system

Abstract

The system according to this embodiment aims to estimate an infant's emotions from their vocalizations and suggest an appropriate response to the parents. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, a suggestion unit, and a provision unit. The collection unit collects vocalization data of the baby. The analysis unit analyzes the vocalization data collected by the collection unit. The estimation unit estimates the baby's emotions based on the data analyzed by the analysis unit. The suggestion unit makes suggestions to the parent based on the emotions estimated by the estimation unit. The provision unit provides the response method suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology presented a challenge in understanding a baby's emotions from their vocalizations and responding appropriately.

[0005] The system according to this embodiment aims to estimate an infant's emotions from their vocalizations and suggest an appropriate response to the parents. [Means for solving the problem]

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an estimation unit, a proposal unit, and a provision unit. The collection unit collects vocalization data of the baby. The analysis unit analyzes the vocalization data collected by the collection unit. The estimation unit estimates the baby's emotions based on the data analyzed by the analysis unit. The proposal unit makes suggestions to the parents based on the emotions estimated by the estimation unit. The provision unit provides the response methods proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can estimate the baby's emotions from the vocalizations and suggest appropriate responses 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) The system according to an embodiment of the present invention is a system that analyzes a baby's vocalizations, interprets their emotions, and proposes appropriate responses to the parents. This system collects the baby's vocalizations, and an AI analyzes the collected vocalization data to estimate the baby's emotions. Based on the estimated emotions, it proposes appropriate responses to the parents. This makes it easier for parents to understand their baby's emotions and activates communication. For example, if a baby cooes, the collection unit collects the sound, and the analysis unit analyzes the sound data. Based on the data analyzed by the analysis unit, the estimation unit estimates the baby's emotions, and based on the emotions estimated by the estimation unit, the suggestion unit proposes specific responses to the parents, such as "The baby is feeling happy. Please play with them using a toy." This makes it easier for parents to understand their baby's emotions and activates communication. In this way, the system can make it easier for parents to understand their baby's emotions and activate communication.

[0029] The system according to this embodiment comprises a collection unit, an analysis unit, an estimation unit, a proposal unit, and a provision unit. The collection unit collects vocal data from the baby. The collection unit can collect vocal data using, for example, a microphone or sensor placed near the baby. The collection unit collects the baby's vocal data in real time and transmits the data to the analysis unit. The analysis unit analyzes the collected vocal data using AI and estimates the baby's emotions. The analysis unit analyzes the audio waveform and frequency components of the vocal data using, for example, AI technologies such as neural networks and deep learning. The estimation unit estimates the baby's emotions based on the data analyzed by the analysis unit. The estimation unit can estimate the baby's emotions with high accuracy based on the analyzed data. The proposal unit proposes specific ways of responding to the parents based on the estimated emotions. For example, the proposal unit suggests specific ways of responding, such as "hold the baby" if the baby is crying, or "play with a toy" if the baby is laughing. The provision unit provides the parents with the ways of responding proposed by the proposal unit. The information providing unit can notify parents via a smartphone or tablet, for example, which allows the system to help parents understand their baby's emotions and stimulate communication.

[0030] The data collection unit may include a microphone or sensor placed near the baby. For example, the data collection unit can collect vocalization data using a condenser microphone or a piezo sensor placed near the baby. Condenser microphones are highly sensitive and cover a wide frequency range, so they can accurately capture even the baby's subtle vocalizations. Piezo sensors detect pressure changes, so they can sense vibrations caused by the baby's vocalizations and collect data. This allows the data collection unit to accurately collect the baby's vocalization data.

[0031] The analysis unit can use AI to analyze a baby's vocalization data and estimate their emotions. For example, the analysis unit uses AI technologies such as neural networks and deep learning to analyze the baby's vocalization data. Neural networks process data using multi-layered artificial neurons and can learn complex patterns. Deep learning trains models using large amounts of data and can perform highly accurate analyses. The analysis unit analyzes the audio waveform and frequency components of the vocalization data to estimate the baby's emotions. For example, the analysis unit analyzes the frequency components of a baby's crying and extracts the characteristics of the crying. It also analyzes the phonemes of a baby's laughter and extracts the characteristics of the laughter. This allows the analysis unit to estimate the baby's emotions with high accuracy.

[0032] The estimation unit can estimate the baby's emotions based on the analyzed data. The estimation unit estimates the baby's emotions based on the data analyzed by the analysis unit. For example, the estimation unit estimates the baby's emotions using speech features and emotion labels. Speech features are features extracted from the speech waveform and frequency components of the vocalization data, and emotion labels are labels that represent the baby's emotions. The estimation unit uses this data to estimate the baby's emotions. For example, the estimation unit analyzes the speech features of the baby's crying and estimates the emotion label of the crying. The estimation unit also analyzes the speech features of the baby's laughter and estimates the emotion label of the laughter. In this way, the estimation unit can accurately estimate the baby's emotions.

[0033] The suggestion unit can suggest a specific response method to the parent based on the estimated emotion. The suggestion unit suggests a specific response method to the parent based on the emotion estimated by the estimation unit. For example, the suggestion unit suggests a specific response method such as "hold the baby" if the baby is crying, or "play with a toy" if the baby is laughing. The suggestion unit suggests an appropriate response method according to the baby's emotion, allowing the parent to take an appropriate response according to the baby's emotion. In this way, the suggestion unit makes it easier for the parent to understand the baby's emotion and can stimulate communication.

[0034] The providing unit can provide the response method suggested by the suggestion unit to the parent. The providing unit provides the response method suggested by the suggestion unit to the parent. The providing unit can notify the parent via a smartphone or tablet, for example. The providing unit can quickly provide the parent with a specific response method, allowing the parent to quickly take an appropriate response in accordance with the baby's emotions. In this way, the providing unit can make it easier for the parent to understand the baby's emotions and stimulate communication.

[0035] The collection unit can analyze the baby's past vocalization data and select an appropriate collection method. The collection unit analyzes the baby's past vocalization data and selects an appropriate collection method. For example, the collection unit can identify the time period when the baby expresses the most emotion based on the past vocalization data and concentrate collection on that time period. The collection unit can also analyze the past vocalization data to understand the frequency with which a specific voice pattern appears and adjust the collection method based on that pattern. Furthermore, the collection unit can analyze the past vocalization data to find a tendency for the baby to vocalize in a specific situation and select a collection method according to that situation. In this way, the collection unit can select the optimal collection method by analyzing the past vocalization data and collect data efficiently.

[0036] The data collection unit can filter vocalization data based on the baby's current activity level and environment. For example, it can refrain from collecting data when the baby is sleeping and collect data when the baby is awake. Furthermore, when the baby is playing, the data collection unit can filter out ambient noise before collecting vocalization data. Additionally, when the baby is eating, the data collection unit can remove noise such as the sound of dishes before collecting vocalization data. In this way, the data collection unit can remove noise and collect accurate data by filtering based on the baby's activity level and environment.

[0037] The data collection unit can simultaneously collect biometric information such as the baby's body temperature or heart rate when collecting vocal data. For example, if the baby's body temperature is high, the data collection unit can collect the corresponding vocal data to understand changes in the baby's condition. Similarly, if the baby's heart rate is elevated, the data collection unit can collect the corresponding vocal data to understand the baby's state of excitement. Furthermore, if the baby's body temperature and heart rate are stable, the data collection unit can collect the corresponding vocal data to understand the stable state. This allows the data collection unit to more accurately understand the baby's condition by simultaneously collecting vocal data and biometric information.

[0038] The data collection unit can remove noise by considering the sound environment surrounding the baby when collecting vocalization data. For example, if a television is on near the baby, the data collection unit can remove that sound as noise. Similarly, if other people are talking near the baby, that sound can be removed as noise. Furthermore, if household appliances are operating near the baby, that sound can be removed as noise. This allows the data collection unit to collect accurate vocalization data by removing noise while considering the surrounding sound environment.

[0039] The analysis unit can perform a detailed analysis of the phonemes and frequency components of the vocalization data during the analysis process. For example, the analysis unit can analyze the phonemes of a baby's vocalization data to determine if specific phonemes are associated with emotions. It can also analyze the frequency components of a baby's vocalization data to determine if specific frequencies are associated with emotions. Furthermore, the analysis unit can combine the phonemes and frequency components of a baby's vocalization data to extract emotional characteristics. This allows the analysis unit to accurately grasp emotional characteristics by performing a detailed analysis of the phonemes and frequency components of the vocalization data.

[0040] The analysis unit can learn the baby's vocalization patterns during analysis to improve analysis accuracy. For example, the analysis unit can learn the baby's vocalization patterns and investigate whether specific patterns are related to emotions. Furthermore, the analysis unit can learn the baby's vocalization patterns and adjust algorithms to improve analysis accuracy. In addition, the analysis unit can learn the baby's vocalization patterns to more accurately grasp emotional characteristics. Thus, by learning the baby's vocalization patterns, the analysis unit can improve analysis accuracy.

[0041] During analysis, the analysis unit can detect abnormalities by comparing the baby's vocalization data with past data. During analysis, the analysis unit can detect abnormalities by comparing the baby's vocalization data with past data. For example, the analysis unit can compare the baby's vocalization data with past data to detect abnormal patterns. The analysis unit can also compare the baby's vocalization data with past data to detect abnormal frequency components. Furthermore, the analysis unit can compare the baby's vocalization data with past data to detect abnormal phonemes. This allows the analysis unit to detect abnormal vocalization patterns by comparing with past data.

[0042] During analysis, the analysis unit can compare the baby's vocalization data with data of other babies to extract features. During analysis, the analysis unit compares the baby's vocalization data with data of other babies to extract features. For example, the analysis unit can compare the baby's vocalization data with data of other babies to extract common features. The analysis unit can also compare the baby's vocalization data with data of other babies to extract features related to specific emotions. Furthermore, the analysis unit can compare the baby's vocalization data with data of other babies to extract individual features. In this way, the analysis unit can extract common features and individual features by comparing with data of other babies.

[0043] The estimation unit can estimate emotions taking into account temporal changes in the baby's vocalization data during estimation. The estimation unit estimates emotions taking into account temporal changes in the baby's vocalization data during estimation. The estimation unit can, for example, analyze temporal changes in the baby's vocalization data to estimate changes in emotions. Furthermore, the estimation unit can improve the accuracy of emotion estimation by taking into account temporal changes in the baby's vocalization data. Furthermore, the estimation unit can estimate changes in emotions in real time based on temporal changes in the baby's vocalization data. This allows the estimation unit to more accurately estimate changes in emotions by taking into account temporal changes in the vocalization data.

[0044] The estimation unit can estimate emotions by combining the baby's vocal data and biometric information during estimation. For example, the estimation unit can estimate emotions by combining the baby's vocal data and body temperature. Furthermore, the estimation unit can estimate emotions by combining the baby's vocal data and heart rate. In addition, the estimation unit can estimate emotions by combining the baby's vocal data and respiratory rate. This allows the estimation unit to improve the accuracy of emotion estimation by combining vocal data and biometric information.

[0045] The estimation unit can estimate emotions by combining the baby's vocalization data and the parent's response data during estimation. The estimation unit can improve the accuracy of emotion estimation by combining the baby's vocalization data and the parent's response data. Furthermore, the estimation unit can estimate changes in emotion in real time based on the baby's vocalization data and the parent's response data. In addition, the estimation unit can analyze the baby's vocalization data and the parent's response data to extract emotional characteristics. This allows the estimation unit to improve the accuracy of emotion estimation by combining vocalization data and parent's response data.

[0046] The estimation unit can estimate emotions by combining the baby's vocal data and environmental data during estimation. For example, the estimation unit can estimate emotions by combining the baby's vocal data with room temperature. It can also estimate emotions by combining the baby's vocal data with lighting brightness. Furthermore, the estimation unit can estimate emotions by combining the baby's vocal data with the surrounding sound environment. This allows the estimation unit to improve the accuracy of emotion estimation by combining vocal data and environmental data.

[0047] The suggestion unit can make appropriate suggestions by referring to the baby's past response data. For example, it can refer to methods that have calmed the baby in the past and make similar suggestions. It can also refer to games the baby has enjoyed in the past and make similar suggestions. Furthermore, it can refer to methods that have helped the baby relax in the past and make similar suggestions. This allows the suggestion unit to make optimal suggestions by referring to past response data.

[0048] The suggestion function can customize its suggestions based on the baby's current activity level. For example, if the baby is playing, it can suggest ways to encourage them to continue playing. If the baby is eating, it can suggest ways to help them enjoy their meal. Furthermore, if the baby is sleeping, it can suggest ways to help them sleep peacefully. This allows the suggestion function to customize its suggestions based on the baby's current activity level and provide more appropriate advice.

[0049] The suggestion function can make appropriate suggestions by considering the baby's biological information. For example, if the baby's body temperature is high, the suggestion function can make suggestions to lower the body temperature. Also, if the baby's heart rate is elevated, the suggestion function can make suggestions to stabilize the heart rate. Furthermore, if the baby's respiratory rate is increasing, the suggestion function can make suggestions to calm the breathing. In this way, the suggestion function can make optimal suggestions by considering biological information.

[0050] The proposal function can adjust its suggestions when making them, taking into account environmental data surrounding the baby. For example, if the baby's surroundings are noisy, the proposal function can suggest creating a quieter environment. Similarly, if the baby's surroundings are dark, it can suggest making them brighter. Furthermore, if the baby's surroundings are cold, it can suggest making them warmer. This allows the proposal function to make more appropriate suggestions by considering environmental data.

[0051] The dispensing unit can select the optimal dispensing method by referring to the parent's past response data at the time of dispensing. The dispensing unit can select the optimal dispensing method by referring to the parent's past response data at the time of dispensing. For example, the dispensing unit can refer to methods that were effective for the parent in the past and select a similar dispensing method. Furthermore, the dispensing unit can refer to methods that the parent avoided in the past and select a different dispensing method. In this way, the dispensing unit can select the optimal dispensing method by referring to past response data.

[0052] The service provider can customize the services offered based on the baby's current activity level at the time of delivery. For example, if the baby is playing, the service provider can offer ways to help them continue playing. If the baby is eating, the service provider can offer ways to help them enjoy their meal. Furthermore, if the baby is sleeping, the service provider can offer ways to help them sleep peacefully. This allows the service provider to customize the services based on the baby's current activity level and provide more appropriate support.

[0053] The providing unit can select an appropriate providing method by taking into consideration device information of the parent at the time of providing. The providing unit can select an appropriate providing method by taking into consideration device information of the parent at the time of providing. For example, if the parent uses a smartphone, the providing unit can select a providing method optimized for the smartphone. Furthermore, if the parent uses a tablet, the providing unit can select a providing method optimized for the tablet. Furthermore, if the parent uses a smartwatch, the providing unit can select a providing method optimized for the smartwatch. In this way, the providing unit can select the optimal providing method by taking into consideration device information.

[0054] The providing unit can adjust the content of the information provided by taking into account environmental data around the baby when providing the information. The providing unit can adjust the content of the information provided by taking into account environmental data around the baby when providing the information. For example, if the environment around the baby is noisy, the providing unit can provide a method for creating a quiet environment. Furthermore, if the environment around the baby is dark, the providing unit can provide a method for making the environment brighter. Furthermore, if the environment around the baby is cold, the providing unit can provide a method for making the environment warmer. In this way, the providing unit can provide a more appropriate response by taking into account the environmental data.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The data collection unit can simultaneously collect the temperature and humidity surrounding the baby when collecting the baby's vocalization data. This allows the data collection unit to combine the baby's vocalization data with the environmental data and transmit it to the analysis unit. The analysis unit analyzes the vocalization data while considering the environmental data, enabling a more accurate estimation of the baby's emotions. For example, if a baby is crying in a hot environment, considering the environmental data allows the analysis unit to estimate that the baby is feeling uncomfortable due to the heat. Similarly, if a baby is crying in a humid environment, it can be estimated that the baby is feeling uncomfortable due to the humidity. Furthermore, if a baby is laughing in an environment with comfortable temperature and humidity, considering the environmental data allows the analysis unit to estimate that the baby is feeling comfortable. In this way, the data collection unit can utilize the environmental data to more accurately estimate the baby's emotions.

[0057] The analysis unit can improve the accuracy of its analysis by referring to the baby's past vocalization data when analyzing the baby's vocalization data. By learning the baby's vocalization patterns based on past vocalization data and comparing them with current vocalization data, the analysis unit can improve the accuracy of emotion estimation. For example, by referring to vocalization data from when the baby cried in the past and comparing it to the current crying, it can determine whether the baby is crying for the same reason. Similarly, by referring to vocalization data from when the baby laughed in the past and comparing it to the current laughing, it can determine whether the baby is laughing for the same reason. Furthermore, by referring to vocalization data from when the baby was quiet in the past and comparing it to the current quiet state, it can determine whether the baby is quiet for the same reason. In this way, the analysis unit can improve the accuracy of emotion estimation by utilizing past vocalization data.

[0058] The service provider can adjust the method of providing the proposed solutions to parents, taking into account the parents' current situation. If the parents are busy, the service provider can provide easily implemented solutions; if they have more time, they can provide more detailed solutions. For example, if the parents are busy at work, they can provide solutions that can be implemented quickly; if they are at home and have time, they can provide solutions that can be implemented over a longer period. Similarly, if the parents are out, they can provide solutions that can be implemented while out; if they are at home, they can provide solutions that can be implemented at home. Furthermore, if the parents are tired, they can provide easily implemented solutions; if they are energetic, they can provide more proactive solutions. This allows the service provider to select the most appropriate solution based on the parents' current situation.

[0059] The data collection unit can simultaneously collect biometric information such as the baby's body temperature and heart rate when collecting vocal data from the baby. This allows the data collection unit to combine the vocal data and biometric information and transmit it to the analysis unit. The analysis unit analyzes the vocal data while considering the biometric information, enabling a more accurate estimation of the baby's emotions. For example, if the baby's body temperature is high, analyzing the vocal data can be used to estimate that the baby is crying due to illness. Similarly, if the baby's heart rate is elevated, analyzing the vocal data can be used to estimate that the baby is excited. Furthermore, if the baby's body temperature and heart rate are stable, analyzing the vocal data can be used to estimate that the baby is relaxed. In this way, the data collection unit can utilize biometric information to more accurately estimate the baby's emotions.

[0060] The estimation unit can estimate a baby's emotions by combining the baby's vocalization data and environmental data. The estimation unit can improve the accuracy of emotion estimation by analyzing vocalization data based on environmental data. For example, if a baby is crying, it can refer to ambient noise data to estimate whether the noise is the cause of the crying. If a baby is laughing, it can refer to ambient brightness data to estimate whether the brightness is influencing the laughing. Furthermore, if a baby is quiet, it can refer to ambient temperature data to estimate whether the temperature is influencing the laughing. In this way, the estimation unit can use environmental data to more accurately estimate the baby's emotions.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects the baby's vocalization data. The collection unit can collect the vocalization data using, for example, a microphone or a sensor installed near the baby. The collection unit collects the baby's vocalization data in real time and transmits the data to the analysis unit. Step 2: The analysis unit uses AI to analyze the collected vocalization data and estimate the baby's emotions. The analysis unit uses AI technologies such as neural networks and deep learning to analyze the voice waveforms and frequency components of the vocalization data. Step 3: The estimation unit estimates the baby's emotion based on the data analyzed by the analysis unit. The estimation unit can estimate the baby's emotion with high accuracy based on the analyzed data. Step 4: The suggestion unit suggests specific ways to respond to the parent based on the estimated emotions. For example, if the baby is crying, the suggestion unit suggests "hold the baby"; if the baby is laughing, the suggestion unit suggests "play with a toy." Step 5: The providing unit provides the parent with the response method suggested by the suggesting unit. The providing unit can notify the parent via a smartphone or tablet, for example.

[0063] (Example 2) The system according to an embodiment of the present invention is a system that analyzes a baby's vocalizations, interprets their emotions, and proposes appropriate responses to the parents. This system collects the baby's vocalizations, and an AI analyzes the collected vocalization data to estimate the baby's emotions. Based on the estimated emotions, it proposes appropriate responses to the parents. This makes it easier for parents to understand their baby's emotions and activates communication. For example, if a baby cooes, the collection unit collects the sound, and the analysis unit analyzes the sound data. Based on the data analyzed by the analysis unit, the estimation unit estimates the baby's emotions, and based on the emotions estimated by the estimation unit, the suggestion unit proposes specific responses to the parents, such as "The baby is feeling happy. Please play with them using a toy." This makes it easier for parents to understand their baby's emotions and activates communication. In this way, the system can make it easier for parents to understand their baby's emotions and activate communication.

[0064] The system according to this embodiment comprises a collection unit, an analysis unit, an estimation unit, a proposal unit, and a provision unit. The collection unit collects vocal data from the baby. The collection unit can collect vocal data using, for example, a microphone or sensor placed near the baby. The collection unit collects the baby's vocal data in real time and transmits the data to the analysis unit. The analysis unit analyzes the collected vocal data using AI and estimates the baby's emotions. The analysis unit analyzes the audio waveform and frequency components of the vocal data using, for example, AI technologies such as neural networks and deep learning. The estimation unit estimates the baby's emotions based on the data analyzed by the analysis unit. The estimation unit can estimate the baby's emotions with high accuracy based on the analyzed data. The proposal unit proposes specific ways of responding to the parents based on the estimated emotions. For example, the proposal unit suggests specific ways of responding, such as "hold the baby" if the baby is crying, or "play with a toy" if the baby is laughing. The provision unit provides the parents with the ways of responding proposed by the proposal unit. The information providing unit can notify parents via a smartphone or tablet, for example, which allows the system to help parents understand their baby's emotions and stimulate communication.

[0065] The data collection unit may include a microphone or sensor placed near the baby. For example, the data collection unit can collect vocalization data using a condenser microphone or a piezo sensor placed near the baby. Condenser microphones are highly sensitive and cover a wide frequency range, so they can accurately capture even the baby's subtle vocalizations. Piezo sensors detect pressure changes, so they can sense vibrations caused by the baby's vocalizations and collect data. This allows the data collection unit to accurately collect the baby's vocalization data.

[0066] The analysis unit can use AI to analyze a baby's vocalization data and estimate their emotions. For example, the analysis unit uses AI technologies such as neural networks and deep learning to analyze the baby's vocalization data. Neural networks process data using multi-layered artificial neurons and can learn complex patterns. Deep learning trains models using large amounts of data and can perform highly accurate analyses. The analysis unit analyzes the audio waveform and frequency components of the vocalization data to estimate the baby's emotions. For example, the analysis unit analyzes the frequency components of a baby's crying and extracts the characteristics of the crying. It also analyzes the phonemes of a baby's laughter and extracts the characteristics of the laughter. This allows the analysis unit to estimate the baby's emotions with high accuracy.

[0067] The estimation unit can estimate the baby's emotions based on the analyzed data. The estimation unit estimates the baby's emotions based on the data analyzed by the analysis unit. For example, the estimation unit estimates the baby's emotions using speech features and emotion labels. Speech features are features extracted from the speech waveform and frequency components of the vocalization data, and emotion labels are labels that represent the baby's emotions. The estimation unit uses this data to estimate the baby's emotions. For example, the estimation unit analyzes the speech features of the baby's crying and estimates the emotion label of the crying. The estimation unit also analyzes the speech features of the baby's laughter and estimates the emotion label of the laughter. In this way, the estimation unit can accurately estimate the baby's emotions.

[0068] The suggestion unit can suggest a specific response method to the parent based on the estimated emotion. The suggestion unit suggests a specific response method to the parent based on the emotion estimated by the estimation unit. For example, the suggestion unit suggests a specific response method such as "hold the baby" if the baby is crying, or "play with a toy" if the baby is laughing. The suggestion unit suggests an appropriate response method according to the baby's emotion, allowing the parent to take an appropriate response according to the baby's emotion. In this way, the suggestion unit makes it easier for the parent to understand the baby's emotion and can stimulate communication.

[0069] The providing unit can provide the response method suggested by the suggestion unit to the parent. The providing unit provides the response method suggested by the suggestion unit to the parent. The providing unit can notify the parent via a smartphone or tablet, for example. The providing unit can quickly provide the parent with a specific response method, allowing the parent to quickly take an appropriate response in accordance with the baby's emotions. In this way, the providing unit can make it easier for the parent to understand the baby's emotions and stimulate communication.

[0070] The data collection unit can estimate the baby's emotions and adjust the timing of vocal data collection based on the estimated emotions. For example, the unit can collect vocal data the moment the baby starts crying, capturing emotional changes in real time. It can also collect vocal data when the baby starts laughing, prioritizing the acquisition of positive emotional data. Furthermore, it can collect vocal data when the baby is quiet, assessing emotional stability. This allows the data collection unit to adjust the timing of vocal data collection according to the baby's emotions, enabling the collection of more appropriate data.

[0071] The collection unit can analyze the baby's past vocalization data and select an appropriate collection method. The collection unit analyzes the baby's past vocalization data and selects an appropriate collection method. For example, the collection unit can identify the time period when the baby expresses the most emotion based on the past vocalization data and concentrate collection on that time period. The collection unit can also analyze the past vocalization data to understand the frequency with which a specific voice pattern appears and adjust the collection method based on that pattern. Furthermore, the collection unit can analyze the past vocalization data to find a tendency for the baby to vocalize in a specific situation and select a collection method according to that situation. In this way, the collection unit can select the optimal collection method by analyzing the past vocalization data and collect data efficiently.

[0072] The data collection unit can filter vocalization data based on the baby's current activity level and environment. For example, it can refrain from collecting data when the baby is sleeping and collect data when the baby is awake. Furthermore, when the baby is playing, the data collection unit can filter out ambient noise before collecting vocalization data. Additionally, when the baby is eating, the data collection unit can remove noise such as the sound of dishes before collecting vocalization data. In this way, the data collection unit can remove noise and collect accurate data by filtering based on the baby's activity level and environment.

[0073] The data collection unit can estimate the baby's emotions and determine the priority of the vocalization data to collect based on the estimated emotions. For example, if the baby is crying, the data collection unit can prioritize collecting that vocalization data. Similarly, if the baby is laughing, the data collection unit can prioritize collecting that vocalization data. Furthermore, if the baby is quiet, the data collection unit can prioritize collecting that vocalization data. This allows the data collection unit to prioritize vocalization data based on the baby's emotions and collect important data preferentially.

[0074] The data collection unit can simultaneously collect biometric information such as the baby's body temperature or heart rate when collecting vocal data. For example, if the baby's body temperature is high, the data collection unit can collect the corresponding vocal data to understand changes in the baby's condition. Similarly, if the baby's heart rate is elevated, the data collection unit can collect the corresponding vocal data to understand the baby's state of excitement. Furthermore, if the baby's body temperature and heart rate are stable, the data collection unit can collect the corresponding vocal data to understand the stable state. This allows the data collection unit to more accurately understand the baby's condition by simultaneously collecting vocal data and biometric information.

[0075] The data collection unit can remove noise by considering the sound environment surrounding the baby when collecting vocalization data. For example, if a television is on near the baby, the data collection unit can remove that sound as noise. Similarly, if other people are talking near the baby, that sound can be removed as noise. Furthermore, if household appliances are operating near the baby, that sound can be removed as noise. This allows the data collection unit to collect accurate vocalization data by removing noise while considering the surrounding sound environment.

[0076] The analysis unit can estimate the baby's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the baby is crying, the analysis unit can focus on the frequency components of the crying sound. Similarly, if the baby is laughing, the analysis unit can focus on the phonemes of the laughing sound. Furthermore, if the baby is quiet, the analysis unit can focus on the characteristics of quiet speech. This allows the analysis unit to adjust the analysis algorithm based on the baby's emotions and improve analysis accuracy.

[0077] The analysis unit can perform a detailed analysis of the phonemes and frequency components of the vocalization data during the analysis process. For example, the analysis unit can analyze the phonemes of a baby's vocalization data to determine if specific phonemes are associated with emotions. It can also analyze the frequency components of a baby's vocalization data to determine if specific frequencies are associated with emotions. Furthermore, the analysis unit can combine the phonemes and frequency components of a baby's vocalization data to extract emotional characteristics. This allows the analysis unit to accurately grasp emotional characteristics by performing a detailed analysis of the phonemes and frequency components of the vocalization data.

[0078] The analysis unit can learn the baby's vocalization patterns during analysis to improve analysis accuracy. For example, the analysis unit can learn the baby's vocalization patterns and investigate whether specific patterns are related to emotions. Furthermore, the analysis unit can learn the baby's vocalization patterns and adjust algorithms to improve analysis accuracy. In addition, the analysis unit can learn the baby's vocalization patterns to more accurately grasp emotional characteristics. Thus, by learning the baby's vocalization patterns, the analysis unit can improve analysis accuracy.

[0079] The analysis unit can estimate the baby's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the baby is crying, the analysis unit can highlight the analysis results for crying sounds. Similarly, if the baby is laughing, the analysis unit can highlight the analysis results for laughing sounds. Furthermore, if the baby is quiet, the analysis unit can highlight the analysis results for quiet sounds. In this way, the analysis unit can adjust the display method of the analysis results based on the baby's emotions and display the results in a way that is easy for parents to understand.

[0080] During analysis, the analysis unit can detect abnormalities by comparing the baby's vocalization data with past data. During analysis, the analysis unit can detect abnormalities by comparing the baby's vocalization data with past data. For example, the analysis unit can compare the baby's vocalization data with past data to detect abnormal patterns. The analysis unit can also compare the baby's vocalization data with past data to detect abnormal frequency components. Furthermore, the analysis unit can compare the baby's vocalization data with past data to detect abnormal phonemes. This allows the analysis unit to detect abnormal vocalization patterns by comparing with past data.

[0081] During analysis, the analysis unit can compare the baby's vocalization data with data of other babies to extract features. During analysis, the analysis unit compares the baby's vocalization data with data of other babies to extract features. For example, the analysis unit can compare the baby's vocalization data with data of other babies to extract common features. The analysis unit can also compare the baby's vocalization data with data of other babies to extract features related to specific emotions. Furthermore, the analysis unit can compare the baby's vocalization data with data of other babies to extract individual features. In this way, the analysis unit can extract common features and individual features by comparing with data of other babies.

[0082] The estimation unit can estimate the baby's emotion and adjust the estimation algorithm based on the estimated baby's emotion. The estimation unit can estimate the baby's emotion and adjust the estimation algorithm based on the estimated baby's emotion. For example, when the baby is crying, the estimation unit can adjust the estimation algorithm by emphasizing features of crying sounds. Furthermore, when the baby is laughing, the estimation unit can adjust the estimation algorithm by emphasizing features of laughter sounds. Furthermore, when the baby is quiet, the estimation unit can adjust the estimation algorithm by emphasizing features of quiet sounds. In this way, the estimation unit can adjust the estimation algorithm based on the baby's emotion and improve estimation accuracy.

[0083] The estimation unit can estimate emotions taking into account temporal changes in the baby's vocalization data during estimation. The estimation unit estimates emotions taking into account temporal changes in the baby's vocalization data during estimation. The estimation unit can, for example, analyze temporal changes in the baby's vocalization data to estimate changes in emotions. Furthermore, the estimation unit can improve the accuracy of emotion estimation by taking into account temporal changes in the baby's vocalization data. Furthermore, the estimation unit can estimate changes in emotions in real time based on temporal changes in the baby's vocalization data. This allows the estimation unit to more accurately estimate changes in emotions by taking into account temporal changes in the vocalization data.

[0084] The estimation unit can estimate emotions by combining the baby's vocal data and biometric information during estimation. For example, the estimation unit can estimate emotions by combining the baby's vocal data and body temperature. Furthermore, the estimation unit can estimate emotions by combining the baby's vocal data and heart rate. In addition, the estimation unit can estimate emotions by combining the baby's vocal data and respiratory rate. This allows the estimation unit to improve the accuracy of emotion estimation by combining vocal data and biometric information.

[0085] The estimation unit can estimate the baby's emotion and adjust the display method of the estimation result based on the estimated baby's emotion. The estimation unit can estimate the baby's emotion and adjust the display method of the estimation result based on the estimated baby's emotion. For example, if the baby is crying, the estimation unit can emphasize and display the estimated result of crying. Also, if the baby is laughing, the estimation unit can emphasize and display the estimated result of laughter. Furthermore, if the baby is quiet, the estimation unit can emphasize and display the estimated result of quiet voices. In this way, the estimation unit can adjust the display method of the estimation result based on the baby's emotion and display the result in a form that is easy for parents to understand.

[0086] The estimation unit can estimate emotions by combining the baby's vocalization data and the parent's response data during estimation. The estimation unit can improve the accuracy of emotion estimation by combining the baby's vocalization data and the parent's response data. Furthermore, the estimation unit can estimate changes in emotion in real time based on the baby's vocalization data and the parent's response data. In addition, the estimation unit can analyze the baby's vocalization data and the parent's response data to extract emotional characteristics. This allows the estimation unit to improve the accuracy of emotion estimation by combining vocalization data and parent's response data.

[0087] The estimation unit can estimate emotions by combining the baby's vocal data and environmental data during estimation. For example, the estimation unit can estimate emotions by combining the baby's vocal data with room temperature. It can also estimate emotions by combining the baby's vocal data with lighting brightness. Furthermore, the estimation unit can estimate emotions by combining the baby's vocal data with the surrounding sound environment. This allows the estimation unit to improve the accuracy of emotion estimation by combining vocal data and environmental data.

[0088] The suggestion unit can estimate the baby's emotions and adjust its suggestions based on those estimates. For example, if the baby is crying, the suggestion unit can suggest ways to soothe the baby. If the baby is laughing, it can suggest ways to further entertain the baby. Furthermore, if the baby is quiet, it can suggest ways to help the baby relax. This allows the suggestion unit to adjust its suggestions based on the baby's emotions and provide more appropriate advice.

[0089] The suggestion unit can make appropriate suggestions by referring to the baby's past response data. For example, it can refer to methods that have calmed the baby in the past and make similar suggestions. It can also refer to games the baby has enjoyed in the past and make similar suggestions. Furthermore, it can refer to methods that have helped the baby relax in the past and make similar suggestions. This allows the suggestion unit to make optimal suggestions by referring to past response data.

[0090] The suggestion function can customize its suggestions based on the baby's current activity level. For example, if the baby is playing, it can suggest ways to encourage them to continue playing. If the baby is eating, it can suggest ways to help them enjoy their meal. Furthermore, if the baby is sleeping, it can suggest ways to help them sleep peacefully. This allows the suggestion function to customize its suggestions based on the baby's current activity level and provide more appropriate advice.

[0091] The suggestion unit can estimate the baby's emotions and determine the priority of suggestions based on those emotions. For example, if the baby is crying, the suggestion unit can prioritize suggestions to soothe the baby. If the baby is laughing, the suggestion unit can prioritize suggestions to entertain the baby. Furthermore, if the baby is quiet, the suggestion unit can prioritize suggestions to help the baby relax. In this way, the suggestion unit can determine the priority of suggestions based on the baby's emotions and prioritize important suggestions.

[0092] The suggestion function can make appropriate suggestions by considering the baby's biological information. For example, if the baby's body temperature is high, the suggestion function can make suggestions to lower the body temperature. Also, if the baby's heart rate is elevated, the suggestion function can make suggestions to stabilize the heart rate. Furthermore, if the baby's respiratory rate is increasing, the suggestion function can make suggestions to calm the breathing. In this way, the suggestion function can make optimal suggestions by considering biological information.

[0093] The proposal function can adjust its suggestions when making them, taking into account environmental data surrounding the baby. For example, if the baby's surroundings are noisy, the proposal function can suggest creating a quieter environment. Similarly, if the baby's surroundings are dark, it can suggest making them brighter. Furthermore, if the baby's surroundings are cold, it can suggest making them warmer. This allows the proposal function to make more appropriate suggestions by considering environmental data.

[0094] The service provider can estimate the baby's emotions and adjust the service delivery method based on the estimated emotions. For example, if the baby is crying, the service provider can quickly provide methods to soothe the baby. If the baby is laughing, the service provider can provide methods to entertain the baby. Furthermore, if the baby is quiet, the service provider can provide methods to help the baby relax. This allows the service provider to adjust the service delivery method based on the baby's emotions and provide a more appropriate response.

[0095] The dispensing unit can select the optimal dispensing method by referring to the parent's past response data at the time of dispensing. The dispensing unit can select the optimal dispensing method by referring to the parent's past response data at the time of dispensing. For example, the dispensing unit can refer to methods that were effective for the parent in the past and select a similar dispensing method. Furthermore, the dispensing unit can refer to methods that the parent avoided in the past and select a different dispensing method. In this way, the dispensing unit can select the optimal dispensing method by referring to past response data.

[0096] The service provider can customize the services offered based on the baby's current activity level at the time of delivery. For example, if the baby is playing, the service provider can offer ways to help them continue playing. If the baby is eating, the service provider can offer ways to help them enjoy their meal. Furthermore, if the baby is sleeping, the service provider can offer ways to help them sleep peacefully. This allows the service provider to customize the services based on the baby's current activity level and provide more appropriate support.

[0097] The service provider can estimate the baby's emotions and determine the priority of services based on those estimates. For example, if the baby is crying, the service provider can prioritize providing methods to soothe the baby. Similarly, if the baby is laughing, the service provider can prioritize providing methods to entertain the baby. Furthermore, if the baby is quiet, the service provider can prioritize providing methods to help the baby relax. This allows the service provider to prioritize services based on the baby's emotions and provide important responses first.

[0098] The providing unit can select an appropriate providing method by taking into consideration device information of the parent at the time of providing. The providing unit can select an appropriate providing method by taking into consideration device information of the parent at the time of providing. For example, if the parent uses a smartphone, the providing unit can select a providing method optimized for the smartphone. Furthermore, if the parent uses a tablet, the providing unit can select a providing method optimized for the tablet. Furthermore, if the parent uses a smartwatch, the providing unit can select a providing method optimized for the smartwatch. In this way, the providing unit can select the optimal providing method by taking into consideration device information.

[0099] The providing unit can adjust the content of the information provided by taking into account environmental data around the baby when providing the information. The providing unit can adjust the content of the information provided by taking into account environmental data around the baby when providing the information. For example, if the environment around the baby is noisy, the providing unit can provide a method for creating a quiet environment. Furthermore, if the environment around the baby is dark, the providing unit can provide a method for making the environment brighter. Furthermore, if the environment around the baby is cold, the providing unit can provide a method for making the environment warmer. In this way, the providing unit can provide a more appropriate response by taking into account the environmental data. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, suggestion 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 collection unit collects vocalization data of the baby using the microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected vocalization data using AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the baby's emotions based on the analyzed data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a specific response method to the parent based on the estimated emotions. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the suggested response method to the parent. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the baby's vocalization data using the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected vocalization data using AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the baby's emotions based on the analyzed data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a specific response method to the parent based on the estimated emotions. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the suggested response method to the parent. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, suggestion 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 collection unit collects the baby's vocalization data using the microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected vocalization data using AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the baby's emotions based on the analyzed data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests specific ways of responding to the baby to the parent based on the estimated emotions. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the suggested ways of responding to the parent. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects vocalization data of the baby using the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected vocalization data using AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the baby's emotions based on the analyzed data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a specific response method to the parent based on the estimated emotions. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the suggested response method to the parent.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The system may include a collection unit, an analysis unit, an estimation unit, a suggestion unit, and a provision unit that collect the baby's vocalization data, as well as a motion detection unit that detects the baby's movements. The motion detection unit detects the baby's movements and transmits the data to the analysis unit. The analysis unit combines and analyzes the motion data and vocalization data to more accurately estimate the baby's emotions. For example, if a baby is crying and waving its hands, combining the motion data and vocalization data can estimate that the baby is likely asking for something. Also, if a baby is laughing and clapping its hands, combining the motion data and vocalization data can estimate that the baby is having a lot of fun. Furthermore, even if the baby is quiet, analyzing the motion data can determine whether the baby is relaxed. This allows the system to utilize the baby's motion data to improve the accuracy of emotion estimation.

[0102] The data collection unit can simultaneously collect the temperature and humidity surrounding the baby when collecting the baby's vocalization data. This allows the data collection unit to combine the baby's vocalization data with the environmental data and transmit it to the analysis unit. The analysis unit analyzes the vocalization data while considering the environmental data, enabling a more accurate estimation of the baby's emotions. For example, if a baby is crying in a hot environment, considering the environmental data allows the analysis unit to estimate that the baby is feeling uncomfortable due to the heat. Similarly, if a baby is crying in a humid environment, it can be estimated that the baby is feeling uncomfortable due to the humidity. Furthermore, if a baby is laughing in an environment with comfortable temperature and humidity, considering the environmental data allows the analysis unit to estimate that the baby is feeling comfortable. In this way, the data collection unit can utilize the environmental data to more accurately estimate the baby's emotions.

[0103] The analysis unit can improve the accuracy of its analysis by referring to the baby's past vocalization data when analyzing the baby's vocalization data. By learning the baby's vocalization patterns based on past vocalization data and comparing them with current vocalization data, the analysis unit can improve the accuracy of emotion estimation. For example, by referring to vocalization data from when the baby cried in the past and comparing it to the current crying, it can determine whether the baby is crying for the same reason. Similarly, by referring to vocalization data from when the baby laughed in the past and comparing it to the current laughing, it can determine whether the baby is laughing for the same reason. Furthermore, by referring to vocalization data from when the baby was quiet in the past and comparing it to the current quiet state, it can determine whether the baby is quiet for the same reason. In this way, the analysis unit can improve the accuracy of emotion estimation by utilizing past vocalization data.

[0104] The estimation unit can utilize the baby's facial expression data when estimating the baby's emotions. By analyzing the baby's facial expression data and combining it with vocalization data, the estimation unit can improve the accuracy of emotion estimation. For example, if the baby is crying, analyzing the facial expression data can more accurately estimate whether the baby is sad or angry. Similarly, if the baby is smiling, analyzing the facial expression data can more accurately estimate whether the baby is having fun or feeling secure. Furthermore, if the baby is expressionless and quiet, analyzing the facial expression data can more accurately estimate whether the baby is relaxed or tired. In this way, the estimation unit can improve the accuracy of emotion estimation by utilizing facial expression data.

[0105] The suggestion function can estimate the baby's emotions and, based on those estimates, can refer to the parents' past response data when suggesting specific ways to respond to the parents. By referring to how the parents have responded in the past, the suggestion function can propose the most appropriate response. For example, if the baby is crying, it can refer to methods the parents have used in the past to soothe the baby and offer similar suggestions. Similarly, if the baby is laughing, it can refer to methods the parents have used in the past to entertain the baby and offer similar suggestions. Furthermore, if the baby is quiet, it can refer to methods the parents have used in the past to relax the baby and offer similar suggestions. This allows the suggestion function to utilize the parents' past response data to propose more appropriate responses.

[0106] The service provider can adjust the method of providing the proposed solutions to parents, taking into account the parents' current situation. If the parents are busy, the service provider can provide easily implemented solutions; if they have more time, they can provide more detailed solutions. For example, if the parents are busy at work, they can provide solutions that can be implemented quickly; if they are at home and have time, they can provide solutions that can be implemented over a longer period. Similarly, if the parents are out, they can provide solutions that can be implemented while out; if they are at home, they can provide solutions that can be implemented at home. Furthermore, if the parents are tired, they can provide easily implemented solutions; if they are energetic, they can provide more proactive solutions. This allows the service provider to select the most appropriate solution based on the parents' current situation.

[0107] The data collection unit can simultaneously collect biometric information such as the baby's body temperature and heart rate when collecting vocal data from the baby. This allows the data collection unit to combine the vocal data and biometric information and transmit it to the analysis unit. The analysis unit analyzes the vocal data while considering the biometric information, enabling a more accurate estimation of the baby's emotions. For example, if the baby's body temperature is high, analyzing the vocal data can be used to estimate that the baby is crying due to illness. Similarly, if the baby's heart rate is elevated, analyzing the vocal data can be used to estimate that the baby is excited. Furthermore, if the baby's body temperature and heart rate are stable, analyzing the vocal data can be used to estimate that the baby is relaxed. In this way, the data collection unit can utilize biometric information to more accurately estimate the baby's emotions.

[0108] The analysis unit can combine the baby's vocalization data with the parent's reaction data when analyzing the baby's vocalization data. Based on the parent's reaction data, the analysis unit can analyze the baby's vocalization data to improve the accuracy of emotion estimation. For example, if the baby is crying, the analysis unit can refer to the parent's reaction and analyze the vocalization data based on that reaction data to more accurately estimate what the baby wants. Similarly, if the baby is laughing, the analysis unit can refer to the parent's reaction and analyze the vocalization data based on that reaction data to more accurately estimate what the baby is enjoying. Furthermore, if the baby is quiet, the analysis unit can refer to the parent's reaction and analyze the vocalization data based on that reaction data to more accurately estimate what the baby is feeling. In this way, the analysis unit can improve the accuracy of emotion estimation by utilizing the parent's reaction data.

[0109] The estimation unit can estimate a baby's emotions by combining the baby's vocalization data and environmental data. The estimation unit can improve the accuracy of emotion estimation by analyzing vocalization data based on environmental data. For example, if a baby is crying, it can refer to ambient noise data to estimate whether the noise is the cause of the crying. If a baby is laughing, it can refer to ambient brightness data to estimate whether the brightness is influencing the laughing. Furthermore, if a baby is quiet, it can refer to ambient temperature data to estimate whether the temperature is influencing the laughing. In this way, the estimation unit can use environmental data to more accurately estimate the baby's emotions.

[0110] The suggestion unit can estimate the baby's emotion and, based on the estimated emotion, consider the baby's current activity status when suggesting a specific response method to the parent. If the baby is playing, the suggestion unit can make a suggestion to continue playing, and if the baby is eating, the suggestion unit can make a suggestion to enjoy the meal. For example, if the baby is playing, the suggestion unit can make a suggestion such as "Play with your toys," and if the baby is eating, the suggestion unit can make a suggestion such as "Speak to the baby slowly so that he or she can enjoy his or her meal." Furthermore, if the baby is sleeping, the suggestion unit can make a suggestion such as "Let the baby sleep quietly." Furthermore, if the baby is crying, the suggestion unit can make a suggestion such as "Hold the baby." In this way, the suggestion unit can propose a more appropriate response method by considering the baby's current activity status.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects the baby's vocalization data. The collection unit can collect the vocalization data using, for example, a microphone or a sensor installed near the baby. The collection unit collects the baby's vocalization data in real time and transmits the data to the analysis unit. Step 2: The analysis unit uses AI to analyze the collected vocalization data and estimate the baby's emotions. The analysis unit uses AI technologies such as neural networks and deep learning to analyze the voice waveforms and frequency components of the vocalization data. Step 3: The estimation unit estimates the baby's emotion based on the data analyzed by the analysis unit. The estimation unit can estimate the baby's emotion with high accuracy based on the analyzed data. Step 4: The suggestion unit suggests specific ways to respond to the parent based on the estimated emotions. For example, if the baby is crying, the suggestion unit suggests "hold the baby"; if the baby is laughing, the suggestion unit suggests "play with a toy." Step 5: The providing unit provides the parent with the response method suggested by the suggesting unit. The providing unit can notify the parent via a smartphone or tablet, for example.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting baby vocalization data; an analysis unit that analyzes the utterance data collected by the collection unit; an estimation unit that estimates the baby's emotion based on the data analyzed by the analysis unit; a suggestion unit that makes a suggestion to a parent based on the emotion estimated by the estimation unit; a providing unit that provides the response method proposed by the suggesting unit; Equipped with A system characterized by:

2. The collecting unit Includes a microphone or sensor placed near the baby The system of claim 1 .

3. The analysis unit Using AI to analyze baby vocalization data and estimate emotions The system of claim 1 .

4. The estimation unit Inferring the baby's emotions based on the analyzed data The system of claim 1 .

5. The proposal unit Suggest specific ways to respond to parents based on estimated emotions The system of claim 1 .

6. The providing unit The response method suggested by the suggestion unit is provided to the parent. The system of claim 1 .

7. The collecting unit The system estimates the baby's emotions and adjusts the timing of vocalization data collection based on the estimated emotions. The system of claim 1 .

8. The collecting unit Analyze the baby's past vocalization data and select the appropriate collection method The system of claim 1 .

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