System

Abstract

An object of the system according to the embodiment is to analyze the daily routine of the baby and the cause of the cry and provide appropriate advice to the parent.SOLUTION: A system includes an acquisition unit, a learning unit, a prediction unit, an analysis unit, and a provision unit. The acquisition unit records daily routines of the baby. The learning unit learns the data recorded by the acquisition unit. The prediction unit predicts a next action based on the data learned by the learning unit. The analysis unit analyzes the cry of the baby. The providing unit provides advice to the parent based on a result of the analysis performed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for parents to accurately understand their baby's daily routine or the cause of their crying, which can lead to delays in taking appropriate action.

[0005] The system according to the embodiment aims to analyze the baby's daily routine and the cause of the baby's cries and provide appropriate advice to the parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a learning unit, a prediction unit, an analysis unit, and a providing unit. The acquisition unit records the baby's daily routine. The learning unit learns the data recorded by the acquisition unit. The prediction unit predicts the baby's next behavior based on the data learned by the learning unit. The analysis unit analyzes the baby's cries. The providing unit provides advice to the parent based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the baby's daily routine and the cause of the crying, and provide appropriate advice to the parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention records a baby's daily routine, predicts its next behavior, and provides advice to parents based on the analysis of the baby's cries. This system records a baby's daily routine, learns from past data, and predicts its next behavior. Furthermore, it analyzes the baby's cries, determines the cause of the cries, and provides suggestions. For example, it can predict the next time the baby will need milk or when to change the baby's diaper. It can also determine the cause of the baby's crying and provide appropriate advice to the parent. This allows parents to address their baby's needs without overlooking their baby's subtle signs. This allows the system to record a baby's daily routine in detail, predict its next behavior, and provide specific advice to the parent based on the analysis of the baby's cries. For example, by quickly identifying the cause of the baby's crying and taking appropriate measures, the baby's stress can be reduced. Furthermore, by understanding the baby's daily routine, parents can more effectively manage their baby's health. This further deepens the bond between parent and child.

[0029] A baby care system according to an embodiment includes an acquisition unit, a learning unit, a prediction unit, an analysis unit, and a provision unit. The acquisition unit records a baby's daily routine. For example, it records in detail the times for feeding the baby, sleep patterns, diaper change times, and the like. For example, the acquisition unit inputs the time and amount of feeding to record the baby's feeding times. The acquisition unit can also input the sleep time and wake-up time to record the baby's sleep patterns. The acquisition unit can also input the number of diaper changes and the time period to record the diaper change times. The learning unit learns from the recorded data. For example, the learning unit learns data such as the baby's feeding times, sleep patterns, and diaper change times, and builds a model for predicting the baby's next behavior. The learning unit learns from the data using, for example, a machine learning algorithm, and builds a model for predicting the baby's next behavior. The learning unit can also perform preprocessing of the data to improve the accuracy of the learning. The prediction unit predicts the baby's next behavior based on the data learned by the learning unit. For example, the prediction unit predicts the next time the baby will want milk or when to change the baby's diaper. The prediction unit uses, for example, an algorithm that predicts the baby's next behavior based on past data. The prediction unit can also perform data preprocessing to improve the accuracy of the prediction. The analysis unit analyzes the baby's cry. For example, the analysis unit determines the cause of the baby's crying and provides appropriate advice to the parent. The analysis unit can, for example, use audio analysis technology to analyze the baby's cry and identify the cause. The analysis unit can also identify the cause of the crying by referring to past data. The provision unit provides advice to the parent based on the results of the analysis by the analysis unit. For example, the provision unit suggests specific ways to respond to the baby if the baby is crying because it is hungry or if there is a reason the baby cannot sleep. The provision unit provides advice to the parent using, for example, a notification method. The provision unit can also customize the content of the advice. As a result, the baby care system according to the embodiment can record the baby's daily routine, predict the baby's next behavior, and provide specific advice to the parent based on the analysis results of the baby's cry.

[0030] The acquisition unit can record daily routines such as feeding times, sleep patterns, and diaper changing times. For example, the acquisition unit inputs the time and amount of milk to record feeding times. The acquisition unit can also input sleep times and wake-up times to record sleep patterns. The acquisition unit can also input the number of diaper changes and the time periods to record diaper changing times. This makes it possible to record the baby's daily routine in detail. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the baby's feeding times, sleep patterns, and diaper changing times to the generation AI, and have the generation AI record the data.

[0031] The learning unit can learn the recorded data and predict the next behavior. The learning unit can learn data such as the baby's feeding times, sleep patterns, and diaper change times, and build a model for predicting the next behavior. The learning unit can learn the data using, for example, a machine learning algorithm, and build a model for predicting the next behavior. The learning unit can also preprocess the data to improve the accuracy of learning. This makes it possible to learn the recorded data and predict the next behavior. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input data such as the baby's feeding times, sleep patterns, and diaper change times into a generation AI, and have the generation AI learn the data.

[0032] The prediction unit can predict the next time the baby will want milk or the timing of changing the diaper. The prediction unit uses, for example, an algorithm that predicts the next behavior based on past data. For example, the prediction unit predicts the next time the baby will want milk. The prediction unit can also predict the timing of changing the diaper. This makes it possible to predict the next time the baby will want milk or the timing of changing the diaper. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the baby's milk time and diaper change time into the generation AI and have the generation AI predict the next behavior.

[0033] The analysis unit can analyze the baby's cry and determine its cause. The analysis unit can, for example, use audio analysis technology to analyze the baby's cry and identify its cause. For example, the analysis unit can determine whether the baby is crying because it is hungry. The analysis unit can also identify the cause of the baby's sleeplessness. The analysis unit can also identify the cause of the cry by referring to past data. For example, the analysis unit can refer to past cry data to identify the cause of the baby's discomfort. This makes it possible to analyze the baby's cry and determine its cause. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the baby's cry data into the generation AI and have the generation AI analyze the cry.

[0034] The providing unit can suggest specific ways of responding to the parent based on the analysis results. The providing unit provides advice to the parent using, for example, a notification method. For example, the providing unit suggests specific ways of responding to the parent when the baby is crying because the baby is hungry or when there is a reason the baby cannot sleep. The providing unit can also customize the content of the advice. For example, the providing unit adjusts the content of the advice by reflecting feedback from the parent. This makes it possible to suggest specific ways of responding to the parent based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the results of the analysis by the analysis unit into a generating AI and causes the generating AI to provide advice.

[0035] The acquisition unit can analyze the baby's past behavioral history and select an appropriate data acquisition method. For example, the acquisition unit can analyze past milk feeding times and predict the next milk feeding time to acquire data. The acquisition unit can also analyze past sleep patterns and predict the next sleep time to acquire data. The acquisition unit can also analyze past diaper changing times and predict the timing of the next diaper change to acquire data. This makes it possible to analyze the baby's past behavioral history and select an optimal data acquisition method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the baby's past behavioral history data into the generation AI and cause the generation AI to select a data acquisition method.

[0036] When acquiring data, the acquisition unit can perform filtering based on the baby's current health condition and environment. For example, if the baby is healthy, the acquisition unit performs normal data acquisition. Furthermore, if the baby has a cold, the acquisition unit can prioritize acquiring specific data (such as body temperature and appetite). Furthermore, if the baby is in a new environment, the acquisition unit can also acquire data appropriate for that environment (such as temperature and humidity). This makes it possible to filter data based on the baby's current health condition and environment. Some or all of the above-mentioned processing in the acquisition unit may be performed using, or without, AI, for example. For example, the acquisition unit can input data on the baby's health condition and environment into the generation AI and have the generation AI perform data filtering.

[0037] When acquiring data, the acquisition unit can select the optimal acquisition means according to the baby's behavioral pattern. For example, if the baby is actively moving, the acquisition unit selects a data acquisition means corresponding to the movement. Furthermore, if the baby is quiet, the acquisition unit can also select a data acquisition means corresponding to a stationary state. Furthermore, if the baby is sleeping, the acquisition unit can also select a data acquisition means for when the baby is sleeping. This makes it possible to select the optimal data acquisition means according to the baby's behavioral pattern. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the baby's behavioral pattern data to the generation AI and cause the generation AI to select a data acquisition means.

[0038] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the baby's geographical location information. For example, when the baby is at home, the acquisition unit prioritizes acquiring data related to behavioral patterns at home. Furthermore, when the baby is out, the acquisition unit can prioritize acquiring data related to behavioral patterns at the destination. Furthermore, when the baby is traveling, the acquisition unit can prioritize acquiring data related to behavioral patterns at the travel destination. This makes it possible to prioritize acquiring highly relevant data by taking into account the baby's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the baby's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant data.

[0039] The acquisition unit can analyze the social media activity of the baby's parent when acquiring data and acquire related data. For example, when the parent posts a photo of the baby on social media, the acquisition unit acquires the baby's behavioral pattern at that time. Furthermore, when the parent posts about the baby's health condition on social media, the acquisition unit can acquire the baby's health data at that time. Furthermore, when the parent posts about the baby's diet on social media, the acquisition unit can acquire the baby's diet data at that time. This makes it possible to analyze the social media activity of the baby's parent and acquire related data. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the parent's social media activity data into the generation AI and cause the generation AI to acquire related data.

[0040] The acquisition unit can customize the acquisition method by reflecting the baby's past feedback when acquiring data. The acquisition unit customizes the data acquisition method based on, for example, feedback provided by the parent in the past. The acquisition unit can also analyze past data acquisition results and select the optimal acquisition method. The acquisition unit can also adjust the frequency and timing of data acquisition by reflecting past feedback. This makes it possible to customize the data acquisition method by reflecting the baby's past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the baby's past feedback data into the generation AI and cause the generation AI to customize the data acquisition method.

[0041] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. For example, the learning unit can optimize the algorithm that predicts the next feeding time by referring to past milk feeding time data. The learning unit can also optimize the algorithm that predicts the next sleep time by referring to past sleep pattern data. The learning unit can also optimize the algorithm that predicts the timing of the next diaper change by referring to past diaper changing time data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input past learning data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0042] The learning unit can analyze fluctuations in the baby's behavioral patterns during learning and adjust the update frequency of the learning data. For example, if the baby's behavioral patterns fluctuate significantly, the learning unit can increase the update frequency of the learning data. Furthermore, if the baby's behavioral patterns are stable, the learning unit can also decrease the update frequency of the learning data. Furthermore, the learning unit can analyze fluctuations in the baby's behavioral patterns in real time and set an appropriate update frequency. This makes it possible to analyze fluctuations in the baby's behavioral patterns and adjust the update frequency of the learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the baby's behavioral pattern data to the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0043] During learning, the learning unit can update the learning data to reflect feedback from the baby's parent. The learning unit updates the learning data based on, for example, feedback provided by the parent. The learning unit can also adjust the learning algorithm to reflect the parent's feedback. The learning unit can also set a priority order for the learning data based on the parent's feedback. This allows the learning data to be updated to reflect the feedback from the baby's parent. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input parent's feedback data into the generation AI and cause the generation AI to update the learning data.

[0044] During learning, the learning unit can weight the learning data based on the time when the baby's behavioral history was submitted. For example, the learning unit weights the learning data by placing emphasis on recent behavioral history. The learning unit can also weight the learning data by placing emphasis on behavioral history from a specific period. The learning unit can also dynamically adjust the weighting of the learning data based on the time when the behavioral history was submitted. This allows the learning data to be weighted based on the time when the baby's behavioral history was submitted. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the baby's behavioral history data to the generation AI and cause the generation AI to weight the learning data.

[0045] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit can integrate health data and behavioral data of the baby to enrich the learning data. The learning unit can also integrate environmental data and behavioral data of the baby to enrich the learning data. The learning unit can also integrate feedback from the baby's parents and behavioral data to enrich the learning data. This makes it possible to enrich the learning data by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to enrich the learning data.

[0046] During learning, the learning unit can adjust the learning algorithm by reflecting feedback from the baby's parent. The learning unit adjusts the learning algorithm based on, for example, feedback provided by the parent. The learning unit can also adjust parameters of the learning algorithm by reflecting parent feedback. The learning unit can also set priorities for the learning algorithm based on parent feedback. This allows the learning algorithm to be adjusted by reflecting feedback from the baby's parent. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input parent feedback data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0047] When making a prediction, the prediction unit can predict the baby's next behavior by referring to the baby's past behavioral patterns. The prediction unit, for example, can predict the next feeding time by referring to past milk feeding time data. The prediction unit can also predict the next sleep time by referring to past sleep pattern data. The prediction unit can also predict the timing of the next diaper change by referring to past diaper changing time data. This makes it possible to predict the baby's next behavior by referring to the baby's past behavioral patterns. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the baby's past behavioral pattern data into the generation AI and cause the generation AI to predict the next behavior.

[0048] The prediction unit can make predictions taking into account the baby's current health condition and environment. For example, if the baby is healthy, the prediction unit predicts a normal behavioral pattern. Furthermore, if the baby has a cold, the prediction unit can also predict specific behaviors (such as resting or eating). Furthermore, if the baby is in a new environment, the prediction unit can predict a behavioral pattern appropriate for that environment. This allows predictions to be made taking into account the baby's current health condition and environment. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the baby's health condition and environment into the generation AI and have the generation AI execute the prediction.

[0049] The prediction unit can optimize the prediction algorithm by reflecting feedback from the baby's parent when making a prediction. The prediction unit optimizes the prediction algorithm based on, for example, feedback provided by the parent. The prediction unit can also adjust parameters of the prediction algorithm by reflecting the parent's feedback. The prediction unit can also set a priority for the prediction algorithm based on the parent's feedback. This allows the prediction algorithm to be optimized by reflecting the feedback from the baby's parent. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input parent's feedback data into the generation AI and cause the generation AI to optimize the prediction algorithm.

[0050] The prediction unit can improve the accuracy of prediction by taking into account fluctuations in the baby's behavioral patterns when making predictions. For example, if the baby's behavioral patterns fluctuate significantly, the prediction unit can improve the accuracy of prediction by taking into account the fluctuations. Furthermore, if the baby's behavioral patterns are stable, the prediction unit can improve the accuracy of prediction by taking into account the stability. Furthermore, the prediction unit can analyze fluctuations in the baby's behavioral patterns in real time to improve the accuracy of prediction. This makes it possible to improve the accuracy of prediction by taking into account fluctuations in the baby's behavioral patterns. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the baby's behavioral patterns into the generation AI and cause the generation AI to improve the accuracy of prediction.

[0051] The prediction unit can improve the accuracy of the prediction by analyzing the social media activity of the baby's parent when making the prediction. For example, if the parent posts about the baby's health on social media, the prediction unit can improve the accuracy of the prediction based on that information. Furthermore, if the parent posts about the baby's diet on social media, the prediction unit can improve the accuracy of the prediction based on that information. Furthermore, if the parent posts about the baby's behavior on social media, the prediction unit can improve the accuracy of the prediction based on that information. In this way, the social media activity of the baby's parent can be analyzed to improve the accuracy of the prediction. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the parent's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0052] The prediction unit can customize the prediction result by reflecting feedback from the baby's parent at the time of prediction. The prediction unit customizes the prediction result based on, for example, feedback provided by the parent. The prediction unit can also adjust the display method of the prediction result by reflecting the parent's feedback. The prediction unit can also set a priority order for the prediction result based on the parent's feedback. This allows the prediction result to be customized by reflecting the feedback from the baby's parent. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input parent's feedback data into the generation AI and cause the generation AI to customize the prediction result.

[0053] When analyzing the baby's cry, the analysis unit can identify the cause by referring to the baby's past cry data. For example, the analysis unit can identify whether the baby is crying because it is hungry by referring to the past cry data. The analysis unit can also identify the cause of the baby's inability to sleep by referring to the past cry data. The analysis unit can also identify the cause of the baby's discomfort by referring to the past cry data. In this way, the cause can be identified by referring to the baby's past cry data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the baby's past cry data into the generation AI and cause the generation AI to identify the cause.

[0054] When analyzing the baby's cries, the analysis unit can take into account the baby's current health condition and environment. For example, if the baby is healthy, the analysis unit performs a normal cry analysis. Furthermore, if the baby has a cold, the analysis unit can prioritize analysis of specific cries (such as coughs and sneezes). Furthermore, if the baby is in a new environment, the analysis unit can perform a cry analysis appropriate to that environment. This makes it possible to analyze the baby's cries while taking into account the baby's current health condition and environment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the baby's health condition and environment into the generation AI and have the generation AI perform an analysis of the baby's cries.

[0055] The analysis unit can optimize the analysis algorithm by reflecting feedback from the baby's parents when analyzing the baby's cries. The analysis unit optimizes the analysis algorithm based on, for example, feedback provided by the parents. The analysis unit can also adjust parameters of the analysis algorithm by reflecting the parent's feedback. The analysis unit can also set priorities for the analysis algorithm based on the parent's feedback. This makes it possible to optimize the analysis algorithm for the baby's cries by reflecting the feedback from the baby's parents. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input parent's feedback data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0056] The analysis unit can improve the accuracy of the analysis by taking into account fluctuations in the baby's behavioral patterns when analyzing the baby's cries. For example, if the baby's behavioral patterns fluctuate significantly, the analysis unit can improve the accuracy of the analysis by taking into account the fluctuations. Furthermore, if the baby's behavioral patterns are stable, the analysis unit can improve the accuracy of the analysis by taking into account the stability. Furthermore, the analysis unit can analyze fluctuations in the baby's behavioral patterns in real time to improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis of the baby's cries by taking into account fluctuations in the baby's behavioral patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the baby's behavioral pattern data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0057] When analyzing the baby's cries, the analysis unit can improve the accuracy of the analysis by analyzing the social media activity of the baby's parents. For example, if the parent posts about the baby's health on social media, the analysis unit can improve the accuracy of the analysis based on that information. Furthermore, if the parent posts about the baby's diet on social media, the analysis unit can improve the accuracy of the analysis based on that information. Furthermore, if the parent posts about the baby's behavior on social media, the analysis unit can improve the accuracy of the analysis based on that information. In this way, the accuracy of the analysis of the baby's cries can be improved by analyzing the social media activity of the baby's parents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the parent's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0058] When analyzing the baby's cries, the analysis unit can customize the analysis results by reflecting feedback from the baby's parents. The analysis unit customizes the analysis results based on, for example, feedback provided by the parents. The analysis unit can also adjust how the analysis results are displayed by reflecting the parent's feedback. The analysis unit can also set priorities for the analysis results based on the parent's feedback. This allows the analysis results of the baby's cries to be customized by reflecting the feedback from the baby's parents. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input parent's feedback data into the generation AI and cause the generation AI to customize the analysis results.

[0059] When providing advice, the providing unit can provide optimal advice by referring to the baby's past behavioral patterns. For example, the providing unit can provide advice on the next time to feed by referring to past milk feeding time data. The providing unit can also provide advice on the next sleep time by referring to past sleep pattern data. The providing unit can also provide advice on the timing of the next diaper change by referring to past diaper changing time data. In this way, optimal advice can be provided by referring to the baby's past behavioral patterns. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the baby's past behavioral pattern data into the generating AI and cause the generating AI to provide optimal advice.

[0060] When providing advice, the providing unit can take into consideration the baby's current health condition and environment. For example, if the baby is healthy, the providing unit can provide normal advice. Furthermore, if the baby has a cold, the providing unit can also provide specific advice (such as rest and food). Furthermore, if the baby is in a new environment, the providing unit can also provide advice appropriate to that environment. This makes it possible to provide advice taking into consideration the baby's current health condition and environment. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data on the baby's health condition and environment into the generating AI and cause the generating AI to provide advice.

[0061] The providing unit can improve the accuracy of advice by reflecting feedback from the baby's parent when providing advice. The providing unit can improve the accuracy of advice, for example, based on feedback provided by the parent. The providing unit can also adjust the content of the advice by reflecting the parent's feedback. The providing unit can also set a priority order for advice based on the parent's feedback. This makes it possible to improve the accuracy of advice by reflecting the feedback from the baby's parent. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input parent's feedback data into the generating AI and cause the generating AI to improve the accuracy of advice.

[0062] The providing unit can improve the accuracy of advice by taking into account fluctuations in the baby's behavioral patterns when providing advice. For example, if the baby's behavioral patterns fluctuate significantly, the providing unit can improve the accuracy of advice by taking into account the fluctuations. Furthermore, if the baby's behavioral patterns are stable, the providing unit can improve the accuracy of advice by taking into account the stability. Furthermore, the providing unit can analyze fluctuations in the baby's behavioral patterns in real time and improve the accuracy of advice. This makes it possible to improve the accuracy of advice by taking into account fluctuations in the baby's behavioral patterns. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the baby's behavioral patterns into a generating AI and cause the generating AI to improve the accuracy of advice.

[0063] When providing advice, the providing unit can analyze the social media activity of the baby's parent to improve the accuracy of the advice. For example, if the parent posts about the baby's health on social media, the providing unit can improve the accuracy of the advice based on that information. Furthermore, if the parent posts about the baby's diet on social media, the providing unit can improve the accuracy of the advice based on that information. Furthermore, if the parent posts about the baby's behavior on social media, the providing unit can improve the accuracy of the advice based on that information. In this way, the social media activity of the baby's parent can be analyzed to improve the accuracy of the advice. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the parent's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0064] The providing unit can customize the advice content by reflecting feedback from the baby's parent when providing advice. The providing unit customizes the advice content based on, for example, feedback provided by the parent. The providing unit can also adjust the way the advice is expressed by reflecting the parent's feedback. The providing unit can also set a priority order for the advice based on the parent's feedback. This makes it possible to customize the advice content by reflecting the feedback from the baby's parent. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input parent's feedback data into a generating AI and cause the generating AI to customize the advice content.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The acquisition unit can also capture biological data such as the baby's temperature and heart rate. For example, if the baby's temperature rises, the acquisition unit records that data and notifies the parent if there is an abnormality. It can also monitor heart rate fluctuations to detect signs of stress or anxiety. It can also record the baby's breathing patterns and issue an alert if breathing difficulties or abnormal patterns are observed. This allows for more detailed monitoring of the baby's health and early detection of abnormalities.

[0067] The learning unit can adjust the learning model according to the baby's developmental stage. For example, different behavioral patterns are seen in the newborn and infant stages, so the learning unit builds models appropriate for each stage. As a baby grows, eating and sleeping patterns also change, so the learning unit can update the model to reflect these changes. Furthermore, the weighting of the learning data can be adjusted according to the baby's individual growth rate, allowing for more accurate predictions. This makes it possible to provide appropriate care that matches the baby's growth.

[0068] The prediction unit can optimize a parent's schedule based on the baby's behavioral patterns. For example, it can predict the next time the baby will want milk and send a reminder to the parent at that time. It can also predict the baby's sleep patterns and suggest the best time for the parent to rest. It can also predict the baby's activity times and provide a schedule that allows the parent to efficiently complete housework and work. This allows parents to create a schedule that meets the baby's needs and reduces stress.

[0069] The analysis unit can analyze not only the baby's cries, but also its facial expressions and movements. For example, if a baby is smiling, its facial expressions can be analyzed to determine that it is in a good mood. If the baby is reaching out, its movements can be analyzed to determine that it wants something. Furthermore, if the baby is squirming, its movements can be analyzed to determine that it is feeling uncomfortable. This makes it possible to comprehensively determine a baby's condition from not only its cries but also its facial expressions and movements.

[0070] The acquisition unit can acquire environmental data (such as temperature, humidity, and noise level) around the baby. For example, if the room temperature is too high, the acquisition unit records the data and prompts the parent to adjust the temperature appropriately. If the humidity is too low, the acquisition unit can also recommend humidification. Furthermore, if the noise level is high, the acquisition unit can notify the parent to provide a quiet environment. This makes it possible to provide data to maintain a comfortable environment for the baby.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The acquisition unit records the baby's daily routine. For example, it records in detail the times for feeding, sleep patterns, and diaper change times. To record the times for feeding the baby, the acquisition unit inputs the time and amount of feeding. The acquisition unit can also input the sleep time and wake-up time to record the baby's sleep pattern. Furthermore, the acquisition unit can also input the number of times the diaper was changed and the time of day to record the times for changing the diaper. Step 2: The learning unit learns from the recorded data. For example, the learning unit learns from data such as the baby's feeding times, sleep patterns, and diaper changing times, and builds a model to predict the baby's next behavior. The learning unit uses a machine learning algorithm to learn from the data and builds a model to predict the baby's next behavior. The learning unit can also preprocess the data to improve the accuracy of learning. Step 3: The prediction unit predicts the next behavior based on the data learned by the learning unit. For example, the prediction unit predicts the next time the baby will want milk or when to change its diaper. The prediction unit uses an algorithm to predict the next behavior based on past data. The prediction unit can also perform data preprocessing to improve the accuracy of the prediction. Step 4: The analysis unit analyzes the baby's cry. For example, the analysis unit determines the reason why the baby is crying and provides appropriate advice to the parents. The analysis unit uses audio analysis technology to analyze the baby's cry and identify the cause. The analysis unit can also refer to past data to identify the cause of the cry. Step 5: The providing unit provides advice to the parent based on the results of the analysis by the analyzing unit. For example, the providing unit suggests specific ways to respond to the parent when the baby is crying because it is hungry or when there is a reason the baby cannot sleep. The providing unit provides advice to the parent using a notification method. The providing unit can also customize the content of the advice.

[0073] (Example 2) A system according to an embodiment of the present invention records a baby's daily routine, predicts its next behavior, and provides advice to parents based on the analysis of the baby's cries. This system records a baby's daily routine, learns from past data, and predicts its next behavior. Furthermore, it analyzes the baby's cries, determines the cause of the cries, and provides suggestions. For example, it can predict the next time the baby will need milk or when to change the baby's diaper. It can also determine the cause of the baby's crying and provide appropriate advice to the parent. This allows parents to address their baby's needs without overlooking their baby's subtle signs. This allows the system to record a baby's daily routine in detail, predict its next behavior, and provide specific advice to the parent based on the analysis of the baby's cries. For example, by quickly identifying the cause of the baby's crying and taking appropriate measures, the baby's stress can be reduced. Furthermore, by understanding the baby's daily routine, parents can more effectively manage their baby's health. This further deepens the bond between parent and child.

[0074] A baby care system according to an embodiment includes an acquisition unit, a learning unit, a prediction unit, an analysis unit, and a provision unit. The acquisition unit records a baby's daily routine. For example, it records in detail the times for feeding the baby, sleep patterns, diaper change times, and the like. For example, the acquisition unit inputs the time and amount of feeding to record the baby's feeding times. The acquisition unit can also input the sleep time and wake-up time to record the baby's sleep patterns. The acquisition unit can also input the number of diaper changes and the time period to record the diaper change times. The learning unit learns from the recorded data. For example, the learning unit learns data such as the baby's feeding times, sleep patterns, and diaper change times, and builds a model for predicting the baby's next behavior. The learning unit learns from the data using, for example, a machine learning algorithm, and builds a model for predicting the baby's next behavior. The learning unit can also perform preprocessing of the data to improve the accuracy of the learning. The prediction unit predicts the baby's next behavior based on the data learned by the learning unit. For example, the prediction unit predicts the next time the baby will want milk or when to change the baby's diaper. The prediction unit uses, for example, an algorithm that predicts the baby's next behavior based on past data. The prediction unit can also perform data preprocessing to improve the accuracy of the prediction. The analysis unit analyzes the baby's cry. For example, the analysis unit determines the cause of the baby's crying and provides appropriate advice to the parent. The analysis unit can, for example, use audio analysis technology to analyze the baby's cry and identify the cause. The analysis unit can also identify the cause of the crying by referring to past data. The provision unit provides advice to the parent based on the results of the analysis by the analysis unit. For example, the provision unit suggests specific ways to respond to the baby if the baby is crying because it is hungry or if there is a reason the baby cannot sleep. The provision unit provides advice to the parent using, for example, a notification method. The provision unit can also customize the content of the advice. As a result, the baby care system according to the embodiment can record the baby's daily routine, predict the baby's next behavior, and provide specific advice to the parent based on the analysis results of the baby's cry.

[0075] The acquisition unit can record daily routines such as feeding times, sleep patterns, and diaper changing times. For example, the acquisition unit inputs the time and amount of milk to record feeding times. The acquisition unit can also input sleep times and wake-up times to record sleep patterns. The acquisition unit can also input the number of diaper changes and the time periods to record diaper changing times. This makes it possible to record the baby's daily routine in detail. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the baby's feeding times, sleep patterns, and diaper changing times to the generation AI, and have the generation AI record the data.

[0076] The learning unit can learn the recorded data and predict the next behavior. The learning unit can learn data such as the baby's feeding times, sleep patterns, and diaper change times, and build a model for predicting the next behavior. The learning unit can learn the data using, for example, a machine learning algorithm, and build a model for predicting the next behavior. The learning unit can also preprocess the data to improve the accuracy of learning. This makes it possible to learn the recorded data and predict the next behavior. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input data such as the baby's feeding times, sleep patterns, and diaper change times into a generation AI, and have the generation AI learn the data.

[0077] The prediction unit can predict the next time the baby will want milk or the timing of changing the diaper. The prediction unit uses, for example, an algorithm that predicts the next behavior based on past data. For example, the prediction unit predicts the next time the baby will want milk. The prediction unit can also predict the timing of changing the diaper. This makes it possible to predict the next time the baby will want milk or the timing of changing the diaper. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the baby's milk time and diaper change time into the generation AI and have the generation AI predict the next behavior.

[0078] The analysis unit can analyze the baby's cry and determine its cause. The analysis unit can, for example, use audio analysis technology to analyze the baby's cry and identify its cause. For example, the analysis unit can determine whether the baby is crying because it is hungry. The analysis unit can also identify the cause of the baby's sleeplessness. The analysis unit can also identify the cause of the cry by referring to past data. For example, the analysis unit can refer to past cry data to identify the cause of the baby's discomfort. This makes it possible to analyze the baby's cry and determine its cause. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the baby's cry data into the generation AI and have the generation AI analyze the cry.

[0079] The providing unit can suggest specific ways of responding to the parent based on the analysis results. The providing unit provides advice to the parent using, for example, a notification method. For example, the providing unit suggests specific ways of responding to the parent when the baby is crying because the baby is hungry or when there is a reason the baby cannot sleep. The providing unit can also customize the content of the advice. For example, the providing unit adjusts the content of the advice by reflecting feedback from the parent. This makes it possible to suggest specific ways of responding to the parent based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the results of the analysis by the analysis unit into a generating AI and causes the generating AI to provide advice.

[0080] The acquisition unit can estimate the baby's emotions and adjust the timing of data acquisition based on the estimated baby's emotions. For example, the acquisition unit records the baby's feeding time and sleep patterns when the baby is in a good mood. The acquisition unit can also acquire data to identify the cause of the baby's crying when the baby is crying. The acquisition unit can also record the sleep patterns when the baby seems sleepy. This makes it possible to adjust the timing of data acquisition based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the acquisition unit may be performed using an AI, for example, or without an AI. For example, the acquisition unit can input the baby's emotion data into the generation AI and cause the generation AI to adjust the timing of data acquisition.

[0081] The acquisition unit can analyze the baby's past behavioral history and select an appropriate data acquisition method. For example, the acquisition unit can analyze past milk feeding times and predict the next milk feeding time to acquire data. The acquisition unit can also analyze past sleep patterns and predict the next sleep time to acquire data. The acquisition unit can also analyze past diaper changing times and predict the timing of the next diaper change to acquire data. This makes it possible to analyze the baby's past behavioral history and select an optimal data acquisition method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the baby's past behavioral history data into the generation AI and cause the generation AI to select a data acquisition method.

[0082] When acquiring data, the acquisition unit can perform filtering based on the baby's current health condition and environment. For example, if the baby is healthy, the acquisition unit performs normal data acquisition. Furthermore, if the baby has a cold, the acquisition unit can prioritize acquiring specific data (such as body temperature and appetite). Furthermore, if the baby is in a new environment, the acquisition unit can also acquire data appropriate for that environment (such as temperature and humidity). This makes it possible to filter data based on the baby's current health condition and environment. Some or all of the above-mentioned processing in the acquisition unit may be performed using, or without, AI, for example. For example, the acquisition unit can input data on the baby's health condition and environment into the generation AI and have the generation AI perform data filtering.

[0083] When acquiring data, the acquisition unit can select the optimal acquisition means according to the baby's behavioral pattern. For example, if the baby is actively moving, the acquisition unit selects a data acquisition means corresponding to the movement. Furthermore, if the baby is quiet, the acquisition unit can also select a data acquisition means corresponding to a stationary state. Furthermore, if the baby is sleeping, the acquisition unit can also select a data acquisition means for when the baby is sleeping. This makes it possible to select the optimal data acquisition means according to the baby's behavioral pattern. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the baby's behavioral pattern data to the generation AI and cause the generation AI to select a data acquisition means.

[0084] The acquisition unit can estimate the baby's emotions and determine the priority of data to be acquired based on the estimated baby's emotions. For example, if the baby is crying, the acquisition unit prioritizes acquiring data to identify the cause of the crying. Furthermore, if the baby is smiling, the acquisition unit can prioritize acquiring information about feeding times and sleep patterns. Furthermore, if the baby seems sleepy, the acquisition unit can prioritize acquiring sleep patterns. This makes it possible to determine the priority of data to be acquired based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the baby's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0085] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the baby's geographical location information. For example, when the baby is at home, the acquisition unit prioritizes acquiring data related to behavioral patterns at home. Furthermore, when the baby is out, the acquisition unit can prioritize acquiring data related to behavioral patterns at the destination. Furthermore, when the baby is traveling, the acquisition unit can prioritize acquiring data related to behavioral patterns at the travel destination. This makes it possible to prioritize acquiring highly relevant data by taking into account the baby's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the baby's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant data.

[0086] The acquisition unit can analyze the social media activity of the baby's parent when acquiring data and acquire related data. For example, when the parent posts a photo of the baby on social media, the acquisition unit acquires the baby's behavioral pattern at that time. Furthermore, when the parent posts about the baby's health condition on social media, the acquisition unit can acquire the baby's health data at that time. Furthermore, when the parent posts about the baby's diet on social media, the acquisition unit can acquire the baby's diet data at that time. This makes it possible to analyze the social media activity of the baby's parent and acquire related data. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the parent's social media activity data into the generation AI and cause the generation AI to acquire related data.

[0087] The acquisition unit can customize the acquisition method by reflecting the baby's past feedback when acquiring data. The acquisition unit customizes the data acquisition method based on, for example, feedback provided by the parent in the past. The acquisition unit can also analyze past data acquisition results and select the optimal acquisition method. The acquisition unit can also adjust the frequency and timing of data acquisition by reflecting past feedback. This makes it possible to customize the data acquisition method by reflecting the baby's past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the baby's past feedback data into the generation AI and cause the generation AI to customize the data acquisition method.

[0088] The learning unit can estimate the baby's emotions and select training data based on the estimated baby's emotions. For example, the learning unit prioritizes learning data when the baby is in a good mood. The learning unit can also prioritize learning data when the baby is crying. The learning unit can also prioritize learning data when the baby is sleepy. This allows the selection of training data based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, for example, or without an AI. For example, the learning unit can input the baby's emotion data into the generation AI and cause the generation AI to select training data.

[0089] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. For example, the learning unit can optimize the algorithm that predicts the next feeding time by referring to past milk feeding time data. The learning unit can also optimize the algorithm that predicts the next sleep time by referring to past sleep pattern data. The learning unit can also optimize the algorithm that predicts the timing of the next diaper change by referring to past diaper changing time data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input past learning data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0090] The learning unit can analyze fluctuations in the baby's behavioral patterns during learning and adjust the update frequency of the learning data. For example, if the baby's behavioral patterns fluctuate significantly, the learning unit can increase the update frequency of the learning data. Furthermore, if the baby's behavioral patterns are stable, the learning unit can also decrease the update frequency of the learning data. Furthermore, the learning unit can analyze fluctuations in the baby's behavioral patterns in real time and set an appropriate update frequency. This makes it possible to analyze fluctuations in the baby's behavioral patterns and adjust the update frequency of the learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the baby's behavioral pattern data to the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0091] During learning, the learning unit can update the learning data to reflect feedback from the baby's parent. The learning unit updates the learning data based on, for example, feedback provided by the parent. The learning unit can also adjust the learning algorithm to reflect the parent's feedback. The learning unit can also set a priority order for the learning data based on the parent's feedback. This allows the learning data to be updated to reflect the feedback from the baby's parent. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input parent's feedback data into the generation AI and cause the generation AI to update the learning data.

[0092] The learning unit can estimate the baby's emotions and adjust the frequency of learning based on the estimated baby's emotions. For example, the learning unit can increase the frequency of learning when the baby is in a good mood. The learning unit can also decrease the frequency of learning when the baby is crying. The learning unit can also adjust the frequency of learning when the baby seems sleepy. This makes it possible to adjust the frequency of learning based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit can be performed using an AI, for example, or without an AI. For example, the learning unit can input the baby's emotion data into the generation AI and cause the generation AI to adjust the frequency of learning.

[0093] During learning, the learning unit can weight the learning data based on the time when the baby's behavioral history was submitted. For example, the learning unit weights the learning data by placing emphasis on recent behavioral history. The learning unit can also weight the learning data by placing emphasis on behavioral history from a specific period. The learning unit can also dynamically adjust the weighting of the learning data based on the time when the behavioral history was submitted. This allows the learning data to be weighted based on the time when the baby's behavioral history was submitted. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the baby's behavioral history data to the generation AI and cause the generation AI to weight the learning data.

[0094] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit can integrate health data and behavioral data of the baby to enrich the learning data. The learning unit can also integrate environmental data and behavioral data of the baby to enrich the learning data. The learning unit can also integrate feedback from the baby's parents and behavioral data to enrich the learning data. This makes it possible to enrich the learning data by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to enrich the learning data.

[0095] During learning, the learning unit can adjust the learning algorithm by reflecting feedback from the baby's parent. The learning unit adjusts the learning algorithm based on, for example, feedback provided by the parent. The learning unit can also adjust parameters of the learning algorithm by reflecting parent feedback. The learning unit can also set priorities for the learning algorithm based on parent feedback. This allows the learning algorithm to be adjusted by reflecting feedback from the baby's parent. Some or all of the above-described processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input parent feedback data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0096] The prediction unit can estimate the baby's emotions and improve the accuracy of prediction based on the estimated baby's emotions. The prediction unit can predict the baby's next behavior based on data when the baby is in a good mood, for example. The prediction unit can also predict the baby's next behavior based on data when the baby is crying. The prediction unit can also predict the baby's next behavior based on data when the baby is sleepy. This improves the accuracy of prediction based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the prediction unit can be performed using an AI, for example, or without an AI. For example, the prediction unit can input the baby's emotion data into the generation AI and cause the generation AI to improve the accuracy of prediction.

[0097] When making a prediction, the prediction unit can predict the baby's next behavior by referring to the baby's past behavioral patterns. The prediction unit, for example, can predict the next feeding time by referring to past milk feeding time data. The prediction unit can also predict the next sleep time by referring to past sleep pattern data. The prediction unit can also predict the timing of the next diaper change by referring to past diaper changing time data. This makes it possible to predict the baby's next behavior by referring to the baby's past behavioral patterns. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the baby's past behavioral pattern data into the generation AI and cause the generation AI to predict the next behavior.

[0098] The prediction unit can make predictions taking into account the baby's current health condition and environment. For example, if the baby is healthy, the prediction unit predicts a normal behavioral pattern. Furthermore, if the baby has a cold, the prediction unit can also predict specific behaviors (such as resting or eating). Furthermore, if the baby is in a new environment, the prediction unit can predict a behavioral pattern appropriate for that environment. This allows predictions to be made taking into account the baby's current health condition and environment. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the baby's health condition and environment into the generation AI and have the generation AI execute the prediction.

[0099] The prediction unit can optimize the prediction algorithm by reflecting feedback from the baby's parent when making a prediction. The prediction unit optimizes the prediction algorithm based on, for example, feedback provided by the parent. The prediction unit can also adjust parameters of the prediction algorithm by reflecting the parent's feedback. The prediction unit can also set a priority for the prediction algorithm based on the parent's feedback. This allows the prediction algorithm to be optimized by reflecting the feedback from the baby's parent. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input parent's feedback data into the generation AI and cause the generation AI to optimize the prediction algorithm.

[0100] The prediction unit can estimate the baby's emotion and adjust the display method of the prediction result based on the estimated emotion of the baby. For example, when the baby is in a good mood, the prediction unit displays a detailed prediction result. When the baby is crying, the prediction unit can also display a concise prediction result. When the baby seems sleepy, the prediction unit can also display a visually easy-to-understand prediction result. This makes it possible to adjust the display method of the prediction result based on the baby's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the prediction unit may be performed using an AI, for example, or without using an AI. For example, the prediction unit can input the baby's emotion data into the generation AI and cause the generation AI to adjust the display method of the prediction result.

[0101] The prediction unit can improve the accuracy of prediction by taking into account fluctuations in the baby's behavioral patterns when making predictions. For example, if the baby's behavioral patterns fluctuate significantly, the prediction unit can improve the accuracy of prediction by taking into account the fluctuations. Furthermore, if the baby's behavioral patterns are stable, the prediction unit can improve the accuracy of prediction by taking into account the stability. Furthermore, the prediction unit can analyze fluctuations in the baby's behavioral patterns in real time to improve the accuracy of prediction. This makes it possible to improve the accuracy of prediction by taking into account fluctuations in the baby's behavioral patterns. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the baby's behavioral patterns into the generation AI and cause the generation AI to improve the accuracy of prediction.

[0102] The prediction unit can improve the accuracy of the prediction by analyzing the social media activity of the baby's parent when making the prediction. For example, if the parent posts about the baby's health on social media, the prediction unit can improve the accuracy of the prediction based on that information. Furthermore, if the parent posts about the baby's diet on social media, the prediction unit can improve the accuracy of the prediction based on that information. Furthermore, if the parent posts about the baby's behavior on social media, the prediction unit can improve the accuracy of the prediction based on that information. In this way, the social media activity of the baby's parent can be analyzed to improve the accuracy of the prediction. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the parent's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0103] The prediction unit can customize the prediction result by reflecting feedback from the baby's parent at the time of prediction. The prediction unit customizes the prediction result based on, for example, feedback provided by the parent. The prediction unit can also adjust the display method of the prediction result by reflecting the parent's feedback. The prediction unit can also set a priority order for the prediction result based on the parent's feedback. This allows the prediction result to be customized by reflecting the feedback from the baby's parent. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input parent's feedback data into the generation AI and cause the generation AI to customize the prediction result.

[0104] The analysis unit can estimate the baby's emotions and improve the analysis accuracy of the baby's cries based on the estimated emotions. The analysis unit can improve the analysis accuracy, for example, based on the baby's cries when it is in a good mood. The analysis unit can also improve the analysis accuracy based on the baby's cries when it is crying. The analysis unit can also improve the analysis accuracy based on the baby's cries when it is sleepy. This makes it possible to improve the analysis accuracy of the baby's cries based on the baby's emotions. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the baby's emotion data into the generation AI and cause the generation AI to improve the analysis accuracy of the baby's cries.

[0105] When analyzing the baby's cry, the analysis unit can identify the cause by referring to the baby's past cry data. For example, the analysis unit can identify whether the baby is crying because it is hungry by referring to the past cry data. The analysis unit can also identify the cause of the baby's inability to sleep by referring to the past cry data. The analysis unit can also identify the cause of the baby's discomfort by referring to the past cry data. In this way, the cause can be identified by referring to the baby's past cry data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the baby's past cry data into the generation AI and cause the generation AI to identify the cause.

[0106] When analyzing the baby's cries, the analysis unit can take into account the baby's current health condition and environment. For example, if the baby is healthy, the analysis unit performs a normal cry analysis. Furthermore, if the baby has a cold, the analysis unit can prioritize analysis of specific cries (such as coughs and sneezes). Furthermore, if the baby is in a new environment, the analysis unit can perform a cry analysis appropriate to that environment. This makes it possible to analyze the baby's cries while taking into account the baby's current health condition and environment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the baby's health condition and environment into the generation AI and have the generation AI perform an analysis of the baby's cries.

[0107] The analysis unit can optimize the analysis algorithm by reflecting feedback from the baby's parents when analyzing the baby's cries. The analysis unit optimizes the analysis algorithm based on, for example, feedback provided by the parents. The analysis unit can also adjust parameters of the analysis algorithm by reflecting the parent's feedback. The analysis unit can also set priorities for the analysis algorithm based on the parent's feedback. This makes it possible to optimize the analysis algorithm for the baby's cries by reflecting the feedback from the baby's parents. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input parent's feedback data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0108] The analysis unit can estimate the baby's emotions and adjust the display method of the analysis results based on the estimated baby's emotions. For example, the analysis unit displays detailed analysis results when the baby is in a good mood. The analysis unit can also display concise analysis results when the baby is crying. The analysis unit can also display visually easy-to-understand analysis results when the baby seems sleepy. This makes it possible to adjust the display method of the analysis results based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the baby's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0109] The analysis unit can improve the accuracy of the analysis by taking into account fluctuations in the baby's behavioral patterns when analyzing the baby's cries. For example, if the baby's behavioral patterns fluctuate significantly, the analysis unit can improve the accuracy of the analysis by taking into account the fluctuations. Furthermore, if the baby's behavioral patterns are stable, the analysis unit can improve the accuracy of the analysis by taking into account the stability. Furthermore, the analysis unit can analyze fluctuations in the baby's behavioral patterns in real time to improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis of the baby's cries by taking into account fluctuations in the baby's behavioral patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the baby's behavioral pattern data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0110] When analyzing the baby's cries, the analysis unit can improve the accuracy of the analysis by analyzing the social media activity of the baby's parents. For example, if the parent posts about the baby's health on social media, the analysis unit can improve the accuracy of the analysis based on that information. Furthermore, if the parent posts about the baby's diet on social media, the analysis unit can improve the accuracy of the analysis based on that information. Furthermore, if the parent posts about the baby's behavior on social media, the analysis unit can improve the accuracy of the analysis based on that information. In this way, the accuracy of the analysis of the baby's cries can be improved by analyzing the social media activity of the baby's parents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the parent's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0111] When analyzing the baby's cries, the analysis unit can customize the analysis results by reflecting feedback from the baby's parents. The analysis unit customizes the analysis results based on, for example, feedback provided by the parents. The analysis unit can also adjust how the analysis results are displayed by reflecting the parent's feedback. The analysis unit can also set priorities for the analysis results based on the parent's feedback. This allows the analysis results of the baby's cries to be customized by reflecting the feedback from the baby's parents. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input parent's feedback data into the generation AI and cause the generation AI to customize the analysis results.

[0112] The providing unit can estimate the baby's emotions and adjust the way in which advice is expressed based on the estimated baby's emotions. For example, when the baby is in a good mood, the providing unit can provide detailed advice. Furthermore, when the baby is crying, the providing unit can provide concise advice. Furthermore, when the baby seems sleepy, the providing unit can provide visually easy-to-understand advice. This makes it possible to adjust the way in which advice is expressed based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or may be performed without using an AI. For example, the providing unit can input the baby's emotion data into the generation AI and cause the generation AI to adjust the way in which advice is expressed.

[0113] When providing advice, the providing unit can provide optimal advice by referring to the baby's past behavioral patterns. For example, the providing unit can provide advice on the next time to feed by referring to past milk feeding time data. The providing unit can also provide advice on the next sleep time by referring to past sleep pattern data. The providing unit can also provide advice on the timing of the next diaper change by referring to past diaper changing time data. In this way, optimal advice can be provided by referring to the baby's past behavioral patterns. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the baby's past behavioral pattern data into the generating AI and cause the generating AI to provide optimal advice.

[0114] When providing advice, the providing unit can take into consideration the baby's current health condition and environment. For example, if the baby is healthy, the providing unit can provide normal advice. Furthermore, if the baby has a cold, the providing unit can also provide specific advice (such as rest and food). Furthermore, if the baby is in a new environment, the providing unit can also provide advice appropriate to that environment. This makes it possible to provide advice taking into consideration the baby's current health condition and environment. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data on the baby's health condition and environment into the generating AI and cause the generating AI to provide advice.

[0115] The providing unit can improve the accuracy of advice by reflecting feedback from the baby's parent when providing advice. The providing unit can improve the accuracy of advice, for example, based on feedback provided by the parent. The providing unit can also adjust the content of the advice by reflecting the parent's feedback. The providing unit can also set a priority order for advice based on the parent's feedback. This makes it possible to improve the accuracy of advice by reflecting the feedback from the baby's parent. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input parent's feedback data into the generating AI and cause the generating AI to improve the accuracy of advice.

[0116] The providing unit can estimate the baby's emotions and determine the priority of advice based on the estimated baby's emotions. For example, if the baby is crying, the providing unit can prioritize advice with a high urgency. Furthermore, if the baby is in a good mood, the providing unit can also provide detailed advice. Furthermore, if the baby seems sleepy, the providing unit can prioritize advice about sleep. This makes it possible to determine the priority of advice based on the baby's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the baby's emotion data to the generation AI and cause the generation AI to determine the priority of advice.

[0117] The providing unit can improve the accuracy of advice by taking into account fluctuations in the baby's behavioral patterns when providing advice. For example, if the baby's behavioral patterns fluctuate significantly, the providing unit can improve the accuracy of advice by taking into account the fluctuations. Furthermore, if the baby's behavioral patterns are stable, the providing unit can improve the accuracy of advice by taking into account the stability. Furthermore, the providing unit can analyze fluctuations in the baby's behavioral patterns in real time and improve the accuracy of advice. This makes it possible to improve the accuracy of advice by taking into account fluctuations in the baby's behavioral patterns. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the baby's behavioral patterns into a generating AI and cause the generating AI to improve the accuracy of advice.

[0118] When providing advice, the providing unit can analyze the social media activity of the baby's parent to improve the accuracy of the advice. For example, if the parent posts about the baby's health on social media, the providing unit can improve the accuracy of the advice based on that information. Furthermore, if the parent posts about the baby's diet on social media, the providing unit can improve the accuracy of the advice based on that information. Furthermore, if the parent posts about the baby's behavior on social media, the providing unit can improve the accuracy of the advice based on that information. In this way, the social media activity of the baby's parent can be analyzed to improve the accuracy of the advice. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the parent's social media activity data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0119] The providing unit can customize the advice content by reflecting feedback from the baby's parent when providing advice. The providing unit customizes the advice content based on, for example, feedback provided by the parent. The providing unit can also adjust the way the advice is expressed by reflecting the parent's feedback. The providing unit can also set a priority order for the advice based on the parent's feedback. This makes it possible to customize the advice content by reflecting the feedback from the baby's parent. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input parent's feedback data into a generating AI and cause the generating AI to customize the advice content. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, learning unit, prediction unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can record the baby's daily routine using the camera 42 or microphone 38B of the smart device 14. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the recorded data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the baby's next behavior based on the learned data. The analysis unit collects the baby's cries using the microphone 38B of the smart device 14 and analyzes them using the specific processing unit 290 of the data processing device 12. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice to the parent based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, learning unit, prediction unit, analysis unit, and provision unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can record the baby's daily routine using the camera 42 and microphone 238 of the smart glasses 214. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the recorded data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the baby's next behavior based on the learned data. The analysis unit collects the baby's cries using the microphone 238 of the smart glasses 214 and analyzes them using the specific processing unit 290 of the data processing device 12. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice to the parent based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, learning unit, prediction unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit can record the baby's daily routine using the camera 42 or the microphone 238 of the headset-type terminal 314. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns from the recorded data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the baby's next behavior based on the learned data. The analysis unit collects the baby's cries using the microphone 238 of the headset-type terminal 314 and analyzes them using the specific processing unit 290 of the data processing device 12. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice to the parent based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, learning unit, prediction unit, analysis unit, and provision unit described above is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can record the baby's daily routine using the camera 42 or microphone 238 of the robot 414. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the recorded data. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the baby's next behavior based on the learned data. The analysis unit collects the baby's cries using the microphone 238 of the robot 414 and analyzes them using the specific processing unit 290 of the data processing device 12. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice to the parent based on the analysis results.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The acquisition unit can also capture biological data such as the baby's temperature and heart rate. For example, if the baby's temperature rises, the acquisition unit records that data and notifies the parent if there is an abnormality. It can also monitor heart rate fluctuations to detect signs of stress or anxiety. It can also record the baby's breathing patterns and issue an alert if breathing difficulties or abnormal patterns are observed. This allows for more detailed monitoring of the baby's health and early detection of abnormalities.

[0122] The learning unit can adjust the learning model according to the baby's developmental stage. For example, different behavioral patterns are seen in the newborn and infant stages, so the learning unit builds models appropriate for each stage. As a baby grows, eating and sleeping patterns also change, so the learning unit can update the model to reflect these changes. Furthermore, the weighting of the learning data can be adjusted according to the baby's individual growth rate, allowing for more accurate predictions. This makes it possible to provide appropriate care that matches the baby's growth.

[0123] The prediction unit can optimize a parent's schedule based on the baby's behavioral patterns. For example, it can predict the next time the baby will want milk and send a reminder to the parent at that time. It can also predict the baby's sleep patterns and suggest the best time for the parent to rest. It can also predict the baby's activity times and provide a schedule that allows the parent to efficiently complete housework and work. This allows parents to create a schedule that meets the baby's needs and reduces stress.

[0124] The analysis unit can analyze not only the baby's cries, but also its facial expressions and movements. For example, if a baby is smiling, its facial expressions can be analyzed to determine that it is in a good mood. If the baby is reaching out, its movements can be analyzed to determine that it wants something. Furthermore, if the baby is squirming, its movements can be analyzed to determine that it is feeling uncomfortable. This makes it possible to comprehensively determine a baby's condition from not only its cries but also its facial expressions and movements.

[0125] The providing unit can estimate the baby's emotions and adjust the tone of advice to the parent based on the estimated emotions. For example, if the baby is in a good mood, the providing unit can provide the parent with advice accompanied by encouraging words. Also, if the baby is crying, the providing unit can suggest specific ways to respond to the parent in a calm tone. Furthermore, if the baby seems sleepy, the providing unit can provide the parent with advice about sleep in a gentle tone. This allows the parent to respond appropriately according to the baby's emotions.

[0126] The acquisition unit can acquire environmental data (such as temperature, humidity, and noise level) around the baby. For example, if the room temperature is too high, the acquisition unit records the data and prompts the parent to adjust the temperature appropriately. If the humidity is too low, the acquisition unit can also recommend humidification. Furthermore, if the noise level is high, the acquisition unit can notify the parent to provide a quiet environment. This makes it possible to provide data to maintain a comfortable environment for the baby.

[0127] The learning unit can estimate the baby's emotions and select learning data based on the estimated emotions. For example, data when the baby is in a good mood can be prioritized for learning. Alternatively, data when the baby is crying can be prioritized for learning. Alternatively, data when the baby is sleepy can be prioritized for learning. This allows the selection of learning data based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input the baby's emotion data into the generation AI and have the generation AI select the learning data.

[0128] The prediction unit can estimate the baby's emotions and improve the accuracy of prediction based on the estimated baby's emotions. For example, the next behavior can be predicted based on data of when the baby is in a good mood. The next behavior can also be predicted based on data of when the baby is crying. The next behavior can also be predicted based on data of when the baby is sleepy. This improves the accuracy of prediction based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the prediction unit can input the baby's emotion data into the generation AI and cause the generation AI to improve the accuracy of prediction.

[0129] The analysis unit can estimate the baby's emotions and improve the analysis accuracy of the baby's cries based on the estimated emotions. For example, the analysis accuracy can be improved based on the baby's cries when it is in a good mood. The analysis unit can also improve the analysis accuracy based on the baby's cries when it is crying. The analysis unit can also improve the analysis accuracy based on the baby's cries when it is sleepy. This improves the analysis accuracy of the baby's cries based on the baby's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the baby's emotion data into the generation AI and cause the generation AI to improve the analysis accuracy of the baby's cries.

[0130] The providing unit can estimate the baby's emotions and determine the priority of advice based on the estimated baby's emotions. For example, if the baby is crying, it can prioritize advice with a high urgency. Furthermore, if the baby is in a good mood, the providing unit can provide detailed advice. Furthermore, if the baby seems sleepy, the providing unit can prioritize advice about sleep. This makes it possible to determine the priority of advice based on the baby's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the baby's emotion data to the generation AI and cause the generation AI to determine the priority of advice.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The acquisition unit records the baby's daily routine. For example, it records in detail the times for feeding, sleep patterns, and diaper change times. To record the times for feeding the baby, the acquisition unit inputs the time and amount of feeding. The acquisition unit can also input the sleep time and wake-up time to record the baby's sleep pattern. Furthermore, the acquisition unit can also input the number of times the diaper was changed and the time of day to record the times for changing the diaper. Step 2: The learning unit learns from the recorded data. For example, the learning unit learns from data such as the baby's feeding times, sleep patterns, and diaper changing times, and builds a model to predict the baby's next behavior. The learning unit uses a machine learning algorithm to learn from the data and builds a model to predict the baby's next behavior. The learning unit can also preprocess the data to improve the accuracy of learning. Step 3: The prediction unit predicts the next behavior based on the data learned by the learning unit. For example, the prediction unit predicts the next time the baby will want milk or when to change its diaper. The prediction unit uses an algorithm to predict the next behavior based on past data. The prediction unit can also perform data preprocessing to improve the accuracy of the prediction. Step 4: The analysis unit analyzes the baby's cry. For example, the analysis unit determines the reason why the baby is crying and provides appropriate advice to the parents. The analysis unit uses audio analysis technology to analyze the baby's cry and identify the cause. The analysis unit can also refer to past data to identify the cause of the cry. Step 5: The providing unit provides advice to the parent based on the results of the analysis by the analyzing unit. For example, the providing unit suggests specific ways to respond to the parent when the baby is crying because it is hungry or when there is a reason the baby cannot sleep. The providing unit provides advice to the parent using a notification method. The providing unit can also customize the content of the advice.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0160] Fig. 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.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0165] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0167] The data processing system 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.

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

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The 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.

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

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] 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 AI 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 acquisition section that records the baby's daily routine; a learning unit that learns the data recorded by the acquisition unit; a prediction unit that predicts a next action based on the data learned by the learning unit; An analysis section that analyzes the baby's cries, a providing unit that provides advice to parents based on the results of the analysis by the analysis unit. A system characterized by:

2. The acquisition unit Track daily routines including feeding times, sleep patterns, and diaper changes 2. The system of claim 1.

3. The learning unit Learn from recorded data and predict next actions 2. The system of claim 1.

4. The prediction unit Predict when your baby will next need milk or when to change their diaper 2. The system of claim 1.

5. The analysis unit Analyze your baby's cries and determine their cause 2. The system of claim 1.

6. The providing unit Propose specific measures to parents based on the analysis results 2. The system of claim 1.

7. The acquisition unit Estimate the baby's emotions and adjust the timing of data acquisition based on the estimated emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the baby's past behavioral history and select the appropriate data collection method 2. The system of claim 1.

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