system

Abstract

We provide the system. [Solution] A means for generating received data to analyze a child's emotions, Means for using an analytical model to process the received data and determine the emotional state, A means of providing a report based on the aforementioned emotional state to the guardian, Based on the aforementioned report, a means of providing specific action proposals to parents, Translation means for translating the aforementioned action proposals and reports into multiple languages, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, mental health problems among children are increasing. In particular, guardians lack means to quickly and accurately grasp the emotional state of children, and thus it may be difficult to provide appropriate support. Against this background, there is a need for a system to promote the sound mental health of children within the family and to effectively support guardians.

Means for Solving the Problems

[0005] The present invention provides a system comprising means for generating received data for analyzing a child's emotions, means for using an analytical model that processes the received data and determines the emotional state, means for providing a report based on the emotional state to the guardian, means for providing a specific action suggestion to the guardian based on the report, and means for translating this information into multiple languages. This enables guardians to understand their child's emotional state in a timely manner and provide appropriate support.

[0006] "Received data" refers to the information and messages entered by the user, which serve as the raw material for the AI ​​system to analyze emotions.

[0007] An "analysis model" is a computational method that uses algorithms, including generative AI, to determine emotional states from data.

[0008] "Emotional state" refers to a state that indicates changes or trends in a user's emotions, and is determined by an analytical model.

[0009] A "report" is information summarizing the results of an emotional analysis, and is provided to parents to encourage appropriate responses.

[0010] "Action suggestions" are specific recommendations for actions or activities presented to parents based on reports of their child's emotional state.

[0011] "Translation means" refers to a function or process for converting generated reports or action proposals into different languages. [Brief explanation of the drawing]

[0012] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] 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.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention relates to a system that supports children's mental health and is realized through interaction between a server, a terminal, and a user. The system consists of the following components:

[0034] First, the device provides an interface for collecting emotions and daily events entered by the child user. The user can input text in a natural way. The device temporarily stores the entered data locally and sends it to the server at the appropriate time.

[0035] Next, the server receives text data sent from the terminal. The server uses a generative AI model to analyze the received data and determine the user's emotional state. Specifically, the AI ​​model identifies emotional labels and scores states such as stress and anxiety based on them. Based on this score, the server creates a report summarizing the characteristics of the emotional state.

[0036] Furthermore, the server generates action suggestions for parents, along with a report based on sentiment analysis. This includes specific advice such as, "You seem stressed today, so let's make time to relax." This information generated by the server is translated based on the parents' language settings.

[0037] Next, the device receives reports and action suggestions sent from the server and displays them in a format that is easy for parents to understand. Parents can use this information to consider appropriate actions for their child.

[0038] As a concrete example of this system, if a user enters "I had a fight with a friend at school today," the server may analyze this and determine that the user's emotions are leaning towards anxiety or sadness. In this case, the system may offer action suggestions to the parent, such as "Your child is feeling sad. Let's create a calm environment to talk things over."

[0039] The system of the present invention, configured in this way, can adapt to diverse family environments and support children's mental health.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal launches the application and displays an interface where the user can log in. The user enters the required authentication information and logs in.

[0043] Step 2:

[0044] After logging in, the device provides the child with a chat interface. Users can freely type their feelings and the events of the day in text format and press the send button.

[0045] Step 3:

[0046] The device sends user input data to the server. This data is used as foundational data for analyzing the user's emotional state.

[0047] Step 4:

[0048] The server prepares to parse the text data received from the terminal. This includes formatting and preprocessing the data.

[0049] Step 5:

[0050] The server inputs the formatted text data into a generative AI model to analyze the emotional state. The model identifies emotional labels and generates corresponding scores.

[0051] Step 6:

[0052] The server generates a report based on the results of the sentiment analysis. The report includes a summary of the user's emotional tendencies, and particularly highlights any signs of stress or anxiety.

[0053] Step 7:

[0054] Based on the generated report, the server creates specific action suggestions for parents. These suggestions include concrete actions that parents can take to support their children.

[0055] Step 8:

[0056] The server translates reports and action suggestions into the user's language. It provides information in the language set by the user.

[0057] Step 9:

[0058] The device receives translated reports and action suggestions sent from the server and notifies the parent / guardian. The parent / guardian can then review the information on the device and consider appropriate action.

[0059] (Example 1)

[0060] 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."

[0061] In today's busy lifestyle, continuously and accurately analyzing a child's emotional state and providing the results to parents in a useful way is extremely important. However, existing methods may miss subtle changes or abnormalities in emotions. Furthermore, it is necessary to clearly communicate information to parents in a variety of languages. This invention aims to address these challenges and improve the mental health of children.

[0062] 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.

[0063] In this invention, the server includes means for collecting information to analyze a child's emotions, means for using a model to process the information and determine the emotional state, and means for scoring the generated emotional state. This allows parents to concretely understand their child's mental health by analyzing the emotional state based on data entered by the child and visualizing it as a numerical value.

[0064] "Means of collecting information" refers to a system for collecting and temporarily storing data on emotions and events entered by users.

[0065] "Methods of using models" refers to techniques that analyze collected data and apply AI models to determine specific emotional states.

[0066] "Means of providing information based on emotional state" refers to methods for generating reports and suggestions based on analysis results and communicating them to parents / guardians.

[0067] "Means of suggesting actions" refers to the process of suggesting specific ways to deal with a child to the guardian based on the judged emotional state.

[0068] "Means for translation into languages" refers to technologies for making generated reports and proposals multilingual.

[0069] "Methods for scoring emotional states" refer to methods that quantify the results of emotional analysis and evaluate them quantitatively.

[0070] "Means of displaying information on a device" refers to a mechanism for visually presenting generated reports and suggestions to the user or their guardian.

[0071] This invention is a system that continuously analyzes a child's emotional state and provides useful information and specific action suggestions to parents. In one embodiment, this system is realized through interaction between a server, a terminal, and a user.

[0072] First, the device provides an interface that allows the child user to naturally input emotions and everyday events. The device temporarily stores the input data in local storage and securely sends it to the server using HTTPS.

[0073] Next, the server utilizes a generative AI model to analyze the received data. During the analysis process, the server assigns sentiment labels to the text data and scores the emotional state. This entire process incorporates a sentiment analysis model, and includes an example where the analysis is performed using a prompt statement, such as: "Analyze the sentiment in the following sentence and identify the sentiment label. Sentence: 'I had a fight with a friend at school today.'"

[0074] The server then creates a report based on the analyzed emotional state and generates specific action suggestions for the parents. This information is translated into multiple languages ​​and displayed visually to the parents through their device.

[0075] For example, if a user enters "I had fun at school today," the server might analyze this and assign the emotion label "joy." Based on the emotion score, it might then provide advice to the parent, such as, "It seems your child had a good day. Please continue to encourage positive experiences."

[0076] This system will enable adaptation to diverse family environments and continuous support for children's mental health.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users input text about their child's feelings and events of the day in a natural way. An example of input data is the sentence, "Today I had fun eating lunch with my friends at school." This text data is temporarily stored in local storage by the device as emotional data. The input data will serve as raw material for later analysis.

[0080] Step 2:

[0081] The terminal periodically or based on event triggers sends stored text data to the server. The HTTPS protocol is used for transmission to ensure data protection and secure communication. The output data becomes data for analysis on the server.

[0082] Step 3:

[0083] The server inputs the received text data into a generating AI model. This process uses the prompt "Analyze the emotions in the following text and identify the emotion labels" to instruct the AI ​​model to analyze the text. Based on the input text, the model generates emotion labels such as joy, sadness, and anxiety. These emotion labels are then output.

[0084] Step 4:

[0085] The server scores the analysis results based on the obtained emotion labels. In the scoring process, for example, a high score is assigned to "joy," thus representing the emotional state numerically. This scoring provides a foundation for quantitatively understanding a child's emotional state. The output is a score.

[0086] Step 5:

[0087] Based on the emotional labels and scores obtained, the server generates a report and develops action suggestions for parents. Specific advice might include, "Your child seems to have had a positive experience today. Provide them with more opportunities for interaction." This information is translated into various languages ​​on the server. The output consists of language-specific reports and suggestions.

[0088] Step 6:

[0089] The device receives reports and action suggestions sent from the server and displays them in a format that is easy for parents to understand. The displayed information allows parents to grasp their child's emotional state and consider appropriate responses. The information is then presented to the parents as output.

[0090] (Application Example 1)

[0091] 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."

[0092] In supporting children's mental health, it is essential to accurately understand changes in their emotions and behavior on a daily basis and provide timely feedback to parents. However, with existing technologies, it has been difficult to recognize changes in children's emotions in real time and suggest specific actions to parents. Furthermore, it has been challenging to provide information in a way that parents can easily understand in diverse language environments.

[0093] 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.

[0094] In this invention, the server includes means for generating received data for analyzing a child's emotions, means for using an analysis model to process the received data and determine the emotional state, and speech recognition means for recognizing the child's voice and converting it into text. This makes it possible to capture the child's daily emotional changes, provide reports and action suggestions in a multilingual and easy-to-understand format to the parent's communication device, and effectively support the child's mental health.

[0095] "Means for generating received data" refers to a device or method that constructs data necessary for analysis based on information entered by a child.

[0096] "Means of using an analytical model" refers to an apparatus or method that processes collected data and performs analytical techniques to determine a child's emotional state.

[0097] "Means of providing information to parents" refers to the means and techniques for presenting analysis results to parents in an easy-to-understand manner.

[0098] "Means of providing concrete action suggestions" refers to a system that generates and provides advice to parents on how to take appropriate action based on the results of emotional analysis.

[0099] "Translation means" refers to technology that translates generated behavioral suggestions and reports into the language necessary to help parents understand them.

[0100] "Speech recognition means" refers to a technology that receives a child's speech as audio data and converts it into text data.

[0101] "Display means" refers to a device or method for visually presenting information transmitted from a server, making it easily verifiable by parents or guardians.

[0102] This invention is built using a specialized platform to realize a system that supports children's mental health. The system is mainly composed of a server, terminals, and the interaction between the child and their guardian as users.

[0103] The server plays a central role primarily in analysis and feedback. Sentimental text data entered by users through their devices is temporarily stored locally before being sent to the server at the appropriate time. Upon receiving this text data, the server analyzes it using natural language processing techniques. Specifically, it uses generative AI models such as Hugging Face's Transformers to label emotional states and calculate stress and anxiety scores.

[0104] After an assessment of the emotional state, the server generates specific action suggestions for the parent. These suggestions are translated into multiple languages ​​to ensure they are easily understood by the parent. The output suggestions are displayed on the parent's device, allowing them to review the recommended actions.

[0105] The device provides a user-friendly interface that allows children to naturally input their emotions. Simultaneously, it incorporates voice recognition technology, enabling real-time input of emotional data. This allows children to easily express their feelings by speaking.

[0106] For example, if a child says, "I had fun playing with my friends today," the speech recognition system converts this into text, and the server determines a positive emotional state based on the "fun" emotional label. This information is then reported to the parent, such as, "It seems your child had fun today. It was a good day."

[0107] An example of a prompt for a generative AI model is as follows: "Today's Sentiment Analysis: I had a fight with a friend. Use the model to classify the emotion and present an anxiety score."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The device receives emotions and daily events entered by the child user in either voice or text format. In the case of voice input, the device's built-in speech recognition system converts it into text data. The input data is temporarily stored in local storage.

[0111] Step 2:

[0112] The device sends the collected text data to the server. The timing of data transmission is triggered either periodically or based on specific events. The data transmitted consists of emotional information entered by the child.

[0113] Step 3:

[0114] The server retrieves text data received from the terminal. Next, it analyzes the text data using natural language processing techniques and labels the emotional state using a generative AI model. The input is text data from children, and the output is an emotional label and an emotional score.

[0115] Step 4:

[0116] The server generates specific action suggestions to provide to parents based on emotion labels and scores. It uses the emotion analysis results to create advice aimed at reducing stress and anxiety. This process generates language that is easy for parents to understand.

[0117] Step 5:

[0118] The server translates the generated action suggestions into the parent's language. This translation process utilizes a multilingual translation library. The translated data provides appropriate feedback to the parent.

[0119] Step 6:

[0120] The parent's device receives translated suggestions sent from the server. The device displays this information in a user-friendly interface to help parents take appropriate action regarding their child's mental health.

[0121] Step 7:

[0122] Parents will review suggested behaviors for engaging with their children and use the feedback to improve their daily communication methods. This will promote support for children's mental health within the home.

[0123] 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.

[0124] This invention is a system that analyzes children's emotions in detail and helps parents provide appropriate support. The system consists of a terminal, a server, and an emotion engine.

[0125] First, the device provides an interface for using the camera and microphone when the user logs in. The user can interact with the system using voice and facial expressions. This information is sent to the emotion engine in real time.

[0126] The emotion engine analyzes received audio and facial expression data to recognize the user's emotions in real time. The engine quantifies the identified emotions and determines their state. Based on information such as voice tone and speed, and changes in facial expressions, the emotion engine identifies emotion labels with high accuracy.

[0127] Next, the server receives the data processed by the emotion engine and uses a generative AI model to analyze the overall emotional state. Based on this information, it automatically generates a report showing the user's emotional state. The server can also store a history of emotional states and detect changes in trends or anomalies by comparing and analyzing them with past patterns.

[0128] The generated reports are translated by the server and converted into a format that matches the parent's language settings. The server also generates and automatically sends action suggestions tailored to each individual's emotional state. This allows parents to gain a detailed understanding of their child's emotional changes and provide prompt and appropriate support.

[0129] For example, if a user says with a positive expression, "Something good happened at school today," the emotion engine recognizes that emotion as "joy," and the server generates action suggestions such as, "Your child is having a good time. Let's continue to enjoy conversations with the family in this way." This system can effectively support children's mental health, regardless of their family environment.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device launches an interface prompting the user to log in, requesting user information and permission to access the camera and microphone. The user enters the required information into the login form and logs in.

[0133] Step 2:

[0134] The device displays an interface for emotion capture after login. Users provide emotion data by talking about daily events and showing facial expressions. Voice and facial data are captured in real time.

[0135] Step 3:

[0136] The device transmits captured audio and facial expression data to the emotion engine. The emotion engine analyzes this data to identify the user's emotions in real time.

[0137] Step 4:

[0138] The emotion engine analyzes the tone, speed, and content of the voice, as well as changes and characteristics of facial expressions, to calculate an emotion label. This result is then quantified to determine the user's emotional state.

[0139] Step 5:

[0140] The server receives emotion labels and scores sent from the emotion engine and uses a generative AI model to analyze the overall emotional state.

[0141] Step 6:

[0142] The server generates a report detailing the user's emotional state. This report includes a summary of the current emotional state, any necessary notes, and a comparison with past data.

[0143] Step 7:

[0144] The server translates the generated report into the parent's preferred language. It also derives specific action suggestions for the parent based on the report and sends the information.

[0145] Step 8:

[0146] The device receives translated reports and action suggestions sent from the server and displays them to the parent as a notification. The parent uses this information to consider how to respond to the user.

[0147] (Example 2)

[0148] 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".

[0149] In today's home environment, it is crucial for parents to promptly detect changes in their children's emotions and provide appropriate support. However, conventional systems have struggled to analyze emotional changes in detail, making it difficult to obtain clear suggestions for appropriate actions from parents. Therefore, there is a need for a system that can effectively support children's mental health.

[0150] 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.

[0151] In this invention, the server includes input means for collecting voice and facial expression data from the user, means for using a recognition engine for analyzing the voice and facial expression data, and means for analyzing the emotional state using the analyzed data and automatically generating a report using a generative AI model. This makes it possible to analyze changes in a child's emotions in detail and quickly provide beneficial behavioral suggestions to parents.

[0152] A "user" is the entity that utilizes the system and provides voice and facial expression data.

[0153] "Input means" refers to devices and interfaces for collecting voice and facial expression data from users.

[0154] A "recognition engine" is an algorithm or software that analyzes audio and facial expression data obtained from input sources to identify emotions.

[0155] A "generative AI model" refers to an artificial intelligence system that automatically generates reports and action suggestions based on analyzed emotional data.

[0156] A "report" is a document automatically generated by a generative AI model to show the user's emotional state.

[0157] "Translation means" refers to systems and processes for converting generated reports and action proposals into multiple languages.

[0158] "Emotional state" refers to the specific nature of a user's emotions, analyzed based on voice and facial expression data.

[0159] "Action suggestions" are specific and helpful recommendations for decisions and actions provided to parents based on the user's emotional state.

[0160] In order to implement this invention, the following system is required.

[0161] The terminal is the device used by users when logging in. The terminal is equipped with a camera and microphone, which are used to collect voice and facial expression data from the user. Specifically, the terminal uses video processing software and speech recognition software to record and accurately capture data in real time. For example, when a user speaks into the terminal's camera, the terminal collects and digitizes their voice and facial expressions.

[0162] The server receives user voice and facial expression data transmitted from the terminal. The server utilizes a recognition engine to analyze this data and identify the user's emotions. The recognition engine analyzes the tone and speed of the voice and changes in facial expressions, quantifying and labeling the emotions. The analysis results in the expression of emotions such as tension or joy.

[0163] Next, the server inputs the analyzed emotional data into a generating AI model, which automatically generates a comprehensive emotional report. The generating AI model uses algorithms learned from past data to perform accurate situational analysis and report creation. This report is displayed in a format easily understood by the user's guardian. The server also generates specific action suggestions based on the report. These action suggestions are designed to improve the user's emotional state and are sent to the guardian in a timely manner.

[0164] For example, if it is determined that a child is experiencing stress, the message sent from the server might say, "Your child is experiencing stress. Increase their outdoor activities and allow them to relax."

[0165] An example of a prompt is, "Analyze the child's emotional state in detail and generate appropriate action suggestions for the parent." Based on this prompt, the generating AI model creates a report and action suggestions using the user's emotional data.

[0166] This system allows for a detailed understanding of changes in a child's emotions within the home, enabling parents to provide prompt and accurate support.

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

[0168] Step 1:

[0169] The user logs into the device. The device activates its camera and microphone and prepares to collect audio and facial expression data. The input is the user's voice and facial expressions, and the output is real-time captured audio files and video data. Specifically, when the user says "hello" into the device's camera, the device records the image and audio of that moment as data.

[0170] Step 2:

[0171] The terminal immediately transmits the collected audio and facial expression data to the server. The input is the audio files and video data captured by the terminal, and the output is the digital data sent to the server. Specifically, the terminal uploads the data to the server via the internet using a data transmission protocol.

[0172] Step 3:

[0173] The server inputs the received audio and facial expression data into the recognition engine. The input consists of audio files and video data sent from the terminal, and the output is analyzed emotion data. Specifically, the server analyzes the tone, speed, and facial expression changes of the voice, and quantifies and labels the user's emotions. For example, the server identifies the emotion of "joy" from the audio.

[0174] Step 4:

[0175] The server sends the analyzed emotion data to a generating AI model, which then generates a comprehensive emotion report. The input is quantified emotion data, and the output is an automatically generated emotion report. Specifically, the generating AI model refers to past data to summarize the current emotional state in a report.

[0176] Step 5:

[0177] The server creates action suggestions based on the generated emotion report. The input is the emotion report, and the output is specific action suggestions. Specifically, the server analyzes the user's emotional state and tendencies and constructs suggestions such as "recommend relaxing activities outdoors."

[0178] Step 6:

[0179] The server translates and provides behavioral suggestions and emotion reports in the language specified by the parent. The input is the behavioral suggestions and emotion reports, and the output is the translated information. Specifically, the server utilizes multilingual translation software to convert the information into a language format that is easy for the parent to understand.

[0180] This series of steps allows the system to analyze the user's emotions in detail and provide parents with specific support information.

[0181] (Application Example 2)

[0182] 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 device 14 will be referred to as the "terminal."

[0183] There is a need to accurately understand the emotional state of elderly individuals and enable caregivers to provide prompt and appropriate support. However, currently, it is difficult to recognize changes in the emotional state of elderly individuals in real time and provide caregivers with appropriate action suggestions. As a result, it may be difficult to respond appropriately to individual situations, potentially leading to a decline in the quality of care. To solve these problems, an emotion recognition system specifically designed for the elderly care field is needed.

[0184] 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.

[0185] In this invention, the server includes means for generating received information for emotion analysis, means for using an analysis engine to process the received information and recognize an emotional state, and means for providing a report based on the recognized emotional state. This makes it possible to grasp changes in the emotions of elderly people in real time and provide appropriate care suggestions to caregivers.

[0186] "Emotion analysis" is a technology that processes data such as voice and facial expressions to identify an individual's emotional state.

[0187] "Received information" refers to data such as the user's voice and facial expressions, which are used for sentiment analysis.

[0188] An "analysis engine" is a technology that includes algorithms and programs for processing received information and recognizing emotional states.

[0189] A "report" is a document that includes a description of the state and an analysis of the situation, generated based on the perceived emotional state.

[0190] An "action suggestion" is a proposal that directs or recommends appropriate actions based on the perceived emotional state, tailored to the specific situation.

[0191] "Translation means" refers to technologies that translate generated reports and action proposals into multiple languages ​​so that they can be understood by users from various cultural backgrounds.

[0192] A "notification" is a message, delivered via audio or visual means, that informs users or caregivers of important information.

[0193] An "information storage device" is a digital data storage medium used to save changes in emotional states and behavioral history.

[0194] A "learning method" is an algorithm and procedure used to analyze past data, learn patterns, and predict future situations.

[0195] A system for carrying out this invention consists of a terminal, a server, and a generative AI model.

[0196] The devices used are typical smartphones and tablets equipped with cameras and microphones. The devices interact with the user and acquire voice and facial expression data in real time. The voice and facial expressions spoken by the user to the device are sent to the server via an emotion analysis platform within the device.

[0197] The server analyzes the received data and uses an analysis engine to determine the emotional state. This analysis engine includes emotion recognition libraries such as Google® Cloud Vision API and TENSORFLOW®. The analyzed emotional state is further analyzed by a generative AI model. This model allows for a comprehensive evaluation of an individual's emotional trends and the generation of appropriate actions.

[0198] The generated emotion-based reports and action suggestions are translated by the server and notified to caregivers in multiple languages. These notifications help caregivers take appropriate actions smoothly. For example, if an elderly person says positively, "I feel good today," the generating AI model might suggest something like, "Since the elderly person is in a good emotional state, we suggest going for a walk together."

[0199] A concrete example of a prompt message is, "If the elderly person expresses positive emotions, generate appropriate action suggestions based on this information." In this way, it is possible to provide consultation tailored to the user's emotional state and support appropriate care.

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

[0201] Step 1:

[0202] The device acquires the user's voice and facial expressions through its camera and microphone. The input consists of the user's voice and facial image, which is captured and converted into initial digital data. This conversion generates the voice as an audio file and the facial image as an image file.

[0203] Step 2:

[0204] The terminal transmits the acquired digital data to the server. This data consists of audio and image files, which serve as input for the next analysis step. Specifically, the terminal uses network communication to transmit data to the server via a secure channel.

[0205] Step 3:

[0206] The server processes the received audio and image data for sentiment analysis. The input consists of the audio and image files sent in the previous step. These are processed using the Google Cloud Vision API or similar sentiment recognition libraries to identify the user's emotions. The output is the analyzed emotional state, with sentiment labels such as "joy" and "sadness" generated.

[0207] Step 4:

[0208] The server sends the analysis results to a generating AI model for a comprehensive sentiment analysis. The input is sentiment labels, and based on these, the AI ​​model evaluates the sentiment trends and generates specific action suggestions as output. These action suggestions serve as guidelines indicating what kind of response is appropriate.

[0209] Step 5:

[0210] The server performs multilingual translation as needed to notify caregivers of the generated action suggestions. The input is the details of the action suggestion, and the server uses a text translation engine to translate the suggestion into a format and language that is readable by the caregiver. The output is the translated action suggestion, which is delivered to the caregiver's terminal.

[0211] Step 6:

[0212] The caregiver's terminal displays action suggestions received from the server and notifies the caregiver. The input is translated action suggestions, which are displayed on the screen to visually present them to the caregiver. The output is reference information that helps the caregiver understand the suggestions and take the most appropriate action for the individual user.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention relates to a system that supports children's mental health and is realized through interaction between a server, a terminal, and a user. The system consists of the following components:

[0230] First, the device provides an interface for collecting emotions and daily events entered by the child user. The user can input text in a natural way. The device temporarily stores the entered data locally and sends it to the server at the appropriate time.

[0231] Next, the server receives text data sent from the terminal. The server uses a generative AI model to analyze the received data and determine the user's emotional state. Specifically, the AI ​​model identifies emotional labels and scores states such as stress and anxiety based on them. Based on this score, the server creates a report summarizing the characteristics of the emotional state.

[0232] Furthermore, the server generates action suggestions for parents, along with a report based on sentiment analysis. This includes specific advice such as, "You seem stressed today, so let's make time to relax." This information generated by the server is translated based on the parents' language settings.

[0233] Next, the device receives reports and action suggestions sent from the server and displays them in a format that is easy for parents to understand. Parents can use this information to consider appropriate actions for their child.

[0234] As a concrete example of this system, if a user enters "I had a fight with a friend at school today," the server may analyze this and determine that the user's emotions are leaning towards anxiety or sadness. In this case, the system may offer action suggestions to the parent, such as "Your child is feeling sad. Let's create a calm environment to talk things over."

[0235] The system of the present invention, configured in this way, can adapt to diverse family environments and support children's mental health.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The terminal launches the application and displays an interface where the user can log in. The user enters the required authentication information and logs in.

[0239] Step 2:

[0240] After logging in, the device provides the child with a chat interface. Users can freely type their feelings and the events of the day in text format and press the send button.

[0241] Step 3:

[0242] The device sends user input data to the server. This data is used as foundational data for analyzing the user's emotional state.

[0243] Step 4:

[0244] The server prepares to parse the text data received from the terminal. This includes formatting and preprocessing the data.

[0245] Step 5:

[0246] The server inputs the formatted text data into a generative AI model to analyze the emotional state. The model identifies emotional labels and generates corresponding scores.

[0247] Step 6:

[0248] The server generates a report based on the results of the sentiment analysis. The report includes a summary of the user's emotional tendencies, and particularly highlights any signs of stress or anxiety.

[0249] Step 7:

[0250] Based on the generated report, the server creates specific action suggestions for parents. These suggestions include concrete actions that parents can take to support their children.

[0251] Step 8:

[0252] The server translates reports and action suggestions into the user's language. It provides information in the language set by the user.

[0253] Step 9:

[0254] The device receives translated reports and action suggestions sent from the server and notifies the parent / guardian. The parent / guardian can then review the information on the device and consider appropriate action.

[0255] (Example 1)

[0256] 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."

[0257] In today's busy lifestyle, continuously and accurately analyzing a child's emotional state and providing the results to parents in a useful way is extremely important. However, existing methods may miss subtle changes or abnormalities in emotions. Furthermore, it is necessary to clearly communicate information to parents in a variety of languages. This invention aims to address these challenges and improve the mental health of children.

[0258] 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.

[0259] In this invention, the server includes means for collecting information to analyze a child's emotions, means for using a model to process the information and determine the emotional state, and means for scoring the generated emotional state. This allows parents to concretely understand their child's mental health by analyzing the emotional state based on data entered by the child and visualizing it as a numerical value.

[0260] "Means of collecting information" refers to a system for collecting and temporarily storing data on emotions and events entered by users.

[0261] "Methods of using models" refers to techniques that analyze collected data and apply AI models to determine specific emotional states.

[0262] "Means of providing information based on emotional state" refers to methods for generating reports and suggestions based on analysis results and communicating them to parents / guardians.

[0263] "Means of suggesting actions" refers to the process of suggesting specific ways to deal with a child to the guardian based on the judged emotional state.

[0264] "Means for translation into languages" refers to technologies for making generated reports and proposals multilingual.

[0265] "Methods for scoring emotional states" refer to methods that quantify the results of emotional analysis and evaluate them quantitatively.

[0266] "Means of displaying information on a device" refers to a mechanism for visually presenting generated reports and suggestions to the user or their guardian.

[0267] This invention is a system that continuously analyzes a child's emotional state and provides useful information and specific action suggestions to parents. In one embodiment, this system is realized through interaction between a server, a terminal, and a user.

[0268] First, the device provides an interface that allows the child user to naturally input emotions and everyday events. The device temporarily stores the input data in local storage and securely sends it to the server using HTTPS.

[0269] Next, the server utilizes a generative AI model to analyze the received data. During the analysis process, the server assigns sentiment labels to the text data and scores the emotional state. This entire process incorporates a sentiment analysis model, and includes an example where the analysis is performed using a prompt statement, such as: "Analyze the sentiment in the following sentence and identify the sentiment label. Sentence: 'I had a fight with a friend at school today.'"

[0270] The server then creates a report based on the analyzed emotional state and generates specific action suggestions for the parents. This information is translated into multiple languages ​​and displayed visually to the parents through their device.

[0271] For example, if a user enters "I had fun at school today," the server might analyze this and assign the emotion label "joy." Based on the emotion score, it might then provide advice to the parent, such as, "It seems your child had a good day. Please continue to encourage positive experiences."

[0272] This system will enable adaptation to diverse family environments and continuous support for children's mental health.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] Users input text about their child's feelings and events of the day in a natural way. An example of input data is the sentence, "Today I had fun eating lunch with my friends at school." This text data is temporarily stored in local storage by the device as emotional data. The input data will serve as raw material for later analysis.

[0276] Step 2:

[0277] The terminal periodically or based on event triggers sends stored text data to the server. The HTTPS protocol is used for transmission to ensure data protection and secure communication. The output data becomes data for analysis on the server.

[0278] Step 3:

[0279] The server inputs the received text data into the generative AI model. In this process, the AI model is made to analyze the text using the prompt sentence "Please analyze the sentiment from the following text and identify the sentiment label." Based on the input text, the model generates sentiment labels such as joy, sadness, uneasiness, etc. This sentiment label is obtained as the output.

[0280] Step 4:

[0281] Based on the obtained sentiment label, the server scores the analysis result. In the scoring process, for example, if "joy" is high, the score is set high to numerically represent the emotional state. This scoring provides a basis for quantitatively understanding the child's emotional state. As the output, a score is generated.

[0282] Step 5:

[0283] Based on the obtained sentiment label and score, the server generates a report and formulates action proposals for the guardians. As specific advice, there is "It seems that the child had a positive experience today. Let's provide more opportunities for interaction." etc. This information is translated into various languages on the server. As the output, reports and proposals in different languages are created.

[0284] Step 6:

[0285] The terminal receives the report and action proposals sent from the server and displays them in a form that is easy for the guardians to understand. Based on the displayed information, the guardians can grasp the child's emotional state and consider corresponding countermeasures. As the output, information is presented to the guardians.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In supporting children's mental health, it is essential to accurately understand changes in their emotions and behavior on a daily basis and provide timely feedback to parents. However, with existing technologies, it has been difficult to recognize changes in children's emotions in real time and suggest specific actions to parents. Furthermore, it has been challenging to provide information in a way that parents can easily understand in diverse language environments.

[0289] 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.

[0290] In this invention, the server includes means for generating received data for analyzing a child's emotions, means for using an analysis model to process the received data and determine the emotional state, and speech recognition means for recognizing the child's voice and converting it into text. This makes it possible to capture the child's daily emotional changes, provide reports and action suggestions in a multilingual and easy-to-understand format to the parent's communication device, and effectively support the child's mental health.

[0291] "Means for generating received data" refers to a device or method that constructs data necessary for analysis based on information entered by a child.

[0292] "Means of using an analytical model" refers to an apparatus or method that processes collected data and performs analytical techniques to determine a child's emotional state.

[0293] "Means of providing information to parents" refers to the means and techniques for presenting analysis results to parents in an easy-to-understand manner.

[0294] "Means of providing concrete action suggestions" refers to a system that generates and provides advice to parents on how to take appropriate action based on the results of emotional analysis.

[0295] "Translation means" refers to technology that translates generated behavioral suggestions and reports into the language necessary to help parents understand them.

[0296] "Speech recognition means" refers to a technology that receives a child's speech as audio data and converts it into text data.

[0297] "Display means" refers to a device or method for visually presenting information transmitted from a server, making it easily verifiable by parents or guardians.

[0298] This invention is built using a specialized platform to realize a system that supports children's mental health. The system is mainly composed of a server, terminals, and the interaction between the child and their guardian as users.

[0299] The server plays a central role primarily in analysis and feedback. Sentimental text data entered by users through their devices is temporarily stored locally before being sent to the server at the appropriate time. Upon receiving this text data, the server analyzes it using natural language processing techniques. Specifically, it uses generative AI models such as Hugging Face's Transformers to label emotional states and calculate stress and anxiety scores.

[0300] After an assessment of the emotional state, the server generates specific action suggestions for the parent. These suggestions are translated into multiple languages ​​to ensure they are easily understood by the parent. The output suggestions are displayed on the parent's device, allowing them to review the recommended actions.

[0301] The device provides a user-friendly interface that allows children to naturally input their emotions. Simultaneously, it incorporates voice recognition technology, enabling real-time input of emotional data. This allows children to easily express their feelings by speaking.

[0302] As a specific example, when a child says, "I had a great time playing with my friends today," the speech recognition system converts this into text, and the server determines a positive emotional state based on the emotional label of happiness. This information is reported to the guardian as, "It seems your child had a great time today. What a wonderful day."

[0303] Examples of prompt texts for the generative AI model are as follows: "Emotion analysis today: I had a fight with my friend. Perform emotion classification with the model and present the anxiety score."

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The terminal receives the emotions and daily events input by the child user in voice or text format. In the case of voice input, it is converted into text data by the speech recognition system within the terminal. The input data is temporarily stored in the local storage.

[0307] Step 2:

[0308] The terminal sends the collected text data to the server. At this time, the data transmission timing is triggered periodically or based on an event. The data to be sent is the emotional information input by the child.

[0309] Step 3:

[0310] The server obtains the text data received from the terminal. Next, it analyzes the text data using natural language processing technology and labels the emotional state using the generative AI model. The input is the child's text data, and the output is the emotional label and emotional score.

[0311] Step 4:

[0312] The server generates specific action suggestions to provide to parents based on emotion labels and scores. It uses the emotion analysis results to create advice aimed at reducing stress and anxiety. This process generates language that is easy for parents to understand.

[0313] Step 5:

[0314] The server translates the generated action suggestions into the parent's language. This translation process utilizes a multilingual translation library. The translated data provides appropriate feedback to the parent.

[0315] Step 6:

[0316] The parent's device receives translated suggestions sent from the server. The device displays this information in a user-friendly interface to help parents take appropriate action regarding their child's mental health.

[0317] Step 7:

[0318] Parents will review suggested behaviors for engaging with their children and use the feedback to improve their daily communication methods. This will promote support for children's mental health within the home.

[0319] 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.

[0320] This invention is a system that analyzes children's emotions in detail and helps parents provide appropriate support. The system consists of a terminal, a server, and an emotion engine.

[0321] First, the device provides an interface for using the camera and microphone when the user logs in. The user can interact with the system using voice and facial expressions. This information is sent to the emotion engine in real time.

[0322] The emotion engine analyzes received audio and facial expression data to recognize the user's emotions in real time. The engine quantifies the identified emotions and determines their state. Based on information such as voice tone and speed, and changes in facial expressions, the emotion engine identifies emotion labels with high accuracy.

[0323] Next, the server receives the data processed by the emotion engine and uses a generative AI model to analyze the overall emotional state. Based on this information, it automatically generates a report showing the user's emotional state. The server can also store a history of emotional states and detect changes in trends or anomalies by comparing and analyzing them with past patterns.

[0324] The generated reports are translated by the server and converted into a format that matches the parent's language settings. The server also generates and automatically sends action suggestions tailored to each individual's emotional state. This allows parents to gain a detailed understanding of their child's emotional changes and provide prompt and appropriate support.

[0325] For example, if a user says with a positive expression, "Something good happened at school today," the emotion engine recognizes that emotion as "joy," and the server generates action suggestions such as, "Your child is having a good time. Let's continue to enjoy conversations with the family in this way." This system can effectively support children's mental health, regardless of their family environment.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The device launches an interface prompting the user to log in, requesting user information and permission to access the camera and microphone. The user enters the required information into the login form and logs in.

[0329] Step 2:

[0330] The device displays an interface for emotion capture after login. Users provide emotion data by talking about daily events and showing facial expressions. Voice and facial data are captured in real time.

[0331] Step 3:

[0332] The device transmits captured audio and facial expression data to the emotion engine. The emotion engine analyzes this data to identify the user's emotions in real time.

[0333] Step 4:

[0334] The emotion engine analyzes the tone, speed, and content of the voice, as well as changes and characteristics of facial expressions, to calculate an emotion label. This result is then quantified to determine the user's emotional state.

[0335] Step 5:

[0336] The server receives emotion labels and scores sent from the emotion engine and uses a generative AI model to analyze the overall emotional state.

[0337] Step 6:

[0338] The server generates a report detailing the user's emotional state. This report includes a summary of the current emotional state, any necessary notes, and a comparison with past data.

[0339] Step 7:

[0340] The server translates the generated report into the parent's preferred language. It also derives specific action suggestions for the parent based on the report and sends the information.

[0341] Step 8:

[0342] The device receives translated reports and action suggestions sent from the server and displays them to the parent as a notification. The parent uses this information to consider how to respond to the user.

[0343] (Example 2)

[0344] 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".

[0345] In today's home environment, it is crucial for parents to promptly detect changes in their children's emotions and provide appropriate support. However, conventional systems have struggled to analyze emotional changes in detail, making it difficult to obtain clear suggestions for appropriate actions from parents. Therefore, there is a need for a system that can effectively support children's mental health.

[0346] 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.

[0347] In this invention, the server includes input means for collecting voice and facial expression data from the user, means for using a recognition engine for analyzing the voice and facial expression data, and means for analyzing the emotional state using the analyzed data and automatically generating a report using a generative AI model. This makes it possible to analyze changes in a child's emotions in detail and quickly provide beneficial behavioral suggestions to parents.

[0348] A "user" is the entity that utilizes the system and provides voice and facial expression data.

[0349] "Input means" refers to devices and interfaces for collecting voice and facial expression data from users.

[0350] A "recognition engine" is an algorithm or software that analyzes audio and facial expression data obtained from input sources to identify emotions.

[0351] A "generative AI model" refers to an artificial intelligence system that automatically generates reports and action suggestions based on analyzed emotional data.

[0352] A "report" is a document automatically generated by a generative AI model to show the user's emotional state.

[0353] "Translation means" refers to systems and processes for converting generated reports and action proposals into multiple languages.

[0354] "Emotional state" refers to the specific nature of a user's emotions, analyzed based on voice and facial expression data.

[0355] "Action suggestions" are specific and helpful recommendations for decisions and actions provided to parents based on the user's emotional state.

[0356] In order to implement this invention, the following system is required.

[0357] The terminal is the device used by users when logging in. The terminal is equipped with a camera and microphone, which are used to collect voice and facial expression data from the user. Specifically, the terminal uses video processing software and speech recognition software to record and accurately capture data in real time. For example, when a user speaks into the terminal's camera, the terminal collects and digitizes their voice and facial expressions.

[0358] The server receives user voice and facial expression data transmitted from the terminal. The server utilizes a recognition engine to analyze this data and identify the user's emotions. The recognition engine analyzes the tone and speed of the voice and changes in facial expressions, quantifying and labeling the emotions. The analysis results in the expression of emotions such as tension or joy.

[0359] Next, the server inputs the analyzed emotional data into a generating AI model, which automatically generates a comprehensive emotional report. The generating AI model uses algorithms learned from past data to perform accurate situational analysis and report creation. This report is displayed in a format easily understood by the user's guardian. The server also generates specific action suggestions based on the report. These action suggestions are designed to improve the user's emotional state and are sent to the guardian in a timely manner.

[0360] For example, if it is determined that a child is experiencing stress, the message sent from the server might say, "Your child is experiencing stress. Increase their outdoor activities and allow them to relax."

[0361] An example of a prompt is, "Analyze the child's emotional state in detail and generate appropriate action suggestions for the parent." Based on this prompt, the generating AI model creates a report and action suggestions using the user's emotional data.

[0362] This system allows for a detailed understanding of changes in a child's emotions within the home, enabling parents to provide prompt and accurate support.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] The user logs into the device. The device activates its camera and microphone and prepares to collect audio and facial expression data. The input is the user's voice and facial expressions, and the output is real-time captured audio files and video data. Specifically, when the user says "hello" into the device's camera, the device records the image and audio of that moment as data.

[0366] Step 2:

[0367] The terminal immediately transmits the collected audio and facial expression data to the server. The input is the audio files and video data captured by the terminal, and the output is the digital data sent to the server. Specifically, the terminal uploads the data to the server via the internet using a data transmission protocol.

[0368] Step 3:

[0369] The server inputs the received audio and facial expression data into the recognition engine. The input consists of audio files and video data sent from the terminal, and the output is analyzed emotion data. Specifically, the server analyzes the tone, speed, and facial expression changes of the voice, and quantifies and labels the user's emotions. For example, the server identifies the emotion of "joy" from the audio.

[0370] Step 4:

[0371] The server sends the analyzed emotion data to a generating AI model, which then generates a comprehensive emotion report. The input is quantified emotion data, and the output is an automatically generated emotion report. Specifically, the generating AI model refers to past data to summarize the current emotional state in a report.

[0372] Step 5:

[0373] The server creates action suggestions based on the generated emotion report. The input is the emotion report, and the output is specific action suggestions. Specifically, the server analyzes the user's emotional state and tendencies and constructs suggestions such as "recommend relaxing activities outdoors."

[0374] Step 6:

[0375] The server translates and provides behavioral suggestions and emotion reports in the language specified by the parent. The input is the behavioral suggestions and emotion reports, and the output is the translated information. Specifically, the server utilizes multilingual translation software to convert the information into a language format that is easy for the parent to understand.

[0376] This series of steps allows the system to analyze the user's emotions in detail and provide parents with specific support information.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0379] There is a need to accurately understand the emotional state of elderly individuals and enable caregivers to provide prompt and appropriate support. However, currently, it is difficult to recognize changes in the emotional state of elderly individuals in real time and provide caregivers with appropriate action suggestions. As a result, it may be difficult to respond appropriately to individual situations, potentially leading to a decline in the quality of care. To solve these problems, an emotion recognition system specifically designed for the elderly care field is needed.

[0380] 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.

[0381] In this invention, the server includes means for generating received information for emotion analysis, means for using an analysis engine to process the received information and recognize an emotional state, and means for providing a report based on the recognized emotional state. This makes it possible to grasp changes in the emotions of elderly people in real time and provide appropriate care suggestions to caregivers.

[0382] "Emotion analysis" is a technology that processes data such as voice and facial expressions to identify an individual's emotional state.

[0383] "Received information" refers to data such as the user's voice and facial expressions, which are used for sentiment analysis.

[0384] An "analysis engine" is a technology that includes algorithms and programs for processing received information and recognizing emotional states.

[0385] A "report" is a document that includes a description of the state and an analysis of the situation, generated based on the perceived emotional state.

[0386] An "action suggestion" is a proposal that directs or recommends appropriate actions based on the perceived emotional state, tailored to the specific situation.

[0387] "Translation means" refers to technologies that translate generated reports and action proposals into multiple languages ​​so that they can be understood by users from various cultural backgrounds.

[0388] A "notification" is a message, delivered via audio or visual means, that informs users or caregivers of important information.

[0389] An "information storage device" is a digital data storage medium used to save changes in emotional states and behavioral history.

[0390] A "learning method" is an algorithm and procedure used to analyze past data, learn patterns, and predict future situations.

[0391] A system for carrying out this invention consists of a terminal, a server, and a generative AI model.

[0392] The devices used are typical smartphones and tablets equipped with cameras and microphones. The devices interact with the user and acquire voice and facial expression data in real time. The voice and facial expressions spoken by the user to the device are sent to the server via an emotion analysis platform within the device.

[0393] The server uses an analysis engine to analyze the received data and determine the emotional state. This analysis engine includes emotion recognition libraries such as Google Cloud Vision API and TensorFlow. The analyzed emotional state is further analyzed by a generative AI model. This model allows for a comprehensive evaluation of an individual's emotional trends and the generation of appropriate actions.

[0394] The generated emotion-based reports and action suggestions are translated by the server and notified to caregivers in multiple languages. These notifications help caregivers take appropriate actions smoothly. For example, if an elderly person says positively, "I feel good today," the generating AI model might suggest something like, "Since the elderly person is in a good emotional state, we suggest going for a walk together."

[0395] A concrete example of a prompt message is, "If the elderly person expresses positive emotions, generate appropriate action suggestions based on this information." In this way, it is possible to provide consultation tailored to the user's emotional state and support appropriate care.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The device acquires the user's voice and facial expressions through its camera and microphone. The input consists of the user's voice and facial image, which is captured and converted into initial digital data. This conversion generates the voice as an audio file and the facial image as an image file.

[0399] Step 2:

[0400] The terminal transmits the acquired digital data to the server. This data consists of audio and image files, which serve as input for the next analysis step. Specifically, the terminal uses network communication to transmit data to the server via a secure channel.

[0401] Step 3:

[0402] The server processes the received audio and image data for sentiment analysis. The input consists of the audio and image files sent in the previous step. These are processed using the Google Cloud Vision API or similar sentiment recognition libraries to identify the user's emotions. The output is the analyzed emotional state, with sentiment labels such as "joy" and "sadness" generated.

[0403] Step 4:

[0404] The server sends the analysis results to a generating AI model for a comprehensive sentiment analysis. The input is sentiment labels, and based on these, the AI ​​model evaluates the sentiment trends and generates specific action suggestions as output. These action suggestions serve as guidelines indicating what kind of response is appropriate.

[0405] Step 5:

[0406] The server performs multilingual translation as needed to notify caregivers of the generated action suggestions. The input is the details of the action suggestion, and the server uses a text translation engine to translate the suggestion into a format and language that is readable by the caregiver. The output is the translated action suggestion, which is delivered to the caregiver's terminal.

[0407] Step 6:

[0408] The caregiver's terminal displays action suggestions received from the server and notifies the caregiver. The input is translated action suggestions, which are displayed on the screen to visually present them to the caregiver. The output is reference information that helps the caregiver understand the suggestions and take the most appropriate action for the individual user.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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".

[0425] This invention relates to a system that supports children's mental health and is realized through interaction between a server, a terminal, and a user. The system consists of the following components:

[0426] First, the device provides an interface for collecting emotions and daily events entered by the child user. The user can input text in a natural way. The device temporarily stores the entered data locally and sends it to the server at the appropriate time.

[0427] Next, the server receives text data sent from the terminal. The server uses a generative AI model to analyze the received data and determine the user's emotional state. Specifically, the AI ​​model identifies emotional labels and scores states such as stress and anxiety based on them. Based on this score, the server creates a report summarizing the characteristics of the emotional state.

[0428] Furthermore, the server generates action suggestions for parents, along with a report based on sentiment analysis. This includes specific advice such as, "You seem stressed today, so let's make time to relax." This information generated by the server is translated based on the parents' language settings.

[0429] Next, the device receives reports and action suggestions sent from the server and displays them in a format that is easy for parents to understand. Parents can use this information to consider appropriate actions for their child.

[0430] As a concrete example of this system, if a user enters "I had a fight with a friend at school today," the server may analyze this and determine that the user's emotions are leaning towards anxiety or sadness. In this case, the system may offer action suggestions to the parent, such as "Your child is feeling sad. Let's create a calm environment to talk things over."

[0431] The system of the present invention, configured in this way, can adapt to diverse family environments and support children's mental health.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The terminal launches the application and displays an interface where the user can log in. The user enters the required authentication information and logs in.

[0435] Step 2:

[0436] After logging in, the device provides the child with a chat interface. Users can freely type their feelings and the events of the day in text format and press the send button.

[0437] Step 3:

[0438] The device sends user input data to the server. This data is used as foundational data for analyzing the user's emotional state.

[0439] Step 4:

[0440] The server prepares to parse the text data received from the terminal. This includes formatting and preprocessing the data.

[0441] Step 5:

[0442] The server inputs the formatted text data into a generative AI model to analyze the emotional state. The model identifies emotional labels and generates corresponding scores.

[0443] Step 6:

[0444] The server generates a report based on the results of the sentiment analysis. The report includes a summary of the user's emotional tendencies, and particularly highlights any signs of stress or anxiety.

[0445] Step 7:

[0446] Based on the generated report, the server creates specific action suggestions for parents. These suggestions include concrete actions that parents can take to support their children.

[0447] Step 8:

[0448] The server translates reports and action suggestions into the user's language. It provides information in the language set by the user.

[0449] Step 9:

[0450] The device receives translated reports and action suggestions sent from the server and notifies the parent / guardian. The parent / guardian can then review the information on the device and consider appropriate action.

[0451] (Example 1)

[0452] 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."

[0453] In today's busy lifestyle, continuously and accurately analyzing a child's emotional state and providing the results to parents in a useful way is extremely important. However, existing methods may miss subtle changes or abnormalities in emotions. Furthermore, it is necessary to clearly communicate information to parents in a variety of languages. This invention aims to address these challenges and improve the mental health of children.

[0454] 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.

[0455] In this invention, the server includes means for collecting information to analyze a child's emotions, means for using a model to process the information and determine the emotional state, and means for scoring the generated emotional state. This allows parents to concretely understand their child's mental health by analyzing the emotional state based on data entered by the child and visualizing it as a numerical value.

[0456] "Means of collecting information" refers to a system for collecting and temporarily storing data on emotions and events entered by users.

[0457] "Methods of using models" refers to techniques that analyze collected data and apply AI models to determine specific emotional states.

[0458] "Means of providing information based on emotional state" refers to methods for generating reports and suggestions based on analysis results and communicating them to parents / guardians.

[0459] "Means of suggesting actions" refers to the process of suggesting specific ways to deal with a child to the guardian based on the judged emotional state.

[0460] "Means for translation into languages" refers to technologies for making generated reports and proposals multilingual.

[0461] "Methods for scoring emotional states" refer to methods that quantify the results of emotional analysis and evaluate them quantitatively.

[0462] "Means of displaying information on a device" refers to a mechanism for visually presenting generated reports and suggestions to the user or their guardian.

[0463] This invention is a system that continuously analyzes a child's emotional state and provides useful information and specific action suggestions to parents. In one embodiment, this system is realized through interaction between a server, a terminal, and a user.

[0464] First, the device provides an interface that allows the child user to naturally input emotions and everyday events. The device temporarily stores the input data in local storage and securely sends it to the server using HTTPS.

[0465] Next, the server utilizes a generative AI model to analyze the received data. During the analysis process, the server assigns sentiment labels to the text data and scores the emotional state. This entire process incorporates a sentiment analysis model, and includes an example where the analysis is performed using a prompt statement, such as: "Analyze the sentiment in the following sentence and identify the sentiment label. Sentence: 'I had a fight with a friend at school today.'"

[0466] The server then creates a report based on the analyzed emotional state and generates specific action suggestions for the parents. This information is translated into multiple languages ​​and displayed visually to the parents through their device.

[0467] For example, if a user enters "I had fun at school today," the server might analyze this and assign the emotion label "joy." Based on the emotion score, it might then provide advice to the parent, such as, "It seems your child had a good day. Please continue to encourage positive experiences."

[0468] This system will enable adaptation to diverse family environments and continuous support for children's mental health.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] Users input text about their child's feelings and events of the day in a natural way. An example of input data is the sentence, "Today I had fun eating lunch with my friends at school." This text data is temporarily stored in local storage by the device as emotional data. The input data will serve as raw material for later analysis.

[0472] Step 2:

[0473] The terminal periodically or based on event triggers sends stored text data to the server. The HTTPS protocol is used for transmission to ensure data protection and secure communication. The output data becomes data for analysis on the server.

[0474] Step 3:

[0475] The server inputs the received text data into a generating AI model. This process uses the prompt "Analyze the emotions in the following text and identify the emotion labels" to instruct the AI ​​model to analyze the text. Based on the input text, the model generates emotion labels such as joy, sadness, and anxiety. These emotion labels are then output.

[0476] Step 4:

[0477] The server scores the analysis results based on the obtained emotion labels. In the scoring process, for example, a high score is assigned to "joy," thus representing the emotional state numerically. This scoring provides a foundation for quantitatively understanding a child's emotional state. The output is a score.

[0478] Step 5:

[0479] Based on the emotional labels and scores obtained, the server generates a report and develops action suggestions for parents. Specific advice might include, "Your child seems to have had a positive experience today. Provide them with more opportunities for interaction." This information is translated into various languages ​​on the server. The output consists of language-specific reports and suggestions.

[0480] Step 6:

[0481] The device receives reports and action suggestions sent from the server and displays them in a format that is easy for parents to understand. The displayed information allows parents to grasp their child's emotional state and consider appropriate responses. The information is then presented to the parents as output.

[0482] (Application Example 1)

[0483] 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."

[0484] In supporting children's mental health, it is essential to accurately understand changes in their emotions and behavior on a daily basis and provide timely feedback to parents. However, with existing technologies, it has been difficult to recognize changes in children's emotions in real time and suggest specific actions to parents. Furthermore, it has been challenging to provide information in a way that parents can easily understand in diverse language environments.

[0485] 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.

[0486] In this invention, the server includes means for generating received data for analyzing a child's emotions, means for using an analysis model to process the received data and determine the emotional state, and speech recognition means for recognizing the child's voice and converting it into text. This makes it possible to capture the child's daily emotional changes, provide reports and action suggestions in a multilingual and easy-to-understand format to the parent's communication device, and effectively support the child's mental health.

[0487] "Means for generating received data" refers to a device or method that constructs data necessary for analysis based on information entered by a child.

[0488] "Means of using an analytical model" refers to an apparatus or method that processes collected data and performs analytical techniques to determine a child's emotional state.

[0489] "Means of providing information to parents" refers to the means and techniques for presenting analysis results to parents in an easy-to-understand manner.

[0490] "Means of providing concrete action suggestions" refers to a system that generates and provides advice to parents on how to take appropriate action based on the results of emotional analysis.

[0491] "Translation means" refers to technology that translates generated behavioral suggestions and reports into the language necessary to help parents understand them.

[0492] "Speech recognition means" refers to a technology that receives a child's speech as audio data and converts it into text data.

[0493] "Display means" refers to a device or method for visually presenting information transmitted from a server, making it easily verifiable by parents or guardians.

[0494] This invention is built using a specialized platform to realize a system that supports children's mental health. The system is mainly composed of a server, terminals, and the interaction between the child and their guardian as users.

[0495] The server plays a central role primarily in analysis and feedback. Sentimental text data entered by users through their devices is temporarily stored locally before being sent to the server at the appropriate time. Upon receiving this text data, the server analyzes it using natural language processing techniques. Specifically, it uses generative AI models such as Hugging Face's Transformers to label emotional states and calculate stress and anxiety scores.

[0496] After an assessment of the emotional state, the server generates specific action suggestions for the parent. These suggestions are translated into multiple languages ​​to ensure they are easily understood by the parent. The output suggestions are displayed on the parent's device, allowing them to review the recommended actions.

[0497] The device provides a user-friendly interface that allows children to naturally input their emotions. Simultaneously, it incorporates voice recognition technology, enabling real-time input of emotional data. This allows children to easily express their feelings by speaking.

[0498] For example, if a child says, "I had fun playing with my friends today," the speech recognition system converts this into text, and the server determines a positive emotional state based on the "fun" emotional label. This information is then reported to the parent, such as, "It seems your child had fun today. It was a good day."

[0499] An example of a prompt for a generative AI model is as follows: "Today's Sentiment Analysis: I had a fight with a friend. Use the model to classify the emotion and present an anxiety score."

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The device receives emotions and daily events entered by the child user in either voice or text format. In the case of voice input, the device's built-in speech recognition system converts it into text data. The input data is temporarily stored in local storage.

[0503] Step 2:

[0504] The device sends the collected text data to the server. The timing of data transmission is triggered either periodically or based on specific events. The data transmitted consists of emotional information entered by the child.

[0505] Step 3:

[0506] The server retrieves text data received from the terminal. Next, it analyzes the text data using natural language processing techniques and labels the emotional state using a generative AI model. The input is text data from children, and the output is an emotional label and an emotional score.

[0507] Step 4:

[0508] The server generates specific action suggestions to provide to parents based on emotion labels and scores. It uses the emotion analysis results to create advice aimed at reducing stress and anxiety. This process generates language that is easy for parents to understand.

[0509] Step 5:

[0510] The server translates the generated action suggestions into the parent's language. This translation process utilizes a multilingual translation library. The translated data provides appropriate feedback to the parent.

[0511] Step 6:

[0512] The parent's device receives translated suggestions sent from the server. The device displays this information in a user-friendly interface to help parents take appropriate action regarding their child's mental health.

[0513] Step 7:

[0514] Parents will review suggested behaviors for engaging with their children and use the feedback to improve their daily communication methods. This will promote support for children's mental health within the home.

[0515] 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.

[0516] This invention is a system that analyzes children's emotions in detail and helps parents provide appropriate support. The system consists of a terminal, a server, and an emotion engine.

[0517] First, the device provides an interface for using the camera and microphone when the user logs in. The user can interact with the system using voice and facial expressions. This information is sent to the emotion engine in real time.

[0518] The emotion engine analyzes received audio and facial expression data to recognize the user's emotions in real time. The engine quantifies the identified emotions and determines their state. Based on information such as voice tone and speed, and changes in facial expressions, the emotion engine identifies emotion labels with high accuracy.

[0519] Next, the server receives the data processed by the emotion engine and uses a generative AI model to analyze the overall emotional state. Based on this information, it automatically generates a report showing the user's emotional state. The server can also store a history of emotional states and detect changes in trends or anomalies by comparing and analyzing them with past patterns.

[0520] The generated reports are translated by the server and converted into a format that matches the parent's language settings. The server also generates and automatically sends action suggestions tailored to each individual's emotional state. This allows parents to gain a detailed understanding of their child's emotional changes and provide prompt and appropriate support.

[0521] For example, if a user says with a positive expression, "Something good happened at school today," the emotion engine recognizes that emotion as "joy," and the server generates action suggestions such as, "Your child is having a good time. Let's continue to enjoy conversations with the family in this way." This system can effectively support children's mental health, regardless of their family environment.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The device launches an interface prompting the user to log in, requesting user information and permission to access the camera and microphone. The user enters the required information into the login form and logs in.

[0525] Step 2:

[0526] The device displays an interface for emotion capture after login. Users provide emotion data by talking about daily events and showing facial expressions. Voice and facial data are captured in real time.

[0527] Step 3:

[0528] The device transmits captured audio and facial expression data to the emotion engine. The emotion engine analyzes this data to identify the user's emotions in real time.

[0529] Step 4:

[0530] The emotion engine analyzes the tone, speed, and content of the voice, as well as changes and characteristics of facial expressions, to calculate an emotion label. This result is then quantified to determine the user's emotional state.

[0531] Step 5:

[0532] The server receives emotion labels and scores sent from the emotion engine and uses a generative AI model to analyze the overall emotional state.

[0533] Step 6:

[0534] The server generates a report detailing the user's emotional state. This report includes a summary of the current emotional state, any necessary notes, and a comparison with past data.

[0535] Step 7:

[0536] The server translates the generated report into the parent's preferred language. It also derives specific action suggestions for the parent based on the report and sends the information.

[0537] Step 8:

[0538] The device receives translated reports and action suggestions sent from the server and displays them to the parent as a notification. The parent uses this information to consider how to respond to the user.

[0539] (Example 2)

[0540] 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."

[0541] In today's home environment, it is crucial for parents to promptly detect changes in their children's emotions and provide appropriate support. However, conventional systems have struggled to analyze emotional changes in detail, making it difficult to obtain clear suggestions for appropriate actions from parents. Therefore, there is a need for a system that can effectively support children's mental health.

[0542] 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.

[0543] In this invention, the server includes input means for collecting voice and facial expression data from the user, means for using a recognition engine for analyzing the voice and facial expression data, and means for analyzing the emotional state using the analyzed data and automatically generating a report using a generative AI model. This makes it possible to analyze changes in a child's emotions in detail and quickly provide beneficial behavioral suggestions to parents.

[0544] A "user" is the entity that utilizes the system and provides voice and facial expression data.

[0545] "Input means" refers to devices and interfaces for collecting voice and facial expression data from users.

[0546] A "recognition engine" is an algorithm or software that analyzes audio and facial expression data obtained from input sources to identify emotions.

[0547] A "generative AI model" refers to an artificial intelligence system that automatically generates reports and action suggestions based on analyzed emotional data.

[0548] A "report" is a document automatically generated by a generative AI model to show the user's emotional state.

[0549] "Translation means" refers to systems and processes for converting generated reports and action proposals into multiple languages.

[0550] "Emotional state" refers to the specific nature of a user's emotions, analyzed based on voice and facial expression data.

[0551] "Action suggestions" are specific and helpful recommendations for decisions and actions provided to parents based on the user's emotional state.

[0552] In order to implement this invention, the following system is required.

[0553] The terminal is the device used by users when logging in. The terminal is equipped with a camera and microphone, which are used to collect voice and facial expression data from the user. Specifically, the terminal uses video processing software and speech recognition software to record and accurately capture data in real time. For example, when a user speaks into the terminal's camera, the terminal collects and digitizes their voice and facial expressions.

[0554] The server receives user voice and facial expression data transmitted from the terminal. The server utilizes a recognition engine to analyze this data and identify the user's emotions. The recognition engine analyzes the tone and speed of the voice and changes in facial expressions, quantifying and labeling the emotions. The analysis results in the expression of emotions such as tension or joy.

[0555] Next, the server inputs the analyzed emotional data into a generating AI model, which automatically generates a comprehensive emotional report. The generating AI model uses algorithms learned from past data to perform accurate situational analysis and report creation. This report is displayed in a format easily understood by the user's guardian. The server also generates specific action suggestions based on the report. These action suggestions are designed to improve the user's emotional state and are sent to the guardian in a timely manner.

[0556] For example, if it is determined that a child is experiencing stress, the message sent from the server might say, "Your child is experiencing stress. Increase their outdoor activities and allow them to relax."

[0557] An example of a prompt is, "Analyze the child's emotional state in detail and generate appropriate action suggestions for the parent." Based on this prompt, the generating AI model creates a report and action suggestions using the user's emotional data.

[0558] This system allows for a detailed understanding of changes in a child's emotions within the home, enabling parents to provide prompt and accurate support.

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] The user logs into the device. The device activates its camera and microphone and prepares to collect audio and facial expression data. The input is the user's voice and facial expressions, and the output is real-time captured audio files and video data. Specifically, when the user says "hello" into the device's camera, the device records the image and audio of that moment as data.

[0562] Step 2:

[0563] The terminal immediately transmits the collected audio and facial expression data to the server. The input is the audio files and video data captured by the terminal, and the output is the digital data sent to the server. Specifically, the terminal uploads the data to the server via the internet using a data transmission protocol.

[0564] Step 3:

[0565] The server inputs the received audio and facial expression data into the recognition engine. The input consists of audio files and video data sent from the terminal, and the output is analyzed emotion data. Specifically, the server analyzes the tone, speed, and facial expression changes of the voice, and quantifies and labels the user's emotions. For example, the server identifies the emotion of "joy" from the audio.

[0566] Step 4:

[0567] The server sends the analyzed emotion data to a generating AI model, which then generates a comprehensive emotion report. The input is quantified emotion data, and the output is an automatically generated emotion report. Specifically, the generating AI model refers to past data to summarize the current emotional state in a report.

[0568] Step 5:

[0569] The server creates action suggestions based on the generated emotion report. The input is the emotion report, and the output is specific action suggestions. Specifically, the server analyzes the user's emotional state and tendencies and constructs suggestions such as "recommend relaxing activities outdoors."

[0570] Step 6:

[0571] The server translates and provides behavioral suggestions and emotion reports in the language specified by the parent. The input is the behavioral suggestions and emotion reports, and the output is the translated information. Specifically, the server utilizes multilingual translation software to convert the information into a language format that is easy for the parent to understand.

[0572] This series of steps allows the system to analyze the user's emotions in detail and provide parents with specific support information.

[0573] (Application Example 2)

[0574] 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."

[0575] There is a need to accurately understand the emotional state of elderly individuals and enable caregivers to provide prompt and appropriate support. However, currently, it is difficult to recognize changes in the emotional state of elderly individuals in real time and provide caregivers with appropriate action suggestions. As a result, it may be difficult to respond appropriately to individual situations, potentially leading to a decline in the quality of care. To solve these problems, an emotion recognition system specifically designed for the elderly care field is needed.

[0576] 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.

[0577] In this invention, the server includes means for generating received information for emotion analysis, means for using an analysis engine to process the received information and recognize an emotional state, and means for providing a report based on the recognized emotional state. This makes it possible to grasp changes in the emotions of elderly people in real time and provide appropriate care suggestions to caregivers.

[0578] "Emotion analysis" is a technology that processes data such as voice and facial expressions to identify an individual's emotional state.

[0579] "Received information" refers to data such as the user's voice and facial expressions, which are used for sentiment analysis.

[0580] An "analysis engine" is a technology that includes algorithms and programs for processing received information and recognizing emotional states.

[0581] A "report" is a document that includes a description of the state and an analysis of the situation, generated based on the perceived emotional state.

[0582] An "action suggestion" is a proposal that directs or recommends appropriate actions based on the perceived emotional state, tailored to the specific situation.

[0583] "Translation means" refers to technologies that translate generated reports and action proposals into multiple languages ​​so that they can be understood by users from various cultural backgrounds.

[0584] A "notification" is a message, delivered via audio or visual means, that informs users or caregivers of important information.

[0585] An "information storage device" is a digital data storage medium used to save changes in emotional states and behavioral history.

[0586] A "learning method" is an algorithm and procedure used to analyze past data, learn patterns, and predict future situations.

[0587] A system for carrying out this invention consists of a terminal, a server, and a generative AI model.

[0588] The devices used are typical smartphones and tablets equipped with cameras and microphones. The devices interact with the user and acquire voice and facial expression data in real time. The voice and facial expressions spoken by the user to the device are sent to the server via an emotion analysis platform within the device.

[0589] The server uses an analysis engine to analyze the received data and determine the emotional state. This analysis engine includes emotion recognition libraries such as Google Cloud Vision API and TensorFlow. The analyzed emotional state is further analyzed by a generative AI model. This model allows for a comprehensive evaluation of an individual's emotional trends and the generation of appropriate actions.

[0590] The generated emotion-based reports and action suggestions are translated by the server and notified to caregivers in multiple languages. These notifications help caregivers take appropriate actions smoothly. For example, if an elderly person says positively, "I feel good today," the generating AI model might suggest something like, "Since the elderly person is in a good emotional state, we suggest going for a walk together."

[0591] A concrete example of a prompt message is, "If the elderly person expresses positive emotions, generate appropriate action suggestions based on this information." In this way, it is possible to provide consultation tailored to the user's emotional state and support appropriate care.

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The device acquires the user's voice and facial expressions through its camera and microphone. The input consists of the user's voice and facial image, which is captured and converted into initial digital data. This conversion generates the voice as an audio file and the facial image as an image file.

[0595] Step 2:

[0596] The terminal transmits the acquired digital data to the server. This data consists of audio and image files, which serve as input for the next analysis step. Specifically, the terminal uses network communication to transmit data to the server via a secure channel.

[0597] Step 3:

[0598] The server processes the received audio and image data for sentiment analysis. The input consists of the audio and image files sent in the previous step. These are processed using the Google Cloud Vision API or similar sentiment recognition libraries to identify the user's emotions. The output is the analyzed emotional state, with sentiment labels such as "joy" and "sadness" generated.

[0599] Step 4:

[0600] The server sends the analysis results to a generating AI model for a comprehensive sentiment analysis. The input is sentiment labels, and based on these, the AI ​​model evaluates the sentiment trends and generates specific action suggestions as output. These action suggestions serve as guidelines indicating what kind of response is appropriate.

[0601] Step 5:

[0602] The server performs multilingual translation as needed to notify caregivers of the generated action suggestions. The input is the details of the action suggestion, and the server uses a text translation engine to translate the suggestion into a format and language that is readable by the caregiver. The output is the translated action suggestion, which is delivered to the caregiver's terminal.

[0603] Step 6:

[0604] The caregiver's terminal displays action suggestions received from the server and notifies the caregiver. The input is translated action suggestions, which are displayed on the screen to visually present them to the caregiver. The output is reference information that helps the caregiver understand the suggestions and take the most appropriate action for the individual user.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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".

[0622] This invention relates to a system that supports children's mental health and is realized through interaction between a server, a terminal, and a user. The system consists of the following components:

[0623] First, the device provides an interface for collecting emotions and daily events entered by the child user. The user can input text in a natural way. The device temporarily stores the entered data locally and sends it to the server at the appropriate time.

[0624] Next, the server receives text data sent from the terminal. The server uses a generative AI model to analyze the received data and determine the user's emotional state. Specifically, the AI ​​model identifies emotional labels and scores states such as stress and anxiety based on them. Based on this score, the server creates a report summarizing the characteristics of the emotional state.

[0625] Furthermore, the server generates action suggestions for parents, along with a report based on sentiment analysis. This includes specific advice such as, "You seem stressed today, so let's make time to relax." This information generated by the server is translated based on the parents' language settings.

[0626] Next, the device receives reports and action suggestions sent from the server and displays them in a format that is easy for parents to understand. Parents can use this information to consider appropriate actions for their child.

[0627] As a concrete example of this system, if a user enters "I had a fight with a friend at school today," the server may analyze this and determine that the user's emotions are leaning towards anxiety or sadness. In this case, the system may offer action suggestions to the parent, such as "Your child is feeling sad. Let's create a calm environment to talk things over."

[0628] The system of the present invention, configured in this way, can adapt to diverse family environments and support children's mental health.

[0629] The following describes the processing flow.

[0630] Step 1:

[0631] The terminal launches the application and displays an interface where the user can log in. The user enters the required authentication information and logs in.

[0632] Step 2:

[0633] After logging in, the device provides the child with a chat interface. Users can freely type their feelings and the events of the day in text format and press the send button.

[0634] Step 3:

[0635] The device sends user input data to the server. This data is used as foundational data for analyzing the user's emotional state.

[0636] Step 4:

[0637] The server prepares to parse the text data received from the terminal. This includes formatting and preprocessing the data.

[0638] Step 5:

[0639] The server inputs the formatted text data into a generative AI model to analyze the emotional state. The model identifies emotional labels and generates corresponding scores.

[0640] Step 6:

[0641] The server generates a report based on the results of the sentiment analysis. The report includes a summary of the user's emotional tendencies, and particularly highlights any signs of stress or anxiety.

[0642] Step 7:

[0643] Based on the generated report, the server creates specific action suggestions for parents. These suggestions include concrete actions that parents can take to support their children.

[0644] Step 8:

[0645] The server translates reports and action suggestions into the user's language. It provides information in the language set by the user.

[0646] Step 9:

[0647] The device receives translated reports and action suggestions sent from the server and notifies the parent / guardian. The parent / guardian can then review the information on the device and consider appropriate action.

[0648] (Example 1)

[0649] 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".

[0650] In today's busy lifestyle, continuously and accurately analyzing a child's emotional state and providing the results to parents in a useful way is extremely important. However, existing methods may miss subtle changes or abnormalities in emotions. Furthermore, it is necessary to clearly communicate information to parents in a variety of languages. This invention aims to address these challenges and improve the mental health of children.

[0651] 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.

[0652] In this invention, the server includes means for collecting information to analyze a child's emotions, means for using a model to process the information and determine the emotional state, and means for scoring the generated emotional state. This allows parents to concretely understand their child's mental health by analyzing the emotional state based on data entered by the child and visualizing it as a numerical value.

[0653] "Means of collecting information" refers to a system for collecting and temporarily storing data on emotions and events entered by users.

[0654] "Methods of using models" refers to techniques that analyze collected data and apply AI models to determine specific emotional states.

[0655] "Means of providing information based on emotional state" refers to methods for generating reports and suggestions based on analysis results and communicating them to parents / guardians.

[0656] "Means of suggesting actions" refers to the process of suggesting specific ways to deal with a child to the guardian based on the judged emotional state.

[0657] "Means for translation into languages" refers to technologies for making generated reports and proposals multilingual.

[0658] "Methods for scoring emotional states" refer to methods that quantify the results of emotional analysis and evaluate them quantitatively.

[0659] "Means of displaying information on a device" refers to a mechanism for visually presenting generated reports and suggestions to the user or their guardian.

[0660] This invention is a system that continuously analyzes a child's emotional state and provides useful information and specific action suggestions to parents. In one embodiment, this system is realized through interaction between a server, a terminal, and a user.

[0661] First, the device provides an interface that allows the child user to naturally input emotions and everyday events. The device temporarily stores the input data in local storage and securely sends it to the server using HTTPS.

[0662] Next, the server utilizes a generative AI model to analyze the received data. During the analysis process, the server assigns sentiment labels to the text data and scores the emotional state. This entire process incorporates a sentiment analysis model, and includes an example where the analysis is performed using a prompt statement, such as: "Analyze the sentiment in the following sentence and identify the sentiment label. Sentence: 'I had a fight with a friend at school today.'"

[0663] The server then creates a report based on the analyzed emotional state and generates specific action suggestions for the parents. This information is translated into multiple languages ​​and displayed visually to the parents through their device.

[0664] For example, if a user enters "I had fun at school today," the server might analyze this and assign the emotion label "joy." Based on the emotion score, it might then provide advice to the parent, such as, "It seems your child had a good day. Please continue to encourage positive experiences."

[0665] This system will enable adaptation to diverse family environments and continuous support for children's mental health.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] Users input text about their child's feelings and events of the day in a natural way. An example of input data is the sentence, "Today I had fun eating lunch with my friends at school." This text data is temporarily stored in local storage by the device as emotional data. The input data will serve as raw material for later analysis.

[0669] Step 2:

[0670] The terminal periodically or based on event triggers sends stored text data to the server. The HTTPS protocol is used for transmission to ensure data protection and secure communication. The output data becomes data for analysis on the server.

[0671] Step 3:

[0672] The server inputs the received text data into a generating AI model. This process uses the prompt "Analyze the emotions in the following text and identify the emotion labels" to instruct the AI ​​model to analyze the text. Based on the input text, the model generates emotion labels such as joy, sadness, and anxiety. These emotion labels are then output.

[0673] Step 4:

[0674] The server scores the analysis results based on the obtained emotion labels. In the scoring process, for example, a high score is assigned to "joy," thus representing the emotional state numerically. This scoring provides a foundation for quantitatively understanding a child's emotional state. The output is a score.

[0675] Step 5:

[0676] Based on the emotional labels and scores obtained, the server generates a report and develops action suggestions for parents. Specific advice might include, "Your child seems to have had a positive experience today. Provide them with more opportunities for interaction." This information is translated into various languages ​​on the server. The output consists of language-specific reports and suggestions.

[0677] Step 6:

[0678] The device receives reports and action suggestions sent from the server and displays them in a format that is easy for parents to understand. The displayed information allows parents to grasp their child's emotional state and consider appropriate responses. The information is then presented to the parents as output.

[0679] (Application Example 1)

[0680] 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".

[0681] In supporting children's mental health, it is essential to accurately understand changes in their emotions and behavior on a daily basis and provide timely feedback to parents. However, with existing technologies, it has been difficult to recognize changes in children's emotions in real time and suggest specific actions to parents. Furthermore, it has been challenging to provide information in a way that parents can easily understand in diverse language environments.

[0682] 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.

[0683] In this invention, the server includes means for generating received data for analyzing a child's emotions, means for using an analysis model to process the received data and determine the emotional state, and speech recognition means for recognizing the child's voice and converting it into text. This makes it possible to capture the child's daily emotional changes, provide reports and action suggestions in a multilingual and easy-to-understand format to the parent's communication device, and effectively support the child's mental health.

[0684] "Means for generating received data" refers to a device or method that constructs data necessary for analysis based on information entered by a child.

[0685] "Means of using an analytical model" refers to an apparatus or method that processes collected data and performs analytical techniques to determine a child's emotional state.

[0686] "Means of providing information to parents" refers to the means and techniques for presenting analysis results to parents in an easy-to-understand manner.

[0687] "Means of providing concrete action suggestions" refers to a system that generates and provides advice to parents on how to take appropriate action based on the results of emotional analysis.

[0688] "Translation means" refers to technology that translates generated behavioral suggestions and reports into the language necessary to help parents understand them.

[0689] "Speech recognition means" refers to a technology that receives a child's speech as audio data and converts it into text data.

[0690] "Display means" refers to a device or method for visually presenting information transmitted from a server, making it easily verifiable by parents or guardians.

[0691] This invention is built using a specialized platform to realize a system that supports children's mental health. The system is mainly composed of a server, terminals, and the interaction between the child and their guardian as users.

[0692] The server plays a central role primarily in analysis and feedback. Sentimental text data entered by users through their devices is temporarily stored locally before being sent to the server at the appropriate time. Upon receiving this text data, the server analyzes it using natural language processing techniques. Specifically, it uses generative AI models such as Hugging Face's Transformers to label emotional states and calculate stress and anxiety scores.

[0693] After an assessment of the emotional state, the server generates specific action suggestions for the parent. These suggestions are translated into multiple languages ​​to ensure they are easily understood by the parent. The output suggestions are displayed on the parent's device, allowing them to review the recommended actions.

[0694] The device provides a user-friendly interface that allows children to naturally input their emotions. Simultaneously, it incorporates voice recognition technology, enabling real-time input of emotional data. This allows children to easily express their feelings by speaking.

[0695] For example, if a child says, "I had fun playing with my friends today," the speech recognition system converts this into text, and the server determines a positive emotional state based on the "fun" emotional label. This information is then reported to the parent, such as, "It seems your child had fun today. It was a good day."

[0696] An example of a prompt for a generative AI model is as follows: "Today's Sentiment Analysis: I had a fight with a friend. Use the model to classify the emotion and present an anxiety score."

[0697] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0698] Step 1:

[0699] The device receives emotions and daily events entered by the child user in either voice or text format. In the case of voice input, the device's built-in speech recognition system converts it into text data. The input data is temporarily stored in local storage.

[0700] Step 2:

[0701] The device sends the collected text data to the server. The timing of data transmission is triggered either periodically or based on specific events. The data transmitted consists of emotional information entered by the child.

[0702] Step 3:

[0703] The server retrieves text data received from the terminal. Next, it analyzes the text data using natural language processing techniques and labels the emotional state using a generative AI model. The input is text data from children, and the output is an emotional label and an emotional score.

[0704] Step 4:

[0705] The server generates specific action suggestions to provide to parents based on emotion labels and scores. It uses the emotion analysis results to create advice aimed at reducing stress and anxiety. This process generates language that is easy for parents to understand.

[0706] Step 5:

[0707] The server translates the generated action suggestions into the parent's language. This translation process utilizes a multilingual translation library. The translated data provides appropriate feedback to the parent.

[0708] Step 6:

[0709] The parent's device receives translated suggestions sent from the server. The device displays this information in a user-friendly interface to help parents take appropriate action regarding their child's mental health.

[0710] Step 7:

[0711] Parents will review suggested behaviors for engaging with their children and use the feedback to improve their daily communication methods. This will promote support for children's mental health within the home.

[0712] 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.

[0713] This invention is a system that analyzes children's emotions in detail and helps parents provide appropriate support. The system consists of a terminal, a server, and an emotion engine.

[0714] First, the device provides an interface for using the camera and microphone when the user logs in. The user can interact with the system using voice and facial expressions. This information is sent to the emotion engine in real time.

[0715] The emotion engine analyzes received audio and facial expression data to recognize the user's emotions in real time. The engine quantifies the identified emotions and determines their state. Based on information such as voice tone and speed, and changes in facial expressions, the emotion engine identifies emotion labels with high accuracy.

[0716] Next, the server receives the data processed by the emotion engine and uses a generative AI model to analyze the overall emotional state. Based on this information, it automatically generates a report showing the user's emotional state. The server can also store a history of emotional states and detect changes in trends or anomalies by comparing and analyzing them with past patterns.

[0717] The generated reports are translated by the server and converted into a format that matches the parent's language settings. The server also generates and automatically sends action suggestions tailored to each individual's emotional state. This allows parents to gain a detailed understanding of their child's emotional changes and provide prompt and appropriate support.

[0718] For example, if a user says with a positive expression, "Something good happened at school today," the emotion engine recognizes that emotion as "joy," and the server generates action suggestions such as, "Your child is having a good time. Let's continue to enjoy conversations with the family in this way." This system can effectively support children's mental health, regardless of their family environment.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The device launches an interface prompting the user to log in, requesting user information and permission to access the camera and microphone. The user enters the required information into the login form and logs in.

[0722] Step 2:

[0723] The device displays an interface for emotion capture after login. Users provide emotion data by talking about daily events and showing facial expressions. Voice and facial data are captured in real time.

[0724] Step 3:

[0725] The device transmits captured audio and facial expression data to the emotion engine. The emotion engine analyzes this data to identify the user's emotions in real time.

[0726] Step 4:

[0727] The emotion engine analyzes the tone, speed, and content of the voice, as well as changes and characteristics of facial expressions, to calculate an emotion label. This result is then quantified to determine the user's emotional state.

[0728] Step 5:

[0729] The server receives emotion labels and scores sent from the emotion engine and uses a generative AI model to analyze the overall emotional state.

[0730] Step 6:

[0731] The server generates a report detailing the user's emotional state. This report includes a summary of the current emotional state, any necessary notes, and a comparison with past data.

[0732] Step 7:

[0733] The server translates the generated report into the parent's preferred language. It also derives specific action suggestions for the parent based on the report and sends the information.

[0734] Step 8:

[0735] The device receives translated reports and action suggestions sent from the server and displays them to the parent as a notification. The parent uses this information to consider how to respond to the user.

[0736] (Example 2)

[0737] 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".

[0738] In today's home environment, it is crucial for parents to promptly detect changes in their children's emotions and provide appropriate support. However, conventional systems have struggled to analyze emotional changes in detail, making it difficult to obtain clear suggestions for appropriate actions from parents. Therefore, there is a need for a system that can effectively support children's mental health.

[0739] 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.

[0740] In this invention, the server includes input means for collecting voice and facial expression data from the user, means for using a recognition engine for analyzing the voice and facial expression data, and means for analyzing the emotional state using the analyzed data and automatically generating a report using a generative AI model. This makes it possible to analyze changes in a child's emotions in detail and quickly provide beneficial behavioral suggestions to parents.

[0741] A "user" is the entity that utilizes the system and provides voice and facial expression data.

[0742] "Input means" refers to devices and interfaces for collecting voice and facial expression data from users.

[0743] A "recognition engine" is an algorithm or software that analyzes audio and facial expression data obtained from input sources to identify emotions.

[0744] A "generative AI model" refers to an artificial intelligence system that automatically generates reports and action suggestions based on analyzed emotional data.

[0745] A "report" is a document automatically generated by a generative AI model to show the user's emotional state.

[0746] "Translation means" refers to systems and processes for converting generated reports and action proposals into multiple languages.

[0747] "Emotional state" refers to the specific nature of a user's emotions, analyzed based on voice and facial expression data.

[0748] "Action suggestions" are specific and helpful recommendations for decisions and actions provided to parents based on the user's emotional state.

[0749] In order to implement this invention, the following system is required.

[0750] The terminal is the device used by users when logging in. The terminal is equipped with a camera and microphone, which are used to collect voice and facial expression data from the user. Specifically, the terminal uses video processing software and speech recognition software to record and accurately capture data in real time. For example, when a user speaks into the terminal's camera, the terminal collects and digitizes their voice and facial expressions.

[0751] The server receives user voice and facial expression data transmitted from the terminal. The server utilizes a recognition engine to analyze this data and identify the user's emotions. The recognition engine analyzes the tone and speed of the voice and changes in facial expressions, quantifying and labeling the emotions. The analysis results in the expression of emotions such as tension or joy.

[0752] Next, the server inputs the analyzed emotional data into a generating AI model, which automatically generates a comprehensive emotional report. The generating AI model uses algorithms learned from past data to perform accurate situational analysis and report creation. This report is displayed in a format easily understood by the user's guardian. The server also generates specific action suggestions based on the report. These action suggestions are designed to improve the user's emotional state and are sent to the guardian in a timely manner.

[0753] For example, if it is determined that a child is experiencing stress, the message sent from the server might say, "Your child is experiencing stress. Increase their outdoor activities and allow them to relax."

[0754] An example of a prompt is, "Analyze the child's emotional state in detail and generate appropriate action suggestions for the parent." Based on this prompt, the generating AI model creates a report and action suggestions using the user's emotional data.

[0755] This system allows for a detailed understanding of changes in a child's emotions within the home, enabling parents to provide prompt and accurate support.

[0756] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0757] Step 1:

[0758] The user logs into the device. The device activates its camera and microphone and prepares to collect audio and facial expression data. The input is the user's voice and facial expressions, and the output is real-time captured audio files and video data. Specifically, when the user says "hello" into the device's camera, the device records the image and audio of that moment as data.

[0759] Step 2:

[0760] The terminal immediately transmits the collected audio and facial expression data to the server. The input is the audio files and video data captured by the terminal, and the output is the digital data sent to the server. Specifically, the terminal uploads the data to the server via the internet using a data transmission protocol.

[0761] Step 3:

[0762] The server inputs the received audio and facial expression data into the recognition engine. The input consists of audio files and video data sent from the terminal, and the output is analyzed emotion data. Specifically, the server analyzes the tone, speed, and facial expression changes of the voice, and quantifies and labels the user's emotions. For example, the server identifies the emotion of "joy" from the audio.

[0763] Step 4:

[0764] The server sends the analyzed emotion data to a generating AI model, which then generates a comprehensive emotion report. The input is quantified emotion data, and the output is an automatically generated emotion report. Specifically, the generating AI model refers to past data to summarize the current emotional state in a report.

[0765] Step 5:

[0766] The server creates action suggestions based on the generated emotion report. The input is the emotion report, and the output is specific action suggestions. Specifically, the server analyzes the user's emotional state and tendencies and constructs suggestions such as "recommend relaxing activities outdoors."

[0767] Step 6:

[0768] The server translates and provides behavioral suggestions and emotion reports in the language specified by the parent. The input is the behavioral suggestions and emotion reports, and the output is the translated information. Specifically, the server utilizes multilingual translation software to convert the information into a language format that is easy for the parent to understand.

[0769] This series of steps allows the system to analyze the user's emotions in detail and provide parents with specific support information.

[0770] (Application Example 2)

[0771] 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".

[0772] There is a need to accurately understand the emotional state of elderly individuals and enable caregivers to provide prompt and appropriate support. However, currently, it is difficult to recognize changes in the emotional state of elderly individuals in real time and provide caregivers with appropriate action suggestions. As a result, it may be difficult to respond appropriately to individual situations, potentially leading to a decline in the quality of care. To solve these problems, an emotion recognition system specifically designed for the elderly care field is needed.

[0773] 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.

[0774] In this invention, the server includes means for generating received information for emotion analysis, means for using an analysis engine to process the received information and recognize an emotional state, and means for providing a report based on the recognized emotional state. This makes it possible to grasp changes in the emotions of elderly people in real time and provide appropriate care suggestions to caregivers.

[0775] "Emotion analysis" is a technology that processes data such as voice and facial expressions to identify an individual's emotional state.

[0776] "Received information" refers to data such as the user's voice and facial expressions, which are used for sentiment analysis.

[0777] An "analysis engine" is a technology that includes algorithms and programs for processing received information and recognizing emotional states.

[0778] A "report" is a document that includes a description of the state and an analysis of the situation, generated based on the perceived emotional state.

[0779] An "action suggestion" is a proposal that directs or recommends appropriate actions based on the perceived emotional state, tailored to the specific situation.

[0780] "Translation means" refers to technologies that translate generated reports and action proposals into multiple languages ​​so that they can be understood by users from various cultural backgrounds.

[0781] A "notification" is a message, delivered via audio or visual means, that informs users or caregivers of important information.

[0782] An "information storage device" is a digital data storage medium used to save changes in emotional states and behavioral history.

[0783] A "learning method" is an algorithm and procedure used to analyze past data, learn patterns, and predict future situations.

[0784] A system for carrying out this invention consists of a terminal, a server, and a generative AI model.

[0785] The devices used are typical smartphones and tablets equipped with cameras and microphones. The devices interact with the user and acquire voice and facial expression data in real time. The voice and facial expressions spoken by the user to the device are sent to the server via an emotion analysis platform within the device.

[0786] The server uses an analysis engine to analyze the received data and determine the emotional state. This analysis engine includes emotion recognition libraries such as Google Cloud Vision API and TensorFlow. The analyzed emotional state is further analyzed by a generative AI model. This model allows for a comprehensive evaluation of an individual's emotional trends and the generation of appropriate actions.

[0787] The generated emotion-based reports and action suggestions are translated by the server and notified to caregivers in multiple languages. These notifications help caregivers take appropriate actions smoothly. For example, if an elderly person says positively, "I feel good today," the generating AI model might suggest something like, "Since the elderly person is in a good emotional state, we suggest going for a walk together."

[0788] A concrete example of a prompt message is, "If the elderly person expresses positive emotions, generate appropriate action suggestions based on this information." In this way, it is possible to provide consultation tailored to the user's emotional state and support appropriate care.

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The device acquires the user's voice and facial expressions through its camera and microphone. The input consists of the user's voice and facial image, which is captured and converted into initial digital data. This conversion generates the voice as an audio file and the facial image as an image file.

[0792] Step 2:

[0793] The terminal transmits the acquired digital data to the server. This data consists of audio and image files, which serve as input for the next analysis step. Specifically, the terminal uses network communication to transmit data to the server via a secure channel.

[0794] Step 3:

[0795] The server processes the received audio and image data for sentiment analysis. The input consists of the audio and image files sent in the previous step. These are processed using the Google Cloud Vision API or similar sentiment recognition libraries to identify the user's emotions. The output is the analyzed emotional state, with sentiment labels such as "joy" and "sadness" generated.

[0796] Step 4:

[0797] The server sends the analysis results to a generating AI model for a comprehensive sentiment analysis. The input is sentiment labels, and based on these, the AI ​​model evaluates the sentiment trends and generates specific action suggestions as output. These action suggestions serve as guidelines indicating what kind of response is appropriate.

[0798] Step 5:

[0799] The server performs multilingual translation as needed to notify caregivers of the generated action suggestions. The input is the details of the action suggestion, and the server uses a text translation engine to translate the suggestion into a format and language that is readable by the caregiver. The output is the translated action suggestion, which is delivered to the caregiver's terminal.

[0800] Step 6:

[0801] The caregiver's terminal displays action suggestions received from the server and notifies the caregiver. The input is translated action suggestions, which are displayed on the screen to visually present them to the caregiver. The output is reference information that helps the caregiver understand the suggestions and take the most appropriate action for the individual user.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A means for generating received data to analyze a child's emotions,

[0826] Means for using an analytical model to process the received data and determine the emotional state,

[0827] A means of providing a report based on the aforementioned emotional state to the guardian,

[0828] Based on the aforementioned report, a means of providing specific action proposals to parents,

[0829] Translation means for translating the aforementioned action proposals and reports into multiple languages,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising means for detecting abnormalities in a child's emotional state and generating an alert.

[0833] (Claim 3)

[0834] The system according to claim 1, comprising data storage and a learning algorithm for continuous learning and improvement of a child's emotional state.

[0835] "Example 1"

[0836] (Claim 1)

[0837] A means of collecting information to analyze children's emotions,

[0838] Means for using a model to process the aforementioned information and determine the emotional state,

[0839] A means of providing information based on the aforementioned emotional state to the guardian,

[0840] A means of proposing specific actions to parents based on the aforementioned information,

[0841] Means for translating the aforementioned action proposals and information into multiple languages,

[0842] A means of scoring the generated emotional state,

[0843] A means of displaying the generated information on the terminal,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, further comprising means for detecting abnormalities in a child's emotional state and generating a notification.

[0847] (Claim 3)

[0848] The system according to claim 1, comprising a memory device and a learning algorithm for continuous analysis and improvement of a child's emotional state.

[0849] "Application Example 1"

[0850] (Claim 1)

[0851] A means for generating received data to analyze a child's emotions,

[0852] Means for using an analytical model to process the received data and determine the emotional state,

[0853] A means of providing a report based on the aforementioned emotional state to the guardian,

[0854] Based on the aforementioned report, a means of providing specific action proposals to parents,

[0855] Translation means for translating the aforementioned action proposals and reports into multiple languages,

[0856] A speech recognition method that recognizes children's voices and converts them into text,

[0857] A display means for presenting the aforementioned report and action suggestions to the parent's communication device,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising means for detecting abnormalities in a child's emotional state and generating an alert.

[0861] (Claim 3)

[0862] The system according to claim 1, comprising data storage and a learning algorithm for continuous learning and improvement of a child's emotional state.

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

[0864] (Claim 1)

[0865] An input means for collecting voice and facial expression data from the user,

[0866] Means for using a recognition engine to analyze the aforementioned voice and facial expression data,

[0867] A means of analyzing emotional states using analyzed data and automatically generating reports using a generative AI model,

[0868] A means of providing specific action proposals to parents based on the aforementioned report,

[0869] Translation means for translating the aforementioned report and action proposals into multiple languages,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for detecting changes or abnormalities in emotional state by comparing voice and facial expression data with accumulated past data.

[0873] (Claim 3)

[0874] The system according to claim 1, comprising continuous analysis of voice and facial expression data and a learning algorithm using a generative AI model.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A means for generating received information for sentiment analysis,

[0878] Means for using an analysis engine to process the received information and recognize the emotional state,

[0879] Means for providing a report based on the recognized emotional state,

[0880] A means of providing specific action proposals based on the aforementioned report,

[0881] A conversion means for converting the aforementioned action proposals and reports into multiple languages,

[0882] A means of recognizing the emotional state of elderly people and generating appropriate care suggestions for caregivers,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, further comprising means for detecting a change in emotional state and generating a notification.

[0886] (Claim 3)

[0887] The system according to claim 1, comprising an information storage device and a learning method for continuous learning and improvement of emotional states. [Explanation of symbols]

[0888] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating received data to analyze a child's emotions, Means for using an analytical model to process the received data and determine the emotional state, A means of providing a report based on the aforementioned emotional state to the guardian, Based on the aforementioned report, a means of providing specific action proposals to parents, Translation means for translating the aforementioned action proposals and reports into multiple languages, A system that includes this.

2. The system according to claim 1, further comprising means for detecting abnormalities in a child's emotional state and generating an alert.

3. The system according to claim 1, comprising data storage and a learning algorithm for continuous learning and improvement of a child's emotional state.

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