system

Abstract

The system according to the embodiment aims to efficiently extract a child's growth record and personality from footage captured by a surveillance camera and provide this information to parents. [Solution] A system according to an embodiment includes an analysis unit, an extraction unit, a summarization unit, a provision unit, and an analysis unit. The analysis unit analyzes video. The extraction unit extracts important moments from the video analyzed by the analysis unit. The summarization unit summarizes a growth record based on the important moments extracted by the extraction unit. The provision unit provides the growth record summarized by the summarization unit to a parent. The analysis unit analyzes the child's personality from the video analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently extract a child's growth record and personality from surveillance camera footage and provide it to parents.

[0005] The system according to the embodiment aims to efficiently extract a child's growth record and personality from footage captured by a surveillance camera and provide this information to parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an extraction unit, a summarization unit, a provision unit, and an analysis unit. The analysis unit analyzes video. The extraction unit extracts important moments from the video analyzed by the analysis unit. The summarization unit summarizes a growth record based on the important moments extracted by the extraction unit. The provision unit provides the growth record summarized by the summarization unit to a parent. The analysis unit analyzes the child's personality from the video analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently extract a child's growth record and personality from the footage captured by the surveillance camera and provide this information to parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A monitoring camera system according to an embodiment of the present invention analyzes video footage and automatically records and summarizes important moments and activities in a child's daily life. This system uses a generative AI to analyze the video footage, identify the child's behavior, facial expressions, and comments, and extract key moments. The generative AI then analyzes the daily video footage and provides parents with a mid- to long-term growth record in the form of reports and digest videos. Furthermore, the generative AI analyzes the child's "personality" from the video footage and uses this information to provide appropriate childcare support. This allows parents to monitor their child's growth and receive appropriate childcare support. For example, the generative AI uses behavioral recognition, facial expression recognition, and voice recognition to analyze the video footage. These recognition technologies are used to identify the child's behavior, facial expressions, and comments and extract key moments. For example, it automatically records important moments for parents, such as their first steps or first words. The generative AI then analyzes the daily video footage and summarizes the mid- to long-term growth record. The generative AI analyzes the daily video footage, extracts key moments, and creates reports and digest videos. This allows parents to understand their child's growth at a glance. Furthermore, the generative AI analyzes the child's "personality" from the video and uses this information to provide childcare support tailored to each child. The generative AI analyzes the child's behavior, facial expressions, and comments to understand the child's characteristics and interests. Based on this, it provides parenting advice and support to parents. For example, if a child is interested in a particular type of play, it will suggest ways to deepen their learning through that play. This system allows parents to receive appropriate childcare support while watching over their child's growth. As a result, the monitoring camera system can record a child's growth in detail and provide appropriate childcare support to parents.

[0029] A monitoring camera system according to an embodiment includes an analysis unit, an extraction unit, a summarization unit, a providing unit, and an analysis unit. The analysis unit analyzes video. The analysis unit analyzes video using, for example, behavior recognition, facial expression recognition, and voice recognition. The behavior recognition is used, for example, to identify a child's movements and behaviors. The facial expression recognition is used, for example, to analyze a child's facial expressions and estimate their emotions. The voice recognition is used, for example, to analyze a child's speech and understand its content. The analysis unit can combine these recognition technologies to perform a detailed analysis of a child's behavior, facial expressions, and speech. The extraction unit extracts important moments from the video analyzed by the analysis unit. The extraction unit extracts important moments, such as a child's first behavior or a specific event. Examples of first behaviors include a child's first step or first word. Examples of specific events include a child's birthday or entrance ceremony. The extraction unit can automatically identify and record these important moments. The summarization unit summarizes a growth record based on the important moments extracted by the extraction unit. The summarizing unit, for example, analyzes daily video footage, extracts important moments, and creates reports or digest videos. The reports are provided in, for example, text or graph format. The digest videos are provided as, for example, short videos summarizing important moments. The summarizing unit allows parents to understand their child's growth at a glance through these summaries. The providing unit provides the growth record summarized by the summarizing unit to parents. The providing unit provides the growth record via, for example, a web application or a mobile application. The providing unit can also provide the growth record via email or paper. Through these methods, the providing unit allows parents to check their child's growth. The analysis unit analyzes the child's personality from the video analyzed by the analysis unit. The analysis unit analyzes, for example, patterns of behavior, facial expressions, and speech to understand the child's characteristics and interests. Based on this, the analysis unit provides parenting advice and support to parents. For example, if a child is interested in a particular game, the analysis unit suggests ways to deepen their learning through that game. As a result, the monitoring camera system according to the embodiment can record a child's growth in detail and provide appropriate parenting support to parents.

[0030] The analysis unit can analyze the video using behavioral recognition, facial expression recognition, and voice recognition. Behavior recognition is used, for example, to identify a child's movements and behaviors. Behavior recognition can analyze a child's movements in real time using, for example, a machine learning algorithm. For example, a generative AI analyzes a child's movements and identifies specific behaviors. Behavior recognition can also learn a child's movement patterns and detect abnormal behavior. For example, a generative AI learns a child's movement patterns and detects unusual behavior. Facial expression recognition can analyze a child's facial expression and infer emotions. Facial expression recognition can analyze a child's facial expression in real time using, for example, a deep learning algorithm. For example, a generative AI analyzes a child's facial expression and infer emotions. Facial expression recognition can also analyze changes in a child's facial expression to detect changes in emotions. For example, a generative AI analyzes changes in a child's facial expression to detect changes in emotions. Speech recognition can analyze a child's speech and understand its content. Speech recognition can analyze a child's speech in real time using, for example, natural language processing technology. For example, generative AI can analyze a child's speech and understand its content. Speech recognition can also learn a child's speech patterns and detect abnormal speech. For example, generative AI can learn a child's speech patterns and detect speech that is out of the ordinary. This improves the accuracy of video analysis by using behavioral recognition, facial expression recognition, and speech recognition.

[0031] The extraction unit can extract important moments of first actions or specific events. Examples of first actions include the first step or the first word. The first step refers, for example, to the moment a child walks on their own for the first time. The generation AI can analyze a child's movements and identify the first step. The first word refers, for example, to the moment a child utters a meaningful word for the first time. The generation AI can analyze a child's utterances and identify the first word. Examples of specific events include birthdays and entrance ceremonies. The birthday refers, for example, to an event celebrating a child's birthday. The generation AI can analyze video and identify birthday events. The entrance ceremony refers, for example, to an event celebrating a child's entrance to school. The generation AI can analyze video and identify entrance ceremony events. This allows important moments, such as first actions or specific events, to be extracted, so that no important moments are missed.

[0032] The summarization unit can analyze the daily video, extract important moments, and create reports or digest videos. The reports are provided in, for example, text or graph format. The text format report describes, for example, detailed information about a child's growth in writing. The generation AI can analyze the daily video, extract important moments, and create a text format report. The graph format report visually displays, for example, data about a child's growth. The generation AI can analyze the daily video, extract important moments, and create a graph format report. The digest video is provided, for example, as a short video summarizing important moments. The digest video visually displays, for example, highlights of a child's growth. The generation AI can analyze the daily video, extract important moments, and create a digest video. By analyzing the daily video, extracting important moments, and creating reports or digest videos, parents can grasp their child's growth at a glance.

[0033] The providing unit can provide the summarized growth record to the guardian. The providing unit provides the growth record through, for example, a web application or a mobile application. The web application is, for example, an application that can be accessed via the Internet, and the guardian can check the growth record using a web browser. The mobile application is, for example, an application that can be accessed using a smartphone or a tablet, and the guardian can check the growth record using the mobile device. The providing unit can also provide the growth record via email or paper media. The email is, for example, a method of sending the growth record to the guardian's email address, and the guardian can check the growth record through email. The paper media is, for example, a method of mailing a printed report or a digest video, and the guardian can check the growth record through paper media. In this way, by providing the summarized growth record to the guardian, the guardian can check the growth of his / her child.

[0034] The analysis unit can analyze patterns of behavior, facial expressions, and speech to understand a child's characteristics and interests. Behavior patterns refer to, for example, a child's daily actions and movements. The generation AI can analyze a child's behavior and identify specific behavioral patterns. For example, if a child repeatedly behaves in a specific manner at a specific time, the behavioral pattern can be identified. Facial expression patterns refer to, for example, changes in a child's facial expressions and emotions. The generation AI can analyze a child's facial expressions and identify specific facial expression patterns. For example, if a child shows a specific facial expression in a specific situation, the facial expression pattern can be identified. Speech patterns refer to, for example, the content and frequency of a child's speech. The generation AI can analyze a child's speech and identify specific speech patterns. For example, if a child frequently uses a specific word, the speech pattern can be identified. Thus, by analyzing patterns of behavior, facial expressions, and speech, a child's characteristics and interests can be understood. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input a child's behavioral data into the generation AI and have the generation AI perform an analysis of the child's characteristics and interests.

[0035] The provision unit can provide childcare advice and support based on the child's characteristics and interests. For example, the provision unit provides childcare advice based on the child's characteristics and interests. The childcare advice includes, for example, suggestions for play and learning methods based on the child's characteristics. The generation AI can analyze the child's characteristics and interests and provide appropriate childcare advice. For example, if a child is interested in a particular game, the provision unit can suggest ways to deepen learning through that game. The provision unit also provides childcare support based on the child's characteristics and interests. Childcare support includes, for example, providing childcare supplies and providing counseling. The generation AI can analyze the child's characteristics and interests and provide appropriate childcare support. For example, if a child needs a particular childcare supply, the provision unit can suggest ways to provide the supply. In this way, by providing childcare advice and support based on the child's characteristics and interests, parents can receive appropriate childcare support.

[0036] When analyzing the video, the analysis unit can learn the child's daily life patterns and detect abnormal behavior. The analysis unit, for example, uses a generation AI to learn the child's daily life patterns. The generation AI can analyze the child's behavioral data and identify daily life patterns. For example, if a child repeats a certain behavior at a certain time, it can learn that behavior pattern. The analysis unit detects abnormal behavior based on the learned daily life patterns. For example, the generation AI can analyze the child's behavioral data and detect behavior that is different from normal. Abnormal behavior includes, for example, behavior that deviates from normal behavior patterns and dangerous behavior. The generation AI can automatically identify this abnormal behavior and notify parents. In this way, by learning the child's daily life patterns and detecting abnormal behavior, abnormal behavior can be detected early.

[0037] When analyzing the video, the analysis unit can apply an analysis algorithm according to the child's developmental stage. For example, the analysis unit uses a generation AI to apply an analysis algorithm according to the child's developmental stage. The generation AI can select an appropriate analysis algorithm based on the child's age and developmental stage. For example, the generation AI learns behavioral patterns according to the child's age and applies an appropriate analysis algorithm. The analysis unit applies a facial expression recognition algorithm according to the developmental stage. For example, the generation AI can apply a facial expression recognition algorithm according to the child's developmental stage to analyze changes in facial expressions. The analysis unit also applies a voice recognition algorithm according to the developmental stage. For example, the generation AI can apply a voice recognition algorithm according to the child's developmental stage to analyze the content of what is being said. This enables more appropriate analysis by applying an analysis algorithm according to the child's developmental stage.

[0038] When analyzing the video, the analysis unit can perform analysis based on environmental information about the child's surroundings. The analysis unit, for example, uses a generation AI to analyze environmental information about the child's surroundings. Environmental information includes, for example, audio information, lighting information, and temperature information. The generation AI can analyze audio information about the child's surroundings and take environmental changes into account. For example, the generation AI analyzes audio information about the child's surroundings and detects changes in the environment. The analysis unit can analyze lighting information about the child's surroundings and adjust the brightness of the video. For example, the generation AI analyzes lighting information about the child's surroundings and adjusts the brightness of the video. The analysis unit can also analyze temperature information about the child's surroundings and take changes in behavior into account. For example, the generation AI analyzes temperature information about the child's surroundings and detects changes in behavior. This allows for more accurate analysis by taking environmental information about the child's surroundings into account.

[0039] The analysis unit can monitor the child's health condition and detect abnormalities during video analysis. The analysis unit monitors the child's health condition using, for example, a generation AI. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature and detect abnormal changes. For example, the generation AI analyzes the child's body temperature data and detects any abnormal changes in body temperature. The analysis unit can analyze the child's breathing pattern and detect abnormalities. For example, the generation AI analyzes the child's breathing pattern and detects any abnormal changes in breathing. The analysis unit can also analyze the child's movement pattern and detect abnormalities in the child's health condition. For example, the generation AI analyzes the child's movement pattern and detects any abnormal changes in movement. In this way, the child's health condition can be monitored and abnormalities can be detected early.

[0040] When extracting key moments, the extraction unit can perform the extraction based on the frequency and duration of the child's behavior. The extraction unit, for example, uses a generation AI to analyze the frequency of the child's behavior. The generation AI can analyze the child's behavior data and identify the frequency of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the frequency of that behavior. The extraction unit analyzes the duration of the child's behavior. The generation AI can analyze the child's behavior data and identify the duration of a specific behavior. For example, if a child continues a specific behavior for a long period of time, it can analyze the duration of that behavior. The extraction unit comprehensively analyzes the frequency and duration of the behavior to extract key moments. The generation AI can comprehensively analyze the child's behavior data and identify key moments based on frequency and duration. This makes it possible to extract more important moments by taking the frequency and duration of the child's behavior into consideration.

[0041] When extracting important moments, the extraction unit can apply an extraction algorithm according to the child's developmental stage. For example, the extraction unit uses a generation AI to apply an extraction algorithm according to the child's developmental stage. The generation AI can select an appropriate extraction algorithm based on the child's age and developmental stage. For example, the generation AI learns behavioral patterns according to the child's age and applies an appropriate extraction algorithm. The extraction unit applies a facial expression recognition algorithm according to the developmental stage. For example, the generation AI can apply a facial expression recognition algorithm according to the child's developmental stage to analyze changes in facial expressions. In addition, the extraction unit applies a voice recognition algorithm according to the developmental stage. For example, the generation AI can apply a voice recognition algorithm according to the child's developmental stage to analyze the content of what is being said. In this way, by applying an extraction algorithm according to the child's developmental stage, more appropriate moments can be extracted.

[0042] When extracting key moments, the extraction unit can perform the extraction based on environmental information surrounding the child. The extraction unit, for example, uses a generation AI to analyze environmental information surrounding the child. Environmental information includes, for example, audio information, lighting information, and temperature information. The generation AI can analyze audio information surrounding the child and extract key moments while taking environmental changes into consideration. For example, the generation AI analyzes audio information surrounding the child, detects environmental changes, and extracts key moments. The extraction unit can analyze lighting information surrounding the child and adjust the brightness of the video to extract key moments. For example, the generation AI analyzes lighting information surrounding the child and adjusts the brightness of the video to extract key moments. The extraction unit can also analyze temperature information surrounding the child and extract key moments while taking behavioral changes into consideration. For example, the generation AI analyzes temperature information surrounding the child, detects behavioral changes, and extracts key moments. This allows for more accurate extraction of moments by taking environmental information surrounding the child into consideration.

[0043] The extraction unit can monitor the child's health condition and detect abnormalities when extracting key moments. The extraction unit, for example, uses a generation AI to monitor the child's health condition. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature, detect abnormal changes, and extract key moments. For example, the generation AI analyzes the child's body temperature data, detects abnormal changes in body temperature, and extracts key moments. The extraction unit can analyze the child's breathing pattern, detect abnormalities, and extract key moments. For example, the generation AI analyzes the child's breathing pattern, detects abnormal changes in breathing, and extracts key moments. The extraction unit can also analyze the child's movement pattern, detect abnormalities in the health condition, and extract key moments. For example, the generation AI analyzes the child's movement pattern, detects abnormal changes in movement, and extracts key moments. This allows the child's health condition to be monitored and detected, enabling early detection of abnormalities in the health condition.

[0044] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the child's behavior. For example, the summarization unit uses a generation AI to analyze the importance of the child's behavior. The generation AI can analyze the child's behavior data and identify the importance of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the importance of that behavior. The summarization unit adjusts the level of detail of the summary based on the importance of the behavior. The generation AI can comprehensively analyze the child's behavior data and adjust the level of detail of the summary based on the importance. For example, the generation AI analyzes the importance of the child's behavior and provides a detailed summary for important behavior. Alternatively, the generation AI analyzes the importance of the child's behavior and provides a concise summary for overall behavior. In this way, by adjusting the level of detail of the summary based on the importance of the child's behavior, a detailed summary is provided for important behavior.

[0045] When generating a summary, the summarization unit can apply a summarization algorithm appropriate for the child's developmental stage. For example, the summarization unit uses a generation AI to apply a summarization algorithm appropriate for the child's developmental stage. The generation AI can select an appropriate summarization algorithm based on the child's age and developmental stage. For example, the generation AI learns behavioral patterns appropriate for the child's age and applies an appropriate summarization algorithm. The summarization unit applies a facial expression recognition algorithm appropriate for the developmental stage. For example, the generation AI can apply a facial expression recognition algorithm appropriate for the child's developmental stage to analyze changes in facial expressions. The summarization unit also applies a voice recognition algorithm appropriate for the developmental stage. For example, the generation AI can apply a voice recognition algorithm appropriate for the child's developmental stage to analyze the content of what is being said. In this way, a more appropriate summary can be provided by applying a summarization algorithm appropriate for the child's developmental stage.

[0046] When generating summaries, the summarization unit can determine the priority of summaries based on the frequency and duration of the child's behavior. The summarization unit, for example, uses a generation AI to analyze the frequency of the child's behavior. The generation AI can analyze the child's behavior data and identify the frequency of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the frequency of that behavior. The summarization unit analyzes the duration of the child's behavior. The generation AI can analyze the child's behavior data and identify the duration of a specific behavior. For example, if a child continues a specific behavior for a long period of time, it can analyze the duration of that behavior. The summarization unit comprehensively analyzes the frequency and duration of the behaviors and determines the priority of summaries. The generation AI can comprehensively analyze the child's behavior data and determine the priority of summaries based on the frequency and duration. In this way, by determining the priority of summaries based on the frequency and duration of the child's behavior, important behaviors are preferentially summarized.

[0047] The summarization unit can monitor the child's health condition and detect abnormalities when generating a summary. The summarization unit monitors the child's health condition using, for example, a generation AI. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature and detect abnormal changes, which are reflected in the summary. For example, the generation AI analyzes the child's body temperature data and detects abnormal body temperature changes that are out of the ordinary and reflects them in the summary. The summarization unit can analyze the child's breathing pattern and detect abnormalities, which are reflected in the summary. For example, the generation AI analyzes the child's breathing pattern and detects abnormal breathing changes that are out of the ordinary and reflects them in the summary. The summarization unit can also analyze the child's movement pattern and detect abnormalities in the health condition, which are reflected in the summary. For example, the generation AI analyzes the child's movement pattern and detects abnormal movement changes that are out of the ordinary and reflects them in the summary. In this way, the child's health condition is monitored and abnormalities are detected, which are reflected in the summary.

[0048] When providing information, the providing unit can select an appropriate method of providing information by referring to the guardian's past feedback. The providing unit, for example, analyzes the guardian's past feedback. The feedback includes, for example, the guardian's preferred information providing methods and evaluations in the past. The generation AI can analyze the guardian's past feedback and select the optimal method of providing information. For example, the generation AI selects the optimal method of providing information based on the guardian's preferred information providing methods in the past. The providing unit can adjust the level of detail of the information based on the guardian's past feedback. For example, the generation AI analyzes the guardian's past feedback and adjusts the level of detail of the information. The providing unit can also select a display format of the information by referring to the guardian's past feedback. For example, the generation AI selects a display format of the information by referring to the guardian's past feedback. In this way, the optimal method of providing information is selected by referring to the guardian's past feedback.

[0049] The providing unit can customize information according to the guardian's areas of interest when providing the information. The providing unit, for example, analyzes the guardian's areas of interest. The areas of interest include, for example, specific childcare information and activities. The generation AI can analyze the guardian's areas of interest and provide customized information. For example, the generation AI provides customized information based on the childcare information in which the guardian is interested. The providing unit can adjust the display order of information based on the guardian's areas of interest. For example, the generation AI analyzes the guardian's areas of interest and provides related information with priority. The providing unit can also adjust the content of the information based on the guardian's areas of interest. For example, the generation AI adjusts the content of the information based on the guardian's areas of interest. In this way, more appropriate information can be provided by customizing the information according to the guardian's areas of interest.

[0050] The providing unit can provide optimal information by taking into account the guardian's geographical location information when providing the information. The providing unit, for example, analyzes the guardian's geographical location information. Geographical location information includes, for example, GPS data and location information services. The generating AI can analyze the guardian's geographical location information and provide optimal information. For example, if the guardian is in their current location, the generating AI provides information on nearby childcare support facilities. The providing unit can adjust the content of the information based on the guardian's geographical location information. For example, if the generating AI analyzes the guardian's geographical location information and is traveling, it provides childcare information for the travel destination. The providing unit can also adjust the display format of the information based on the guardian's geographical location information. For example, the generating AI analyzes the guardian's geographical location information and provides childcare information related to a specific area. In this way, more appropriate information is provided by taking into account the guardian's geographical location information.

[0051] At the time of providing the information, the providing unit can analyze the parent's social media activity and provide relevant information. The providing unit, for example, analyzes the parent's social media activity. Social media activity includes, for example, the content of posts and the number of likes. The generation AI can analyze the parent's social media activity and provide relevant information. For example, the generation AI analyzes the parent's social media posts and provides relevant childcare information. The providing unit can analyze the parent's social media interests and provide relevant information. For example, the generation AI analyzes the parent's social media interests and provides relevant information. The providing unit can also analyze the parent's social media follower information and provide relevant information. For example, the generation AI analyzes the parent's social media follower information and provides relevant information. In this way, more relevant information can be provided by analyzing the parent's social media activity.

[0052] The analysis unit can take into account the frequency and duration of a child's behavior when analyzing personality. The analysis unit, for example, uses a generation AI to analyze the frequency of a child's behavior. The generation AI can analyze the child's behavioral data and identify the frequency of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the frequency of that behavior. The analysis unit analyzes the duration of the child's behavior. The generation AI can analyze the child's behavioral data and identify the duration of a specific behavior. For example, if a child continues a specific behavior for a long period of time, it can analyze the duration of that behavior. The analysis unit comprehensively analyzes the frequency and duration of the behavior and reflects this in the personality analysis. The generation AI can comprehensively analyze the child's behavioral data and perform personality analysis based on the frequency and duration. This enables more accurate personality analysis by taking into account the frequency and duration of a child's behavior.

[0053] When analyzing a child's personality, the analysis unit can apply an analysis algorithm that corresponds to the child's developmental stage. For example, the analysis unit uses a generation AI to apply an analysis algorithm that corresponds to the child's developmental stage. The generation AI can select an appropriate analysis algorithm based on the child's age and developmental stage. For example, the generation AI learns behavioral patterns according to the child's age and applies an appropriate analysis algorithm. The analysis unit applies a facial expression recognition algorithm that corresponds to the child's developmental stage. For example, the generation AI can apply a facial expression recognition algorithm that corresponds to the child's developmental stage to analyze changes in facial expressions. The analysis unit also applies a voice recognition algorithm that corresponds to the child's developmental stage. For example, the generation AI can apply a voice recognition algorithm that corresponds to the child's developmental stage to analyze the content of speech. This enables more accurate personality analysis by applying an analysis algorithm that corresponds to the child's developmental stage.

[0054] The analysis unit can perform personality analysis based on environmental information surrounding the child. The analysis unit, for example, uses a generation AI to analyze environmental information surrounding the child. Environmental information includes, for example, audio information, lighting information, and temperature information. The generation AI can analyze audio information surrounding the child and perform personality analysis while taking environmental changes into account. For example, the generation AI analyzes audio information surrounding the child, detects environmental changes, and performs personality analysis. The analysis unit can analyze lighting information surrounding the child and adjust the brightness of the image to perform personality analysis. For example, the generation AI analyzes lighting information surrounding the child and adjusts the brightness of the image to perform personality analysis. The analysis unit can also analyze temperature information surrounding the child and perform personality analysis while taking behavioral changes into account. For example, the generation AI analyzes temperature information surrounding the child, detects behavioral changes, and performs personality analysis. This allows for more accurate personality analysis by taking environmental information surrounding the child into account.

[0055] The analysis unit can monitor the child's health condition and detect abnormalities during personality analysis. The analysis unit, for example, uses a generation AI to monitor the child's health condition. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature and detect abnormal changes, which are reflected in the personality analysis. For example, the generation AI analyzes the child's body temperature data and detects abnormal body temperature changes, which are reflected in the personality analysis. The analysis unit can analyze the child's breathing pattern and detect abnormalities, which are reflected in the personality analysis. For example, the generation AI analyzes the child's breathing pattern and detects abnormal breathing changes, which are reflected in the personality analysis. The analysis unit can also analyze the child's movement patterns and detect abnormalities in the child's health condition, which are reflected in the personality analysis. For example, the generation AI analyzes the child's movement patterns and detects abnormal movement changes, which are reflected in the personality analysis. In this way, the child's health condition is monitored and abnormalities are detected, which are reflected in the personality analysis.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When analyzing the video, the analysis unit can perform analysis based on environmental information about the child's surroundings. The analysis unit, for example, uses a generation AI to analyze environmental information about the child's surroundings. Environmental information includes, for example, audio information, lighting information, and temperature information. The generation AI can analyze audio information about the child's surroundings and take environmental changes into account. For example, the generation AI analyzes audio information about the child's surroundings and detects changes in the environment. The analysis unit can analyze lighting information about the child's surroundings and adjust the brightness of the video. For example, the generation AI analyzes lighting information about the child's surroundings and adjusts the brightness of the video. The analysis unit can also analyze temperature information about the child's surroundings and take changes in behavior into account. For example, the generation AI analyzes temperature information about the child's surroundings and detects changes in behavior. This allows for more accurate analysis by taking environmental information about the child's surroundings into account.

[0058] The extraction unit can monitor the child's health condition and detect abnormalities when extracting key moments. The extraction unit, for example, uses a generation AI to monitor the child's health condition. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature, detect abnormal changes, and extract key moments. For example, the generation AI analyzes the child's body temperature data, detects abnormal changes in body temperature, and extracts key moments. The extraction unit can analyze the child's breathing pattern, detect abnormalities, and extract key moments. For example, the generation AI analyzes the child's breathing pattern, detects abnormal changes in breathing, and extracts key moments. The extraction unit can also analyze the child's movement pattern, detect abnormalities in the health condition, and extract key moments. For example, the generation AI analyzes the child's movement pattern, detects abnormal changes in movement, and extracts key moments. This allows the child's health condition to be monitored and detected, enabling early detection of abnormalities in the health condition.

[0059] When generating summaries, the summarization unit can determine the priority of summaries based on the frequency and duration of the child's behavior. The summarization unit, for example, uses a generation AI to analyze the frequency of the child's behavior. The generation AI can analyze the child's behavior data and identify the frequency of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the frequency of that behavior. The summarization unit analyzes the duration of the child's behavior. The generation AI can analyze the child's behavior data and identify the duration of a specific behavior. For example, if a child continues a specific behavior for a long period of time, it can analyze the duration of that behavior. The summarization unit comprehensively analyzes the frequency and duration of the behaviors and determines the priority of summaries. The generation AI can comprehensively analyze the child's behavior data and determine the priority of summaries based on the frequency and duration. In this way, by determining the priority of summaries based on the frequency and duration of the child's behavior, important behaviors are preferentially summarized.

[0060] When providing information, the providing unit can select an appropriate method of providing information by referring to the guardian's past feedback. The providing unit, for example, analyzes the guardian's past feedback. The feedback includes, for example, the guardian's preferred information providing methods and evaluations in the past. The generation AI can analyze the guardian's past feedback and select the optimal method of providing information. For example, the generation AI selects the optimal method of providing information based on the guardian's preferred information providing methods in the past. The providing unit can adjust the level of detail of the information based on the guardian's past feedback. For example, the generation AI analyzes the guardian's past feedback and adjusts the level of detail of the information. The providing unit can also select a display format of the information by referring to the guardian's past feedback. For example, the generation AI selects a display format of the information by referring to the guardian's past feedback. In this way, the optimal method of providing information is selected by referring to the guardian's past feedback.

[0061] The providing unit can provide optimal information by taking into account the guardian's geographical location information when providing the information. The providing unit, for example, analyzes the guardian's geographical location information. Geographical location information includes, for example, GPS data and location information services. The generating AI can analyze the guardian's geographical location information and provide optimal information. For example, if the guardian is in their current location, the generating AI provides information on nearby childcare support facilities. The providing unit can adjust the content of the information based on the guardian's geographical location information. For example, if the generating AI analyzes the guardian's geographical location information and is traveling, it provides childcare information for the travel destination. The providing unit can also adjust the display format of the information based on the guardian's geographical location information. For example, the generating AI analyzes the guardian's geographical location information and provides childcare information related to a specific area. In this way, more appropriate information is provided by taking into account the guardian's geographical location information.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The analysis unit analyzes the video. The analysis unit uses behavioral, facial, and voice recognition to analyze the video and perform a detailed analysis of the child's behavior, facial expressions, and speech. Behavior recognition identifies the child's movements and actions, facial expression recognition analyzes the child's facial expressions to infer emotions, and voice recognition analyzes the child's speech to understand its content. Step 2: The extraction unit extracts important moments from the video analyzed by the analysis unit. The extraction unit automatically identifies and records important moments such as first actions and specific events. First actions include taking a first step or saying a first word, and specific events include birthdays and entrance ceremonies. Step 3: The summarization unit summarizes the growth record based on the key moments extracted by the extraction unit. The summarization unit analyzes the daily video, extracts key moments, and creates reports and digest videos. The reports are provided in text and graph formats, and the digest videos are provided as short videos summarizing key moments. Step 4: The providing unit provides the growth record summarized by the summarizing unit to the guardian. The providing unit can provide the growth record through a web application or a mobile application, and can also provide it by email or in paper form. Step 5: The analysis unit analyzes the child's personality from the video. The analysis unit analyzes patterns of behavior, facial expressions, and speech to understand the child's characteristics and interests. Based on this, it provides parenting advice and support to the parents.

[0064] (Example 2) A monitoring camera system according to an embodiment of the present invention analyzes video footage and automatically records and summarizes important moments and activities in a child's daily life. This system uses a generative AI to analyze the video footage, identify the child's behavior, facial expressions, and comments, and extract key moments. The generative AI then analyzes the daily video footage and provides parents with a mid- to long-term growth record in the form of reports and digest videos. Furthermore, the generative AI analyzes the child's "personality" from the video footage and uses this information to provide appropriate childcare support. This allows parents to monitor their child's growth and receive appropriate childcare support. For example, the generative AI uses behavioral recognition, facial expression recognition, and voice recognition to analyze the video footage. These recognition technologies are used to identify the child's behavior, facial expressions, and comments and extract key moments. For example, it automatically records important moments for parents, such as their first steps or first words. The generative AI then analyzes the daily video footage and summarizes the mid- to long-term growth record. The generative AI analyzes the daily video footage, extracts key moments, and creates reports and digest videos. This allows parents to understand their child's growth at a glance. Furthermore, the generative AI analyzes the child's "personality" from the video and uses this information to provide childcare support tailored to each child. The generative AI analyzes the child's behavior, facial expressions, and comments to understand the child's characteristics and interests. Based on this, it provides parenting advice and support to parents. For example, if a child is interested in a particular type of play, it will suggest ways to deepen their learning through that play. This system allows parents to receive appropriate childcare support while watching over their child's growth. As a result, the monitoring camera system can record a child's growth in detail and provide appropriate childcare support to parents.

[0065] A monitoring camera system according to an embodiment includes an analysis unit, an extraction unit, a summarization unit, a providing unit, and an analysis unit. The analysis unit analyzes video. The analysis unit analyzes video using, for example, behavior recognition, facial expression recognition, and voice recognition. The behavior recognition is used, for example, to identify a child's movements and behaviors. The facial expression recognition is used, for example, to analyze a child's facial expressions and estimate their emotions. The voice recognition is used, for example, to analyze a child's speech and understand its content. The analysis unit can combine these recognition technologies to perform a detailed analysis of a child's behavior, facial expressions, and speech. The extraction unit extracts important moments from the video analyzed by the analysis unit. The extraction unit extracts important moments, such as a child's first behavior or a specific event. Examples of first behaviors include a child's first step or first word. Examples of specific events include a child's birthday or entrance ceremony. The extraction unit can automatically identify and record these important moments. The summarization unit summarizes a growth record based on the important moments extracted by the extraction unit. The summarizing unit, for example, analyzes daily video footage, extracts important moments, and creates reports or digest videos. The reports are provided in, for example, text or graph format. The digest videos are provided as, for example, short videos summarizing important moments. The summarizing unit allows parents to understand their child's growth at a glance through these summaries. The providing unit provides the growth record summarized by the summarizing unit to parents. The providing unit provides the growth record via, for example, a web application or a mobile application. The providing unit can also provide the growth record via email or paper. Through these methods, the providing unit allows parents to check their child's growth. The analysis unit analyzes the child's personality from the video analyzed by the analysis unit. The analysis unit analyzes, for example, patterns of behavior, facial expressions, and speech to understand the child's characteristics and interests. Based on this, the analysis unit provides parenting advice and support to parents. For example, if a child is interested in a particular game, the analysis unit suggests ways to deepen their learning through that game. As a result, the monitoring camera system according to the embodiment can record a child's growth in detail and provide appropriate parenting support to parents.

[0066] The analysis unit can analyze the video using behavioral recognition, facial expression recognition, and voice recognition. Behavior recognition is used, for example, to identify a child's movements and behaviors. Behavior recognition can analyze a child's movements in real time using, for example, a machine learning algorithm. For example, a generative AI analyzes a child's movements and identifies specific behaviors. Behavior recognition can also learn a child's movement patterns and detect abnormal behavior. For example, a generative AI learns a child's movement patterns and detects unusual behavior. Facial expression recognition can analyze a child's facial expression and infer emotions. Facial expression recognition can analyze a child's facial expression in real time using, for example, a deep learning algorithm. For example, a generative AI analyzes a child's facial expression and infer emotions. Facial expression recognition can also analyze changes in a child's facial expression to detect changes in emotions. For example, a generative AI analyzes changes in a child's facial expression to detect changes in emotions. Speech recognition can analyze a child's speech and understand its content. Speech recognition can analyze a child's speech in real time using, for example, natural language processing technology. For example, generative AI can analyze a child's speech and understand its content. Speech recognition can also learn a child's speech patterns and detect abnormal speech. For example, generative AI can learn a child's speech patterns and detect speech that is out of the ordinary. This improves the accuracy of video analysis by using behavioral recognition, facial expression recognition, and speech recognition.

[0067] The extraction unit can extract important moments of first actions or specific events. Examples of first actions include the first step or the first word. The first step refers, for example, to the moment a child walks on their own for the first time. The generation AI can analyze a child's movements and identify the first step. The first word refers, for example, to the moment a child utters a meaningful word for the first time. The generation AI can analyze a child's utterances and identify the first word. Examples of specific events include birthdays and entrance ceremonies. The birthday refers, for example, to an event celebrating a child's birthday. The generation AI can analyze video and identify birthday events. The entrance ceremony refers, for example, to an event celebrating a child's entrance to school. The generation AI can analyze video and identify entrance ceremony events. This allows important moments, such as first actions or specific events, to be extracted, so that no important moments are missed.

[0068] The summarization unit can analyze the daily video, extract important moments, and create reports or digest videos. The reports are provided in, for example, text or graph format. The text format report describes, for example, detailed information about a child's growth in writing. The generation AI can analyze the daily video, extract important moments, and create a text format report. The graph format report visually displays, for example, data about a child's growth. The generation AI can analyze the daily video, extract important moments, and create a graph format report. The digest video is provided, for example, as a short video summarizing important moments. The digest video visually displays, for example, highlights of a child's growth. The generation AI can analyze the daily video, extract important moments, and create a digest video. By analyzing the daily video, extracting important moments, and creating reports or digest videos, parents can grasp their child's growth at a glance.

[0069] The providing unit can provide the summarized growth record to the guardian. The providing unit provides the growth record through, for example, a web application or a mobile application. The web application is, for example, an application that can be accessed via the Internet, and the guardian can check the growth record using a web browser. The mobile application is, for example, an application that can be accessed using a smartphone or a tablet, and the guardian can check the growth record using the mobile device. The providing unit can also provide the growth record via email or paper media. The email is, for example, a method of sending the growth record to the guardian's email address, and the guardian can check the growth record through email. The paper media is, for example, a method of mailing a printed report or a digest video, and the guardian can check the growth record through paper media. In this way, by providing the summarized growth record to the guardian, the guardian can check the growth of his / her child.

[0070] The analysis unit can analyze patterns of behavior, facial expressions, and speech to understand a child's characteristics and interests. Behavior patterns refer to, for example, a child's daily actions and movements. The generation AI can analyze a child's behavior and identify specific behavioral patterns. For example, if a child repeatedly behaves in a specific manner at a specific time, the behavioral pattern can be identified. Facial expression patterns refer to, for example, changes in a child's facial expressions and emotions. The generation AI can analyze a child's facial expressions and identify specific facial expression patterns. For example, if a child shows a specific facial expression in a specific situation, the facial expression pattern can be identified. Speech patterns refer to, for example, the content and frequency of a child's speech. The generation AI can analyze a child's speech and identify specific speech patterns. For example, if a child frequently uses a specific word, the speech pattern can be identified. Thus, by analyzing patterns of behavior, facial expressions, and speech, a child's characteristics and interests can be understood. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input a child's behavioral data into the generation AI and have the generation AI perform an analysis of the child's characteristics and interests.

[0071] The provision unit can provide childcare advice and support based on the child's characteristics and interests. For example, the provision unit provides childcare advice based on the child's characteristics and interests. The childcare advice includes, for example, suggestions for play and learning methods based on the child's characteristics. The generation AI can analyze the child's characteristics and interests and provide appropriate childcare advice. For example, if a child is interested in a particular game, the provision unit can suggest ways to deepen learning through that game. The provision unit also provides childcare support based on the child's characteristics and interests. Childcare support includes, for example, providing childcare supplies and providing counseling. The generation AI can analyze the child's characteristics and interests and provide appropriate childcare support. For example, if a child needs a particular childcare supply, the provision unit can suggest ways to provide the supply. In this way, by providing childcare advice and support based on the child's characteristics and interests, parents can receive appropriate childcare support.

[0072] The analysis unit can estimate the child's emotions and adjust the accuracy of video analysis based on the estimated child's emotions. The analysis unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate their emotions. The generation AI can analyze the child's facial expressions and estimate their emotions. Voice analysis is used, for example, to analyze the child's speech and estimate their emotions. The generation AI can analyze the child's speech and estimate their emotions. The analysis unit adjusts the accuracy of video analysis based on the child's estimated emotions. For example, if the child is excited, the generation AI increases the accuracy of video analysis to capture subtle movements and changes in facial expressions. If the child is relaxed, the generation AI adjusts the accuracy of video analysis to capture overall movements and facial expressions. Furthermore, if the child is tired, the generation AI reduces the accuracy of video analysis and focuses on key movements and facial expressions. This allows for more accurate analysis by adjusting the accuracy of video analysis based on the child's emotions.

[0073] When analyzing the video, the analysis unit can learn the child's daily life patterns and detect abnormal behavior. The analysis unit, for example, uses a generation AI to learn the child's daily life patterns. The generation AI can analyze the child's behavioral data and identify daily life patterns. For example, if a child repeats a certain behavior at a certain time, it can learn that behavior pattern. The analysis unit detects abnormal behavior based on the learned daily life patterns. For example, the generation AI can analyze the child's behavioral data and detect behavior that is different from normal. Abnormal behavior includes, for example, behavior that deviates from normal behavior patterns and dangerous behavior. The generation AI can automatically identify this abnormal behavior and notify parents. In this way, by learning the child's daily life patterns and detecting abnormal behavior, abnormal behavior can be detected early.

[0074] When analyzing the video, the analysis unit can apply an analysis algorithm according to the child's developmental stage. For example, the analysis unit uses a generation AI to apply an analysis algorithm according to the child's developmental stage. The generation AI can select an appropriate analysis algorithm based on the child's age and developmental stage. For example, the generation AI learns behavioral patterns according to the child's age and applies an appropriate analysis algorithm. The analysis unit applies a facial expression recognition algorithm according to the developmental stage. For example, the generation AI can apply a facial expression recognition algorithm according to the child's developmental stage to analyze changes in facial expressions. The analysis unit also applies a voice recognition algorithm according to the developmental stage. For example, the generation AI can apply a voice recognition algorithm according to the child's developmental stage to analyze the content of what is being said. This enables more appropriate analysis by applying an analysis algorithm according to the child's developmental stage.

[0075] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated child's emotions. The analysis unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate their emotions. The generation AI can analyze the child's facial expressions and estimate their emotions. Voice analysis is used, for example, to analyze the child's speech and estimate their emotions. The generation AI can analyze the child's speech and estimate their emotions. The analysis unit adjusts the display method of the analysis results based on the child's estimated emotions. For example, if the child is excited, the generation AI displays detailed analysis results. If the child is relaxed, the generation AI displays overall analysis results. Furthermore, if the child is tired, the generation AI displays main analysis results. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results based on the child's emotions.

[0076] When analyzing the video, the analysis unit can perform analysis based on environmental information about the child's surroundings. The analysis unit, for example, uses a generation AI to analyze environmental information about the child's surroundings. Environmental information includes, for example, audio information, lighting information, and temperature information. The generation AI can analyze audio information about the child's surroundings and take environmental changes into account. For example, the generation AI analyzes audio information about the child's surroundings and detects changes in the environment. The analysis unit can analyze lighting information about the child's surroundings and adjust the brightness of the video. For example, the generation AI analyzes lighting information about the child's surroundings and adjusts the brightness of the video. The analysis unit can also analyze temperature information about the child's surroundings and take changes in behavior into account. For example, the generation AI analyzes temperature information about the child's surroundings and detects changes in behavior. This allows for more accurate analysis by taking environmental information about the child's surroundings into account.

[0077] The analysis unit can monitor the child's health condition and detect abnormalities during video analysis. The analysis unit monitors the child's health condition using, for example, a generation AI. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature and detect abnormal changes. For example, the generation AI analyzes the child's body temperature data and detects any abnormal changes in body temperature. The analysis unit can analyze the child's breathing pattern and detect abnormalities. For example, the generation AI analyzes the child's breathing pattern and detects any abnormal changes in breathing. The analysis unit can also analyze the child's movement pattern and detect abnormalities in the child's health condition. For example, the generation AI analyzes the child's movement pattern and detects any abnormal changes in movement. In this way, the child's health condition can be monitored and abnormalities can be detected early.

[0078] The extraction unit can estimate the child's emotions and adjust the extraction criteria for key moments based on the estimated child's emotions. The extraction unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate the emotions. The generation AI can analyze the child's facial expressions and estimate the emotions. Voice analysis is used, for example, to analyze the child's speech and estimate the emotions. The generation AI can analyze the child's speech and estimate the emotions. The extraction unit adjusts the extraction criteria for key moments based on the estimated child's emotions. For example, if the child is excited, the generation AI extracts small movements and changes in facial expressions as key moments. Furthermore, if the child is relaxed, the generation AI extracts overall movements and facial expressions as key moments. Furthermore, if the child is tired, the generation AI extracts major movements and facial expressions as key moments. This allows more appropriate moments to be extracted by adjusting the extraction criteria for key moments based on the child's emotions.

[0079] When extracting key moments, the extraction unit can perform the extraction based on the frequency and duration of the child's behavior. The extraction unit, for example, uses a generation AI to analyze the frequency of the child's behavior. The generation AI can analyze the child's behavior data and identify the frequency of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the frequency of that behavior. The extraction unit analyzes the duration of the child's behavior. The generation AI can analyze the child's behavior data and identify the duration of a specific behavior. For example, if a child continues a specific behavior for a long period of time, it can analyze the duration of that behavior. The extraction unit comprehensively analyzes the frequency and duration of the behavior to extract key moments. The generation AI can comprehensively analyze the child's behavior data and identify key moments based on frequency and duration. This makes it possible to extract more important moments by taking the frequency and duration of the child's behavior into consideration.

[0080] When extracting important moments, the extraction unit can apply an extraction algorithm according to the child's developmental stage. For example, the extraction unit uses a generation AI to apply an extraction algorithm according to the child's developmental stage. The generation AI can select an appropriate extraction algorithm based on the child's age and developmental stage. For example, the generation AI learns behavioral patterns according to the child's age and applies an appropriate extraction algorithm. The extraction unit applies a facial expression recognition algorithm according to the developmental stage. For example, the generation AI can apply a facial expression recognition algorithm according to the child's developmental stage to analyze changes in facial expressions. In addition, the extraction unit applies a voice recognition algorithm according to the developmental stage. For example, the generation AI can apply a voice recognition algorithm according to the child's developmental stage to analyze the content of what is being said. In this way, by applying an extraction algorithm according to the child's developmental stage, more appropriate moments can be extracted.

[0081] The extraction unit can estimate the child's emotions and adjust the display method of the extraction results based on the estimated child's emotions. The extraction unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate their emotions. The generation AI can analyze the child's facial expressions and estimate their emotions. Voice analysis is used, for example, to analyze the child's speech and estimate their emotions. The generation AI can analyze the child's speech and estimate their emotions. The extraction unit adjusts the display method of the extraction results based on the estimated child's emotions. For example, if the child is excited, the generation AI displays detailed extraction results. If the child is relaxed, the generation AI displays overall extraction results. Furthermore, if the child is tired, the generation AI displays main extraction results. This makes it possible to provide more appropriate information by adjusting the display method of the extraction results based on the child's emotions.

[0082] When extracting key moments, the extraction unit can perform the extraction based on environmental information surrounding the child. The extraction unit, for example, uses a generation AI to analyze environmental information surrounding the child. Environmental information includes, for example, audio information, lighting information, and temperature information. The generation AI can analyze audio information surrounding the child and extract key moments while taking environmental changes into consideration. For example, the generation AI analyzes audio information surrounding the child, detects environmental changes, and extracts key moments. The extraction unit can analyze lighting information surrounding the child and adjust the brightness of the video to extract key moments. For example, the generation AI analyzes lighting information surrounding the child and adjusts the brightness of the video to extract key moments. The extraction unit can also analyze temperature information surrounding the child and extract key moments while taking behavioral changes into consideration. For example, the generation AI analyzes temperature information surrounding the child, detects behavioral changes, and extracts key moments. This allows for more accurate extraction of moments by taking environmental information surrounding the child into consideration.

[0083] The extraction unit can monitor the child's health condition and detect abnormalities when extracting key moments. The extraction unit, for example, uses a generation AI to monitor the child's health condition. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature, detect abnormal changes, and extract key moments. For example, the generation AI analyzes the child's body temperature data, detects abnormal changes in body temperature, and extracts key moments. The extraction unit can analyze the child's breathing pattern, detect abnormalities, and extract key moments. For example, the generation AI analyzes the child's breathing pattern, detects abnormal changes in breathing, and extracts key moments. The extraction unit can also analyze the child's movement pattern, detect abnormalities in the health condition, and extract key moments. For example, the generation AI analyzes the child's movement pattern, detects abnormal changes in movement, and extracts key moments. This allows the child's health condition to be monitored and detected, enabling early detection of abnormalities in the health condition.

[0084] The summarization unit can estimate the child's emotions and adjust the way the summary is presented based on the estimated child's emotions. The summarization unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate the emotions. The generation AI can analyze the child's facial expressions and estimate the emotions. Voice analysis is used, for example, to analyze the child's utterances and estimate the emotions. The generation AI can analyze the child's utterances and estimate the emotions. The summarization unit adjusts the way the summary is presented based on the estimated child's emotions. For example, if the child is excited, the generation AI provides a detailed summary. Also, if the child is relaxed, the generation AI provides an overall summary. Furthermore, if the child is tired, the generation AI provides a main summary. In this way, a more appropriate summary can be provided by adjusting the way the summary is presented based on the child's emotions.

[0085] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the child's behavior. For example, the summarization unit uses a generation AI to analyze the importance of the child's behavior. The generation AI can analyze the child's behavior data and identify the importance of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the importance of that behavior. The summarization unit adjusts the level of detail of the summary based on the importance of the behavior. The generation AI can comprehensively analyze the child's behavior data and adjust the level of detail of the summary based on the importance. For example, the generation AI analyzes the importance of the child's behavior and provides a detailed summary for important behavior. Alternatively, the generation AI analyzes the importance of the child's behavior and provides a concise summary for overall behavior. In this way, by adjusting the level of detail of the summary based on the importance of the child's behavior, a detailed summary is provided for important behavior.

[0086] When generating a summary, the summarization unit can apply a summarization algorithm appropriate for the child's developmental stage. For example, the summarization unit uses a generation AI to apply a summarization algorithm appropriate for the child's developmental stage. The generation AI can select an appropriate summarization algorithm based on the child's age and developmental stage. For example, the generation AI learns behavioral patterns appropriate for the child's age and applies an appropriate summarization algorithm. The summarization unit applies a facial expression recognition algorithm appropriate for the developmental stage. For example, the generation AI can apply a facial expression recognition algorithm appropriate for the child's developmental stage to analyze changes in facial expressions. The summarization unit also applies a voice recognition algorithm appropriate for the developmental stage. For example, the generation AI can apply a voice recognition algorithm appropriate for the child's developmental stage to analyze the content of what is being said. In this way, a more appropriate summary can be provided by applying a summarization algorithm appropriate for the child's developmental stage.

[0087] The summarization unit can estimate the child's emotions and adjust the length of the summary based on the estimated child's emotions. The summarization unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate the emotions. The generation AI can analyze the child's facial expressions and estimate the emotions. Voice analysis is used, for example, to analyze the child's utterances and estimate the emotions. The generation AI can analyze the child's utterances and estimate the emotions. The summarization unit adjusts the length of the summary based on the estimated child's emotions. For example, if the child is excited, the generation AI provides a detailed summary. Also, if the child is relaxed, the generation AI provides an overall summary. Furthermore, if the child is tired, the generation AI provides a main summary. In this way, a more appropriate summary can be provided by adjusting the length of the summary based on the child's emotions.

[0088] When generating summaries, the summarization unit can determine the priority of summaries based on the frequency and duration of the child's behavior. The summarization unit, for example, uses a generation AI to analyze the frequency of the child's behavior. The generation AI can analyze the child's behavior data and identify the frequency of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the frequency of that behavior. The summarization unit analyzes the duration of the child's behavior. The generation AI can analyze the child's behavior data and identify the duration of a specific behavior. For example, if a child continues a specific behavior for a long period of time, it can analyze the duration of that behavior. The summarization unit comprehensively analyzes the frequency and duration of the behaviors and determines the priority of summaries. The generation AI can comprehensively analyze the child's behavior data and determine the priority of summaries based on the frequency and duration. In this way, by determining the priority of summaries based on the frequency and duration of the child's behavior, important behaviors are preferentially summarized.

[0089] The summarization unit can monitor the child's health condition and detect abnormalities when generating a summary. The summarization unit monitors the child's health condition using, for example, a generation AI. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature and detect abnormal changes, which are reflected in the summary. For example, the generation AI analyzes the child's body temperature data and detects abnormal body temperature changes that are out of the ordinary and reflects them in the summary. The summarization unit can analyze the child's breathing pattern and detect abnormalities, which are reflected in the summary. For example, the generation AI analyzes the child's breathing pattern and detects abnormal breathing changes that are out of the ordinary and reflects them in the summary. The summarization unit can also analyze the child's movement pattern and detect abnormalities in the health condition, which are reflected in the summary. For example, the generation AI analyzes the child's movement pattern and detects abnormal movement changes that are out of the ordinary and reflects them in the summary. In this way, the child's health condition is monitored and abnormalities are detected, which are reflected in the summary.

[0090] The providing unit can estimate the child's emotions and adjust the way in which information is presented based on the estimated child's emotions. The providing unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate the emotions. The generating AI can analyze the child's facial expressions and estimate the emotions. Voice analysis is used, for example, to analyze the child's speech and estimate the emotions. The generating AI can analyze the child's speech and estimate the emotions. The providing unit adjusts the way in which information is presented based on the estimated child's emotions. For example, if the child is excited, the generating AI provides detailed information. If the child is relaxed, the generating AI provides general information. Furthermore, if the child is tired, the generating AI provides main information. In this way, more appropriate information is provided by adjusting the way in which information is presented based on the child's emotions.

[0091] When providing information, the providing unit can select an appropriate method of providing information by referring to the guardian's past feedback. The providing unit, for example, analyzes the guardian's past feedback. The feedback includes, for example, the guardian's preferred information providing methods and evaluations in the past. The generation AI can analyze the guardian's past feedback and select the optimal method of providing information. For example, the generation AI selects the optimal method of providing information based on the guardian's preferred information providing methods in the past. The providing unit can adjust the level of detail of the information based on the guardian's past feedback. For example, the generation AI analyzes the guardian's past feedback and adjusts the level of detail of the information. The providing unit can also select a display format of the information by referring to the guardian's past feedback. For example, the generation AI selects a display format of the information by referring to the guardian's past feedback. In this way, the optimal method of providing information is selected by referring to the guardian's past feedback.

[0092] The providing unit can customize information according to the guardian's areas of interest when providing the information. The providing unit, for example, analyzes the guardian's areas of interest. The areas of interest include, for example, specific childcare information and activities. The generation AI can analyze the guardian's areas of interest and provide customized information. For example, the generation AI provides customized information based on the childcare information in which the guardian is interested. The providing unit can adjust the display order of information based on the guardian's areas of interest. For example, the generation AI analyzes the guardian's areas of interest and provides related information with priority. The providing unit can also adjust the content of the information based on the guardian's areas of interest. For example, the generation AI adjusts the content of the information based on the guardian's areas of interest. In this way, more appropriate information can be provided by customizing the information according to the guardian's areas of interest.

[0093] The providing unit can estimate the child's emotions and determine the priority of information to be provided based on the estimated child's emotions. The providing unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate the emotions. The generation AI can analyze the child's facial expressions and estimate the emotions. Voice analysis is used, for example, to analyze the child's speech and estimate the emotions. The generation AI can analyze the child's speech and estimate the emotions. The providing unit determines the priority of information to be provided based on the estimated child's emotions. For example, if the child is excited, the generation AI prioritizes providing detailed information. Also, if the child is relaxed, the generation AI prioritizes providing overall information. Furthermore, if the child is tired, the generation AI prioritizes providing main information. In this way, by determining the priority of information to be provided based on the child's emotions, more important information is provided preferentially.

[0094] The providing unit can provide optimal information by taking into account the guardian's geographical location information when providing the information. The providing unit, for example, analyzes the guardian's geographical location information. Geographical location information includes, for example, GPS data and location information services. The generating AI can analyze the guardian's geographical location information and provide optimal information. For example, if the guardian is in their current location, the generating AI provides information on nearby childcare support facilities. The providing unit can adjust the content of the information based on the guardian's geographical location information. For example, if the generating AI analyzes the guardian's geographical location information and is traveling, it provides childcare information for the travel destination. The providing unit can also adjust the display format of the information based on the guardian's geographical location information. For example, the generating AI analyzes the guardian's geographical location information and provides childcare information related to a specific area. In this way, more appropriate information is provided by taking into account the guardian's geographical location information.

[0095] At the time of providing the information, the providing unit can analyze the parent's social media activity and provide relevant information. The providing unit, for example, analyzes the parent's social media activity. Social media activity includes, for example, the content of posts and the number of likes. The generation AI can analyze the parent's social media activity and provide relevant information. For example, the generation AI analyzes the parent's social media posts and provides relevant childcare information. The providing unit can analyze the parent's social media interests and provide relevant information. For example, the generation AI analyzes the parent's social media interests and provides relevant information. The providing unit can also analyze the parent's social media follower information and provide relevant information. For example, the generation AI analyzes the parent's social media follower information and provides relevant information. In this way, more relevant information can be provided by analyzing the parent's social media activity.

[0096] The analysis unit can estimate the child's emotions and adjust the criteria for personality analysis based on the estimated child's emotions. The analysis unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate their emotions. The generation AI can analyze the child's facial expressions and estimate their emotions. Voice analysis is used, for example, to analyze the child's speech and estimate their emotions. The generation AI can analyze the child's speech and estimate their emotions. The analysis unit adjusts the criteria for personality analysis based on the estimated child's emotions. For example, if the child is excited, the generation AI performs a detailed personality analysis. If the child is relaxed, the generation AI performs a general personality analysis. Furthermore, if the child is tired, the generation AI performs a major personality analysis. This enables more accurate personality analysis by adjusting the criteria for personality analysis based on the child's emotions.

[0097] The analysis unit can take into account the frequency and duration of a child's behavior when analyzing personality. The analysis unit, for example, uses a generation AI to analyze the frequency of a child's behavior. The generation AI can analyze the child's behavioral data and identify the frequency of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the frequency of that behavior. The analysis unit analyzes the duration of the child's behavior. The generation AI can analyze the child's behavioral data and identify the duration of a specific behavior. For example, if a child continues a specific behavior for a long period of time, it can analyze the duration of that behavior. The analysis unit comprehensively analyzes the frequency and duration of the behavior and reflects this in the personality analysis. The generation AI can comprehensively analyze the child's behavioral data and perform personality analysis based on the frequency and duration. This enables more accurate personality analysis by taking into account the frequency and duration of a child's behavior.

[0098] When analyzing a child's personality, the analysis unit can apply an analysis algorithm that corresponds to the child's developmental stage. For example, the analysis unit uses a generation AI to apply an analysis algorithm that corresponds to the child's developmental stage. The generation AI can select an appropriate analysis algorithm based on the child's age and developmental stage. For example, the generation AI learns behavioral patterns according to the child's age and applies an appropriate analysis algorithm. The analysis unit applies a facial expression recognition algorithm that corresponds to the child's developmental stage. For example, the generation AI can apply a facial expression recognition algorithm that corresponds to the child's developmental stage to analyze changes in facial expressions. The analysis unit also applies a voice recognition algorithm that corresponds to the child's developmental stage. For example, the generation AI can apply a voice recognition algorithm that corresponds to the child's developmental stage to analyze the content of speech. This enables more accurate personality analysis by applying an analysis algorithm that corresponds to the child's developmental stage.

[0099] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated child's emotions. The analysis unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate their emotions. The generation AI can analyze the child's facial expressions and estimate their emotions. Voice analysis is used, for example, to analyze the child's speech and estimate their emotions. The generation AI can analyze the child's speech and estimate their emotions. The analysis unit adjusts the display method of the analysis results based on the estimated child's emotions. For example, if the child is excited, the generation AI displays detailed analysis results. If the child is relaxed, the generation AI displays overall analysis results. Furthermore, if the child is tired, the generation AI displays main analysis results. In this way, more appropriate information can be provided by adjusting the display method of the analysis results based on the child's emotions.

[0100] The analysis unit can perform personality analysis based on environmental information surrounding the child. The analysis unit, for example, uses a generation AI to analyze environmental information surrounding the child. Environmental information includes, for example, audio information, lighting information, and temperature information. The generation AI can analyze audio information surrounding the child and perform personality analysis while taking environmental changes into account. For example, the generation AI analyzes audio information surrounding the child, detects environmental changes, and performs personality analysis. The analysis unit can analyze lighting information surrounding the child and adjust the brightness of the image to perform personality analysis. For example, the generation AI analyzes lighting information surrounding the child and adjusts the brightness of the image to perform personality analysis. The analysis unit can also analyze temperature information surrounding the child and perform personality analysis while taking behavioral changes into account. For example, the generation AI analyzes temperature information surrounding the child, detects behavioral changes, and performs personality analysis. This allows for more accurate personality analysis by taking environmental information surrounding the child into account.

[0101] The analysis unit can monitor the child's health condition and detect abnormalities during personality analysis. The analysis unit, for example, uses a generation AI to monitor the child's health condition. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature and detect abnormal changes, which are reflected in the personality analysis. For example, the generation AI analyzes the child's body temperature data and detects abnormal body temperature changes, which are reflected in the personality analysis. The analysis unit can analyze the child's breathing pattern and detect abnormalities, which are reflected in the personality analysis. For example, the generation AI analyzes the child's breathing pattern and detects abnormal breathing changes, which are reflected in the personality analysis. The analysis unit can also analyze the child's movement patterns and detect abnormalities in the child's health condition, which are reflected in the personality analysis. For example, the generation AI analyzes the child's movement patterns and detects abnormal movement changes, which are reflected in the personality analysis. In this way, the child's health condition is monitored and abnormalities are detected, which are reflected in the personality analysis. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, extraction unit, summarization unit, provision unit, and analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit analyzes the child's behavior, facial expressions, and speech using the camera 42 and microphone 38B of the smart device 14. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important moments. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the growth record. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the growth record to the parent. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the child's personality. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, extraction unit, summarization unit, provision unit, and analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit analyzes the child's behavior, facial expressions, and speech using the camera 42 and microphone 238 of the smart glasses 214. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important moments. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the growth record. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the growth record to the guardian. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the child's personality. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, summarization unit, provision unit, and analysis unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit analyzes the child's behavior, facial expressions, and utterances using the camera 42 and microphone 238 of the headset-type terminal 314. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important moments. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the growth record. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the growth record to the parent. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the child's personality. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, summarization unit, provision unit, and analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit analyzes the child's behavior, facial expressions, and speech using the camera 42 and microphone 238 of the robot 414. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important moments. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the growth record. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the growth record to the parent. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the child's personality.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When analyzing the video, the analysis unit can perform analysis based on environmental information about the child's surroundings. The analysis unit, for example, uses a generation AI to analyze environmental information about the child's surroundings. Environmental information includes, for example, audio information, lighting information, and temperature information. The generation AI can analyze audio information about the child's surroundings and take environmental changes into account. For example, the generation AI analyzes audio information about the child's surroundings and detects changes in the environment. The analysis unit can analyze lighting information about the child's surroundings and adjust the brightness of the video. For example, the generation AI analyzes lighting information about the child's surroundings and adjusts the brightness of the video. The analysis unit can also analyze temperature information about the child's surroundings and take changes in behavior into account. For example, the generation AI analyzes temperature information about the child's surroundings and detects changes in behavior. This allows for more accurate analysis by taking environmental information about the child's surroundings into account.

[0104] The extraction unit can monitor the child's health condition and detect abnormalities when extracting key moments. The extraction unit, for example, uses a generation AI to monitor the child's health condition. Health conditions include, for example, body temperature, heart rate, and breathing patterns. The generation AI can analyze the child's body temperature, detect abnormal changes, and extract key moments. For example, the generation AI analyzes the child's body temperature data, detects abnormal changes in body temperature, and extracts key moments. The extraction unit can analyze the child's breathing pattern, detect abnormalities, and extract key moments. For example, the generation AI analyzes the child's breathing pattern, detects abnormal changes in breathing, and extracts key moments. The extraction unit can also analyze the child's movement pattern, detect abnormalities in the health condition, and extract key moments. For example, the generation AI analyzes the child's movement pattern, detects abnormal changes in movement, and extracts key moments. This allows the child's health condition to be monitored and detected, enabling early detection of abnormalities in the health condition.

[0105] When generating summaries, the summarization unit can determine the priority of summaries based on the frequency and duration of the child's behavior. The summarization unit, for example, uses a generation AI to analyze the frequency of the child's behavior. The generation AI can analyze the child's behavior data and identify the frequency of a specific behavior. For example, if a child frequently performs a specific behavior, it can analyze the frequency of that behavior. The summarization unit analyzes the duration of the child's behavior. The generation AI can analyze the child's behavior data and identify the duration of a specific behavior. For example, if a child continues a specific behavior for a long period of time, it can analyze the duration of that behavior. The summarization unit comprehensively analyzes the frequency and duration of the behaviors and determines the priority of summaries. The generation AI can comprehensively analyze the child's behavior data and determine the priority of summaries based on the frequency and duration. In this way, by determining the priority of summaries based on the frequency and duration of the child's behavior, important behaviors are preferentially summarized.

[0106] When providing information, the providing unit can select an appropriate method of providing information by referring to the guardian's past feedback. The providing unit, for example, analyzes the guardian's past feedback. The feedback includes, for example, the guardian's preferred information providing methods and evaluations in the past. The generation AI can analyze the guardian's past feedback and select the optimal method of providing information. For example, the generation AI selects the optimal method of providing information based on the guardian's preferred information providing methods in the past. The providing unit can adjust the level of detail of the information based on the guardian's past feedback. For example, the generation AI analyzes the guardian's past feedback and adjusts the level of detail of the information. The providing unit can also select a display format of the information by referring to the guardian's past feedback. For example, the generation AI selects a display format of the information by referring to the guardian's past feedback. In this way, the optimal method of providing information is selected by referring to the guardian's past feedback.

[0107] The providing unit can provide optimal information by taking into account the guardian's geographical location information when providing the information. The providing unit, for example, analyzes the guardian's geographical location information. Geographical location information includes, for example, GPS data and location information services. The generating AI can analyze the guardian's geographical location information and provide optimal information. For example, if the guardian is in their current location, the generating AI provides information on nearby childcare support facilities. The providing unit can adjust the content of the information based on the guardian's geographical location information. For example, if the generating AI analyzes the guardian's geographical location information and is traveling, it provides childcare information for the travel destination. The providing unit can also adjust the display format of the information based on the guardian's geographical location information. For example, the generating AI analyzes the guardian's geographical location information and provides childcare information related to a specific area. In this way, more appropriate information is provided by taking into account the guardian's geographical location information.

[0108] The analysis unit can estimate the child's emotions and adjust the accuracy of video analysis based on the estimated child's emotions. The analysis unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate their emotions. The generation AI can analyze the child's facial expressions and estimate their emotions. Voice analysis is used, for example, to analyze the child's speech and estimate their emotions. The generation AI can analyze the child's speech and estimate their emotions. The analysis unit adjusts the accuracy of video analysis based on the child's estimated emotions. For example, if the child is excited, the generation AI increases the accuracy of video analysis to capture subtle movements and changes in facial expressions. If the child is relaxed, the generation AI adjusts the accuracy of video analysis to capture overall movements and facial expressions. Furthermore, if the child is tired, the generation AI reduces the accuracy of video analysis and focuses on key movements and facial expressions. This allows for more accurate analysis by adjusting the accuracy of video analysis based on the child's emotions.

[0109] The extraction unit can estimate the child's emotions and adjust the extraction criteria for key moments based on the estimated child's emotions. The extraction unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate the emotions. The generation AI can analyze the child's facial expressions and estimate the emotions. Voice analysis is used, for example, to analyze the child's speech and estimate the emotions. The generation AI can analyze the child's speech and estimate the emotions. The extraction unit adjusts the extraction criteria for key moments based on the estimated child's emotions. For example, if the child is excited, the generation AI extracts small movements and changes in facial expressions as key moments. Furthermore, if the child is relaxed, the generation AI extracts overall movements and facial expressions as key moments. Furthermore, if the child is tired, the generation AI extracts major movements and facial expressions as key moments. This allows more appropriate moments to be extracted by adjusting the extraction criteria for key moments based on the child's emotions.

[0110] The summarization unit can estimate the child's emotions and adjust the way the summary is presented based on the estimated child's emotions. The summarization unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate the emotions. The generation AI can analyze the child's facial expressions and estimate the emotions. Voice analysis is used, for example, to analyze the child's utterances and estimate the emotions. The generation AI can analyze the child's utterances and estimate the emotions. The summarization unit adjusts the way the summary is presented based on the estimated child's emotions. For example, if the child is excited, the generation AI provides a detailed summary. Also, if the child is relaxed, the generation AI provides an overall summary. Furthermore, if the child is tired, the generation AI provides a main summary. In this way, a more appropriate summary can be provided by adjusting the way the summary is presented based on the child's emotions.

[0111] The providing unit can estimate the child's emotions and adjust the way in which information is presented based on the estimated child's emotions. The providing unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate the emotions. The generating AI can analyze the child's facial expressions and estimate the emotions. Voice analysis is used, for example, to analyze the child's speech and estimate the emotions. The generating AI can analyze the child's speech and estimate the emotions. The providing unit adjusts the way in which information is presented based on the estimated child's emotions. For example, if the child is excited, the generating AI provides detailed information. If the child is relaxed, the generating AI provides general information. Furthermore, if the child is tired, the generating AI provides main information. In this way, more appropriate information is provided by adjusting the way in which information is presented based on the child's emotions.

[0112] The analysis unit can estimate the child's emotions and adjust the criteria for personality analysis based on the estimated child's emotions. The analysis unit uses, for example, facial expression analysis and voice analysis to estimate the child's emotions. Facial expression analysis is used, for example, to analyze the child's facial expressions and estimate their emotions. The generation AI can analyze the child's facial expressions and estimate their emotions. Voice analysis is used, for example, to analyze the child's speech and estimate their emotions. The generation AI can analyze the child's speech and estimate their emotions. The analysis unit adjusts the criteria for personality analysis based on the estimated child's emotions. For example, if the child is excited, the generation AI performs a detailed personality analysis. If the child is relaxed, the generation AI performs a general personality analysis. Furthermore, if the child is tired, the generation AI performs a major personality analysis. This enables more accurate personality analysis by adjusting the criteria for personality analysis based on the child's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The analysis unit analyzes the video. The analysis unit uses behavioral, facial, and voice recognition to analyze the video and perform a detailed analysis of the child's behavior, facial expressions, and speech. Behavior recognition identifies the child's movements and actions, facial expression recognition analyzes the child's facial expressions to infer emotions, and voice recognition analyzes the child's speech to understand its content. Step 2: The extraction unit extracts important moments from the video analyzed by the analysis unit. The extraction unit automatically identifies and records important moments such as first actions and specific events. First actions include taking a first step or saying a first word, and specific events include birthdays and entrance ceremonies. Step 3: The summarization unit summarizes the growth record based on the key moments extracted by the extraction unit. The summarization unit analyzes the daily video, extracts key moments, and creates reports and digest videos. The reports are provided in text and graph formats, and the digest videos are provided as short videos summarizing key moments. Step 4: The providing unit provides the growth record summarized by the summarizing unit to the guardian. The providing unit can provide the growth record through a web application or a mobile application, and can also provide it by email or in paper form. Step 5: The analysis unit analyzes the child's personality from the video. The analysis unit analyzes patterns of behavior, facial expressions, and speech to understand the child's characteristics and interests. Based on this, it provides parenting advice and support to the parents.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the video; an extraction unit that extracts important moments from the video analyzed by the analysis unit; a summarizing unit that summarizes the growth record based on the important moments extracted by the extracting unit; a providing unit that provides the growth record summarized by the summarizing unit to a guardian; an analysis unit that analyzes the personality of the child from the video analyzed by the analysis unit; A system characterized by:

2. The analysis unit Analyzing video using behavioral, facial, and voice recognition The system of claim 1 .

3. The extraction unit Extracting first-time actions or key moments of specific events The system of claim 1 .

4. The summary section Analyze daily footage, extract important moments, and create reports and digest videos The system of claim 1 .

5. The providing unit Providing parents with a summary progress report The system of claim 1 .

6. The analysis unit Analyzing patterns of behavior, facial expressions, and speech to understand children's characteristics and interests The system of claim 1 .

7. The providing unit Providing parenting advice and support based on children's characteristics and interests The system of claim 1 .

8. The analysis unit Estimate the child's emotions and adjust the accuracy of video analysis based on the estimated emotions. The system of claim 1 .

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