system

Abstract

We provide the system. [Solution] A means of collecting information on children's behavior using recording devices placed in the home environment, A means of evaluating the developmental characteristics of children using an intelligent model for analyzing the diverse information that has been collected, Based on the evaluation results, a means of providing advice to promote the education and development of children, A means of analyzing the emotional state of children and providing immediate reports as needed, A means of predicting developmental patterns through continuous data accumulation and detecting potential developmental challenges early, A means of monitoring children's activities using a mobile autonomous device and providing interactive educational experiences, A system that includes means to provide immediate feedback to parents based on analysis results and recommend individualized parenting methods.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is to solve the problem that in the conventional development diagnosis method, children get nervous and appropriate evaluation cannot be performed for the needs of parents who are worried about the development of their children. In addition, due to the long waiting time for diagnosis, early detection is difficult and appropriate support cannot be provided in time. Furthermore, there is a problem that opinions of various experts do not match and the information lacks consistency. Under such circumstances, it is required to provide an environment in which parents can support the development of their children with confidence.

Means for Solving the Problems

[0005] This invention utilizes a device installed in the home to collect data on children's activities in a natural environment, and then uses AI to analyze this data from multiple perspectives, enabling more accurate developmental assessment. Furthermore, it provides parents with expert-supervised feedback based on the analysis results, and suggests specific support measures for raising children. This system can also analyze the child's emotional state in real time and notify parents to reduce anxiety and stress. As a result, it is possible to accumulate long-term developmental data, predict growth patterns, and detect potential developmental problems early and respond efficiently.

[0006] "Home environment" refers to the living space at home and its surroundings where children can relax and behave naturally through their daily activities.

[0007] "Devices" refer to equipment such as cameras, microphones, and wearable sensors that are installed in the home to collect data on children's behavior.

[0008] "Activity data" refers to information about a child's physical, mental, and social behavior, and includes motion data and audio recordings.

[0009] An "artificial intelligence model" refers to a computer program used to analyze collected data and evaluate development based on various indicators.

[0010] "Assessment" refers to analyzing a child's developmental stage and making a comprehensive judgment based on aspects such as language, motor skills, and social skills.

[0011] "Feedback" refers to information and advice provided to parents based on analysis results, with the aim of supporting the upbringing of their children.

[0012] "Emotional state" refers to a child's momentary or sustained psychological state, including feelings of security, anxiety, joy, and excitement.

[0013] "Long-term data accumulation" refers to the process of continuously collecting and storing data, and then analyzing its changes and trends over time.

[0014] "Growth patterns" refer to a series of changes and trends in a child's developmental process, revealed based on collected data.

[0015] "Potential developmental problems" refer to developmental delays or disorders that may occur in the future based on current observational data. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a developmental monitoring system that observes and more accurately assesses a child's development in a natural environment. By collecting activity data from a child using a device installed in the home and analyzing this data with an artificial intelligence model, it is possible to understand the child's developmental state within the home environment. The specific implementation of this system is described below.

[0038] The server receives video and audio data of children transmitted from terminals and stores it securely. The server also supplies this data to an artificial intelligence model for multimodal analysis. The analysis meticulously evaluates the child's physical movements, language development, and even emotional states based on facial expressions. This allows the server to calculate a comprehensive developmental index for the child and create feedback based on the analysis results.

[0039] Meanwhile, users can use their devices to receive real-time evaluation results and feedback provided by the server. This feedback includes advice on specific training methods and how to respond, all supervised by experts.

[0040] Furthermore, the device uses an interactive AI character to provide children with learning and play experiences. This AI character is designed to be fun to play with in response to the child's reactions, and it plays a role in complementing learning.

[0041] This system allows users to continuously monitor their child's development at home and prepare appropriate responses before problems arise. Furthermore, by accumulating data over a long period, it can predict a child's developmental growth patterns and detect potential future developmental problems early. This provides users with the advantage of being able to watch their child grow with peace of mind.

[0042] Through the embodiments described above, the present invention overcomes the limitations of conventional developmental diagnostic methods and provides a system that can accurately evaluate a child's developmental state in a manner suitable for the home environment.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device activates a remote camera and microphone installed in the home, recording the child's activities in real time. The recorded data includes what the child is doing while playing and what they are saying.

[0046] Step 2:

[0047] The device sends the recorded activity data to the server. During this process, the data is compressed to reduce the network load associated with transmission.

[0048] Step 3:

[0049] The server stores the received data in temporary storage and performs preprocessing such as data formatting and noise reduction. This prepares the data for smooth analysis.

[0050] Step 4:

[0051] The server supplies pre-processed data to multiple artificial intelligence models. Each model is responsible for analyzing different aspects, such as motor skills, language development, and emotional states.

[0052] Step 5:

[0053] The server aggregates the analysis results from each model and creates a comprehensive developmental index. Based on this, it evaluates the child's developmental status and generates feedback.

[0054] Step 6:

[0055] Users review evaluation results and feedback sent from the server via their devices. This feedback includes expert-reviewed advice and suggestions for the next steps.

[0056] Step 7:

[0057] The device activates an interactive AI character and initiates educational and playful interactions with the child. This facilitates learning while the child enjoys themselves.

[0058] Step 8:

[0059] Users can adjust their child-rearing policies based on feedback as needed and incorporate developmental support measures, reflecting these changes in their parenting at home.

[0060] Through this series of steps, the KidoTrack system can effectively monitor a child's development and provide beneficial support.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] Many assessment methods used to understand the developmental status of children today are based on limited information gathered in unusual environments or time periods, and therefore lack sufficient accuracy and comprehensiveness. Furthermore, there is a lack of analysis to detect growth-related changes and potential challenges early on. This results in a problem where appropriate and timely responses to the individual developmental stages of children are not possible.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting information on a child's activities using a device installed in the home environment, means for evaluating indicators of the child's growth using a machine learning model for analyzing the diverse forms of information collected, and means for providing activities that complement the child's learning using an interactive character. This makes it possible to monitor the child's developmental status in the home environment with high accuracy and to formulate appropriate parenting plans.

[0066] "Home environment" refers to the physical and emotional environment within the home where a child spends their daily life, and it means that observation and information gathering can take place in a natural and comfortable setting.

[0067] "Device" refers to equipment installed in the home environment to collect information on a child's activities, and includes devices such as cameras, microphones, and sensors.

[0068] "Activity information" refers to various forms of data, including a child's physical movements, language use, and psychological state.

[0069] "Analysis" refers to the process of inputting collected activity information into a machine learning model and interpreting its patterns and meanings.

[0070] A "machine learning model" refers to an algorithm that automatically analyzes the characteristics of collected data and uses it to perform developmental indicators and other assessments.

[0071] "Growth indicators" refer to data and information that serve as criteria for evaluating a child's developmental stage, and include physical, intellectual, and emotional aspects.

[0072] An "interactive character" refers to a virtual person or animal designed to interact with children interactively and provide education or entertainment.

[0073] "Activities that complement learning" refer to activities and programs designed to strengthen and develop a child's existing learning.

[0074] In one embodiment of this invention, the system collects information on a child's activities using a terminal installed in the home. The terminal includes devices such as a camera and microphone, which record the child's movements and voice in real time. This information is transmitted to a server via secure communication. The server ensures data security by storing the received data in a central database.

[0075] The server feeds the collected data into a generative AI model. The generative AI model analyzes the diverse forms of data collected and uses this to evaluate indicators of the child's development. The AI ​​model analyzes in detail the child's physical growth, language ability, and psychological state, including facial expressions. Based on the results of this analysis, the server generates and provides feedback on the child's developmental status to the user. This allows the user to understand the child's development in the home environment and create an appropriate parenting plan.

[0076] Furthermore, the device provides children with activities that complement their learning through an interactive character. This character is controlled by AI and suggests educationally valuable activities based on the child's responses.

[0077] As a concrete example, the server performs a process such as "analyzing a child's actions and speech during playtime and creating a report showing developmental trends." In this case, an example of a prompt message would be, "Analyze video and audio data of a 3-year-old child playing with building blocks and evaluate their physical and language development."

[0078] This system design allows users to continuously and comprehensively monitor their child's development and prepare for preventative interventions.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] (Data collection)

[0082] The device uses cameras and microphones installed in the home to collect information about the child's activities. Specifically, this information includes the child's movement patterns and audio clips.

[0083] Input: Real-time captured video and audio data.

[0084] Output: Generates an activity dataset based on children's actions and voices.

[0085] Step 2:

[0086] (Data transmission)

[0087] The device encrypts the collected activity data and sends it to the server via a secure communication channel.

[0088] Input: Children's activity dataset.

[0089] Output: Activity data sent to the server and securely stored.

[0090] Step 3:

[0091] (Data storage)

[0092] The server stores the received activity data in a database. Access restrictions are implemented to ensure data integrity and privacy.

[0093] Input: Submitted activity data.

[0094] Output: Activity data as stored in the database.

[0095] Step 4:

[0096] (Data analysis)

[0097] The server supplies the stored data to the generating AI model. The model analyzes physical movements and facial expressions from video data and evaluates language ability from audio data.

[0098] Input: Activity data retrieved from the database.

[0099] Output: A comprehensive developmental indicator for children.

[0100] Step 5:

[0101] (Feedback generation)

[0102] The server generates feedback based on the analysis results. This feedback includes specific areas for growth and areas for improvement.

[0103] Input: Analysis results of a generated AI model.

[0104] Output: Feedback report on the child's developmental status.

[0105] Step 6:

[0106] (Result provided)

[0107] The server sends the generated feedback to the user's device. The user then uses this feedback to monitor the child's developmental progress.

[0108] Input: Feedback report.

[0109] Output: Feedback in a format viewable on the user's terminal.

[0110] Step 7:

[0111] (Providing interactive experiences)

[0112] The device allows children to interact with an interactive character and provides activities that complement their learning. The character suggests appropriate play and learning opportunities based on the child's reactions.

[0113] Input: Children's reaction data.

[0114] Output: An adapted, interactive educational experience.

[0115] (Application Example 1)

[0116] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0117] There is a need to effectively monitor children's development in the home environment, achieve more precise developmental assessments, and explore methods for early detection of developmental challenges and providing appropriate educational experiences. Furthermore, recording daily behaviors without disrupting relaxation and developing individualized development plans based on expert feedback presents a significant challenge.

[0118] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0119] In this invention, the server includes means for accumulating information on a child's behavior using a recording device placed in the home environment, means for evaluating the child's developmental characteristics using an intelligent model for analyzing the diverse information accumulated, and means for monitoring the child's activities using a mobile autonomous device and providing an interactive educational experience. This makes it possible to collect detailed developmental information from natural behaviors within the home and to quickly formulate an individually tailored educational policy.

[0120] "Home environment" refers to the entire physical space within a house where a child lives and engages in daily activities.

[0121] A "recording device" refers to an electronic device used to acquire various types of information, such as audio and video, and store them as data.

[0122] "Behavioral information" refers to all behavioral data from a child's daily life, including their movements, speech, and facial expressions.

[0123] "Diverse information" refers to multimodal information, which comprehensively encompasses information in different modes such as visual, auditory, and emotional.

[0124] An "intelligent model" refers to a program based on artificial intelligence that analyzes information, extracts patterns and trends, and evaluates them.

[0125] "Developmental characteristics" refer to the physical, mental, and linguistic traits observed in children as they grow.

[0126] An "autonomous device" refers to a robotic device that uses sensors and algorithms to interact with its environment independently and perform specific actions.

[0127] "Interactive educational experience" refers to a learning method in which education is conducted in an interactive manner, and the educational content is adapted in response to the children's responses.

[0128] "Expert feedback" refers to suggestions and advice on child development provided by individuals with expertise in development and education.

[0129] A "development plan" refers to a specific instructional process designed to support a child's development from a long-term perspective.

[0130] To implement this invention, a recording device installed in a home environment, a server system, and a terminal for user use are required. First, the recording device has a high-performance camera and microphone, and can quickly transmit collected audio and video data to the server. Specifically, edge computing devices such as Raspberry Pi or Jetson Nano are used. The server analyzes the received data using machine learning software such as TENSORFLOW® or PyTorch. This allows for a detailed understanding of the child's physical movements, language development, emotional state, etc.

[0131] The server further evaluates developmental characteristics based on the analysis results and sends immediate feedback to the parent's device as needed. This feedback includes data processed in a secure cloud environment utilizing Google Cloud Platform and AWS, and includes advice from experts on child development. Parents can receive this feedback through a smartphone application.

[0132] Furthermore, robots, acting as mobile autonomous devices, are installed in homes to provide interactive educational experiences for children. Through voice interaction and reactions, they can support learning while engaging children's interest.

[0133] As a concrete example, the robot can assess a child's numerical understanding through number games and can switch to a mode that tells calming stories if the child is emotionally unstable. In this system, the prompt using a generative AI model sets the data analysis guidelines in the form of "How can we design an application that provides comprehensive monitoring and real-time feedback on how to visualize a day in the life of a 3-year-old child?"

[0134] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0135] Step 1:

[0136] The recording device collects audio and video data of children in a home environment in real time. The input is audio and video obtained from the device's microphone and camera, and the output is data converted into a digital format. This data is temporarily stored within the recording device in preparation for the next processing step.

[0137] Step 2:

[0138] The recording device transfers the collected audio and video data to the server. The input is the digital data obtained in step 1, and the output is a data stream transmitted via a stable communication protocol. Specifically, the recording device uses the server's IP address to open a secure connection and upload the data.

[0139] Step 3:

[0140] The server begins processing the received data. The input is the data stream from step 2, and the output is a dataset optimized for processing. The server first performs preprocessing essential for improving data quality, such as noise reduction and data correction.

[0141] Step 4:

[0142] The server inputs the pre-processed data into an intelligent model and performs analysis. The input is the high-quality data obtained in step 3, and the output is the analysis results showing developmental characteristics. Specifically, inference is performed based on a generative AI model using the TensorFlow library. Through this process, characteristics such as physical development and emotional state are determined.

[0143] Step 5:

[0144] The server sends the analysis results to the parent's device. The input is the analysis results obtained in step 4, and the output is digitally distributed content including feedback messages. The server connects the data to the mobile app while maintaining SSL security. This data also includes prompts generated by an AI model, providing detailed guidance to the parent.

[0145] Step 6:

[0146] The device displays the received feedback message to the parent. The input is the digitally delivered content from step 5, and the output is the feedback displayed on the device's screen. Based on the feedback, the device provides a user interface for searching for additional information and developing a development plan if necessary.

[0147] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0148] This invention provides more comprehensive and effective childcare support by combining a system for monitoring child development in the home environment with an emotion engine that analyzes the user's emotions in real time. The specific implementation of this system is described below.

[0149] The device, installed in the home, records the child's actions and speech and sends this data to a server. The server analyzes the received multimodal data using an artificial intelligence model to evaluate the child's motor skills, language development, social skills, and more. Furthermore, it uses facial recognition technology to analyze the child's emotional state and notifies parents in real time as needed.

[0150] On the other hand, the emotion engine recognizes the user's emotional state and analyzes the parent's psychological state. This engine determines how the parent feels about receiving feedback and advice, and adjusts the information provided based on that state. For example, if the parent is feeling anxious or worried, it can offer specific advice that provides reassurance.

[0151] As a concrete example, the system analyzes the child's learning activities recorded by the device, evaluates the frequency of language use within the home, and generates advice such as "increasing read-aloud time would be beneficial" based on the results. This advice is optimized by the server, taking into account the user's sentiment analysis, so that parents can confidently put it into practice.

[0152] Furthermore, its long-term data accumulation capabilities allow for the detection of children's growth patterns and the early identification of potential developmental problems. Based on this, users have the opportunity to take early action before problems arise. In this way, this system, which combines an emotional engine, can provide total support that goes beyond conventional developmental monitoring, including the psychological aspects of parents.

[0153] Through such embodiments, the present invention provides a groundbreaking system that more comprehensively supports child development and realizes the well-being of both parents and children.

[0154] The following describes the processing flow.

[0155] Step 1:

[0156] The device activates a remote camera and microphone installed in the home to record the child's activities and speech. This data includes the child's movements, speech, and background sounds.

[0157] Step 2:

[0158] The device temporarily stores the data it records, compresses it as needed, and sends the data to the server via the network.

[0159] Step 3:

[0160] The server prepares the received data for analysis. This process includes formatting and noise reduction of the data, and in particular, processing it so that children's facial expressions can be clearly analyzed by face detection algorithms.

[0161] Step 4:

[0162] The server uses the formatted data to run multiple artificial intelligence models. This allows for the individual analysis and evaluation of children's motor skills, language development, social interaction, and other aspects.

[0163] Step 5:

[0164] The server analyzes the child's facial expression data to understand their emotional state. For example, it identifies typical emotional responses such as smiles and surprise, deepening the psychological understanding of their activities.

[0165] Step 6:

[0166] The emotional engine analyzes the user's, or parent's, emotional state. This analysis determines the presence or absence of anxiety or stress based on traditional contact time and response patterns, and selects an appropriate feedback format.

[0167] Step 7:

[0168] The server considers the child's developmental assessment results and the parents' emotional analysis, and generates expert-reviewed feedback. This includes suggestions for specific parenting strategies and daily support.

[0169] Step 8:

[0170] Users receive feedback through their devices and adjust their home parenting strategies based on the displayed advice.

[0171] Step 9:

[0172] The device activates an interactive AI character and provides children with educational games and conversations that reflect the feedback they receive. This allows children to develop spontaneously while having fun.

[0173] This series of steps supports more effective and reassuring childcare at home.

[0174] (Example 2)

[0175] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0176] Support for child development in the home environment lacks a comprehensive approach that takes into account the individual characteristics of each child and the psychological state of the parents. In particular, it is difficult to collect data in real time during daily life and accurately assess a child's developmental and emotional state. Furthermore, there is a need for methods of providing advice that take into account the emotional state of the parents themselves.

[0177] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0178] In this invention, the server includes means for collecting information on a child's activities using a device installed in the home environment, means for evaluating developmental indicators of the child using an artificial intelligence system for analyzing the collected multimodal information, and means for analyzing the parent's psychological state and providing optimal information according to their emotional state. This makes it possible to comprehensively monitor a child's development and emotional state in daily life and provide optimal childcare support in real time, taking into account the parent's emotions.

[0179] A "device" is a device installed in the home environment to collect information about a child's activities.

[0180] "Information" refers to data such as audio and video, including children's activities and statements.

[0181] "Multimodal information" refers to information that combines different types of data (e.g., audio, video).

[0182] An "artificial intelligence system" is a computational algorithm used to analyze developmental indicators in children.

[0183] "Developmental indicators" are standards used to evaluate a child's motor skills, language development, social skills, and other abilities.

[0184] "Emotional state" refers to the psychological condition that can be inferred from a child's facial expressions and tone of voice.

[0185] "Real-time notification" is a system that immediately conveys relevant information to parents.

[0186] "Psychological state" refers to the parents' emotions and mental condition.

[0187] "Optimal information" refers to advice and feedback provided in a way that is most appropriate to the circumstances of the parents and children.

[0188] "Information storage" refers to the process of storing data for long-term use in future analysis and evaluation.

[0189] A "growth pattern" refers to the trajectory or tendency of a child's development observed over time.

[0190] A "potential developmental problem" is a developmental challenge that is not outwardly apparent but has the potential to manifest as a problem in the future.

[0191] This invention is a comprehensive system that supports child development in the home environment. The system utilizes various devices within the home to analyze the child's activities and emotions.

[0192] The devices installed in the home collect information about the child's activities. Specific equipment includes a high-resolution camera and a high-sensitivity microphone. This hardware captures the child's daily movements and speech, transmitting the data to a server in real time.

[0193] The server analyzes the received information using an advanced artificial intelligence system. The system is built using machine learning libraries such as TensorFlow to evaluate child development indicators. The OpenCV library is used for facial recognition and emotion analysis, allowing for the estimation of the child's emotional state. The analysis results may include evaluations such as "motor skills are significantly ahead of the standard."

[0194] Furthermore, the server also analyzes the parent's psychological state. The analysis process involves collecting actions taken by the user within the smartphone application and using an emotion engine to understand the parent's psychological state. Based on this information, it prepares to provide the parent with the most appropriate feedback. For example, if the parent is feeling anxious, it will offer specific advice to help them feel more secure.

[0195] Users can receive information provided by the server via their home PCs or smartphones. The displayed dashboard visually shows information and analysis results related to the child's development. Based on this information, parents can create a child-rearing plan for their child.

[0196] For example, the server analyzes the frequency of a child's language use and generates personalized advice such as, "It would be good to increase the amount of time spent reading aloud." The parent's psychological state is also evaluated to see how they react to this advice, and the advice is adjusted as needed.

[0197] Example of a prompt:

[0198] "What kind of play is suitable for developing a child's motor skills?"

[0199] In this way, the system is structured to monitor a child's development from multiple perspectives and provide parents with appropriate support information in real time.

[0200] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0201] Step 1:

[0202] The device collects information about children's activities using a camera and microphone installed in the home. It acquires video footage of the children's movements and audio recordings as input. This information is processed in real time as digital data and packaged into data packets. It generates data packets for transmission to the server as output.

[0203] Step 2:

[0204] The terminal sends the generated data packets to the server via the internet connection. The input is a pre-packaged data packet. This packet is encrypted using the TLS protocol for security purposes and securely transferred to the server. The output is the data the server receives.

[0205] Step 3:

[0206] The server analyzes received data packets and processes video and audio data separately. It receives encrypted data packets as input and decrypts them. OpenCV is used for face recognition and facial expression analysis on the video data, and the BERT model is used for language analysis on the audio data. The output provides evaluation results regarding children's motor skills, language development, and emotional state.

[0207] Step 4:

[0208] The server generates prompts based on the analyzed information and prepares appropriate feedback for parents. Inputs include developmental indicators and emotional state assessments of the child. Based on this, an AI algorithm written in Python is used to generate advice such as "increasing read-aloud time is effective." The output is a feedback message for the parents.

[0209] Step 5:

[0210] The server uses an emotion engine to analyze the parent's emotional state. It receives a log of recent user actions on the application as input. This data is analyzed to infer the parent's emotional state and optimize the feedback. The output is adjusted feedback tailored to the parent's state.

[0211] Step 6:

[0212] Users receive the feedback provided via their home PC or smartphone. The input is feedback messages sent from the server. This is displayed on a dashboard, allowing users to check their child's developmental status and receive parenting advice. The output provides information on specific parenting plans that parents can implement.

[0213] Step 7:

[0214] The server stores all analysis data long-term and models growth patterns. It uses past evaluation data about the child and parental feedback history as input. This data is analyzed over time to accumulate data for predicting future development and necessary support. As output, it updates a database for predicting future development and identifying potential problems.

[0215] (Application Example 2)

[0216] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0217] Systems for monitoring child development at home require accurate assessment of a child's growth and the provision of appropriate childcare support that takes into account the parents' psychological state. However, conventional systems do not adequately grasp the child's growth and the parents' psychological state, nor do they adequately provide appropriate advice to parents. Therefore, the challenge is to provide a system that can monitor a child's development in the home environment in real time and enable childcare support that responds to the parents' emotional state.

[0218] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0219] In this invention, the server includes means for acquiring information on a child's activities using information devices within the home environment, means for evaluating developmental indicators of the child using a machine learning algorithm for analyzing the acquired multi-format information, and means for evaluating the emotional state of the parent and generating and providing advice tailored to the parent's psychological state. This enables accurate understanding of the child's growth and appropriate childcare support that takes into account the parent's psychological state.

[0220] "Information devices within the home environment" refers to devices and equipment installed in the home to acquire information about children's activities.

[0221] "Activity information" refers to data about a child's behavior and statements within the home.

[0222] "Multi-format information" refers to data that includes multiple means of expression, such as language, actions, and facial expressions.

[0223] A "machine learning algorithm" is a data processing technique used to analyze collected data and evaluate developmental indicators in children.

[0224] "Developmental indicators" are criteria used to evaluate a child's growth and development.

[0225] "Parental emotional state" refers to the psychological and emotional state of a caregiver who has children.

[0226] "Real-time notification" means promptly reporting status changes and important information.

[0227] "Potential developmental challenges" are unresolved developmental issues that may become problems in a child's future growth.

[0228] "Long-term data accumulation" refers to the continuous collection and storage of data over a certain period of time.

[0229] In this invention, the user installs information equipment in their home environment, and the terminal acquires information about the child's activities. The terminal uses hardware devices equipped with sensors such as a camera and a microphone to record the child's movements and speech. The recorded activity information is transmitted to a server via a network.

[0230] The server applies machine learning algorithms to the received multi-format information to evaluate child development indicators. This allows the server to assess the child's language, motor skills, and social skills. Machine learning frameworks such as TensorFlow and PyTorch can be used for this analysis.

[0231] Furthermore, the server analyzes the parents' emotional state and generates advice tailored to that state using psychological algorithms. Considering the parents' psychological state, feedback is provided that offers reassurance, especially in stressful situations. For example, if a parent is feeling anxious, it can generate a message such as, "Recent development is progressing well; continue language practice."

[0232] In this embodiment, the server can monitor the child's development in real time and immediately provide parents with the information and reassurance they need.

[0233] A concrete example of a prompt could be: "Based on data of children playing with toys, generate an assessment of language development and advice for parents. If parents are feeling anxious, optimize the advice to provide reassurance."

[0234] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0235] Step 1:

[0236] The device acquires information about the child's activities within the home environment. It records the child's actions and speech through the camera and microphone, and saves this information as activity data. The input is the child's actual actions and voice, while the output is activity data in digital format.

[0237] Step 2:

[0238] The terminal transmits acquired activity information to the server via the network. The input here is digital activity information, and the output is the data sent to the server. The terminal ensures a stable network connection to perform real-time data transfer.

[0239] Step 3:

[0240] The server analyzes the received activity information as multi-format data. It uses machine learning algorithms to evaluate the child's developmental indicators. The input is the transmitted multi-format information, and the output is the result of the child's developmental assessment. Specifically, the server uses TensorFlow to assess motor skills from motion data and natural language processing techniques to assess language development from speech data.

[0241] Step 4:

[0242] The server incorporates information about the parent into a feedback process to recognize the parent's emotional state. The input is the parent's emotional data, and the output is an evaluation of the parent's psychological state by a psychological algorithm. This allows the server to understand the parent's stress level and anxiety.

[0243] Step 5:

[0244] The server generates appropriate advice based on these assessment results and the parents' emotional state. The generating AI model is prompted to construct feedback optimized for the child's situation. The input is developmental assessment results and the parents' emotional state, and the output is optimized parenting advice.

[0245] Step 6:

[0246] The server notifies the user of the generated advice. The user receives this advice and uses it in childcare. The input is feedback from the server, and the output is the advice displayed on the user's device. Notifications are delivered instantly using push notifications to smartphones.

[0247] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0248] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0249] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0250] [Second Embodiment]

[0251] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0252] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0253] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0254] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0255] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0256] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0257] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0258] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0259] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0260] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0261] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0262] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0263] This invention is a developmental monitoring system that observes and more accurately assesses a child's development in a natural environment. By collecting activity data from a child using a device installed in the home and analyzing this data with an artificial intelligence model, it is possible to understand the child's developmental state within the home environment. The specific implementation of this system is described below.

[0264] The server receives video and audio data of children transmitted from terminals and stores it securely. The server also supplies this data to an artificial intelligence model for multimodal analysis. The analysis meticulously evaluates the child's physical movements, language development, and even emotional states based on facial expressions. This allows the server to calculate a comprehensive developmental index for the child and create feedback based on the analysis results.

[0265] Meanwhile, users can use their devices to receive real-time evaluation results and feedback provided by the server. This feedback includes advice on specific training methods and how to respond, all supervised by experts.

[0266] Furthermore, the device uses an interactive AI character to provide children with learning and play experiences. This AI character is designed to be fun to play with in response to the child's reactions, and it plays a role in complementing learning.

[0267] This system allows users to continuously monitor their child's development at home and prepare appropriate responses before problems arise. Furthermore, by accumulating data over a long period, it can predict a child's developmental growth patterns and detect potential future developmental problems early. This provides users with the advantage of being able to watch their child grow with peace of mind.

[0268] Through the embodiments described above, the present invention overcomes the limitations of conventional developmental diagnostic methods and provides a system that can accurately evaluate a child's developmental state in a manner suitable for the home environment.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] The device activates a remote camera and microphone installed in the home, recording the child's activities in real time. The recorded data includes what the child is doing while playing and what they are saying.

[0272] Step 2:

[0273] The device sends the recorded activity data to the server. During this process, the data is compressed to reduce the network load associated with transmission.

[0274] Step 3:

[0275] The server stores the received data in temporary storage and performs preprocessing such as data formatting and noise reduction. This prepares the data for smooth analysis.

[0276] Step 4:

[0277] The server supplies pre-processed data to multiple artificial intelligence models. Each model is responsible for analyzing different aspects, such as motor skills, language development, and emotional states.

[0278] Step 5:

[0279] The server aggregates the analysis results from each model and creates a comprehensive developmental index. Based on this, it evaluates the child's developmental status and generates feedback.

[0280] Step 6:

[0281] Users review evaluation results and feedback sent from the server via their devices. This feedback includes expert-reviewed advice and suggestions for the next steps.

[0282] Step 7:

[0283] The terminal activates an interactive AI character and starts an interaction with the child that includes education and play. As a result, the child's learning is promoted while having fun.

[0284] Step 8:

[0285] The user adjusts the child-rearing policy based on feedback as needed and incorporates development support measures, thereby reflecting them in child-rearing at home.

[0286] Through this series of steps, the KidoTrack system can effectively monitor the development of the child and provide useful support.

[0287] (Example 1)

[0288] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] Evaluation methods for understanding the development status of modern children are often based on information limited to non-daily environments and periods, and do not have sufficient accuracy and comprehensiveness. In addition, there is a lack of analysis for early detection of changes and potential problems associated with growth. As a result, there is a problem that appropriate and timely responses cannot be made to the individual growth process of the child.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0291] In this invention, the server includes means for collecting the activity information of the child using a device installed in the home environment, means for evaluating the growth indicators of the child using a machine learning model for analyzing the collected information in various forms, and means for providing activities to complement the learning of the child using an interactive character. As a result, it becomes possible to monitor the development status of the child in the home environment with high accuracy and establish an appropriate child-rearing policy.

[0292] "Home environment" refers to the physical and emotional environment within the home where a child spends their daily life, and it means that observation and information gathering can take place in a natural and comfortable setting.

[0293] "Device" refers to equipment installed in the home environment to collect information on a child's activities, and includes devices such as cameras, microphones, and sensors.

[0294] "Activity information" refers to various forms of data, including a child's physical movements, language use, and psychological state.

[0295] "Analysis" refers to the process of inputting collected activity information into a machine learning model and interpreting its patterns and meanings.

[0296] A "machine learning model" refers to an algorithm that automatically analyzes the characteristics of collected data and uses it to perform developmental indicators and other assessments.

[0297] "Growth indicators" refer to data and information that serve as criteria for evaluating a child's developmental stage, and include physical, intellectual, and emotional aspects.

[0298] An "interactive character" refers to a virtual person or animal designed to interact with children interactively and provide education or entertainment.

[0299] "Activities that complement learning" refer to activities and programs designed to strengthen and develop a child's existing learning.

[0300] In one embodiment of this invention, the system collects information on a child's activities using a terminal installed in the home. The terminal includes devices such as a camera and microphone, which record the child's movements and voice in real time. This information is transmitted to a server via secure communication. The server ensures data security by storing the received data in a central database.

[0301] The server inputs the collected data into the generative AI model. The generative AI model analyzes the collected data in various forms and evaluates the child's growth indicators based on this. The AI model particularly analyzes in detail the child's physical growth, language ability, and mental state including expressions. Based on the results of this analysis, the server generates feedback on the child's development status and provides it to the user. Thereby, the user can grasp the growth of the child in the home environment and formulate an appropriate nurturing plan.

[0302] In addition, the terminal provides activities that complement learning to the child through an interactive character. This character is operated by AI and proposes educational activities based on the child's responses.

[0303] As a specific example, the server executes processes such as "analyze the actions and conversations during the child's playtime and create a report showing the development trend". At this time, an example of a prompt sentence is "Please analyze the video data and audio data of a 3-year-old child playing with building blocks and evaluate the physical movement and language development".

[0304] With this system design, it is possible for the user to continuously and comprehensively monitor the growth status of the child and have a function that can prepare for preventive intervention.

[0305] The flow of the specific process in Example 1 will be described using FIG. 11.

[0306] Step 1:

[0307] (Data collection)

[0308] The terminal collects the child's activity information using the camera and microphone installed in the home. This information is specifically the child's motion pattern and audio clip.

[0309] Input: Video and audio data captured in real time.

[0310] Output: Generates an activity dataset based on children's actions and voices.

[0311] Step 2:

[0312] (Data transmission)

[0313] The device encrypts the collected activity data and sends it to the server via a secure communication channel.

[0314] Input: Children's activity dataset.

[0315] Output: Activity data sent to the server and securely stored.

[0316] Step 3:

[0317] (Data storage)

[0318] The server stores the received activity data in a database. Access restrictions are implemented to ensure data integrity and privacy.

[0319] Input: Submitted activity data.

[0320] Output: Activity data as stored in the database.

[0321] Step 4:

[0322] (Data analysis)

[0323] The server supplies the stored data to the generating AI model. The model analyzes physical movements and facial expressions from video data and evaluates language ability from audio data.

[0324] Input: Activity data retrieved from the database.

[0325] Output: A comprehensive developmental indicator for children.

[0326] Step 5:

[0327] (Feedback generation)

[0328] The server generates feedback based on the analysis results. This feedback includes specific areas for growth and areas for improvement.

[0329] Input: Analysis results of a generated AI model.

[0330] Output: Feedback report on the child's developmental status.

[0331] Step 6:

[0332] (Result provided)

[0333] The server sends the generated feedback to the user's device. The user then uses this feedback to monitor the child's developmental progress.

[0334] Input: Feedback report.

[0335] Output: Feedback in a format viewable on the user's terminal.

[0336] Step 7:

[0337] (Providing interactive experiences)

[0338] The device allows children to interact with an interactive character and provides activities that complement their learning. The character suggests appropriate play and learning opportunities based on the child's reactions.

[0339] Input: Children's reaction data.

[0340] Output: An adapted, interactive educational experience.

[0341] (Application Example 1)

[0342] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0343] There is a need to effectively monitor children's development in the home environment, achieve more precise developmental assessments, and explore methods for early detection of developmental challenges and providing appropriate educational experiences. Furthermore, recording daily behaviors without disrupting relaxation and developing individualized development plans based on expert feedback presents a significant challenge.

[0344] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0345] In this invention, the server includes means for accumulating information on a child's behavior using a recording device placed in the home environment, means for evaluating the child's developmental characteristics using an intelligent model for analyzing the diverse information accumulated, and means for monitoring the child's activities using a mobile autonomous device and providing an interactive educational experience. This makes it possible to collect detailed developmental information from natural behaviors within the home and to quickly formulate an individually tailored educational policy.

[0346] "Home environment" refers to the entire physical space within a house where a child lives and engages in daily activities.

[0347] A "recording device" refers to an electronic device used to acquire various types of information, such as audio and video, and store them as data.

[0348] "Behavioral information" refers to all behavioral data from a child's daily life, including their movements, speech, and facial expressions.

[0349] "Diverse information" refers to multimodal information, which comprehensively encompasses information in different modes such as visual, auditory, and emotional.

[0350] An "intelligent model" refers to a program based on artificial intelligence that analyzes information, extracts patterns and trends, and evaluates them.

[0351] "Developmental characteristics" refer to the physical, mental, and linguistic traits observed in children as they grow.

[0352] An "autonomous device" refers to a robotic device that uses sensors and algorithms to interact with its environment independently and perform specific actions.

[0353] "Interactive educational experience" refers to a learning method in which education is conducted in an interactive manner, and the educational content is adapted in response to the children's responses.

[0354] "Expert feedback" refers to suggestions and advice on child development provided by individuals with expertise in development and education.

[0355] A "development plan" refers to a specific instructional process designed to support a child's development from a long-term perspective.

[0356] To implement this invention, a recording device installed in the home environment, a server system, and a terminal for user use are required. First, the recording device has a high-performance camera and microphone, and can quickly transmit collected audio and video data to the server. Specifically, edge computing devices such as Raspberry Pi or Jetson Nano are used. The server analyzes the received data using machine learning software such as TensorFlow or PyTorch. This allows for a detailed understanding of the child's physical movements, language development, emotional state, etc.

[0357] The server further evaluates developmental characteristics based on the analysis results and sends immediate feedback to the parent's device as needed. This feedback includes advice from child development experts, with data processed in a secure cloud environment utilizing Google Cloud Platform and AWS. Parents can receive this feedback through a smartphone application.

[0358] Furthermore, robots, acting as mobile autonomous devices, are installed in homes to provide interactive educational experiences for children. Through voice interaction and reactions, they can support learning while engaging children's interest.

[0359] As a concrete example, the robot can assess a child's numerical understanding through number games and can switch to a mode that tells calming stories if the child is emotionally unstable. In this system, the prompt using a generative AI model sets the data analysis guidelines in the form of "How can we design an application that provides comprehensive monitoring and real-time feedback on how to visualize a day in the life of a 3-year-old child?"

[0360] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0361] Step 1:

[0362] The recording device collects audio and video data of children in a home environment in real time. The input is audio and video obtained from the device's microphone and camera, and the output is data converted into a digital format. This data is temporarily stored within the recording device in preparation for the next processing step.

[0363] Step 2:

[0364] The recording device transfers the collected audio and video data to the server. The input is the digital data obtained in step 1, and the output is a data stream transmitted via a stable communication protocol. Specifically, the recording device uses the server's IP address to open a secure connection and upload the data.

[0365] Step 3:

[0366] The server begins processing the received data. The input is the data stream from step 2, and the output is a dataset optimized for processing. The server first performs preprocessing essential for improving data quality, such as noise reduction and data correction.

[0367] Step 4:

[0368] The server inputs the pre-processed data into an intelligent model and performs analysis. The input is the high-quality data obtained in step 3, and the output is the analysis results showing developmental characteristics. Specifically, inference is performed based on a generative AI model using the TensorFlow library. Through this process, characteristics such as physical development and emotional state are determined.

[0369] Step 5:

[0370] The server sends the analysis results to the parent's device. The input is the analysis results obtained in step 4, and the output is digitally distributed content including feedback messages. The server connects the data to the mobile app while maintaining SSL security. This data also includes prompts generated by an AI model, providing detailed guidance to the parent.

[0371] Step 6:

[0372] The device displays the received feedback message to the parent. The input is the digitally delivered content from step 5, and the output is the feedback displayed on the device's screen. Based on the feedback, the device provides a user interface for searching for additional information and developing a development plan if necessary.

[0373] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0374] This invention provides more comprehensive and effective childcare support by combining a system for monitoring child development in the home environment with an emotion engine that analyzes the user's emotions in real time. The specific implementation of this system is described below.

[0375] The device, installed in the home, records the child's actions and speech and sends this data to a server. The server analyzes the received multimodal data using an artificial intelligence model to evaluate the child's motor skills, language development, social skills, and more. Furthermore, it uses facial recognition technology to analyze the child's emotional state and notifies parents in real time as needed.

[0376] On the other hand, the emotion engine recognizes the user's emotional state and analyzes the parent's psychological state. This engine determines how the parent feels about receiving feedback and advice, and adjusts the information provided based on that state. For example, if the parent is feeling anxious or worried, it can offer specific advice that provides reassurance.

[0377] As a concrete example, the system analyzes the child's learning activities recorded by the device, evaluates the frequency of language use within the home, and generates advice such as "increasing read-aloud time would be beneficial" based on the results. This advice is optimized by the server, taking into account the user's sentiment analysis, so that parents can confidently put it into practice.

[0378] Furthermore, its long-term data accumulation capabilities allow for the detection of children's growth patterns and the early identification of potential developmental problems. Based on this, users have the opportunity to take early action before problems arise. In this way, this system, which combines an emotional engine, can provide total support that goes beyond conventional developmental monitoring, including the psychological aspects of parents.

[0379] Through such embodiments, the present invention provides a groundbreaking system that more comprehensively supports child development and realizes the well-being of both parents and children.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The device activates a remote camera and microphone installed in the home to record the child's activities and speech. This data includes the child's movements, speech, and background sounds.

[0383] Step 2:

[0384] The device temporarily stores the data it records, compresses it as needed, and sends the data to the server via the network.

[0385] Step 3:

[0386] The server prepares the received data for analysis. This process includes formatting and noise reduction of the data, and in particular, processing it so that children's facial expressions can be clearly analyzed by face detection algorithms.

[0387] Step 4:

[0388] The server uses the formatted data to run multiple artificial intelligence models. This allows for the individual analysis and evaluation of children's motor skills, language development, social interaction, and other aspects.

[0389] Step 5:

[0390] The server analyzes the child's facial expression data to understand their emotional state. For example, it identifies typical emotional responses such as smiles and surprise, deepening the psychological understanding of their activities.

[0391] Step 6:

[0392] The emotional engine analyzes the user's, or parent's, emotional state. This analysis determines the presence or absence of anxiety or stress based on traditional contact time and response patterns, and selects an appropriate feedback format.

[0393] Step 7:

[0394] The server considers the child's developmental assessment results and the parents' emotional analysis, and generates expert-reviewed feedback. This includes suggestions for specific parenting strategies and daily support.

[0395] Step 8:

[0396] Users receive feedback through their devices and adjust their home parenting strategies based on the displayed advice.

[0397] Step 9:

[0398] The device activates an interactive AI character and provides children with educational games and conversations that reflect the feedback they receive. This allows children to develop spontaneously while having fun.

[0399] This series of steps supports more effective and reassuring childcare at home.

[0400] (Example 2)

[0401] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0402] Support for child development in the home environment lacks a comprehensive approach that takes into account the individual characteristics of each child and the psychological state of the parents. In particular, it is difficult to collect data in real time during daily life and accurately assess a child's developmental and emotional state. Furthermore, there is a need for methods of providing advice that take into account the emotional state of the parents themselves.

[0403] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0404] In this invention, the server includes means for collecting information on a child's activities using a device installed in the home environment, means for evaluating developmental indicators of the child using an artificial intelligence system for analyzing the collected multimodal information, and means for analyzing the parent's psychological state and providing optimal information according to their emotional state. This makes it possible to comprehensively monitor a child's development and emotional state in daily life and provide optimal childcare support in real time, taking into account the parent's emotions.

[0405] A "device" is a device installed in the home environment to collect information about a child's activities.

[0406] "Information" refers to data such as audio and video, including children's activities and statements.

[0407] "Multimodal information" refers to information that combines different types of data (e.g., audio, video).

[0408] An "artificial intelligence system" is a computational algorithm used to analyze developmental indicators in children.

[0409] "Developmental indicators" are standards used to evaluate a child's motor skills, language development, social skills, and other abilities.

[0410] "Emotional state" refers to the psychological condition that can be inferred from a child's facial expressions and tone of voice.

[0411] "Real-time notification" is a system that immediately conveys relevant information to parents.

[0412] "Psychological state" refers to the parents' emotions and mental condition.

[0413] "Optimal information" refers to advice and feedback provided in a way that is most appropriate to the circumstances of the parents and children.

[0414] "Information storage" refers to the process of storing data for long-term use in future analysis and evaluation.

[0415] A "growth pattern" refers to the trajectory or tendency of a child's development observed over time.

[0416] A "potential developmental problem" is a developmental challenge that is not outwardly apparent but has the potential to manifest as a problem in the future.

[0417] This invention is a comprehensive system that supports child development in the home environment. The system utilizes various devices within the home to analyze the child's activities and emotions.

[0418] The devices installed in the home collect information about the child's activities. Specific equipment includes a high-resolution camera and a high-sensitivity microphone. This hardware captures the child's daily movements and speech, transmitting the data to a server in real time.

[0419] The server analyzes the received information using an advanced artificial intelligence system. The system is built using machine learning libraries such as TensorFlow to evaluate child development indicators. The OpenCV library is used for facial recognition and emotion analysis, allowing for the estimation of the child's emotional state. The analysis results may include evaluations such as "motor skills are significantly ahead of the standard."

[0420] Furthermore, the server also analyzes the parent's psychological state. The analysis process involves collecting actions taken by the user within the smartphone application and using an emotion engine to understand the parent's psychological state. Based on this information, it prepares to provide the parent with the most appropriate feedback. For example, if the parent is feeling anxious, it will offer specific advice to help them feel more secure.

[0421] Users can receive information provided by the server via their home PCs or smartphones. The displayed dashboard visually shows information and analysis results related to the child's development. Based on this information, parents can create a child-rearing plan for their child.

[0422] For example, the server analyzes the frequency of a child's language use and generates personalized advice such as, "It would be good to increase the amount of time spent reading aloud." The parent's psychological state is also evaluated to see how they react to this advice, and the advice is adjusted as needed.

[0423] Example of a prompt:

[0424] "What kind of play is suitable for developing a child's motor skills?"

[0425] In this way, the system is structured to monitor a child's development from multiple perspectives and provide parents with appropriate support information in real time.

[0426] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0427] Step 1:

[0428] The device collects information about children's activities using a camera and microphone installed in the home. It acquires video footage of the children's movements and audio recordings as input. This information is processed in real time as digital data and packaged into data packets. It generates data packets for transmission to the server as output.

[0429] Step 2:

[0430] The terminal sends the generated data packets to the server via the internet connection. The input is a pre-packaged data packet. This packet is encrypted using the TLS protocol for security purposes and securely transferred to the server. The output is the data the server receives.

[0431] Step 3:

[0432] The server analyzes received data packets and processes video and audio data separately. It receives encrypted data packets as input and decrypts them. OpenCV is used for face recognition and facial expression analysis on the video data, and the BERT model is used for language analysis on the audio data. The output provides evaluation results regarding children's motor skills, language development, and emotional state.

[0433] Step 4:

[0434] The server generates prompts based on the analyzed information and prepares appropriate feedback for parents. Inputs include developmental indicators and emotional state assessments of the child. Based on this, an AI algorithm written in Python is used to generate advice such as "increasing read-aloud time is effective." The output is a feedback message for the parents.

[0435] Step 5:

[0436] The server uses an emotion engine to analyze the parent's emotional state. It receives a log of recent user actions on the application as input. This data is analyzed to infer the parent's emotional state and optimize the feedback. The output is adjusted feedback tailored to the parent's state.

[0437] Step 6:

[0438] Users receive the feedback provided via their home PC or smartphone. The input is feedback messages sent from the server. This is displayed on a dashboard, allowing users to check their child's developmental status and receive parenting advice. The output provides information on specific parenting plans that parents can implement.

[0439] Step 7:

[0440] The server stores all analysis data long-term and models growth patterns. It uses past evaluation data about the child and parental feedback history as input. This data is analyzed over time to accumulate data for predicting future development and necessary support. As output, it updates a database for predicting future development and identifying potential problems.

[0441] (Application Example 2)

[0442] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0443] Systems for monitoring child development at home require accurate assessment of a child's growth and the provision of appropriate childcare support that takes into account the parents' psychological state. However, conventional systems do not adequately grasp the child's growth and the parents' psychological state, nor do they adequately provide appropriate advice to parents. Therefore, the challenge is to provide a system that can monitor a child's development in the home environment in real time and enable childcare support that responds to the parents' emotional state.

[0444] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0445] In this invention, the server includes means for acquiring information on a child's activities using information devices within the home environment, means for evaluating developmental indicators of the child using a machine learning algorithm for analyzing the acquired multi-format information, and means for evaluating the emotional state of the parent and generating and providing advice tailored to the parent's psychological state. This enables accurate understanding of the child's growth and appropriate childcare support that takes into account the parent's psychological state.

[0446] "Information devices within the home environment" refers to devices and equipment installed in the home to acquire information about children's activities.

[0447] "Activity information" refers to data about a child's behavior and statements within the home.

[0448] "Multi-format information" refers to data that includes multiple means of expression, such as language, actions, and facial expressions.

[0449] A "machine learning algorithm" is a data processing technique used to analyze collected data and evaluate developmental indicators in children.

[0450] "Developmental indicators" are criteria used to evaluate a child's growth and development.

[0451] "Parental emotional state" refers to the psychological and emotional state of a caregiver who has children.

[0452] "Real-time notification" means promptly reporting status changes and important information.

[0453] "Potential developmental challenges" are unresolved developmental issues that may become problems in a child's future growth.

[0454] "Long-term data accumulation" refers to the continuous collection and storage of data over a certain period of time.

[0455] In this invention, the user installs information equipment in their home environment, and the terminal acquires information about the child's activities. The terminal uses hardware devices equipped with sensors such as a camera and a microphone to record the child's movements and speech. The recorded activity information is transmitted to a server via a network.

[0456] The server applies machine learning algorithms to the received multi-format information to evaluate child development indicators. This allows the server to assess the child's language, motor skills, and social skills. Machine learning frameworks such as TensorFlow and PyTorch can be used for this analysis.

[0457] Furthermore, the server analyzes the parents' emotional state and generates advice tailored to that state using psychological algorithms. Considering the parents' psychological state, feedback is provided that offers reassurance, especially in stressful situations. For example, if a parent is feeling anxious, it can generate a message such as, "Recent development is progressing well; continue language practice."

[0458] In this embodiment, the server can monitor the child's development in real time and immediately provide parents with the information and reassurance they need.

[0459] A concrete example of a prompt could be: "Based on data of children playing with toys, generate an assessment of language development and advice for parents. If parents are feeling anxious, optimize the advice to provide reassurance."

[0460] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0461] Step 1:

[0462] The device acquires information about the child's activities within the home environment. It records the child's actions and speech through the camera and microphone, and saves this information as activity data. The input is the child's actual actions and voice, while the output is activity data in digital format.

[0463] Step 2:

[0464] The terminal transmits acquired activity information to the server via the network. The input here is digital activity information, and the output is the data sent to the server. The terminal ensures a stable network connection to perform real-time data transfer.

[0465] Step 3:

[0466] The server analyzes the received activity information as multi-format data. It uses machine learning algorithms to evaluate the child's developmental indicators. The input is the transmitted multi-format information, and the output is the result of the child's developmental assessment. Specifically, the server uses TensorFlow to assess motor skills from motion data and natural language processing techniques to assess language development from speech data.

[0467] Step 4:

[0468] The server incorporates information about the parent into a feedback process to recognize the parent's emotional state. The input is the parent's emotional data, and the output is an evaluation of the parent's psychological state by a psychological algorithm. This allows the server to understand the parent's stress level and anxiety.

[0469] Step 5:

[0470] The server generates appropriate advice based on these assessment results and the parents' emotional state. The generating AI model is prompted to construct feedback optimized for the child's situation. The input is developmental assessment results and the parents' emotional state, and the output is optimized parenting advice.

[0471] Step 6:

[0472] The server notifies the user of the generated advice. The user receives this advice and uses it in childcare. The input is feedback from the server, and the output is the advice displayed on the user's device. Notifications are delivered instantly using push notifications to smartphones.

[0473] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0474] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0475] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0476] [Third Embodiment]

[0477] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0478] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0479] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0480] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0481] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0482] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0483] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0484] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0485] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0486] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0487] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0488] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0489] This invention is a developmental monitoring system that observes and more accurately assesses a child's development in a natural environment. By collecting activity data from a child using a device installed in the home and analyzing this data with an artificial intelligence model, it is possible to understand the child's developmental state within the home environment. The specific implementation of this system is described below.

[0490] The server receives video and audio data of children transmitted from terminals and stores it securely. The server also supplies this data to an artificial intelligence model for multimodal analysis. The analysis meticulously evaluates the child's physical movements, language development, and even emotional states based on facial expressions. This allows the server to calculate a comprehensive developmental index for the child and create feedback based on the analysis results.

[0491] Meanwhile, users can use their devices to receive real-time evaluation results and feedback provided by the server. This feedback includes advice on specific training methods and how to respond, all supervised by experts.

[0492] Furthermore, the device uses an interactive AI character to provide children with learning and play experiences. This AI character is designed to be fun to play with in response to the child's reactions, and it plays a role in complementing learning.

[0493] This system allows users to continuously monitor their child's development at home and prepare appropriate responses before problems arise. Furthermore, by accumulating data over a long period, it can predict a child's developmental growth patterns and detect potential future developmental problems early. This provides users with the advantage of being able to watch their child grow with peace of mind.

[0494] Through the embodiments described above, the present invention overcomes the limitations of conventional developmental diagnostic methods and provides a system that can accurately evaluate a child's developmental state in a manner suitable for the home environment.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The device activates a remote camera and microphone installed in the home, recording the child's activities in real time. The recorded data includes what the child is doing while playing and what they are saying.

[0498] Step 2:

[0499] The device sends the recorded activity data to the server. During this process, the data is compressed to reduce the network load associated with transmission.

[0500] Step 3:

[0501] The server stores the received data in temporary storage and performs preprocessing such as data formatting and noise reduction. This prepares the data for smooth analysis.

[0502] Step 4:

[0503] The server supplies pre-processed data to multiple artificial intelligence models. Each model is responsible for analyzing different aspects, such as motor skills, language development, and emotional states.

[0504] Step 5:

[0505] The server aggregates the analysis results from each model and creates a comprehensive developmental index. Based on this, it evaluates the child's developmental status and generates feedback.

[0506] Step 6:

[0507] Users review evaluation results and feedback sent from the server via their devices. This feedback includes expert-reviewed advice and suggestions for the next steps.

[0508] Step 7:

[0509] The device activates an interactive AI character and initiates educational and playful interactions with the child. This facilitates learning while the child enjoys themselves.

[0510] Step 8:

[0511] Users can adjust their child-rearing policies based on feedback as needed and incorporate developmental support measures, reflecting these changes in their parenting at home.

[0512] Through this series of steps, the KidoTrack system can effectively monitor a child's development and provide beneficial support.

[0513] (Example 1)

[0514] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] Many assessment methods used to understand the developmental status of children today are based on limited information gathered in unusual environments or time periods, and therefore lack sufficient accuracy and comprehensiveness. Furthermore, there is a lack of analysis to detect growth-related changes and potential challenges early on. This results in a problem where appropriate and timely responses to the individual developmental stages of children are not possible.

[0516] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0517] In this invention, the server includes means for collecting information on a child's activities using a device installed in the home environment, means for evaluating indicators of the child's growth using a machine learning model for analyzing the diverse forms of information collected, and means for providing activities that complement the child's learning using an interactive character. This makes it possible to monitor the child's developmental status in the home environment with high accuracy and to formulate appropriate parenting plans.

[0518] "Home environment" refers to the physical and emotional environment within the home where a child spends their daily life, and it means that observation and information gathering can take place in a natural and comfortable setting.

[0519] "Device" refers to equipment installed in the home environment to collect information on a child's activities, and includes devices such as cameras, microphones, and sensors.

[0520] "Activity information" refers to various forms of data, including a child's physical movements, language use, and psychological state.

[0521] "Analysis" refers to the process of inputting collected activity information into a machine learning model and interpreting its patterns and meanings.

[0522] A "machine learning model" refers to an algorithm that automatically analyzes the characteristics of collected data and uses it to perform developmental indicators and other assessments.

[0523] "Growth indicators" refer to data and information that serve as criteria for evaluating a child's developmental stage, and include physical, intellectual, and emotional aspects.

[0524] An "interactive character" refers to a virtual person or animal designed to interact with children interactively and provide education or entertainment.

[0525] "Activities that complement learning" refer to activities and programs designed to strengthen and develop a child's existing learning.

[0526] In one embodiment of this invention, the system collects information on a child's activities using a terminal installed in the home. The terminal includes devices such as a camera and microphone, which record the child's movements and voice in real time. This information is transmitted to a server via secure communication. The server ensures data security by storing the received data in a central database.

[0527] The server feeds the collected data into a generative AI model. The generative AI model analyzes the diverse forms of data collected and uses this to evaluate indicators of the child's development. The AI ​​model analyzes in detail the child's physical growth, language ability, and psychological state, including facial expressions. Based on the results of this analysis, the server generates and provides feedback on the child's developmental status to the user. This allows the user to understand the child's development in the home environment and create an appropriate parenting plan.

[0528] Furthermore, the device provides children with activities that complement their learning through an interactive character. This character is controlled by AI and suggests educationally valuable activities based on the child's responses.

[0529] As a concrete example, the server performs a process such as "analyzing a child's actions and speech during playtime and creating a report showing developmental trends." In this case, an example of a prompt message would be, "Analyze video and audio data of a 3-year-old child playing with building blocks and evaluate their physical and language development."

[0530] This system design allows users to continuously and comprehensively monitor their child's development and prepare for preventative interventions.

[0531] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0532] Step 1:

[0533] (Data collection)

[0534] The device uses cameras and microphones installed in the home to collect information about the child's activities. Specifically, this information includes the child's movement patterns and audio clips.

[0535] Input: Real-time captured video and audio data.

[0536] Output: Generates an activity dataset based on children's actions and voices.

[0537] Step 2:

[0538] (Data transmission)

[0539] The device encrypts the collected activity data and sends it to the server via a secure communication channel.

[0540] Input: Children's activity dataset.

[0541] Output: Activity data sent to the server and securely stored.

[0542] Step 3:

[0543] (Data storage)

[0544] The server stores the received activity data in a database. Access restrictions are implemented to ensure data integrity and privacy.

[0545] Input: Submitted activity data.

[0546] Output: Activity data as stored in the database.

[0547] Step 4:

[0548] (Data analysis)

[0549] The server supplies the stored data to the generating AI model. The model analyzes physical movements and facial expressions from video data and evaluates language ability from audio data.

[0550] Input: Activity data retrieved from the database.

[0551] Output: A comprehensive developmental indicator for children.

[0552] Step 5:

[0553] (Feedback generation)

[0554] The server generates feedback based on the analysis results. This feedback includes specific areas for growth and areas for improvement.

[0555] Input: Analysis results of a generated AI model.

[0556] Output: Feedback report on the child's developmental status.

[0557] Step 6:

[0558] (Result provided)

[0559] The server sends the generated feedback to the user's device. The user then uses this feedback to monitor the child's developmental progress.

[0560] Input: Feedback report.

[0561] Output: Feedback in a format viewable on the user's terminal.

[0562] Step 7:

[0563] (Providing interactive experiences)

[0564] The device allows children to interact with an interactive character and provides activities that complement their learning. The character suggests appropriate play and learning opportunities based on the child's reactions.

[0565] Input: Children's reaction data.

[0566] Output: An adapted, interactive educational experience.

[0567] (Application Example 1)

[0568] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0569] There is a need to effectively monitor children's development in the home environment, achieve more precise developmental assessments, and explore methods for early detection of developmental challenges and providing appropriate educational experiences. Furthermore, recording daily behaviors without disrupting relaxation and developing individualized development plans based on expert feedback presents a significant challenge.

[0570] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0571] In this invention, the server includes means for accumulating information on a child's behavior using a recording device placed in the home environment, means for evaluating the child's developmental characteristics using an intelligent model for analyzing the diverse information accumulated, and means for monitoring the child's activities using a mobile autonomous device and providing an interactive educational experience. This makes it possible to collect detailed developmental information from natural behaviors within the home and to quickly formulate an individually tailored educational policy.

[0572] "Home environment" refers to the entire physical space within a house where a child lives and engages in daily activities.

[0573] A "recording device" refers to an electronic device used to acquire various types of information, such as audio and video, and store them as data.

[0574] "Behavioral information" refers to all behavioral data from a child's daily life, including their movements, speech, and facial expressions.

[0575] "Diverse information" refers to multimodal information, which comprehensively encompasses information in different modes such as visual, auditory, and emotional.

[0576] An "intelligent model" refers to a program based on artificial intelligence that analyzes information, extracts patterns and trends, and evaluates them.

[0577] "Developmental characteristics" refer to the physical, mental, and linguistic traits observed in children as they grow.

[0578] An "autonomous device" refers to a robotic device that uses sensors and algorithms to interact with its environment independently and perform specific actions.

[0579] "Interactive educational experience" refers to a learning method in which education is conducted in an interactive manner, and the educational content is adapted in response to the children's responses.

[0580] "Expert feedback" refers to suggestions and advice on child development provided by individuals with expertise in development and education.

[0581] A "development plan" refers to a specific instructional process designed to support a child's development from a long-term perspective.

[0582] To implement this invention, a recording device installed in the home environment, a server system, and a terminal for user use are required. First, the recording device has a high-performance camera and microphone, and can quickly transmit collected audio and video data to the server. Specifically, edge computing devices such as Raspberry Pi or Jetson Nano are used. The server analyzes the received data using machine learning software such as TensorFlow or PyTorch. This allows for a detailed understanding of the child's physical movements, language development, emotional state, etc.

[0583] The server further evaluates developmental characteristics based on the analysis results and sends immediate feedback to the parent's device as needed. This feedback includes advice from child development experts, with data processed in a secure cloud environment utilizing Google Cloud Platform and AWS. Parents can receive this feedback through a smartphone application.

[0584] Furthermore, robots, acting as mobile autonomous devices, are installed in homes to provide interactive educational experiences for children. Through voice interaction and reactions, they can support learning while engaging children's interest.

[0585] As a concrete example, the robot can assess a child's numerical understanding through number games and can switch to a mode that tells calming stories if the child is emotionally unstable. In this system, the prompt using a generative AI model sets the data analysis guidelines in the form of "How can we design an application that provides comprehensive monitoring and real-time feedback on how to visualize a day in the life of a 3-year-old child?"

[0586] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0587] Step 1:

[0588] The recording device collects audio and video data of children in a home environment in real time. The input is audio and video obtained from the device's microphone and camera, and the output is data converted into a digital format. This data is temporarily stored within the recording device in preparation for the next processing step.

[0589] Step 2:

[0590] The recording device transfers the collected audio and video data to the server. The input is the digital data obtained in step 1, and the output is a data stream transmitted via a stable communication protocol. Specifically, the recording device uses the server's IP address to open a secure connection and upload the data.

[0591] Step 3:

[0592] The server begins processing the received data. The input is the data stream from step 2, and the output is a dataset optimized for processing. The server first performs preprocessing essential for improving data quality, such as noise reduction and data correction.

[0593] Step 4:

[0594] The server inputs the pre-processed data into an intelligent model and performs analysis. The input is the high-quality data obtained in step 3, and the output is the analysis results showing developmental characteristics. Specifically, inference is performed based on a generative AI model using the TensorFlow library. Through this process, characteristics such as physical development and emotional state are determined.

[0595] Step 5:

[0596] The server sends the analysis results to the parent's device. The input is the analysis results obtained in step 4, and the output is digitally distributed content including feedback messages. The server connects the data to the mobile app while maintaining SSL security. This data also includes prompts generated by an AI model, providing detailed guidance to the parent.

[0597] Step 6:

[0598] The device displays the received feedback message to the parent. The input is the digitally delivered content from step 5, and the output is the feedback displayed on the device's screen. Based on the feedback, the device provides a user interface for searching for additional information and developing a development plan if necessary.

[0599] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0600] This invention provides more comprehensive and effective childcare support by combining a system for monitoring child development in the home environment with an emotion engine that analyzes the user's emotions in real time. The specific implementation of this system is described below.

[0601] The device, installed in the home, records the child's actions and speech and sends this data to a server. The server analyzes the received multimodal data using an artificial intelligence model to evaluate the child's motor skills, language development, social skills, and more. Furthermore, it uses facial recognition technology to analyze the child's emotional state and notifies parents in real time as needed.

[0602] On the other hand, the emotion engine recognizes the user's emotional state and analyzes the parent's psychological state. This engine determines how the parent feels about receiving feedback and advice, and adjusts the information provided based on that state. For example, if the parent is feeling anxious or worried, it can offer specific advice that provides reassurance.

[0603] As a concrete example, the system analyzes the child's learning activities recorded by the device, evaluates the frequency of language use within the home, and generates advice such as "increasing read-aloud time would be beneficial" based on the results. This advice is optimized by the server, taking into account the user's sentiment analysis, so that parents can confidently put it into practice.

[0604] Furthermore, its long-term data accumulation capabilities allow for the detection of children's growth patterns and the early identification of potential developmental problems. Based on this, users have the opportunity to take early action before problems arise. In this way, this system, which combines an emotional engine, can provide total support that goes beyond conventional developmental monitoring, including the psychological aspects of parents.

[0605] Through such embodiments, the present invention provides a groundbreaking system that more comprehensively supports child development and realizes the well-being of both parents and children.

[0606] The following describes the processing flow.

[0607] Step 1:

[0608] The device activates a remote camera and microphone installed in the home to record the child's activities and speech. This data includes the child's movements, speech, and background sounds.

[0609] Step 2:

[0610] The device temporarily stores the data it records, compresses it as needed, and sends the data to the server via the network.

[0611] Step 3:

[0612] The server prepares the received data for analysis. This process includes formatting and noise reduction of the data, and in particular, processing it so that children's facial expressions can be clearly analyzed by face detection algorithms.

[0613] Step 4:

[0614] The server uses the formatted data to run multiple artificial intelligence models. This allows for the individual analysis and evaluation of children's motor skills, language development, social interaction, and other aspects.

[0615] Step 5:

[0616] The server analyzes the child's facial expression data to understand their emotional state. For example, it identifies typical emotional responses such as smiles and surprise, deepening the psychological understanding of their activities.

[0617] Step 6:

[0618] The emotional engine analyzes the user's, or parent's, emotional state. This analysis determines the presence or absence of anxiety or stress based on traditional contact time and response patterns, and selects an appropriate feedback format.

[0619] Step 7:

[0620] The server considers the child's developmental assessment results and the parents' emotional analysis, and generates expert-reviewed feedback. This includes suggestions for specific parenting strategies and daily support.

[0621] Step 8:

[0622] Users receive feedback through their devices and adjust their home parenting strategies based on the displayed advice.

[0623] Step 9:

[0624] The device activates an interactive AI character and provides children with educational games and conversations that reflect the feedback they receive. This allows children to develop spontaneously while having fun.

[0625] This series of steps supports more effective and reassuring childcare at home.

[0626] (Example 2)

[0627] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0628] Support for child development in the home environment lacks a comprehensive approach that takes into account the individual characteristics of each child and the psychological state of the parents. In particular, it is difficult to collect data in real time during daily life and accurately assess a child's developmental and emotional state. Furthermore, there is a need for methods of providing advice that take into account the emotional state of the parents themselves.

[0629] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0630] In this invention, the server includes means for collecting information on a child's activities using a device installed in the home environment, means for evaluating developmental indicators of the child using an artificial intelligence system for analyzing the collected multimodal information, and means for analyzing the parent's psychological state and providing optimal information according to their emotional state. This makes it possible to comprehensively monitor a child's development and emotional state in daily life and provide optimal childcare support in real time, taking into account the parent's emotions.

[0631] A "device" is a device installed in the home environment to collect information about a child's activities.

[0632] "Information" refers to data such as audio and video, including children's activities and statements.

[0633] "Multimodal information" refers to information that combines different types of data (e.g., audio, video).

[0634] An "artificial intelligence system" is a computational algorithm used to analyze developmental indicators in children.

[0635] "Developmental indicators" are standards used to evaluate a child's motor skills, language development, social skills, and other abilities.

[0636] "Emotional state" refers to the psychological condition that can be inferred from a child's facial expressions and tone of voice.

[0637] "Real-time notification" is a system that immediately conveys relevant information to parents.

[0638] "Psychological state" refers to the parents' emotions and mental condition.

[0639] "Optimal information" refers to advice and feedback provided in a way that is most appropriate to the circumstances of the parents and children.

[0640] "Information storage" refers to the process of storing data for long-term use in future analysis and evaluation.

[0641] A "growth pattern" refers to the trajectory or tendency of a child's development observed over time.

[0642] A "potential developmental problem" is a developmental challenge that is not outwardly apparent but has the potential to manifest as a problem in the future.

[0643] This invention is a comprehensive system that supports child development in the home environment. The system utilizes various devices within the home to analyze the child's activities and emotions.

[0644] The devices installed in the home collect information about the child's activities. Specific equipment includes a high-resolution camera and a high-sensitivity microphone. This hardware captures the child's daily movements and speech, transmitting the data to a server in real time.

[0645] The server analyzes the received information using an advanced artificial intelligence system. The system is built using machine learning libraries such as TensorFlow to evaluate child development indicators. The OpenCV library is used for facial recognition and emotion analysis, allowing for the estimation of the child's emotional state. The analysis results may include evaluations such as "motor skills are significantly ahead of the standard."

[0646] Furthermore, the server also analyzes the parent's psychological state. The analysis process involves collecting actions taken by the user within the smartphone application and using an emotion engine to understand the parent's psychological state. Based on this information, it prepares to provide the parent with the most appropriate feedback. For example, if the parent is feeling anxious, it will offer specific advice to help them feel more secure.

[0647] Users can receive information provided by the server via their home PCs or smartphones. The displayed dashboard visually shows information and analysis results related to the child's development. Based on this information, parents can create a child-rearing plan for their child.

[0648] For example, the server analyzes the frequency of a child's language use and generates personalized advice such as, "It would be good to increase the amount of time spent reading aloud." The parent's psychological state is also evaluated to see how they react to this advice, and the advice is adjusted as needed.

[0649] Example of a prompt:

[0650] "What kind of play is suitable for developing a child's motor skills?"

[0651] In this way, the system is structured to monitor a child's development from multiple perspectives and provide parents with appropriate support information in real time.

[0652] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0653] Step 1:

[0654] The device collects information about children's activities using a camera and microphone installed in the home. It acquires video footage of the children's movements and audio recordings as input. This information is processed in real time as digital data and packaged into data packets. It generates data packets for transmission to the server as output.

[0655] Step 2:

[0656] The terminal sends the generated data packets to the server via the internet connection. The input is a pre-packaged data packet. This packet is encrypted using the TLS protocol for security purposes and securely transferred to the server. The output is the data the server receives.

[0657] Step 3:

[0658] The server analyzes received data packets and processes video and audio data separately. It receives encrypted data packets as input and decrypts them. OpenCV is used for face recognition and facial expression analysis on the video data, and the BERT model is used for language analysis on the audio data. The output provides evaluation results regarding children's motor skills, language development, and emotional state.

[0659] Step 4:

[0660] The server generates prompts based on the analyzed information and prepares appropriate feedback for parents. Inputs include developmental indicators and emotional state assessments of the child. Based on this, an AI algorithm written in Python is used to generate advice such as "increasing read-aloud time is effective." The output is a feedback message for the parents.

[0661] Step 5:

[0662] The server uses an emotion engine to analyze the parent's emotional state. It receives a log of recent user actions on the application as input. This data is analyzed to infer the parent's emotional state and optimize the feedback. The output is adjusted feedback tailored to the parent's state.

[0663] Step 6:

[0664] Users receive the feedback provided via their home PC or smartphone. The input is feedback messages sent from the server. This is displayed on a dashboard, allowing users to check their child's developmental status and receive parenting advice. The output provides information on specific parenting plans that parents can implement.

[0665] Step 7:

[0666] The server stores all analysis data long-term and models growth patterns. It uses past evaluation data about the child and parental feedback history as input. This data is analyzed over time to accumulate data for predicting future development and necessary support. As output, it updates a database for predicting future development and identifying potential problems.

[0667] (Application Example 2)

[0668] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0669] Systems for monitoring child development at home require accurate assessment of a child's growth and the provision of appropriate childcare support that takes into account the parents' psychological state. However, conventional systems do not adequately grasp the child's growth and the parents' psychological state, nor do they adequately provide appropriate advice to parents. Therefore, the challenge is to provide a system that can monitor a child's development in the home environment in real time and enable childcare support that responds to the parents' emotional state.

[0670] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0671] In this invention, the server includes means for acquiring information on a child's activities using information devices within the home environment, means for evaluating developmental indicators of the child using a machine learning algorithm for analyzing the acquired multi-format information, and means for evaluating the emotional state of the parent and generating and providing advice tailored to the parent's psychological state. This enables accurate understanding of the child's growth and appropriate childcare support that takes into account the parent's psychological state.

[0672] "Information devices within the home environment" refers to devices and equipment installed in the home to acquire information about children's activities.

[0673] "Activity information" refers to data about a child's behavior and statements within the home.

[0674] "Multi-format information" refers to data that includes multiple means of expression, such as language, actions, and facial expressions.

[0675] A "machine learning algorithm" is a data processing technique used to analyze collected data and evaluate developmental indicators in children.

[0676] "Developmental indicators" are criteria used to evaluate a child's growth and development.

[0677] "Parental emotional state" refers to the psychological and emotional state of a caregiver who has children.

[0678] "Real-time notification" means promptly reporting status changes and important information.

[0679] "Potential developmental challenges" are unresolved developmental issues that may become problems in a child's future growth.

[0680] "Long-term data accumulation" refers to the continuous collection and storage of data over a certain period of time.

[0681] In this invention, the user installs information equipment in their home environment, and the terminal acquires information about the child's activities. The terminal uses hardware devices equipped with sensors such as a camera and a microphone to record the child's movements and speech. The recorded activity information is transmitted to a server via a network.

[0682] The server applies machine learning algorithms to the received multi-format information to evaluate child development indicators. This allows the server to assess the child's language, motor skills, and social skills. Machine learning frameworks such as TensorFlow and PyTorch can be used for this analysis.

[0683] Furthermore, the server analyzes the parents' emotional state and generates advice tailored to that state using psychological algorithms. Considering the parents' psychological state, feedback is provided that offers reassurance, especially in stressful situations. For example, if a parent is feeling anxious, it can generate a message such as, "Recent development is progressing well; continue language practice."

[0684] In this embodiment, the server can monitor the child's development in real time and immediately provide parents with the information and reassurance they need.

[0685] A concrete example of a prompt could be: "Based on data of children playing with toys, generate an assessment of language development and advice for parents. If parents are feeling anxious, optimize the advice to provide reassurance."

[0686] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0687] Step 1:

[0688] The device acquires information about the child's activities within the home environment. It records the child's actions and speech through the camera and microphone, and saves this information as activity data. The input is the child's actual actions and voice, while the output is activity data in digital format.

[0689] Step 2:

[0690] The terminal transmits acquired activity information to the server via the network. The input here is digital activity information, and the output is the data sent to the server. The terminal ensures a stable network connection to perform real-time data transfer.

[0691] Step 3:

[0692] The server analyzes the received activity information as multi-format data. It uses machine learning algorithms to evaluate the child's developmental indicators. The input is the transmitted multi-format information, and the output is the result of the child's developmental assessment. Specifically, the server uses TensorFlow to assess motor skills from motion data and natural language processing techniques to assess language development from speech data.

[0693] Step 4:

[0694] The server incorporates information about the parent into a feedback process to recognize the parent's emotional state. The input is the parent's emotional data, and the output is an evaluation of the parent's psychological state by a psychological algorithm. This allows the server to understand the parent's stress level and anxiety.

[0695] Step 5:

[0696] The server generates appropriate advice based on these assessment results and the parents' emotional state. The generating AI model is prompted to construct feedback optimized for the child's situation. The input is developmental assessment results and the parents' emotional state, and the output is optimized parenting advice.

[0697] Step 6:

[0698] The server notifies the user of the generated advice. The user receives this advice and uses it in childcare. The input is feedback from the server, and the output is the advice displayed on the user's device. Notifications are delivered instantly using push notifications to smartphones.

[0699] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0700] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0701] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0702] [Fourth Embodiment]

[0703] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0704] As shown in Figure 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.

[0705] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0706] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0707] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0708] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0709] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0710] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0711] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0712] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0713] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0714] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0715] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] This invention is a developmental monitoring system that observes and more accurately assesses a child's development in a natural environment. By collecting activity data from a child using a device installed in the home and analyzing this data with an artificial intelligence model, it is possible to understand the child's developmental state within the home environment. The specific implementation of this system is described below.

[0717] The server receives video and audio data of children transmitted from terminals and stores it securely. The server also supplies this data to an artificial intelligence model for multimodal analysis. The analysis meticulously evaluates the child's physical movements, language development, and even emotional states based on facial expressions. This allows the server to calculate a comprehensive developmental index for the child and create feedback based on the analysis results.

[0718] Meanwhile, users can use their devices to receive real-time evaluation results and feedback provided by the server. This feedback includes advice on specific training methods and how to respond, all supervised by experts.

[0719] Furthermore, the device uses an interactive AI character to provide children with learning and play experiences. This AI character is designed to be fun to play with in response to the child's reactions, and it plays a role in complementing learning.

[0720] This system allows users to continuously monitor their child's development at home and prepare appropriate responses before problems arise. Furthermore, by accumulating data over a long period, it can predict a child's developmental growth patterns and detect potential future developmental problems early. This provides users with the advantage of being able to watch their child grow with peace of mind.

[0721] Through the embodiments described above, the present invention overcomes the limitations of conventional developmental diagnostic methods and provides a system that can accurately evaluate a child's developmental state in a manner suitable for the home environment.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The device activates a remote camera and microphone installed in the home, recording the child's activities in real time. The recorded data includes what the child is doing while playing and what they are saying.

[0725] Step 2:

[0726] The device sends the recorded activity data to the server. During this process, the data is compressed to reduce the network load associated with transmission.

[0727] Step 3:

[0728] The server stores the received data in temporary storage and performs preprocessing such as data formatting and noise reduction. This prepares the data for smooth analysis.

[0729] Step 4:

[0730] The server supplies pre-processed data to multiple artificial intelligence models. Each model is responsible for analyzing different aspects, such as motor skills, language development, and emotional states.

[0731] Step 5:

[0732] The server aggregates the analysis results from each model and creates a comprehensive developmental index. Based on this, it evaluates the child's developmental status and generates feedback.

[0733] Step 6:

[0734] Users review evaluation results and feedback sent from the server via their devices. This feedback includes expert-reviewed advice and suggestions for the next steps.

[0735] Step 7:

[0736] The device activates an interactive AI character and initiates educational and playful interactions with the child. This facilitates learning while the child enjoys themselves.

[0737] Step 8:

[0738] Users can adjust their child-rearing policies based on feedback as needed and incorporate developmental support measures, reflecting these changes in their parenting at home.

[0739] Through this series of steps, the KidoTrack system can effectively monitor a child's development and provide beneficial support.

[0740] (Example 1)

[0741] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0742] Many assessment methods used to understand the developmental status of children today are based on limited information gathered in unusual environments or time periods, and therefore lack sufficient accuracy and comprehensiveness. Furthermore, there is a lack of analysis to detect growth-related changes and potential challenges early on. This results in a problem where appropriate and timely responses to the individual developmental stages of children are not possible.

[0743] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0744] In this invention, the server includes means for collecting information on a child's activities using a device installed in the home environment, means for evaluating indicators of the child's growth using a machine learning model for analyzing the diverse forms of information collected, and means for providing activities that complement the child's learning using an interactive character. This makes it possible to monitor the child's developmental status in the home environment with high accuracy and to formulate appropriate parenting plans.

[0745] "Home environment" refers to the physical and emotional environment within the home where a child spends their daily life, and it means that observation and information gathering can take place in a natural and comfortable setting.

[0746] "Device" refers to equipment installed in the home environment to collect information on a child's activities, and includes devices such as cameras, microphones, and sensors.

[0747] "Activity information" refers to various forms of data, including a child's physical movements, language use, and psychological state.

[0748] "Analysis" refers to the process of inputting collected activity information into a machine learning model and interpreting its patterns and meanings.

[0749] A "machine learning model" refers to an algorithm that automatically analyzes the characteristics of collected data and uses it to perform developmental indicators and other assessments.

[0750] "Growth indicators" refer to data and information that serve as criteria for evaluating a child's developmental stage, and include physical, intellectual, and emotional aspects.

[0751] An "interactive character" refers to a virtual person or animal designed to interact with children interactively and provide education or entertainment.

[0752] "Activities that complement learning" refer to activities and programs designed to strengthen and develop a child's existing learning.

[0753] In one embodiment of this invention, the system collects information on a child's activities using a terminal installed in the home. The terminal includes devices such as a camera and microphone, which record the child's movements and voice in real time. This information is transmitted to a server via secure communication. The server ensures data security by storing the received data in a central database.

[0754] The server feeds the collected data into a generative AI model. The generative AI model analyzes the diverse forms of data collected and uses this to evaluate indicators of the child's development. The AI ​​model analyzes in detail the child's physical growth, language ability, and psychological state, including facial expressions. Based on the results of this analysis, the server generates and provides feedback on the child's developmental status to the user. This allows the user to understand the child's development in the home environment and create an appropriate parenting plan.

[0755] Furthermore, the device provides children with activities that complement their learning through an interactive character. This character is controlled by AI and suggests educationally valuable activities based on the child's responses.

[0756] As a concrete example, the server performs a process such as "analyzing a child's actions and speech during playtime and creating a report showing developmental trends." In this case, an example of a prompt message would be, "Analyze video and audio data of a 3-year-old child playing with building blocks and evaluate their physical and language development."

[0757] This system design allows users to continuously and comprehensively monitor their child's development and prepare for preventative interventions.

[0758] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0759] Step 1:

[0760] (Data collection)

[0761] The device uses cameras and microphones installed in the home to collect information about the child's activities. Specifically, this information includes the child's movement patterns and audio clips.

[0762] Input: Real-time captured video and audio data.

[0763] Output: Generates an activity dataset based on children's actions and voices.

[0764] Step 2:

[0765] (Data transmission)

[0766] The device encrypts the collected activity data and sends it to the server via a secure communication channel.

[0767] Input: Children's activity dataset.

[0768] Output: Activity data sent to the server and securely stored.

[0769] Step 3:

[0770] (Data storage)

[0771] The server stores the received activity data in a database. Access restrictions are implemented to ensure data integrity and privacy.

[0772] Input: Submitted activity data.

[0773] Output: Activity data as stored in the database.

[0774] Step 4:

[0775] (Data analysis)

[0776] The server supplies the stored data to the generating AI model. The model analyzes physical movements and facial expressions from video data and evaluates language ability from audio data.

[0777] Input: Activity data retrieved from the database.

[0778] Output: A comprehensive developmental indicator for children.

[0779] Step 5:

[0780] (Feedback generation)

[0781] The server generates feedback based on the analysis results. This feedback includes specific areas for growth and areas for improvement.

[0782] Input: Analysis results of a generated AI model.

[0783] Output: Feedback report on the child's developmental status.

[0784] Step 6:

[0785] (Result provided)

[0786] The server sends the generated feedback to the user's device. The user then uses this feedback to monitor the child's developmental progress.

[0787] Input: Feedback report.

[0788] Output: Feedback in a format viewable on the user's terminal.

[0789] Step 7:

[0790] (Providing interactive experiences)

[0791] The device allows children to interact with an interactive character and provides activities that complement their learning. The character suggests appropriate play and learning opportunities based on the child's reactions.

[0792] Input: Children's reaction data.

[0793] Output: An adapted, interactive educational experience.

[0794] (Application Example 1)

[0795] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0796] There is a need to effectively monitor children's development in the home environment, achieve more precise developmental assessments, and explore methods for early detection of developmental challenges and providing appropriate educational experiences. Furthermore, recording daily behaviors without disrupting relaxation and developing individualized development plans based on expert feedback presents a significant challenge.

[0797] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0798] In this invention, the server includes means for accumulating information on a child's behavior using a recording device placed in the home environment, means for evaluating the child's developmental characteristics using an intelligent model for analyzing the diverse information accumulated, and means for monitoring the child's activities using a mobile autonomous device and providing an interactive educational experience. This makes it possible to collect detailed developmental information from natural behaviors within the home and to quickly formulate an individually tailored educational policy.

[0799] "Home environment" refers to the entire physical space within a house where a child lives and engages in daily activities.

[0800] A "recording device" refers to an electronic device used to acquire various types of information, such as audio and video, and store them as data.

[0801] "Behavioral information" refers to all behavioral data from a child's daily life, including their movements, speech, and facial expressions.

[0802] "Diverse information" refers to multimodal information, which comprehensively encompasses information in different modes such as visual, auditory, and emotional.

[0803] An "intelligent model" refers to a program based on artificial intelligence that analyzes information, extracts patterns and trends, and evaluates them.

[0804] "Developmental characteristics" refer to the physical, mental, and linguistic traits observed in children as they grow.

[0805] An "autonomous device" refers to a robotic device that uses sensors and algorithms to interact with its environment independently and perform specific actions.

[0806] "Interactive educational experience" refers to a learning method in which education is conducted in an interactive manner, and the educational content is adapted in response to the children's responses.

[0807] "Expert feedback" refers to suggestions and advice on child development provided by individuals with expertise in development and education.

[0808] A "development plan" refers to a specific instructional process designed to support a child's development from a long-term perspective.

[0809] To implement this invention, a recording device installed in the home environment, a server system, and a terminal for user use are required. First, the recording device has a high-performance camera and microphone, and can quickly transmit collected audio and video data to the server. Specifically, edge computing devices such as Raspberry Pi or Jetson Nano are used. The server analyzes the received data using machine learning software such as TensorFlow or PyTorch. This allows for a detailed understanding of the child's physical movements, language development, emotional state, etc.

[0810] The server further evaluates developmental characteristics based on the analysis results and sends immediate feedback to the parent's device as needed. This feedback includes advice from child development experts, with data processed in a secure cloud environment utilizing Google Cloud Platform and AWS. Parents can receive this feedback through a smartphone application.

[0811] Furthermore, robots, acting as mobile autonomous devices, are installed in homes to provide interactive educational experiences for children. Through voice interaction and reactions, they can support learning while engaging children's interest.

[0812] As a concrete example, the robot can assess a child's numerical understanding through number games and can switch to a mode that tells calming stories if the child is emotionally unstable. In this system, the prompt using a generative AI model sets the data analysis guidelines in the form of "How can we design an application that provides comprehensive monitoring and real-time feedback on how to visualize a day in the life of a 3-year-old child?"

[0813] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0814] Step 1:

[0815] The recording device collects audio and video data of children in a home environment in real time. The input is audio and video obtained from the device's microphone and camera, and the output is data converted into a digital format. This data is temporarily stored within the recording device in preparation for the next processing step.

[0816] Step 2:

[0817] The recording device transfers the collected audio and video data to the server. The input is the digital data obtained in step 1, and the output is a data stream transmitted via a stable communication protocol. Specifically, the recording device uses the server's IP address to open a secure connection and upload the data.

[0818] Step 3:

[0819] The server begins processing the received data. The input is the data stream from step 2, and the output is a dataset optimized for processing. The server first performs preprocessing essential for improving data quality, such as noise reduction and data correction.

[0820] Step 4:

[0821] The server inputs the pre-processed data into an intelligent model and performs analysis. The input is the high-quality data obtained in step 3, and the output is the analysis results showing developmental characteristics. Specifically, inference is performed based on a generative AI model using the TensorFlow library. Through this process, characteristics such as physical development and emotional state are determined.

[0822] Step 5:

[0823] The server sends the analysis results to the parent's device. The input is the analysis results obtained in step 4, and the output is digitally distributed content including feedback messages. The server connects the data to the mobile app while maintaining SSL security. This data also includes prompts generated by an AI model, providing detailed guidance to the parent.

[0824] Step 6:

[0825] The device displays the received feedback message to the parent. The input is the digitally delivered content from step 5, and the output is the feedback displayed on the device's screen. Based on the feedback, the device provides a user interface for searching for additional information and developing a development plan if necessary.

[0826] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0827] This invention provides more comprehensive and effective childcare support by combining a system for monitoring child development in the home environment with an emotion engine that analyzes the user's emotions in real time. The specific implementation of this system is described below.

[0828] The device, installed in the home, records the child's actions and speech and sends this data to a server. The server analyzes the received multimodal data using an artificial intelligence model to evaluate the child's motor skills, language development, social skills, and more. Furthermore, it uses facial recognition technology to analyze the child's emotional state and notifies parents in real time as needed.

[0829] On the other hand, the emotion engine recognizes the user's emotional state and analyzes the parent's psychological state. This engine determines how the parent feels about receiving feedback and advice, and adjusts the information provided based on that state. For example, if the parent is feeling anxious or worried, it can offer specific advice that provides reassurance.

[0830] As a concrete example, the system analyzes the child's learning activities recorded by the device, evaluates the frequency of language use within the home, and generates advice such as "increasing read-aloud time would be beneficial" based on the results. This advice is optimized by the server, taking into account the user's sentiment analysis, so that parents can confidently put it into practice.

[0831] Furthermore, its long-term data accumulation capabilities allow for the detection of children's growth patterns and the early identification of potential developmental problems. Based on this, users have the opportunity to take early action before problems arise. In this way, this system, which combines an emotional engine, can provide total support that goes beyond conventional developmental monitoring, including the psychological aspects of parents.

[0832] Through such embodiments, the present invention provides a groundbreaking system that more comprehensively supports child development and realizes the well-being of both parents and children.

[0833] The following describes the processing flow.

[0834] Step 1:

[0835] The device activates a remote camera and microphone installed in the home to record the child's activities and speech. This data includes the child's movements, speech, and background sounds.

[0836] Step 2:

[0837] The device temporarily stores the data it records, compresses it as needed, and sends the data to the server via the network.

[0838] Step 3:

[0839] The server prepares the received data for analysis. This process includes formatting and noise reduction of the data, and in particular, processing it so that children's facial expressions can be clearly analyzed by face detection algorithms.

[0840] Step 4:

[0841] The server uses the formatted data to run multiple artificial intelligence models. This allows for the individual analysis and evaluation of children's motor skills, language development, social interaction, and other aspects.

[0842] Step 5:

[0843] The server analyzes the child's facial expression data to understand their emotional state. For example, it identifies typical emotional responses such as smiles and surprise, deepening the psychological understanding of their activities.

[0844] Step 6:

[0845] The emotional engine analyzes the user's, or parent's, emotional state. This analysis determines the presence or absence of anxiety or stress based on traditional contact time and response patterns, and selects an appropriate feedback format.

[0846] Step 7:

[0847] The server considers the child's developmental assessment results and the parents' emotional analysis, and generates expert-reviewed feedback. This includes suggestions for specific parenting strategies and daily support.

[0848] Step 8:

[0849] Users receive feedback through their devices and adjust their home parenting strategies based on the displayed advice.

[0850] Step 9:

[0851] The device activates an interactive AI character and provides children with educational games and conversations that reflect the feedback they receive. This allows children to develop spontaneously while having fun.

[0852] This series of steps supports more effective and reassuring childcare at home.

[0853] (Example 2)

[0854] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0855] Support for child development in the home environment lacks a comprehensive approach that takes into account the individual characteristics of each child and the psychological state of the parents. In particular, it is difficult to collect data in real time during daily life and accurately assess a child's developmental and emotional state. Furthermore, there is a need for methods of providing advice that take into account the emotional state of the parents themselves.

[0856] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0857] In this invention, the server includes means for collecting information on a child's activities using a device installed in the home environment, means for evaluating developmental indicators of the child using an artificial intelligence system for analyzing the collected multimodal information, and means for analyzing the parent's psychological state and providing optimal information according to their emotional state. This makes it possible to comprehensively monitor a child's development and emotional state in daily life and provide optimal childcare support in real time, taking into account the parent's emotions.

[0858] A "device" is a device installed in the home environment to collect information about a child's activities.

[0859] "Information" refers to data such as audio and video, including children's activities and statements.

[0860] "Multimodal information" refers to information that combines different types of data (e.g., audio, video).

[0861] An "artificial intelligence system" is a computational algorithm used to analyze developmental indicators in children.

[0862] "Developmental indicators" are standards used to evaluate a child's motor skills, language development, social skills, and other abilities.

[0863] "Emotional state" refers to the psychological condition that can be inferred from a child's facial expressions and tone of voice.

[0864] "Real-time notification" is a system that immediately conveys relevant information to parents.

[0865] "Psychological state" refers to the parents' emotions and mental condition.

[0866] "Optimal information" refers to advice and feedback provided in a way that is most appropriate to the circumstances of the parents and children.

[0867] "Information storage" refers to the process of storing data for long-term use in future analysis and evaluation.

[0868] A "growth pattern" refers to the trajectory or tendency of a child's development observed over time.

[0869] A "potential developmental problem" is a developmental challenge that is not outwardly apparent but has the potential to manifest as a problem in the future.

[0870] This invention is a comprehensive system that supports child development in the home environment. The system utilizes various devices within the home to analyze the child's activities and emotions.

[0871] The devices installed in the home collect information about the child's activities. Specific equipment includes a high-resolution camera and a high-sensitivity microphone. This hardware captures the child's daily movements and speech, transmitting the data to a server in real time.

[0872] The server analyzes the received information using an advanced artificial intelligence system. The system is built using machine learning libraries such as TensorFlow to evaluate child development indicators. The OpenCV library is used for facial recognition and emotion analysis, allowing for the estimation of the child's emotional state. The analysis results may include evaluations such as "motor skills are significantly ahead of the standard."

[0873] Furthermore, the server also analyzes the parent's psychological state. The analysis process involves collecting actions taken by the user within the smartphone application and using an emotion engine to understand the parent's psychological state. Based on this information, it prepares to provide the parent with the most appropriate feedback. For example, if the parent is feeling anxious, it will offer specific advice to help them feel more secure.

[0874] Users can receive information provided by the server via their home PCs or smartphones. The displayed dashboard visually shows information and analysis results related to the child's development. Based on this information, parents can create a child-rearing plan for their child.

[0875] For example, the server analyzes the frequency of a child's language use and generates personalized advice such as, "It would be good to increase the amount of time spent reading aloud." The parent's psychological state is also evaluated to see how they react to this advice, and the advice is adjusted as needed.

[0876] Example of a prompt:

[0877] "What kind of play is suitable for developing a child's motor skills?"

[0878] In this way, the system is structured to monitor a child's development from multiple perspectives and provide parents with appropriate support information in real time.

[0879] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0880] Step 1:

[0881] The device collects information about children's activities using a camera and microphone installed in the home. It acquires video footage of the children's movements and audio recordings as input. This information is processed in real time as digital data and packaged into data packets. It generates data packets for transmission to the server as output.

[0882] Step 2:

[0883] The terminal sends the generated data packets to the server via the internet connection. The input is a pre-packaged data packet. This packet is encrypted using the TLS protocol for security purposes and securely transferred to the server. The output is the data the server receives.

[0884] Step 3:

[0885] The server analyzes received data packets and processes video and audio data separately. It receives encrypted data packets as input and decrypts them. OpenCV is used for face recognition and facial expression analysis on the video data, and the BERT model is used for language analysis on the audio data. The output provides evaluation results regarding children's motor skills, language development, and emotional state.

[0886] Step 4:

[0887] The server generates prompts based on the analyzed information and prepares appropriate feedback for parents. Inputs include developmental indicators and emotional state assessments of the child. Based on this, an AI algorithm written in Python is used to generate advice such as "increasing read-aloud time is effective." The output is a feedback message for the parents.

[0888] Step 5:

[0889] The server uses an emotion engine to analyze the parent's emotional state. It receives a log of recent user actions on the application as input. This data is analyzed to infer the parent's emotional state and optimize the feedback. The output is adjusted feedback tailored to the parent's state.

[0890] Step 6:

[0891] Users receive the feedback provided via their home PC or smartphone. The input is feedback messages sent from the server. This is displayed on a dashboard, allowing users to check their child's developmental status and receive parenting advice. The output provides information on specific parenting plans that parents can implement.

[0892] Step 7:

[0893] The server stores all analysis data long-term and models growth patterns. It uses past evaluation data about the child and parental feedback history as input. This data is analyzed over time to accumulate data for predicting future development and necessary support. As output, it updates a database for predicting future development and identifying potential problems.

[0894] (Application Example 2)

[0895] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0896] Systems for monitoring child development at home require accurate assessment of a child's growth and the provision of appropriate childcare support that takes into account the parents' psychological state. However, conventional systems do not adequately grasp the child's growth and the parents' psychological state, nor do they adequately provide appropriate advice to parents. Therefore, the challenge is to provide a system that can monitor a child's development in the home environment in real time and enable childcare support that responds to the parents' emotional state.

[0897] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0898] In this invention, the server includes means for acquiring information on a child's activities using information devices within the home environment, means for evaluating developmental indicators of the child using a machine learning algorithm for analyzing the acquired multi-format information, and means for evaluating the emotional state of the parent and generating and providing advice tailored to the parent's psychological state. This enables accurate understanding of the child's growth and appropriate childcare support that takes into account the parent's psychological state.

[0899] "Information devices within the home environment" refers to devices and equipment installed in the home to acquire information about children's activities.

[0900] "Activity information" refers to data about a child's behavior and statements within the home.

[0901] "Multi-format information" refers to data that includes multiple means of expression, such as language, actions, and facial expressions.

[0902] A "machine learning algorithm" is a data processing technique used to analyze collected data and evaluate developmental indicators in children.

[0903] "Developmental indicators" are criteria used to evaluate a child's growth and development.

[0904] "Parental emotional state" refers to the psychological and emotional state of a caregiver who has children.

[0905] "Real-time notification" means promptly reporting status changes and important information.

[0906] "Potential developmental challenges" are unresolved developmental issues that may become problems in a child's future growth.

[0907] "Long-term data accumulation" refers to the continuous collection and storage of data over a certain period of time.

[0908] In this invention, the user installs information equipment in their home environment, and the terminal acquires information about the child's activities. The terminal uses hardware devices equipped with sensors such as a camera and a microphone to record the child's movements and speech. The recorded activity information is transmitted to a server via a network.

[0909] The server applies machine learning algorithms to the received multi-format information to evaluate child development indicators. This allows the server to assess the child's language, motor skills, and social skills. Machine learning frameworks such as TensorFlow and PyTorch can be used for this analysis.

[0910] Furthermore, the server analyzes the parents' emotional state and generates advice tailored to that state using psychological algorithms. Considering the parents' psychological state, feedback is provided that offers reassurance, especially in stressful situations. For example, if a parent is feeling anxious, it can generate a message such as, "Recent development is progressing well; continue language practice."

[0911] In this embodiment, the server can monitor the child's development in real time and immediately provide parents with the information and reassurance they need.

[0912] A concrete example of a prompt could be: "Based on data of children playing with toys, generate an assessment of language development and advice for parents. If parents are feeling anxious, optimize the advice to provide reassurance."

[0913] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0914] Step 1:

[0915] The device acquires information about the child's activities within the home environment. It records the child's actions and speech through the camera and microphone, and saves this information as activity data. The input is the child's actual actions and voice, while the output is activity data in digital format.

[0916] Step 2:

[0917] The terminal transmits acquired activity information to the server via the network. The input here is digital activity information, and the output is the data sent to the server. The terminal ensures a stable network connection to perform real-time data transfer.

[0918] Step 3:

[0919] The server analyzes the received activity information as multi-format data. It uses machine learning algorithms to evaluate the child's developmental indicators. The input is the transmitted multi-format information, and the output is the result of the child's developmental assessment. Specifically, the server uses TensorFlow to assess motor skills from motion data and natural language processing techniques to assess language development from speech data.

[0920] Step 4:

[0921] The server incorporates information about the parent into a feedback process to recognize the parent's emotional state. The input is the parent's emotional data, and the output is an evaluation of the parent's psychological state by a psychological algorithm. This allows the server to understand the parent's stress level and anxiety.

[0922] Step 5:

[0923] The server generates appropriate advice based on these assessment results and the parents' emotional state. The generating AI model is prompted to construct feedback optimized for the child's situation. The input is developmental assessment results and the parents' emotional state, and the output is optimized parenting advice.

[0924] Step 6:

[0925] The server notifies the user of the generated advice. The user receives this advice and uses it in childcare. The input is feedback from the server, and the output is the advice displayed on the user's device. Notifications are delivered instantly using push notifications to smartphones.

[0926] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0927] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0928] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0929] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0930] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0931] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0932] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0933] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0934] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0935] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0936] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0937] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0938] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0939] 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.

[0940] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0941] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0942] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0943] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0944] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0945] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0946] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0947] The following is further disclosed regarding the embodiments described above.

[0948] (Claim 1)

[0949] A means of collecting children's activity data using devices installed in the home environment,

[0950] A means of evaluating child development indicators using an artificial intelligence model for analyzing collected multimodal data,

[0951] Based on the evaluation results, a means of providing feedback to support children's education and development,

[0952] A means of analyzing a child's emotional state and providing real-time notifications as needed,

[0953] A system that includes means to predict growth patterns through long-term data accumulation and to detect potential developmental problems early.

[0954] (Claim 2)

[0955] The system according to claim 1, which performs accurate developmental assessment based on data collected in a home environment where the child can relax.

[0956] (Claim 3)

[0957] The system according to claim 1, which provides parents with feedback supervised by experts and develops individualized parenting plans.

[0958] "Example 1"

[0959] (Claim 1)

[0960] A means of collecting information on a child's activities using devices installed in the home environment,

[0961] A means of evaluating children's growth indicators using machine learning models to analyze diverse forms of collected information,

[0962] Based on the evaluation results, means to provide outcomes that support children's growth and education,

[0963] A means of analyzing a child's psychological state and reporting immediately as needed,

[0964] By accumulating information over a long period, we can predict growth trends and identify potential problems early.

[0965] A system that includes means of providing activities that complement children's learning using interactive characters.

[0966] (Claim 2)

[0967] The system according to claim 1, which performs a detailed growth assessment based on data collected in a home environment where children can behave naturally.

[0968] (Claim 3)

[0969] The system according to claim 1, which provides parents with the results of expert advice and formulates individual growth plans.

[0970] "Application Example 1"

[0971] (Claim 1)

[0972] A means of collecting information on children's behavior using recording devices placed in the home environment,

[0973] A means of evaluating the developmental characteristics of children using an intelligent model for analyzing the diverse information that has been collected,

[0974] Based on the evaluation results, a means of providing advice to promote the education and development of children,

[0975] A means of analyzing the emotional state of children and providing immediate reports as needed,

[0976] A means of predicting developmental patterns through continuous data accumulation and detecting potential developmental challenges early,

[0977] A means of monitoring children's activities using a mobile autonomous device and providing interactive educational experiences,

[0978] A system that includes means to provide immediate feedback to parents based on analysis results and recommend individualized parenting methods.

[0979] (Claim 2)

[0980] The system according to claim 1, which performs accurate developmental assessments based on information obtained in a safe and secure home environment for children.

[0981] (Claim 3)

[0982] The system according to claim 1, which provides expert-led feedback and creates individualized training plans.

[0983] "Example 2 of combining an emotion engine"

[0984] (Claim 1)

[0985] A means of collecting information about children's activities using devices installed in the home environment,

[0986] A means of evaluating child development indicators using an artificial intelligence system for analyzing collected multimodal information,

[0987] Based on the evaluation results, a means of providing advice to support the education and development of children,

[0988] A means of analyzing a child's emotional state and providing real-time notifications as needed,

[0989] A means of analyzing the psychological state of parents and providing optimal information according to their emotional state,

[0990] A system that includes means to predict growth patterns through long-term data accumulation and to detect potential developmental problems early.

[0991] (Claim 2)

[0992] The system according to claim 1, which performs an accurate developmental assessment based on information obtained in a home environment where the child can relax.

[0993] (Claim 3)

[0994] The system according to claim 1, which provides parents with advice supervised by experts and develops individualized child-rearing plans.

[0995] "Application example 2 when combining with an emotional engine"

[0996] (Claim 1)

[0997] A means of acquiring information about a child's activities using information devices within the home environment,

[0998] A means of evaluating child development indicators using a machine learning algorithm to analyze acquired multi-format information,

[0999] A means of providing responses to support the education and development of children based on the evaluation results,

[1000] A means of analyzing a child's emotional state and providing instant notification as needed,

[1001] By accumulating long-term data, we can predict growth patterns and identify potential developmental challenges early.

[1002] A system that includes means for evaluating the emotional state of parents and generating and providing advice tailored to the parents' psychological state.

[1003] (Claim 2)

[1004] The system according to claim 1, which performs highly accurate developmental assessments based on information obtained in a calm living environment for the child.

[1005] (Claim 3)

[1006] The system according to claim 1, which provides parents with responses supervised by experts and formulates individualized child-rearing plans. [Explanation of Symbols]

[1007] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information on children's behavior using recording devices placed in the home environment, A means of evaluating the developmental characteristics of children using an intelligent model for analyzing the diverse information that has been collected, Based on the evaluation results, a means of providing advice to promote the education and development of children, A means of analyzing the emotional state of children and providing immediate reports as needed, A means of predicting developmental patterns through continuous data accumulation and detecting potential developmental challenges early, A means of monitoring children's activities using a mobile autonomous device and providing interactive educational experiences, A system that includes means to provide immediate feedback to parents based on analysis results and recommend individualized parenting methods.

2. The system according to claim 1, which performs accurate developmental assessments based on information obtained in a home environment where children feel secure.

3. The system according to claim 1, which provides expert-led feedback and creates individualized training plans.

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