system

Abstract

We provide the system. [Solution] A speech recognition means that receives audio data and converts it into text data, An image recognition means that receives video data and analyzes the emotional state of the subject, A means for extracting behavioral information from text data using natural language processing, A report generation means that automatically generates a report based on the analysis results of the image recognition means, A system that includes a notification mechanism that uses this data to send notifications to users.

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] In school childcare, it is required that staff carefully observe the behavior and emotions of individual children and respond appropriately. However, the workload of this task is large, and a lot of time is spent on reporting to parents and creating logs, so there is a problem that efficient operation is difficult. Under such a background, a system for reducing the workload of staff and realizing rapid and accurate information sharing with parents is required.

Means for Solving the Problems

[0005] This invention provides a system comprising: speech recognition means for receiving audio data and converting it into text data; image recognition means for receiving video data and analyzing the emotional state of a subject; means for extracting behavioral information from text data using natural language processing; report generation means for automatically generating reports based on the analysis results; and notification means for sending notifications to users using the data. This reduces the burden on staff and enables the automatic creation of reports to parents and daily logs. Furthermore, this system can be used by multiple terminals via a network, and by receiving user feedback and updating the database, it realizes more effective after-school care.

[0006] "Speech recognition means" refers to a device or system that converts speech signals into text data.

[0007] "Image recognition means" refers to a technology or device that recognizes and analyzes a specific object or its state from video data.

[0008] "Natural language processing means" refers to algorithms and programs for understanding text data and extracting or generating necessary information.

[0009] A "report generation method" refers to a system that has the function of automatically creating documents from analyzed data and providing them in report format.

[0010] "Notification means" refers to a communication method or protocol for sending specific information to a user in real time.

[0011] A "database" refers to an information management system that systematically stores information and allows for easy searching and updating.

[0012] "Feedback" refers to the process of receiving responses and opinions from users, as well as the information itself.

[0013] A "terminal" refers to a hardware device that a user directly operates to input and output data. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered 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.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention combines speech recognition, image recognition, and natural language processing technologies in an after-school care system to reduce the workload of staff and streamline reporting to parents. The following describes a specific embodiment of the system and its operation.

[0036] The server functions as the central hub of this system, receiving audio and video data transmitted from terminals. First, the server uses a speech recognition engine to convert the received audio data into text. Based on this text data, it performs natural language processing to analyze specific actions and situations. For example, if a staff member inputs "The children are doing crafts," the server generates an activity tag called "crafts."

[0037] Meanwhile, the server performs image recognition on the video data to analyze the children's emotional state and behavior. If the system detects, for example, that a child is excited, it tags that state as "excited" and reflects it in the analysis results.

[0038] The server automatically generates a report using these analysis results. The report includes each child's emotional state and activity history, and is output in a format that can be used as a staff member's daily report or a report to parents.

[0039] The terminal functions as a staff operating interface, receiving voice input and feedback, as well as notifications from the server. Staff can use the terminal to monitor the children's condition in real time and take appropriate action as needed. For example, if the terminal receives a notification such as "○○ is a little quiet," staff can consider the child's feelings and take appropriate action.

[0040] Users, primarily parents, can view reports through the application from their own devices. This allows parents to gain a detailed understanding of their child's daily activities and submit feedback. This feedback information is collected on a server and used to improve the system in the future.

[0041] Thus, the system of the present invention, by effectively analyzing and providing information using voice, video, and natural language processing, enables higher quality individualized care in the setting of after-school care.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal receives audio data from staff and converts it into text data using speech recognition technology. For example, if a staff member says, "The children will have their snack soon," the audio is converted to text in real time.

[0045] Step 2:

[0046] The terminal transmits video footage from a camera installed in the classroom to a server. The video includes the children's actions and facial expressions, which are used as data for image recognition.

[0047] Step 3:

[0048] The server applies natural language processing to the received text data to extract information about specific activities and situations. For example, it detects the word "snack" and records it as a tag.

[0049] Step 4:

[0050] The server performs image recognition on the video data to analyze the children's emotional state. For example, it detects "smiles" from facial expressions and saves the results to a database.

[0051] Step 5:

[0052] The server automatically generates a report based on the results of speech recognition and image recognition. The report includes a summary of the children's activities and emotional state.

[0053] Step 6:

[0054] The terminal receives the report sent from the server and notifies the staff. The staff then reviews the report and considers the next steps as needed.

[0055] Step 7:

[0056] Users view the generated reports via their own devices. Based on these reports, parents can understand their child's daily activities and provide feedback through their devices.

[0057] (Example 1)

[0058] 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."

[0059] In modern times, after-school care settings place a heavy workload on staff, making it particularly difficult to monitor and record children's activities and psychological states in real time. Furthermore, with the demand for detailed reports to parents, there is a need for an efficient system that simultaneously meets the demands of both the busy staff and the information processing needs. Conventional methods result in fragmented audio and video analysis and information provision, failing to deliver high-quality information that meets the needs of staff and parents. Therefore, this invention aims to build a system that integrates audio, video, and natural language processing to enable automated information provision.

[0060] 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.

[0061] In this invention, the server includes a voice conversion means for receiving voice information and converting it into text information, a video analysis means for receiving video information and analyzing the psychological state of the subject, and a means for extracting data related to actions from the text information using natural language processing. This makes it possible to grasp the real-time psychological state and activities of children in after-school care settings and to efficiently record and provide the necessary information.

[0062] "Voice conversion means" refers to a function or device for converting received voice information into text information.

[0063] "Video analysis means" refers to a function or device for analyzing the psychological state of a subject based on video information, and for recording or utilizing the results.

[0064] "Natural language processing means" refers to a function or device for extracting data related to actions from textual information and organizing that information.

[0065] "Report generation means" refers to a function or device for automatically generating a report based on the analyzed information.

[0066] "Communication means" refers to a function or device for transmitting analyzed information or notifications to a user.

[0067] "Information aggregation means" refers to a function or device that receives feedback from users, aggregates it as information, and uses it to improve the system.

[0068] An "information network" refers to a network that connects multiple information devices and allows them to exchange data with each other.

[0069] This invention is a system designed to reduce the workload of staff and streamline reporting to parents in after-school care settings. The server integrates and analyzes audio and video information to automatically record and report on the children's daily activities and psychological state.

[0070] Specifically, the server receives audio data transmitted from terminals operated by staff and converts it into text information using a speech recognition engine (e.g., a general speech recognition API) as a means of speech conversion. In addition, video data is analyzed using image recognition software (e.g., a general image recognition library) as a means of video analysis. This allows the server to detect the children's psychological state and activities from their facial expressions and movements. Based on this information, the server uses natural language processing to organize specific actions and situations and obtain analysis results.

[0071] Based on the analysis results, the server automatically generates a report using a report generation system. This report includes emotional states, activity details, and points that staff should pay particular attention to. The server also has communication mechanisms to notify staff and guardians of important information and to receive real-time communication and feedback. This feedback is collected by information aggregation mechanisms and used to improve the system.

[0072] Parents, as users, can view reports through their own devices, gaining a detailed understanding of their children's daily activities. This allows parents to take appropriate measures to support their children's healthy development. Furthermore, feedback from parents provides valuable data for future system performance improvements and on-site service enhancements.

[0073] A specific example of a prompt message is, "Your child has started a new game. Observe their activity and create a report on what they are learning and what emotions they are experiencing." In response to this prompt, the system collects relevant analytical data and generates a detailed report for parents.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server receives audio data transmitted from the terminal. It takes audio data as input and converts it into text information using a speech-to-text conversion method. Specifically, it activates a speech recognition engine, analyzes the audio data, and outputs text data. This text data is used for subsequent analysis.

[0077] Step 2:

[0078] The server receives video data transmitted from the terminal. It receives video data as input and analyzes the psychological state using video analysis tools. Using image recognition software, it analyzes the children's facial expressions and movements, and tags their activities and emotional states. The analyzed tag information is generated as output.

[0079] Step 3:

[0080] The server processes text data using natural language processing to extract information about actions. It receives textual information as input, performs natural language processing, and analyzes specific actions and situations. The output provides information about the characteristics of the actions and events.

[0081] Step 4:

[0082] The server generates a report using the analysis results. It integrates the analyzed text data, video analysis results, and behavioral information, and automatically creates a report using a report generation mechanism. The output is a report comprehensively detailing each child's activity history and emotional state, which is then made available for use.

[0083] Step 5:

[0084] The server sends the generated report to the terminal and notifies the staff. Using communication methods, the server provides staff with the report's contents and important information in real time. This allows staff to closely monitor the children's condition and take prompt action as needed.

[0085] Step 6:

[0086] Parents, as users, view the generated reports on their own devices. Through the application, parents can access the reports to gain a detailed understanding of their children's daily activities and emotions. This allows them to provide further support and follow-up at home.

[0087] Step 7:

[0088] Users submit feedback on reports to the system. This feedback information is aggregated on the server and used through data aggregation to improve the system and services in the future. This results in a better learning environment.

[0089] (Application Example 1)

[0090] 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."

[0091] In educational settings, there is a challenge in tracking individual learning progress in real time and providing appropriate guidance. Furthermore, there is a lack of means for parents to understand their child's learning progress and provide necessary feedback quickly. This makes it difficult to respond flexibly to learners' levels of concentration and psychological state, resulting in cases where learning effectiveness is not maximized.

[0092] 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.

[0093] In this invention, the server includes speech recognition means for receiving audio data and converting it into text data, image recognition means for receiving video data and analyzing the emotional state of an object, means for extracting information about behavior from text data using natural language processing means, and state monitoring means for tracking the level of concentration and responses of individuals in an educational setting. This enables detailed monitoring of the state of individual learners in educational settings, allowing for appropriate guidance and prompt provision of information to parents.

[0094] "Speech recognition means" refers to a device that has the function of receiving speech data and converting it into text data.

[0095] "Image recognition means" refers to a device that receives video data and has the function of analyzing the emotional state of an object.

[0096] "Natural language processing means" refers to a function that utilizes technology to extract information about behavior from text data.

[0097] A "report generation means" is a device that has the function of automatically creating a report based on the analysis results.

[0098] "Communication means" refers to a device that has the function of transmitting information using this data.

[0099] A "state monitoring device" is a device that has the function of tracking an individual's level of concentration and response in an educational setting.

[0100] A "communication network" is a network in which multiple devices are interconnected and used to exchange information.

[0101] A "device" is a set of hardware or software components designed to perform a specific function.

[0102] The system for carrying out this invention includes means for speech recognition, image recognition, natural language processing, report generation, communication, and status monitoring. The server receives audio and video data used in educational settings. For example, an instructor can use smart glasses to record students' speech and facial expressions in real time.

[0103] Audio data is converted to text using speech recognition software such as Google® Speech-to-Text API. This allows the server to quickly retrieve and analyze what students are saying. Image data is analyzed using image recognition software such as OpenCV or AWS® Rekognition to understand students' emotional states and behaviors. This analysis allows the server to understand students' concentration levels and psychological states, and send notifications to instructors and parents as needed.

[0104] Natural language processing is used to extract information about behavior from text data. This allows the server to analyze students' activity history based on their statements. Furthermore, a report generation system can automatically create a report on learning progress based on the analysis results and provide it to users, i.e., instructors and parents.

[0105] For example, if image recognition technology detects that a student is struggling to solve a problem, the instructor can immediately provide follow-up support. Furthermore, if a generative AI model detects a decrease in a learner's concentration level through natural language processing, the system can input a prompt such as, "Measure the student's concentration level and notify me in real time if they are losing focus."

[0106] In this way, the system facilitates individualized learning support in educational settings and provides an environment where learners can learn more efficiently.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server receives audio and video data from the educational setting. The input is real-time streaming data from smart glasses or tablets used by instructors. The server stores this data and prepares it for subsequent processing.

[0110] Step 2:

[0111] The server converts received audio data into text data using speech recognition technology. By utilizing the Google Speech-to-Text API, it generates text with high accuracy from the audio. The output of this process is text data documenting the content of the instructor's and students' speech.

[0112] Step 3:

[0113] The server analyzes video data using image recognition. The input is the video data received in step 1, and the server analyzes the students' facial expressions and movements using OpenCV or AWS Rekognition. The output is tag data indicating the students' emotional state and learning attitude.

[0114] Step 4:

[0115] The server uses natural language processing to extract information about behavior from the text data obtained in step 2. The input is text data, and a generative AI model is used to understand the topic of the discussion and the intent behind the statements. The output is an information set showing the results of the behavioral analysis.

[0116] Step 5:

[0117] Based on the analysis results from steps 3 and 4, the server automatically generates a report using the report generation mechanism. The report includes a comprehensive evaluation of the students' learning activities and emotional state. The output is a report that can be viewed by instructors and parents.

[0118] Step 6:

[0119] The server uses communication methods to send the generated report to the user's device, specifically to the instructor or parent. The input is the report generated in step 5, and the output is a digital report in a viewable format.

[0120] Step 7:

[0121] Users can view submitted reports and provide feedback on students' learning progress. This feedback is sent to the server and used to improve the system in the future. This process accumulates data that helps improve the quality of instruction in educational settings.

[0122] 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.

[0123] This invention improves the performance of a childcare system by incorporating voice recognition means, image recognition means, natural language processing means, report generation means, and notification means, and further integrating an emotion engine. This system provides an approach to gain a deeper understanding of the user's emotions and improve the quality of individualized care.

[0124] The server first receives the audio data transmitted from the terminal and converts it into text data through a speech recognition engine. This text data is then analyzed using natural language processing to extract information about the child's behavior. For example, if a staff member inputs "They're having fun playing games," the server adds the tags "games" and "excitement" to the text data.

[0125] Next, the server receives the video data. Image recognition measures analyze the child's facial expressions in the video, and the emotion engine uses the results to detect the emotional state. For example, from a video in which the child is smiling, the emotion of "joy" is recognized and recorded as data.

[0126] The analysis results from the emotion engine are reflected in the report in real time by the report generation system. The generated report provides detailed information on the children's emotional fluctuations and activity history, offering valuable information to staff and parents.

[0127] The terminals serve to display notifications and reports received from the server to the staff. Based on this information, the staff can monitor the children's condition in real time and take appropriate action. For example, if an alert appears on the terminal stating, "○○ seems a little anxious," the staff can consider the background and observe the child further.

[0128] Parents, as users, can view this report through a dedicated application. This allows parents to gain a detailed understanding of their child's daily activities and emotional changes, and to use this information in conversations at home.

[0129] In this way, this system uses three types of information—voice, video, and emotion—to provide a solution for more accurately understanding and efficiently responding to children's conditions.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The terminal receives voice input from the staff. Using speech recognition technology, the voice is converted into text data. For example, the voice saying "The weather is nice today" is converted into text.

[0133] Step 2:

[0134] The device acquires video data from a camera installed in the classroom and transmits it to a server via the network.

[0135] Step 3:

[0136] The server analyzes the received text data using natural language processing techniques to extract specific activities and situations. For example, "good weather" is organized as information related to an activity.

[0137] Step 4:

[0138] The server performs image recognition on the received video data and analyzes the child's facial expressions. The emotion engine estimates the emotional state from the facial expressions. For example, it recognizes "joy" from a video of a smile.

[0139] Step 5:

[0140] The server automatically generates daily reports using a report generation method based on the results of speech recognition, natural language processing, and emotion engine analysis. The reports include content such as, "Today, XX seemed to be having fun playing."

[0141] Step 6:

[0142] The terminal receives a report from the server and notifies the staff. The staff then reviews the information and provides support to the child as needed.

[0143] Step 7:

[0144] Parents, who are the users, can view reports generated through a dedicated application. This allows parents to understand their child's daytime activities and improve communication at home.

[0145] (Example 2)

[0146] 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".

[0147] Traditional after-school care systems have faced challenges in fully understanding children's emotions and behaviors by analyzing only audio and video information individually, making it difficult to improve the quality of individualized care. Furthermore, the generation of real-time reports and the integrated use of various information are limited, making it difficult for parents and staff to immediately grasp a child's condition.

[0148] 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.

[0149] In this invention, the server includes recognition means for receiving audio information and converting it into text information, recognition means for receiving video information and analyzing the emotional state of the subject, and means for extracting behavioral data from the text information using natural language processing technology. This enables comprehensive analysis of data from audio and video, allowing for a detailed understanding of emotions and behaviors.

[0150] "Audio information" refers to data transmitted through sound, and serves as input material for converting it into text information.

[0151] "Textual information" refers to string data obtained by converting audio information using recognition means, and is the information that is analyzed by natural language processing technology.

[0152] "Recognition means" refers to technologies and devices that process audio and video information and convert it into textual information or emotional states.

[0153] "Visual information" refers to visual data acquired through video, and is material used to analyze emotional states using recognition tools.

[0154] "Emotional state" refers to information that indicates the type and intensity of emotions of the subject, as analyzed from video information.

[0155] "Natural language processing technology" is a computer technology that analyzes textual information to extract specific actions or intentions.

[0156] A "report" is a document automatically generated based on the analysis results, intended to record and share the child's emotions and behavior.

[0157] An "information set" refers to a series of data that is updated by accumulating analysis results and feedback.

[0158] An "information and communication network" refers to a network infrastructure used for sending and receiving data, and is generally considered to be a system that connects multiple information terminals.

[0159] This invention provides a system for comprehensively understanding children's emotions and behaviors and providing appropriate responses within a school-age child care system. The system utilizes audio information, video information, and natural language processing technology. Specifically, it implements speech recognition technology to convert audio information into text information, image recognition technology to receive and analyze video information, and behavioral data extraction technology using natural language processing.

[0160] After receiving audio information, the server converts it into text using a speech recognition module. A commonly used speech recognition engine can be used for this process. The converted text is then analyzed by a natural language processing engine to extract information related to the behavior.

[0161] In addition, the server receives video information and uses image recognition technology to analyze the emotional state contained in the video. Face recognition algorithms and facial expression analysis algorithms are applied to the analysis, and the acquired emotional state is recorded in a database. These results are integrated by an advanced analysis engine and reflected in the final report in real time.

[0162] The device receives and displays generated notifications and reports to staff. Based on this information, staff can take immediate action. At the same time, parents, who are users, can view the reports through a dedicated application. This application allows them to check daily activities and emotional changes, and can be used to facilitate communication between parents and children.

[0163] As a concrete example, if you input the prompt "Please provide a report on today's school activities and emotions" into the generative AI model, the system will generate and provide a detailed report based on the analysis results. This allows parents to visualize various aspects of their child's day and obtain specific information to strengthen support at home.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server receives audio information from the terminal. Audio files containing recordings of everyday conversations and activities are input from the terminal. The server uses a speech recognition module to convert the audio information into text. This process includes filtering, such as noise reduction, and the output is text.

[0167] Step 2:

[0168] The server uses a natural language processing engine to analyze textual information as input. This engine performs grammatical analysis and extracts information related to behavior. Through this process, interesting keywords and phrases are identified from the input textual information, and tags are created based on these. The output is the analyzed behavioral information.

[0169] Step 3:

[0170] The server receives video information from the terminal and uses it as input. The received video is analyzed using image recognition technology. The server uses a facial recognition algorithm to analyze the emotional state of the child in the video. For example, emotions such as smiling or sadness are identified, and emotional state data is generated as output.

[0171] Step 4:

[0172] The server integrates the generated emotional state data and behavioral information. Using an emotion engine, it performs detailed emotional analysis based on this data. The integrated information is output as a report in real time through a report generation function. The report includes information such as the child's emotional fluctuations and activity history.

[0173] Step 5:

[0174] The terminal receives and displays reports and notifications provided by the server. Staff use this information as input to immediately understand the child's situation and take appropriate action. Based on the displayed specific activity status and emotions, staff action plans are formulated. As output, staff action records and feedback are obtained.

[0175] Step 6:

[0176] Parents, as users, can view the generated reports as input through a dedicated application. The application has an intuitive interface and features functions to track daily activities and emotional fluctuations. As output, parents can obtain information that can be used for support and communication at home.

[0177] (Application Example 2)

[0178] 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".

[0179] In commercial and service spaces, understanding users' emotions and interests in real time and providing effective responses and suggestions is not easy. In particular, for staff to provide appropriate communication and service on the spot, rapid and accurate information acquisition is essential. However, the process of manually acquiring this information is time-consuming and often inaccurate.

[0180] 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.

[0181] In this invention, the server includes a speech conversion means for receiving audio data and converting it into text data, an image analysis means for receiving video data and analyzing the emotional state of the subject, and a means for extracting information about behavior from the text data using natural language processing. This enables immediate interaction based on the user's emotions and behavior.

[0182] A "speech conversion means" is a means for receiving speech data and converting it into text data.

[0183] "Image analysis means" refers to a means for receiving video data and analyzing the emotional state of the subject within it.

[0184] "Natural language processing tools" are methods for extracting information about behavior from text data.

[0185] "Information generation means" refers to means for automatically generating reports based on analysis results.

[0186] "Notification delivery means" refers to a means of sending notifications to users using analyzed data.

[0187] "Interaction enhancement means" refers to methods for supporting immediate on-site responses using wearable devices.

[0188] A "communication network" is a network that connects voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means, enabling multiple devices to use it.

[0189] A specific system for carrying out this invention is a networked configuration including voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means. The system is designed to analyze users' emotions and behavior in real time in stores and service spaces and to provide appropriate responses.

[0190] The server first uses a speech-to-text converter to convert the audio data received from the user into text data. This process uses speech recognition software such as Google Cloud Speech-to-Text. Next, the image analysis converter receives the video data and analyzes the user's emotional state. Specifically, image recognition solutions such as Google Cloud Vision are used. The emotional data obtained from the image analysis is then analyzed by an emotion engine to determine emotions such as "joy" or "interest."

[0191] In parallel, natural language processing (NLP) is used to extract behavioral information from the text conversion results of the audio data. This extraction utilizes natural language processing with Google Cloud Natural Language. The analysis results are aggregated into an information generation system, and a report is automatically generated. The report visually shows the progression of the user's emotions and behavior, facilitating rapid decision-making. Users are notified in real time of events and responses via a notification system.

[0192] Furthermore, wearable devices are used as a means of enhancing interaction. A typical example is the use of smart glasses to receive real-time information feedback on-site and respond immediately. These devices are equipped with the ability to connect to the aforementioned server system via Bluetooth® or Wi-Fi.

[0193] For example, when a customer smiles while looking at a specific product in a store, the system recognizes this as "interest" and sends a notification to staff saying, "The customer is showing interest in the new sneakers," thereby supporting sales promotion activities. An example of a prompt using the generative AI model is, "Analyze the emotions from the facial expression of the customer looking at the new sneakers and generate a notification to promote sales."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server receives audio data from a terminal or wearable device. Using a speech-to-text conversion method, this audio data is converted into text data. The input is audio data, and the output is text data. Google Cloud Speech-to-Text is used to perform the audio-to-text conversion.

[0197] Step 2:

[0198] The server receives video data from a terminal or wearable device. Using image analysis tools, it analyzes the emotional state of the subject based on this video data. The input is video data, and the output is emotional state data. Using Google Cloud Vision, the customer's facial expressions are analyzed, and the emotion engine determines the emotion.

[0199] Step 3:

[0200] The server receives the text data obtained in Step 1 and extracts behavioral information using natural language processing. The input is text data, and the output is behavioral information data. Natural language processing is performed using Google Cloud Natural Language to analyze customer intent and behavior.

[0201] Step 4:

[0202] The server aggregates the outputs from steps 2 and 3 and automatically generates a report using an information generation mechanism. The input consists of emotional state data and behavioral information data, and the output is a report. This allows for a visual representation of the user's emotional progression and behavioral history.

[0203] Step 5:

[0204] The server sends notifications to terminals or wearable devices using notification delivery methods, based on the generated reports. The input is reports, and the output is real-time notifications. Users can instantly receive information about customer activities and emotions through wearable devices such as smart glasses.

[0205] Step 6:

[0206] Users receive notifications and respond to them within the store, offering service suggestions. Based on customer emotions and behavioral information, they implement optimal communication and product recommendations. Input is real-time notifications, and output is customer interaction. This step is expected to improve the customer experience and boost sales.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention combines speech recognition, image recognition, and natural language processing technologies in an after-school care system to reduce the workload of staff and streamline reporting to parents. The following describes a specific embodiment of the system and its operation.

[0224] The server functions as the central hub of this system, receiving audio and video data transmitted from terminals. First, the server uses a speech recognition engine to convert the received audio data into text. Based on this text data, it performs natural language processing to analyze specific actions and situations. For example, if a staff member inputs "The children are doing crafts," the server generates an activity tag called "crafts."

[0225] Meanwhile, the server performs image recognition on the video data to analyze the children's emotional state and behavior. If the system detects, for example, that a child is excited, it tags that state as "excited" and reflects it in the analysis results.

[0226] The server automatically generates a report using these analysis results. The report includes each child's emotional state and activity history, and is output in a format that can be used as a staff member's daily report or a report to parents.

[0227] The terminal functions as a staff operating interface, receiving voice input and feedback, as well as notifications from the server. Staff can use the terminal to monitor the children's condition in real time and take appropriate action as needed. For example, if the terminal receives a notification such as "○○ is a little quiet," staff can consider the child's feelings and take appropriate action.

[0228] Users, primarily parents, can view reports through the application from their own devices. This allows parents to gain a detailed understanding of their child's daily activities and submit feedback. This feedback information is collected on a server and used to improve the system in the future.

[0229] Thus, the system of the present invention, by effectively analyzing and providing information using voice, video, and natural language processing, enables higher quality individualized care in the setting of after-school care.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The terminal receives audio data from staff and converts it into text data using speech recognition technology. For example, if a staff member says, "The children will have their snack soon," the audio is converted to text in real time.

[0233] Step 2:

[0234] The terminal transmits video footage from a camera installed in the classroom to a server. The video includes the children's actions and facial expressions, which are used as data for image recognition.

[0235] Step 3:

[0236] The server applies natural language processing to the received text data to extract information about specific activities and situations. For example, it detects the word "snack" and records it as a tag.

[0237] Step 4:

[0238] The server performs image recognition on the video data to analyze the children's emotional state. For example, it detects "smiles" from facial expressions and saves the results to a database.

[0239] Step 5:

[0240] The server automatically generates a report based on the results of speech recognition and image recognition. The report includes a summary of the children's activities and emotional state.

[0241] Step 6:

[0242] The terminal receives the report sent from the server and notifies the staff. The staff then reviews the report and considers the next steps as needed.

[0243] Step 7:

[0244] Users view the generated reports via their own devices. Based on these reports, parents can understand their child's daily activities and provide feedback through their devices.

[0245] (Example 1)

[0246] 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."

[0247] In modern times, after-school care settings place a heavy workload on staff, making it particularly difficult to monitor and record children's activities and psychological states in real time. Furthermore, with the demand for detailed reports to parents, there is a need for an efficient system that simultaneously meets the demands of both the busy staff and the information processing needs. Conventional methods result in fragmented audio and video analysis and information provision, failing to deliver high-quality information that meets the needs of staff and parents. Therefore, this invention aims to build a system that integrates audio, video, and natural language processing to enable automated information provision.

[0248] 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.

[0249] In this invention, the server includes a voice conversion means for receiving voice information and converting it into text information, a video analysis means for receiving video information and analyzing the psychological state of the subject, and a means for extracting data related to actions from the text information using natural language processing. This makes it possible to grasp the real-time psychological state and activities of children in after-school care settings and to efficiently record and provide the necessary information.

[0250] "Voice conversion means" refers to a function or device for converting received voice information into text information.

[0251] "Video analysis means" refers to a function or device for analyzing the psychological state of a subject based on video information, and for recording or utilizing the results.

[0252] "Natural language processing means" refers to a function or device for extracting data related to actions from textual information and organizing that information.

[0253] "Report generation means" refers to a function or device for automatically generating a report based on the analyzed information.

[0254] "Communication means" refers to a function or device for transmitting analyzed information or notifications to a user.

[0255] "Information aggregation means" refers to a function or device that receives feedback from users, aggregates it as information, and uses it to improve the system.

[0256] An "information network" refers to a network that connects multiple information devices and allows them to exchange data with each other.

[0257] This invention is a system designed to reduce the workload of staff and streamline reporting to parents in after-school care settings. The server integrates and analyzes audio and video information to automatically record and report on the children's daily activities and psychological state.

[0258] Specifically, the server receives audio data transmitted from terminals operated by staff and converts it into text information using a speech recognition engine (e.g., a general speech recognition API) as a means of speech conversion. In addition, video data is analyzed using image recognition software (e.g., a general image recognition library) as a means of video analysis. This allows the server to detect the children's psychological state and activities from their facial expressions and movements. Based on this information, the server uses natural language processing to organize specific actions and situations and obtain analysis results.

[0259] Based on the analysis results, the server automatically generates a report using a report generation system. This report includes emotional states, activity details, and points that staff should pay particular attention to. The server also has communication mechanisms to notify staff and guardians of important information and to receive real-time communication and feedback. This feedback is collected by information aggregation mechanisms and used to improve the system.

[0260] Parents, as users, can view reports through their own devices, gaining a detailed understanding of their children's daily activities. This allows parents to take appropriate measures to support their children's healthy development. Furthermore, feedback from parents provides valuable data for future system performance improvements and on-site service enhancements.

[0261] A specific example of a prompt message is, "Your child has started a new game. Observe their activity and create a report on what they are learning and what emotions they are experiencing." In response to this prompt, the system collects relevant analytical data and generates a detailed report for parents.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The server receives audio data transmitted from the terminal. It takes audio data as input and converts it into text information using a speech-to-text conversion method. Specifically, it activates a speech recognition engine, analyzes the audio data, and outputs text data. This text data is used for subsequent analysis.

[0265] Step 2:

[0266] The server receives video data transmitted from the terminal. It receives video data as input and analyzes the psychological state using video analysis tools. Using image recognition software, it analyzes the children's facial expressions and movements, and tags their activities and emotional states. The analyzed tag information is generated as output.

[0267] Step 3:

[0268] The server processes text data using natural language processing to extract information about actions. It receives textual information as input, performs natural language processing, and analyzes specific actions and situations. The output provides information about the characteristics of the actions and events.

[0269] Step 4:

[0270] The server generates a report using the analysis results. It integrates the analyzed text data, video analysis results, and behavioral information, and automatically creates a report using a report generation mechanism. The output is a report comprehensively detailing each child's activity history and emotional state, which is then made available for use.

[0271] Step 5:

[0272] The server sends the generated report to the terminal and notifies the staff. Using communication methods, the server provides staff with the report's contents and important information in real time. This allows staff to closely monitor the children's condition and take prompt action as needed.

[0273] Step 6:

[0274] Parents, as users, view the generated reports on their own devices. Through the application, parents can access the reports to gain a detailed understanding of their children's daily activities and emotions. This allows them to provide further support and follow-up at home.

[0275] Step 7:

[0276] Users submit feedback on reports to the system. This feedback information is aggregated on the server and used through data aggregation to improve the system and services in the future. This results in a better learning environment.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In educational settings, there is a challenge in tracking individual learning progress in real time and providing appropriate guidance. Furthermore, there is a lack of means for parents to understand their child's learning progress and provide necessary feedback quickly. This makes it difficult to respond flexibly to learners' levels of concentration and psychological state, resulting in cases where learning effectiveness is not maximized.

[0280] 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.

[0281] In this invention, the server includes speech recognition means for receiving audio data and converting it into text data, image recognition means for receiving video data and analyzing the emotional state of an object, means for extracting information about behavior from text data using natural language processing means, and state monitoring means for tracking the level of concentration and responses of individuals in an educational setting. This enables detailed monitoring of the state of individual learners in educational settings, allowing for appropriate guidance and prompt provision of information to parents.

[0282] "Speech recognition means" refers to a device that has the function of receiving speech data and converting it into text data.

[0283] "Image recognition means" refers to a device that receives video data and has the function of analyzing the emotional state of an object.

[0284] The "natural language processing means" is a function that utilizes technologies for extracting information related to actions from text data.

[0285] The "report generation means" is a device having a function of automatically creating a report based on the analysis results.

[0286] The "communication means" is a device having a function of transmitting information using these data.

[0287] The "state monitoring means" is a device having a function of tracking the concentration and reactions of an individual in an educational setting.

[0288] The "communication network" is a network in which a plurality of devices are interconnected to exchange information.

[0289] The "device" is a hardware or software component designed to realize a specific function.

[0290] The system for implementing this invention includes means for speech recognition, image recognition, natural language processing, report generation, communication, and state monitoring. The server receives audio data and video data used in the educational field. For example, a teacher can use smart glasses to record the speech and expressions of students in real time.

[0291] The audio data is converted into text by speech recognition software such as Google Speech-to-Text API. Thereby, the server can quickly obtain and analyze the speech content of students. The image data is analyzed using image recognition software such as OpenCV or AWS Rekognition to analyze the emotional state and actions of students. Through this analysis, the server grasps the concentration and mental state of students and sends notifications to teachers or guardians as necessary.

[0292] Natural language processing is used to extract information about behavior from text data. This allows the server to analyze students' activity history based on their statements. Furthermore, a report generation system can automatically create a report on learning progress based on the analysis results and provide it to users, i.e., instructors and parents.

[0293] For example, if image recognition technology detects that a student is struggling to solve a problem, the instructor can immediately provide follow-up support. Furthermore, if a generative AI model detects a decrease in a learner's concentration level through natural language processing, the system can input a prompt such as, "Measure the student's concentration level and notify me in real time if they are losing focus."

[0294] In this way, the system facilitates individualized learning support in educational settings and provides an environment where learners can learn more efficiently.

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] The server receives audio and video data from the educational setting. The input is real-time streaming data from smart glasses or tablets used by instructors. The server stores this data and prepares it for subsequent processing.

[0298] Step 2:

[0299] The server converts received audio data into text data using speech recognition technology. By utilizing the Google Speech-to-Text API, it generates text with high accuracy from the audio. The output of this process is text data documenting the content of the instructor's and students' speech.

[0300] Step 3:

[0301] The server analyzes the video data using image recognition means. The input is the video data received in Step 1, and OpenCV or AWS Rekognition is used to analyze the expressions and actions of students. The output is tag data indicating the emotional state and learning attitude of the students.

[0302] Step 4:

[0303] The server extracts information related to actions from the text data obtained in Step 2 using natural language processing means. The input is the text data, and by using a generative AI model, the topic of the discussion and the intention of the speech are grasped. The output is an information set indicating the action analysis result.

[0304] Step 5:

[0305] Based on the analysis results of Step 3 and Step 4, the server automatically creates a report using the report generation means. The report includes a comprehensive evaluation of the learning activities and emotional state of the students. The output is a report that can be viewed by teachers and guardians.

[0306] Step 6:

[0307] The server transmits the report generated using the communication means to the user's terminal, specifically the terminal of the teacher or guardian. The input is the report generated in Step 5, and the output is a digital report in a viewable format.

[0308] [[ID=W28]] Step 7:

[0309] The user can view the transmitted report and provide feedback on the learning situation of the students. The feedback is transmitted to the server and utilized for system improvement in subsequent sessions. As a result, data for improving the quality of teaching in the educational field is accumulated.

[0310] 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.

[0311] This invention improves the performance of a childcare system by incorporating voice recognition means, image recognition means, natural language processing means, report generation means, and notification means, and further integrating an emotion engine. This system provides an approach to gain a deeper understanding of the user's emotions and improve the quality of individualized care.

[0312] The server first receives the audio data transmitted from the terminal and converts it into text data through a speech recognition engine. This text data is then analyzed using natural language processing to extract information about the child's behavior. For example, if a staff member inputs "They're having fun playing games," the server adds the tags "games" and "excitement" to the text data.

[0313] Next, the server receives the video data. Image recognition measures analyze the child's facial expressions in the video, and the emotion engine uses the results to detect the emotional state. For example, from a video in which the child is smiling, the emotion of "joy" is recognized and recorded as data.

[0314] The analysis results from the emotion engine are reflected in the report in real time by the report generation system. The generated report provides detailed information on the children's emotional fluctuations and activity history, offering valuable information to staff and parents.

[0315] The terminals serve to display notifications and reports received from the server to the staff. Based on this information, the staff can monitor the children's condition in real time and take appropriate action. For example, if an alert appears on the terminal stating, "○○ seems a little anxious," the staff can consider the background and observe the child further.

[0316] Parents, as users, can view this report through a dedicated application. This allows parents to gain a detailed understanding of their child's daily activities and emotional changes, and to use this information in conversations at home.

[0317] In this way, this system uses three types of information—voice, video, and emotion—to provide a solution for more accurately understanding and efficiently responding to children's conditions.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The terminal receives voice input from the staff. Using speech recognition technology, the voice is converted into text data. For example, the voice saying "The weather is nice today" is converted into text.

[0321] Step 2:

[0322] The device acquires video data from a camera installed in the classroom and transmits it to a server via the network.

[0323] Step 3:

[0324] The server analyzes the received text data using natural language processing techniques to extract specific activities and situations. For example, "good weather" is organized as information related to an activity.

[0325] Step 4:

[0326] The server performs image recognition on the received video data and analyzes the child's facial expressions. The emotion engine estimates the emotional state from the facial expressions. For example, it recognizes "joy" from a video of a smile.

[0327] Step 5:

[0328] The server automatically generates daily reports using a report generation method based on the results of speech recognition, natural language processing, and emotion engine analysis. The reports include content such as, "Today, XX seemed to be having fun playing."

[0329] Step 6:

[0330] The terminal receives a report from the server and notifies the staff. The staff then reviews the information and provides support to the child as needed.

[0331] Step 7:

[0332] Parents, who are the users, can view reports generated through a dedicated application. This allows parents to understand their child's daytime activities and improve communication at home.

[0333] (Example 2)

[0334] 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".

[0335] Traditional after-school care systems have faced challenges in fully understanding children's emotions and behaviors by analyzing only audio and video information individually, making it difficult to improve the quality of individualized care. Furthermore, the generation of real-time reports and the integrated use of various information are limited, making it difficult for parents and staff to immediately grasp a child's condition.

[0336] 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.

[0337] In this invention, the server includes recognition means for receiving audio information and converting it into text information, recognition means for receiving video information and analyzing the emotional state of the subject, and means for extracting behavioral data from the text information using natural language processing technology. This enables comprehensive analysis of data from audio and video, allowing for a detailed understanding of emotions and behaviors.

[0338] "Audio information" refers to data transmitted through sound, and serves as input material for converting it into text information.

[0339] "Textual information" refers to string data obtained by converting audio information using recognition means, and is the information that is analyzed by natural language processing technology.

[0340] "Recognition means" refers to technologies and devices that process audio and video information and convert it into textual information or emotional states.

[0341] "Visual information" refers to visual data acquired through video, and is material used to analyze emotional states using recognition tools.

[0342] "Emotional state" refers to information that indicates the type and intensity of emotions of the subject, as analyzed from video information.

[0343] "Natural language processing technology" is a computer technology that analyzes textual information to extract specific actions or intentions.

[0344] A "report" is a document automatically generated based on the analysis results, intended to record and share the child's emotions and behavior.

[0345] An "information set" refers to a series of data that is updated by accumulating analysis results and feedback.

[0346] An "information and communication network" refers to a network infrastructure used for sending and receiving data, and is generally considered to be a system that connects multiple information terminals.

[0347] This invention provides a system for comprehensively understanding children's emotions and behaviors and providing appropriate responses within a school-age child care system. The system utilizes audio information, video information, and natural language processing technology. Specifically, it implements speech recognition technology to convert audio information into text information, image recognition technology to receive and analyze video information, and behavioral data extraction technology using natural language processing.

[0348] After receiving audio information, the server converts it into text using a speech recognition module. A commonly used speech recognition engine can be used for this process. The converted text is then analyzed by a natural language processing engine to extract information related to the behavior.

[0349] In addition, the server receives video information and uses image recognition technology to analyze the emotional state contained in the video. Face recognition algorithms and facial expression analysis algorithms are applied to the analysis, and the acquired emotional state is recorded in a database. These results are integrated by an advanced analysis engine and reflected in the final report in real time.

[0350] The device receives and displays generated notifications and reports to staff. Based on this information, staff can take immediate action. At the same time, parents, who are users, can view the reports through a dedicated application. This application allows them to check daily activities and emotional changes, and can be used to facilitate communication between parents and children.

[0351] As a concrete example, if you input the prompt "Please provide a report on today's school activities and emotions" into the generative AI model, the system will generate and provide a detailed report based on the analysis results. This allows parents to visualize various aspects of their child's day and obtain specific information to strengthen support at home.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The server receives audio information from the terminal. Audio files containing recordings of everyday conversations and activities are input from the terminal. The server uses a speech recognition module to convert the audio information into text. This process includes filtering, such as noise reduction, and the output is text.

[0355] Step 2:

[0356] The server uses a natural language processing engine to analyze textual information as input. This engine performs grammatical analysis and extracts information related to behavior. Through this process, interesting keywords and phrases are identified from the input textual information, and tags are created based on these. The output is the analyzed behavioral information.

[0357] Step 3:

[0358] The server receives video information from the terminal and uses it as input. The received video is analyzed using image recognition technology. The server uses a facial recognition algorithm to analyze the emotional state of the child in the video. For example, emotions such as smiling or sadness are identified, and emotional state data is generated as output.

[0359] Step 4:

[0360] The server integrates the generated emotional state data and behavioral information. Using an emotion engine, it performs detailed emotional analysis based on this data. The integrated information is output as a report in real time through a report generation function. The report includes information such as the child's emotional fluctuations and activity history.

[0361] Step 5:

[0362] The terminal receives and displays reports and notifications provided by the server. Staff use this information as input to immediately understand the child's situation and take appropriate action. Based on the displayed specific activity status and emotions, staff action plans are formulated. As output, staff action records and feedback are obtained.

[0363] Step 6:

[0364] Parents, as users, can view the generated reports as input through a dedicated application. The application has an intuitive interface and features functions to track daily activities and emotional fluctuations. As output, parents can obtain information that can be used for support and communication at home.

[0365] (Application Example 2)

[0366] 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."

[0367] In commercial and service spaces, understanding users' emotions and interests in real time and providing effective responses and suggestions is not easy. In particular, for staff to provide appropriate communication and service on the spot, rapid and accurate information acquisition is essential. However, the process of manually acquiring this information is time-consuming and often inaccurate.

[0368] 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.

[0369] In this invention, the server includes a speech conversion means for receiving audio data and converting it into text data, an image analysis means for receiving video data and analyzing the emotional state of the subject, and a means for extracting information about behavior from the text data using natural language processing. This enables immediate interaction based on the user's emotions and behavior.

[0370] A "speech conversion means" is a means for receiving speech data and converting it into text data.

[0371] "Image analysis means" refers to a means for receiving video data and analyzing the emotional state of the subject within it.

[0372] "Natural language processing tools" are methods for extracting information about behavior from text data.

[0373] "Information generation means" refers to means for automatically generating reports based on analysis results.

[0374] "Notification delivery means" refers to a means of sending notifications to users using analyzed data.

[0375] "Interaction enhancement means" refers to methods for supporting immediate on-site responses using wearable devices.

[0376] A "communication network" is a network that connects voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means, enabling multiple devices to use it.

[0377] A specific system for carrying out this invention is a networked configuration including voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means. The system is designed to analyze users' emotions and behavior in real time in stores and service spaces and to provide appropriate responses.

[0378] The server first uses a speech-to-text converter to convert the audio data received from the user into text data. This process uses speech recognition software such as Google Cloud Speech-to-Text. Next, the image analysis converter receives the video data and analyzes the user's emotional state. Specifically, image recognition solutions such as Google Cloud Vision are used. The emotional data obtained from the image analysis is then analyzed by an emotion engine to determine emotions such as "joy" or "interest."

[0379] In parallel, natural language processing (NLP) is used to extract behavioral information from the text conversion results of the audio data. This extraction utilizes natural language processing with Google Cloud Natural Language. The analysis results are aggregated into an information generation system, and a report is automatically generated. The report visually shows the progression of the user's emotions and behavior, facilitating rapid decision-making. Users are notified in real time of events and responses via a notification system.

[0380] Furthermore, wearable devices are used as a means of enhancing interaction. A typical example is the use of smart glasses to receive real-time information feedback on-site and respond immediately. These devices are equipped with the ability to connect to the aforementioned server system via Bluetooth or Wi-Fi.

[0381] For example, when a customer smiles while looking at a specific product in a store, the system recognizes this as "interest" and sends a notification to staff saying, "The customer is showing interest in the new sneakers," thereby supporting sales promotion activities. An example of a prompt using the generative AI model is, "Analyze the emotions from the facial expression of the customer looking at the new sneakers and generate a notification to promote sales."

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server receives audio data from a terminal or wearable device. Using a speech-to-text conversion method, this audio data is converted into text data. The input is audio data, and the output is text data. Google Cloud Speech-to-Text is used to perform the audio-to-text conversion.

[0385] Step 2:

[0386] The server receives video data from a terminal or wearable device. Using image analysis tools, it analyzes the emotional state of the subject based on this video data. The input is video data, and the output is emotional state data. Using Google Cloud Vision, the customer's facial expressions are analyzed, and the emotion engine determines the emotion.

[0387] Step 3:

[0388] The server receives the text data obtained in Step 1 and extracts behavioral information using natural language processing. The input is text data, and the output is behavioral information data. Natural language processing is performed using Google Cloud Natural Language to analyze customer intent and behavior.

[0389] Step 4:

[0390] The server aggregates the outputs from steps 2 and 3 and automatically generates a report using an information generation mechanism. The input consists of emotional state data and behavioral information data, and the output is a report. This allows for a visual representation of the user's emotional progression and behavioral history.

[0391] Step 5:

[0392] The server sends notifications to terminals or wearable devices using notification delivery methods, based on the generated reports. The input is reports, and the output is real-time notifications. Users can instantly receive information about customer activities and emotions through wearable devices such as smart glasses.

[0393] Step 6:

[0394] Users receive notifications and respond to them within the store, offering service suggestions. Based on customer emotions and behavioral information, they implement optimal communication and product recommendations. Input is real-time notifications, and output is customer interaction. This step is expected to improve the customer experience and boost sales.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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".

[0411] This invention combines speech recognition, image recognition, and natural language processing technologies in an after-school care system to reduce the workload of staff and streamline reporting to parents. The following describes a specific embodiment of the system and its operation.

[0412] The server functions as the central hub of this system, receiving audio and video data transmitted from terminals. First, the server uses a speech recognition engine to convert the received audio data into text. Based on this text data, it performs natural language processing to analyze specific actions and situations. For example, if a staff member inputs "The children are doing crafts," the server generates an activity tag called "crafts."

[0413] Meanwhile, the server performs image recognition on the video data to analyze the children's emotional state and behavior. If the system detects, for example, that a child is excited, it tags that state as "excited" and reflects it in the analysis results.

[0414] The server automatically generates a report using these analysis results. The report includes each child's emotional state and activity history, and is output in a format that can be used as a staff member's daily report or a report to parents.

[0415] The terminal functions as a staff operating interface, receiving voice input and feedback, as well as notifications from the server. Staff can use the terminal to monitor the children's condition in real time and take appropriate action as needed. For example, if the terminal receives a notification such as "○○ is a little quiet," staff can consider the child's feelings and take appropriate action.

[0416] Users, primarily parents, can view reports through the application from their own devices. This allows parents to gain a detailed understanding of their child's daily activities and submit feedback. This feedback information is collected on a server and used to improve the system in the future.

[0417] Thus, the system of the present invention, by effectively analyzing and providing information using voice, video, and natural language processing, enables higher quality individualized care in the setting of after-school care.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The terminal receives audio data from staff and converts it into text data using speech recognition technology. For example, if a staff member says, "The children will have their snack soon," the audio is converted to text in real time.

[0421] Step 2:

[0422] The terminal transmits video footage from a camera installed in the classroom to a server. The video includes the children's actions and facial expressions, which are used as data for image recognition.

[0423] Step 3:

[0424] The server applies natural language processing to the received text data to extract information about specific activities and situations. For example, it detects the word "snack" and records it as a tag.

[0425] Step 4:

[0426] The server performs image recognition on the video data to analyze the children's emotional state. For example, it detects "smiles" from facial expressions and saves the results to a database.

[0427] Step 5:

[0428] The server automatically generates a report based on the results of speech recognition and image recognition. The report includes a summary of the children's activities and emotional state.

[0429] Step 6:

[0430] The terminal receives the report sent from the server and notifies the staff. The staff then reviews the report and considers the next steps as needed.

[0431] Step 7:

[0432] Users view the generated reports via their own devices. Based on these reports, parents can understand their child's daily activities and provide feedback through their devices.

[0433] (Example 1)

[0434] 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."

[0435] In modern times, after-school care settings place a heavy workload on staff, making it particularly difficult to monitor and record children's activities and psychological states in real time. Furthermore, with the demand for detailed reports to parents, there is a need for an efficient system that simultaneously meets the demands of both the busy staff and the information processing needs. Conventional methods result in fragmented audio and video analysis and information provision, failing to deliver high-quality information that meets the needs of staff and parents. Therefore, this invention aims to build a system that integrates audio, video, and natural language processing to enable automated information provision.

[0436] 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.

[0437] In this invention, the server includes a voice conversion means for receiving voice information and converting it into text information, a video analysis means for receiving video information and analyzing the psychological state of the subject, and a means for extracting data related to actions from the text information using natural language processing. This makes it possible to grasp the real-time psychological state and activities of children in after-school care settings and to efficiently record and provide the necessary information.

[0438] "Voice conversion means" refers to a function or device for converting received voice information into text information.

[0439] "Video analysis means" refers to a function or device for analyzing the psychological state of a subject based on video information, and for recording or utilizing the results.

[0440] "Natural language processing means" refers to a function or device for extracting data related to actions from textual information and organizing that information.

[0441] "Report generation means" refers to a function or device for automatically generating a report based on the analyzed information.

[0442] "Communication means" refers to a function or device for transmitting analyzed information or notifications to a user.

[0443] "Information aggregation means" refers to a function or device that receives feedback from users, aggregates it as information, and uses it to improve the system.

[0444] An "information network" refers to a network that connects multiple information devices and allows them to exchange data with each other.

[0445] This invention is a system designed to reduce the workload of staff and streamline reporting to parents in after-school care settings. The server integrates and analyzes audio and video information to automatically record and report on the children's daily activities and psychological state.

[0446] Specifically, the server receives audio data transmitted from terminals operated by staff and converts it into text information using a speech recognition engine (e.g., a general speech recognition API) as a means of speech conversion. In addition, video data is analyzed using image recognition software (e.g., a general image recognition library) as a means of video analysis. This allows the server to detect the children's psychological state and activities from their facial expressions and movements. Based on this information, the server uses natural language processing to organize specific actions and situations and obtain analysis results.

[0447] Based on the analysis results, the server automatically generates a report using a report generation system. This report includes emotional states, activity details, and points that staff should pay particular attention to. The server also has communication mechanisms to notify staff and guardians of important information and to receive real-time communication and feedback. This feedback is collected by information aggregation mechanisms and used to improve the system.

[0448] Parents, as users, can view reports through their own devices, gaining a detailed understanding of their children's daily activities. This allows parents to take appropriate measures to support their children's healthy development. Furthermore, feedback from parents provides valuable data for future system performance improvements and on-site service enhancements.

[0449] A specific example of a prompt message is, "Your child has started a new game. Observe their activity and create a report on what they are learning and what emotions they are experiencing." In response to this prompt, the system collects relevant analytical data and generates a detailed report for parents.

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] The server receives audio data transmitted from the terminal. It takes audio data as input and converts it into text information using a speech-to-text conversion method. Specifically, it activates a speech recognition engine, analyzes the audio data, and outputs text data. This text data is used for subsequent analysis.

[0453] Step 2:

[0454] The server receives video data transmitted from the terminal. It receives video data as input and analyzes the psychological state using video analysis tools. Using image recognition software, it analyzes the children's facial expressions and movements, and tags their activities and emotional states. The analyzed tag information is generated as output.

[0455] Step 3:

[0456] The server processes text data using natural language processing to extract information about actions. It receives textual information as input, performs natural language processing, and analyzes specific actions and situations. The output provides information about the characteristics of the actions and events.

[0457] Step 4:

[0458] The server generates a report using the analysis results. It integrates the analyzed text data, video analysis results, and behavioral information, and automatically creates a report using a report generation mechanism. The output is a report comprehensively detailing each child's activity history and emotional state, which is then made available for use.

[0459] Step 5:

[0460] The server sends the generated report to the terminal and notifies the staff. Using communication methods, the server provides staff with the report's contents and important information in real time. This allows staff to closely monitor the children's condition and take prompt action as needed.

[0461] Step 6:

[0462] Parents, as users, view the generated reports on their own devices. Through the application, parents can access the reports to gain a detailed understanding of their children's daily activities and emotions. This allows them to provide further support and follow-up at home.

[0463] Step 7:

[0464] Users submit feedback on reports to the system. This feedback information is aggregated on the server and used through data aggregation to improve the system and services in the future. This results in a better learning environment.

[0465] (Application Example 1)

[0466] 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."

[0467] In educational settings, there is a challenge in tracking individual learning progress in real time and providing appropriate guidance. Furthermore, there is a lack of means for parents to understand their child's learning progress and provide necessary feedback quickly. This makes it difficult to respond flexibly to learners' levels of concentration and psychological state, resulting in cases where learning effectiveness is not maximized.

[0468] 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.

[0469] In this invention, the server includes speech recognition means for receiving audio data and converting it into text data, image recognition means for receiving video data and analyzing the emotional state of an object, means for extracting information about behavior from text data using natural language processing means, and state monitoring means for tracking the level of concentration and responses of individuals in an educational setting. This enables detailed monitoring of the state of individual learners in educational settings, allowing for appropriate guidance and prompt provision of information to parents.

[0470] "Speech recognition means" refers to a device that has the function of receiving speech data and converting it into text data.

[0471] "Image recognition means" refers to a device that receives video data and has the function of analyzing the emotional state of an object.

[0472] "Natural language processing means" refers to a function that utilizes technology to extract information about behavior from text data.

[0473] A "report generation means" is a device that has the function of automatically creating a report based on the analysis results.

[0474] "Communication means" refers to a device that has the function of transmitting information using this data.

[0475] A "state monitoring device" is a device that has the function of tracking an individual's level of concentration and response in an educational setting.

[0476] A "communication network" is a network in which multiple devices are interconnected and used to exchange information.

[0477] A "device" is a set of hardware or software components designed to perform a specific function.

[0478] The system for carrying out this invention includes means for speech recognition, image recognition, natural language processing, report generation, communication, and status monitoring. The server receives audio and video data used in educational settings. For example, an instructor can use smart glasses to record students' speech and facial expressions in real time.

[0479] Audio data is converted to text using speech recognition software such as the Google Speech-to-Text API. This allows the server to quickly retrieve and analyze what students are saying. Image data is analyzed using image recognition software such as OpenCV or AWS Rekognition to understand students' emotional states and behaviors. This analysis allows the server to understand students' concentration levels and psychological states, and send notifications to instructors and parents as needed.

[0480] Natural language processing is used to extract information about behavior from text data. This allows the server to analyze students' activity history based on their statements. Furthermore, a report generation system can automatically create a report on learning progress based on the analysis results and provide it to users, i.e., instructors and parents.

[0481] For example, if image recognition technology detects that a student is struggling to solve a problem, the instructor can immediately provide follow-up support. Furthermore, if a generative AI model detects a decrease in a learner's concentration level through natural language processing, the system can input a prompt such as, "Measure the student's concentration level and notify me in real time if they are losing focus."

[0482] In this way, the system facilitates individualized learning support in educational settings and provides an environment where learners can learn more efficiently.

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] The server receives audio and video data from the educational setting. The input is real-time streaming data from smart glasses or tablets used by instructors. The server stores this data and prepares it for subsequent processing.

[0486] Step 2:

[0487] The server converts received audio data into text data using speech recognition technology. By utilizing the Google Speech-to-Text API, it generates text with high accuracy from the audio. The output of this process is text data documenting the content of the instructor's and students' speech.

[0488] Step 3:

[0489] The server analyzes video data using image recognition. The input is the video data received in step 1, and the server analyzes the students' facial expressions and movements using OpenCV or AWS Rekognition. The output is tag data indicating the students' emotional state and learning attitude.

[0490] Step 4:

[0491] The server uses natural language processing to extract information about behavior from the text data obtained in step 2. The input is text data, and a generative AI model is used to understand the topic of the discussion and the intent behind the statements. The output is an information set showing the results of the behavioral analysis.

[0492] Step 5:

[0493] Based on the analysis results from steps 3 and 4, the server automatically generates a report using the report generation mechanism. The report includes a comprehensive evaluation of the students' learning activities and emotional state. The output is a report that can be viewed by instructors and parents.

[0494] Step 6:

[0495] The server uses communication methods to send the generated report to the user's device, specifically to the instructor or parent. The input is the report generated in step 5, and the output is a digital report in a viewable format.

[0496] Step 7:

[0497] Users can view submitted reports and provide feedback on students' learning progress. This feedback is sent to the server and used to improve the system in the future. This process accumulates data that helps improve the quality of instruction in educational settings.

[0498] 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.

[0499] This invention improves the performance of a childcare system by incorporating voice recognition means, image recognition means, natural language processing means, report generation means, and notification means, and further integrating an emotion engine. This system provides an approach to gain a deeper understanding of the user's emotions and improve the quality of individualized care.

[0500] The server first receives the audio data transmitted from the terminal and converts it into text data through a speech recognition engine. This text data is then analyzed using natural language processing to extract information about the child's behavior. For example, if a staff member inputs "They're having fun playing games," the server adds the tags "games" and "excitement" to the text data.

[0501] Next, the server receives the video data. Image recognition measures analyze the child's facial expressions in the video, and the emotion engine uses the results to detect the emotional state. For example, from a video in which the child is smiling, the emotion of "joy" is recognized and recorded as data.

[0502] The analysis results from the emotion engine are reflected in the report in real time by the report generation system. The generated report provides detailed information on the children's emotional fluctuations and activity history, offering valuable information to staff and parents.

[0503] The terminals serve to display notifications and reports received from the server to the staff. Based on this information, the staff can monitor the children's condition in real time and take appropriate action. For example, if an alert appears on the terminal stating, "○○ seems a little anxious," the staff can consider the background and observe the child further.

[0504] Parents, as users, can view this report through a dedicated application. This allows parents to gain a detailed understanding of their child's daily activities and emotional changes, and to use this information in conversations at home.

[0505] In this way, this system uses three types of information—voice, video, and emotion—to provide a solution for more accurately understanding and efficiently responding to children's conditions.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The terminal receives voice input from the staff. Using speech recognition technology, the voice is converted into text data. For example, the voice saying "The weather is nice today" is converted into text.

[0509] Step 2:

[0510] The device acquires video data from a camera installed in the classroom and transmits it to a server via the network.

[0511] Step 3:

[0512] The server analyzes the received text data using natural language processing techniques to extract specific activities and situations. For example, "good weather" is organized as information related to an activity.

[0513] Step 4:

[0514] The server performs image recognition on the received video data and analyzes the child's facial expressions. The emotion engine estimates the emotional state from the facial expressions. For example, it recognizes "joy" from a video of a smile.

[0515] Step 5:

[0516] The server automatically generates daily reports using a report generation method based on the results of speech recognition, natural language processing, and emotion engine analysis. The reports include content such as, "Today, XX seemed to be having fun playing."

[0517] Step 6:

[0518] The terminal receives a report from the server and notifies the staff. The staff then reviews the information and provides support to the child as needed.

[0519] Step 7:

[0520] Parents, who are the users, can view reports generated through a dedicated application. This allows parents to understand their child's daytime activities and improve communication at home.

[0521] (Example 2)

[0522] 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."

[0523] Traditional after-school care systems have faced challenges in fully understanding children's emotions and behaviors by analyzing only audio and video information individually, making it difficult to improve the quality of individualized care. Furthermore, the generation of real-time reports and the integrated use of various information are limited, making it difficult for parents and staff to immediately grasp a child's condition.

[0524] 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.

[0525] In this invention, the server includes recognition means for receiving audio information and converting it into text information, recognition means for receiving video information and analyzing the emotional state of the subject, and means for extracting behavioral data from the text information using natural language processing technology. This enables comprehensive analysis of data from audio and video, allowing for a detailed understanding of emotions and behaviors.

[0526] "Audio information" refers to data transmitted through sound, and serves as input material for converting it into text information.

[0527] "Textual information" refers to string data obtained by converting audio information using recognition means, and is the information that is analyzed by natural language processing technology.

[0528] "Recognition means" refers to technologies and devices that process audio and video information and convert it into textual information or emotional states.

[0529] "Visual information" refers to visual data acquired through video, and is material used to analyze emotional states using recognition tools.

[0530] "Emotional state" refers to information that indicates the type and intensity of emotions of the subject, as analyzed from video information.

[0531] "Natural language processing technology" is a computer technology that analyzes textual information to extract specific actions or intentions.

[0532] A "report" is a document automatically generated based on the analysis results, intended to record and share the child's emotions and behavior.

[0533] An "information set" refers to a series of data that is updated by accumulating analysis results and feedback.

[0534] An "information and communication network" refers to a network infrastructure used for sending and receiving data, and is generally considered to be a system that connects multiple information terminals.

[0535] This invention provides a system for comprehensively understanding children's emotions and behaviors and providing appropriate responses within a school-age child care system. The system utilizes audio information, video information, and natural language processing technology. Specifically, it implements speech recognition technology to convert audio information into text information, image recognition technology to receive and analyze video information, and behavioral data extraction technology using natural language processing.

[0536] After receiving audio information, the server converts it into text using a speech recognition module. A commonly used speech recognition engine can be used for this process. The converted text is then analyzed by a natural language processing engine to extract information related to the behavior.

[0537] In addition, the server receives video information and uses image recognition technology to analyze the emotional state contained in the video. Face recognition algorithms and facial expression analysis algorithms are applied to the analysis, and the acquired emotional state is recorded in a database. These results are integrated by an advanced analysis engine and reflected in the final report in real time.

[0538] The device receives and displays generated notifications and reports to staff. Based on this information, staff can take immediate action. At the same time, parents, who are users, can view the reports through a dedicated application. This application allows them to check daily activities and emotional changes, and can be used to facilitate communication between parents and children.

[0539] As a concrete example, if you input the prompt "Please provide a report on today's school activities and emotions" into the generative AI model, the system will generate and provide a detailed report based on the analysis results. This allows parents to visualize various aspects of their child's day and obtain specific information to strengthen support at home.

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] The server receives audio information from the terminal. Audio files containing recordings of everyday conversations and activities are input from the terminal. The server uses a speech recognition module to convert the audio information into text. This process includes filtering, such as noise reduction, and the output is text.

[0543] Step 2:

[0544] The server uses a natural language processing engine to analyze textual information as input. This engine performs grammatical analysis and extracts information related to behavior. Through this process, interesting keywords and phrases are identified from the input textual information, and tags are created based on these. The output is the analyzed behavioral information.

[0545] Step 3:

[0546] The server receives video information from the terminal and uses it as input. The received video is analyzed using image recognition technology. The server uses a facial recognition algorithm to analyze the emotional state of the child in the video. For example, emotions such as smiling or sadness are identified, and emotional state data is generated as output.

[0547] Step 4:

[0548] The server integrates the generated emotional state data and behavioral information. Using an emotion engine, it performs detailed emotional analysis based on this data. The integrated information is output as a report in real time through a report generation function. The report includes information such as the child's emotional fluctuations and activity history.

[0549] Step 5:

[0550] The terminal receives and displays reports and notifications provided by the server. Staff use this information as input to immediately understand the child's situation and take appropriate action. Based on the displayed specific activity status and emotions, staff action plans are formulated. As output, staff action records and feedback are obtained.

[0551] Step 6:

[0552] Parents, as users, can view the generated reports as input through a dedicated application. The application has an intuitive interface and features functions to track daily activities and emotional fluctuations. As output, parents can obtain information that can be used for support and communication at home.

[0553] (Application Example 2)

[0554] 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."

[0555] In commercial and service spaces, understanding users' emotions and interests in real time and providing effective responses and suggestions is not easy. In particular, for staff to provide appropriate communication and service on the spot, rapid and accurate information acquisition is essential. However, the process of manually acquiring this information is time-consuming and often inaccurate.

[0556] 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.

[0557] In this invention, the server includes a speech conversion means for receiving audio data and converting it into text data, an image analysis means for receiving video data and analyzing the emotional state of the subject, and a means for extracting information about behavior from the text data using natural language processing. This enables immediate interaction based on the user's emotions and behavior.

[0558] A "speech conversion means" is a means for receiving speech data and converting it into text data.

[0559] "Image analysis means" refers to a means for receiving video data and analyzing the emotional state of the subject within it.

[0560] "Natural language processing tools" are methods for extracting information about behavior from text data.

[0561] "Information generation means" refers to means for automatically generating reports based on analysis results.

[0562] "Notification delivery means" refers to a means of sending notifications to users using analyzed data.

[0563] "Interaction enhancement means" refers to methods for supporting immediate on-site responses using wearable devices.

[0564] A "communication network" is a network that connects voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means, enabling multiple devices to use it.

[0565] A specific system for carrying out this invention is a networked configuration including voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means. The system is designed to analyze users' emotions and behavior in real time in stores and service spaces and to provide appropriate responses.

[0566] The server first uses a speech-to-text converter to convert the audio data received from the user into text data. This process uses speech recognition software such as Google Cloud Speech-to-Text. Next, the image analysis converter receives the video data and analyzes the user's emotional state. Specifically, image recognition solutions such as Google Cloud Vision are used. The emotional data obtained from the image analysis is then analyzed by an emotion engine to determine emotions such as "joy" or "interest."

[0567] In parallel, natural language processing (NLP) is used to extract behavioral information from the text conversion results of the audio data. This extraction utilizes natural language processing with Google Cloud Natural Language. The analysis results are aggregated into an information generation system, and a report is automatically generated. The report visually shows the progression of the user's emotions and behavior, facilitating rapid decision-making. Users are notified in real time of events and responses via a notification system.

[0568] Furthermore, wearable devices are used as a means of enhancing interaction. A typical example is the use of smart glasses to receive real-time information feedback on-site and respond immediately. These devices are equipped with the ability to connect to the aforementioned server system via Bluetooth or Wi-Fi.

[0569] For example, when a customer smiles while looking at a specific product in a store, the system recognizes this as "interest" and sends a notification to staff saying, "The customer is showing interest in the new sneakers," thereby supporting sales promotion activities. An example of a prompt using the generative AI model is, "Analyze the emotions from the facial expression of the customer looking at the new sneakers and generate a notification to promote sales."

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The server receives audio data from a terminal or wearable device. Using a speech-to-text conversion method, this audio data is converted into text data. The input is audio data, and the output is text data. Google Cloud Speech-to-Text is used to perform the audio-to-text conversion.

[0573] Step 2:

[0574] The server receives video data from a terminal or wearable device. Using image analysis tools, it analyzes the emotional state of the subject based on this video data. The input is video data, and the output is emotional state data. Using Google Cloud Vision, the customer's facial expressions are analyzed, and the emotion engine determines the emotion.

[0575] Step 3:

[0576] The server receives the text data obtained in Step 1 and extracts behavioral information using natural language processing. The input is text data, and the output is behavioral information data. Natural language processing is performed using Google Cloud Natural Language to analyze customer intent and behavior.

[0577] Step 4:

[0578] The server aggregates the outputs from steps 2 and 3 and automatically generates a report using an information generation mechanism. The input consists of emotional state data and behavioral information data, and the output is a report. This allows for a visual representation of the user's emotional progression and behavioral history.

[0579] Step 5:

[0580] The server sends notifications to terminals or wearable devices using notification delivery methods, based on the generated reports. The input is reports, and the output is real-time notifications. Users can instantly receive information about customer activities and emotions through wearable devices such as smart glasses.

[0581] Step 6:

[0582] Users receive notifications and respond to them within the store, offering service suggestions. Based on customer emotions and behavioral information, they implement optimal communication and product recommendations. Input is real-time notifications, and output is customer interaction. This step is expected to improve the customer experience and boost sales.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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".

[0600] This invention combines speech recognition, image recognition, and natural language processing technologies in an after-school care system to reduce the workload of staff and streamline reporting to parents. The following describes a specific embodiment of the system and its operation.

[0601] The server functions as the central hub of this system, receiving audio and video data transmitted from terminals. First, the server uses a speech recognition engine to convert the received audio data into text. Based on this text data, it performs natural language processing to analyze specific actions and situations. For example, if a staff member inputs "The children are doing crafts," the server generates an activity tag called "crafts."

[0602] Meanwhile, the server performs image recognition on the video data to analyze the children's emotional state and behavior. If the system detects, for example, that a child is excited, it tags that state as "excited" and reflects it in the analysis results.

[0603] The server automatically generates a report using these analysis results. The report includes each child's emotional state and activity history, and is output in a format that can be used as a staff member's daily report or a report to parents.

[0604] The terminal functions as a staff operating interface, receiving voice input and feedback, as well as notifications from the server. Staff can use the terminal to monitor the children's condition in real time and take appropriate action as needed. For example, if the terminal receives a notification such as "○○ is a little quiet," staff can consider the child's feelings and take appropriate action.

[0605] Users, primarily parents, can view reports through the application from their own devices. This allows parents to gain a detailed understanding of their child's daily activities and submit feedback. This feedback information is collected on a server and used to improve the system in the future.

[0606] Thus, the system of the present invention, by effectively analyzing and providing information using voice, video, and natural language processing, enables higher quality individualized care in the setting of after-school care.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The terminal receives audio data from staff and converts it into text data using speech recognition technology. For example, if a staff member says, "The children will have their snack soon," the audio is converted to text in real time.

[0610] Step 2:

[0611] The terminal transmits video footage from a camera installed in the classroom to a server. The video includes the children's actions and facial expressions, which are used as data for image recognition.

[0612] Step 3:

[0613] The server applies natural language processing to the received text data to extract information about specific activities and situations. For example, it detects the word "snack" and records it as a tag.

[0614] Step 4:

[0615] The server performs image recognition on the video data to analyze the children's emotional state. For example, it detects "smiles" from facial expressions and saves the results to a database.

[0616] Step 5:

[0617] The server automatically generates a report based on the results of speech recognition and image recognition. The report includes a summary of the children's activities and emotional state.

[0618] Step 6:

[0619] The terminal receives the report sent from the server and notifies the staff. The staff then reviews the report and considers the next steps as needed.

[0620] Step 7:

[0621] Users view the generated reports via their own devices. Based on these reports, parents can understand their child's daily activities and provide feedback through their devices.

[0622] (Example 1)

[0623] 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".

[0624] In modern times, after-school care settings place a heavy workload on staff, making it particularly difficult to monitor and record children's activities and psychological states in real time. Furthermore, with the demand for detailed reports to parents, there is a need for an efficient system that simultaneously meets the demands of both the busy staff and the information processing needs. Conventional methods result in fragmented audio and video analysis and information provision, failing to deliver high-quality information that meets the needs of staff and parents. Therefore, this invention aims to build a system that integrates audio, video, and natural language processing to enable automated information provision.

[0625] 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.

[0626] In this invention, the server includes a voice conversion means for receiving voice information and converting it into text information, a video analysis means for receiving video information and analyzing the psychological state of the subject, and a means for extracting data related to actions from the text information using natural language processing. This makes it possible to grasp the real-time psychological state and activities of children in after-school care settings and to efficiently record and provide the necessary information.

[0627] "Voice conversion means" refers to a function or device for converting received voice information into text information.

[0628] "Video analysis means" refers to a function or device for analyzing the psychological state of a subject based on video information, and for recording or utilizing the results.

[0629] "Natural language processing means" refers to a function or device for extracting data related to actions from textual information and organizing that information.

[0630] "Report generation means" refers to a function or device for automatically generating a report based on the analyzed information.

[0631] "Communication means" refers to a function or device for transmitting analyzed information or notifications to a user.

[0632] "Information aggregation means" refers to a function or device that receives feedback from users, aggregates it as information, and uses it to improve the system.

[0633] An "information network" refers to a network that connects multiple information devices and allows them to exchange data with each other.

[0634] This invention is a system designed to reduce the workload of staff and streamline reporting to parents in after-school care settings. The server integrates and analyzes audio and video information to automatically record and report on the children's daily activities and psychological state.

[0635] Specifically, the server receives audio data transmitted from terminals operated by staff and converts it into text information using a speech recognition engine (e.g., a general speech recognition API) as a means of speech conversion. In addition, video data is analyzed using image recognition software (e.g., a general image recognition library) as a means of video analysis. This allows the server to detect the children's psychological state and activities from their facial expressions and movements. Based on this information, the server uses natural language processing to organize specific actions and situations and obtain analysis results.

[0636] Based on the analysis results, the server automatically generates a report using a report generation system. This report includes emotional states, activity details, and points that staff should pay particular attention to. The server also has communication mechanisms to notify staff and guardians of important information and to receive real-time communication and feedback. This feedback is collected by information aggregation mechanisms and used to improve the system.

[0637] Parents, as users, can view reports through their own devices, gaining a detailed understanding of their children's daily activities. This allows parents to take appropriate measures to support their children's healthy development. Furthermore, feedback from parents provides valuable data for future system performance improvements and on-site service enhancements.

[0638] A specific example of a prompt message is, "Your child has started a new game. Observe their activity and create a report on what they are learning and what emotions they are experiencing." In response to this prompt, the system collects relevant analytical data and generates a detailed report for parents.

[0639] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0640] Step 1:

[0641] The server receives audio data transmitted from the terminal. It takes audio data as input and converts it into text information using a speech-to-text conversion method. Specifically, it activates a speech recognition engine, analyzes the audio data, and outputs text data. This text data is used for subsequent analysis.

[0642] Step 2:

[0643] The server receives video data transmitted from the terminal. It receives video data as input and analyzes the psychological state using video analysis tools. Using image recognition software, it analyzes the children's facial expressions and movements, and tags their activities and emotional states. The analyzed tag information is generated as output.

[0644] Step 3:

[0645] The server processes text data using natural language processing to extract information about actions. It receives textual information as input, performs natural language processing, and analyzes specific actions and situations. The output provides information about the characteristics of the actions and events.

[0646] Step 4:

[0647] The server generates a report using the analysis results. It integrates the analyzed text data, video analysis results, and behavioral information, and automatically creates a report using a report generation mechanism. The output is a report comprehensively detailing each child's activity history and emotional state, which is then made available for use.

[0648] Step 5:

[0649] The server sends the generated report to the terminal and notifies the staff. Using communication methods, the server provides staff with the report's contents and important information in real time. This allows staff to closely monitor the children's condition and take prompt action as needed.

[0650] Step 6:

[0651] Parents, as users, view the generated reports on their own devices. Through the application, parents can access the reports to gain a detailed understanding of their children's daily activities and emotions. This allows them to provide further support and follow-up at home.

[0652] Step 7:

[0653] Users submit feedback on reports to the system. This feedback information is aggregated on the server and used through data aggregation to improve the system and services in the future. This results in a better learning environment.

[0654] (Application Example 1)

[0655] 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".

[0656] In educational settings, there is a challenge in tracking individual learning progress in real time and providing appropriate guidance. Furthermore, there is a lack of means for parents to understand their child's learning progress and provide necessary feedback quickly. This makes it difficult to respond flexibly to learners' levels of concentration and psychological state, resulting in cases where learning effectiveness is not maximized.

[0657] 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.

[0658] In this invention, the server includes speech recognition means for receiving audio data and converting it into text data, image recognition means for receiving video data and analyzing the emotional state of an object, means for extracting information about behavior from text data using natural language processing means, and state monitoring means for tracking the level of concentration and responses of individuals in an educational setting. This enables detailed monitoring of the state of individual learners in educational settings, allowing for appropriate guidance and prompt provision of information to parents.

[0659] "Speech recognition means" refers to a device that has the function of receiving speech data and converting it into text data.

[0660] "Image recognition means" refers to a device that receives video data and has the function of analyzing the emotional state of an object.

[0661] "Natural language processing means" refers to a function that utilizes technology to extract information about behavior from text data.

[0662] A "report generation means" is a device that has the function of automatically creating a report based on the analysis results.

[0663] "Communication means" refers to a device that has the function of transmitting information using this data.

[0664] A "state monitoring device" is a device that has the function of tracking an individual's level of concentration and response in an educational setting.

[0665] A "communication network" is a network in which multiple devices are interconnected and used to exchange information.

[0666] A "device" is a set of hardware or software components designed to perform a specific function.

[0667] The system for carrying out this invention includes means for speech recognition, image recognition, natural language processing, report generation, communication, and status monitoring. The server receives audio and video data used in educational settings. For example, an instructor can use smart glasses to record students' speech and facial expressions in real time.

[0668] Audio data is converted to text using speech recognition software such as the Google Speech-to-Text API. This allows the server to quickly retrieve and analyze what students are saying. Image data is analyzed using image recognition software such as OpenCV or AWS Rekognition to understand students' emotional states and behaviors. This analysis allows the server to understand students' concentration levels and psychological states, and send notifications to instructors and parents as needed.

[0669] Natural language processing is used to extract information about behavior from text data. This allows the server to analyze students' activity history based on their statements. Furthermore, a report generation system can automatically create a report on learning progress based on the analysis results and provide it to users, i.e., instructors and parents.

[0670] For example, if image recognition technology detects that a student is struggling to solve a problem, the instructor can immediately provide follow-up support. Furthermore, if a generative AI model detects a decrease in a learner's concentration level through natural language processing, the system can input a prompt such as, "Measure the student's concentration level and notify me in real time if they are losing focus."

[0671] In this way, the system facilitates individualized learning support in educational settings and provides an environment where learners can learn more efficiently.

[0672] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0673] Step 1:

[0674] The server receives audio and video data from the educational setting. The input is real-time streaming data from smart glasses or tablets used by instructors. The server stores this data and prepares it for subsequent processing.

[0675] Step 2:

[0676] The server converts received audio data into text data using speech recognition technology. By utilizing the Google Speech-to-Text API, it generates text with high accuracy from the audio. The output of this process is text data documenting the content of the instructor's and students' speech.

[0677] Step 3:

[0678] The server analyzes video data using image recognition. The input is the video data received in step 1, and the server analyzes the students' facial expressions and movements using OpenCV or AWS Rekognition. The output is tag data indicating the students' emotional state and learning attitude.

[0679] Step 4:

[0680] The server uses natural language processing to extract information about behavior from the text data obtained in step 2. The input is text data, and a generative AI model is used to understand the topic of the discussion and the intent behind the statements. The output is an information set showing the results of the behavioral analysis.

[0681] Step 5:

[0682] Based on the analysis results from steps 3 and 4, the server automatically generates a report using the report generation mechanism. The report includes a comprehensive evaluation of the students' learning activities and emotional state. The output is a report that can be viewed by instructors and parents.

[0683] Step 6:

[0684] The server uses communication methods to send the generated report to the user's device, specifically to the instructor or parent. The input is the report generated in step 5, and the output is a digital report in a viewable format.

[0685] Step 7:

[0686] Users can view submitted reports and provide feedback on students' learning progress. This feedback is sent to the server and used to improve the system in the future. This process accumulates data that helps improve the quality of instruction in educational settings.

[0687] 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.

[0688] This invention improves the performance of a childcare system by incorporating voice recognition means, image recognition means, natural language processing means, report generation means, and notification means, and further integrating an emotion engine. This system provides an approach to gain a deeper understanding of the user's emotions and improve the quality of individualized care.

[0689] The server first receives the audio data transmitted from the terminal and converts it into text data through a speech recognition engine. This text data is then analyzed using natural language processing to extract information about the child's behavior. For example, if a staff member inputs "They're having fun playing games," the server adds the tags "games" and "excitement" to the text data.

[0690] Next, the server receives the video data. Image recognition measures analyze the child's facial expressions in the video, and the emotion engine uses the results to detect the emotional state. For example, from a video in which the child is smiling, the emotion of "joy" is recognized and recorded as data.

[0691] The analysis results from the emotion engine are reflected in the report in real time by the report generation system. The generated report provides detailed information on the children's emotional fluctuations and activity history, offering valuable information to staff and parents.

[0692] The terminals serve to display notifications and reports received from the server to the staff. Based on this information, the staff can monitor the children's condition in real time and take appropriate action. For example, if an alert appears on the terminal stating, "○○ seems a little anxious," the staff can consider the background and observe the child further.

[0693] Parents, as users, can view this report through a dedicated application. This allows parents to gain a detailed understanding of their child's daily activities and emotional changes, and to use this information in conversations at home.

[0694] In this way, this system uses three types of information—voice, video, and emotion—to provide a solution for more accurately understanding and efficiently responding to children's conditions.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The terminal receives voice input from the staff. Using speech recognition technology, the voice is converted into text data. For example, the voice saying "The weather is nice today" is converted into text.

[0698] Step 2:

[0699] The device acquires video data from a camera installed in the classroom and transmits it to a server via the network.

[0700] Step 3:

[0701] The server analyzes the received text data using natural language processing techniques to extract specific activities and situations. For example, "good weather" is organized as information related to an activity.

[0702] Step 4:

[0703] The server performs image recognition on the received video data and analyzes the child's facial expressions. The emotion engine estimates the emotional state from the facial expressions. For example, it recognizes "joy" from a video of a smile.

[0704] Step 5:

[0705] The server automatically generates daily reports using a report generation method based on the results of speech recognition, natural language processing, and emotion engine analysis. The reports include content such as, "Today, XX seemed to be having fun playing."

[0706] Step 6:

[0707] The terminal receives a report from the server and notifies the staff. The staff then reviews the information and provides support to the child as needed.

[0708] Step 7:

[0709] Parents, who are the users, can view reports generated through a dedicated application. This allows parents to understand their child's daytime activities and improve communication at home.

[0710] (Example 2)

[0711] 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".

[0712] Traditional after-school care systems have faced challenges in fully understanding children's emotions and behaviors by analyzing only audio and video information individually, making it difficult to improve the quality of individualized care. Furthermore, the generation of real-time reports and the integrated use of various information are limited, making it difficult for parents and staff to immediately grasp a child's condition.

[0713] 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.

[0714] In this invention, the server includes recognition means for receiving audio information and converting it into text information, recognition means for receiving video information and analyzing the emotional state of the subject, and means for extracting behavioral data from the text information using natural language processing technology. This enables comprehensive analysis of data from audio and video, allowing for a detailed understanding of emotions and behaviors.

[0715] "Audio information" refers to data transmitted through sound, and serves as input material for converting it into text information.

[0716] "Textual information" refers to string data obtained by converting audio information using recognition means, and is the information that is analyzed by natural language processing technology.

[0717] "Recognition means" refers to technologies and devices that process audio and video information and convert it into textual information or emotional states.

[0718] "Visual information" refers to visual data acquired through video, and is material used to analyze emotional states using recognition tools.

[0719] "Emotional state" refers to information that indicates the type and intensity of emotions of the subject, as analyzed from video information.

[0720] "Natural language processing technology" is a computer technology that analyzes textual information to extract specific actions or intentions.

[0721] A "report" is a document automatically generated based on the analysis results, intended to record and share the child's emotions and behavior.

[0722] An "information set" refers to a series of data that is updated by accumulating analysis results and feedback.

[0723] An "information and communication network" refers to a network infrastructure used for sending and receiving data, and is generally considered to be a system that connects multiple information terminals.

[0724] This invention provides a system for comprehensively understanding children's emotions and behaviors and providing appropriate responses within a school-age child care system. The system utilizes audio information, video information, and natural language processing technology. Specifically, it implements speech recognition technology to convert audio information into text information, image recognition technology to receive and analyze video information, and behavioral data extraction technology using natural language processing.

[0725] After receiving audio information, the server converts it into text using a speech recognition module. A commonly used speech recognition engine can be used for this process. The converted text is then analyzed by a natural language processing engine to extract information related to the behavior.

[0726] In addition, the server receives video information and uses image recognition technology to analyze the emotional state contained in the video. Face recognition algorithms and facial expression analysis algorithms are applied to the analysis, and the acquired emotional state is recorded in a database. These results are integrated by an advanced analysis engine and reflected in the final report in real time.

[0727] The device receives and displays generated notifications and reports to staff. Based on this information, staff can take immediate action. At the same time, parents, who are users, can view the reports through a dedicated application. This application allows them to check daily activities and emotional changes, and can be used to facilitate communication between parents and children.

[0728] As a concrete example, if you input the prompt "Please provide a report on today's school activities and emotions" into the generative AI model, the system will generate and provide a detailed report based on the analysis results. This allows parents to visualize various aspects of their child's day and obtain specific information to strengthen support at home.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The server receives audio information from the terminal. Audio files containing recordings of everyday conversations and activities are input from the terminal. The server uses a speech recognition module to convert the audio information into text. This process includes filtering, such as noise reduction, and the output is text.

[0732] Step 2:

[0733] The server uses a natural language processing engine to analyze textual information as input. This engine performs grammatical analysis and extracts information related to behavior. Through this process, interesting keywords and phrases are identified from the input textual information, and tags are created based on these. The output is the analyzed behavioral information.

[0734] Step 3:

[0735] The server receives video information from the terminal and uses it as input. The received video is analyzed using image recognition technology. The server uses a facial recognition algorithm to analyze the emotional state of the child in the video. For example, emotions such as smiling or sadness are identified, and emotional state data is generated as output.

[0736] Step 4:

[0737] The server integrates the generated emotional state data and behavioral information. Using an emotion engine, it performs detailed emotional analysis based on this data. The integrated information is output as a report in real time through a report generation function. The report includes information such as the child's emotional fluctuations and activity history.

[0738] Step 5:

[0739] The terminal receives and displays reports and notifications provided by the server. Staff use this information as input to immediately understand the child's situation and take appropriate action. Based on the displayed specific activity status and emotions, staff action plans are formulated. As output, staff action records and feedback are obtained.

[0740] Step 6:

[0741] Parents, as users, can view the generated reports as input through a dedicated application. The application has an intuitive interface and features functions to track daily activities and emotional fluctuations. As output, parents can obtain information that can be used for support and communication at home.

[0742] (Application Example 2)

[0743] 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".

[0744] In commercial and service spaces, understanding users' emotions and interests in real time and providing effective responses and suggestions is not easy. In particular, for staff to provide appropriate communication and service on the spot, rapid and accurate information acquisition is essential. However, the process of manually acquiring this information is time-consuming and often inaccurate.

[0745] 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.

[0746] In this invention, the server includes a speech conversion means for receiving audio data and converting it into text data, an image analysis means for receiving video data and analyzing the emotional state of the subject, and a means for extracting information about behavior from the text data using natural language processing. This enables immediate interaction based on the user's emotions and behavior.

[0747] A "speech conversion means" is a means for receiving speech data and converting it into text data.

[0748] "Image analysis means" refers to a means for receiving video data and analyzing the emotional state of the subject within it.

[0749] "Natural language processing tools" are methods for extracting information about behavior from text data.

[0750] "Information generation means" refers to means for automatically generating reports based on analysis results.

[0751] "Notification delivery means" refers to a means of sending notifications to users using analyzed data.

[0752] "Interaction enhancement means" refers to methods for supporting immediate on-site responses using wearable devices.

[0753] A "communication network" is a network that connects voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means, enabling multiple devices to use it.

[0754] A specific system for carrying out this invention is a networked configuration including voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means. The system is designed to analyze users' emotions and behavior in real time in stores and service spaces and to provide appropriate responses.

[0755] The server first uses a speech-to-text converter to convert the audio data received from the user into text data. This process uses speech recognition software such as Google Cloud Speech-to-Text. Next, the image analysis converter receives the video data and analyzes the user's emotional state. Specifically, image recognition solutions such as Google Cloud Vision are used. The emotional data obtained from the image analysis is then analyzed by an emotion engine to determine emotions such as "joy" or "interest."

[0756] In parallel, natural language processing (NLP) is used to extract behavioral information from the text conversion results of the audio data. This extraction utilizes natural language processing with Google Cloud Natural Language. The analysis results are aggregated into an information generation system, and a report is automatically generated. The report visually shows the progression of the user's emotions and behavior, facilitating rapid decision-making. Users are notified in real time of events and responses via a notification system.

[0757] Furthermore, wearable devices are used as a means of enhancing interaction. A typical example is the use of smart glasses to receive real-time information feedback on-site and respond immediately. These devices are equipped with the ability to connect to the aforementioned server system via Bluetooth or Wi-Fi.

[0758] For example, when a customer smiles while looking at a specific product in a store, the system recognizes this as "interest" and sends a notification to staff saying, "The customer is showing interest in the new sneakers," thereby supporting sales promotion activities. An example of a prompt using the generative AI model is, "Analyze the emotions from the facial expression of the customer looking at the new sneakers and generate a notification to promote sales."

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The server receives audio data from a terminal or wearable device. Using a speech-to-text conversion method, this audio data is converted into text data. The input is audio data, and the output is text data. Google Cloud Speech-to-Text is used to perform the audio-to-text conversion.

[0762] Step 2:

[0763] The server receives video data from a terminal or wearable device. Using image analysis tools, it analyzes the emotional state of the subject based on this video data. The input is video data, and the output is emotional state data. Using Google Cloud Vision, the customer's facial expressions are analyzed, and the emotion engine determines the emotion.

[0764] Step 3:

[0765] The server receives the text data obtained in Step 1 and extracts behavioral information using natural language processing. The input is text data, and the output is behavioral information data. Natural language processing is performed using Google Cloud Natural Language to analyze customer intent and behavior.

[0766] Step 4:

[0767] The server aggregates the outputs from steps 2 and 3 and automatically generates a report using an information generation mechanism. The input consists of emotional state data and behavioral information data, and the output is a report. This allows for a visual representation of the user's emotional progression and behavioral history.

[0768] Step 5:

[0769] The server sends notifications to terminals or wearable devices using notification delivery methods, based on the generated reports. The input is reports, and the output is real-time notifications. Users can instantly receive information about customer activities and emotions through wearable devices such as smart glasses.

[0770] Step 6:

[0771] Users receive notifications and respond to them within the store, offering service suggestions. Based on customer emotions and behavioral information, they implement optimal communication and product recommendations. Input is real-time notifications, and output is customer interaction. This step is expected to improve the customer experience and boost sales.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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."

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] The following is further disclosed regarding the embodiments described above.

[0794] (Claim 1)

[0795] A speech recognition means that receives audio data and converts it into text data,

[0796] An image recognition means that receives video data and analyzes the emotional state of the subject,

[0797] A means for extracting behavioral information from text data using natural language processing,

[0798] Based on the above analysis results, a report generation means is provided to automatically generate a report,

[0799] A system that includes a notification mechanism that uses this data to send notifications to users.

[0800] (Claim 2)

[0801] The system according to claim 1, further comprising means for receiving user feedback using the above analysis results and updating a database based thereon.

[0802] (Claim 3)

[0803] The system according to claim 1, characterized in that multiple terminals can be used by connecting the above-mentioned speech recognition means, image recognition means, natural language processing means, report generation means, and notification means via a network.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A speech conversion means that receives audio information and converts it into text information,

[0807] A video analysis means that receives video information and analyzes the psychological state of the subject,

[0808] A means for extracting data related to actions from textual information using natural language processing,

[0809] Based on the above analysis results, a report generation means is provided to automatically generate a report,

[0810] A means of communication that uses this information to send notifications to users,

[0811] A method for identifying and tagging learning activities and psychological states based on the analysis results,

[0812] A means of collecting user feedback and using it to improve the system,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, further comprising means for receiving user feedback using the above analysis results and for aggregating information based thereon.

[0816] (Claim 3)

[0817] The system according to claim 1, characterized in that multiple information devices can be used by connecting the above-mentioned voice conversion means, video analysis means, natural language analysis means, report generation means, and communication means via an information network.

[0818] "Application Example 1"

[0819] (Claim 1)

[0820] A speech recognition means that receives audio data and converts it into text data,

[0821] An image recognition means that receives video data and analyzes the emotional state of an object,

[0822] A means for extracting behavioral information from text data using natural language processing,

[0823] Based on the above analysis results, a report generation means is provided to automatically generate a report,

[0824] A means of communication that uses this data to transmit information,

[0825] A system including a state monitoring means for tracking the level of concentration and response of individuals in an educational setting.

[0826] (Claim 2)

[0827] The system according to claim 1, further comprising means for receiving user feedback using the above analysis results and updating an information storage unit based thereon.

[0828] (Claim 3)

[0829] The system according to claim 1, characterized in that multiple devices can be used by connecting the above-mentioned speech recognition means, image recognition means, natural language processing means, report generation means, communication means, and status monitoring means via a communication network.

[0830] "Example 2 of combining an emotion engine"

[0831] (Claim 1)

[0832] A recognition means that receives audio information and converts it into text information,

[0833] A recognition means that receives video information and analyzes the emotional state of the subject,

[0834] A means of extracting behavioral data from textual information using natural language processing technology,

[0835] Based on the above analysis results, a generation means for automatically generating a report,

[0836] A means of sending notifications to users using this information,

[0837] A means to integrate an emotion engine and detect emotional states in detail,

[0838] A means of reflecting sentiment analysis results in reports in real time,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, further comprising means for receiving user feedback using the above analysis results and updating the information set based thereon.

[0842] (Claim 3)

[0843] The system according to claim 1, characterized in that multiple information terminals can be used by connecting the above-mentioned speech recognition means, image recognition means, natural language processing means, report generation means, and notification means via an information and communication network.

[0844] "Application example 2 when combining with an emotional engine"

[0845] (Claim 1)

[0846] A speech conversion means that receives audio data and converts it into text data,

[0847] An image analysis means that receives video data and analyzes the emotional state of the subject,

[0848] A means for extracting information about behavior from text data using natural language processing,

[0849] Based on the above analysis results, an information generation means for automatically generating a report,

[0850] A notification provision method that uses this data to send notifications to users,

[0851] A means of enhancing interaction to support immediate response on-site using wearable devices,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for receiving user responses using the above analysis results and updating the information infrastructure based thereon.

[0855] (Claim 3)

[0856] The system according to claim 1, characterized in that multiple devices can be used by connecting the above-mentioned voice conversion means, image analysis means, natural language analysis means, information generation means, notification provision means, and interaction enhancement means via a communication network. [Explanation of Symbols]

[0857] 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 speech recognition means that receives audio data and converts it into text data, An image recognition means that receives video data and analyzes the emotional state of the subject, A means for extracting behavioral information from text data using natural language processing, A report generation means that automatically generates a report based on the analysis results of the image recognition means, A system that includes a notification mechanism that uses this data to send notifications to users.

2. The system according to claim 1, further comprising means for receiving user feedback using the analysis results and updating a database based thereon.

3. The system according to claim 1, characterized in that multiple terminals can be used by connecting the speech recognition means, the image recognition means, the natural language processing means, the report generation means, and the notification means via a network.

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