system

Abstract

The system according to the embodiment aims to record a child's growth process using audio and images and to easily share the data. [Solution] A system according to an embodiment includes an audio recording unit, a transmission unit, an image recording unit, a cloud server, and a sharing unit. The audio recording unit detects and records audio. The transmission unit transmits audio data recorded by the audio recording unit to the cloud server. The image recording unit detects objects and takes photos. The transmission unit transmits image data captured by the image recording unit to the cloud server. The cloud server stores the data transmitted by the transmission unit. The sharing unit has specific functions for sharing data stored on the cloud server via social networking services.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to record a child's growth process using audio and images and share them easily.

[0005] The system according to the embodiment aims to record a child's growth process using audio and images and to easily share the data. [Means for solving the problem]

[0006] The system according to the embodiment includes an audio recording unit, a transmission unit, an image recording unit, a cloud server, and a sharing unit. The audio recording unit detects and records audio. The transmission unit transmits audio data recorded by the audio recording unit to the cloud server. The image recording unit detects objects and takes photos. The transmission unit transmits image data captured by the image recording unit to the cloud server. The cloud server stores the data transmitted by the transmission unit. The sharing unit has a specific function for sharing data stored in the cloud server via SNS. [Effects of the Invention]

[0007] The system according to the embodiment allows a child's growth process to be recorded using audio and images and easily shared. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automatically collects, timestamps, and saves a child's growth record, and shares it via a social networking site (SNS). This system uses an audio recording device to record a child's speech and a home camera to record what the child is holding. For example, the audio recording device automatically starts recording when it detects sound, and saves the recording with a timestamp. Next, the home camera automatically takes a photo when it detects an object the child is holding, and saves the photo with a timestamp. These recorded audio and images are automatically shared via a social networking site. For example, they can be shared with family and friends via a social networking site. This allows the system to easily share a child's growth record and preserve memories. For example, parents can easily keep a record of their child's growth without missing anything.

[0029] A growth recording system according to an embodiment includes an audio recording unit, a transmission unit, an image recording unit, a cloud server, and a sharing unit. The audio recording unit detects and records audio. For example, the audio recording unit automatically starts recording when it detects a child's speech and saves the audio with a timestamp. The transmission unit transmits audio data recorded by the audio recording unit to a cloud server. For example, the transmission unit transmits data to the cloud server using wireless communication. The image recording unit detects an object and takes a photograph. For example, the image recording unit automatically takes a photograph when it detects an object being held by a child and saves the photograph with a timestamp. The transmission unit transmits image data captured by the image recording unit to the cloud server. The cloud server saves the data saved by the transmission unit. For example, the cloud server protects the data using encryption technology. The sharing unit shares the data saved on the cloud server via social networking services (SNS). For example, the sharing unit automatically shares the data via the SNS. As a result, the growth recording system according to an embodiment can automatically collect audio and image data, save the data with a timestamp, and share the data via the SNS.

[0030] The audio recording unit can automatically start recording when it detects sound, and save the sound with a timestamp. For example, when it detects a child speaking, the audio recording unit can automatically start recording and save the sound with a timestamp. Voice recognition technology, for example, can be used to detect sound. Recording can start when a certain volume is exceeded or when a specific keyword is detected, for example. Timestamps can be added, for example, by recording the recording start time or by adding the timestamp to the recorded data. This allows audio to be automatically recorded and saved with a timestamp, so important moments can be recorded without missing a thing.

[0031] The image recording unit can automatically take a photo when it detects an object, and save the photo with a timestamp. For example, when the image recording unit detects an object being held by a child, it automatically takes a photo and saves the photo with a timestamp. Image recognition technology, for example, can be used to detect objects. The conditions for starting photography include when an object enters the camera's field of view or when a specific movement is detected. The timestamp can be added, for example, by recording the time of photography or by adding it to the image data as metadata. This allows objects to be automatically photographed and saved with a timestamp, so that important moments are not missed.

[0032] The transmitting unit can transmit data to a cloud server using wireless communication. The transmitting unit transmits data acquired by, for example, the audio recording unit or the image recording unit to the cloud server using wireless communication. Specific wireless communication technologies include, for example, Wi-Fi, Bluetooth (registered trademark), and 5G. The transmitting unit can perform high-speed and stable data transmission using, for example, Wi-Fi. The transmitting unit can also perform short-distance data transmission using Bluetooth. Furthermore, the transmitting unit can also perform high-speed data transmission over a wide range using 5G. This makes it easier to transmit data using wireless communication.

[0033] The cloud server can protect data using encryption technology. The cloud server protects data transmitted by a transmitter using encryption technology, for example. Specific encryption technologies include, for example, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and TLS (Transport Layer Security). The cloud server can encrypt data using, for example, AES to ensure high security. The cloud server can also encrypt and decrypt data using RSA. Furthermore, the cloud server can encrypt data transmission and reception using TLS to achieve secure communication. As a result, the use of encryption technology improves data security.

[0034] The sharing unit can automatically share data through an SNS. For example, the sharing unit automatically shares data stored in a cloud server through an SNS. Specific methods for sharing data through an SNS include, for example, setting the type of data to be shared and the timing of sharing. For example, the sharing unit can automatically post audio data or image data to an SNS. The sharing unit can also be set to share data at a specific timing. Furthermore, the sharing unit can set privacy settings for the data to be shared so that only specific users can access the data. This makes it easy to share information with family and friends by automatically sharing data through an SNS.

[0035] The audio recording unit can automatically filter background noise during recording to improve audio quality. For example, the audio recording unit uses AI to detect and filter ambient noise in real time during recording. A noise reduction algorithm, for example, can be used to filter background noise. Criteria for improving audio quality include noise removal rate and voice clarity. For example, AI can detect and filter wind noise during recording. AI can also detect and filter other people's voices during recording. Furthermore, AI can detect and filter mechanical sounds during recording. This improves the quality of the recorded audio by filtering out background noise.

[0036] The audio recording unit can identify and classify the type of audio during recording. For example, the audio recording unit identifies and classifies the type of audio (e.g., laughter, crying, words) during recording. A voice recognition algorithm can be used to identify the type of audio. Classification criteria include, for example, audio features and patterns. For example, AI can detect laughter and tag the recorded data. AI can also detect crying and tag the recorded data. Furthermore, AI can detect words and tag the recorded data. This makes it easier to manage recorded data by identifying and classifying the type of audio.

[0037] The audio recording unit can automatically adjust the recording sensitivity according to the volume of the audio during recording. For example, the audio recording unit detects the volume of the audio during recording and automatically adjusts the recording sensitivity according to that volume. A volume sensor can be used, for example, to detect the volume. The criteria for adjusting the recording sensitivity include a volume threshold and a sensitivity adjustment algorithm. For example, if a child's voice is quiet, the AI ​​can increase the recording sensitivity. Also, if the child's voice is loud, the AI ​​can decrease the recording sensitivity. Furthermore, the AI ​​can automatically adjust the recording sensitivity according to the ambient volume. This allows recording at an appropriate volume by adjusting the recording sensitivity according to the audio volume.

[0038] The audio recording unit can prioritize recording highly relevant audio by taking into account the child's location information during recording. The audio recording unit, for example, prioritizes recording highly relevant audio by taking into account the child's location information during recording. GPS data can be used, for example, to acquire location information. The relevance evaluation criterion can be, for example, the relevance between location information and audio content. For example, if a child is at a playground, audio at that location can be prioritized for recording. Also, if a child is inside the house, audio at that location can be postponed. Furthermore, if a child is in a specific location, audio at that location can also be prioritized for recording. In this way, highly relevant audio can be prioritized for recording by taking into account the child's location information.

[0039] The audio recording unit can analyze the child's activity history during recording and automatically detect and record specific events. For example, the audio recording unit can analyze the child's activity history during recording and automatically detect and record specific events (for example, the first word). The activity history can be analyzed using, for example, a historical data analysis algorithm. Event detection criteria include, for example, specific keywords and behavioral patterns. For example, AI can detect and record the moment a child says "mom" for the first time. AI can also detect and record the moment a child says "daddy" for the first time. AI can also detect and record the moment a child says "thank you" for the first time. In this way, by analyzing a child's activity history, specific events can be automatically detected and recorded.

[0040] The audio recording unit can customize the recording method by reflecting parental feedback during recording. The audio recording unit, for example, customizes the recording method by reflecting parental feedback during recording. Methods for obtaining feedback include, for example, voice or text input provided by the parent. Criteria for customizing the recording method include, for example, the content of the feedback and the parent's wishes. For example, the parent can provide feedback to prioritize recording of a specific audio. The parent can also provide feedback to postpone recording of a specific audio. Furthermore, the parent can provide feedback to adjust the recording sensitivity. In this way, the recording method can be customized by reflecting parental feedback.

[0041] The transmitting unit can automatically detect the communication environment when transmitting data and select the optimal transmission method. The transmitting unit, for example, automatically detects the communication environment when transmitting data and selects the optimal transmission method. Methods for detecting the communication environment include, for example, technology for monitoring the network status. Criteria for selecting the transmission method include, for example, communication speed and stability. For example, if Wi-Fi is available, data can be transmitted with priority given to Wi-Fi. Also, if mobile data communication is available, data can be transmitted using mobile data communication. Furthermore, if the communication environment is unstable, data transmission can be temporarily suspended. In this way, the optimal transmission method can be selected by automatically detecting the communication environment.

[0042] The transmitting unit can determine the transmission priority based on the importance of the data when transmitting the data. For example, the transmitting unit determines the transmission priority based on the importance of the data when transmitting the data. Criteria for evaluating the importance of the data include, for example, the content of the data and urgency. Methods for determining the transmission priority include, for example, an algorithm for preferentially transmitting data with high importance. For example, important data can be transmitted with priority. It is also possible to postpone data with low importance. Furthermore, the transmission priority can be adjusted according to the importance of the data. In this way, by determining the transmission priority based on the importance of the data, important data can be transmitted with priority.

[0043] The transmitting unit can improve transmission efficiency by automatically adjusting the compression rate of the transmission data when transmitting data. The transmitting unit, for example, improves transmission efficiency by automatically adjusting the compression rate of the transmission data when transmitting data. Methods for adjusting the compression rate include, for example, a technique for applying a compression algorithm depending on the type of data and the transmission environment. Criteria for evaluating transmission efficiency include, for example, the data compression rate and transmission speed. For example, the data compression rate can be increased to improve transmission efficiency. It is also possible to maintain transmission quality by lowering the data compression rate. Furthermore, the compression rate can be automatically adjusted depending on the type of data. In this way, transmission efficiency is improved by automatically adjusting the compression rate of the transmission data.

[0044] The transmitting unit can select the optimal transmission method by taking into consideration the device information of the destination when transmitting data. For example, the transmitting unit selects the optimal transmission method by taking into consideration the device information of the destination when transmitting data. Methods for acquiring the device information of the destination include, for example, technology for detecting the type of device and connection status. Criteria for selecting the transmission method include, for example, the device performance and communication environment. For example, if the destination is a smartphone, mobile data communication can be used. Also, if the destination is a tablet, Wi-Fi can be prioritized for transmission. Furthermore, if the destination is a PC, a wired connection can be used for transmission. In this way, the optimal transmission method can be selected by taking into consideration the device information of the destination.

[0045] The transmitting unit can customize the transmission method according to the type of data being transmitted when transmitting data. For example, the transmitting unit customizes the transmission method according to the type of data being transmitted (audio, image) when transmitting data. Methods for identifying the type of data being transmitted include, for example, technology that analyzes the metadata or file format of the data. Criteria for customizing the transmission method include, for example, the type of data and the transmission environment. For example, audio data can be transmitted with a higher compression rate. Also, image data can be transmitted with a lower compression rate. Furthermore, the optimal transmission method can be selected according to the type of data. In this way, by customizing the transmission method according to the type of data being transmitted, transmission efficiency is improved.

[0046] The sending unit can adjust the sending method by reflecting parental feedback when sending data. The sending unit, for example, adjusts the sending method by reflecting parental feedback when sending data. Methods for obtaining feedback include, for example, voice or text input provided by the parent. Criteria for adjusting the sending method include, for example, the content of the feedback or the parent's wishes. For example, the parent can provide feedback to send specific data preferentially. The parent can also provide feedback to postpone specific data. Furthermore, the parent can provide feedback to adjust the sending method. In this way, the sending method can be customized by reflecting parental feedback.

[0047] The image recording unit can improve image quality by automatically removing unnecessary objects from the background during shooting. For example, the image recording unit uses AI to detect and remove unnecessary objects from the background in real time during shooting. Methods for removing unnecessary objects from the background can include, for example, an object detection algorithm. Image quality can be evaluated based on, for example, the removal rate of unnecessary objects and image clarity. For example, AI can detect and remove background noise in real time during shooting. AI can also detect and remove unnecessary objects during shooting. Furthermore, AI can detect and remove the figures of other people during shooting. This improves image quality by removing unnecessary objects from the background.

[0048] The image recording unit can identify and classify the type of object when capturing an image. For example, the image recording unit identifies and classifies the type of object (e.g., toy, natural object) while capturing an image. An object recognition algorithm, for example, can be used as a method for identifying the type of object. Classification criteria include, for example, the features and patterns of the object. For example, AI can detect toys and tag the image data. AI can also detect natural objects and tag the image data. Furthermore, AI can detect other objects and tag the image data. This makes it easier to manage image data by identifying and classifying the type of object.

[0049] The image recording unit can automatically adjust the camera settings according to the light conditions when taking a picture. For example, the image recording unit detects the light conditions during shooting and automatically adjusts the camera settings according to those conditions. A light sensor, for example, can be used to detect the light conditions. Criteria for adjusting the camera settings include, for example, the intensity and direction of light. For example, if the light is strong, the AI ​​can adjust the camera exposure. Also, if the light is weak, the AI ​​can adjust the camera sensitivity. Furthermore, the AI ​​can automatically adjust the camera settings according to the light conditions. As a result, by adjusting the camera settings according to the light conditions, it is possible to take pictures with appropriate exposure.

[0050] The image recording unit can take priority in taking highly relevant images by taking into account the child's location information during shooting. The image recording unit, for example, takes priority in taking highly relevant images by taking into account the child's location information during shooting. GPS data can be used, for example, to acquire the location information. The relevance evaluation criteria can include, for example, the relevance between the location information and the content of the image. For example, if the child is at a playground, images at that location can be taken priority. Also, if the child is inside the house, images at that location can be postponed. Furthermore, if the child is at a specific location, images at that location can be taken priority. In this way, highly relevant images can be taken priority by taking into account the child's location information.

[0051] The image recording unit can analyze the child's activity history when taking a photo and automatically detect and capture a specific event. The image recording unit can, for example, analyze the child's activity history while taking a photo and automatically detect and capture a specific event (for example, a first discovery). A method for analyzing the activity history can use, for example, a history data analysis algorithm. Event detection criteria include, for example, the detection of a specific behavioral pattern or object. For example, the AI ​​can detect and capture the first stone the child picks up. The AI ​​can also detect and capture the first leaf the child finds. The AI ​​can also detect and capture the first flower the child touches. In this way, by analyzing the child's activity history, specific events can be automatically detected and captured.

[0052] The image recording unit can customize the shooting method by reflecting parental feedback during shooting. The image recording unit customizes the shooting method by reflecting parental feedback during shooting, for example. Feedback can be obtained, for example, by voice or text input provided by the parent. Criteria for customizing the shooting method include, for example, the content of the feedback and the parent's wishes. For example, the parent can provide feedback to prioritize shooting of a specific object. The parent can also provide feedback to postpone shooting of a specific object. Furthermore, the parent can provide feedback to adjust the shooting sensitivity. In this way, the shooting method can be customized by reflecting parental feedback.

[0053] A cloud server can determine the storage priority based on the importance of the data when saving data. For example, a cloud server determines the storage priority based on the importance of the data when saving data. Criteria for evaluating the importance of data include, for example, the content of the data and urgency. Methods for determining the storage priority include, for example, an algorithm that prioritizes saving data with high importance. For example, important data can be saved with priority. It is also possible to postpone saving data with low importance. Furthermore, it is possible to adjust the storage priority according to the importance of the data. In this way, by determining the storage priority based on the importance of the data, important data can be saved with priority.

[0054] Cloud servers can customize the storage method depending on the type of data when saving it. For example, cloud servers customize the storage method depending on the type of data (audio, image) when saving data. Methods for identifying the type of data include, for example, technology that analyzes the metadata and file format of the data. Criteria for customizing the storage method include, for example, the type of data and the storage environment. For example, audio data can be saved with a high compression rate. Image data can also be saved with a low compression rate. Furthermore, the optimal storage method can be selected depending on the type of data. In this way, customizing the storage method depending on the type of data improves storage efficiency.

[0055] A cloud server can improve data security by automatically adjusting the data backup frequency when saving data. A cloud server can improve data security by automatically adjusting the data backup frequency when saving data, for example. Methods for adjusting the backup frequency include, for example, applying a backup algorithm depending on the importance of the data and the storage environment. Criteria for evaluating data security include, for example, the frequency of backups and data restorability. For example, the backup frequency of important data can be increased. Also, the backup frequency of less important data can be decreased. Furthermore, the backup frequency can be automatically adjusted depending on the importance of the data. In this way, data security is improved by automatically adjusting the data backup frequency.

[0056] A cloud server can select the optimal storage method by taking into account destination server information when saving data. For example, a cloud server can select the optimal storage method by taking into account destination server information when saving data. Methods for obtaining destination server information include, for example, technology for detecting the server type and connection status. Criteria for selecting a storage method include, for example, server performance and security. For example, data can be saved with priority given to high-speed servers. Data can also be saved with priority given to highly secure servers. Furthermore, the optimal storage method can be selected according to destination server information. This allows the optimal storage method to be selected by taking into account destination server information.

[0057] A cloud server can improve storage efficiency by automatically adjusting the compression rate of stored data when saving data. For example, a cloud server can improve storage efficiency by automatically adjusting the compression rate of stored data when saving data. Methods for adjusting the compression rate include, for example, a technique for applying a compression algorithm depending on the type of data and the storage environment. Criteria for evaluating storage efficiency include, for example, the data compression rate and storage speed. For example, the data compression rate can be increased to improve storage efficiency. It is also possible to maintain storage quality by lowering the data compression rate. Furthermore, the compression rate can be automatically adjusted depending on the type of data. In this way, automatic adjustment of the compression rate of stored data improves storage efficiency.

[0058] The cloud server can adjust the storage method by reflecting parental feedback when saving data. The cloud server can adjust the storage method by reflecting parental feedback when saving data, for example. Feedback can be obtained by, for example, voice or text input provided by the parent. Criteria for adjusting the storage method include, for example, the content of the feedback or the parent's wishes. For example, the parent can provide feedback to prioritize saving of specific data. The parent can also provide feedback to postpone saving of specific data. Furthermore, the parent can provide feedback to adjust the storage method. In this way, the storage method can be customized by reflecting parental feedback.

[0059] The sharing unit can automatically select a destination SNS platform when sharing data. The sharing unit, for example, automatically selects a destination SNS platform when sharing data. Criteria for selecting an SNS platform include, for example, the type of data to be shared and the target user demographic. Selection methods include, for example, an algorithm that takes into account the platform's characteristics and usage status. For example, it is possible to select an SNS that prioritizes sharing data for family members. It is also possible to select an SNS that prioritizes sharing data for friends. Furthermore, it is possible to select the optimal SNS platform depending on the type of data. In this way, by automatically selecting a destination SNS platform, data can be shared on the optimal platform.

[0060] The sharing unit can determine the priority of sharing based on the importance of the shared data when sharing data. For example, the sharing unit determines the priority of sharing based on the importance of the shared data when sharing data. Criteria for evaluating the importance of shared data include, for example, the content of the data and urgency. Methods for determining the priority of sharing include, for example, an algorithm that prioritizes sharing of data with high importance. For example, important data can be shared with priority. It is also possible to postpone sharing of data with low importance. Furthermore, the priority of sharing can be adjusted according to the importance of the data. In this way, by determining the priority of sharing based on the importance of the shared data, important data can be shared with priority.

[0061] The sharing unit can improve sharing efficiency by automatically adjusting the format of the shared data when data is shared. The sharing unit can improve sharing efficiency by, for example, automatically adjusting the format of the shared data when data is shared. Methods for adjusting the format include, for example, a technique for applying a format conversion algorithm depending on the type of data and the sharing environment. Criteria for evaluating sharing efficiency include, for example, the data format conversion rate and sharing speed. For example, the sharing efficiency can be improved by optimizing the data format. Sharing efficiency can also be improved by simplifying the data format. Furthermore, the format can be automatically adjusted depending on the type of data. In this way, sharing efficiency is improved by automatically adjusting the format of the shared data.

[0062] The sharing unit can select the optimal sharing method by taking into account device information of the sharee when sharing data. For example, the sharing unit selects the optimal sharing method by taking into account device information of the sharee when sharing data. Methods for acquiring device information of the sharee include, for example, technology for detecting the device type and connection status. Criteria for selecting the sharing method include, for example, device performance and communication environment. For example, if the sharee is a smartphone, mobile data communication can be used. Also, if the sharee is a tablet, sharing can be prioritized over Wi-Fi. Furthermore, if the sharee is a PC, sharing can be performed using a wired connection. In this way, the optimal sharing method can be selected by taking into account device information of the sharee.

[0063] The sharing unit can customize the sharing method according to the type of data to be shared when sharing data. For example, the sharing unit customizes the sharing method according to the type of data to be shared (audio, image) when sharing data. Methods for identifying the type of data to be shared include, for example, technology that analyzes the metadata or file format of the data. Criteria for customizing the sharing method include, for example, the type of data and the sharing environment. For example, audio data can be shared with a higher compression rate. Also, image data can be shared with a lower compression rate. Furthermore, the optimal sharing method can be selected according to the type of data. In this way, customizing the sharing method according to the type of data to be shared improves sharing efficiency.

[0064] The sharing unit can adjust the sharing method by reflecting parental feedback when sharing data. For example, the sharing unit adjusts the sharing method by reflecting parental feedback when sharing data. Methods for obtaining feedback include, for example, voice or text input provided by the parent. Criteria for adjusting the sharing method include, for example, the content of the feedback and the parent's wishes. For example, the parent can provide feedback to prioritize sharing of specific data. The parent can also provide feedback to postpone sharing of specific data. Furthermore, the parent can provide feedback to adjust the sharing method. In this way, the sharing method can be customized by reflecting parental feedback.

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

[0066] The growth record system may further include a health record unit that collects health data of the child. The health record unit collects, time-stamps, and stores data such as the child's body temperature, heart rate, and sleep patterns. This allows parents to understand their child's health status in real time. For example, if the body temperature exceeds a certain range, an alert can be issued. Also, if the heart rate shows an abnormal value, a medical institution can be automatically notified. Furthermore, the system can analyze sleep patterns and provide advice on improving the child's sleep quality.

[0067] The growth record system can further include a learning record unit that collects a child's learning data. For example, the learning record unit records what a child is learning and their progress, and saves the data with a timestamp. This allows parents to understand their child's learning situation and provide appropriate support. For example, if progress in a particular subject is falling behind, the parent can be notified. The system can also evaluate learning results and identify areas that need praise. Furthermore, the system can analyze learning trends and suggest the best learning method for the child.

[0068] The growth record system may further include a friend record unit that records a child's friendships. The friend record unit, for example, records which friends a child has and what activities they have done with them, and stores the records with a timestamp. This allows parents to understand their child's social growth. For example, if a child frequently plays with a particular friend, the system can suggest activities to deepen the relationship with that friend. Also, if a child makes a new friend, the system can record the child's first activity with that friend. Furthermore, the system can analyze changes in friendships and support the child's improvement of social skills.

[0069] The growth recording system may further include a pattern analysis unit that analyzes the child's activity data and detects specific patterns. For example, if a child tends to engage in a specific activity at a specific time of day, the pattern analysis unit can detect that pattern and notify the parent. This allows the parent to understand the child's activity pattern and provide appropriate support. For example, if a child takes a nap at the same time every day, the parent can adjust the schedule accordingly. Also, if a child tends to engage in a specific activity on a specific day of the week, the parent can make preparations to support that activity. Furthermore, the pattern analysis unit can analyze changes in the child's activity pattern and take appropriate measures according to the child's growth.

[0070] The growth record system may further include a growth prediction unit that predicts future growth based on the child's activity data. The growth prediction unit, for example, analyzes past activity data and predicts future growth patterns. This allows parents to make plans that take their child's future growth into consideration. For example, it can predict how a specific activity will affect future learning ability. It can also predict how specific health data will affect future health conditions. It can also predict how specific friendships will affect future social skills.

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

[0072] Step 1: The voice recording unit detects and records voice. For example, when the voice recording unit detects a child speaking, it automatically starts recording and saves the recording with a timestamp. Step 2: The transmitting unit transmits the audio data recorded by the audio recording unit to the cloud server. For example, the transmitting unit transmits the data to the cloud server using wireless communication. Step 3: The image recording unit detects the object and takes a photo. For example, when the image recording unit detects an object being held by a child, it automatically takes a photo and saves it with a timestamp. Step 4: The transmitting unit transmits the image data captured by the image recording unit to the cloud server. Step 5: The cloud server stores the data transmitted by the transmitter. For example, the cloud server protects the data using encryption technology. Step 6: The sharing unit shares the data stored in the cloud server through SNS. For example, the sharing unit automatically shares the data through SNS.

[0073] (Example 2) A system according to an embodiment of the present invention automatically collects, timestamps, and saves a child's growth record, and shares it via a social networking site (SNS). This system uses an audio recording device to record a child's speech and a home camera to record what the child is holding. For example, the audio recording device automatically starts recording when it detects sound, and saves the recording with a timestamp. Next, the home camera automatically takes a photo when it detects an object the child is holding, and saves the photo with a timestamp. These recorded audio and images are automatically shared via a social networking site. For example, they can be shared with family and friends via a social networking site. This allows the system to easily share a child's growth record and preserve memories. For example, parents can easily keep a record of their child's growth without missing anything.

[0074] A growth recording system according to an embodiment includes an audio recording unit, a transmission unit, an image recording unit, a cloud server, and a sharing unit. The audio recording unit detects and records audio. For example, the audio recording unit automatically starts recording when it detects a child's speech and saves the audio with a timestamp. The transmission unit transmits audio data recorded by the audio recording unit to a cloud server. For example, the transmission unit transmits data to the cloud server using wireless communication. The image recording unit detects an object and takes a photograph. For example, the image recording unit automatically takes a photograph when it detects an object being held by a child and saves the photograph with a timestamp. The transmission unit transmits image data captured by the image recording unit to the cloud server. The cloud server saves the data saved by the transmission unit. For example, the cloud server protects the data using encryption technology. The sharing unit shares the data saved on the cloud server via social networking services (SNS). For example, the sharing unit automatically shares the data via the SNS. As a result, the growth recording system according to an embodiment can automatically collect audio and image data, save the data with a timestamp, and share the data via the SNS.

[0075] The audio recording unit can automatically start recording when it detects sound, and save the sound with a timestamp. For example, when it detects a child speaking, the audio recording unit can automatically start recording and save the sound with a timestamp. Voice recognition technology, for example, can be used to detect sound. Recording can start when a certain volume is exceeded or when a specific keyword is detected, for example. Timestamps can be added, for example, by recording the recording start time or by adding the timestamp to the recorded data. This allows audio to be automatically recorded and saved with a timestamp, so important moments can be recorded without missing a thing.

[0076] The image recording unit can automatically take a photo when it detects an object, and save the photo with a timestamp. For example, when the image recording unit detects an object being held by a child, it automatically takes a photo and saves the photo with a timestamp. Image recognition technology, for example, can be used to detect objects. The conditions for starting photography include when an object enters the camera's field of view or when a specific movement is detected. The timestamp can be added, for example, by recording the time of photography or by adding it to the image data as metadata. This allows objects to be automatically photographed and saved with a timestamp, so that important moments are not missed.

[0077] The transmitting unit can transmit data to a cloud server using wireless communication. The transmitting unit transmits data acquired by, for example, the audio recording unit or the image recording unit to the cloud server using wireless communication. Specific wireless communication technologies include, for example, Wi-Fi, Bluetooth, and 5G. The transmitting unit can perform high-speed and stable data transmission using, for example, Wi-Fi. The transmitting unit can also perform short-distance data transmission using Bluetooth. Furthermore, the transmitting unit can also perform high-speed data transmission over a wide range using 5G. This makes it easier to transmit data using wireless communication.

[0078] The cloud server can protect data using encryption technology. The cloud server protects data transmitted by a transmitter using encryption technology, for example. Specific encryption technologies include, for example, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and TLS (Transport Layer Security). The cloud server can encrypt data using, for example, AES to ensure high security. The cloud server can also encrypt and decrypt data using RSA. Furthermore, the cloud server can encrypt data transmission and reception using TLS to achieve secure communication. As a result, the use of encryption technology improves data security.

[0079] The sharing unit can automatically share data through an SNS. For example, the sharing unit automatically shares data stored in a cloud server through an SNS. Specific methods for sharing data through an SNS include, for example, setting the type of data to be shared and the timing of sharing. For example, the sharing unit can automatically post audio data or image data to an SNS. The sharing unit can also be set to share data at a specific timing. Furthermore, the sharing unit can set privacy settings for the data to be shared so that only specific users can access the data. This makes it easy to share information with family and friends by automatically sharing data through an SNS.

[0080] The audio recording unit can estimate the child's emotion and adjust the start timing of recording based on the estimated emotion. The audio recording unit, for example, estimates the child's emotion and adjusts the start timing of recording based on the estimated emotion. The emotion estimation method can use, for example, an emotion recognition algorithm. The criteria for adjusting the start timing of recording include, for example, the intensity and type of emotion. For example, if the child is excited, the AI ​​can detect that emotion and immediately start recording. Also, if the child is calm, the AI ​​can detect that emotion and delay the start of recording. Furthermore, if the child is sad, the AI ​​can detect that emotion and adjust the start of recording. This allows for more appropriate timing by adjusting the start timing of recording according to the child's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The audio recording unit can automatically filter background noise during recording to improve audio quality. For example, the audio recording unit uses AI to detect and filter ambient noise in real time during recording. A noise reduction algorithm, for example, can be used to filter background noise. Criteria for improving audio quality include noise removal rate and voice clarity. For example, AI can detect and filter wind noise during recording. AI can also detect and filter other people's voices during recording. Furthermore, AI can detect and filter mechanical sounds during recording. This improves the quality of the recorded audio by filtering out background noise.

[0082] The audio recording unit can identify and classify the type of audio during recording. For example, the audio recording unit identifies and classifies the type of audio (e.g., laughter, crying, words) during recording. A voice recognition algorithm can be used to identify the type of audio. Classification criteria include, for example, audio features and patterns. For example, AI can detect laughter and tag the recorded data. AI can also detect crying and tag the recorded data. Furthermore, AI can detect words and tag the recorded data. This makes it easier to manage recorded data by identifying and classifying the type of audio.

[0083] The audio recording unit can automatically adjust the recording sensitivity according to the volume of the audio during recording. For example, the audio recording unit detects the volume of the audio during recording and automatically adjusts the recording sensitivity according to that volume. A volume sensor can be used, for example, to detect the volume. The criteria for adjusting the recording sensitivity include a volume threshold and a sensitivity adjustment algorithm. For example, if a child's voice is quiet, the AI ​​can increase the recording sensitivity. Also, if the child's voice is loud, the AI ​​can decrease the recording sensitivity. Furthermore, the AI ​​can automatically adjust the recording sensitivity according to the ambient volume. This allows recording at an appropriate volume by adjusting the recording sensitivity according to the audio volume.

[0084] The audio recording unit can estimate the child's emotion and determine the priority of audio to be recorded based on the estimated emotion. The audio recording unit, for example, estimates the child's emotion and determines the priority of audio to be recorded based on the estimated emotion. An emotion recognition algorithm, for example, can be used as a method of estimating emotion. Criteria for determining the priority of audio include, for example, the intensity or type of emotion. For example, if the child is excited, that audio can be recorded with priority. Also, if the child is calm, that audio can be postponed. Furthermore, if the child is sad, that audio can be recorded with priority. In this way, by determining the priority of audio to be recorded according to the child's emotion, important audio can be recorded with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The audio recording unit can prioritize recording highly relevant audio by taking into account the child's location information during recording. The audio recording unit, for example, prioritizes recording highly relevant audio by taking into account the child's location information during recording. GPS data can be used, for example, to acquire location information. The relevance evaluation criterion can be, for example, the relevance between location information and audio content. For example, if a child is at a playground, audio at that location can be prioritized for recording. Also, if a child is inside the house, audio at that location can be postponed. Furthermore, if a child is in a specific location, audio at that location can also be prioritized for recording. In this way, highly relevant audio can be prioritized for recording by taking into account the child's location information.

[0086] The audio recording unit can analyze the child's activity history during recording and automatically detect and record specific events. For example, the audio recording unit can analyze the child's activity history during recording and automatically detect and record specific events (for example, the first word). The activity history can be analyzed using, for example, a historical data analysis algorithm. Event detection criteria include, for example, specific keywords and behavioral patterns. For example, AI can detect and record the moment a child says "mom" for the first time. AI can also detect and record the moment a child says "daddy" for the first time. AI can also detect and record the moment a child says "thank you" for the first time. In this way, by analyzing a child's activity history, specific events can be automatically detected and recorded.

[0087] The audio recording unit can customize the recording method by reflecting parental feedback during recording. The audio recording unit, for example, customizes the recording method by reflecting parental feedback during recording. Methods for obtaining feedback include, for example, voice or text input provided by the parent. Criteria for customizing the recording method include, for example, the content of the feedback and the parent's wishes. For example, the parent can provide feedback to prioritize recording of a specific audio. The parent can also provide feedback to postpone recording of a specific audio. Furthermore, the parent can provide feedback to adjust the recording sensitivity. In this way, the recording method can be customized by reflecting parental feedback.

[0088] The transmitting unit can estimate the child's emotion and adjust the timing of data transmission based on the estimated emotion. The transmitting unit, for example, estimates the child's emotion and adjusts the timing of data transmission based on the emotion. An emotion recognition algorithm can be used as an emotion estimation method. Criteria for adjusting the timing of data transmission include, for example, the intensity and type of emotion. For example, if the child is excited, data transmission can be performed immediately. Also, if the child is calm, data transmission can be delayed. Furthermore, if the child is sad, the timing of data transmission can be adjusted. In this way, by adjusting the timing of data transmission according to the child's emotion, data can be transmitted at an appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The transmitting unit can automatically detect the communication environment when transmitting data and select the optimal transmission method. The transmitting unit, for example, automatically detects the communication environment when transmitting data and selects the optimal transmission method. Methods for detecting the communication environment include, for example, technology for monitoring the network status. Criteria for selecting the transmission method include, for example, communication speed and stability. For example, if Wi-Fi is available, data can be transmitted with priority given to Wi-Fi. Also, if mobile data communication is available, data can be transmitted using mobile data communication. Furthermore, if the communication environment is unstable, data transmission can be temporarily suspended. In this way, the optimal transmission method can be selected by automatically detecting the communication environment.

[0090] The transmitting unit can determine the transmission priority based on the importance of the data when transmitting the data. For example, the transmitting unit determines the transmission priority based on the importance of the data when transmitting the data. Criteria for evaluating the importance of the data include, for example, the content of the data and urgency. Methods for determining the transmission priority include, for example, an algorithm for preferentially transmitting data with high importance. For example, important data can be transmitted with priority. It is also possible to postpone data with low importance. Furthermore, the transmission priority can be adjusted according to the importance of the data. In this way, by determining the transmission priority based on the importance of the data, important data can be transmitted with priority.

[0091] The transmitting unit can improve transmission efficiency by automatically adjusting the compression rate of the transmission data when transmitting data. The transmitting unit, for example, improves transmission efficiency by automatically adjusting the compression rate of the transmission data when transmitting data. Methods for adjusting the compression rate include, for example, a technique for applying a compression algorithm depending on the type of data and the transmission environment. Criteria for evaluating transmission efficiency include, for example, the data compression rate and transmission speed. For example, the data compression rate can be increased to improve transmission efficiency. It is also possible to maintain transmission quality by lowering the data compression rate. Furthermore, the compression rate can be automatically adjusted depending on the type of data. In this way, transmission efficiency is improved by automatically adjusting the compression rate of the transmission data.

[0092] The transmission unit can estimate the child's emotion and determine the priority of data to be transmitted based on the estimated emotion. The transmission unit, for example, estimates the child's emotion and determines the priority of data to be transmitted based on the estimated emotion. An emotion recognition algorithm, for example, can be used as a method of estimating emotion. Criteria for determining the priority of data include, for example, the intensity or type of emotion. For example, if the child is excited, that data can be transmitted preferentially. Also, if the child is calm, that data can be postponed. Furthermore, if the child is sad, that data can be transmitted preferentially. In this way, by determining the priority of data to be transmitted according to the child's emotion, important data can be transmitted preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The transmitting unit can select the optimal transmission method by taking into consideration the device information of the destination when transmitting data. For example, the transmitting unit selects the optimal transmission method by taking into consideration the device information of the destination when transmitting data. Methods for acquiring the device information of the destination include, for example, technology for detecting the type of device and connection status. Criteria for selecting the transmission method include, for example, the device performance and communication environment. For example, if the destination is a smartphone, mobile data communication can be used. Also, if the destination is a tablet, Wi-Fi can be prioritized for transmission. Furthermore, if the destination is a PC, a wired connection can be used for transmission. In this way, the optimal transmission method can be selected by taking into consideration the device information of the destination.

[0094] The transmitting unit can customize the transmission method according to the type of data being transmitted when transmitting data. For example, the transmitting unit customizes the transmission method according to the type of data being transmitted (audio, image) when transmitting data. Methods for identifying the type of data being transmitted include, for example, technology that analyzes the metadata or file format of the data. Criteria for customizing the transmission method include, for example, the type of data and the transmission environment. For example, audio data can be transmitted with a higher compression rate. Also, image data can be transmitted with a lower compression rate. Furthermore, the optimal transmission method can be selected according to the type of data. In this way, by customizing the transmission method according to the type of data being transmitted, transmission efficiency is improved.

[0095] The sending unit can adjust the sending method by reflecting parental feedback when sending data. The sending unit, for example, adjusts the sending method by reflecting parental feedback when sending data. Methods for obtaining feedback include, for example, voice or text input provided by the parent. Criteria for adjusting the sending method include, for example, the content of the feedback or the parent's wishes. For example, the parent can provide feedback to send specific data preferentially. The parent can also provide feedback to postpone specific data. Furthermore, the parent can provide feedback to adjust the sending method. In this way, the sending method can be customized by reflecting parental feedback.

[0096] The image recording unit can estimate the child's emotion and adjust the timing of capturing images based on the estimated emotion. The image recording unit, for example, estimates the child's emotion and adjusts the timing of capturing images based on the estimated emotion. The emotion estimation method can use, for example, an emotion recognition algorithm. The criteria for adjusting the timing of capturing images include, for example, the intensity and type of emotion. For example, if the child is excited, the AI ​​can detect the emotion and immediately start capturing images. Also, if the child is calm, the AI ​​can detect the emotion and delay the start of capturing images. Furthermore, if the child is sad, the AI ​​can detect the emotion and adjust the start of capturing images. Thus, by adjusting the timing of capturing images according to the child's emotion, capturing images can be performed at an appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The image recording unit can improve image quality by automatically removing unnecessary objects from the background during shooting. For example, the image recording unit uses AI to detect and remove unnecessary objects from the background in real time during shooting. Methods for removing unnecessary objects from the background can include, for example, an object detection algorithm. Image quality can be evaluated based on, for example, the removal rate of unnecessary objects and image clarity. For example, AI can detect and remove background noise in real time during shooting. AI can also detect and remove unnecessary objects during shooting. Furthermore, AI can detect and remove the figures of other people during shooting. This improves image quality by removing unnecessary objects from the background.

[0098] The image recording unit can identify and classify the type of object when capturing an image. For example, the image recording unit identifies and classifies the type of object (e.g., toy, natural object) while capturing an image. An object recognition algorithm, for example, can be used as a method for identifying the type of object. Classification criteria include, for example, the features and patterns of the object. For example, AI can detect toys and tag the image data. AI can also detect natural objects and tag the image data. Furthermore, AI can detect other objects and tag the image data. This makes it easier to manage image data by identifying and classifying the type of object.

[0099] The image recording unit can automatically adjust the camera settings according to the light conditions when taking a picture. For example, the image recording unit detects the light conditions during shooting and automatically adjusts the camera settings according to those conditions. A light sensor, for example, can be used to detect the light conditions. Criteria for adjusting the camera settings include, for example, the intensity and direction of light. For example, if the light is strong, the AI ​​can adjust the camera exposure. Also, if the light is weak, the AI ​​can adjust the camera sensitivity. Furthermore, the AI ​​can automatically adjust the camera settings according to the light conditions. As a result, by adjusting the camera settings according to the light conditions, it is possible to take pictures with appropriate exposure.

[0100] The image recording unit can estimate the child's emotion and determine the priority of images to be captured based on the estimated emotion. The image recording unit, for example, estimates the child's emotion and determines the priority of images to be captured based on the emotion. An emotion recognition algorithm, for example, can be used as a method of estimating emotion. Criteria for determining the priority of images include, for example, the intensity or type of emotion. For example, if the child is excited, images of that emotion can be captured first. Also, if the child is calm, images of that emotion can be postponed. Furthermore, if the child is sad, images of that emotion can be captured first. In this way, by determining the priority of images to be captured according to the child's emotion, important images can be captured first. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The image recording unit can take priority in taking highly relevant images by taking into account the child's location information during shooting. The image recording unit, for example, takes priority in taking highly relevant images by taking into account the child's location information during shooting. GPS data can be used, for example, to acquire the location information. The relevance evaluation criteria can include, for example, the relevance between the location information and the content of the image. For example, if the child is at a playground, images at that location can be taken priority. Also, if the child is inside the house, images at that location can be postponed. Furthermore, if the child is at a specific location, images at that location can be taken priority. In this way, highly relevant images can be taken priority by taking into account the child's location information.

[0102] The image recording unit can analyze the child's activity history when taking a photo and automatically detect and capture a specific event. The image recording unit can, for example, analyze the child's activity history while taking a photo and automatically detect and capture a specific event (for example, a first discovery). A method for analyzing the activity history can use, for example, a history data analysis algorithm. Event detection criteria include, for example, the detection of a specific behavioral pattern or object. For example, the AI ​​can detect and capture the first stone the child picks up. The AI ​​can also detect and capture the first leaf the child finds. The AI ​​can also detect and capture the first flower the child touches. In this way, by analyzing the child's activity history, specific events can be automatically detected and captured.

[0103] The image recording unit can customize the shooting method by reflecting parental feedback during shooting. The image recording unit customizes the shooting method by reflecting parental feedback during shooting, for example. Feedback can be obtained, for example, by voice or text input provided by the parent. Criteria for customizing the shooting method include, for example, the content of the feedback and the parent's wishes. For example, the parent can provide feedback to prioritize shooting of a specific object. The parent can also provide feedback to postpone shooting of a specific object. Furthermore, the parent can provide feedback to adjust the shooting sensitivity. In this way, the shooting method can be customized by reflecting parental feedback.

[0104] The cloud server can estimate the child's emotion and adjust the data storage method based on the estimated emotion. The cloud server can, for example, estimate the child's emotion and adjust the data storage method based on the estimated emotion. An emotion recognition algorithm can be used to estimate the emotion. Criteria for adjusting the data storage method include, for example, the intensity and type of emotion. For example, if the child is excited, that data can be saved preferentially. Alternatively, if the child is calm, that data can be saved later. Furthermore, if the child is sad, that data can be saved preferentially. In this way, by adjusting the data storage method according to the child's emotion, data can be saved in an appropriate manner. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] A cloud server can determine the storage priority based on the importance of the data when saving data. For example, a cloud server determines the storage priority based on the importance of the data when saving data. Criteria for evaluating the importance of data include, for example, the content of the data and urgency. Methods for determining the storage priority include, for example, an algorithm that prioritizes saving data with high importance. For example, important data can be saved with priority. It is also possible to postpone saving data with low importance. Furthermore, it is possible to adjust the storage priority according to the importance of the data. In this way, by determining the storage priority based on the importance of the data, important data can be saved with priority.

[0106] Cloud servers can customize the storage method depending on the type of data when saving it. For example, cloud servers customize the storage method depending on the type of data (audio, image) when saving data. Methods for identifying the type of data include, for example, technology that analyzes the metadata and file format of the data. Criteria for customizing the storage method include, for example, the type of data and the storage environment. For example, audio data can be saved with a high compression rate. Image data can also be saved with a low compression rate. Furthermore, the optimal storage method can be selected depending on the type of data. In this way, customizing the storage method depending on the type of data improves storage efficiency.

[0107] A cloud server can improve data security by automatically adjusting the data backup frequency when saving data. A cloud server can improve data security by automatically adjusting the data backup frequency when saving data, for example. Methods for adjusting the backup frequency include, for example, applying a backup algorithm depending on the importance of the data and the storage environment. Criteria for evaluating data security include, for example, the frequency of backups and data restorability. For example, the backup frequency of important data can be increased. Also, the backup frequency of less important data can be decreased. Furthermore, the backup frequency can be automatically adjusted depending on the importance of the data. In this way, data security is improved by automatically adjusting the data backup frequency.

[0108] The cloud server can estimate the child's emotions and determine the priority of data to be saved based on the estimated child's emotions. The cloud server can estimate the child's emotions and determine the priority of data to be saved based on the estimated emotions. An emotion recognition algorithm, for example, can be used to estimate emotions. Criteria for determining the priority of data include the intensity and type of emotion. For example, if the child is excited, that data can be saved preferentially. Also, if the child is calm, that data can be saved later. Furthermore, if the child is sad, that data can be saved preferentially. In this way, by determining the priority of data to be saved according to the child's emotions, important data can be saved preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] A cloud server can select the optimal storage method by taking into account destination server information when saving data. For example, a cloud server can select the optimal storage method by taking into account destination server information when saving data. Methods for obtaining destination server information include, for example, technology for detecting the server type and connection status. Criteria for selecting a storage method include, for example, server performance and security. For example, data can be saved with priority given to high-speed servers. Data can also be saved with priority given to highly secure servers. Furthermore, the optimal storage method can be selected according to destination server information. This allows the optimal storage method to be selected by taking into account destination server information.

[0110] A cloud server can improve storage efficiency by automatically adjusting the compression rate of stored data when saving data. For example, a cloud server can improve storage efficiency by automatically adjusting the compression rate of stored data when saving data. Methods for adjusting the compression rate include, for example, a technique for applying a compression algorithm depending on the type of data and the storage environment. Criteria for evaluating storage efficiency include, for example, the data compression rate and storage speed. For example, the data compression rate can be increased to improve storage efficiency. It is also possible to maintain storage quality by lowering the data compression rate. Furthermore, the compression rate can be automatically adjusted depending on the type of data. In this way, automatic adjustment of the compression rate of stored data improves storage efficiency.

[0111] The cloud server can adjust the storage method by reflecting parental feedback when saving data. The cloud server can adjust the storage method by reflecting parental feedback when saving data, for example. Feedback can be obtained by, for example, voice or text input provided by the parent. Criteria for adjusting the storage method include, for example, the content of the feedback or the parent's wishes. For example, the parent can provide feedback to prioritize saving of specific data. The parent can also provide feedback to postpone saving of specific data. Furthermore, the parent can provide feedback to adjust the storage method. In this way, the storage method can be customized by reflecting parental feedback.

[0112] The sharing unit can estimate the child's emotion and adjust the timing of sharing based on the estimated emotion. The sharing unit, for example, estimates the child's emotion and adjusts the timing of sharing based on the emotion. An emotion recognition algorithm, for example, can be used as an emotion estimation method. Criteria for adjusting the timing of sharing include, for example, the intensity or type of emotion. For example, if the child is excited, the data can be shared immediately. Also, if the child is calm, the sharing of the data can be delayed. Furthermore, if the child is sad, the timing of sharing the data can be adjusted. In this way, by adjusting the timing of sharing according to the child's emotion, data can be shared at an appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The sharing unit can automatically select a destination SNS platform when sharing data. The sharing unit, for example, automatically selects a destination SNS platform when sharing data. Criteria for selecting an SNS platform include, for example, the type of data to be shared and the target user demographic. Selection methods include, for example, an algorithm that takes into account the platform's characteristics and usage status. For example, it is possible to select an SNS that prioritizes sharing data for family members. It is also possible to select an SNS that prioritizes sharing data for friends. Furthermore, it is possible to select the optimal SNS platform depending on the type of data. In this way, by automatically selecting a destination SNS platform, data can be shared on the optimal platform.

[0114] The sharing unit can determine the priority of sharing based on the importance of the shared data when sharing data. For example, the sharing unit determines the priority of sharing based on the importance of the shared data when sharing data. Criteria for evaluating the importance of shared data include, for example, the content of the data and urgency. Methods for determining the priority of sharing include, for example, an algorithm that prioritizes sharing of data with high importance. For example, important data can be shared with priority. It is also possible to postpone sharing of data with low importance. Furthermore, the priority of sharing can be adjusted according to the importance of the data. In this way, by determining the priority of sharing based on the importance of the shared data, important data can be shared with priority.

[0115] The sharing unit can improve sharing efficiency by automatically adjusting the format of the shared data when data is shared. The sharing unit can improve sharing efficiency by, for example, automatically adjusting the format of the shared data when data is shared. Methods for adjusting the format include, for example, a technique for applying a format conversion algorithm depending on the type of data and the sharing environment. Criteria for evaluating sharing efficiency include, for example, the data format conversion rate and sharing speed. For example, the sharing efficiency can be improved by optimizing the data format. Sharing efficiency can also be improved by simplifying the data format. Furthermore, the format can be automatically adjusted depending on the type of data. In this way, sharing efficiency is improved by automatically adjusting the format of the shared data.

[0116] The sharing unit can estimate the child's emotions and determine the priority of data to be shared based on the estimated emotions. The sharing unit, for example, estimates the child's emotions and determines the priority of data to be shared based on the estimated emotions. An emotion recognition algorithm, for example, can be used as a method of estimating emotions. Criteria for determining the priority of data to be shared include, for example, the intensity or type of emotion. For example, if the child is excited, that data can be shared preferentially. Also, if the child is calm, that data can be postponed. Furthermore, if the child is sad, that data can be shared preferentially. In this way, by determining the priority of data to be shared according to the child's emotions, important data can be shared preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The sharing unit can select the optimal sharing method by taking into account device information of the sharee when sharing data. For example, the sharing unit selects the optimal sharing method by taking into account device information of the sharee when sharing data. Methods for acquiring device information of the sharee include, for example, technology for detecting the device type and connection status. Criteria for selecting the sharing method include, for example, device performance and communication environment. For example, if the sharee is a smartphone, mobile data communication can be used. Also, if the sharee is a tablet, sharing can be prioritized over Wi-Fi. Furthermore, if the sharee is a PC, sharing can be performed using a wired connection. In this way, the optimal sharing method can be selected by taking into account device information of the sharee.

[0118] The sharing unit can customize the sharing method according to the type of data to be shared when sharing data. For example, the sharing unit customizes the sharing method according to the type of data to be shared (audio, image) when sharing data. Methods for identifying the type of data to be shared include, for example, technology that analyzes the metadata or file format of the data. Criteria for customizing the sharing method include, for example, the type of data and the sharing environment. For example, audio data can be shared with a higher compression rate. Also, image data can be shared with a lower compression rate. Furthermore, the optimal sharing method can be selected according to the type of data. In this way, customizing the sharing method according to the type of data to be shared improves sharing efficiency.

[0119] The sharing unit can adjust the sharing method by reflecting parental feedback when sharing data. For example, the sharing unit adjusts the sharing method by reflecting parental feedback when sharing data. Methods for obtaining feedback include, for example, voice or text input provided by the parent. Criteria for adjusting the sharing method include, for example, the content of the feedback and the parent's wishes. For example, the parent can provide feedback to prioritize sharing of specific data. The parent can also provide feedback to postpone sharing of specific data. Furthermore, the parent can provide feedback to adjust the sharing method. In this way, the sharing method can be customized by reflecting parental feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the audio recording unit, transmission unit, image recording unit, cloud server, and sharing 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 audio recording unit is realized by the microphone 38B and control unit 46A of the smart device 14, detects the child's speech, and starts recording. The transmission unit is realized by the communication I / F 44 of the smart device 14, and transmits the recorded audio data to the cloud server. The image recording unit is realized by the camera 42 and control unit 46A of the smart device 14, detects an object the child is holding, and takes a photo. The cloud server is realized by the database 24 of the data processing device 12, and stores the transmitted data. The sharing unit is realized by the specific processing unit 290 of the data processing device 12, and shares the stored data via SNS. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned voice recording unit, transmission unit, image recording unit, cloud server, and sharing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice recording unit is realized by the microphone 238 and control unit 46A of the smart glasses 214, detects the child's speech, and starts recording. The transmission unit is realized by the communication I / F 44 of the smart glasses 214, and transmits the recorded voice data to the cloud server. The image recording unit is realized by the camera 42 and control unit 46A of the smart glasses 214, detects an object held by the child, and takes a photograph. The cloud server is realized by the database 24 of the data processing device 12, and stores the transmitted data. The sharing unit is realized by the specific processing unit 290 of the data processing device 12, and shares the stored data via SNS. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned audio recording unit, transmission unit, image recording unit, cloud server, and sharing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the audio recording unit is realized by the microphone 238 and control unit 46A of the headset-type terminal 314, detects the child's speech, and starts recording. The transmission unit is realized by the communication I / F 44 of the headset-type terminal 314, and transmits the recorded audio data to the cloud server. The image recording unit is realized by the camera 42 and control unit 46A of the headset-type terminal 314, detects an object the child is holding in their hand, and takes a photo. The cloud server is realized by the database 24 of the data processing device 12, and stores the transmitted data. The sharing unit is realized by the specific processing unit 290 of the data processing device 12, and shares the stored data via SNS. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice recording unit, transmission unit, image recording unit, cloud server, and sharing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice recording unit is realized by the microphone 238 and control unit 46A of the robot 414, detects the child's speech, and starts recording. The transmission unit is realized by the communication I / F 44 of the robot 414, and transmits the recorded voice data to the cloud server. The image recording unit is realized by the camera 42 and control unit 46A of the robot 414, detects an object the child is holding in their hand, and takes a photo. The cloud server is realized by the database 24 of the data processing device 12, and stores the transmitted data. The sharing unit is realized by the specific processing unit 290 of the data processing device 12, and shares the stored data via SNS.

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

[0121] The growth record system may further include a health record unit that collects health data of the child. The health record unit collects, time-stamps, and stores data such as the child's body temperature, heart rate, and sleep patterns. This allows parents to understand their child's health status in real time. For example, if the body temperature exceeds a certain range, an alert can be issued. Also, if the heart rate shows an abnormal value, a medical institution can be automatically notified. Furthermore, the system can analyze sleep patterns and provide advice on improving the child's sleep quality.

[0122] The growth record system can further include a learning record unit that collects a child's learning data. For example, the learning record unit records what a child is learning and their progress, and saves the data with a timestamp. This allows parents to understand their child's learning situation and provide appropriate support. For example, if progress in a particular subject is falling behind, the parent can be notified. The system can also evaluate learning results and identify areas that need praise. Furthermore, the system can analyze learning trends and suggest the best learning method for the child.

[0123] The growth record system may further include a friend record unit that records a child's friendships. The friend record unit, for example, records which friends a child has and what activities they have done with them, and stores the records with a timestamp. This allows parents to understand their child's social growth. For example, if a child frequently plays with a particular friend, the system can suggest activities to deepen the relationship with that friend. Also, if a child makes a new friend, the system can record the child's first activity with that friend. Furthermore, the system can analyze changes in friendships and support the child's improvement of social skills.

[0124] The growth record system may further include an emotion alert unit that estimates the child's emotion and issues an alert to the parent based on the estimated emotion. For example, the emotion alert unit can notify the parent if the child is sad. This allows the parent to understand the child's emotional state in real time and take appropriate action. For example, if the child is excited, the parent can be notified and can schedule time to play with the child. Also, if the child is anxious, the parent can be notified and take measures to reassure the child. Furthermore, if the child is happy, the moment can be recorded and can be reviewed later.

[0125] The growth recording system may further include a pattern analysis unit that analyzes the child's activity data and detects specific patterns. For example, if a child tends to engage in a specific activity at a specific time of day, the pattern analysis unit can detect that pattern and notify the parent. This allows the parent to understand the child's activity pattern and provide appropriate support. For example, if a child takes a nap at the same time every day, the parent can adjust the schedule accordingly. Also, if a child tends to engage in a specific activity on a specific day of the week, the parent can make preparations to support that activity. Furthermore, the pattern analysis unit can analyze changes in the child's activity pattern and take appropriate measures according to the child's growth.

[0126] The growth record system may further include a learning adjustment unit that estimates the child's emotions and adjusts the learning content based on the estimated emotions. For example, if the child is excited, the learning adjustment unit may provide more challenging learning content, thereby increasing the child's motivation to learn. For example, if the child is calm, the learning adjustment unit may provide learning content that requires concentration. Also, if the child is anxious, the learning adjustment unit may provide learning content that helps the child relax. Furthermore, if the child is happy, the learning adjustment unit may utilize the child's emotions to provide fun learning content.

[0127] The growth recording system may further include a friend support unit that estimates a child's emotions and supports friendships based on the estimated emotions. For example, when a child meets a new friend, the friend support unit can suggest an activity to deepen the relationship with the friend, thereby improving the child's social skills. For example, when a child is excited while playing with a friend, the friend support unit can suggest an activity to support the play with the friend. Also, when a child feels anxious while playing with a friend, the friend support unit can suggest an activity to improve the relationship with the friend. Furthermore, when a child is happy while playing with a friend, the moment can be recorded and reviewed later.

[0128] The growth record system may further include a health management unit that estimates the child's emotions and manages the child's health based on the estimated emotions. For example, if the child is feeling anxious, the health management unit can identify the cause and take appropriate measures. This allows the child's health to be maintained in a better state. For example, if the child is excited, the health management unit can identify the cause and provide a relaxing environment. Also, if the child is sad, the health management unit can identify the cause and notify the parent so that appropriate measures can be taken. Furthermore, if the child is happy, the health management unit can use the child's emotions to suggest healthy activities.

[0129] The growth record system may further include a sharing adjustment unit that estimates the child's emotions and adjusts the content to be shared on the social networking site based on the estimated emotions. For example, if a child is excited, the sharing adjustment unit can share that moment on the social networking site, allowing the child's emotions to be shared with family and friends. For example, if a child is calm, the sharing adjustment unit can record that moment and share it later. Also, if a child is feeling anxious, the sharing adjustment unit can record that moment and notify only the parents. Furthermore, if a child is happy, the sharing adjustment unit can share that moment on the social networking site, allowing the child to share their joy with family and friends.

[0130] The growth record system may further include a growth prediction unit that predicts future growth based on the child's activity data. The growth prediction unit, for example, analyzes past activity data and predicts future growth patterns. This allows parents to make plans that take their child's future growth into consideration. For example, it can predict how a specific activity will affect future learning ability. It can also predict how specific health data will affect future health conditions. It can also predict how specific friendships will affect future social skills.

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

[0132] Step 1: The voice recording unit detects and records voice. For example, when the voice recording unit detects a child speaking, it automatically starts recording and saves the recording with a timestamp. Step 2: The transmitting unit transmits the audio data recorded by the audio recording unit to the cloud server. For example, the transmitting unit transmits the data to the cloud server using wireless communication. Step 3: The image recording unit detects the object and takes a photo. For example, when the image recording unit detects an object being held by a child, it automatically takes a photo and saves it with a timestamp. Step 4: The transmitting unit transmits the image data captured by the image recording unit to the cloud server. Step 5: The cloud server stores the data transmitted by the transmitter. For example, the cloud server protects the data using encryption technology. Step 6: The sharing unit shares the data stored in the cloud server through SNS. For example, the sharing unit automatically shares the data through SNS.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

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

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice recording unit that detects and records voice; a transmitting unit that transmits the voice data recorded by the voice recording unit to a cloud server; an image recording unit that detects an object and takes a photograph; a transmitting unit that transmits image data captured by the image recording unit to a cloud server; a cloud server that stores the data transmitted by the transmission unit; a sharing unit having a specific function for sharing data stored in the cloud server through SNS. A system characterized by:

2. The voice recording unit When audio is detected, recording starts automatically and is saved with a timestamp.

2. The system of claim 1.

3. The image recording unit When an object is detected, it automatically takes a photo and saves it with a timestamp.

2. The system of claim 1.

4. The transmission unit Send data to a cloud server using wireless communication 2. The system of claim 1.

5. The cloud server Protect your data with encryption technology 2. The system of claim 1.

6. The common part is Automatically share data via social media 2. The system of claim 1.

7. The voice recording unit Estimate the child's emotions and adjust the start of recording based on the estimated emotions 2. The system of claim 1.

8. The voice recording unit Automatically filter background noise when recording to improve audio quality 2. The system of claim 1.

Images