system

Abstract

We provide the system. [Solution] A means of recording data based on the activities of young children, A means for analyzing the aforementioned data to identify the learning style of a young child, A means for generating an optimized learning and play program based on the aforementioned learning format, Means for providing the generated program, An automated device for observing and collecting data on the activities of young children in real time, A means of executing the generated program to suggest the most suitable educational activities for children, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Selecting appropriate learning content to promote the healthy development of infants is a complicated and difficult task for parents. Although there are a variety of contents in the market, it is impossible to identify the optimal content that suits each infant's interests and development stage, and a lot of time and effort may be wasted. In addition, there are limitations in manual selection, and it is difficult to provide individualized learning support.

Means for Solving the Problems

[0005] This invention provides a method for automatically generating personalized and optimal learning and play programs by collecting daily activity data of infants, analyzing it with AI technology, identifying each infant's learning pattern, and automatically generating personalized and optimal learning and play programs. This enables the efficient suggestion of content suitable for each infant using activity data including diverse data formats, thereby reducing the burden on parents and promoting the healthy development of infants.

[0006] The term "infant" typically refers to children from birth to around 6 years old, who are in a stage of significant physical and mental development.

[0007] "Activity data" refers to data that records the actions and speech of young children in their daily lives, and includes information such as audio data, image data, and motion data.

[0008] "Analysis" refers to the process of processing and analyzing data using AI technology to identify patterns and characteristics based on the collected data.

[0009] "Learning patterns" refer to identifying the learning behaviors and interests that young children exhibit, and describe behavioral patterns based on their individual developmental stages and interests.

[0010] "Generation" refers to the act of constructing new learning and play programs based on existing data and analysis results.

[0011] A "program" refers to a set of tasks and activities designed with a specific purpose in mind, provided as content suitable for the development of young children.

[0012] "Presentation" refers to the act of visually or audibly informing a user of a generated learning or play program. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It 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 Example 2 when an 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 an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled 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), etc.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that effectively collects and analyzes activity data of infants and generates personalized learning and play programs based on that data. The system aims to provide optimal content by allowing parents to record their child's daily behavior and understand their learning patterns.

[0035] First, the user (parent) records the child's daily activities via the device. This record may include video, audio, and even motion data from motion sensors. Once the user collects this data using a dedicated application, the device formats the data and sends it to the server.

[0036] The server stores the received data and analyzes it using AI algorithms. For example, computer vision technology can be applied to video data to identify what the toddler is interested in. For audio data, natural language processing technology is used to evaluate the development of sounds and language. In this way, the server understands the toddler's interests and tendencies and identifies their learning patterns.

[0037] Next, the server generates learning and play programs best suited to the child based on the analysis results. This process creates a plan that develops specific developmental skills by combining multiple educational elements. For example, for a child who shows interest in color recognition, a game app designed to cultivate their sense of color will be generated.

[0038] Finally, the generated program is sent from the server to the terminal and provided to the user. The terminal notifies the user of any new available content and presents instructions on how to use it and the expected learning effects. The user then has the child use it and provides feedback to the system, which further optimizes the program.

[0039] This system allows parents to provide their toddlers with suitable learning experiences with less effort, thereby supporting their development.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users use the device to record their child's daily activities. The device has a dedicated application installed that allows for video and audio recording. Furthermore, in some cases, motion sensors are used to collect the child's movement data.

[0043] Step 2:

[0044] The device converts the collected data into a predetermined format. Video data is compressed, and audio data is converted to a format suitable for audio analysis as needed. During this process, data anonymization and security protection are also applied.

[0045] Step 3:

[0046] The terminal sends the converted data to the server. During this transmission, secure protocols such as SSL are used to protect the confidentiality of the data.

[0047] Step 4:

[0048] The server stores the received data in a database. Since the stored data is to be analyzed by AI, preprocessing is performed as needed.

[0049] Step 5:

[0050] The server begins data analysis. Specifically, for video data, computer vision technology is used to identify the child's behavior and objects of interest. For audio data, natural language processing is applied to analyze pronunciation patterns and word usage frequency.

[0051] Step 6:

[0052] The server identifies the child's learning pattern based on the analysis results. This learning pattern includes the child's current developmental stage, areas of interest, and skills.

[0053] Step 7:

[0054] The server generates an optimal learning and play program based on identified learning patterns. At this stage, personalized content is designed using a generative AI. For example, if a toddler is interested in colors, an educational game about colors will be created.

[0055] Step 8:

[0056] The server sends the generated learning and play programs to the terminal. During transmission, the programs are reformatted to a format easily understood by the user.

[0057] Step 9:

[0058] The device notifies the user that the program is available. The application then displays detailed information and instructions, preparing the child to actually use the program.

[0059] Step 10:

[0060] Users observe how young children use the program and provide feedback to the system regarding their performance and reactions. This allows the system to accumulate information necessary for future program optimization.

[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] To provide individualized educational experiences tailored to the growth and development of young children, it is necessary to accurately understand their ever-changing interests and behaviors and to quickly and effectively adjust learning programs accordingly. However, conventional systems have struggled to provide such individualized support. Therefore, there were limitations to collecting diverse information about children's daily activities in real time and using that information to provide the optimal educational experience.

[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 recording information on daily activities, means for analyzing the information on daily activities to identify a learning method, and means for generating an individualized educational program based on the learning method. This makes it possible to provide an optimal educational experience tailored to the individual learning patterns of each child.

[0066] "Information on daily activities" refers to audio, visual, and motion information related to the daily activities of young children.

[0067] "Analysis" is a process performed to identify a child's learning methods based on collected information about their daily activities.

[0068] "Learning methods" refer to patterns that show a child's specific learning tendencies and interests, and serve as the foundation for generating optimal educational programs.

[0069] An "educational program" is individualized learning and play content designed to promote the development of young children and cultivate specific skills.

[0070] "Visual recognition technology" is a technique that uses computer vision to analyze video information and estimate the behavior and interests of young children.

[0071] "Natural language processing technology" is a technology that uses collected audio information to evaluate the degree to which young children recognize words and sounds.

[0072] This invention relates to a system for recording and analyzing the daily activities of young children and generating personalized educational programs based on the results. The details are described below.

[0073] First, the user uses a device with a dedicated application installed to collect information about the child's daily activities. This information includes audio, video, and movement data. This data is then organized on the device and transmitted to the server using a secure communication protocol.

[0074] The server stores received information about daily activities and performs analysis on a high-performance computing environment. This analysis utilizes visual recognition technology and natural language processing technology. Specifically, visual recognition technology is used to analyze video information and identify the child's interests and behaviors. Natural language processing technology is used to analyze audio information and evaluate the child's language and sound development.

[0075] Based on the learning methods of young children obtained from the analysis, the server generates educational programs using a generated AI model. These programs are tailored to the individual needs and interests of each child, providing high value to the user. For example, for a child interested in colors, an application is generated that includes a game for identifying and learning colors.

[0076] The generated educational program is delivered from the server to the terminal and the user is notified. The user has the child use the program, observes the child's response, and provides feedback to the system. This feedback is used to further optimize the program.

[0077] As a concrete example of a prompt, the text "Analyze the video data to identify the interests of young children and suggest educational content" is provided to the generating AI model, and the analysis and generation process begins. This allows users to provide young children with personalized learning experiences, effectively supporting their growth and development.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users collect information about their child's daily activities using a device with a dedicated application installed. This information includes the child's voice, video, and movement data. For example, users can record videos with their smartphone's camera and audio with the built-in microphone. They can also collect movement data using the device's motion sensor. The input is this raw data, and the output is data formatted by the application.

[0081] Step 2:

[0082] The terminal organizes and formats the collected data. This processing includes compressing video files, denoising audio files, and filtering motion data. This reduces unnecessary data and prepares it for easier analysis. The input is raw audio, video, and motion data, while the output is formatted, effective data.

[0083] Step 3:

[0084] The terminal sends formatted data to the server. A secure communication protocol is used to ensure data integrity and confidentiality. The input is formatted data, and the output is a notification of data arrival by sending it to the server.

[0085] Step 4:

[0086] The server receives and stores the transmitted data. The database is organized by date, time, and data type, preparing it for later analysis. The input is formatted data received from the terminal, and the output is data stored in the database.

[0087] Step 5:

[0088] The server analyzes data using visual recognition and natural language processing technologies. Specifically, visual recognition technology analyzes video data to identify the child's interests (e.g., specific colors or objects). Natural language processing technology analyzes audio data to evaluate the recognition rate of sounds and words. The input is the received data, and the output is data on the identified child's interests and learning methods.

[0089] Step 6:

[0090] The server uses the analysis results to generate an AI model and create an educational program. Here, a program is constructed to cultivate specific skills based on the child's learning style. For example, for a child who shows interest in color recognition, content is generated that allows them to learn about color recognition. The input is the analysis results, and the output is a personalized educational program.

[0091] Step 7:

[0092] The server transfers the generated educational program to the terminal. The terminal notifies the user that the program has been provided and prompts them to begin using it. The input is the educational program, and the output is the notification of transfer to the terminal and the preparation for program startup.

[0093] Step 8:

[0094] The user provides an educational program delivered to the device to a child and runs the program. During this process, the user observes the child's responses, provides feedback through the application, and enables further optimization. The input is the educational program, and the output is the observed responses and feedback data.

[0095] (Application Example 1)

[0096] 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."

[0097] To promote the development of young children, it is crucial to provide optimal learning and play programs tailored to each child's individual characteristics and interests. However, conventional technologies do not adequately provide systems that effectively collect and analyze children's daily activities and propose customized educational activities in real time.

[0098] 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.

[0099] In this invention, the server includes means for recording data based on the child's activities, means for analyzing the data to identify the child's learning style, and means for generating optimized learning and play programs based on the learning style. This makes it possible to observe the child's activities in real time and propose optimal educational activities for each individual child based on the results.

[0100] "Data based on infant activities" refers to acoustic, visual, and physical movement information that includes infants' daily behaviors and responses.

[0101] "Means of recording" refers to the equipment and technology necessary to reliably preserve and later analyze the activities of young children.

[0102] "Methods for analyzing and identifying the learning patterns of young children" refer to algorithms and software that analyze collected data to identify the learning tendencies and interests that young children exhibit.

[0103] "Means for generating learning and play programs" refers to a system for designing and building customized learning and play activities that promote the development of young children, based on analysis results.

[0104] "Means of provision" refers to interfaces or devices that present the generated program to the child or their guardian, making it available for use.

[0105] "Automated devices" are robots and sensor devices used to observe the activities of young children in real time and efficiently collect data.

[0106] "Means for proposing educational activities" refers to a system for making specific suggestions regarding young children's learning and play, based on a generated program.

[0107] This application provides a system to support the development of young children in a home environment. The system records and analyzes the child's activities in real time and generates personalized learning and play programs based on that analysis. Specifically, the following technologies and processes are involved:

[0108] First, an automated device installed in the home (e.g., a home robot) collects data on the child's daily activities using cameras and sensors. This data comprehensively captures the child's behavior, attitude, and reactions to the environment. In this process, visual data is analyzed using the open-source library OpenCV, and audio data is collected using PyAudio.

[0109] Next, this data is transmitted to a server via the device. The server analyzes this data using AI technologies such as TENSORFLOW® to identify the child's learning style and areas of interest. Based on the analysis, the system generates appropriate learning and play programs. These programs are designed to stimulate the child's specific interests and promote their development.

[0110] The generated program is provided to the user via a terminal. At this stage, the user runs the program and develops educational activities tailored to the child. For example, for a child interested in color recognition, an activity is suggested in which the robot identifies colorful objects and describes them verbally.

[0111] An example of a prompt message is, "Identify themes that the child is interested in and suggest learning activities based on them." This allows the system to respond to the child's dynamic interests and flexibly adapt the educational plan.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The device collects activity data of infants via an automated system. Visual data captured by a camera and audio data recorded by a microphone are input. To process the data, OpenCV is used to divide the video into frames, and PyAudio is used to sample the audio signal along the time axis, outputting it as visual and acoustic data.

[0115] Step 2:

[0116] The terminal formats the collected visual and auditory data into an appropriate format and sends it to the server. The formatted data is then organized as input and output, which is transferred to the server.

[0117] Step 3:

[0118] The server applies computer vision technology to the received visual data to infer the child's interests and concerns. Specifically, it uses TensorFlow to perform object recognition and behavioral analysis within images. From the input visual data, it extracts information such as the number of times a child gazes at an object and their movement patterns, and generates output that indicates the child's interest characteristics.

[0119] Step 4:

[0120] The server uses natural language processing on acoustic data to evaluate the development of language and sound. The acoustic data, as input, is processed by speech recognition and language analysis, yielding outputs such as speech patterns and word frequency.

[0121] Step 5:

[0122] The server identifies the child's learning style based on the analysis of visual and auditory data and generates appropriate learning and play programs. It utilizes prompt statements, processes data based on the generated AI model, and outputs a customized program. Specifically, it might generate a program such as, "For this child, we suggest a building block game using colored blocks as a way to teach colors."

[0123] Step 6:

[0124] The terminal receives the generated program and provides it to the user. The program is input, and output is produced showing how the user interacts with the infant through visual and audio guidance.

[0125] Step 7:

[0126] The user engages in activities with the infant according to the provided program and provides feedback on the infant's responses to the system. This feedback becomes input data and is output as basic data for generating the next optimal program.

[0127] 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.

[0128] This invention combines a system that collects and analyzes activity data from infants to generate optimal learning and play programs with an emotion engine that recognizes the user's emotions, thereby achieving further personalization. This system records the infant's daily activities and suggests content that takes the user's emotional state into consideration, thereby promoting the healthy development of the infant.

[0129] First, the user records the child's activities through the device. These activities include video recording and audio recording. Furthermore, the device uses a built-in emotion engine to recognize the user's (parent's or guardian's) emotional state. This emotion engine determines the user's emotions in real time, primarily by analyzing audio and image data.

[0130] Next, the device sends this data to the server. The server uses artificial intelligence to analyze the collected activity data of the infants and identify their learning patterns. At the same time, the user's emotional state, as recognized by the emotion engine, is also taken into consideration. For example, if the system detects that the user is stressed, the suggested content will reflect elements that promote relaxation and ease of use.

[0131] Furthermore, the server uses this information to generate the most suitable learning and play programs for the toddler. The generated programs are personalized, taking into account not only the toddler's developmental stage but also the user's emotional state. For example, if the toddler prefers active movement and the user is detected to be in a cheerful emotional state, games incorporating physical activity will be recommended.

[0132] Finally, these programs are delivered to the user via a device. The user can try out the provided content with their child and provide feedback to the system. Based on this feedback, the program is further optimized.

[0133] In this way, the present invention provides multifaceted support for the development of infants and reduces the burden on parents in raising their children. By integrating an emotion engine with AI-based analysis, it becomes possible to provide a more personalized experience.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user uses the device to record the child's daily activities. By collecting video and audio through the available camera and microphone, the child's behavior and speech are recorded in detail. In addition, data is simultaneously collected to recognize emotions from the user's facial expressions and voice.

[0137] Step 2:

[0138] The device uses a built-in emotion engine to analyze the user's emotional state. It identifies emotions such as joy, stress, and surprise from voice intonation and facial expressions, and uses this information, along with activity data, to proceed to the next step.

[0139] Step 3:

[0140] The device transmits collected activity and emotion data to the server. Transmission is conducted via a secure protocol, and efforts are made to ensure data security.

[0141] Step 4:

[0142] The server stores the received data in a database and begins AI analysis. Here, computer vision technology is used to analyze the child's interests and behavioral patterns, and natural language processing is used to analyze the audio data and identify learning patterns.

[0143] Step 5:

[0144] The server generates an optimal learning and play program, taking into account the user's emotional state along with the analyzed learning patterns. For example, if the user indicates a desire to relax, the server will suggest a program that includes calming music and activities that promote relaxation.

[0145] Step 6:

[0146] The server sends the generated program to the terminal. The terminal notifies the user and immediately displays the program's contents and instructions for use.

[0147] Step 7:

[0148] The user has the child participate in the provided program and observes their reactions. During this time, the user carefully observes how the child engages with the content and provides supplementary support as needed.

[0149] Step 8:

[0150] Users record the infant's reactions and their own emotional changes as feedback and send it to the server via their device. This feedback data will be used to optimize future programs.

[0151] This series of processing steps enables the present invention to provide individualized learning experiences for young children and to offer flexible content suggestions that take into account the user's emotional state.

[0152] (Example 2)

[0153] 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".

[0154] Traditional early childhood education systems have struggled to provide individualized learning programs that take into account the unique characteristics of each child and the emotional state of their parents. Furthermore, there were technical challenges in analyzing children's interests and activity patterns in real time. As a result, effective developmental support for children and a reduction in the burden of childcare for parents have not been fully achieved.

[0155] 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.

[0156] In this invention, the server includes means for collecting information on the child's activities, means for analyzing the information to identify the child's learning tendencies, means for generating an optimal learning and play program based on the tendencies, and means for considering the user's emotional state in the generated program. This makes it possible to provide a learning program that is individualized according to the child's development, further maximize the effectiveness of education, and reduce the burden of childcare on parents.

[0157] "Information regarding the activities of young children" refers to data about the actions and activities that young children engage in in their daily lives, such as play, and includes audio information, image information, and motion information.

[0158] "Learning tendencies" refer to the unique learning patterns, interests, and abilities of a child, identified by analyzing their activity data.

[0159] "Means for generating programs" refers to technology that automatically creates customized programs by selecting the most suitable learning and play activities for young children based on their analyzed learning tendencies.

[0160] "Means of considering emotional state" refers to methods of analyzing user emotional data and adjusting the content of learning programs and play programs to reflect that state.

[0161] "Methods for collecting feedback and optimizing the program" refers to the process of collecting reactions and results obtained after users and children try the proposed program, and using that information to improve the quality of future programs.

[0162] This invention is a system for collecting and analyzing activity information of infants and providing personalized learning and play programs. The user records and audio recordings of the infant's activities using a terminal. This terminal is equipped with a high-performance camera and microphone, allowing it to collect audio, visual, and motion information of the infant's daily activities. Furthermore, the terminal implements software with emotion recognition capabilities, enabling it to analyze the user's emotional state.

[0163] Information collected by the device is sent to a server, which analyzes this information using an artificial intelligence model. This analysis employs advanced machine learning algorithms and data processing techniques. Based on this data, the server identifies the child's learning tendencies and, taking into account the user's emotional state, generates an optimal learning and play program. The server utilizes a generative AI model in generating the program. The generated program is personalized and optimized for the child's interests and abilities, as well as the user's emotional state.

[0164] For example, if a toddler enjoys being read picture books, the server can suggest new picture books and related visual and auditory content. Also, if the user is relaxed, using calming music can provide an optimal experience for both parent and child.

[0165] An example of a prompt to input into the generating AI model is: "Please suggest a play program for an active 3-year-old boy, including new physical activities. Since the user is relaxed, please include the idea of ​​combining it with calming music."

[0166] In this way, the system can provide multifaceted support for both the development of young children and the user's parenting experience.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user records information about the child's activities through the device. The input consists of audio and video data related to the child's activities. The device's camera and microphone are used to capture and record everyday play and learning scenes. The output consists of recorded audio information, image information, and video data.

[0170] Step 2:

[0171] The device recognizes the user's emotional state using recorded data. The emotion recognition engine receives voice tone and facial expression data as input and performs emotion analysis. The user's emotional state is displayed in real time as output.

[0172] Step 3:

[0173] The device sends the collected data to the server. The input consists of recorded audio, image, and emotion data. This data is packaged and sent to the server via Wi-Fi or a data network. The output is a notification indicating that the data transfer is complete.

[0174] Step 4:

[0175] The server analyzes received activity data to identify the learning tendencies of young children. The input consists of activity data and emotional data, which an AI model then analyzes. Machine learning algorithms are used for data processing, and the output is a report on learning tendencies.

[0176] Step 5:

[0177] The server uses prompt statements to generate programs using a generative AI model. For example, it might take a prompt statement like, "Please suggest a play program that includes new physical activities for an active 3-year-old boy," and the generative AI will output the optimal program.

[0178] Step 6:

[0179] The server optimizes the program considering the user's emotional state. The input consists of the generated program and the user's emotional data. The output is a modified learning / play program.

[0180] Step 7:

[0181] The generated program is provided to the user via the terminal. The user reviews and executes the program. As output, the program is displayed to the user and becomes available for testing.

[0182] Step 8:

[0183] The user inputs the results of their execution as feedback into the terminal. This feedback data is sent to the server and reflected in subsequent programs. Program update information is then notified as output.

[0184] (Application Example 2)

[0185] 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".

[0186] In early childhood care and education, it is crucial to provide optimal learning and play programs tailored to each child's individual characteristics and developmental stage. However, conventional systems have struggled to generate content that comprehensively considers each child's unique activity data and their parents' emotional state. As a result, flexible responses to on-the-spot situations have been difficult, and it has been difficult to adequately support the reduction of childcare effort and the healthy development of young children.

[0187] 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.

[0188] In this invention, the server includes means for collecting information on the child's activities, means for analyzing the activity information to identify the child's learning patterns, means for generating an optimal learning and play program based on the learning patterns and the user's emotional state, and means including an emotion recognition engine for recognizing the user's emotions. This makes it possible to provide personalized programs that are tailored to the child's characteristics and the situation at hand.

[0189] "Infant activity information" is a general term for data including audio, image, and motion information related to the daily activities of infants.

[0190] A "learning pattern" is a pattern that indicates the tendencies and characteristics of a child's learning and play, identified by analyzing their activity information.

[0191] An "optimal learning and play program" is individualized and generated educational and play content for young children, based on the child's learning patterns and the user's emotional state.

[0192] An "emotion recognition engine" is software or hardware that analyzes audio and image information to determine the user's emotional state in real time.

[0193] "User's emotional state" refers to information about the emotional state of the child's caregiver or parent as detected by the emotion recognition engine.

[0194] A "server" is a computer system located on a network that receives, analyzes, and generates programs based on information about the activities of infants.

[0195] To implement this invention, the system first requires a terminal for collecting activity information of infants. This terminal is equipped with sensors such as a camera and microphone, and is capable of acquiring audio, image, and motion information about the infant's daily activities in real time. The activity information is transmitted to a server via wireless communication.

[0196] The server analyzes received activity information to identify the child's learning patterns. It also uses an emotion recognition engine to analyze the user (parent)'s voice and image information to determine their emotional state in real time. This analysis utilizes cloud-based speech recognition and image analysis technologies such as Google® Cloud Speech-to-Text and Google Cloud Vision API. Based on the analysis results, an optimal learning and play program is generated using a machine learning model (e.g., TensorFlow).

[0197] The generated programs take into account the user's emotional state. For example, if the system detects that the parent is tired, it will suggest relaxing activities such as reading a quiet picture book. On the other hand, if the parent is energetic, it can suggest active programs using dancing or singing.

[0198] Users can receive programs provided by the server via their terminal and implement them on their infants. Furthermore, by sending feedback on the implemented programs to the system, the programs can be further optimized.

[0199] As a concrete example, if the system determines that a parent is feeling anxious during the busy morning hours, it will suggest a quick finger exercise. An example of a prompt to the generating AI model is as follows: "The system has recognized that the parent is feeling stressed. To alleviate the parent's burden, please suggest a relaxing activity for the infant."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The device uses a camera and microphone to collect audio, image, and motion information to record the child's daily activities. This input data is transmitted to a server in real time.

[0203] Step 2:

[0204] The server converts received audio information into text using Google Cloud Speech-to-Text. Image information is analyzed using the Google Cloud Vision API to extract information about the child's activities and environment. This process outputs both audio-text and image analysis results.

[0205] Step 3:

[0206] The server uses an emotion recognition engine to estimate the parent's emotional state from audio and image information. It analyzes changes in voice tone and facial expressions and outputs the estimation results as emotion data.

[0207] Step 4:

[0208] The server uses the acquired activity information and emotional data of the infant to run a generative AI model (e.g., TensorFlow) and analyze the infant's learning patterns. During this process, learning pattern data is output.

[0209] Step 5:

[0210] The server generates an optimal learning and play program based on learning pattern data and emotional data. This involves prompting the user with a generation AI model (e.g., "The parent's emotional state has been identified as stressed. To alleviate the parent's burden, please suggest relaxing activities for the child.") to determine appropriate program content. The generated program is then sent to the device.

[0211] Step 6:

[0212] Users implement learning and play programs provided through their devices for their young children. After implementation, they input feedback on the children's reactions and the effectiveness of the programs, and send this feedback to the server.

[0213] Step 7:

[0214] The server analyzes the feedback received from users and uses it to further improve the program. The feedback data is then used in the next program generation process to output optimized content.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention is a system that effectively collects and analyzes activity data of infants and generates personalized learning and play programs based on that data. The system aims to provide optimal content by allowing parents to record their child's daily behavior and understand their learning patterns.

[0232] First, the user (parent) records the child's daily activities via the device. This record may include video, audio, and even motion data from motion sensors. Once the user collects this data using a dedicated application, the device formats the data and sends it to the server.

[0233] The server stores the received data and analyzes it using AI algorithms. For example, computer vision technology can be applied to video data to identify what the toddler is interested in. For audio data, natural language processing technology is used to evaluate the development of sounds and language. In this way, the server understands the toddler's interests and tendencies and identifies their learning patterns.

[0234] Next, the server generates learning and play programs best suited to the child based on the analysis results. This process creates a plan that develops specific developmental skills by combining multiple educational elements. For example, for a child who shows interest in color recognition, a game app designed to cultivate their sense of color will be generated.

[0235] Finally, the generated program is sent from the server to the terminal and provided to the user. The terminal notifies the user of any new available content and presents instructions on how to use it and the expected learning effects. The user then has the child use it and provides feedback to the system, which further optimizes the program.

[0236] This system allows parents to provide their toddlers with suitable learning experiences with less effort, thereby supporting their development.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] Users use the device to record their child's daily activities. The device has a dedicated application installed that allows for video and audio recording. Furthermore, in some cases, motion sensors are used to collect the child's movement data.

[0240] Step 2:

[0241] The device converts the collected data into a predetermined format. Video data is compressed, and audio data is converted to a format suitable for audio analysis as needed. During this process, data anonymization and security protection are also applied.

[0242] Step 3:

[0243] The terminal sends the converted data to the server. During this transmission, secure protocols such as SSL are used to protect the confidentiality of the data.

[0244] Step 4:

[0245] The server stores the received data in a database. Since the stored data is to be analyzed by AI, preprocessing is performed as needed.

[0246] Step 5:

[0247] The server begins data analysis. Specifically, for video data, computer vision technology is used to identify the child's behavior and objects of interest. For audio data, natural language processing is applied to analyze pronunciation patterns and word usage frequency.

[0248] Step 6:

[0249] The server identifies the child's learning pattern based on the analysis results. This learning pattern includes the child's current developmental stage, areas of interest, and skills.

[0250] Step 7:

[0251] The server generates an optimal learning and play program based on identified learning patterns. At this stage, personalized content is designed using a generative AI. For example, if a toddler is interested in colors, an educational game about colors will be created.

[0252] Step 8:

[0253] The server sends the generated learning and play programs to the terminal. During transmission, the programs are reformatted to a format easily understood by the user.

[0254] Step 9:

[0255] The device notifies the user that the program is available. The application then displays detailed information and instructions, preparing the child to actually use the program.

[0256] Step 10:

[0257] Users observe how young children use the program and provide feedback to the system regarding their performance and reactions. This allows the system to accumulate information necessary for future program optimization.

[0258] (Example 1)

[0259] 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 glasses 214 will be referred to as the "terminal".

[0260] To provide individualized educational experiences tailored to the growth and development of young children, it is necessary to accurately understand their ever-changing interests and behaviors and to quickly and effectively adjust learning programs accordingly. However, conventional systems have struggled to provide such individualized support. Therefore, there were limitations to collecting diverse information about children's daily activities in real time and using that information to provide the optimal educational experience.

[0261] 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.

[0262] In this invention, the server includes means for recording information on daily activities, means for analyzing the information on daily activities to identify a learning method, and means for generating an individualized educational program based on the learning method. This makes it possible to provide an optimal educational experience tailored to the individual learning patterns of each child.

[0263] "Information on daily activities" refers to audio, visual, and motion information related to the daily activities of young children.

[0264] "Analysis" is a process performed to identify a child's learning methods based on collected information about their daily activities.

[0265] "Learning methods" refer to patterns that show a child's specific learning tendencies and interests, and serve as the foundation for generating optimal educational programs.

[0266] An "educational program" is individualized learning and play content designed to promote the development of young children and cultivate specific skills.

[0267] "Visual recognition technology" is a technique that uses computer vision to analyze video information and estimate the behavior and interests of young children.

[0268] "Natural language processing technology" is a technology that uses collected audio information to evaluate the degree to which young children recognize words and sounds.

[0269] This invention relates to a system for recording and analyzing the daily activities of young children and generating personalized educational programs based on the results. The details are described below.

[0270] First, the user uses a device with a dedicated application installed to collect information about the child's daily activities. This information includes audio, video, and movement data. This data is then organized on the device and transmitted to the server using a secure communication protocol.

[0271] The server stores received information about daily activities and performs analysis on a high-performance computing environment. This analysis utilizes visual recognition technology and natural language processing technology. Specifically, visual recognition technology is used to analyze video information and identify the child's interests and behaviors. Natural language processing technology is used to analyze audio information and evaluate the child's language and sound development.

[0272] Based on the learning methods of young children obtained from the analysis, the server generates educational programs using a generated AI model. These programs are tailored to the individual needs and interests of each child, providing high value to the user. For example, for a child interested in colors, an application is generated that includes a game for identifying and learning colors.

[0273] The generated educational program is delivered from the server to the terminal and the user is notified. The user has the child use the program, observes the child's response, and provides feedback to the system. This feedback is used to further optimize the program.

[0274] As a concrete example of a prompt, the text "Analyze the video data to identify the interests of young children and suggest educational content" is provided to the generating AI model, and the analysis and generation process begins. This allows users to provide young children with personalized learning experiences, effectively supporting their growth and development.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user uses a terminal installed with a dedicated application to collect information on the life movements of an infant. This information includes the infant's voice, video, and movement information. For example, the user can shoot a video with the camera of a smartphone and record voice with the built-in microphone. Also, movement data can be collected using the motion sensor of the terminal. The input is these raw data, and the output is the data formatted by the application.

[0278] Step 2:

[0279] The terminal sorts and formats the collected data. In the process, compression of video files, noise removal of audio files, and filtering of movement data are performed. As a result, unnecessary data is reduced and it is arranged in a form that is easy to analyze. The input is the collected raw data of voice, video, and movement, and the output is the effective data in a formatted state.

[0280] Step 3:

[0281] The terminal sends the formatted data to the server. A secure communication protocol is used to ensure the integrity and confidentiality of the data. The input is the formatted data, and the output is the data arrival notification by transmission to the server.

[0282] Step 4:

[0283] The server receives and stores the transmitted data. The database is neatly managed by date, time, and data type in preparation for later analysis. The input is the formatted data received from the terminal, and the output is the data stored in the database.

[0284] Step 5:

[0285] The server analyzes data using visual recognition technology and natural language processing technology. Specifically, the visual recognition technology analyzes video data to identify the objects of interest of the infant (e.g., specific colors or objects). The natural language processing technology analyzes audio data to evaluate the pronunciation and recognition degree of words. The input is the received data, and the output is the data on the identified interests and learning methods of the infant.

[0286] Step 6:

[0287] The server makes full use of the generated AI model based on the analysis results to generate an educational program. Here, based on the obtained learning methods of the infant, a program for cultivating specific skills is constructed. For example, for an infant showing interest in color discrimination, content that can be learned by discriminating colors is generated. The input is the analysis result, and the output is an individualized educational program.

[0288] Step 7:

[0289] The server transfers the generated educational program to the terminal. The terminal notifies the user that the program has been provided and prompts for the start of use. The input is the educational program, and the output is the transfer notification to the terminal and the preparation for starting the program.

[0290] Step 8:

[0291] The user provides the educational program delivered to the terminal to the infant and executes the program. In this process, the reactions of the infant are observed and fed back through the application, enabling further optimization. The input is the educational program, and the output is the observed reactions and feedback data.

[0292] (Application Example 1)

[0293] Next, Application 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".

[0294] To promote the development of young children, it is crucial to provide optimal learning and play programs tailored to each child's individual characteristics and interests. However, conventional technologies do not adequately provide systems that effectively collect and analyze children's daily activities and propose customized educational activities in real time.

[0295] 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.

[0296] In this invention, the server includes means for recording data based on the child's activities, means for analyzing the data to identify the child's learning style, and means for generating optimized learning and play programs based on the learning style. This makes it possible to observe the child's activities in real time and propose optimal educational activities for each individual child based on the results.

[0297] "Data based on infant activities" refers to acoustic, visual, and physical movement information that includes infants' daily behaviors and responses.

[0298] "Means of recording" refers to the equipment and technology necessary to reliably preserve and later analyze the activities of young children.

[0299] "Methods for analyzing and identifying the learning patterns of young children" refer to algorithms and software that analyze collected data to identify the learning tendencies and interests that young children exhibit.

[0300] "Means for generating learning and play programs" refers to a system for designing and building customized learning and play activities that promote the development of young children, based on analysis results.

[0301] "Means of provision" refers to interfaces or devices that present the generated program to the child or their guardian, making it available for use.

[0302] The "automation device" is a robot or sensor device used to observe the activities of young children in real time and efficiently collect data.

[0303] The "means for proposing educational activities" is a system for making specific proposals in the learning and play of young children along with the generated program.

[0304] In this application example, a system for supporting the growth of young children in a home environment is provided. The system records, analyzes the activities of young children in real time, and generates individualized learning and play programs based on this. Specifically, the following technologies and processes are involved.

[0305] First, the automation device (for example, a home robot) installed in the home uses cameras and sensors to collect daily activity data of young children. This data comprehensively captures the actions, attitudes, and reactions of young children from the environment. In this process, the open-source library OpenCV is used to analyze visual data, and PyAudio is used to collect audio data.

[0306] Next, this data is sent to the server through the terminal. The server analyzes this data using AI technologies such as TensorFlow to identify the learning patterns and areas of interest of young children. Based on the analyzed results, the system generates appropriate learning and play programs. This program is designed to draw out the specific interests of young children and promote their growth.

[0307] The generated program is provided to the user via the terminal. At this stage, the user executes the program and conducts educational activities suitable for young children. For example, for young children interested in color recognition, activities where the robot identifies colorful objects and explains them in words are proposed.

[0308] An example of a prompt message is, "Identify themes that the child is interested in and suggest learning activities based on them." This allows the system to respond to the child's dynamic interests and flexibly adapt the educational plan.

[0309] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0310] Step 1:

[0311] The device collects activity data of infants via an automated system. Visual data captured by a camera and audio data recorded by a microphone are input. To process the data, OpenCV is used to divide the video into frames, and PyAudio is used to sample the audio signal along the time axis, outputting it as visual and acoustic data.

[0312] Step 2:

[0313] The terminal formats the collected visual and auditory data into an appropriate format and sends it to the server. The formatted data is then organized as input and output, which is transferred to the server.

[0314] Step 3:

[0315] The server applies computer vision technology to the received visual data to infer the child's interests and concerns. Specifically, it uses TensorFlow to perform object recognition and behavioral analysis within images. From the input visual data, it extracts information such as the number of times a child gazes at an object and their movement patterns, and generates output that indicates the child's interest characteristics.

[0316] Step 4:

[0317] The server uses natural language processing on acoustic data to evaluate the development of language and sound. The acoustic data, as input, is processed by speech recognition and language analysis, yielding outputs such as speech patterns and word frequency.

[0318] Step 5:

[0319] The server identifies the child's learning style based on the analysis of visual and auditory data and generates appropriate learning and play programs. It utilizes prompt statements, processes data based on the generated AI model, and outputs a customized program. Specifically, it might generate a program such as, "For this child, we suggest a building block game using colored blocks as a way to teach colors."

[0320] Step 6:

[0321] The terminal receives the generated program and provides it to the user. The program is input, and output is produced showing how the user interacts with the infant through visual and audio guidance.

[0322] Step 7:

[0323] The user engages in activities with the infant according to the provided program and provides feedback on the infant's responses to the system. This feedback becomes input data and is output as basic data for generating the next optimal program.

[0324] 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.

[0325] This invention combines a system that collects and analyzes activity data from infants to generate optimal learning and play programs with an emotion engine that recognizes the user's emotions, thereby achieving further personalization. This system records the infant's daily activities and suggests content that takes the user's emotional state into consideration, thereby promoting the healthy development of the infant.

[0326] First, the user records the child's activities through the device. These activities include video recording and audio recording. Furthermore, the device uses a built-in emotion engine to recognize the user's (parent's or guardian's) emotional state. This emotion engine determines the user's emotions in real time, primarily by analyzing audio and image data.

[0327] Next, the device sends this data to the server. The server uses artificial intelligence to analyze the collected activity data of the infants and identify their learning patterns. At the same time, the user's emotional state, as recognized by the emotion engine, is also taken into consideration. For example, if the system detects that the user is stressed, the suggested content will reflect elements that promote relaxation and ease of use.

[0328] Furthermore, the server uses this information to generate the most suitable learning and play programs for the toddler. The generated programs are personalized, taking into account not only the toddler's developmental stage but also the user's emotional state. For example, if the toddler prefers active movement and the user is detected to be in a cheerful emotional state, games incorporating physical activity will be recommended.

[0329] Finally, these programs are delivered to the user via a device. The user can try out the provided content with their child and provide feedback to the system. Based on this feedback, the program is further optimized.

[0330] In this way, the present invention provides multifaceted support for the development of infants and reduces the burden on parents in raising their children. By integrating an emotion engine with AI-based analysis, it becomes possible to provide a more personalized experience.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The user uses the device to record the child's daily activities. By collecting video and audio through the available camera and microphone, the child's behavior and speech are recorded in detail. In addition, data is simultaneously collected to recognize emotions from the user's facial expressions and voice.

[0334] Step 2:

[0335] The device uses a built-in emotion engine to analyze the user's emotional state. It identifies emotions such as joy, stress, and surprise from voice intonation and facial expressions, and uses this information, along with activity data, to proceed to the next step.

[0336] Step 3:

[0337] The device transmits collected activity and emotion data to the server. Transmission is conducted via a secure protocol, and efforts are made to ensure data security.

[0338] Step 4:

[0339] The server stores the received data in a database and begins AI analysis. Here, computer vision technology is used to analyze the child's interests and behavioral patterns, and natural language processing is used to analyze the audio data and identify learning patterns.

[0340] Step 5:

[0341] The server generates an optimal learning and play program, taking into account the user's emotional state along with the analyzed learning patterns. For example, if the user indicates a desire to relax, the server will suggest a program that includes calming music and activities that promote relaxation.

[0342] Step 6:

[0343] The server sends the generated program to the terminal. The terminal notifies the user and immediately displays the program's contents and instructions for use.

[0344] Step 7:

[0345] The user has the child participate in the provided program and observes their reactions. During this time, the user carefully observes how the child engages with the content and provides supplementary support as needed.

[0346] Step 8:

[0347] Users record the infant's reactions and their own emotional changes as feedback and send it to the server via their device. This feedback data will be used to optimize future programs.

[0348] This series of processing steps enables the present invention to provide individualized learning experiences for young children and to offer flexible content suggestions that take into account the user's emotional state.

[0349] (Example 2)

[0350] 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".

[0351] Traditional early childhood education systems have struggled to provide individualized learning programs that take into account the unique characteristics of each child and the emotional state of their parents. Furthermore, there were technical challenges in analyzing children's interests and activity patterns in real time. As a result, effective developmental support for children and a reduction in the burden of childcare for parents have not been fully achieved.

[0352] 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.

[0353] In this invention, the server includes means for collecting information on the child's activities, means for analyzing the information to identify the child's learning tendencies, means for generating an optimal learning and play program based on the tendencies, and means for considering the user's emotional state in the generated program. This makes it possible to provide a learning program that is individualized according to the child's development, further maximize the effectiveness of education, and reduce the burden of childcare on parents.

[0354] "Information regarding the activities of young children" refers to data about the actions and activities that young children engage in in their daily lives, such as play, and includes audio information, image information, and motion information.

[0355] "Learning tendencies" refer to the unique learning patterns, interests, and abilities of a child, identified by analyzing their activity data.

[0356] "Means for generating programs" refers to technology that automatically creates customized programs by selecting the most suitable learning and play activities for young children based on their analyzed learning tendencies.

[0357] "Means of considering emotional state" refers to methods of analyzing user emotional data and adjusting the content of learning programs and play programs to reflect that state.

[0358] "Methods for collecting feedback and optimizing the program" refers to the process of collecting reactions and results obtained after users and children try the proposed program, and using that information to improve the quality of future programs.

[0359] This invention is a system for collecting and analyzing activity information of infants and providing personalized learning and play programs. The user records and audio recordings of the infant's activities using a terminal. This terminal is equipped with a high-performance camera and microphone, allowing it to collect audio, visual, and motion information of the infant's daily activities. Furthermore, the terminal implements software with emotion recognition capabilities, enabling it to analyze the user's emotional state.

[0360] Information collected by the device is sent to a server, which analyzes this information using an artificial intelligence model. This analysis employs advanced machine learning algorithms and data processing techniques. Based on this data, the server identifies the child's learning tendencies and, taking into account the user's emotional state, generates an optimal learning and play program. The server utilizes a generative AI model in generating the program. The generated program is personalized and optimized for the child's interests and abilities, as well as the user's emotional state.

[0361] For example, if a toddler enjoys being read picture books, the server can suggest new picture books and related visual and auditory content. Also, if the user is relaxed, using calming music can provide an optimal experience for both parent and child.

[0362] An example of a prompt to input into the generating AI model is: "Please suggest a play program for an active 3-year-old boy, including new physical activities. Since the user is relaxed, please include the idea of ​​combining it with calming music."

[0363] In this way, the system can provide multifaceted support for both the development of young children and the user's parenting experience.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The user records information about the child's activities through the device. The input consists of audio and video data related to the child's activities. The device's camera and microphone are used to capture and record everyday play and learning scenes. The output consists of recorded audio information, image information, and video data.

[0367] Step 2:

[0368] The device recognizes the user's emotional state using recorded data. The emotion recognition engine receives voice tone and facial expression data as input and performs emotion analysis. The user's emotional state is displayed in real time as output.

[0369] Step 3:

[0370] The device sends the collected data to the server. The input consists of recorded audio, image, and emotion data. This data is packaged and sent to the server via Wi-Fi or a data network. The output is a notification indicating that the data transfer is complete.

[0371] Step 4:

[0372] The server analyzes received activity data to identify the learning tendencies of young children. The input consists of activity data and emotional data, which an AI model then analyzes. Machine learning algorithms are used for data processing, and the output is a report on learning tendencies.

[0373] Step 5:

[0374] The server uses prompt statements to generate programs using a generative AI model. For example, it might take a prompt statement like, "Please suggest a play program that includes new physical activities for an active 3-year-old boy," and the generative AI will output the optimal program.

[0375] Step 6:

[0376] The server optimizes the program considering the user's emotional state. The input consists of the generated program and the user's emotional data. The output is a modified learning / play program.

[0377] Step 7:

[0378] The generated program is provided to the user via the terminal. The user reviews and executes the program. As output, the program is displayed to the user and becomes available for testing.

[0379] Step 8:

[0380] The user inputs the results of their execution as feedback into the terminal. This feedback data is sent to the server and reflected in subsequent programs. Program update information is then notified as output.

[0381] (Application Example 2)

[0382] 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 will be referred to as the "terminal."

[0383] In early childhood care and education, it is crucial to provide optimal learning and play programs tailored to each child's individual characteristics and developmental stage. However, conventional systems have struggled to generate content that comprehensively considers each child's unique activity data and their parents' emotional state. As a result, flexible responses to on-the-spot situations have been difficult, and it has been difficult to adequately support the reduction of childcare effort and the healthy development of young children.

[0384] 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.

[0385] In this invention, the server includes means for collecting information on the child's activities, means for analyzing the activity information to identify the child's learning patterns, means for generating an optimal learning and play program based on the learning patterns and the user's emotional state, and means including an emotion recognition engine for recognizing the user's emotions. This makes it possible to provide personalized programs that are tailored to the child's characteristics and the situation at hand.

[0386] "Infant activity information" is a general term for data including audio, image, and motion information related to the daily activities of infants.

[0387] A "learning pattern" is a pattern that indicates the tendencies and characteristics of a child's learning and play, identified by analyzing their activity information.

[0388] An "optimal learning and play program" is individualized and generated educational and play content for young children, based on the child's learning patterns and the user's emotional state.

[0389] An "emotion recognition engine" is software or hardware that analyzes audio and image information to determine the user's emotional state in real time.

[0390] "User's emotional state" refers to information about the emotional state of the child's caregiver or parent as detected by the emotion recognition engine.

[0391] A "server" is a computer system located on a network that receives, analyzes, and generates programs based on information about the activities of infants.

[0392] To implement this invention, the system first requires a terminal for collecting activity information of infants. This terminal is equipped with sensors such as a camera and microphone, and is capable of acquiring audio, image, and motion information about the infant's daily activities in real time. The activity information is transmitted to a server via wireless communication.

[0393] The server analyzes received activity information to identify the child's learning patterns. It also uses an emotion recognition engine to analyze the user (parent)'s voice and image information to determine their emotional state in real time. This analysis utilizes cloud-based speech recognition and image analysis technologies such as Google Cloud Speech-to-Text and Google Cloud Vision API. Based on the analysis results, an optimal learning and play program is generated using a machine learning model (e.g., TensorFlow).

[0394] The generated programs take into account the user's emotional state. For example, if the system detects that the parent is tired, it will suggest relaxing activities such as reading a quiet picture book. On the other hand, if the parent is energetic, it can suggest active programs using dancing or singing.

[0395] Users can receive programs provided by the server via their terminal and implement them on their infants. Furthermore, by sending feedback on the implemented programs to the system, the programs can be further optimized.

[0396] As a concrete example, if the system determines that a parent is feeling anxious during the busy morning hours, it will suggest a quick finger exercise. An example of a prompt to the generating AI model is as follows: "The system has recognized that the parent is feeling stressed. To alleviate the parent's burden, please suggest a relaxing activity for the infant."

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The device uses a camera and microphone to collect audio, image, and motion information to record the child's daily activities. This input data is transmitted to a server in real time.

[0400] Step 2:

[0401] The server converts received audio information into text using Google Cloud Speech-to-Text. Image information is analyzed using the Google Cloud Vision API to extract information about the child's activities and environment. This process outputs both audio-text and image analysis results.

[0402] Step 3:

[0403] The server uses an emotion recognition engine to estimate the parent's emotional state from audio and image information. It analyzes changes in voice tone and facial expressions and outputs the estimation results as emotion data.

[0404] Step 4:

[0405] The server uses the acquired activity information and emotional data of the infant to run a generative AI model (e.g., TensorFlow) and analyze the infant's learning patterns. During this process, learning pattern data is output.

[0406] Step 5:

[0407] The server generates an optimal learning and play program based on learning pattern data and emotional data. This involves prompting the user with a generation AI model (e.g., "The parent's emotional state has been identified as stressed. To alleviate the parent's burden, please suggest relaxing activities for the child.") to determine appropriate program content. The generated program is then sent to the device.

[0408] Step 6:

[0409] Users implement learning and play programs provided through their devices for their young children. After implementation, they input feedback on the children's reactions and the effectiveness of the programs, and send this feedback to the server.

[0410] Step 7:

[0411] The server analyzes the feedback received from users and uses it to further improve the program. The feedback data is then used in the next program generation process to output optimized content.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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".

[0428] This invention is a system that effectively collects and analyzes activity data of infants and generates personalized learning and play programs based on that data. The system aims to provide optimal content by allowing parents to record their child's daily behavior and understand their learning patterns.

[0429] First, the user (parent) records the child's daily activities via the device. This record may include video, audio, and even motion data from motion sensors. Once the user collects this data using a dedicated application, the device formats the data and sends it to the server.

[0430] The server stores the received data and analyzes it using AI algorithms. For example, computer vision technology can be applied to video data to identify what the toddler is interested in. For audio data, natural language processing technology is used to evaluate the development of sounds and language. In this way, the server understands the toddler's interests and tendencies and identifies their learning patterns.

[0431] Next, the server generates learning and play programs best suited to the child based on the analysis results. This process creates a plan that develops specific developmental skills by combining multiple educational elements. For example, for a child who shows interest in color recognition, a game app designed to cultivate their sense of color will be generated.

[0432] Finally, the generated program is sent from the server to the terminal and provided to the user. The terminal notifies the user of any new available content and presents instructions on how to use it and the expected learning effects. The user then has the child use it and provides feedback to the system, which further optimizes the program.

[0433] This system allows parents to provide their toddlers with suitable learning experiences with less effort, thereby supporting their development.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] Users use the device to record their child's daily activities. The device has a dedicated application installed that allows for video and audio recording. Furthermore, in some cases, motion sensors are used to collect the child's movement data.

[0437] Step 2:

[0438] The device converts the collected data into a predetermined format. Video data is compressed, and audio data is converted to a format suitable for audio analysis as needed. During this process, data anonymization and security protection are also applied.

[0439] Step 3:

[0440] The terminal sends the converted data to the server. During this transmission, secure protocols such as SSL are used to protect the confidentiality of the data.

[0441] Step 4:

[0442] The server stores the received data in a database. Since the stored data is to be analyzed by AI, preprocessing is performed as needed.

[0443] Step 5:

[0444] The server begins data analysis. Specifically, for video data, computer vision technology is used to identify the child's behavior and objects of interest. For audio data, natural language processing is applied to analyze pronunciation patterns and word usage frequency.

[0445] Step 6:

[0446] The server identifies the child's learning pattern based on the analysis results. This learning pattern includes the child's current developmental stage, areas of interest, and skills.

[0447] Step 7:

[0448] The server generates an optimal learning and play program based on identified learning patterns. At this stage, personalized content is designed using a generative AI. For example, if a toddler is interested in colors, an educational game about colors will be created.

[0449] Step 8:

[0450] The server sends the generated learning and play programs to the terminal. During transmission, the programs are reformatted to a format easily understood by the user.

[0451] Step 9:

[0452] The device notifies the user that the program is available. The application then displays detailed information and instructions, preparing the child to actually use the program.

[0453] Step 10:

[0454] Users observe how young children use the program and provide feedback to the system regarding their performance and reactions. This allows the system to accumulate information necessary for future program optimization.

[0455] (Example 1)

[0456] 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."

[0457] To provide individualized educational experiences tailored to the growth and development of young children, it is necessary to accurately understand their ever-changing interests and behaviors and to quickly and effectively adjust learning programs accordingly. However, conventional systems have struggled to provide such individualized support. Therefore, there were limitations to collecting diverse information about children's daily activities in real time and using that information to provide the optimal educational experience.

[0458] 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.

[0459] In this invention, the server includes means for recording information on daily activities, means for analyzing the information on daily activities to identify a learning method, and means for generating an individualized educational program based on the learning method. This makes it possible to provide an optimal educational experience tailored to the individual learning patterns of each child.

[0460] "Information on daily activities" refers to audio, visual, and motion information related to the daily activities of young children.

[0461] "Analysis" is a process performed to identify a child's learning methods based on collected information about their daily activities.

[0462] "Learning methods" refer to patterns that show a child's specific learning tendencies and interests, and serve as the foundation for generating optimal educational programs.

[0463] An "educational program" is individualized learning and play content designed to promote the development of young children and cultivate specific skills.

[0464] "Visual recognition technology" is a technique that uses computer vision to analyze video information and estimate the behavior and interests of young children.

[0465] "Natural language processing technology" is a technology that uses collected audio information to evaluate the degree to which young children recognize words and sounds.

[0466] This invention relates to a system for recording and analyzing the daily activities of young children and generating personalized educational programs based on the results. The details are described below.

[0467] First, the user uses a device with a dedicated application installed to collect information about the child's daily activities. This information includes audio, video, and movement data. This data is then organized on the device and transmitted to the server using a secure communication protocol.

[0468] The server stores received information about daily activities and performs analysis on a high-performance computing environment. This analysis utilizes visual recognition technology and natural language processing technology. Specifically, visual recognition technology is used to analyze video information and identify the child's interests and behaviors. Natural language processing technology is used to analyze audio information and evaluate the child's language and sound development.

[0469] Based on the learning methods of young children obtained from the analysis, the server generates educational programs using a generated AI model. These programs are tailored to the individual needs and interests of each child, providing high value to the user. For example, for a child interested in colors, an application is generated that includes a game for identifying and learning colors.

[0470] The generated educational program is delivered from the server to the terminal and the user is notified. The user has the child use the program, observes the child's response, and provides feedback to the system. This feedback is used to further optimize the program.

[0471] As a concrete example of a prompt, the text "Analyze the video data to identify the interests of young children and suggest educational content" is provided to the generating AI model, and the analysis and generation process begins. This allows users to provide young children with personalized learning experiences, effectively supporting their growth and development.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] Users collect information about their child's daily activities using a device with a dedicated application installed. This information includes the child's voice, video, and movement data. For example, users can record videos with their smartphone's camera and audio with the built-in microphone. They can also collect movement data using the device's motion sensor. The input is this raw data, and the output is data formatted by the application.

[0475] Step 2:

[0476] The terminal organizes and formats the collected data. This processing includes compressing video files, denoising audio files, and filtering motion data. This reduces unnecessary data and prepares it for easier analysis. The input is raw audio, video, and motion data, while the output is formatted, effective data.

[0477] Step 3:

[0478] The terminal sends formatted data to the server. A secure communication protocol is used to ensure data integrity and confidentiality. The input is formatted data, and the output is a notification of data arrival by sending it to the server.

[0479] Step 4:

[0480] The server receives and stores the transmitted data. The database is organized by date, time, and data type, preparing it for later analysis. The input is formatted data received from the terminal, and the output is data stored in the database.

[0481] Step 5:

[0482] The server analyzes data using visual recognition and natural language processing technologies. Specifically, visual recognition technology analyzes video data to identify the child's interests (e.g., specific colors or objects). Natural language processing technology analyzes audio data to evaluate the recognition rate of sounds and words. The input is the received data, and the output is data on the identified child's interests and learning methods.

[0483] Step 6:

[0484] The server uses the analysis results to generate an AI model and create an educational program. Here, a program is constructed to cultivate specific skills based on the child's learning style. For example, for a child who shows interest in color recognition, content is generated that allows them to learn about color recognition. The input is the analysis results, and the output is a personalized educational program.

[0485] Step 7:

[0486] The server transfers the generated educational program to the terminal. The terminal notifies the user that the program has been provided and prompts them to begin using it. The input is the educational program, and the output is the notification of transfer to the terminal and the preparation for program startup.

[0487] Step 8:

[0488] The user provides an educational program delivered to the device to a child and runs the program. During this process, the user observes the child's responses, provides feedback through the application, and enables further optimization. The input is the educational program, and the output is the observed responses and feedback data.

[0489] (Application Example 1)

[0490] 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."

[0491] To promote the development of young children, it is crucial to provide optimal learning and play programs tailored to each child's individual characteristics and interests. However, conventional technologies do not adequately provide systems that effectively collect and analyze children's daily activities and propose customized educational activities in real time.

[0492] 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.

[0493] In this invention, the server includes means for recording data based on the child's activities, means for analyzing the data to identify the child's learning style, and means for generating optimized learning and play programs based on the learning style. This makes it possible to observe the child's activities in real time and propose optimal educational activities for each individual child based on the results.

[0494] "Data based on infant activities" refers to acoustic, visual, and physical movement information that includes infants' daily behaviors and responses.

[0495] "Means of recording" refers to the equipment and technology necessary to reliably preserve and later analyze the activities of young children.

[0496] "Methods for analyzing and identifying the learning patterns of young children" refer to algorithms and software that analyze collected data to identify the learning tendencies and interests that young children exhibit.

[0497] "Means for generating learning and play programs" refers to a system for designing and building customized learning and play activities that promote the development of young children, based on analysis results.

[0498] "Means of provision" refers to interfaces or devices that present the generated program to the child or their guardian, making it available for use.

[0499] "Automated devices" are robots and sensor devices used to observe the activities of young children in real time and efficiently collect data.

[0500] "Means for proposing educational activities" refers to a system for making specific suggestions regarding young children's learning and play, based on a generated program.

[0501] This application provides a system to support the development of young children in a home environment. The system records and analyzes the child's activities in real time and generates personalized learning and play programs based on that analysis. Specifically, the following technologies and processes are involved:

[0502] First, an automated device installed in the home (e.g., a home robot) collects data on the child's daily activities using cameras and sensors. This data comprehensively captures the child's behavior, attitude, and reactions to the environment. In this process, visual data is analyzed using the open-source library OpenCV, and audio data is collected using PyAudio.

[0503] Next, this data is sent to a server via the device. The server analyzes this data using AI technologies such as TensorFlow to identify the child's learning style and areas of interest. Based on the analysis, the system generates appropriate learning and play programs. These programs are designed to stimulate the child's specific interests and promote their development.

[0504] The generated program is provided to the user via a terminal. At this stage, the user runs the program and develops educational activities tailored to the child. For example, for a child interested in color recognition, an activity is suggested in which the robot identifies colorful objects and describes them verbally.

[0505] An example of a prompt message is, "Identify themes that the child is interested in and suggest learning activities based on them." This allows the system to respond to the child's dynamic interests and flexibly adapt the educational plan.

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The device collects activity data of infants via an automated system. Visual data captured by a camera and audio data recorded by a microphone are input. To process the data, OpenCV is used to divide the video into frames, and PyAudio is used to sample the audio signal along the time axis, outputting it as visual and acoustic data.

[0509] Step 2:

[0510] The terminal formats the collected visual and auditory data into an appropriate format and sends it to the server. The formatted data is then organized as input and output, which is transferred to the server.

[0511] Step 3:

[0512] The server applies computer vision technology to the received visual data to infer the child's interests and concerns. Specifically, it uses TensorFlow to perform object recognition and behavioral analysis within images. From the input visual data, it extracts information such as the number of times a child gazes at an object and their movement patterns, and generates output that indicates the child's interest characteristics.

[0513] Step 4:

[0514] The server uses natural language processing on acoustic data to evaluate the development of language and sound. The acoustic data, as input, is processed by speech recognition and language analysis, yielding outputs such as speech patterns and word frequency.

[0515] Step 5:

[0516] The server identifies the child's learning style based on the analysis of visual and auditory data and generates appropriate learning and play programs. It utilizes prompt statements, processes data based on the generated AI model, and outputs a customized program. Specifically, it might generate a program such as, "For this child, we suggest a building block game using colored blocks as a way to teach colors."

[0517] Step 6:

[0518] The terminal receives the generated program and provides it to the user. The program is input, and output is produced showing how the user interacts with the infant through visual and audio guidance.

[0519] Step 7:

[0520] The user engages in activities with the infant according to the provided program and provides feedback on the infant's responses to the system. This feedback becomes input data and is output as basic data for generating the next optimal program.

[0521] 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.

[0522] This invention combines a system that collects and analyzes activity data from infants to generate optimal learning and play programs with an emotion engine that recognizes the user's emotions, thereby achieving further personalization. This system records the infant's daily activities and suggests content that takes the user's emotional state into consideration, thereby promoting the healthy development of the infant.

[0523] First, the user records the child's activities through the device. These activities include video recording and audio recording. Furthermore, the device uses a built-in emotion engine to recognize the user's (parent's or guardian's) emotional state. This emotion engine determines the user's emotions in real time, primarily by analyzing audio and image data.

[0524] Next, the device sends this data to the server. The server uses artificial intelligence to analyze the collected activity data of the infants and identify their learning patterns. At the same time, the user's emotional state, as recognized by the emotion engine, is also taken into consideration. For example, if the system detects that the user is stressed, the suggested content will reflect elements that promote relaxation and ease of use.

[0525] Furthermore, the server uses this information to generate the most suitable learning and play programs for the toddler. The generated programs are personalized, taking into account not only the toddler's developmental stage but also the user's emotional state. For example, if the toddler prefers active movement and the user is detected to be in a cheerful emotional state, games incorporating physical activity will be recommended.

[0526] Finally, these programs are delivered to the user via a device. The user can try out the provided content with their child and provide feedback to the system. Based on this feedback, the program is further optimized.

[0527] In this way, the present invention provides multifaceted support for the development of infants and reduces the burden on parents in raising their children. By integrating an emotion engine with AI-based analysis, it becomes possible to provide a more personalized experience.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] The user uses the device to record the child's daily activities. By collecting video and audio through the available camera and microphone, the child's behavior and speech are recorded in detail. In addition, data is simultaneously collected to recognize emotions from the user's facial expressions and voice.

[0531] Step 2:

[0532] The device uses a built-in emotion engine to analyze the user's emotional state. It identifies emotions such as joy, stress, and surprise from voice intonation and facial expressions, and uses this information, along with activity data, to proceed to the next step.

[0533] Step 3:

[0534] The device transmits collected activity and emotion data to the server. Transmission is conducted via a secure protocol, and efforts are made to ensure data security.

[0535] Step 4:

[0536] The server stores the received data in a database and begins AI analysis. Here, computer vision technology is used to analyze the child's interests and behavioral patterns, and natural language processing is used to analyze the audio data and identify learning patterns.

[0537] Step 5:

[0538] The server generates an optimal learning and play program, taking into account the user's emotional state along with the analyzed learning patterns. For example, if the user indicates a desire to relax, the server will suggest a program that includes calming music and activities that promote relaxation.

[0539] Step 6:

[0540] The server sends the generated program to the terminal. The terminal notifies the user and immediately displays the program's contents and instructions for use.

[0541] Step 7:

[0542] The user has the child participate in the provided program and observes their reactions. During this time, the user carefully observes how the child engages with the content and provides supplementary support as needed.

[0543] Step 8:

[0544] Users record the infant's reactions and their own emotional changes as feedback and send it to the server via their device. This feedback data will be used to optimize future programs.

[0545] This series of processing steps enables the present invention to provide individualized learning experiences for young children and to offer flexible content suggestions that take into account the user's emotional state.

[0546] (Example 2)

[0547] 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."

[0548] Traditional early childhood education systems have struggled to provide individualized learning programs that take into account the unique characteristics of each child and the emotional state of their parents. Furthermore, there were technical challenges in analyzing children's interests and activity patterns in real time. As a result, effective developmental support for children and a reduction in the burden of childcare for parents have not been fully achieved.

[0549] 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.

[0550] In this invention, the server includes means for collecting information on the child's activities, means for analyzing the information to identify the child's learning tendencies, means for generating an optimal learning and play program based on the tendencies, and means for considering the user's emotional state in the generated program. This makes it possible to provide a learning program that is individualized according to the child's development, further maximize the effectiveness of education, and reduce the burden of childcare on parents.

[0551] "Information regarding the activities of young children" refers to data about the actions and activities that young children engage in in their daily lives, such as play, and includes audio information, image information, and motion information.

[0552] "Learning tendencies" refer to the unique learning patterns, interests, and abilities of a child, identified by analyzing their activity data.

[0553] "Means for generating programs" refers to technology that automatically creates customized programs by selecting the most suitable learning and play activities for young children based on their analyzed learning tendencies.

[0554] "Means of considering emotional state" refers to methods of analyzing user emotional data and adjusting the content of learning programs and play programs to reflect that state.

[0555] "Methods for collecting feedback and optimizing the program" refers to the process of collecting reactions and results obtained after users and children try the proposed program, and using that information to improve the quality of future programs.

[0556] This invention is a system for collecting and analyzing activity information of infants and providing personalized learning and play programs. The user records and audio recordings of the infant's activities using a terminal. This terminal is equipped with a high-performance camera and microphone, allowing it to collect audio, visual, and motion information of the infant's daily activities. Furthermore, the terminal implements software with emotion recognition capabilities, enabling it to analyze the user's emotional state.

[0557] Information collected by the device is sent to a server, which analyzes this information using an artificial intelligence model. This analysis employs advanced machine learning algorithms and data processing techniques. Based on this data, the server identifies the child's learning tendencies and, taking into account the user's emotional state, generates an optimal learning and play program. The server utilizes a generative AI model in generating the program. The generated program is personalized and optimized for the child's interests and abilities, as well as the user's emotional state.

[0558] For example, if a toddler enjoys being read picture books, the server can suggest new picture books and related visual and auditory content. Also, if the user is relaxed, using calming music can provide an optimal experience for both parent and child.

[0559] An example of a prompt to input into the generating AI model is: "Please suggest a play program for an active 3-year-old boy, including new physical activities. Since the user is relaxed, please include the idea of ​​combining it with calming music."

[0560] In this way, the system can provide multifaceted support for both the development of young children and the user's parenting experience.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The user records information about the child's activities through the device. The input consists of audio and video data related to the child's activities. The device's camera and microphone are used to capture and record everyday play and learning scenes. The output consists of recorded audio information, image information, and video data.

[0564] Step 2:

[0565] The device recognizes the user's emotional state using recorded data. The emotion recognition engine receives voice tone and facial expression data as input and performs emotion analysis. The user's emotional state is displayed in real time as output.

[0566] Step 3:

[0567] The device sends the collected data to the server. The input consists of recorded audio, image, and emotion data. This data is packaged and sent to the server via Wi-Fi or a data network. The output is a notification indicating that the data transfer is complete.

[0568] Step 4:

[0569] The server analyzes received activity data to identify the learning tendencies of young children. The input consists of activity data and emotional data, which an AI model then analyzes. Machine learning algorithms are used for data processing, and the output is a report on learning tendencies.

[0570] Step 5:

[0571] The server uses prompt statements to generate programs using a generative AI model. For example, it might take a prompt statement like, "Please suggest a play program that includes new physical activities for an active 3-year-old boy," and the generative AI will output the optimal program.

[0572] Step 6:

[0573] The server optimizes the program considering the user's emotional state. The input consists of the generated program and the user's emotional data. The output is a modified learning / play program.

[0574] Step 7:

[0575] The generated program is provided to the user via the terminal. The user reviews and executes the program. As output, the program is displayed to the user and becomes available for testing.

[0576] Step 8:

[0577] The user inputs the results of their execution as feedback into the terminal. This feedback data is sent to the server and reflected in subsequent programs. Program update information is then notified as output.

[0578] (Application Example 2)

[0579] 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."

[0580] In early childhood care and education, it is crucial to provide optimal learning and play programs tailored to each child's individual characteristics and developmental stage. However, conventional systems have struggled to generate content that comprehensively considers each child's unique activity data and their parents' emotional state. As a result, flexible responses to on-the-spot situations have been difficult, and it has been difficult to adequately support the reduction of childcare effort and the healthy development of young children.

[0581] 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.

[0582] In this invention, the server includes means for collecting information on the child's activities, means for analyzing the activity information to identify the child's learning patterns, means for generating an optimal learning and play program based on the learning patterns and the user's emotional state, and means including an emotion recognition engine for recognizing the user's emotions. This makes it possible to provide personalized programs that are tailored to the child's characteristics and the situation at hand.

[0583] "Infant activity information" is a general term for data including audio, image, and motion information related to the daily activities of infants.

[0584] A "learning pattern" is a pattern that indicates the tendencies and characteristics of a child's learning and play, identified by analyzing their activity information.

[0585] An "optimal learning and play program" is individualized and generated educational and play content for young children, based on the child's learning patterns and the user's emotional state.

[0586] An "emotion recognition engine" is software or hardware that analyzes audio and image information to determine the user's emotional state in real time.

[0587] "User's emotional state" refers to information about the emotional state of the child's caregiver or parent as detected by the emotion recognition engine.

[0588] A "server" is a computer system located on a network that receives, analyzes, and generates programs based on information about the activities of infants.

[0589] To implement this invention, the system first requires a terminal for collecting activity information of infants. This terminal is equipped with sensors such as a camera and microphone, and is capable of acquiring audio, image, and motion information about the infant's daily activities in real time. The activity information is transmitted to a server via wireless communication.

[0590] The server analyzes received activity information to identify the child's learning patterns. It also uses an emotion recognition engine to analyze the user (parent)'s voice and image information to determine their emotional state in real time. This analysis utilizes cloud-based speech recognition and image analysis technologies such as Google Cloud Speech-to-Text and Google Cloud Vision API. Based on the analysis results, an optimal learning and play program is generated using a machine learning model (e.g., TensorFlow).

[0591] The generated programs take into account the user's emotional state. For example, if the system detects that the parent is tired, it will suggest relaxing activities such as reading a quiet picture book. On the other hand, if the parent is energetic, it can suggest active programs using dancing or singing.

[0592] Users can receive programs provided by the server via their terminal and implement them on their infants. Furthermore, by sending feedback on the implemented programs to the system, the programs can be further optimized.

[0593] As a concrete example, if the system determines that a parent is feeling anxious during the busy morning hours, it will suggest a quick finger exercise. An example of a prompt to the generating AI model is as follows: "The system has recognized that the parent is feeling stressed. To alleviate the parent's burden, please suggest a relaxing activity for the infant."

[0594] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0595] Step 1:

[0596] The device uses a camera and microphone to collect audio, image, and motion information to record the child's daily activities. This input data is transmitted to a server in real time.

[0597] Step 2:

[0598] The server converts received audio information into text using Google Cloud Speech-to-Text. Image information is analyzed using the Google Cloud Vision API to extract information about the child's activities and environment. This process outputs both audio-text and image analysis results.

[0599] Step 3:

[0600] The server uses an emotion recognition engine to estimate the parent's emotional state from audio and image information. It analyzes changes in voice tone and facial expressions and outputs the estimation results as emotion data.

[0601] Step 4:

[0602] The server uses the acquired activity information and emotional data of the infant to run a generative AI model (e.g., TensorFlow) and analyze the infant's learning patterns. During this process, learning pattern data is output.

[0603] Step 5:

[0604] The server generates an optimal learning and play program based on learning pattern data and emotional data. This involves prompting the user with a generation AI model (e.g., "The parent's emotional state has been identified as stressed. To alleviate the parent's burden, please suggest relaxing activities for the child.") to determine appropriate program content. The generated program is then sent to the device.

[0605] Step 6:

[0606] Users implement learning and play programs provided through their devices for their young children. After implementation, they input feedback on the children's reactions and the effectiveness of the programs, and send this feedback to the server.

[0607] Step 7:

[0608] The server analyzes the feedback received from users and uses it to further improve the program. The feedback data is then used in the next program generation process to output optimized content.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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".

[0626] This invention is a system that effectively collects and analyzes activity data of infants and generates personalized learning and play programs based on that data. The system aims to provide optimal content by allowing parents to record their child's daily behavior and understand their learning patterns.

[0627] First, the user (parent) records the child's daily activities via the device. This record may include video, audio, and even motion data from motion sensors. Once the user collects this data using a dedicated application, the device formats the data and sends it to the server.

[0628] The server stores the received data and analyzes it using AI algorithms. For example, computer vision technology can be applied to video data to identify what the toddler is interested in. For audio data, natural language processing technology is used to evaluate the development of sounds and language. In this way, the server understands the toddler's interests and tendencies and identifies their learning patterns.

[0629] Next, the server generates learning and play programs best suited to the child based on the analysis results. This process creates a plan that develops specific developmental skills by combining multiple educational elements. For example, for a child who shows interest in color recognition, a game app designed to cultivate their sense of color will be generated.

[0630] Finally, the generated program is sent from the server to the terminal and provided to the user. The terminal notifies the user of any new available content and presents instructions on how to use it and the expected learning effects. The user then has the child use it and provides feedback to the system, which further optimizes the program.

[0631] This system allows parents to provide their toddlers with suitable learning experiences with less effort, thereby supporting their development.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] Users use the device to record their child's daily activities. The device has a dedicated application installed that allows for video and audio recording. Furthermore, in some cases, motion sensors are used to collect the child's movement data.

[0635] Step 2:

[0636] The device converts the collected data into a predetermined format. Video data is compressed, and audio data is converted to a format suitable for audio analysis as needed. During this process, data anonymization and security protection are also applied.

[0637] Step 3:

[0638] The terminal sends the converted data to the server. During this transmission, secure protocols such as SSL are used to protect the confidentiality of the data.

[0639] Step 4:

[0640] The server stores the received data in a database. Since the stored data is to be analyzed by AI, preprocessing is performed as needed.

[0641] Step 5:

[0642] The server begins data analysis. Specifically, for video data, computer vision technology is used to identify the child's behavior and objects of interest. For audio data, natural language processing is applied to analyze pronunciation patterns and word usage frequency.

[0643] Step 6:

[0644] The server identifies the child's learning pattern based on the analysis results. This learning pattern includes the child's current developmental stage, areas of interest, and skills.

[0645] Step 7:

[0646] The server generates an optimal learning and play program based on identified learning patterns. At this stage, personalized content is designed using a generative AI. For example, if a toddler is interested in colors, an educational game about colors will be created.

[0647] Step 8:

[0648] The server sends the generated learning and play programs to the terminal. During transmission, the programs are reformatted to a format easily understood by the user.

[0649] Step 9:

[0650] The device notifies the user that the program is available. The application then displays detailed information and instructions, preparing the child to actually use the program.

[0651] Step 10:

[0652] Users observe how young children use the program and provide feedback to the system regarding their performance and reactions. This allows the system to accumulate information necessary for future program optimization.

[0653] (Example 1)

[0654] 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".

[0655] To provide individualized educational experiences tailored to the growth and development of young children, it is necessary to accurately understand their ever-changing interests and behaviors and to quickly and effectively adjust learning programs accordingly. However, conventional systems have struggled to provide such individualized support. Therefore, there were limitations to collecting diverse information about children's daily activities in real time and using that information to provide the optimal educational experience.

[0656] 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.

[0657] In this invention, the server includes means for recording information on daily activities, means for analyzing the information on daily activities to identify a learning method, and means for generating an individualized educational program based on the learning method. This makes it possible to provide an optimal educational experience tailored to the individual learning patterns of each child.

[0658] "Information on daily activities" refers to audio, visual, and motion information related to the daily activities of young children.

[0659] "Analysis" is a process performed to identify a child's learning methods based on collected information about their daily activities.

[0660] "Learning methods" refer to patterns that show a child's specific learning tendencies and interests, and serve as the foundation for generating optimal educational programs.

[0661] An "educational program" is individualized learning and play content designed to promote the development of young children and cultivate specific skills.

[0662] "Visual recognition technology" is a technique that uses computer vision to analyze video information and estimate the behavior and interests of young children.

[0663] "Natural language processing technology" is a technology that uses collected audio information to evaluate the degree to which young children recognize words and sounds.

[0664] This invention relates to a system for recording and analyzing the daily activities of young children and generating personalized educational programs based on the results. The details are described below.

[0665] First, the user uses a device with a dedicated application installed to collect information about the child's daily activities. This information includes audio, video, and movement data. This data is then organized on the device and transmitted to the server using a secure communication protocol.

[0666] The server stores received information about daily activities and performs analysis on a high-performance computing environment. This analysis utilizes visual recognition technology and natural language processing technology. Specifically, visual recognition technology is used to analyze video information and identify the child's interests and behaviors. Natural language processing technology is used to analyze audio information and evaluate the child's language and sound development.

[0667] Based on the learning methods of young children obtained from the analysis, the server generates educational programs using a generated AI model. These programs are tailored to the individual needs and interests of each child, providing high value to the user. For example, for a child interested in colors, an application is generated that includes a game for identifying and learning colors.

[0668] The generated educational program is delivered from the server to the terminal and the user is notified. The user has the child use the program, observes the child's response, and provides feedback to the system. This feedback is used to further optimize the program.

[0669] As a concrete example of a prompt, the text "Analyze the video data to identify the interests of young children and suggest educational content" is provided to the generating AI model, and the analysis and generation process begins. This allows users to provide young children with personalized learning experiences, effectively supporting their growth and development.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] Users collect information about their child's daily activities using a device with a dedicated application installed. This information includes the child's voice, video, and movement data. For example, users can record videos with their smartphone's camera and audio with the built-in microphone. They can also collect movement data using the device's motion sensor. The input is this raw data, and the output is data formatted by the application.

[0673] Step 2:

[0674] The terminal organizes and formats the collected data. This processing includes compressing video files, denoising audio files, and filtering motion data. This reduces unnecessary data and prepares it for easier analysis. The input is raw audio, video, and motion data, while the output is formatted, effective data.

[0675] Step 3:

[0676] The terminal sends formatted data to the server. A secure communication protocol is used to ensure data integrity and confidentiality. The input is formatted data, and the output is a notification of data arrival by sending it to the server.

[0677] Step 4:

[0678] The server receives and stores the transmitted data. The database is organized by date, time, and data type, preparing it for later analysis. The input is formatted data received from the terminal, and the output is data stored in the database.

[0679] Step 5:

[0680] The server analyzes data using visual recognition and natural language processing technologies. Specifically, visual recognition technology analyzes video data to identify the child's interests (e.g., specific colors or objects). Natural language processing technology analyzes audio data to evaluate the recognition rate of sounds and words. The input is the received data, and the output is data on the identified child's interests and learning methods.

[0681] Step 6:

[0682] The server uses the analysis results to generate an AI model and create an educational program. Here, a program is constructed to cultivate specific skills based on the child's learning style. For example, for a child who shows interest in color recognition, content is generated that allows them to learn about color recognition. The input is the analysis results, and the output is a personalized educational program.

[0683] Step 7:

[0684] The server transfers the generated educational program to the terminal. The terminal notifies the user that the program has been provided and prompts them to begin using it. The input is the educational program, and the output is the notification of transfer to the terminal and the preparation for program startup.

[0685] Step 8:

[0686] The user provides an educational program delivered to the device to a child and runs the program. During this process, the user observes the child's responses, provides feedback through the application, and enables further optimization. The input is the educational program, and the output is the observed responses and feedback data.

[0687] (Application Example 1)

[0688] 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".

[0689] To promote the development of young children, it is crucial to provide optimal learning and play programs tailored to each child's individual characteristics and interests. However, conventional technologies do not adequately provide systems that effectively collect and analyze children's daily activities and propose customized educational activities in real time.

[0690] 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.

[0691] In this invention, the server includes means for recording data based on the child's activities, means for analyzing the data to identify the child's learning style, and means for generating optimized learning and play programs based on the learning style. This makes it possible to observe the child's activities in real time and propose optimal educational activities for each individual child based on the results.

[0692] "Data based on infant activities" refers to acoustic, visual, and physical movement information that includes infants' daily behaviors and responses.

[0693] "Means of recording" refers to the equipment and technology necessary to reliably preserve and later analyze the activities of young children.

[0694] "Methods for analyzing and identifying the learning patterns of young children" refer to algorithms and software that analyze collected data to identify the learning tendencies and interests that young children exhibit.

[0695] "Means for generating learning and play programs" refers to a system for designing and building customized learning and play activities that promote the development of young children, based on analysis results.

[0696] "Means of provision" refers to interfaces or devices that present the generated program to the child or their guardian, making it available for use.

[0697] "Automated devices" are robots and sensor devices used to observe the activities of young children in real time and efficiently collect data.

[0698] "Means for proposing educational activities" refers to a system for making specific suggestions regarding young children's learning and play, based on a generated program.

[0699] This application provides a system to support the development of young children in a home environment. The system records and analyzes the child's activities in real time and generates personalized learning and play programs based on that analysis. Specifically, the following technologies and processes are involved:

[0700] First, an automated device installed in the home (e.g., a home robot) collects data on the child's daily activities using cameras and sensors. This data comprehensively captures the child's behavior, attitude, and reactions to the environment. In this process, visual data is analyzed using the open-source library OpenCV, and audio data is collected using PyAudio.

[0701] Next, this data is sent to a server via the device. The server analyzes this data using AI technologies such as TensorFlow to identify the child's learning style and areas of interest. Based on the analysis, the system generates appropriate learning and play programs. These programs are designed to stimulate the child's specific interests and promote their development.

[0702] The generated program is provided to the user via a terminal. At this stage, the user runs the program and develops educational activities tailored to the child. For example, for a child interested in color recognition, an activity is suggested in which the robot identifies colorful objects and describes them verbally.

[0703] An example of a prompt message is, "Identify themes that the child is interested in and suggest learning activities based on them." This allows the system to respond to the child's dynamic interests and flexibly adapt the educational plan.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The device collects activity data of infants via an automated system. Visual data captured by a camera and audio data recorded by a microphone are input. To process the data, OpenCV is used to divide the video into frames, and PyAudio is used to sample the audio signal along the time axis, outputting it as visual and acoustic data.

[0707] Step 2:

[0708] The terminal formats the collected visual and auditory data into an appropriate format and sends it to the server. The formatted data is then organized as input and output, which is transferred to the server.

[0709] Step 3:

[0710] The server applies computer vision technology to the received visual data to infer the child's interests and concerns. Specifically, it uses TensorFlow to perform object recognition and behavioral analysis within images. From the input visual data, it extracts information such as the number of times a child gazes at an object and their movement patterns, and generates output that indicates the child's interest characteristics.

[0711] Step 4:

[0712] The server uses natural language processing on acoustic data to evaluate the development of language and sound. The acoustic data, as input, is processed by speech recognition and language analysis, yielding outputs such as speech patterns and word frequency.

[0713] Step 5:

[0714] The server identifies the child's learning style based on the analysis of visual and auditory data and generates appropriate learning and play programs. It utilizes prompt statements, processes data based on the generated AI model, and outputs a customized program. Specifically, it might generate a program such as, "For this child, we suggest a building block game using colored blocks as a way to teach colors."

[0715] Step 6:

[0716] The terminal receives the generated program and provides it to the user. The program is input, and output is produced showing how the user interacts with the infant through visual and audio guidance.

[0717] Step 7:

[0718] The user engages in activities with the infant according to the provided program and provides feedback on the infant's responses to the system. This feedback becomes input data and is output as basic data for generating the next optimal program.

[0719] 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.

[0720] This invention combines a system that collects and analyzes activity data from infants to generate optimal learning and play programs with an emotion engine that recognizes the user's emotions, thereby achieving further personalization. This system records the infant's daily activities and suggests content that takes the user's emotional state into consideration, thereby promoting the healthy development of the infant.

[0721] First, the user records the child's activities through the device. These activities include video recording and audio recording. Furthermore, the device uses a built-in emotion engine to recognize the user's (parent's or guardian's) emotional state. This emotion engine determines the user's emotions in real time, primarily by analyzing audio and image data.

[0722] Next, the device sends this data to the server. The server uses artificial intelligence to analyze the collected activity data of the infants and identify their learning patterns. At the same time, the user's emotional state, as recognized by the emotion engine, is also taken into consideration. For example, if the system detects that the user is stressed, the suggested content will reflect elements that promote relaxation and ease of use.

[0723] Furthermore, the server uses this information to generate the most suitable learning and play programs for the toddler. The generated programs are personalized, taking into account not only the toddler's developmental stage but also the user's emotional state. For example, if the toddler prefers active movement and the user is detected to be in a cheerful emotional state, games incorporating physical activity will be recommended.

[0724] Finally, these programs are delivered to the user via a device. The user can try out the provided content with their child and provide feedback to the system. Based on this feedback, the program is further optimized.

[0725] In this way, the present invention provides multifaceted support for the development of infants and reduces the burden on parents in raising their children. By integrating an emotion engine with AI-based analysis, it becomes possible to provide a more personalized experience.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The user uses the device to record the child's daily activities. By collecting video and audio through the available camera and microphone, the child's behavior and speech are recorded in detail. In addition, data is simultaneously collected to recognize emotions from the user's facial expressions and voice.

[0729] Step 2:

[0730] The device uses a built-in emotion engine to analyze the user's emotional state. It identifies emotions such as joy, stress, and surprise from voice intonation and facial expressions, and uses this information, along with activity data, to proceed to the next step.

[0731] Step 3:

[0732] The device transmits collected activity and emotion data to the server. Transmission is conducted via a secure protocol, and efforts are made to ensure data security.

[0733] Step 4:

[0734] The server stores the received data in a database and begins AI analysis. Here, computer vision technology is used to analyze the child's interests and behavioral patterns, and natural language processing is used to analyze the audio data and identify learning patterns.

[0735] Step 5:

[0736] The server generates an optimal learning and play program, taking into account the user's emotional state along with the analyzed learning patterns. For example, if the user indicates a desire to relax, the server will suggest a program that includes calming music and activities that promote relaxation.

[0737] Step 6:

[0738] The server sends the generated program to the terminal. The terminal notifies the user and immediately displays the program's contents and instructions for use.

[0739] Step 7:

[0740] The user has the child participate in the provided program and observes their reactions. During this time, the user carefully observes how the child engages with the content and provides supplementary support as needed.

[0741] Step 8:

[0742] Users record the infant's reactions and their own emotional changes as feedback and send it to the server via their device. This feedback data will be used to optimize future programs.

[0743] This series of processing steps enables the present invention to provide individualized learning experiences for young children and to offer flexible content suggestions that take into account the user's emotional state.

[0744] (Example 2)

[0745] 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".

[0746] Traditional early childhood education systems have struggled to provide individualized learning programs that take into account the unique characteristics of each child and the emotional state of their parents. Furthermore, there were technical challenges in analyzing children's interests and activity patterns in real time. As a result, effective developmental support for children and a reduction in the burden of childcare for parents have not been fully achieved.

[0747] 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.

[0748] In this invention, the server includes means for collecting information on the child's activities, means for analyzing the information to identify the child's learning tendencies, means for generating an optimal learning and play program based on the tendencies, and means for considering the user's emotional state in the generated program. This makes it possible to provide a learning program that is individualized according to the child's development, further maximize the effectiveness of education, and reduce the burden of childcare on parents.

[0749] "Information regarding the activities of young children" refers to data about the actions and activities that young children engage in in their daily lives, such as play, and includes audio information, image information, and motion information.

[0750] "Learning tendencies" refer to the unique learning patterns, interests, and abilities of a child, identified by analyzing their activity data.

[0751] "Means for generating programs" refers to technology that automatically creates customized programs by selecting the most suitable learning and play activities for young children based on their analyzed learning tendencies.

[0752] "Means of considering emotional state" refers to methods of analyzing user emotional data and adjusting the content of learning programs and play programs to reflect that state.

[0753] "Methods for collecting feedback and optimizing the program" refers to the process of collecting reactions and results obtained after users and children try the proposed program, and using that information to improve the quality of future programs.

[0754] This invention is a system for collecting and analyzing activity information of infants and providing personalized learning and play programs. The user records and audio recordings of the infant's activities using a terminal. This terminal is equipped with a high-performance camera and microphone, allowing it to collect audio, visual, and motion information of the infant's daily activities. Furthermore, the terminal implements software with emotion recognition capabilities, enabling it to analyze the user's emotional state.

[0755] Information collected by the device is sent to a server, which analyzes this information using an artificial intelligence model. This analysis employs advanced machine learning algorithms and data processing techniques. Based on this data, the server identifies the child's learning tendencies and, taking into account the user's emotional state, generates an optimal learning and play program. The server utilizes a generative AI model in generating the program. The generated program is personalized and optimized for the child's interests and abilities, as well as the user's emotional state.

[0756] For example, if a toddler enjoys being read picture books, the server can suggest new picture books and related visual and auditory content. Also, if the user is relaxed, using calming music can provide an optimal experience for both parent and child.

[0757] An example of a prompt to input into the generating AI model is: "Please suggest a play program for an active 3-year-old boy, including new physical activities. Since the user is relaxed, please include the idea of ​​combining it with calming music."

[0758] In this way, the system can provide multifaceted support for both the development of young children and the user's parenting experience.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The user records information about the child's activities through the device. The input consists of audio and video data related to the child's activities. The device's camera and microphone are used to capture and record everyday play and learning scenes. The output consists of recorded audio information, image information, and video data.

[0762] Step 2:

[0763] The device recognizes the user's emotional state using recorded data. The emotion recognition engine receives voice tone and facial expression data as input and performs emotion analysis. The user's emotional state is displayed in real time as output.

[0764] Step 3:

[0765] The device sends the collected data to the server. The input consists of recorded audio, image, and emotion data. This data is packaged and sent to the server via Wi-Fi or a data network. The output is a notification indicating that the data transfer is complete.

[0766] Step 4:

[0767] The server analyzes received activity data to identify the learning tendencies of young children. The input consists of activity data and emotional data, which an AI model then analyzes. Machine learning algorithms are used for data processing, and the output is a report on learning tendencies.

[0768] Step 5:

[0769] The server uses prompt statements to generate programs using a generative AI model. For example, it might take a prompt statement like, "Please suggest a play program that includes new physical activities for an active 3-year-old boy," and the generative AI will output the optimal program.

[0770] Step 6:

[0771] The server optimizes the program considering the user's emotional state. The input consists of the generated program and the user's emotional data. The output is a modified learning / play program.

[0772] Step 7:

[0773] The generated program is provided to the user via the terminal. The user reviews and executes the program. As output, the program is displayed to the user and becomes available for testing.

[0774] Step 8:

[0775] The user inputs the results of their execution as feedback into the terminal. This feedback data is sent to the server and reflected in subsequent programs. Program update information is then notified as output.

[0776] (Application Example 2)

[0777] 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".

[0778] In early childhood care and education, it is crucial to provide optimal learning and play programs tailored to each child's individual characteristics and developmental stage. However, conventional systems have struggled to generate content that comprehensively considers each child's unique activity data and their parents' emotional state. As a result, flexible responses to on-the-spot situations have been difficult, and it has been difficult to adequately support the reduction of childcare effort and the healthy development of young children.

[0779] 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.

[0780] In this invention, the server includes means for collecting information on the child's activities, means for analyzing the activity information to identify the child's learning patterns, means for generating an optimal learning and play program based on the learning patterns and the user's emotional state, and means including an emotion recognition engine for recognizing the user's emotions. This makes it possible to provide personalized programs that are tailored to the child's characteristics and the situation at hand.

[0781] "Infant activity information" is a general term for data including audio, image, and motion information related to the daily activities of infants.

[0782] A "learning pattern" is a pattern that indicates the tendencies and characteristics of a child's learning and play, identified by analyzing their activity information.

[0783] An "optimal learning and play program" is individualized and generated educational and play content for young children, based on the child's learning patterns and the user's emotional state.

[0784] An "emotion recognition engine" is software or hardware that analyzes audio and image information to determine the user's emotional state in real time.

[0785] "User's emotional state" refers to information about the emotional state of the child's caregiver or parent as detected by the emotion recognition engine.

[0786] A "server" is a computer system located on a network that receives, analyzes, and generates programs based on information about the activities of infants.

[0787] To implement this invention, the system first requires a terminal for collecting activity information of infants. This terminal is equipped with sensors such as a camera and microphone, and is capable of acquiring audio, image, and motion information about the infant's daily activities in real time. The activity information is transmitted to a server via wireless communication.

[0788] The server analyzes received activity information to identify the child's learning patterns. It also uses an emotion recognition engine to analyze the user (parent)'s voice and image information to determine their emotional state in real time. This analysis utilizes cloud-based speech recognition and image analysis technologies such as Google Cloud Speech-to-Text and Google Cloud Vision API. Based on the analysis results, an optimal learning and play program is generated using a machine learning model (e.g., TensorFlow).

[0789] The generated programs take into account the user's emotional state. For example, if the system detects that the parent is tired, it will suggest relaxing activities such as reading a quiet picture book. On the other hand, if the parent is energetic, it can suggest active programs using dancing or singing.

[0790] Users can receive programs provided by the server via their terminal and implement them on their infants. Furthermore, by sending feedback on the implemented programs to the system, the programs can be further optimized.

[0791] As a concrete example, if the system determines that a parent is feeling anxious during the busy morning hours, it will suggest a quick finger exercise. An example of a prompt to the generating AI model is as follows: "The system has recognized that the parent is feeling stressed. To alleviate the parent's burden, please suggest a relaxing activity for the infant."

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] The device uses a camera and microphone to collect audio, image, and motion information to record the child's daily activities. This input data is transmitted to a server in real time.

[0795] Step 2:

[0796] The server converts received audio information into text using Google Cloud Speech-to-Text. Image information is analyzed using the Google Cloud Vision API to extract information about the child's activities and environment. This process outputs both audio-text and image analysis results.

[0797] Step 3:

[0798] The server uses an emotion recognition engine to estimate the parent's emotional state from audio and image information. It analyzes changes in voice tone and facial expressions and outputs the estimation results as emotion data.

[0799] Step 4:

[0800] The server uses the acquired activity information and emotional data of the infant to run a generative AI model (e.g., TensorFlow) and analyze the infant's learning patterns. During this process, learning pattern data is output.

[0801] Step 5:

[0802] The server generates an optimal learning and play program based on learning pattern data and emotional data. This involves prompting the user with a generation AI model (e.g., "The parent's emotional state has been identified as stressed. To alleviate the parent's burden, please suggest relaxing activities for the child.") to determine appropriate program content. The generated program is then sent to the device.

[0803] Step 6:

[0804] Users implement learning and play programs provided through their devices for their young children. After implementation, they input feedback on the children's reactions and the effectiveness of the programs, and send this feedback to the server.

[0805] Step 7:

[0806] The server analyzes the feedback received from users and uses it to further improve the program. The feedback data is then used in the next program generation process to output optimized content.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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."

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] The following is further disclosed regarding the embodiments described above.

[0829] (Claim 1)

[0830] Methods for collecting activity data of young children,

[0831] A means for analyzing the aforementioned activity data to identify the learning patterns of infants,

[0832] Means for generating an optimal learning / play program based on the aforementioned learning pattern,

[0833] Means for presenting the generated program,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein the activity data includes audio data, image data, and motion data.

[0837] (Claim 3)

[0838] The system according to claim 1, wherein the analysis is performed by computer vision and natural language processing.

[0839] "Example 1"

[0840] (Claim 1)

[0841] A means of recording information about daily living activities,

[0842] A means for analyzing the information on the aforementioned daily living activities to identify a learning method,

[0843] Means for generating an individualized educational program based on the learning method described above,

[0844] The generated educational program is provided, and means are used to receive feedback for optimization.

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the information of the aforementioned daily living activities includes audio information, video information, and motion information.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the analysis is performed by visual recognition technology and natural language processing technology.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] A means of recording data based on the activities of young children,

[0853] A means for analyzing the aforementioned data to identify the learning style of a young child,

[0854] A means for generating an optimized learning and play program based on the aforementioned learning format,

[0855] Means for providing the generated program,

[0856] An automated device for observing and collecting data on the activities of young children in real time,

[0857] A means of executing the generated program to suggest the most suitable educational activities for children,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the activity data includes acoustic information, visual information, and bodily movement information.

[0861] (Claim 3)

[0862] The system according to claim 1, wherein the analysis is performed by computer vision and natural language understanding.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] Means of collecting information on the activities of young children,

[0866] A means for analyzing the aforementioned information to identify trends in early childhood learning,

[0867] Means for generating optimal learning and play programs based on the aforementioned trends,

[0868] The generated program includes a means to consider the user's emotional state,

[0869] Means for presenting the aforementioned program,

[0870] A means of collecting feedback and optimizing the program,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the information includes audio information, image information, and motion information, and comprises means for identifying the user's emotional state.

[0874] (Claim 3)

[0875] The system according to claim 1, wherein the analysis is performed using an artificial intelligence model, and program generation is performed using generation AI technology and prompt statements.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] Means for collecting information on the activities of young children,

[0879] A means for analyzing the aforementioned activity information to identify the learning patterns of infants,

[0880] A means for generating an optimal learning and play program based on the aforementioned learning pattern and the user's emotional state,

[0881] Means for presenting the generated program,

[0882] Means including an emotion recognition engine for recognizing the emotions of the user,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the activity information includes audio information, image information, and motion information, and further, the emotion recognition is based on the audio information and image information.

[0886] (Claim 3)

[0887] The system according to claim 1, wherein the analysis and emotion recognition are performed by machine learning models and computer vision techniques. [Explanation of symbols]

[0888] 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 recording data based on the activities of young children, A means for analyzing the aforementioned data to identify the learning style of a young child, A means for generating an optimized learning and play program based on the aforementioned learning format, Means for providing the generated program, An automated device for observing and collecting data on the activities of young children in real time, A means of executing the generated program to suggest the most suitable educational activities for children, A system that includes this.

2. The system according to claim 1, wherein the activity data includes acoustic information, visual information, and bodily movement information.

3. The system according to claim 1, wherein the analysis is performed by computer vision and natural language understanding.

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