system

Abstract

We provide the system. [Solution] A system that includes means to understand children's worries about relationships and romance in their school and daily lives, means to understand situations where children compare themselves to others or are unable to express their true feelings due to worrying about what others think, and means to help children build confidence by giving them encouragement and advice.

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 as a 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] Children have various worries in school life and daily life, such as human relationships and love. Also, the environment surrounding children is changing, including the nuclear family trend in urban areas, the increase in dual-income families, and the academic society. There are also more children who compare themselves with others, care about the eyes of those around them, and cannot speak their true feelings. However, there is not enough means to appropriately understand these children's worries and give them encouragement and advice to boost their confidence.

Means for Solving the Problems

[0005] This invention provides a means to understand children's worries about relationships and romance in their school and daily lives, a means to understand situations where children compare themselves to others or are unable to express their true feelings due to concerns about what others think, and a means to help children build confidence by providing encouragement and advice. These means are realized using AI that analyzes children's words and actions, and generates optimal encouragement and advice according to the children's feelings and circumstances. [Brief explanation of the drawing]

[0006] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12]This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] This is a sequence diagram showing the processing flow of the data processing system in Example 1 of the Form 1 when an emotion engine is combined. [Figure 18] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of the Form 2 when an emotion engine is combined. [Figure 20] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] This is a sequence diagram showing the processing flow of the data processing system in Example 3 of the Form 3 when an emotion engine is combined. [Figure 22] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. [Figure 23] This is a sequence diagram showing the processing flow of a data processing system in another embodiment. [Modes for carrying out the invention]

[0007] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] 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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)), etc.

[0010] 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.

[0011] 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.

[0012] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0013] 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."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] In one embodiment of the present invention, an AI is used to analyze children's words and actions as a means of understanding their worries about relationships and romantic relationships in their school and daily lives. This AI estimates the feelings and worries of children from their words and actions. For example, if a child says, "I don't want to go to school," the AI ​​estimates from that statement that the child may be having some kind of worry about school life.

[0029] "Example of form 2"

[0030] Furthermore, as a means of understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about what others think, the AI ​​analyzes children's words and actions to estimate their feelings and circumstances. For example, if a child says, "I'm no good compared to other kids," the AI ​​will estimate from that statement that the child may be struggling with self-esteem.

[0031] "Example of form 3"

[0032] Furthermore, as a means of providing encouragement and advice to children, the AI ​​generates optimal encouragement and advice according to the children's feelings and circumstances. For example, if the AI ​​estimates that a child is struggling with school life, it will generate an encouraging message for that child such as, "School life is tough, but I know you can overcome it."

[0033] The following describes the processing flow for each example of the form.

[0034] "Example of form 1"

[0035] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0036] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate the children's feelings and concerns.

[0037] Step 3: The AI ​​generates encouragement and advice for the children based on their estimated feelings and concerns. This is done based on pre-set rules and learning models.

[0038] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0039] "Example of form 2"

[0040] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0041] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate situations where children are comparing themselves to others or are unable to express their true feelings due to concerns about what others think.

[0042] Step 3: The AI ​​generates encouragement and advice for the children based on the estimated situation. This is done based on pre-set rules and learning models.

[0043] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0044] "Example of form 3"

[0045] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0046] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate the children's feelings and situations.

[0047] Step 3: The AI ​​generates encouragement and advice for the children based on their estimated feelings and circumstances. This is done based on pre-set rules and learning models.

[0048] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0049] (Example 1)

[0050] Next, we will describe Example 1 of Form 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."

[0051] In today's educational environment, children often face interpersonal and emotional problems, and it is crucial to identify these issues early and provide appropriate support. However, accurately inferring children's feelings and problems from their words and actions is difficult, resulting in a lack of information for educators and support staff to respond appropriately.

[0052] 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.

[0053] In this invention, the server includes means for understanding interpersonal relationships and emotional problems in children's educational and daily environments, means for analyzing children's statements and actions to estimate their emotions and intentions, and means for providing the analysis results to educators and support staff. This makes it possible to grasp children's feelings and problems early and provide appropriate support and guidance.

[0054] The term "educational environment" refers to the physical and social environment in which children learn and grow, and includes schools, homes, and other similar settings.

[0055] "Everyday environment" refers to the environment that children encounter in their daily lives, including their home and community.

[0056] "Interpersonal relationships" refers to the human relationships that children form with others, including friendships and relationships with teachers.

[0057] "Emotional problems" refer to emotionally related issues that children experience, including anxiety and stress.

[0058] "Means of analyzing speech and actions" refers to techniques and methods for analyzing children's words and actions and inferring the emotions and intentions behind them.

[0059] "Means of inferring emotions and intentions" refers to techniques and methods for inferring children's feelings and intentions from their words and actions.

[0060] "Means of providing analysis results" refers to the technologies and methods for communicating the analyzed information to educators and support staff.

[0061] "Educators and supporters" refers to people who have a role in supporting children's learning and growth, and includes teachers, counselors, and others.

[0062] The following system is constructed as an embodiment of this invention.

[0063] The server generates a program to analyze the children's statements and actions. This program is designed to analyze the children's statements and actions using natural language processing techniques. Specifically, the server uses a cloud-based natural language processing API to analyze the text data and extract emotions and intentions. This API is provided by a common cloud service provider.

[0064] The devices are installed in classrooms and homes to collect children's speech as audio data. This audio data is sent to a server and converted into text data. The server analyzes the converted text data to estimate the children's feelings and problems.

[0065] Educators and support staff can view the analysis results provided by the server through a dedicated dashboard. This dashboard displays a list of children's emotional states and estimated problems. This allows users to provide appropriate support and guidance to the children.

[0066] For example, if a child says, "I don't want to go to school," the server analyzes this statement and estimates their anxiety and stress regarding school life. Users can then view this information on a dashboard and consider appropriate responses for their child.

[0067] An example of a prompt is, "If a child says they don't want to go to school, estimate the reason." Using this prompt, the server estimates the child's underlying concerns from their statements and generates information to provide appropriate support.

[0068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0069] Step 1:

[0070] The devices are installed in classrooms and homes to collect children's speech as audio data. Specifically, they use microphones to record conversations and send that audio data to a server. The input is audio data, and the output is an audio file sent to the server.

[0071] Step 2:

[0072] The server converts the received audio data into text data. Specifically, it uses speech recognition software to convert the audio data into a string of characters. This process outputs the audio data as text data. The input is an audio file, and the output is text data.

[0073] Step 3:

[0074] The server analyzes text data using natural language processing techniques. Specifically, it uses a cloud-based natural language processing API to extract emotions and intentions from the text. This process yields information about emotions and intentions from the text data. The input is text data, and the output is the analysis results, including emotions and intentions.

[0075] Step 4:

[0076] The server estimates the children's feelings and problems based on the analysis results. Using a generative AI model, it identifies potential problems underlying their statements. This process reveals the children's underlying anxieties. The input is the analysis results, and the output is the estimated feelings and problems.

[0077] Step 5:

[0078] The server provides educators and support staff with estimated feelings and problems. Specifically, it displays the information through a dedicated dashboard. This dashboard lists each child's emotional state and estimated problems. The input is the estimated feelings and problems, and the output is the information displayed on the dashboard.

[0079] Step 6:

[0080] Educators and support staff, who are the users, can check the children's status through the dashboard and provide appropriate support and guidance. Specifically, they consider countermeasures for the children based on the information on the dashboard. The input is the information on the dashboard, and the output is the countermeasures taken by the educators and support staff.

[0081] (Application Example 1)

[0082] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0083] In children's school and daily lives, there is a challenge in understanding the relationship and romantic problems they face, comparing themselves to others, and understanding situations where they cannot express their true feelings due to concern about what others think. Furthermore, there is a need to provide appropriate encouragement and advice tailored to the children's feelings and circumstances. In addition, it is necessary to promptly inform parents and educators of this information to enable appropriate intervention.

[0084] 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.

[0085] In this invention, the server includes means for analyzing children's words and actions in real time and notifying parents and educators, means for understanding children's concerns, and means for providing encouragement and advice to children. This makes it possible to quickly understand children's feelings and concerns and provide appropriate support.

[0086] "Methods for analyzing children's speech and actions in real time" refers to technologies that instantly acquire children's voices and movements as data and analyze their content using information processing equipment.

[0087] "Means of notifying parents and educators" refers to communication technologies that, based on analyzed information, quickly transmit important information about children's feelings and circumstances to parents and educators.

[0088] "Means for understanding children's worries" refers to information processing technology that identifies and understands the problems and feelings children are experiencing through their words and actions.

[0089] "Means of providing encouragement and advice" refers to information processing technology that generates and provides appropriate encouragement and advice according to the feelings and circumstances of children.

[0090] As a form of implementing the invention, this system has the function of analyzing children's speech and actions in real time and notifying parents and educators. The server uses a speech recognition API (e.g., Google® Cloud Speech-to-Text) to convert children's speech into text data and an image recognition API (e.g., Google Cloud Vision) to analyze their actions. This obtains data to estimate the children's feelings and concerns.

[0091] The server uses natural language processing libraries (e.g., NLTK, spaCy) to perform sentiment analysis on text data. Based on these analysis results, a generative AI model estimates the children's feelings and concerns and generates appropriate encouragement and advice. The generated information is sent to parents and educators via push notifications using communication technology.

[0092] For example, if a child says "I don't want to go to school," the server uses a speech recognition API to convert this statement into text, and a natural language processing library analyzes the negative emotion expressed by "I don't want to go." The generative AI model estimates that the child has problems related to school life and sends a notification to the parent saying, "Your child is not wanting to go to school. There may be a problem."

[0093] An example of a prompt question would be, "What kinds of feelings or worries might a child have if they say, 'I don't want to go to school'?"

[0094] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0095] Step 1:

[0096] The device captures the children's speech as audio data via a microphone. The captured audio data is then sent to a server.

[0097] Step 2:

[0098] The server uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the received audio data into text data. This process transforms the audio data into a format that can be parsed as text information.

[0099] Step 3:

[0100] The server performs sentiment analysis on text data using natural language processing libraries (e.g., NLTK, spaCy). It analyzes emotions and intentions from the input text data and generates data to estimate children's feelings and concerns.

[0101] Step 4:

[0102] The server uses a generative AI model to estimate children's feelings and worries based on the results of emotion analysis. Based on the estimation results, it generates appropriate encouragement and advice.

[0103] Step 5:

[0104] The server uses communication technology to send estimated results and generated encouragement and advice to parents and educators via push notifications. This allows parents and educators to understand their children's situation and provide the necessary support.

[0105] (Example 2)

[0106] Next, we will describe Example 2 of Form 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".

[0107] Children often struggle to express their true feelings at school and in their daily lives because they are overly concerned with comparing themselves to others and the opinions of those around them. In such situations, it is difficult to accurately understand children's feelings and provide appropriate encouragement and advice. Furthermore, it is not easy to infer the emotions and circumstances behind children's words and actions.

[0108] 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.

[0109] In this invention, the server includes means for analyzing children's statements and actions and estimating the emotions and circumstances behind them; means for estimating the children's feelings based on the analyzed data and providing appropriate support information; and means for estimating emotions using a generative AI model. This makes it possible to accurately understand the children's feelings and provide appropriate encouragement and advice tailored to their individual circumstances.

[0110] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[0111] "Relationships" refers to the social connections and interactions that children build with others.

[0112] "Personal worries" refer to the internal problems and anxieties that children individually face.

[0113] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0114] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new information or predictions from data.

[0115] "Inferring emotions" refers to inferring the emotions behind children's words and actions.

[0116] "Support information" refers to information such as encouragement and advice provided according to the feelings and circumstances of the children.

[0117] One embodiment of this invention is to provide a system that analyzes children's words and actions and estimates the emotions and circumstances behind them. A specific embodiment is shown below.

[0118] The server collects data on children's statements and actions. This data is obtained through the terminal as voice input or text input. For example, if a child says to the terminal, "I'm no good compared to other children," that voice data is sent to the server.

[0119] The server uses speech recognition software to convert the collected audio data into text data. Specifically, it can utilize APIs commonly used for speech recognition technology.

[0120] Next, the server analyzes the converted text data using natural language processing techniques. This analysis utilizes a generative AI model, which includes algorithms for understanding the context of natural language and estimating sentiment.

[0121] Based on the analysis, the server estimates the children's feelings and circumstances and generates appropriate support information. This support information is provided to the children as encouragement and advice.

[0122] As a concrete example, a user can input the following prompt message into the AI ​​model:

[0123] "What emotions might be at play when a child says, 'I'm no good compared to other kids'?"

[0124] "What kind of support do children need when they are worried about what others think of them?"

[0125] This allows users to gain a deeper understanding of children's feelings and obtain clues to provide appropriate support.

[0126] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0127] Step 1:

[0128] The server receives audio data from the terminal regarding the children's speech and actions. The user's spoken words into the terminal are input as audio data. This audio data is then sent to the server.

[0129] Step 2:

[0130] The server uses speech recognition software to convert the received audio data into text data. Specifically, it analyzes the audio data using a speech recognition API and outputs it as text data. This conversion transforms the audio information into text information.

[0131] Step 3:

[0132] The server analyzes the converted text data using natural language processing techniques. A generative AI model is used to understand the context of the text data and estimate emotions. This analysis outputs data that allows for the estimation of children's emotions and situations from the text data.

[0133] Step 4:

[0134] The server estimates the children's feelings and circumstances based on the analysis results. Using the output of the generative AI model, it estimates the emotions and worries the children may be experiencing. These estimation results serve as the basic data for generating appropriate support information for the children.

[0135] Step 5:

[0136] The server generates support information for the children based on the estimation results. Using a generation AI model, it generates encouragement and advice tailored to the children's feelings and circumstances. This support information is provided to the user through the terminal.

[0137] Step 6:

[0138] Users receive support information through their devices and provide appropriate support to the children. Based on this information, users can understand the children's feelings and offer necessary advice and encouragement.

[0139] (Application Example 2)

[0140] Next, we will describe Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0141] Modern children often struggle with self-esteem and are unable to express their true feelings due to excessive comparison with others and concern for how they are perceived in school and daily life. This situation can negatively impact children's mental health. However, there is a lack of means to identify these feelings and situations early and provide appropriate support. Therefore, there is a need for a system that analyzes children's words and actions, estimates their feelings, identifies their problems early, and provides appropriate encouragement and advice.

[0142] 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.

[0143] In this invention, the server includes means for transcribing children's speech into text using speech recognition technology and analyzing it using natural language processing technology, means for estimating the children's feelings using a generative AI model and notifying them of the results, and means for generating optimal encouragement and advice according to the children's feelings and circumstances. This makes it possible to accurately understand the children's feelings and provide appropriate support.

[0144] "School life" refers to the daily activities and experiences that children have while at school.

[0145] "Daily life" refers to the activities and experiences that children engage in on a daily basis at home and in their community.

[0146] "Human relationships" refers to the reciprocal relationships that children build with others.

[0147] "Romantic love" refers to the affection and feelings that children have for others.

[0148] "Worries" refer to the mental burdens and problems that children face.

[0149] "Comparison" refers to the act of children comparing themselves to others.

[0150] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[0151] "Speech recognition technology" refers to the technology that converts speech into text data.

[0152] "Natural language processing technology" refers to the technology used to analyze text data and understand its meaning.

[0153] A "generative AI model" refers to a model that uses artificial intelligence to generate new information from data.

[0154] "Sentiments" refers to the inner feelings and emotions of children.

[0155] "Notification" refers to the act of communicating analysis results or information to others.

[0156] "Encouragement" refers to words and actions used to cheer up children.

[0157] "Advice" refers to the guidance and instruction given to children.

[0158] The system for implementing this invention analyzes children's words and actions and estimates their feelings to provide appropriate support. The system has the following configuration:

[0159] The server uses speech recognition technology to convert children's speech into text data. This process utilizes speech recognition software such as the Google Speech-to-Text API. The converted text data is then analyzed using natural language processing technology. Natural language processing software, such as the Google Cloud Natural Language API, is used to understand the children's feelings and circumstances from the text data.

[0160] Next, the server uses a generative AI model to estimate the child's feelings from the analyzed data. It uses generative AI models such as OpenAI's GPT-3 to generate the estimated feelings. These estimated results are then communicated to teachers and parents through notification systems.

[0161] For example, if a child says, "I'm worse at drawing than the other kids," the server uses speech recognition technology to transcribe this statement into text and analyzes it using natural language processing technology. A generative AI model estimates from this statement whether the child is struggling with self-esteem and notifies the user of the result. An example of a prompt would be, "If a child says, 'I'm worse at drawing than the other kids,' please estimate their feelings."

[0162] This system makes it possible to accurately understand children's feelings and provide appropriate encouragement and advice.

[0163] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0164] Step 1:

[0165] The user (child) makes a statement. The device acquires this statement as audio data via the microphone. The input is audio data, and the output is the same audio data.

[0166] Step 2:

[0167] The device sends the acquired audio data to the server. The server converts the audio data into text data using speech recognition technology. Specifically, it uses the Google Speech-to-Text API to convert speech to text. The input is audio data, and the output is text data.

[0168] Step 3:

[0169] The server analyzes the converted text data using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract emotions and intentions from the text data. The input is text data, and the output is emotion data as a result of the analysis.

[0170] Step 4:

[0171] The server uses a generative AI model to estimate the child's emotions from the analysis results. Specifically, it uses OpenAI's GPT-3 to estimate emotions based on emotion data. The input is emotion data, and the output is the estimated emotion result.

[0172] Step 5:

[0173] The server communicates the estimated mood to teachers and parents through notification methods. Specifically, it sends the estimated mood results using email or app notifications. The input is the estimated mood result, and the output is the notification message.

[0174] Step 6:

[0175] The server generates optimal encouragement and advice based on the child's feelings and situation. It uses a generative AI model to create appropriate messages. The input is an estimated feeling, and the output is a message of encouragement or advice.

[0176] Step 7:

[0177] The server sends the generated encouragement and advice to the terminal and displays it to the user. The terminal displays the message on the screen and provides feedback to the user. The input is the message of encouragement and advice, and the output is what is displayed to the user.

[0178] (Example 3)

[0179] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0180] Modern children face a variety of problems and psychological stress in their living environments, but there is a lack of means to properly understand these issues and provide appropriate responses tailored to their individual circumstances. Therefore, there is a need to effectively resolve the problems children face and promote their psychological stability.

[0181] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0182] In this invention, the server includes means for understanding problems in the children's living environment, means for analyzing the children's psychological state, and means for generating appropriate responses for the children. This makes it possible to provide appropriate responses tailored to the individual circumstances of the children and promote their psychological stability.

[0183] "Problems in children's living environments" refers to the various difficulties and challenges that children face in their daily lives and social environments.

[0184] "Psychological state" refers to a child's emotions, mood, and mental health.

[0185] "Appropriate responses" refer to reactions such as encouragement and advice that are provided according to the individual circumstances and psychological state of the children.

[0186] An "information processing device" refers to a computing device used to analyze data and perform processing according to a specific purpose.

[0187] One embodiment of this invention is to provide a system that identifies problems in children's living environments, analyzes their psychological state, and generates appropriate responses.

[0188] Users input text data about children's feelings and situations using a device. This data specifically describes the problems and worries the children are facing. The device sends this input data to a server. The server receives the data and analyzes it using a generative AI model. Specifically, it uses natural language processing technology to analyze the data in order to understand the children's psychological state. Based on the analysis results, the server generates an appropriate response. This response includes encouragement and advice tailored to each child's individual situation.

[0189] The hardware used includes high-performance computers equipped with NVIDIA GPUs, and the software includes large-scale language models such as OpenAI's GPT series. This allows the server to generate optimal responses tailored to the children's feelings and situations.

[0190] For example, if a user enters text such as "I'm lonely because I can't make friends at school," the server will use this information to generate a message such as "It's hard to make new friends, but try talking to people little by little. I'm sure you can do it." An example of a prompt message could be something like, "What kind of encouraging message should I send to a child who is having trouble with relationships at school?" The flow of specific processing in Example 3 will be explained using Figure 15.

[0191] Step 1:

[0192] Users input text data about the children's feelings and circumstances using a terminal. This input data describes the specific problems and worries the children are facing. The input data is sent from the terminal to the server.

[0193] Step 2:

[0194] The server inputs text data received from the terminals into a generative AI model for analysis. Specifically, natural language processing techniques are used to extract information from the text data that helps understand the children's psychological state and circumstances. This analysis provides detailed data about the children's feelings and situations.

[0195] Step 3:

[0196] Based on the analysis results, the server uses a generative AI model to generate appropriate responses. The generated responses include encouragement and advice tailored to each child's individual situation. For example, in response to the input "I'm lonely because I can't make friends at school," a message such as "It's hard to make new friends, but try talking to people little by little. I'm sure you can do it" is generated.

[0197] Step 4:

[0198] The server sends the generated response to the terminal. The terminal displays the received message to the user. The user can review the displayed message and provide appropriate encouragement or advice to the children.

[0199] (Application Example 3)

[0200] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0201] Modern children face various problems and stresses in their school and daily lives, but there is a lack of means to properly understand and support them. Furthermore, children often have difficulty expressing their emotions, and opportunities to receive appropriate encouragement and advice are limited. Improving this situation and providing children with a safe and secure environment is essential.

[0202] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0203] In this invention, the server includes means for understanding problems in the children's living environment, means for analyzing the children's emotional state, and means for providing appropriate encouragement and advice to the children. This makes it possible to provide appropriate support in real time according to the children's emotions and circumstances.

[0204] "Means for understanding problems in the living environment" refers to technologies for detecting and recording various problems that children face in their daily lives and school life.

[0205] "Methods for analyzing emotional states" refer to technologies that analyze children's voices and facial expressions to estimate their emotions and psychological states.

[0206] "Means of providing appropriate encouragement and advice" refers to techniques for generating and providing optimal encouragement and advice tailored to the child's situation and emotions.

[0207] "Means of recognizing voice and facial expressions" refers to technologies for acquiring and analyzing children's speech and facial expressions as digital data.

[0208] "Methods for generating encouragement and advice using generative AI models" refers to technologies that utilize AI technology to automatically generate encouragement and advice tailored to the specific circumstances of children.

[0209] "Methods for converting generated messages into audio" refers to technology that converts encouraging and advice messages generated in text format into audio for delivery to children.

[0210] The system for implementing this invention is designed to identify problems in children's living environments, analyze their emotional states, and provide appropriate encouragement and advice.

[0211] The server uses speech recognition and facial recognition technologies to acquire children's voices and facial expressions as digital data. Specifically, it uses Google Cloud Speech-to-Text for speech recognition and Microsoft® Azure® Face API for facial expression recognition. This makes it possible to estimate emotions from children's speech and facial expressions.

[0212] Next, the server uses a generative AI model to generate encouragement and advice tailored to the children's situations. This process utilizes prompt text as input and employs generative AI models such as OpenAI GPT-3. The generated messages are then converted into speech using speech synthesis technology such as Amazon Polly and delivered to the children.

[0213] For example, if a child says, "Today's lesson was difficult," the server converts the statement into text and estimates the child's emotions from their facial expression. Then, it inputs the following prompt into the generative AI model: "The child said, 'Today's lesson was difficult.' Their expression seems a little down. Please generate an encouraging message for this situation." As a result, the AI ​​can generate an encouraging message such as, "It's great to challenge yourself with difficult things. Let's try our best next time!" and deliver it to the child verbally.

[0214] In this way, the system can provide appropriate support in real time, tailored to the children's emotions and circumstances.

[0215] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0216] Step 1:

[0217] The user (child) speaks into the device. The device acquires the user's voice through its microphone. This voice data becomes the input.

[0218] Step 2:

[0219] The device sends the acquired audio data to the server. The server uses speech recognition technology (Google Cloud Speech-to-Text) to convert the audio data into text data. This converted text data becomes the output.

[0220] Step 3:

[0221] The server receives video data from the user transmitted from the terminal and analyzes the user's facial expressions using facial recognition technology (Microsoft Azure Face API). As a result of the analysis, it estimates the user's emotional state, and this data is output.

[0222] Step 4:

[0223] The server takes text data and emotional state data as input and generates prompt sentences for a generative AI model (OpenAI GPT-3). These prompt sentences include the user's statements and emotional state. Using these prompt sentences, the AI ​​model generates encouraging and advice messages. These messages become the output.

[0224] Step 5:

[0225] The server converts the generated message into audio data using speech synthesis technology (Amazon Polly). This audio data becomes the output.

[0226] Step 6:

[0227] The server sends the generated audio data to the terminal. The terminal plays the audio message to the user through its speaker. This allows the user to receive encouragement and advice.

[0228] 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.

[0229] "Example of form 1"

[0230] One embodiment of the present invention combines an AI that analyzes children's speech and actions with an emotion engine that recognizes the user's emotions. Specifically, it collects input from devices and platforms used by children in real time, and the AI ​​analyzes this data. At the same time, the emotion engine recognizes emotions from the children's facial expressions and tone of voice, and feeds this information back into the AI's analysis. This makes it possible to more accurately understand the children's feelings and worries.

[0231] "Example of form 2"

[0232] Furthermore, the means of providing encouragement and advice to children also combine AI and an emotion engine. Specifically, the AI ​​generates optimal encouragement and advice according to the child's feelings and situation. In doing so, it refers to information obtained from the emotion engine, enabling it to respond appropriately to the child's emotions. For example, if it senses that a child is feeling down, it generates an encouraging message. Conversely, if it senses that a child is happy, it generates a message that shares that happiness.

[0233] "Example of form 3"

[0234] Furthermore, it is possible to use an emotion engine to both understand children's worries and to provide them with encouragement and advice. Specifically, the emotion engine recognizes children's emotions in real time and feeds that information back to the AI. This allows the AI ​​to respond appropriately to the children's emotions. For example, if the AI ​​senses that a child is angry, it will generate advice to calm that anger. Conversely, if the AI ​​senses that a child is having fun, it will generate advice to amplify that fun.

[0235] The following describes the processing flow for each example of the form.

[0236] "Example of form 1"

[0237] Step 1: Collect input in real time from the devices and platforms that children use.

[0238] Step 2: The AI ​​analyzes the collected data.

[0239] Step 3: Simultaneously, the emotional engine recognizes emotions from the children's facial expressions, tone of voice, and other factors.

[0240] Step 4: The information obtained from the emotion engine is fed back into the AI ​​analysis.

[0241] "Example of form 2"

[0242] Step 1: The AI ​​generates optimal encouragement and advice based on the children's feelings and circumstances.

[0243] Step 2: Use the information obtained from the emotion engine as a reference.

[0244] Step 3: It becomes possible to respond appropriately to children's emotions.

[0245] "Example of form 3"

[0246] Step 1: The emotion engine recognizes children's emotions in real time.

[0247] Step 2: Provide that information as feedback to the AI.

[0248] Step 3: The AI ​​responds appropriately to the children's emotions.

[0249] (Example 1)

[0250] Next, we will describe Example 1 of Form 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."

[0251] Modern children often face social and emotional problems in their educational and daily environments. However, it is difficult to identify these problems early and provide appropriate support and guidance. In particular, when children compare themselves to others or are unable to express their true feelings due to concerns about how others perceive them, the seriousness of the problems may be overlooked. There is a need to improve this situation, accurately understand children's feelings and worries, and provide appropriate support.

[0252] 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.

[0253] In this invention, the server includes means for understanding the social relationships and emotional problems of children in their educational and daily environments, means for understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about the opinions of those around them, and means for improving children's self-confidence by providing support and guidance. This makes it possible to accurately grasp children's feelings and worries and provide appropriate support.

[0254] The term "educational environment" refers to the physical and social environment in which children learn and grow, and includes schools, homes, and other learning facilities.

[0255] "Everyday environment" refers to the environment in which children go about their daily lives, and includes their home, community, and friendships.

[0256] "Social relationships" refer to the interpersonal relationships that children form with others, including relationships with friends, family, teachers, and others.

[0257] "Emotional problems" refer to emotional issues that children experience, including anxiety, stress, and loneliness.

[0258] An "information processing device" refers to a device used to analyze children's speech and actions, and includes electronic devices such as computers and servers.

[0259] An "emotion analysis device" refers to a device used to recognize children's emotions, and it has the function of analyzing facial expressions and tone of voice using a camera and microphone.

[0260] "Artificial intelligence" refers to the technology of computer systems that imitate human intelligence to learn and reason, and includes natural language processing and machine learning.

[0261] "Support and guidance" refers to advice and educational support provided to children, aimed at problem-solving and improving their self-confidence.

[0262] This invention is a system that understands the social relationships and emotional problems of children in their educational and daily environments and provides appropriate support. The system uses an information processing device and an emotion analysis device to analyze children's statements, actions, and emotions.

[0263] The server collects data on children's speech and actions in real time from devices they use (smartphones, tablets, computers, etc.). Specifically, it acquires data such as social media posts, chat messages, and voice input. This data is encrypted before being sent to the server.

[0264] The server passes the received data to an information processing unit, which analyzes the text data using natural language processing technology. The information processing unit then estimates the children's feelings and worries from their statements. For example, from a statement like "I don't want to go to school," it estimates worries related to school life.

[0265] Simultaneously, the device uses its camera and microphone to capture the children's facial expressions and voice tones. This data is sent to an emotion analysis device. The emotion analysis device recognizes emotions from the facial expressions and voice tones and identifies emotions such as joy, sadness, and anger.

[0266] The server integrates and analyzes data obtained from the information processing device and the emotion analysis device. This allows for a more accurate understanding of the children's feelings and concerns. Based on the integrated analysis results, users can provide appropriate support and guidance to the children.

[0267] For example, if a child posts on social media that they "had a fight with a friend," the device collects the post and sends it to the server. The server uses an information processing device to estimate the possibility that the child is having trouble with their friendships. At the same time, if the emotion analysis device recognizes anger from the tone of voice captured by the device, the server integrates this information and suggests that the friendship problems may be serious.

[0268] As an example of a prompt, the prompt "What kinds of problems might a child have if they say they don't want to go to school?" can be input into the AI ​​model, and the AI ​​can output the type of problem and its cause that it estimates.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0270] Step 1:

[0271] The device collects data on speech and actions from the devices used by children in real time. Inputs include social media posts, chat messages, and voice input. This data is temporarily stored on the device, encrypted, and sent to the server. The output is the encrypted data sent to the server.

[0272] Step 2:

[0273] The server decrypts the encrypted data received from the terminal and passes it to the information processing device. The input is encrypted data sent from the terminal. The server decrypts the data and provides it to the information processing device in an appropriate format. The output is analyzable data passed to the information processing device.

[0274] Step 3:

[0275] The information processing device analyzes text data using natural language processing technology. The input is analyzable data provided by a server. The information processing device analyzes the content of the statements and estimates the feelings and worries of the children. For example, from the statement "I don't want to go to school," it estimates worries related to school life. The output is estimated data of feelings and worries as a result of the analysis.

[0276] Step 4:

[0277] The terminal captures the expressions and voice tones of children using a camera and a microphone. The input is video and audio data obtained in real time. The terminal sends this data to an emotion analysis device. The output is the video and audio data sent to the emotion analysis device.

[0278] Step 5:

[0279] The emotion analysis device recognizes emotions from expressions and voice tones. The input is the video and audio data sent from the terminal. The emotion analysis device identifies emotions such as joy, sadness, anger, etc. The output is the recognized emotion data.

[0280] Step 6:

[0281] The server integrates and analyzes the data obtained from the information processing device and the emotion analysis device. The input is the estimated data of mood and troubles and the recognized emotion data. The server integrates these data to more accurately grasp the mood and troubles of children. The output is the integrated analysis result.

[0282] Step 7:

[0283] Based on the integrated analysis result provided by the server, the user provides appropriate support and guidance to children. The input is the integrated analysis result from the server. The user considers countermeasures for children's problems based on this. The output is the support and guidance provided to children.

[0284] (Application Example 1)

[0285] Next, Application Example 1 of the first morphological example will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0286] In children's school and daily lives, there is a need to identify early on any relationship or romantic problems they may be experiencing and provide appropriate support. However, children often compare themselves to others and are hesitant to express their true feelings due to concerns about what others think, making it difficult to understand their problems. Furthermore, it is difficult for parents and educators to understand children's feelings in real time and provide the necessary support. Effective means to solve these challenges are needed.

[0287] 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.

[0288] In this invention, the server includes means for collecting and analyzing children's statements and actions in real time, means for analyzing children's facial expressions and tone of voice to recognize their emotions, and means for sending notifications to parents or educators when an abnormality is detected. This makes it possible to accurately understand children's feelings and worries, and for parents and educators to intervene at the appropriate time.

[0289] "Methods for collecting and analyzing children's statements and actions in real time" refers to technologies that instantly acquire voice and motion data from devices used by children and analyze it to understand their feelings and circumstances.

[0290] "A means of analyzing children's facial expressions and tone of voice to recognize their emotions" refers to a technology that uses cameras and microphones to capture children's facial expressions and voices, and then uses that data to have an emotion engine estimate their emotional state.

[0291] The "means of sending notifications to parents and educators when an abnormality is detected" refers to a technology that uses AI to analyze children's data and quickly warn parents and educators when unusual emotions or behaviors are detected.

[0292] The system for implementing this invention has the function of understanding children's feelings and concerns in real time and sending notifications to parents and educators as needed. The system uses terminals such as smartphones and smart glasses to collect children's words and actions in real time. These terminals acquire audio and video data through microphones and cameras.

[0293] The server uses software such as Python, TensorFlow®, and OpenCV to analyze the collected data. Audio data is converted to text using TensorFlow, and the content of the speech is analyzed using natural language processing. Video data is analyzed for facial expressions using OpenCV, and an emotion engine analyzes the tone of voice to estimate the emotional state of the children.

[0294] If an anomaly is detected, the server will promptly send a notification to parents or educators. This notification will include information about the children's feelings and circumstances, prompting appropriate action.

[0295] For example, if a child says, "I had a fight with a friend," and their expression is sad and their voice is low, the AI ​​will estimate that the child is having trouble with interpersonal relationships and will send a notification to the parent. An example of a prompt to input into the generating AI model would be, "What kind of feelings might a child have if they say they don't want to go to school?"

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0297] Step 1:

[0298] The device collects audio and video data of children in real time. It takes audio data from the microphone and video data from the camera as input. This data is sent to the server in an appropriate format for use in subsequent analysis steps.

[0299] Step 2:

[0300] The server converts the received voice data into text. Using the voice data transmitted from the terminal as input, it performs voice recognition using TensorFlow. As output, the voice data is converted into text data. This text data serves as input for natural language processing.

[0301] Step 3:

[0302] The server analyzes the text data through natural language processing. Using the text data generated in Step 2 as input, it analyzes the speech content using a generative AI model. As output, estimation results of the mood and situation based on the children's speech are obtained.

[0303] Step 4:

[0304] The server analyzes the video data to recognize expressions. Using the video data transmitted from the terminal as input, it performs expression analysis using OpenCV. As output, the emotional state based on the children's expressions is estimated.

[0305] Step 5:

[0306] The server analyzes the tone of the voice to recognize emotions. Using the voice data transmitted from the terminal as input, it analyzes the tone of the voice using an emotion engine. As output, the emotional state based on the tone of the voice is estimated.

[0307] Step 6:

[0308] The server integrates the results of Steps 3, 4, and 5 and detects anomalies. Using the estimation results of the mood and situation, the emotional state based on the expression, and the emotional state based on the tone of the voice as input. As output, alert information is generated when an anomaly is detected.

[0309] Step 7:

[0310] The server sends notifications to parents and educators if an anomaly is detected. It uses the alert information generated in step 6 as input. As output, it sends notifications to parents and educators regarding the children's feelings and circumstances.

[0311] (Example 2)

[0312] Next, we will describe Example 2 of Form 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".

[0313] Children often struggle to express their true feelings and harbor anxieties because they are overly concerned with comparing themselves to others and the opinions of those around them in school and daily life. This can lead to a decline in children's self-esteem and negatively impact their mental health. Therefore, it is crucial to accurately understand children's emotions and situations and provide appropriate encouragement and advice.

[0314] 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.

[0315] In this invention, the server includes means for analyzing children's speech and actions and estimating their emotional state, means for generating optimal encouragement and advice based on the estimated emotions, and means for presenting the generated messages to the children. This enables the provision of appropriate support that is sensitive to the children's emotions, improving their self-esteem and maintaining their mental health.

[0316] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[0317] "Relationships" refers to the state of human relationships and interactions that children build with others.

[0318] "Personal problems" refer to the mental or emotional issues or anxieties that children individually face.

[0319] "Natural language processing technology" refers to the technology used to analyze and understand human language using computers.

[0320] "Emotional analysis technology" refers to the technology that extracts and analyzes emotions from text and audio data.

[0321] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new text or information.

[0322] "Encouragement and advice" refers to words and guidance that provide emotional support to children.

[0323] "Means of presenting a message" refers to methods and devices for conveying a generated message to children.

[0324] This invention is a system for understanding children's emotions and situations and providing appropriate encouragement and advice. The system consists of three components: a server, a terminal, and a user.

[0325] The server uses natural language processing and sentiment analysis technologies to analyze children's statements and actions. Specifically, the server uses a common cloud-based language analysis API as its natural language processing technology to analyze children's statements as text data. The analyzed data is then used with sentiment analysis technology to estimate the children's emotional state. For this sentiment analysis, a general sentiment analysis engine is used.

[0326] The server uses a generative AI model to generate optimal encouragement and advice based on estimated emotions. The generative AI model uses pre-trained algorithms to produce messages that resonate with the children's emotions. These messages are customized according to the children's emotional state.

[0327] The terminal's role is to present messages sent from the server to the user. The terminal communicates the generated messages to the user through voice output and text display. This allows the user to receive feedback from the AI.

[0328] For example, if a user enters the prompt "My child seems to be losing confidence because they are comparing themselves to their friends at school. How can I encourage them?", the server will use this information and a generative AI model to generate a message such as "You are wonderful just the way you are. There's no need to compare yourself to others," and present it to the user through their device.

[0329] This system provides appropriate support that is sensitive to children's emotions, enabling them to improve their self-esteem and maintain their mental health.

[0330] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0331] Step 1:

[0332] Users input their children's statements and actions through their devices. The input data is sent to the server in either voice or text format. For example, if a user inputs "I didn't do well at school today," that text data is sent to the server.

[0333] Step 2:

[0334] The server analyzes the received text data using natural language processing techniques. Specifically, the server uses a language analysis API to extract the grammatical structure and keywords from the input text. This analysis allows the server to identify the subject and emotion of the statement. As a result of the analysis, the emotional nuances of the text are extracted.

[0335] Step 3:

[0336] Based on the analysis results, the server uses emotion analysis technology to estimate the children's emotional state. For example, from the phrase "it didn't go well," the server might determine that the child is feeling down. This estimation result is output as data indicating the emotional state.

[0337] Step 4:

[0338] The server uses a generative AI model to generate optimal encouragement and advice based on the estimated emotional state. The generative AI model uses a pre-trained algorithm to generate messages appropriate to the emotional state. For example, it might generate a message such as, "You're doing great. I'm sure you'll do better next time."

[0339] Step 5:

[0340] The server sends the generated message to the terminal. The terminal presents the generated message to the user through voice output or text display. This allows the user to receive feedback from the AI.

[0341] (Application Example 2)

[0342] Next, we will describe Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0343] Modern children often struggle to express their true feelings at school and at home because they are overly concerned with comparing themselves to others and the opinions of those around them. Furthermore, there is a lack of systems that can properly understand these anxieties and provide encouragement and advice. In this situation, there is a need to understand children's feelings in real time and provide appropriate feedback.

[0344] 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.

[0345] This invention includes a server that provides means for understanding the interpersonal relationships and personal concerns of children in their living environment, means for understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about how others perceive them, and means for analyzing children's statements and actions in real time to understand their feelings and provide appropriate feedback. This makes it possible to accurately grasp the feelings of children and provide them with appropriate encouragement and advice.

[0346] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[0347] "Relationships" refers to the social connections and interactions that children form with others.

[0348] "Personal worries" refer to the internal problems and anxieties that children individually face.

[0349] "Comparison" refers to the act of children comparing themselves to others and evaluating them in that way.

[0350] "Gaze" refers to the attention and interest that people around children direct towards them.

[0351] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[0352] "Real-time" refers to the immediate processing of children's statements and actions the moment they occur.

[0353] "Feedback" refers to the reactions and evaluations given in response to children's actions and statements.

[0354] "Encouragement" refers to words and actions used to cheer up and encourage children.

[0355] "Advice" refers to guidance and suggestions provided to children regarding problems they face.

[0356] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server uses artificial intelligence to analyze children's speech and actions in real time in order to understand their interpersonal relationships and personal concerns in their living environment. Specifically, the server uses speech recognition technology to convert children's speech into text data and analyzes their emotions and intentions through natural language processing. In this process, an emotion engine is used to more accurately estimate the children's feelings.

[0357] The devices are those that children use on a daily basis, such as smartphones and tablets. These devices receive feedback sent from a server and display appropriate encouragement and advice to the user. This allows children to receive real-time feedback tailored to their feelings.

[0358] The users are children who use the system. Through their devices, users receive feedback from the server, allowing them to receive encouragement and advice tailored to their feelings and circumstances. This enables users to improve their self-esteem and reduce anxiety caused by comparisons with others.

[0359] For example, if a user says, "Things didn't go well at school today," the server analyzes this statement and sends an encouraging message to the device such as, "Failure is the foundation of success. You'll do better next time." This helps the user regain their confidence.

[0360] Examples of prompts for a generative AI model include the following:

[0361] "Prompt: How would you encourage your child if they said, 'I didn't do well at school today'?"

[0362] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0363] Step 1:

[0364] The user inputs their speech through a terminal. The terminal converts the user's voice into text data using speech recognition technology. This text data is then input to the server.

[0365] Step 2:

[0366] The server analyzes the received text data using natural language processing techniques. Specifically, it uses an emotion engine to extract emotions and intentions from the text data. This process outputs data that estimates the user's feelings.

[0367] Step 3:

[0368] The server generates appropriate feedback using a generative AI model based on estimated emotional data. Prompt messages are input to the generative AI model, which generates encouragement and advice tailored to the user's emotional state. This feedback is then output from the server to the terminal.

[0369] Step 4:

[0370] The device displays feedback received from the server to the user. Specifically, it displays encouraging and advice messages on the screen for the user to see. This allows the user to receive appropriate feedback in real time.

[0371] (Example 3)

[0372] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0373] Modern children face a great deal of stress and anxiety in their school and home lives, but there is a lack of means to properly understand and support them. In particular, there is a need to understand children's emotions in real time and provide appropriate encouragement and advice accordingly.

[0374] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0375] In this invention, the server includes means for understanding the emotional state of children in their living environment, means for analyzing children's emotions in real time, and means for generating messages appropriate for children using a generative model. This makes it possible to provide appropriate support in real time according to the children's emotions.

[0376] "Living environment" refers to the places and situations in which children spend their days, encompassing the entire environment, including home and school.

[0377] "Emotional state" refers to the feelings and psychological state that children experience at a particular moment, and includes emotions such as joy, sadness, and anger.

[0378] "Real-time analysis" refers to a process where data is analyzed immediately upon collection, and results are obtained instantly.

[0379] A "generative model" refers to an algorithm or system that uses artificial intelligence technology to generate an appropriate output based on a specific input.

[0380] "Generating a message" refers to creating a message that is appropriate for children based on specific information and circumstances.

[0381] The following systems are conceivable as embodiments for carrying out this invention.

[0382] The user first collects data to understand the emotional state of children in their living environment. Specifically, sensors and microphones are used to acquire data on children's daily conversations and behaviors. This data serves as foundational information for analyzing children's emotions in real time.

[0383] The server inputs the collected data into an emotion engine, which uses natural language processing technology to analyze the children's emotions. The emotion engine identifies emotions such as anger, sadness, and happiness, and feeds the results back into a generative AI model.

[0384] The generative AI model generates prompt messages based on the results of sentiment analysis. For example, if the analysis indicates that a child is struggling with interpersonal relationships at school, it will generate a prompt message such as, "What kind of encouraging message should be generated if a child is struggling with interpersonal relationships at school?"

[0385] The server inputs prompt text into a generation AI model, which then generates encouraging and advice messages tailored to the children. These generated messages are delivered to the children via their devices. The devices, such as smartphones and tablets, display the messages, allowing the children to receive them.

[0386] This system allows children to receive appropriate support in real time, tailored to their emotions. An example of a prompt message is, "If a child is struggling with interpersonal relationships at school, what kind of encouraging message should be generated?" The specific processing flow in Example 3 is explained using Figure 21.

[0387] Step 1:

[0388] Users collect data to understand the emotional state of children in their living environment. Specifically, they use sensors and microphones to acquire data on children's daily conversations and behaviors. This data is transmitted to the server in voice and text format.

[0389] Step 2:

[0390] The server inputs the collected data into an emotion engine, which uses natural language processing technology to analyze the children's emotions in real time. The input audio and text data is analyzed by the emotion engine to identify the children's emotional states (e.g., joy, sadness, anger). This analysis result is used in the next step.

[0391] Step 3:

[0392] The server generates prompt messages to input into the generative AI model based on the results of the sentiment analysis. For example, if the analysis indicates that a child is struggling with interpersonal relationships at school, the server will generate a prompt message such as, "What kind of encouraging message should be generated if a child is struggling with interpersonal relationships at school?" This prompt message is then used as input to the generative AI model.

[0393] Step 4:

[0394] The server inputs prompt text into a generative AI model, which generates encouraging and advice messages tailored to the children. Based on the prompt text, the generative AI model utilizes past data and learned knowledge to create specific and effective messages. These messages are then delivered in the next step.

[0395] Step 5:

[0396] The device delivers the generated messages to the children. Specifically, it displays the messages via smartphones and tablets, allowing the children to receive them. This enables the children to receive encouragement and advice in real time.

[0397] (Application Example 3)

[0398] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0399] Modern children face numerous challenges in school and daily life, often comparing themselves to others or feeling unable to express their true feelings due to concerns about what others think. Similarly, workers in factories and other workplaces may experience emotional stress, negatively impacting work efficiency and the overall work environment. To address these issues, a system is needed that provides appropriate encouragement and advice tailored to individual emotions and circumstances.

[0400] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0401] This invention includes a server that provides means for understanding children's worries about relationships and romance in their school and daily lives, understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about what others think, providing encouragement and advice to help children build confidence, recognizing workers' emotions in real time and providing encouragement and advice according to the progress and situation of their work, analyzing workers' facial expressions and tone of voice, and generating appropriate encouragement and advice based on the analyzed emotional data. This makes it possible to provide individualized and appropriate responses to the emotional problems faced by children and workers.

[0402] "Children" refers to young people who are in the process of growing up in their school and daily lives.

[0403] "School life" refers to the daily life of children as they engage in learning and activities at educational institutions.

[0404] "Daily life" refers to all aspects of the daily life that children engage in at home and in their community.

[0405] "Relationships" refers to the social connections and interactions that children form with others.

[0406] "Romantic love" refers to the affection and intimacy that children feel towards others.

[0407] "Worries" refers to the mental burdens and problems that children face.

[0408] "Comparison" refers to the act of children comparing themselves to others and evaluating them in that way.

[0409] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[0410] "Confidence" refers to a child's belief in their own abilities and worth.

[0411] "Workers" refers to people who perform tasks in factories or work sites.

[0412] "Emotions" refers to the mental processes such as joy, anger, sadness, and happiness that children and workers experience.

[0413] "Real-time" refers to processing events that are currently unfolding immediately.

[0414] "Progress" refers to the degree or status of work or a plan's progress.

[0415] "Facial expressions" refer to the expressions that show emotions and feelings on the faces of children and workers.

[0416] "Voice tone" refers to the pitch and intensity of the sounds produced by children or workers.

[0417] "Analysis" refers to the act of thoroughly analyzing data and information to derive meaning from it.

[0418] "Data" refers to information about children and workers expressed using numbers and symbols.

[0419] "Generation" refers to the act of AI creating new encouragement or advice.

[0420] The system for realizing this invention involves a server and terminals working together. The server plays a central role in recognizing the emotions of children and workers in real time and generating appropriate encouragement and advice. Specifically, the server uses an emotion recognition engine to analyze facial expression data and voice tone transmitted from the terminals. The analyzed data is input into a generative AI model, which generates encouragement and advice tailored to each individual's situation.

[0421] The device uses a camera and microphone to collect facial expressions and voices of children and workers. This data is sent to a server and processed in real time. The device then provides users with generated encouragement and advice.

[0422] For example, if the server determines that a worker is tired, it will generate a message such as, "Take a short break and refresh yourself." This message will then be transmitted to the worker via their terminal.

[0423] Examples of prompt messages include the following:

[0424] Worker's facial expression data: tired face

[0425] Worker's voice tone: Low

[0426] Input to the AI ​​model: "The worker has been determined to be tired. Please generate an appropriate encouraging message."

[0427] In this way, the server and terminals can cooperate to provide individualized and appropriate responses to children and workers.

[0428] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0429] Step 1:

[0430] The device uses a camera and microphone to collect data on the user's facial expressions and voice tone. This data serves as input for understanding the user's emotions in real time.

[0431] Step 2:

[0432] The device sends collected facial expression data and voice tone to the server. The server receives this data and analyzes it using an emotion recognition engine. The analysis results in the user's emotional state being output.

[0433] Step 3:

[0434] The server inputs prompt messages into the generative AI model based on the analyzed emotional state. These prompt messages include instructions for generating encouragement and advice tailored to the user's emotions.

[0435] Step 4:

[0436] The generative AI model receives a prompt and generates encouragement and advice appropriate to the user's emotions. This generated message is then output.

[0437] Step 5:

[0438] The server sends the generated encouragement and advice to the terminal. The terminal displays or audibly communicates this message to the user. This allows the user to receive appropriate encouragement and advice.

[0439] (Other examples)

[0440] Next, other embodiments 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".

[0441] In today's information society, users face challenges when performing procedures such as changing their address, as manual document creation and data entry are cumbersome, time-consuming, and laborious. Furthermore, generating accurate documents based on user input is difficult, potentially leading to problems due to input errors or incomplete information. Additionally, the identity verification process can be complex and burdensome for users.

[0442] The identification processing performed by the identification processing unit 290 of the data processing device 12 in other embodiments is realized by the following means.

[0443] In this invention, the server includes means for providing an interface for receiving input information from the user, means for analyzing the user's communication application or information retrieval tool identifier to supplement the necessary information, and means for generating and inputting prompt sentences to a generating AI model. This enables the user to automate the creation of documents related to address changes and complete the procedure accurately and quickly.

[0444] "User input information" refers to data and instructions that users provide to the system, including specific details such as address changes and updates to personal information.

[0445] A "communication application or information retrieval tool identifier" is information used to identify the application or tool used by the user, and is used to identify the type of device or application used by the user.

[0446] A "prompt" is a document used to instruct a generative AI model to generate a specific response, and it includes specific instructions that are constructed based on the user's input.

[0447] A "generative AI model" is a model that uses artificial intelligence technology to generate responses based on user input, and has the ability to automatically generate documents and data through natural language processing.

[0448] "Identity verification" is a process for confirming a user's identity, and it is a procedure that verifies the user's legitimacy using authentication systems such as the OAuth 2.0 protocol.

[0449] This invention provides a system for users to efficiently perform procedures such as changing their address. The system has the function of receiving input information from the user and automatically generating documents using a generative AI model.

[0450] The server provides a web interface using HTML and JavaScript (registered trademark) to allow users to enter information regarding their address change. The information entered by the user is sent to the server asynchronously using AJAX.

[0451] The server uses a Python script to parse the user's communication application or information retrieval tool identifier. This parse retrieves necessary information from a MySQL® database to supplement the user's input. For example, it retrieves the user's past address history and related personal information from the database.

[0452] Based on the supplemented information, the server uses the pandas library to process the data and insert the necessary information into the address change notification template. This automates the document creation process.

[0453] Next, the server uses the OAuth 2.0 protocol to provide a system for user authentication. The user verifies their identity through the authentication process, and the authentication result is sent to the server.

[0454] Once authentication is complete, the server generates a prompt for the generated AI model (e.g., OpenAI's GPT-3). This prompt is based on the user's input and includes specific instructions. Example prompt: "Please create an address change notification that reflects the user's new address."

[0455] The generative AI model generates a response based on the received prompt. This response is a template for a change of address notification that reflects the user's new address.

[0456] Finally, the server displays the generated response to the user via a web interface. The user can review this response and make corrections as needed. Once corrections are complete, the user can download or print the final document.

[0457] In this way, users can complete the address change procedure quickly and accurately.

[0458] The flow of specific processing in other embodiments will be explained using Figure 23.

[0459] Step 1:

[0460] The server provides a web interface using HTML and JavaScript, displaying a form for users to enter information about their address change. Users enter information such as their name, old and new addresses, and contact information, and then click the submit button. The entered data is sent asynchronously to the server using AJAX. The input data is passed to the server in JSON format.

[0461] Step 2:

[0462] The server parses the received user input data in JSON format using a Python script. During the parsing process, it extracts the user's communication application or information retrieval tool identifier and retrieves relevant information from the MySQL database. For example, it retrieves the user's past address history and related personal information from the database to supplement the input data. The supplemented data is then passed on to the next processing step.

[0463] Step 3:

[0464] The server processes the data using the pandas library based on the completed data. Specifically, it formats the data to insert the necessary information into the address change notification form template. This formatted data is then used as the base data for document creation.

[0465] Step 4:

[0466] The server uses the OAuth 2.0 protocol to provide an authentication system for user verification. The user verifies their identity through the authentication process, and the authentication result is sent to the server. If authentication is successful, the process proceeds to the next step.

[0467] Step 5:

[0468] The server generates prompts for the generative AI model based on the formatted data. These prompts include specific instructions such as, "Create an address change notification that reflects the user's new address." These prompts are then input into the generative AI model.

[0469] Step 6:

[0470] The generative AI model generates a response based on the received prompt. The response is a template for a change of address notification that reflects the user's new address. The generated response is returned to the server.

[0471] Step 7:

[0472] The server displays the generated response to the user via a web interface. The user can review this response and make corrections as needed. Once corrections are complete, the user can download or print the final document.

[0473] 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.

[0474] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence).

[0475] One example of a data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. Data generation model 58 is

[0476] This is obtained by performing deep learning on a neural network. The data generation model 58 receives prompts containing instructions, as well as 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.

[0477] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0478] 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.

[0479] [Second Embodiment]

[0480] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0481] 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.

[0482] 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).

[0483] 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.

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0492] "Example of form 1"

[0493] In one embodiment of the present invention, an AI is used to analyze children's words and actions as a means of understanding their worries about relationships and romantic relationships in their school and daily lives. This AI estimates the feelings and worries of children from their words and actions. For example, if a child says, "I don't want to go to school," the AI ​​estimates from that statement that the child may be having some kind of worry about school life.

[0494] "Example of form 2"

[0495] Furthermore, as a means of understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about what others think, the AI ​​analyzes children's words and actions to estimate their feelings and circumstances. For example, if a child says, "I'm no good compared to other kids," the AI ​​will estimate from that statement that the child may be struggling with self-esteem.

[0496] "Example of form 3"

[0497] Furthermore, as a means of providing encouragement and advice to children, the AI ​​generates optimal encouragement and advice according to the children's feelings and circumstances. For example, if the AI ​​estimates that a child is struggling with school life, it will generate an encouraging message for that child such as, "School life is tough, but I know you can overcome it."

[0498] The following describes the processing flow for each example of the form.

[0499] "Example of form 1"

[0500] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0501] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate the children's feelings and concerns.

[0502] Step 3: The AI ​​generates encouragement and advice for the children based on their estimated feelings and concerns. This is done based on pre-set rules and learning models.

[0503] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0504] "Example of form 2"

[0505] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0506] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate situations where children are comparing themselves to others or are unable to express their true feelings due to concerns about what others think.

[0507] Step 3: The AI ​​generates encouragement and advice for the children based on the estimated situation. This is done based on pre-set rules and learning models.

[0508] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0509] "Example of form 3"

[0510] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0511] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate the children's feelings and situations.

[0512] Step 3: The AI ​​generates encouragement and advice for the children based on their estimated feelings and circumstances. This is done based on pre-set rules and learning models.

[0513] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0514] (Example 1)

[0515] Next, we will describe Example 1 of Form 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".

[0516] In today's educational environment, children often face interpersonal and emotional problems, and it is crucial to identify these issues early and provide appropriate support. However, accurately inferring children's feelings and problems from their words and actions is difficult, resulting in a lack of information for educators and support staff to respond appropriately.

[0517] 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.

[0518] In this invention, the server includes means for understanding interpersonal relationships and emotional problems in children's educational and daily environments, means for analyzing children's statements and actions to estimate their emotions and intentions, and means for providing the analysis results to educators and support staff. This makes it possible to grasp children's feelings and problems early and provide appropriate support and guidance.

[0519] The term "educational environment" refers to the physical and social environment in which children learn and grow, and includes schools, homes, and other similar settings.

[0520] "Everyday environment" refers to the environment that children encounter in their daily lives, including their home and community.

[0521] "Interpersonal relationships" refers to the human relationships that children form with others, including friendships and relationships with teachers.

[0522] "Emotional problems" refer to emotionally related issues that children experience, including anxiety and stress.

[0523] "Means of analyzing speech and actions" refers to techniques and methods for analyzing children's words and actions and inferring the emotions and intentions behind them.

[0524] "Means of inferring emotions and intentions" refers to techniques and methods for inferring children's feelings and intentions from their words and actions.

[0525] "Means of providing analysis results" refers to the technologies and methods for communicating the analyzed information to educators and support staff.

[0526] "Educators and supporters" refers to people who have a role in supporting children's learning and growth, and includes teachers, counselors, and others.

[0527] The following system is constructed as an embodiment of this invention.

[0528] The server generates a program to analyze the children's statements and actions. This program is designed to analyze the children's statements and actions using natural language processing techniques. Specifically, the server uses a cloud-based natural language processing API to analyze the text data and extract emotions and intentions. This API is provided by a common cloud service provider.

[0529] The devices are installed in classrooms and homes to collect children's speech as audio data. This audio data is sent to a server and converted into text data. The server analyzes the converted text data to estimate the children's feelings and problems.

[0530] Educators and support staff can view the analysis results provided by the server through a dedicated dashboard. This dashboard displays a list of children's emotional states and estimated problems. This allows users to provide appropriate support and guidance to the children.

[0531] For example, if a child says, "I don't want to go to school," the server analyzes this statement and estimates their anxiety and stress regarding school life. Users can then view this information on a dashboard and consider appropriate responses for their child.

[0532] An example of a prompt is, "If a child says they don't want to go to school, estimate the reason." Using this prompt, the server estimates the child's underlying concerns from their statements and generates information to provide appropriate support.

[0533] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0534] Step 1:

[0535] The devices are installed in classrooms and homes to collect children's speech as audio data. Specifically, they use microphones to record conversations and send that audio data to a server. The input is audio data, and the output is an audio file sent to the server.

[0536] Step 2:

[0537] The server converts the received audio data into text data. Specifically, it uses speech recognition software to convert the audio data into a string of characters. This process outputs the audio data as text data. The input is an audio file, and the output is text data.

[0538] Step 3:

[0539] The server analyzes text data using natural language processing techniques. Specifically, it uses a cloud-based natural language processing API to extract emotions and intentions from the text. This process yields information about emotions and intentions from the text data. The input is text data, and the output is the analysis results, including emotions and intentions.

[0540] Step 4:

[0541] The server estimates the children's feelings and problems based on the analysis results. Using a generative AI model, it identifies potential problems underlying their statements. This process reveals the children's underlying anxieties. The input is the analysis results, and the output is the estimated feelings and problems.

[0542] Step 5:

[0543] The server provides educators and support staff with estimated feelings and problems. Specifically, it displays the information through a dedicated dashboard. This dashboard lists each child's emotional state and estimated problems. The input is the estimated feelings and problems, and the output is the information displayed on the dashboard.

[0544] Step 6:

[0545] Educators and support staff, who are the users, can check the children's status through the dashboard and provide appropriate support and guidance. Specifically, they consider countermeasures for the children based on the information on the dashboard. The input is the information on the dashboard, and the output is the countermeasures taken by the educators and support staff.

[0546] (Application Example 1)

[0547] Next, we will describe Application Example 1 of Form 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."

[0548] In children's school and daily lives, there is a challenge in understanding the relationship and romantic problems they face, comparing themselves to others, and understanding situations where they cannot express their true feelings due to concern about what others think. Furthermore, there is a need to provide appropriate encouragement and advice tailored to the children's feelings and circumstances. In addition, it is necessary to promptly inform parents and educators of this information to enable appropriate intervention.

[0549] 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.

[0550] In this invention, the server includes means for analyzing children's words and actions in real time and notifying parents and educators, means for understanding children's concerns, and means for providing encouragement and advice to children. This makes it possible to quickly understand children's feelings and concerns and provide appropriate support.

[0551] "Methods for analyzing children's speech and actions in real time" refers to technologies that instantly acquire children's voices and movements as data and analyze their content using information processing equipment.

[0552] "Means of notifying parents and educators" refers to communication technologies that, based on analyzed information, quickly transmit important information about children's feelings and circumstances to parents and educators.

[0553] "Means for understanding children's worries" refers to information processing technology that identifies and understands the problems and feelings children are experiencing through their words and actions.

[0554] "Means of providing encouragement and advice" refers to information processing technology that generates and provides appropriate encouragement and advice according to the feelings and circumstances of children.

[0555] As a form of implementing the invention, this system has the function of analyzing children's speech and actions in real time and notifying parents and educators. The server converts children's speech into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text) and analyzes their actions using an image recognition API (e.g., Google Cloud Vision). This obtains data to estimate the children's feelings and concerns.

[0556] The server uses natural language processing libraries (e.g., NLTK, spaCy) to perform sentiment analysis on text data. Based on these analysis results, a generative AI model estimates the children's feelings and concerns and generates appropriate encouragement and advice. The generated information is sent to parents and educators via push notifications using communication technology.

[0557] For example, if a child says "I don't want to go to school," the server uses a speech recognition API to convert this statement into text, and a natural language processing library analyzes the negative emotion expressed by "I don't want to go." The generative AI model estimates that the child has problems related to school life and sends a notification to the parent saying, "Your child is not wanting to go to school. There may be a problem."

[0558] An example of a prompt question would be, "What kinds of feelings or worries might a child have if they say, 'I don't want to go to school'?"

[0559] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0560] Step 1:

[0561] The device captures the children's speech as audio data via a microphone. The captured audio data is then sent to a server.

[0562] Step 2:

[0563] The server uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the received audio data into text data. This process transforms the audio data into a format that can be parsed as text information.

[0564] Step 3:

[0565] The server performs sentiment analysis on text data using natural language processing libraries (e.g., NLTK, spaCy). It analyzes emotions and intentions from the input text data and generates data to estimate children's feelings and concerns.

[0566] Step 4:

[0567] The server uses a generative AI model to estimate children's feelings and worries based on the results of emotion analysis. Based on the estimation results, it generates appropriate encouragement and advice.

[0568] Step 5:

[0569] The server uses communication technology to send estimated results and generated encouragement and advice to parents and educators via push notifications. This allows parents and educators to understand their children's situation and provide the necessary support.

[0570] (Example 2)

[0571] Next, we will describe Example 2 of Form 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".

[0572] Children often struggle to express their true feelings at school and in their daily lives because they are overly concerned with comparing themselves to others and the opinions of those around them. In such situations, it is difficult to accurately understand children's feelings and provide appropriate encouragement and advice. Furthermore, it is not easy to infer the emotions and circumstances behind children's words and actions.

[0573] 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.

[0574] In this invention, the server includes means for analyzing children's statements and actions and estimating the emotions and circumstances behind them; means for estimating the children's feelings based on the analyzed data and providing appropriate support information; and means for estimating emotions using a generative AI model. This makes it possible to accurately understand the children's feelings and provide appropriate encouragement and advice tailored to their individual circumstances.

[0575] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[0576] "Relationships" refers to the social connections and interactions that children build with others.

[0577] "Personal worries" refer to the internal problems and anxieties that children individually face.

[0578] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0579] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new information or predictions from data.

[0580] "Inferring emotions" refers to inferring the emotions behind children's words and actions.

[0581] "Support information" refers to information such as encouragement and advice provided according to the feelings and circumstances of the children.

[0582] One embodiment of this invention is to provide a system that analyzes children's words and actions and estimates the emotions and circumstances behind them. A specific embodiment is shown below.

[0583] The server collects data on children's statements and actions. This data is obtained through the terminal as voice input or text input. For example, if a child says to the terminal, "I'm no good compared to other children," that voice data is sent to the server.

[0584] The server uses speech recognition software to convert the collected audio data into text data. Specifically, it can utilize APIs commonly used for speech recognition technology.

[0585] Next, the server analyzes the converted text data using natural language processing techniques. This analysis utilizes a generative AI model, which includes algorithms for understanding the context of natural language and estimating sentiment.

[0586] Based on the analysis, the server estimates the children's feelings and circumstances and generates appropriate support information. This support information is provided to the children as encouragement and advice.

[0587] As a concrete example, a user can input the following prompt message into the AI ​​model:

[0588] "What emotions might be at play when a child says, 'I'm no good compared to other kids'?"

[0589] "What kind of support do children need when they are worried about what others think of them?"

[0590] This allows users to gain a deeper understanding of children's feelings and obtain clues to provide appropriate support.

[0591] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0592] Step 1:

[0593] The server receives audio data from the terminal regarding the children's speech and actions. The user's spoken words into the terminal are input as audio data. This audio data is then sent to the server.

[0594] Step 2:

[0595] The server uses speech recognition software to convert the received audio data into text data. Specifically, it analyzes the audio data using a speech recognition API and outputs it as text data. This conversion transforms the audio information into text information.

[0596] Step 3:

[0597] The server analyzes the converted text data using natural language processing techniques. A generative AI model is used to understand the context of the text data and estimate emotions. This analysis outputs data that allows for the estimation of children's emotions and situations from the text data.

[0598] Step 4:

[0599] The server estimates the children's feelings and circumstances based on the analysis results. Using the output of the generative AI model, it estimates the emotions and worries the children may be experiencing. These estimation results serve as the basic data for generating appropriate support information for the children.

[0600] Step 5:

[0601] The server generates support information for the children based on the estimation results. Using a generation AI model, it generates encouragement and advice tailored to the children's feelings and circumstances. This support information is provided to the user through the terminal.

[0602] Step 6:

[0603] Users receive support information through their devices and provide appropriate support to the children. Based on this information, users can understand the children's feelings and offer necessary advice and encouragement.

[0604] (Application Example 2)

[0605] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0606] Modern children often struggle with self-esteem and are unable to express their true feelings due to excessive comparison with others and concern for how they are perceived in school and daily life. This situation can negatively impact children's mental health. However, there is a lack of means to identify these feelings and situations early and provide appropriate support. Therefore, there is a need for a system that analyzes children's words and actions, estimates their feelings, identifies their problems early, and provides appropriate encouragement and advice.

[0607] 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.

[0608] In this invention, the server includes means for transcribing children's speech into text using speech recognition technology and analyzing it using natural language processing technology, means for estimating the children's feelings using a generative AI model and notifying them of the results, and means for generating optimal encouragement and advice according to the children's feelings and circumstances. This makes it possible to accurately understand the children's feelings and provide appropriate support.

[0609] "School life" refers to the daily activities and experiences that children have while at school.

[0610] "Daily life" refers to the activities and experiences that children engage in on a daily basis at home and in their community.

[0611] "Human relationships" refers to the reciprocal relationships that children build with others.

[0612] "Romantic love" refers to the affection and feelings that children have for others.

[0613] "Worries" refer to the mental burdens and problems that children face.

[0614] "Comparison" refers to the act of children comparing themselves to others.

[0615] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[0616] "Speech recognition technology" refers to the technology that converts speech into text data.

[0617] "Natural language processing technology" refers to the technology used to analyze text data and understand its meaning.

[0618] A "generative AI model" refers to a model that uses artificial intelligence to generate new information from data.

[0619] "Sentiments" refers to the inner feelings and emotions of children.

[0620] "Notification" refers to the act of communicating analysis results or information to others.

[0621] "Encouragement" refers to words and actions used to cheer up children.

[0622] "Advice" refers to the guidance and instruction given to children.

[0623] The system for implementing this invention analyzes children's words and actions and estimates their feelings to provide appropriate support. The system has the following configuration:

[0624] The server uses speech recognition technology to convert children's speech into text data. This process utilizes speech recognition software such as the Google Speech-to-Text API. The converted text data is then analyzed using natural language processing technology. Natural language processing software, such as the Google Cloud Natural Language API, is used to understand the children's feelings and circumstances from the text data.

[0625] Next, the server uses a generative AI model to estimate the child's feelings from the analyzed data. It uses generative AI models such as OpenAI's GPT-3 to generate the estimated feelings. These estimated results are then communicated to teachers and parents through a notification system.

[0626] For example, if a child says, "I'm worse at drawing than the other kids," the server uses speech recognition technology to transcribe this statement into text and analyzes it using natural language processing technology. A generative AI model estimates from this statement whether the child is struggling with self-esteem and notifies the user of the result. An example of a prompt would be, "If a child says, 'I'm worse at drawing than the other kids,' please estimate their feelings."

[0627] This system makes it possible to accurately understand children's feelings and provide appropriate encouragement and advice.

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The user (child) makes a statement. The device acquires this statement as audio data via the microphone. The input is audio data, and the output is the same audio data.

[0631] Step 2:

[0632] The device sends the acquired audio data to the server. The server converts the audio data into text data using speech recognition technology. Specifically, it uses the Google Speech-to-Text API to convert speech to text. The input is audio data, and the output is text data.

[0633] Step 3:

[0634] The server analyzes the converted text data using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract emotions and intentions from the text data. The input is text data, and the output is emotion data as a result of the analysis.

[0635] Step 4:

[0636] The server uses a generative AI model to estimate the child's emotions from the analysis results. Specifically, it uses OpenAI's GPT-3 to estimate emotions based on emotion data. The input is emotion data, and the output is the estimated emotion result.

[0637] Step 5:

[0638] The server communicates the estimated mood to teachers and parents through notification methods. Specifically, it sends the estimated mood results using email or app notifications. The input is the estimated mood result, and the output is the notification message.

[0639] Step 6:

[0640] The server generates optimal encouragement and advice based on the child's feelings and situation. It uses a generative AI model to create appropriate messages. The input is an estimated feeling, and the output is a message of encouragement or advice.

[0641] Step 7:

[0642] The server sends the generated encouragement and advice to the terminal and displays it to the user. The terminal displays the message on the screen and provides feedback to the user. The input is the message of encouragement and advice, and the output is what is displayed to the user.

[0643] (Example 3)

[0644] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0645] Modern children face a variety of problems and psychological stress in their living environments, but there is a lack of means to properly understand these issues and provide appropriate responses tailored to their individual circumstances. Therefore, there is a need to effectively resolve the problems children face and promote their psychological stability.

[0646] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0647] In this invention, the server includes means for understanding problems in the children's living environment, means for analyzing the children's psychological state, and means for generating appropriate responses for the children. This makes it possible to provide appropriate responses tailored to the individual circumstances of the children and promote their psychological stability.

[0648] "Problems in children's living environments" refers to the various difficulties and challenges that children face in their daily lives and social environments.

[0649] "Psychological state" refers to a child's emotions, mood, and mental health.

[0650] "Appropriate responses" refer to reactions such as encouragement and advice that are provided according to the individual circumstances and psychological state of the children.

[0651] An "information processing device" refers to a computing device used to analyze data and perform processing according to a specific purpose.

[0652] One embodiment of this invention is to provide a system that identifies problems in children's living environments, analyzes their psychological state, and generates appropriate responses.

[0653] Users input text data about children's feelings and situations using a device. This data specifically describes the problems and worries the children are facing. The device sends this input data to a server. The server receives the data and analyzes it using a generative AI model. Specifically, it uses natural language processing technology to analyze the data in order to understand the children's psychological state. Based on the analysis results, the server generates an appropriate response. This response includes encouragement and advice tailored to each child's individual situation.

[0654] The hardware used includes high-performance computers equipped with NVIDIA GPUs, and the software includes large-scale language models such as OpenAI's GPT series. This allows the server to generate optimal responses tailored to the children's feelings and situations.

[0655] For example, if a user enters text such as "I'm lonely because I can't make friends at school," the server will use this information to generate a message such as "It's hard to make new friends, but try talking to people little by little. I'm sure you can do it." An example of a prompt message could be something like, "What kind of encouraging message should I send to a child who is having trouble with relationships at school?" The flow of specific processing in Example 3 will be explained using Figure 15.

[0656] Step 1:

[0657] Users input text data about the children's feelings and circumstances using a terminal. This input data describes the specific problems and worries the children are facing. The input data is sent from the terminal to the server.

[0658] Step 2:

[0659] The server inputs text data received from the terminals into a generative AI model for analysis. Specifically, natural language processing techniques are used to extract information from the text data that helps understand the children's psychological state and circumstances. This analysis provides detailed data about the children's feelings and situations.

[0660] Step 3:

[0661] Based on the analysis results, the server uses a generative AI model to generate appropriate responses. The generated responses include encouragement and advice tailored to each child's individual situation. For example, in response to the input "I'm lonely because I can't make friends at school," a message such as "It's hard to make new friends, but try talking to people little by little. I'm sure you can do it" is generated.

[0662] Step 4:

[0663] The server sends the generated response to the terminal. The terminal displays the received message to the user. The user can review the displayed message and provide appropriate encouragement or advice to the children.

[0664] (Application Example 3)

[0665] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0666] Modern children face various problems and stresses in their school and daily lives, but there is a lack of means to properly understand and support them. Furthermore, children often have difficulty expressing their emotions, and opportunities to receive appropriate encouragement and advice are limited. Improving this situation and providing children with a safe and secure environment is essential.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0668] In this invention, the server includes means for understanding problems in the children's living environment, means for analyzing the children's emotional state, and means for providing appropriate encouragement and advice to the children. This makes it possible to provide appropriate support in real time according to the children's emotions and circumstances.

[0669] "Means for understanding problems in the living environment" refers to technologies for detecting and recording various problems that children face in their daily lives and school life.

[0670] "Methods for analyzing emotional states" refer to technologies that analyze children's voices and facial expressions to estimate their emotions and psychological states.

[0671] "Means of providing appropriate encouragement and advice" refers to techniques for generating and providing optimal encouragement and advice tailored to the child's situation and emotions.

[0672] "Means of recognizing voice and facial expressions" refers to technologies for acquiring and analyzing children's speech and facial expressions as digital data.

[0673] "Methods for generating encouragement and advice using generative AI models" refers to technologies that utilize AI technology to automatically generate encouragement and advice tailored to the specific circumstances of children.

[0674] "Methods for converting generated messages into audio" refers to technology that converts encouraging and advice messages generated in text format into audio for delivery to children.

[0675] The system for implementing this invention is designed to identify problems in children's living environments, analyze their emotional states, and provide appropriate encouragement and advice.

[0676] The server uses speech recognition and facial recognition technologies to acquire children's voices and facial expressions as digital data. Specifically, it uses Google Cloud Speech-to-Text for speech recognition and Microsoft Azure Face API for facial expression recognition. This makes it possible to estimate emotions from children's speech and facial expressions.

[0677] Next, the server uses a generative AI model to generate encouragement and advice tailored to the children's situations. This process utilizes prompt text as input and employs generative AI models such as OpenAI GPT-3. The generated messages are then converted into speech using speech synthesis technology such as Amazon Polly and delivered to the children.

[0678] For example, if a child says, "Today's lesson was difficult," the server converts the statement into text and estimates the child's emotions from their facial expression. Then, it inputs the following prompt into the generative AI model: "The child said, 'Today's lesson was difficult.' Their expression seems a little down. Please generate an encouraging message for this situation." As a result, the AI ​​can generate an encouraging message such as, "It's great to challenge yourself with difficult things. Let's try our best next time!" and deliver it to the child verbally.

[0679] In this way, the system can provide appropriate support in real time, tailored to the children's emotions and circumstances.

[0680] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0681] Step 1:

[0682] The user (child) speaks into the device. The device acquires the user's voice through its microphone. This voice data becomes the input.

[0683] Step 2:

[0684] The device sends the acquired audio data to the server. The server uses speech recognition technology (Google Cloud Speech-to-Text) to convert the audio data into text data. This converted text data becomes the output.

[0685] Step 3:

[0686] The server receives video data from the user transmitted from the terminal and analyzes the user's facial expressions using facial recognition technology (Microsoft Azure Face API). As a result of the analysis, it estimates the user's emotional state, and this data is output.

[0687] Step 4:

[0688] The server takes text data and emotional state data as input and generates prompt sentences for a generative AI model (OpenAI GPT-3). These prompt sentences include the user's statements and emotional state. Using these prompt sentences, the AI ​​model generates encouraging and advice messages. These messages become the output.

[0689] Step 5:

[0690] The server converts the generated message into audio data using speech synthesis technology (Amazon Polly). This audio data becomes the output.

[0691] Step 6:

[0692] The server sends the generated audio data to the terminal. The terminal plays the audio message to the user through its speaker. This allows the user to receive encouragement and advice.

[0693] 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.

[0694] "Example of form 1"

[0695] One embodiment of the present invention combines an AI that analyzes children's speech and actions with an emotion engine that recognizes the user's emotions. Specifically, it collects input from devices and platforms used by children in real time, and the AI ​​analyzes this data. At the same time, the emotion engine recognizes emotions from the children's facial expressions and tone of voice, and feeds this information back into the AI's analysis. This makes it possible to more accurately understand the children's feelings and worries.

[0696] "Example of form 2"

[0697] Furthermore, the means of providing encouragement and advice to children also combine AI and an emotion engine. Specifically, the AI ​​generates optimal encouragement and advice according to the child's feelings and situation. In doing so, it refers to information obtained from the emotion engine, enabling it to respond appropriately to the child's emotions. For example, if it senses that a child is feeling down, it generates an encouraging message. Conversely, if it senses that a child is happy, it generates a message that shares that happiness.

[0698] "Example of form 3"

[0699] Furthermore, it is possible to use an emotion engine to both understand children's worries and to provide them with encouragement and advice. Specifically, the emotion engine recognizes children's emotions in real time and feeds that information back to the AI. This allows the AI ​​to respond appropriately to the children's emotions. For example, if the AI ​​senses that a child is angry, it will generate advice to calm that anger. Conversely, if the AI ​​senses that a child is having fun, it will generate advice to amplify that fun.

[0700] The following describes the processing flow for each example of the form.

[0701] "Example of form 1"

[0702] Step 1: Collect input in real time from the devices and platforms that children use.

[0703] Step 2: The AI ​​analyzes the collected data.

[0704] Step 3: Simultaneously, the emotional engine recognizes emotions from the children's facial expressions, tone of voice, and other factors.

[0705] Step 4: The information obtained from the emotion engine is fed back into the AI ​​analysis.

[0706] "Example of form 2"

[0707] Step 1: The AI ​​generates optimal encouragement and advice based on the children's feelings and circumstances.

[0708] Step 2: Use the information obtained from the emotion engine as a reference.

[0709] Step 3: It becomes possible to respond appropriately to children's emotions.

[0710] "Example of form 3"

[0711] Step 1: The emotion engine recognizes children's emotions in real time.

[0712] Step 2: Provide that information as feedback to the AI.

[0713] Step 3: The AI ​​responds appropriately to the children's emotions.

[0714] (Example 1)

[0715] Next, we will describe Example 1 of Form 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".

[0716] Modern children often face social and emotional problems in their educational and daily environments. However, it is difficult to identify these problems early and provide appropriate support and guidance. In particular, when children compare themselves to others or are unable to express their true feelings due to concerns about how others perceive them, the seriousness of the problems may be overlooked. There is a need to improve this situation, accurately understand children's feelings and worries, and provide appropriate support.

[0717] 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.

[0718] In this invention, the server includes means for understanding the social relationships and emotional problems of children in their educational and daily environments, means for understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about the opinions of those around them, and means for improving children's self-confidence by providing support and guidance. This makes it possible to accurately grasp children's feelings and worries and provide appropriate support.

[0719] The term "educational environment" refers to the physical and social environment in which children learn and grow, and includes schools, homes, and other learning facilities.

[0720] "Everyday environment" refers to the environment in which children go about their daily lives, and includes their home, community, and friendships.

[0721] "Social relationships" refer to the interpersonal relationships that children form with others, including relationships with friends, family, teachers, and others.

[0722] "Emotional problems" refer to emotional issues that children experience, including anxiety, stress, and loneliness.

[0723] An "information processing device" refers to a device used to analyze children's speech and actions, and includes electronic devices such as computers and servers.

[0724] An "emotion analysis device" refers to a device used to recognize children's emotions, and it has the function of analyzing facial expressions and tone of voice using a camera and microphone.

[0725] "Artificial intelligence" refers to the technology of computer systems that imitate human intelligence to learn and reason, and includes natural language processing and machine learning.

[0726] "Support and guidance" refers to advice and educational support provided to children, aimed at problem-solving and improving their self-confidence.

[0727] This invention is a system that understands the social relationships and emotional problems of children in their educational and daily environments and provides appropriate support. The system uses an information processing device and an emotion analysis device to analyze children's statements, actions, and emotions.

[0728] The server collects data on children's speech and actions in real time from devices they use (smartphones, tablets, computers, etc.). Specifically, it acquires data such as social media posts, chat messages, and voice input. This data is encrypted before being sent to the server.

[0729] The server passes the received data to an information processing unit, which analyzes the text data using natural language processing technology. The information processing unit then estimates the children's feelings and worries from their statements. For example, from a statement like "I don't want to go to school," it estimates worries related to school life.

[0730] Simultaneously, the device uses its camera and microphone to capture the children's facial expressions and voice tones. This data is sent to an emotion analysis device. The emotion analysis device recognizes emotions from the facial expressions and voice tones and identifies emotions such as joy, sadness, and anger.

[0731] The server integrates and analyzes data obtained from the information processing device and the emotion analysis device. This allows for a more accurate understanding of the children's feelings and concerns. Based on the integrated analysis results, users can provide appropriate support and guidance to the children.

[0732] For example, if a child posts on social media that they "had a fight with a friend," the device collects the post and sends it to the server. The server uses an information processing device to estimate the possibility that the child is having trouble with their friendships. At the same time, if the emotion analysis device recognizes anger from the tone of voice captured by the device, the server integrates this information and suggests that the friendship problems may be serious.

[0733] As an example of a prompt, the prompt "What kinds of problems might a child have if they say they don't want to go to school?" can be input into the AI ​​model, and the AI ​​can output the type of problem and its cause that it estimates.

[0734] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0735] Step 1:

[0736] The device collects data on speech and actions from the devices used by children in real time. Inputs include social media posts, chat messages, and voice input. This data is temporarily stored on the device, encrypted, and sent to the server. The output is the encrypted data sent to the server.

[0737] Step 2:

[0738] The server decrypts the encrypted data received from the terminal and passes it to the information processing device. The input is encrypted data sent from the terminal. The server decrypts the data and provides it to the information processing device in an appropriate format. The output is analyzable data passed to the information processing device.

[0739] Step 3:

[0740] The information processing device analyzes text data using natural language processing technology. The input is analyzable data provided by a server. The information processing device analyzes the content of the statements and estimates the feelings and worries of the children. For example, from the statement "I don't want to go to school," it estimates worries related to school life. The output is estimated data of feelings and worries as a result of the analysis.

[0741] Step 4:

[0742] The device uses a camera and microphone to capture the children's facial expressions and voice tones. The input is real-time video and audio data. The device transmits this data to an emotion analysis device. The output is video and audio data transmitted to the emotion analysis device.

[0743] Step 5:

[0744] The emotion analyzer recognizes emotions from facial expressions and tone of voice. The input is video and audio data transmitted from a terminal. The emotion analyzer identifies emotions such as joy, sadness, and anger. The output is the recognized emotion data.

[0745] Step 6:

[0746] The server integrates and analyzes data obtained from the information processing device and the emotion analysis device. The input consists of estimated data on feelings and worries, and recognized emotion data. The server integrates this data to gain a more accurate understanding of the children's feelings and worries. The output is the integrated analysis result.

[0747] Step 7:

[0748] The user provides appropriate support and guidance to the children based on the integrated analysis results provided by the server. The input is the integrated analysis results from the server. The user uses this as a reference to consider countermeasures for the children's problems. The output is the support and guidance provided to the children.

[0749] (Application Example 1)

[0750] Next, we will describe Application Example 1 of Form 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."

[0751] In children's school and daily lives, there is a need to identify early on any relationship or romantic problems they may be experiencing and provide appropriate support. However, children often compare themselves to others and are hesitant to express their true feelings due to concerns about what others think, making it difficult to understand their problems. Furthermore, it is difficult for parents and educators to understand children's feelings in real time and provide the necessary support. Effective means to solve these challenges are needed.

[0752] 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.

[0753] In this invention, the server includes means for collecting and analyzing children's statements and actions in real time, means for analyzing children's facial expressions and tone of voice to recognize their emotions, and means for sending notifications to parents or educators when an abnormality is detected. This makes it possible to accurately understand children's feelings and worries, and for parents and educators to intervene at the appropriate time.

[0754] "Methods for collecting and analyzing children's statements and actions in real time" refers to technologies that instantly acquire voice and motion data from devices used by children and analyze it to understand their feelings and circumstances.

[0755] "A means of analyzing children's facial expressions and tone of voice to recognize their emotions" refers to a technology that uses cameras and microphones to capture children's facial expressions and voices, and then uses that data to have an emotion engine estimate their emotional state.

[0756] The "means of sending notifications to parents and educators when an abnormality is detected" refers to a technology that uses AI to analyze children's data and quickly warn parents and educators when unusual emotions or behaviors are detected.

[0757] The system for implementing this invention has the function of understanding children's feelings and concerns in real time and sending notifications to parents and educators as needed. The system uses terminals such as smartphones and smart glasses to collect children's words and actions in real time. These terminals acquire audio and video data through microphones and cameras.

[0758] The server uses software such as Python, TensorFlow, and OpenCV to analyze the collected data. Audio data is converted to text using TensorFlow, and the content of the speech is analyzed using natural language processing. Video data is analyzed using OpenCV for facial expression analysis, and an emotion engine analyzes the tone of voice to estimate the emotional state of the children.

[0759] If an anomaly is detected, the server will promptly send a notification to parents or educators. This notification will include information about the children's feelings and circumstances, prompting appropriate action.

[0760] For example, if a child says, "I had a fight with a friend," and their expression is sad and their voice is low, the AI ​​will estimate that the child is having trouble with interpersonal relationships and will send a notification to the parent. An example of a prompt to input into the generating AI model would be, "What kind of feelings might a child have if they say they don't want to go to school?"

[0761] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0762] Step 1:

[0763] The device collects audio and video data of children in real time. It takes audio data from the microphone and video data from the camera as input. This data is sent to the server in an appropriate format for use in subsequent analysis steps.

[0764] Step 2:

[0765] The server converts received audio data into text. Using audio data sent from the terminal as input, it performs speech recognition using TensorFlow. The output is the audio data converted back into text data. This text data becomes input for natural language processing.

[0766] Step 3:

[0767] The server analyzes the text data using natural language processing. It uses the text data generated in step 2 as input and analyzes the content of the statements using a generative AI model. The output provides estimated results of the children's feelings and situations based on their statements.

[0768] Step 4:

[0769] The server analyzes video data to recognize facial expressions. It uses video data transmitted from the terminal as input and performs facial expression analysis using OpenCV. The output is an estimate of the children's emotional state based on their facial expressions.

[0770] Step 5:

[0771] The server analyzes the tone of voice to recognize emotions. It uses voice data transmitted from the terminal as input and analyzes the tone of voice using an emotion engine. The output is an estimated emotional state based on the tone of voice.

[0772] Step 6:

[0773] The server integrates the results from steps 3, 4, and 5 to detect anomalies. It uses estimated feelings and situations, emotional states based on facial expressions, and emotional states based on tone of voice as input. The output generates alert information if an anomaly is detected.

[0774] Step 7:

[0775] The server sends notifications to parents and educators if an anomaly is detected. It uses the alert information generated in step 6 as input. As output, it sends notifications to parents and educators regarding the children's feelings and circumstances.

[0776] (Example 2)

[0777] Next, we will describe Example 2 of Form 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".

[0778] Children often struggle to express their true feelings and harbor anxieties because they are overly concerned with comparing themselves to others and the opinions of those around them in school and daily life. This can lead to a decline in children's self-esteem and negatively impact their mental health. Therefore, it is crucial to accurately understand children's emotions and situations and provide appropriate encouragement and advice.

[0779] 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.

[0780] In this invention, the server includes means for analyzing children's speech and actions and estimating their emotional state, means for generating optimal encouragement and advice based on the estimated emotions, and means for presenting the generated messages to the children. This enables the provision of appropriate support that is sensitive to the children's emotions, improving their self-esteem and maintaining their mental health.

[0781] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[0782] "Relationships" refers to the state of human relationships and interactions that children build with others.

[0783] "Personal problems" refer to the mental or emotional issues or anxieties that children individually face.

[0784] "Natural language processing technology" refers to the technology used to analyze and understand human language using computers.

[0785] "Emotional analysis technology" refers to the technology that extracts and analyzes emotions from text and audio data.

[0786] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new text or information.

[0787] "Encouragement and advice" refers to words and guidance that provide emotional support to children.

[0788] "Means of presenting a message" refers to methods and devices for conveying a generated message to children.

[0789] This invention is a system for understanding children's emotions and situations and providing appropriate encouragement and advice. The system consists of three components: a server, a terminal, and a user.

[0790] The server uses natural language processing and sentiment analysis technologies to analyze children's statements and actions. Specifically, the server uses a common cloud-based language analysis API as its natural language processing technology to analyze children's statements as text data. The analyzed data is then used with sentiment analysis technology to estimate the children's emotional state. For this sentiment analysis, a general sentiment analysis engine is used.

[0791] The server uses a generative AI model to generate optimal encouragement and advice based on estimated emotions. The generative AI model uses pre-trained algorithms to produce messages that resonate with the children's emotions. These messages are customized according to the children's emotional state.

[0792] The terminal's role is to present messages sent from the server to the user. The terminal communicates the generated messages to the user through voice output and text display. This allows the user to receive feedback from the AI.

[0793] For example, if a user enters the prompt "My child seems to be losing confidence because they are comparing themselves to their friends at school. How can I encourage them?", the server will use this information and a generative AI model to generate a message such as "You are wonderful just the way you are. There's no need to compare yourself to others," and present it to the user through their device.

[0794] This system provides appropriate support that is sensitive to children's emotions, enabling them to improve their self-esteem and maintain their mental health.

[0795] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0796] Step 1:

[0797] Users input their children's statements and actions through their devices. The input data is sent to the server in either voice or text format. For example, if a user inputs "I didn't do well at school today," that text data is sent to the server.

[0798] Step 2:

[0799] The server analyzes the received text data using natural language processing techniques. Specifically, the server uses a language analysis API to extract the grammatical structure and keywords from the input text. This analysis allows the server to identify the subject and emotion of the statement. As a result of the analysis, the emotional nuances of the text are extracted.

[0800] Step 3:

[0801] Based on the analysis results, the server uses emotion analysis technology to estimate the children's emotional state. For example, from the phrase "it didn't go well," the server might determine that the child is feeling down. This estimation result is output as data indicating the emotional state.

[0802] Step 4:

[0803] The server uses a generative AI model to generate optimal encouragement and advice based on the estimated emotional state. The generative AI model uses a pre-trained algorithm to generate messages appropriate to the emotional state. For example, it might generate a message such as, "You're doing great. I'm sure you'll do better next time."

[0804] Step 5:

[0805] The server sends the generated message to the terminal. The terminal presents the generated message to the user through voice output or text display. This allows the user to receive feedback from the AI.

[0806] (Application Example 2)

[0807] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0808] Modern children often struggle to express their true feelings at school and at home because they are overly concerned with comparing themselves to others and the opinions of those around them. Furthermore, there is a lack of systems that can properly understand these anxieties and provide encouragement and advice. In this situation, there is a need to understand children's feelings in real time and provide appropriate feedback.

[0809] 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.

[0810] This invention includes a server that provides means for understanding the interpersonal relationships and personal concerns of children in their living environment, means for understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about how others perceive them, and means for analyzing children's statements and actions in real time to understand their feelings and provide appropriate feedback. This makes it possible to accurately grasp the feelings of children and provide them with appropriate encouragement and advice.

[0811] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[0812] "Relationships" refers to the social connections and interactions that children form with others.

[0813] "Personal worries" refer to the internal problems and anxieties that children individually face.

[0814] "Comparison" refers to the act of children comparing themselves to others and evaluating them in that way.

[0815] "Gaze" refers to the attention and interest that people around children direct towards them.

[0816] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[0817] "Real-time" refers to the immediate processing of children's statements and actions the moment they occur.

[0818] "Feedback" refers to the reactions and evaluations given in response to children's actions and statements.

[0819] "Encouragement" refers to words and actions used to cheer up and encourage children.

[0820] "Advice" refers to guidance and suggestions provided to children regarding problems they face.

[0821] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server uses artificial intelligence to analyze children's speech and actions in real time in order to understand their interpersonal relationships and personal concerns in their living environment. Specifically, the server uses speech recognition technology to convert children's speech into text data and analyzes their emotions and intentions through natural language processing. In this process, an emotion engine is used to more accurately estimate the children's feelings.

[0822] The devices are those that children use on a daily basis, such as smartphones and tablets. These devices receive feedback sent from a server and display appropriate encouragement and advice to the user. This allows children to receive real-time feedback tailored to their feelings.

[0823] The users are children who use the system. Through their devices, users receive feedback from the server, allowing them to receive encouragement and advice tailored to their feelings and circumstances. This enables users to improve their self-esteem and reduce anxiety caused by comparisons with others.

[0824] For example, if a user says, "Things didn't go well at school today," the server analyzes this statement and sends an encouraging message to the device such as, "Failure is the foundation of success. You'll do better next time." This helps the user regain their confidence.

[0825] Examples of prompts for a generative AI model include the following:

[0826] "Prompt: How would you encourage your child if they said, 'I didn't do well at school today'?"

[0827] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0828] Step 1:

[0829] The user inputs their speech through a terminal. The terminal converts the user's voice into text data using speech recognition technology. This text data is then input to the server.

[0830] Step 2:

[0831] The server analyzes the received text data using natural language processing techniques. Specifically, it uses an emotion engine to extract emotions and intentions from the text data. This process outputs data that estimates the user's feelings.

[0832] Step 3:

[0833] The server generates appropriate feedback using a generative AI model based on estimated emotional data. Prompt messages are input to the generative AI model, which generates encouragement and advice tailored to the user's emotional state. This feedback is then output from the server to the terminal.

[0834] Step 4:

[0835] The device displays feedback received from the server to the user. Specifically, it displays encouraging and advice messages on the screen for the user to see. This allows the user to receive appropriate feedback in real time.

[0836] (Example 3)

[0837] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0838] Modern children face a great deal of stress and anxiety in their school and home lives, but there is a lack of means to properly understand and support them. In particular, there is a need to understand children's emotions in real time and provide appropriate encouragement and advice accordingly.

[0839] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0840] In this invention, the server includes means for understanding the emotional state of children in their living environment, means for analyzing children's emotions in real time, and means for generating messages appropriate for children using a generative model. This makes it possible to provide appropriate support in real time according to the children's emotions.

[0841] "Living environment" refers to the places and situations in which children spend their days, encompassing the entire environment, including home and school.

[0842] "Emotional state" refers to the feelings and psychological state that children experience at a particular moment, and includes emotions such as joy, sadness, and anger.

[0843] "Real-time analysis" refers to a process where data is analyzed immediately upon collection, and results are obtained instantly.

[0844] A "generative model" refers to an algorithm or system that uses artificial intelligence technology to generate an appropriate output based on a specific input.

[0845] "Generating a message" refers to creating a message that is appropriate for children based on specific information and circumstances.

[0846] The following systems are conceivable as embodiments for carrying out this invention.

[0847] The user first collects data to understand the emotional state of children in their living environment. Specifically, sensors and microphones are used to acquire data on children's daily conversations and behaviors. This data serves as foundational information for analyzing children's emotions in real time.

[0848] The server inputs the collected data into an emotion engine, which uses natural language processing technology to analyze the children's emotions. The emotion engine identifies emotions such as anger, sadness, and happiness, and feeds the results back into a generative AI model.

[0849] The generative AI model generates prompt messages based on the results of sentiment analysis. For example, if the analysis indicates that a child is struggling with interpersonal relationships at school, it will generate a prompt message such as, "What kind of encouraging message should be generated if a child is struggling with interpersonal relationships at school?"

[0850] The server inputs prompt text into a generation AI model, which then generates encouraging and advice messages tailored to the children. These generated messages are delivered to the children via their devices. The devices, such as smartphones and tablets, display the messages, allowing the children to receive them.

[0851] This system allows children to receive appropriate support in real time, tailored to their emotions. An example of a prompt message is, "If a child is struggling with interpersonal relationships at school, what kind of encouraging message should be generated?" The specific processing flow in Example 3 is explained using Figure 21.

[0852] Step 1:

[0853] Users collect data to understand the emotional state of children in their living environment. Specifically, they use sensors and microphones to acquire data on children's daily conversations and behaviors. This data is transmitted to the server in voice and text format.

[0854] Step 2:

[0855] The server inputs the collected data into an emotion engine, which uses natural language processing technology to analyze the children's emotions in real time. The input audio and text data is analyzed by the emotion engine to identify the children's emotional states (e.g., joy, sadness, anger). This analysis result is used in the next step.

[0856] Step 3:

[0857] The server generates prompt messages to input into the generative AI model based on the results of the sentiment analysis. For example, if the analysis indicates that a child is struggling with interpersonal relationships at school, the server will generate a prompt message such as, "What kind of encouraging message should be generated if a child is struggling with interpersonal relationships at school?" This prompt message is then used as input to the generative AI model.

[0858] Step 4:

[0859] The server inputs prompt text into a generative AI model, which generates encouraging and advice messages tailored to the children. Based on the prompt text, the generative AI model utilizes past data and learned knowledge to create specific and effective messages. These messages are then delivered in the next step.

[0860] Step 5:

[0861] The device delivers the generated messages to the children. Specifically, it displays the messages via smartphones and tablets, allowing the children to receive them. This enables the children to receive encouragement and advice in real time.

[0862] (Application Example 3)

[0863] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0864] Modern children face numerous challenges in school and daily life, often comparing themselves to others or feeling unable to express their true feelings due to concerns about what others think. Similarly, workers in factories and other workplaces may experience emotional stress, negatively impacting work efficiency and the overall work environment. To address these issues, a system is needed that provides appropriate encouragement and advice tailored to individual emotions and circumstances.

[0865] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0866] This invention includes a server that provides means for understanding children's worries about relationships and romance in their school and daily lives, understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about what others think, providing encouragement and advice to help children build confidence, recognizing workers' emotions in real time and providing encouragement and advice according to the progress and situation of their work, analyzing workers' facial expressions and tone of voice, and generating appropriate encouragement and advice based on the analyzed emotional data. This makes it possible to provide individualized and appropriate responses to the emotional problems faced by children and workers.

[0867] "Children" refers to young people who are in the process of growing up in their school and daily lives.

[0868] "School life" refers to the daily life of children as they engage in learning and activities at educational institutions.

[0869] "Daily life" refers to all aspects of the daily life that children engage in at home and in their community.

[0870] "Relationships" refers to the social connections and interactions that children form with others.

[0871] "Romantic love" refers to the affection and intimacy that children feel towards others.

[0872] "Worries" refers to the mental burdens and problems that children face.

[0873] "Comparison" refers to the act of children comparing themselves to others and evaluating them in that way.

[0874] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[0875] "Confidence" refers to a child's belief in their own abilities and worth.

[0876] "Workers" refers to people who perform tasks in factories or work sites.

[0877] "Emotions" refers to the mental processes such as joy, anger, sadness, and happiness that children and workers experience.

[0878] "Real-time" refers to processing events that are currently unfolding immediately.

[0879] "Progress" refers to the degree or status of work or a plan's progress.

[0880] "Facial expressions" refer to the expressions that show emotions and feelings on the faces of children and workers.

[0881] "Voice tone" refers to the pitch and intensity of the sounds produced by children or workers.

[0882] "Analysis" refers to the act of thoroughly analyzing data and information to derive meaning from it.

[0883] "Data" refers to information about children and workers expressed using numbers and symbols.

[0884] "Generation" refers to the act of AI creating new encouragement or advice.

[0885] The system for realizing this invention involves a server and terminals working together. The server plays a central role in recognizing the emotions of children and workers in real time and generating appropriate encouragement and advice. Specifically, the server uses an emotion recognition engine to analyze facial expression data and voice tone transmitted from the terminals. The analyzed data is input into a generative AI model, which generates encouragement and advice tailored to each individual's situation.

[0886] The device uses a camera and microphone to collect facial expressions and voices of children and workers. This data is sent to a server and processed in real time. The device then provides users with generated encouragement and advice.

[0887] For example, if the server determines that a worker is tired, it will generate a message such as, "Take a short break and refresh yourself." This message will then be transmitted to the worker via their terminal.

[0888] Examples of prompt messages include the following:

[0889] Worker's facial expression data: tired face

[0890] Worker's voice tone: Low

[0891] Input to the AI ​​model: "The worker has been determined to be tired. Please generate an appropriate encouraging message."

[0892] In this way, the server and terminals can cooperate to provide individualized and appropriate responses to children and workers.

[0893] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0894] Step 1:

[0895] The device uses a camera and microphone to collect data on the user's facial expressions and voice tone. This data serves as input for understanding the user's emotions in real time.

[0896] Step 2:

[0897] The device sends collected facial expression data and voice tone to the server. The server receives this data and analyzes it using an emotion recognition engine. The analysis results in the user's emotional state being output.

[0898] Step 3:

[0899] The server inputs prompt messages into the generative AI model based on the analyzed emotional state. These prompt messages include instructions for generating encouragement and advice tailored to the user's emotions.

[0900] Step 4:

[0901] The generative AI model receives a prompt and generates encouragement and advice appropriate to the user's emotions. This generated message is then output.

[0902] Step 5:

[0903] The server sends the generated encouragement and advice to the terminal. The terminal displays or audibly communicates this message to the user. This allows the user to receive appropriate encouragement and advice.

[0904] (Other examples)

[0905] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0906] 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.

[0907] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[0908] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0909] 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.

[0910] [Third Embodiment]

[0911] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0912] 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.

[0913] 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).

[0914] 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.

[0915] 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.

[0916] 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).

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0923] "Example of form 1"

[0924] In one embodiment of the present invention, an AI is used to analyze children's words and actions as a means of understanding their worries about relationships and romantic relationships in their school and daily lives. This AI estimates the feelings and worries of children from their words and actions. For example, if a child says, "I don't want to go to school," the AI ​​estimates from that statement that the child may be having some kind of worry about school life.

[0925] "Example of form 2"

[0926] Furthermore, as a means of understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about what others think, the AI ​​analyzes children's words and actions to estimate their feelings and circumstances. For example, if a child says, "I'm no good compared to other kids," the AI ​​will estimate from that statement that the child may be struggling with self-esteem.

[0927] "Example of form 3"

[0928] Furthermore, as a means of providing encouragement and advice to children, the AI ​​generates optimal encouragement and advice according to the children's feelings and circumstances. For example, if the AI ​​estimates that a child is struggling with school life, it will generate an encouraging message for that child such as, "School life is tough, but I know you can overcome it."

[0929] The following describes the processing flow for each example of the form.

[0930] "Example of form 1"

[0931] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0932] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate the children's feelings and concerns.

[0933] Step 3: The AI ​​generates encouragement and advice for the children based on their estimated feelings and concerns. This is done based on pre-set rules and learning models.

[0934] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0935] "Example of form 2"

[0936] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0937] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate situations where children are comparing themselves to others or are unable to express their true feelings due to concerns about what others think.

[0938] Step 3: The AI ​​generates encouragement and advice for the children based on the estimated situation. This is done based on pre-set rules and learning models.

[0939] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0940] "Example of form 3"

[0941] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[0942] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate the children's feelings and situations.

[0943] Step 3: The AI ​​generates encouragement and advice for the children based on their estimated feelings and circumstances. This is done based on pre-set rules and learning models.

[0944] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[0945] (Example 1)

[0946] Next, we will describe Embodiment 1 of 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."

[0947] In today's educational environment, children often face interpersonal and emotional problems, and it is crucial to identify these issues early and provide appropriate support. However, accurately inferring children's feelings and problems from their words and actions is difficult, resulting in a lack of information for educators and support staff to respond appropriately.

[0948] 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.

[0949] In this invention, the server includes means for understanding interpersonal relationships and emotional problems in children's educational and daily environments, means for analyzing children's statements and actions to estimate their emotions and intentions, and means for providing the analysis results to educators and support staff. This makes it possible to grasp children's feelings and problems early and provide appropriate support and guidance.

[0950] The term "educational environment" refers to the physical and social environment in which children learn and grow, and includes schools, homes, and other similar settings.

[0951] "Everyday environment" refers to the environment that children encounter in their daily lives, including their home and community.

[0952] "Interpersonal relationships" refers to the human relationships that children form with others, including friendships and relationships with teachers.

[0953] "Emotional problems" refer to emotionally related issues that children experience, including anxiety and stress.

[0954] "Means of analyzing speech and actions" refers to techniques and methods for analyzing children's words and actions and inferring the emotions and intentions behind them.

[0955] "Means of inferring emotions and intentions" refers to techniques and methods for inferring children's feelings and intentions from their words and actions.

[0956] "Means of providing analysis results" refers to the technologies and methods for communicating the analyzed information to educators and support staff.

[0957] "Educators and supporters" refers to people who have a role in supporting children's learning and growth, and includes teachers, counselors, and others.

[0958] The following system is constructed as an embodiment of this invention.

[0959] The server generates a program to analyze the children's statements and actions. This program is designed to analyze the children's statements and actions using natural language processing techniques. Specifically, the server uses a cloud-based natural language processing API to analyze the text data and extract emotions and intentions. This API is provided by a common cloud service provider.

[0960] The devices are installed in classrooms and homes to collect children's speech as audio data. This audio data is sent to a server and converted into text data. The server analyzes the converted text data to estimate the children's feelings and problems.

[0961] Educators and support staff can view the analysis results provided by the server through a dedicated dashboard. This dashboard displays a list of children's emotional states and estimated problems. This allows users to provide appropriate support and guidance to the children.

[0962] For example, if a child says, "I don't want to go to school," the server analyzes this statement and estimates their anxiety and stress regarding school life. Users can then view this information on a dashboard and consider appropriate responses for their child.

[0963] An example of a prompt is, "If a child says they don't want to go to school, estimate the reason." Using this prompt, the server estimates the child's underlying concerns from their statements and generates information to provide appropriate support.

[0964] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0965] Step 1:

[0966] The devices are installed in classrooms and homes to collect children's speech as audio data. Specifically, they use microphones to record conversations and send that audio data to a server. The input is audio data, and the output is an audio file sent to the server.

[0967] Step 2:

[0968] The server converts the received audio data into text data. Specifically, it uses speech recognition software to convert the audio data into a string of characters. This process outputs the audio data as text data. The input is an audio file, and the output is text data.

[0969] Step 3:

[0970] The server analyzes text data using natural language processing techniques. Specifically, it uses a cloud-based natural language processing API to extract emotions and intentions from the text. This process yields information about emotions and intentions from the text data. The input is text data, and the output is the analysis results, including emotions and intentions.

[0971] Step 4:

[0972] The server estimates the children's feelings and problems based on the analysis results. Using a generative AI model, it identifies potential problems underlying their statements. This process reveals the children's underlying anxieties. The input is the analysis results, and the output is the estimated feelings and problems.

[0973] Step 5:

[0974] The server provides educators and support staff with estimated feelings and problems. Specifically, it displays the information through a dedicated dashboard. This dashboard lists each child's emotional state and estimated problems. The input is the estimated feelings and problems, and the output is the information displayed on the dashboard.

[0975] Step 6:

[0976] Educators and support staff, who are the users, can check the children's status through the dashboard and provide appropriate support and guidance. Specifically, they consider countermeasures for the children based on the information on the dashboard. The input is the information on the dashboard, and the output is the countermeasures taken by the educators and support staff.

[0977] (Application Example 1)

[0978] Next, we will describe Application Example 1 of Form 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."

[0979] In children's school and daily lives, there is a challenge in understanding the relationship and romantic problems they face, comparing themselves to others, and understanding situations where they cannot express their true feelings due to concern about what others think. Furthermore, there is a need to provide appropriate encouragement and advice tailored to the children's feelings and circumstances. In addition, it is necessary to promptly inform parents and educators of this information to enable appropriate intervention.

[0980] 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.

[0981] In this invention, the server includes means for analyzing children's words and actions in real time and notifying parents and educators, means for understanding children's concerns, and means for providing encouragement and advice to children. This makes it possible to quickly understand children's feelings and concerns and provide appropriate support.

[0982] "Methods for analyzing children's speech and actions in real time" refers to technologies that instantly acquire children's voices and movements as data and analyze their content using information processing equipment.

[0983] "Means of notifying parents and educators" refers to communication technologies that, based on analyzed information, quickly transmit important information about children's feelings and circumstances to parents and educators.

[0984] "Means for understanding children's worries" refers to information processing technology that identifies and understands the problems and feelings children are experiencing through their words and actions.

[0985] "Means of providing encouragement and advice" refers to information processing technology that generates and provides appropriate encouragement and advice according to the feelings and circumstances of children.

[0986] As a form of implementing the invention, this system has the function of analyzing children's speech and actions in real time and notifying parents and educators. The server converts children's speech into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text) and analyzes their actions using an image recognition API (e.g., Google Cloud Vision). This obtains data to estimate the children's feelings and concerns.

[0987] The server uses natural language processing libraries (e.g., NLTK, spaCy) to perform sentiment analysis on text data. Based on these analysis results, a generative AI model estimates the children's feelings and concerns and generates appropriate encouragement and advice. The generated information is sent to parents and educators via push notifications using communication technology.

[0988] For example, if a child says "I don't want to go to school," the server uses a speech recognition API to convert this statement into text, and a natural language processing library analyzes the negative emotion expressed by "I don't want to go." The generative AI model estimates that the child has problems related to school life and sends a notification to the parent saying, "Your child is not wanting to go to school. There may be a problem."

[0989] An example of a prompt question would be, "What kinds of feelings or worries might a child have if they say, 'I don't want to go to school'?"

[0990] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0991] Step 1:

[0992] The device captures the children's speech as audio data via a microphone. The captured audio data is then sent to a server.

[0993] Step 2:

[0994] The server uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the received audio data into text data. This process transforms the audio data into a format that can be parsed as text information.

[0995] Step 3:

[0996] The server performs sentiment analysis on text data using natural language processing libraries (e.g., NLTK, spaCy). It analyzes emotions and intentions from the input text data and generates data to estimate children's feelings and concerns.

[0997] Step 4:

[0998] The server uses a generative AI model to estimate children's feelings and worries based on the results of emotion analysis. Based on the estimation results, it generates appropriate encouragement and advice.

[0999] Step 5:

[1000] The server uses communication technology to send estimated results and generated encouragement and advice to parents and educators via push notifications. This allows parents and educators to understand their children's situation and provide the necessary support.

[1001] (Example 2)

[1002] Next, we will describe Example 2 of the morphological example. 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."

[1003] Children often struggle to express their true feelings at school and in their daily lives because they are overly concerned with comparing themselves to others and the opinions of those around them. In such situations, it is difficult to accurately understand children's feelings and provide appropriate encouragement and advice. Furthermore, it is not easy to infer the emotions and circumstances behind children's words and actions.

[1004] 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.

[1005] In this invention, the server includes means for analyzing children's statements and actions and estimating the emotions and circumstances behind them; means for estimating the children's feelings based on the analyzed data and providing appropriate support information; and means for estimating emotions using a generative AI model. This makes it possible to accurately understand the children's feelings and provide appropriate encouragement and advice tailored to their individual circumstances.

[1006] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[1007] "Relationships" refers to the social connections and interactions that children build with others.

[1008] "Personal worries" refer to the internal problems and anxieties that children individually face.

[1009] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[1010] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new information or predictions from data.

[1011] "Inferring emotions" refers to inferring the emotions behind children's words and actions.

[1012] "Support information" refers to information such as encouragement and advice provided according to the feelings and circumstances of the children.

[1013] One embodiment of this invention is to provide a system that analyzes children's words and actions and estimates the emotions and circumstances behind them. A specific embodiment is shown below.

[1014] The server collects data on children's statements and actions. This data is obtained through the terminal as voice input or text input. For example, if a child says to the terminal, "I'm no good compared to other children," that voice data is sent to the server.

[1015] The server uses speech recognition software to convert the collected audio data into text data. Specifically, it can utilize APIs commonly used for speech recognition technology.

[1016] Next, the server analyzes the converted text data using natural language processing techniques. This analysis utilizes a generative AI model, which includes algorithms for understanding the context of natural language and estimating sentiment.

[1017] Based on the analysis, the server estimates the children's feelings and circumstances and generates appropriate support information. This support information is provided to the children as encouragement and advice.

[1018] As a concrete example, a user can input the following prompt message into the AI ​​model:

[1019] "What emotions might be at play when a child says, 'I'm no good compared to other kids'?"

[1020] "What kind of support do children need when they are worried about what others think of them?"

[1021] This allows users to gain a deeper understanding of children's feelings and obtain clues to provide appropriate support.

[1022] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1023] Step 1:

[1024] The server receives audio data from the terminal regarding the children's speech and actions. The user's spoken words into the terminal are input as audio data. This audio data is then sent to the server.

[1025] Step 2:

[1026] The server uses speech recognition software to convert the received audio data into text data. Specifically, it analyzes the audio data using a speech recognition API and outputs it as text data. This conversion transforms the audio information into text information.

[1027] Step 3:

[1028] The server analyzes the converted text data using natural language processing techniques. A generative AI model is used to understand the context of the text data and estimate emotions. This analysis outputs data that allows for the estimation of children's emotions and situations from the text data.

[1029] Step 4:

[1030] The server estimates the children's feelings and circumstances based on the analysis results. Using the output of the generative AI model, it estimates the emotions and worries the children may be experiencing. These estimation results serve as the basic data for generating appropriate support information for the children.

[1031] Step 5:

[1032] The server generates support information for the children based on the estimation results. Using a generation AI model, it generates encouragement and advice tailored to the children's feelings and circumstances. This support information is provided to the user through the terminal.

[1033] Step 6:

[1034] Users receive support information through their devices and provide appropriate support to the children. Based on this information, users can understand the children's feelings and offer necessary advice and encouragement.

[1035] (Application Example 2)

[1036] Next, we will describe application example 2 of form 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."

[1037] Modern children often struggle with self-esteem and are unable to express their true feelings due to excessive comparison with others and concern for how they are perceived in school and daily life. This situation can negatively impact children's mental health. However, there is a lack of means to identify these feelings and situations early and provide appropriate support. Therefore, there is a need for a system that analyzes children's words and actions, estimates their feelings, identifies their problems early, and provides appropriate encouragement and advice.

[1038] 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.

[1039] In this invention, the server includes means for transcribing children's speech into text using speech recognition technology and analyzing it using natural language processing technology, means for estimating the children's feelings using a generative AI model and notifying them of the results, and means for generating optimal encouragement and advice according to the children's feelings and circumstances. This makes it possible to accurately understand the children's feelings and provide appropriate support.

[1040] "School life" refers to the daily activities and experiences that children have while at school.

[1041] "Daily life" refers to the activities and experiences that children engage in on a daily basis at home and in their community.

[1042] "Human relationships" refers to the reciprocal relationships that children build with others.

[1043] "Romantic love" refers to the affection and feelings that children have for others.

[1044] "Worries" refer to the mental burdens and problems that children face.

[1045] "Comparison" refers to the act of children comparing themselves to others.

[1046] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[1047] "Speech recognition technology" refers to the technology that converts speech into text data.

[1048] "Natural language processing technology" refers to the technology used to analyze text data and understand its meaning.

[1049] A "generative AI model" refers to a model that uses artificial intelligence to generate new information from data.

[1050] "Sentiments" refers to the inner feelings and emotions of children.

[1051] "Notification" refers to the act of communicating analysis results or information to others.

[1052] "Encouragement" refers to words and actions used to cheer up children.

[1053] "Advice" refers to the guidance and instruction given to children.

[1054] The system for implementing this invention analyzes children's words and actions and estimates their feelings to provide appropriate support. The system has the following configuration:

[1055] The server uses speech recognition technology to convert children's speech into text data. This process utilizes speech recognition software such as the Google Speech-to-Text API. The converted text data is then analyzed using natural language processing technology. Natural language processing software, such as the Google Cloud Natural Language API, is used to understand the children's feelings and circumstances from the text data.

[1056] Next, the server uses a generative AI model to estimate the child's feelings from the analyzed data. It uses generative AI models such as OpenAI's GPT-3 to generate the estimated feelings. These estimated results are then communicated to teachers and parents through a notification system.

[1057] For example, if a child says, "I'm worse at drawing than the other kids," the server uses speech recognition technology to transcribe this statement into text and analyzes it using natural language processing technology. A generative AI model estimates from this statement whether the child is struggling with self-esteem and notifies the user of the result. An example of a prompt would be, "If a child says, 'I'm worse at drawing than the other kids,' please estimate their feelings."

[1058] This system makes it possible to accurately understand children's feelings and provide appropriate encouragement and advice.

[1059] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1060] Step 1:

[1061] The user (child) makes a statement. The device acquires this statement as audio data via the microphone. The input is audio data, and the output is the same audio data.

[1062] Step 2:

[1063] The device sends the acquired audio data to the server. The server converts the audio data into text data using speech recognition technology. Specifically, it uses the Google Speech-to-Text API to convert speech to text. The input is audio data, and the output is text data.

[1064] Step 3:

[1065] The server analyzes the converted text data using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract emotions and intentions from the text data. The input is text data, and the output is emotion data as a result of the analysis.

[1066] Step 4:

[1067] The server uses a generative AI model to estimate the child's emotions from the analysis results. Specifically, it uses OpenAI's GPT-3 to estimate emotions based on emotion data. The input is emotion data, and the output is the estimated emotion result.

[1068] Step 5:

[1069] The server communicates the estimated mood to teachers and parents through notification methods. Specifically, it sends the estimated mood results using email or app notifications. The input is the estimated mood result, and the output is the notification message.

[1070] Step 6:

[1071] The server generates optimal encouragement and advice based on the child's feelings and situation. It uses a generative AI model to create appropriate messages. The input is an estimated feeling, and the output is a message of encouragement or advice.

[1072] Step 7:

[1073] The server sends the generated encouragement and advice to the terminal and displays it to the user. The terminal displays the message on the screen and provides feedback to the user. The input is the message of encouragement and advice, and the output is what is displayed to the user.

[1074] (Example 3)

[1075] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1076] Modern children face a variety of problems and psychological stress in their living environments, but there is a lack of means to properly understand these issues and provide appropriate responses tailored to their individual circumstances. Therefore, there is a need to effectively resolve the problems children face and promote their psychological stability.

[1077] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1078] In this invention, the server includes means for understanding problems in the children's living environment, means for analyzing the children's psychological state, and means for generating appropriate responses for the children. This makes it possible to provide appropriate responses tailored to the individual circumstances of the children and promote their psychological stability.

[1079] "Problems in children's living environments" refers to the various difficulties and challenges that children face in their daily lives and social environments.

[1080] "Psychological state" refers to a child's emotions, mood, and mental health.

[1081] "Appropriate responses" refer to reactions such as encouragement and advice that are provided according to the individual circumstances and psychological state of the children.

[1082] An "information processing device" refers to a computing device used to analyze data and perform processing according to a specific purpose.

[1083] One embodiment of this invention is to provide a system that identifies problems in children's living environments, analyzes their psychological state, and generates appropriate responses.

[1084] Users input text data about children's feelings and situations using a device. This data specifically describes the problems and worries the children are facing. The device sends this input data to a server. The server receives the data and analyzes it using a generative AI model. Specifically, it uses natural language processing technology to analyze the data in order to understand the children's psychological state. Based on the analysis results, the server generates an appropriate response. This response includes encouragement and advice tailored to each child's individual situation.

[1085] The hardware used includes high-performance computers equipped with NVIDIA GPUs, and the software includes large-scale language models such as OpenAI's GPT series. This allows the server to generate optimal responses tailored to the children's feelings and situations.

[1086] For example, if a user enters text such as "I'm lonely because I can't make friends at school," the server will use this information to generate a message such as "It's hard to make new friends, but try talking to people little by little. I'm sure you can do it." An example of a prompt message could be something like, "What kind of encouraging message should I send to a child who is having trouble with relationships at school?" The flow of specific processing in Example 3 will be explained using Figure 15.

[1087] Step 1:

[1088] Users input text data about the children's feelings and circumstances using a terminal. This input data describes the specific problems and worries the children are facing. The input data is sent from the terminal to the server.

[1089] Step 2:

[1090] The server inputs text data received from the terminals into a generative AI model for analysis. Specifically, natural language processing techniques are used to extract information from the text data that helps understand the children's psychological state and circumstances. This analysis provides detailed data about the children's feelings and situations.

[1091] Step 3:

[1092] Based on the analysis results, the server uses a generative AI model to generate appropriate responses. The generated responses include encouragement and advice tailored to each child's individual situation. For example, in response to the input "I'm lonely because I can't make friends at school," a message such as "It's hard to make new friends, but try talking to people little by little. I'm sure you can do it" is generated.

[1093] Step 4:

[1094] The server sends the generated response to the terminal. The terminal displays the received message to the user. The user can review the displayed message and provide appropriate encouragement or advice to the children.

[1095] (Application Example 3)

[1096] Next, we will describe application example 3 of form example 3. 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."

[1097] Modern children face various problems and stresses in their school and daily lives, but there is a lack of means to properly understand and support them. Furthermore, children often have difficulty expressing their emotions, and opportunities to receive appropriate encouragement and advice are limited. Improving this situation and providing children with a safe and secure environment is essential.

[1098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1099] In this invention, the server includes means for understanding problems in the children's living environment, means for analyzing the children's emotional state, and means for providing appropriate encouragement and advice to the children. This makes it possible to provide appropriate support in real time according to the children's emotions and circumstances.

[1100] "Means for understanding problems in the living environment" refers to technologies for detecting and recording various problems that children face in their daily lives and school life.

[1101] "Methods for analyzing emotional states" refer to technologies that analyze children's voices and facial expressions to estimate their emotions and psychological states.

[1102] "Means of providing appropriate encouragement and advice" refers to techniques for generating and providing optimal encouragement and advice tailored to the child's situation and emotions.

[1103] "Means of recognizing voice and facial expressions" refers to technologies for acquiring and analyzing children's speech and facial expressions as digital data.

[1104] "Methods for generating encouragement and advice using generative AI models" refers to technologies that utilize AI technology to automatically generate encouragement and advice tailored to the specific circumstances of children.

[1105] "Methods for converting generated messages into audio" refers to technology that converts encouraging and advice messages generated in text format into audio for delivery to children.

[1106] The system for implementing this invention is designed to identify problems in children's living environments, analyze their emotional states, and provide appropriate encouragement and advice.

[1107] The server uses speech recognition and facial recognition technologies to acquire children's voices and facial expressions as digital data. Specifically, it uses Google Cloud Speech-to-Text for speech recognition and Microsoft Azure Face API for facial expression recognition. This makes it possible to estimate emotions from children's speech and facial expressions.

[1108] Next, the server uses a generative AI model to generate encouragement and advice tailored to the children's situations. This process utilizes prompt text as input and employs generative AI models such as OpenAI GPT-3. The generated messages are then converted into speech using speech synthesis technology such as Amazon Polly and delivered to the children.

[1109] For example, if a child says, "Today's lesson was difficult," the server converts the statement into text and estimates the child's emotions from their facial expression. Then, it inputs the following prompt into the generative AI model: "The child said, 'Today's lesson was difficult.' Their expression seems a little down. Please generate an encouraging message for this situation." As a result, the AI ​​can generate an encouraging message such as, "It's great to challenge yourself with difficult things. Let's try our best next time!" and deliver it to the child verbally.

[1110] In this way, the system can provide appropriate support in real time, tailored to the children's emotions and circumstances.

[1111] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1112] Step 1:

[1113] The user (child) speaks into the device. The device acquires the user's voice through its microphone. This voice data becomes the input.

[1114] Step 2:

[1115] The device sends the acquired audio data to the server. The server uses speech recognition technology (Google Cloud Speech-to-Text) to convert the audio data into text data. This converted text data becomes the output.

[1116] Step 3:

[1117] The server receives video data from the user transmitted from the terminal and analyzes the user's facial expressions using facial recognition technology (Microsoft Azure Face API). As a result of the analysis, it estimates the user's emotional state, and this data is output.

[1118] Step 4:

[1119] The server takes text data and emotional state data as input and generates prompt sentences for a generative AI model (OpenAI GPT-3). These prompt sentences include the user's statements and emotional state. Using these prompt sentences, the AI ​​model generates encouraging and advice messages. These messages become the output.

[1120] Step 5:

[1121] The server converts the generated message into audio data using speech synthesis technology (Amazon Polly). This audio data becomes the output.

[1122] Step 6:

[1123] The server sends the generated audio data to the terminal. The terminal plays the audio message to the user through its speaker. This allows the user to receive encouragement and advice.

[1124] 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.

[1125] "Example of form 1"

[1126] One embodiment of the present invention combines an AI that analyzes children's speech and actions with an emotion engine that recognizes the user's emotions. Specifically, it collects input from devices and platforms used by children in real time, and the AI ​​analyzes this data. At the same time, the emotion engine recognizes emotions from the children's facial expressions and tone of voice, and feeds this information back into the AI's analysis. This makes it possible to more accurately understand the children's feelings and worries.

[1127] "Example of form 2"

[1128] Furthermore, the means of providing encouragement and advice to children also combine AI and an emotion engine. Specifically, the AI ​​generates optimal encouragement and advice according to the child's feelings and situation. In doing so, it refers to information obtained from the emotion engine, enabling it to respond appropriately to the child's emotions. For example, if it senses that a child is feeling down, it generates an encouraging message. Conversely, if it senses that a child is happy, it generates a message that shares that happiness.

[1129] "Example of form 3"

[1130] Furthermore, it is possible to use an emotion engine to both understand children's worries and to provide them with encouragement and advice. Specifically, the emotion engine recognizes children's emotions in real time and feeds that information back to the AI. This allows the AI ​​to respond appropriately to the children's emotions. For example, if the AI ​​senses that a child is angry, it will generate advice to calm that anger. Conversely, if the AI ​​senses that a child is having fun, it will generate advice to amplify that fun.

[1131] The following describes the processing flow for each example of the form.

[1132] "Example of form 1"

[1133] Step 1: Collect input in real time from the devices and platforms that children use.

[1134] Step 2: The AI ​​analyzes the collected data.

[1135] Step 3: Simultaneously, the emotional engine recognizes emotions from the children's facial expressions, tone of voice, and other factors.

[1136] Step 4: The information obtained from the emotion engine is fed back into the AI ​​analysis.

[1137] "Example of form 2"

[1138] Step 1: The AI ​​generates optimal encouragement and advice based on the children's feelings and circumstances.

[1139] Step 2: Use the information obtained from the emotion engine as a reference.

[1140] Step 3: It becomes possible to respond appropriately to children's emotions.

[1141] "Example of form 3"

[1142] Step 1: The emotion engine recognizes children's emotions in real time.

[1143] Step 2: Provide that information as feedback to the AI.

[1144] Step 3: The AI ​​responds appropriately to the children's emotions.

[1145] (Example 1)

[1146] Next, we will describe Embodiment 1 of 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."

[1147] Modern children often face social and emotional problems in their educational and daily environments. However, it is difficult to identify these problems early and provide appropriate support and guidance. In particular, when children compare themselves to others or are unable to express their true feelings due to concerns about how others perceive them, the seriousness of the problems may be overlooked. There is a need to improve this situation, accurately understand children's feelings and worries, and provide appropriate support.

[1148] 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.

[1149] In this invention, the server includes means for understanding the social relationships and emotional problems of children in their educational and daily environments, means for understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about the opinions of those around them, and means for improving children's self-confidence by providing support and guidance. This makes it possible to accurately grasp children's feelings and worries and provide appropriate support.

[1150] The term "educational environment" refers to the physical and social environment in which children learn and grow, and includes schools, homes, and other learning facilities.

[1151] "Everyday environment" refers to the environment in which children go about their daily lives, and includes their home, community, and friendships.

[1152] "Social relationships" refer to the interpersonal relationships that children form with others, including relationships with friends, family, teachers, and others.

[1153] "Emotional problems" refer to emotional issues that children experience, including anxiety, stress, and loneliness.

[1154] An "information processing device" refers to a device used to analyze children's speech and actions, and includes electronic devices such as computers and servers.

[1155] An "emotion analysis device" refers to a device used to recognize children's emotions, and it has the function of analyzing facial expressions and tone of voice using a camera and microphone.

[1156] "Artificial intelligence" refers to the technology of computer systems that imitate human intelligence to learn and reason, and includes natural language processing and machine learning.

[1157] "Support and guidance" refers to advice and educational support provided to children, aimed at problem-solving and improving their self-confidence.

[1158] This invention is a system that understands the social relationships and emotional problems of children in their educational and daily environments and provides appropriate support. The system uses an information processing device and an emotion analysis device to analyze children's statements, actions, and emotions.

[1159] The server collects data on children's speech and actions in real time from devices they use (smartphones, tablets, computers, etc.). Specifically, it acquires data such as social media posts, chat messages, and voice input. This data is encrypted before being sent to the server.

[1160] The server passes the received data to an information processing unit, which analyzes the text data using natural language processing technology. The information processing unit then estimates the children's feelings and worries from their statements. For example, from a statement like "I don't want to go to school," it estimates worries related to school life.

[1161] Simultaneously, the device uses its camera and microphone to capture the children's facial expressions and voice tones. This data is sent to an emotion analysis device. The emotion analysis device recognizes emotions from the facial expressions and voice tones and identifies emotions such as joy, sadness, and anger.

[1162] The server integrates and analyzes data obtained from the information processing device and the emotion analysis device. This allows for a more accurate understanding of the children's feelings and concerns. Based on the integrated analysis results, users can provide appropriate support and guidance to the children.

[1163] For example, if a child posts on social media that they "had a fight with a friend," the device collects the post and sends it to the server. The server uses an information processing device to estimate the possibility that the child is having trouble with their friendships. At the same time, if the emotion analysis device recognizes anger from the tone of voice captured by the device, the server integrates this information and suggests that the friendship problems may be serious.

[1164] As an example of a prompt, the prompt "What kinds of problems might a child have if they say they don't want to go to school?" can be input into the AI ​​model, and the AI ​​can output the type of problem and its cause that it estimates.

[1165] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1166] Step 1:

[1167] The device collects data on speech and actions from the devices used by children in real time. Inputs include social media posts, chat messages, and voice input. This data is temporarily stored on the device, encrypted, and sent to the server. The output is the encrypted data sent to the server.

[1168] Step 2:

[1169] The server decrypts the encrypted data received from the terminal and passes it to the information processing device. The input is encrypted data sent from the terminal. The server decrypts the data and provides it to the information processing device in an appropriate format. The output is analyzable data passed to the information processing device.

[1170] Step 3:

[1171] The information processing device analyzes text data using natural language processing technology. The input is analyzable data provided by a server. The information processing device analyzes the content of the statements and estimates the feelings and worries of the children. For example, from the statement "I don't want to go to school," it estimates worries related to school life. The output is estimated data of feelings and worries as a result of the analysis.

[1172] Step 4:

[1173] The device uses a camera and microphone to capture the children's facial expressions and voice tones. The input is real-time video and audio data. The device transmits this data to an emotion analysis device. The output is video and audio data transmitted to the emotion analysis device.

[1174] Step 5:

[1175] The emotion analyzer recognizes emotions from facial expressions and tone of voice. The input is video and audio data transmitted from a terminal. The emotion analyzer identifies emotions such as joy, sadness, and anger. The output is the recognized emotion data.

[1176] Step 6:

[1177] The server integrates and analyzes data obtained from the information processing device and the emotion analysis device. The input consists of estimated data on feelings and worries, and recognized emotion data. The server integrates this data to gain a more accurate understanding of the children's feelings and worries. The output is the integrated analysis result.

[1178] Step 7:

[1179] The user provides appropriate support and guidance to the children based on the integrated analysis results provided by the server. The input is the integrated analysis results from the server. The user uses this as a reference to consider countermeasures for the children's problems. The output is the support and guidance provided to the children.

[1180] (Application Example 1)

[1181] Next, we will describe Application Example 1 of Form 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."

[1182] In children's school and daily lives, there is a need to identify early on any relationship or romantic problems they may be experiencing and provide appropriate support. However, children often compare themselves to others and are hesitant to express their true feelings due to concerns about what others think, making it difficult to understand their problems. Furthermore, it is difficult for parents and educators to understand children's feelings in real time and provide the necessary support. Effective means to solve these challenges are needed.

[1183] 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.

[1184] In this invention, the server includes means for collecting and analyzing children's statements and actions in real time, means for analyzing children's facial expressions and tone of voice to recognize their emotions, and means for sending notifications to parents or educators when an abnormality is detected. This makes it possible to accurately understand children's feelings and worries, and for parents and educators to intervene at the appropriate time.

[1185] "Methods for collecting and analyzing children's statements and actions in real time" refers to technologies that instantly acquire voice and motion data from devices used by children and analyze it to understand their feelings and circumstances.

[1186] "A means of analyzing children's facial expressions and tone of voice to recognize their emotions" refers to a technology that uses cameras and microphones to capture children's facial expressions and voices, and then uses that data to have an emotion engine estimate their emotional state.

[1187] The "means of sending notifications to parents and educators when an abnormality is detected" refers to a technology that uses AI to analyze children's data and quickly warn parents and educators when unusual emotions or behaviors are detected.

[1188] The system for implementing this invention has the function of understanding children's feelings and concerns in real time and sending notifications to parents and educators as needed. The system uses terminals such as smartphones and smart glasses to collect children's words and actions in real time. These terminals acquire audio and video data through microphones and cameras.

[1189] The server uses software such as Python, TensorFlow, and OpenCV to analyze the collected data. Audio data is converted to text using TensorFlow, and the content of the speech is analyzed using natural language processing. Video data is analyzed using OpenCV for facial expression analysis, and an emotion engine analyzes the tone of voice to estimate the emotional state of the children.

[1190] If an anomaly is detected, the server will promptly send a notification to parents or educators. This notification will include information about the children's feelings and circumstances, prompting appropriate action.

[1191] For example, if a child says, "I had a fight with a friend," and their expression is sad and their voice is low, the AI ​​will estimate that the child is having trouble with interpersonal relationships and will send a notification to the parent. An example of a prompt to input into the generating AI model would be, "What kind of feelings might a child have if they say they don't want to go to school?"

[1192] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1193] Step 1:

[1194] The device collects audio and video data of children in real time. It takes audio data from the microphone and video data from the camera as input. This data is sent to the server in an appropriate format for use in subsequent analysis steps.

[1195] Step 2:

[1196] The server converts received audio data into text. Using audio data sent from the terminal as input, it performs speech recognition using TensorFlow. The output is the audio data converted back into text data. This text data becomes input for natural language processing.

[1197] Step 3:

[1198] The server analyzes the text data using natural language processing. It uses the text data generated in step 2 as input and analyzes the content of the statements using a generative AI model. The output provides estimated results of the children's feelings and situations based on their statements.

[1199] Step 4:

[1200] The server analyzes video data to recognize facial expressions. It uses video data transmitted from the terminal as input and performs facial expression analysis using OpenCV. The output is an estimate of the children's emotional state based on their facial expressions.

[1201] Step 5:

[1202] The server analyzes the tone of voice to recognize emotions. It uses voice data transmitted from the terminal as input and analyzes the tone of voice using an emotion engine. The output is an estimated emotional state based on the tone of voice.

[1203] Step 6:

[1204] The server integrates the results from steps 3, 4, and 5 to detect anomalies. It uses estimated feelings and situations, emotional states based on facial expressions, and emotional states based on tone of voice as input. The output generates alert information if an anomaly is detected.

[1205] Step 7:

[1206] The server sends notifications to parents and educators if an anomaly is detected. It uses the alert information generated in step 6 as input. As output, it sends notifications to parents and educators regarding the children's feelings and circumstances.

[1207] (Example 2)

[1208] Next, we will describe Example 2 of the morphological example. 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."

[1209] Children often struggle to express their true feelings and harbor anxieties because they are overly concerned with comparing themselves to others and the opinions of those around them in school and daily life. This can lead to a decline in children's self-esteem and negatively impact their mental health. Therefore, it is crucial to accurately understand children's emotions and situations and provide appropriate encouragement and advice.

[1210] 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.

[1211] In this invention, the server includes means for analyzing children's speech and actions and estimating their emotional state, means for generating optimal encouragement and advice based on the estimated emotions, and means for presenting the generated messages to the children. This enables the provision of appropriate support that is sensitive to the children's emotions, improving their self-esteem and maintaining their mental health.

[1212] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[1213] "Relationships" refers to the state of human relationships and interactions that children build with others.

[1214] "Personal problems" refer to the mental or emotional issues or anxieties that children individually face.

[1215] "Natural language processing technology" refers to the technology used to analyze and understand human language using computers.

[1216] "Emotional analysis technology" refers to the technology that extracts and analyzes emotions from text and audio data.

[1217] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new text or information.

[1218] "Encouragement and advice" refers to words and guidance that provide emotional support to children.

[1219] "Means of presenting a message" refers to methods and devices for conveying a generated message to children.

[1220] This invention is a system for understanding children's emotions and situations and providing appropriate encouragement and advice. The system consists of three components: a server, a terminal, and a user.

[1221] The server uses natural language processing and sentiment analysis technologies to analyze children's statements and actions. Specifically, the server uses a common cloud-based language analysis API as its natural language processing technology to analyze children's statements as text data. The analyzed data is then used with sentiment analysis technology to estimate the children's emotional state. For this sentiment analysis, a general sentiment analysis engine is used.

[1222] The server uses a generative AI model to generate optimal encouragement and advice based on estimated emotions. The generative AI model uses pre-trained algorithms to produce messages that resonate with the children's emotions. These messages are customized according to the children's emotional state.

[1223] The terminal's role is to present messages sent from the server to the user. The terminal communicates the generated messages to the user through voice output and text display. This allows the user to receive feedback from the AI.

[1224] For example, if a user enters the prompt "My child seems to be losing confidence because they are comparing themselves to their friends at school. How can I encourage them?", the server will use this information and a generative AI model to generate a message such as "You are wonderful just the way you are. There's no need to compare yourself to others," and present it to the user through their device.

[1225] This system provides appropriate support that is sensitive to children's emotions, enabling them to improve their self-esteem and maintain their mental health.

[1226] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1227] Step 1:

[1228] Users input their children's statements and actions through their devices. The input data is sent to the server in either voice or text format. For example, if a user inputs "I didn't do well at school today," that text data is sent to the server.

[1229] Step 2:

[1230] The server analyzes the received text data using natural language processing techniques. Specifically, the server uses a language analysis API to extract the grammatical structure and keywords from the input text. This analysis allows the server to identify the subject and emotion of the statement. As a result of the analysis, the emotional nuances of the text are extracted.

[1231] Step 3:

[1232] Based on the analysis results, the server uses emotion analysis technology to estimate the children's emotional state. For example, from the phrase "it didn't go well," the server might determine that the child is feeling down. This estimation result is output as data indicating the emotional state.

[1233] Step 4:

[1234] The server uses a generative AI model to generate optimal encouragement and advice based on the estimated emotional state. The generative AI model uses a pre-trained algorithm to generate messages appropriate to the emotional state. For example, it might generate a message such as, "You're doing great. I'm sure you'll do better next time."

[1235] Step 5:

[1236] The server sends the generated message to the terminal. The terminal presents the generated message to the user through voice output or text display. This allows the user to receive feedback from the AI.

[1237] (Application Example 2)

[1238] Next, we will describe application example 2 of form 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."

[1239] Modern children often struggle to express their true feelings at school and at home because they are overly concerned with comparing themselves to others and the opinions of those around them. Furthermore, there is a lack of systems that can properly understand these anxieties and provide encouragement and advice. In this situation, there is a need to understand children's feelings in real time and provide appropriate feedback.

[1240] 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.

[1241] This invention includes a server that provides means for understanding the interpersonal relationships and personal concerns of children in their living environment, means for understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about how others perceive them, and means for analyzing children's statements and actions in real time to understand their feelings and provide appropriate feedback. This makes it possible to accurately grasp the feelings of children and provide them with appropriate encouragement and advice.

[1242] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[1243] "Relationships" refers to the social connections and interactions that children form with others.

[1244] "Personal worries" refer to the internal problems and anxieties that children individually face.

[1245] "Comparison" refers to the act of children comparing themselves to others and evaluating them in that way.

[1246] "Gaze" refers to the attention and interest that people around children direct towards them.

[1247] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[1248] "Real-time" refers to the immediate processing of children's statements and actions the moment they occur.

[1249] "Feedback" refers to the reactions and evaluations given in response to children's actions and statements.

[1250] "Encouragement" refers to words and actions used to cheer up and encourage children.

[1251] "Advice" refers to guidance and suggestions provided to children regarding problems they face.

[1252] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server uses artificial intelligence to analyze children's speech and actions in real time in order to understand their interpersonal relationships and personal concerns in their living environment. Specifically, the server uses speech recognition technology to convert children's speech into text data and analyzes their emotions and intentions through natural language processing. In this process, an emotion engine is used to more accurately estimate the children's feelings.

[1253] The devices are those that children use on a daily basis, such as smartphones and tablets. These devices receive feedback sent from a server and display appropriate encouragement and advice to the user. This allows children to receive real-time feedback tailored to their feelings.

[1254] The users are children who use the system. Through their devices, users receive feedback from the server, allowing them to receive encouragement and advice tailored to their feelings and circumstances. This enables users to improve their self-esteem and reduce anxiety caused by comparisons with others.

[1255] For example, if a user says, "Things didn't go well at school today," the server analyzes this statement and sends an encouraging message to the device such as, "Failure is the foundation of success. You'll do better next time." This helps the user regain their confidence.

[1256] Examples of prompts for a generative AI model include the following:

[1257] "Prompt: How would you encourage your child if they said, 'I didn't do well at school today'?"

[1258] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1259] Step 1:

[1260] The user inputs their speech through a terminal. The terminal converts the user's voice into text data using speech recognition technology. This text data is then input to the server.

[1261] Step 2:

[1262] The server analyzes the received text data using natural language processing techniques. Specifically, it uses an emotion engine to extract emotions and intentions from the text data. This process outputs data that estimates the user's feelings.

[1263] Step 3:

[1264] The server generates appropriate feedback using a generative AI model based on estimated emotional data. Prompt messages are input to the generative AI model, which generates encouragement and advice tailored to the user's emotional state. This feedback is then output from the server to the terminal.

[1265] Step 4:

[1266] The device displays feedback received from the server to the user. Specifically, it displays encouraging and advice messages on the screen for the user to see. This allows the user to receive appropriate feedback in real time.

[1267] (Example 3)

[1268] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1269] Modern children face a great deal of stress and anxiety in their school and home lives, but there is a lack of means to properly understand and support them. In particular, there is a need to understand children's emotions in real time and provide appropriate encouragement and advice accordingly.

[1270] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1271] In this invention, the server includes means for understanding the emotional state of children in their living environment, means for analyzing children's emotions in real time, and means for generating messages appropriate for children using a generative model. This makes it possible to provide appropriate support in real time according to the children's emotions.

[1272] "Living environment" refers to the places and situations in which children spend their days, encompassing the entire environment, including home and school.

[1273] "Emotional state" refers to the feelings and psychological state that children experience at a particular moment, and includes emotions such as joy, sadness, and anger.

[1274] "Real-time analysis" refers to a process where data is analyzed immediately upon collection, and results are obtained instantly.

[1275] A "generative model" refers to an algorithm or system that uses artificial intelligence technology to generate an appropriate output based on a specific input.

[1276] "Generating a message" refers to creating a message that is appropriate for children based on specific information and circumstances.

[1277] The following systems are conceivable as embodiments for carrying out this invention.

[1278] The user first collects data to understand the emotional state of children in their living environment. Specifically, sensors and microphones are used to acquire data on children's daily conversations and behaviors. This data serves as foundational information for analyzing children's emotions in real time.

[1279] The server inputs the collected data into an emotion engine, which uses natural language processing technology to analyze the children's emotions. The emotion engine identifies emotions such as anger, sadness, and happiness, and feeds the results back into a generative AI model.

[1280] The generative AI model generates prompt messages based on the results of sentiment analysis. For example, if the analysis indicates that a child is struggling with interpersonal relationships at school, it will generate a prompt message such as, "What kind of encouraging message should be generated if a child is struggling with interpersonal relationships at school?"

[1281] The server inputs prompt text into a generation AI model, which then generates encouraging and advice messages tailored to the children. These generated messages are delivered to the children via their devices. The devices, such as smartphones and tablets, display the messages, allowing the children to receive them.

[1282] This system allows children to receive appropriate support in real time, tailored to their emotions. An example of a prompt message is, "If a child is struggling with interpersonal relationships at school, what kind of encouraging message should be generated?" The specific processing flow in Example 3 is explained using Figure 21.

[1283] Step 1:

[1284] Users collect data to understand the emotional state of children in their living environment. Specifically, they use sensors and microphones to acquire data on children's daily conversations and behaviors. This data is transmitted to the server in voice and text format.

[1285] Step 2:

[1286] The server inputs the collected data into an emotion engine, which uses natural language processing technology to analyze the children's emotions in real time. The input audio and text data is analyzed by the emotion engine to identify the children's emotional states (e.g., joy, sadness, anger). This analysis result is used in the next step.

[1287] Step 3:

[1288] The server generates prompt messages to input into the generative AI model based on the results of the sentiment analysis. For example, if the analysis indicates that a child is struggling with interpersonal relationships at school, the server will generate a prompt message such as, "What kind of encouraging message should be generated if a child is struggling with interpersonal relationships at school?" This prompt message is then used as input to the generative AI model.

[1289] Step 4:

[1290] The server inputs prompt text into a generative AI model, which generates encouraging and advice messages tailored to the children. Based on the prompt text, the generative AI model utilizes past data and learned knowledge to create specific and effective messages. These messages are then delivered in the next step.

[1291] Step 5:

[1292] The device delivers the generated messages to the children. Specifically, it displays the messages via smartphones and tablets, allowing the children to receive them. This enables the children to receive encouragement and advice in real time.

[1293] (Application Example 3)

[1294] Next, we will describe application example 3 of form example 3. 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."

[1295] Modern children face numerous challenges in school and daily life, often comparing themselves to others or feeling unable to express their true feelings due to concerns about what others think. Similarly, workers in factories and other workplaces may experience emotional stress, negatively impacting work efficiency and the overall work environment. To address these issues, a system is needed that provides appropriate encouragement and advice tailored to individual emotions and circumstances.

[1296] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1297] This invention includes a server that provides means for understanding children's worries about relationships and romance in their school and daily lives, understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about what others think, providing encouragement and advice to help children build confidence, recognizing workers' emotions in real time and providing encouragement and advice according to the progress and situation of their work, analyzing workers' facial expressions and tone of voice, and generating appropriate encouragement and advice based on the analyzed emotional data. This makes it possible to provide individualized and appropriate responses to the emotional problems faced by children and workers.

[1298] "Children" refers to young people who are in the process of growing up in their school and daily lives.

[1299] "School life" refers to the daily life of children as they engage in learning and activities at educational institutions.

[1300] "Daily life" refers to all aspects of the daily life that children engage in at home and in their community.

[1301] "Relationships" refers to the social connections and interactions that children form with others.

[1302] "Romantic love" refers to the affection and intimacy that children feel towards others.

[1303] "Worries" refers to the mental burdens and problems that children face.

[1304] "Comparison" refers to the act of children comparing themselves to others and evaluating them in that way.

[1305] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[1306] "Confidence" refers to a child's belief in their own abilities and worth.

[1307] "Workers" refers to people who perform tasks in factories or work sites.

[1308] "Emotions" refers to the mental processes such as joy, anger, sadness, and happiness that children and workers experience.

[1309] "Real-time" refers to processing events that are currently unfolding immediately.

[1310] "Progress" refers to the degree or status of work or a plan's progress.

[1311] "Facial expressions" refer to the expressions that show emotions and feelings on the faces of children and workers.

[1312] "Voice tone" refers to the pitch and intensity of the sounds produced by children or workers.

[1313] "Analysis" refers to the act of thoroughly analyzing data and information to derive meaning from it.

[1314] "Data" refers to information about children and workers expressed using numbers and symbols.

[1315] "Generation" refers to the act of AI creating new encouragement or advice.

[1316] The system for realizing this invention involves a server and terminals working together. The server plays a central role in recognizing the emotions of children and workers in real time and generating appropriate encouragement and advice. Specifically, the server uses an emotion recognition engine to analyze facial expression data and voice tone transmitted from the terminals. The analyzed data is input into a generative AI model, which generates encouragement and advice tailored to each individual's situation.

[1317] The device uses a camera and microphone to collect facial expressions and voices of children and workers. This data is sent to a server and processed in real time. The device then provides users with generated encouragement and advice.

[1318] For example, if the server determines that a worker is tired, it will generate a message such as, "Take a short break and refresh yourself." This message will then be transmitted to the worker via their terminal.

[1319] Examples of prompt messages include the following:

[1320] Worker's facial expression data: tired face

[1321] Worker's voice tone: Low

[1322] Input to the AI ​​model: "The worker has been determined to be tired. Please generate an appropriate encouraging message."

[1323] In this way, the server and terminals can cooperate to provide individualized and appropriate responses to children and workers.

[1324] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1325] Step 1:

[1326] The device uses a camera and microphone to collect data on the user's facial expressions and voice tone. This data serves as input for understanding the user's emotions in real time.

[1327] Step 2:

[1328] The device sends collected facial expression data and voice tone to the server. The server receives this data and analyzes it using an emotion recognition engine. The analysis results in the user's emotional state being output.

[1329] Step 3:

[1330] The server inputs prompt messages into the generative AI model based on the analyzed emotional state. These prompt messages include instructions for generating encouragement and advice tailored to the user's emotions.

[1331] Step 4:

[1332] The generative AI model receives a prompt and generates encouragement and advice appropriate to the user's emotions. This generated message is then output.

[1333] Step 5:

[1334] The server sends the generated encouragement and advice to the terminal. The terminal displays or audibly communicates this message to the user. This allows the user to receive appropriate encouragement and advice.

[1335] (Other examples)

[1336] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[1337] 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.

[1338] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[1339] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1340] 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.

[1341] [Fourth Embodiment]

[1342] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1343] 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.

[1344] 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).

[1345] 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.

[1346] 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.

[1347] 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).

[1348] 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.

[1349] 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.

[1350] 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.

[1351] 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.

[1352] 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.

[1353] 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.

[1354] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1355] "Example of form 1"

[1356] In one embodiment of the present invention, an AI is used to analyze children's words and actions as a means of understanding their worries about relationships and romantic relationships in their school and daily lives. This AI estimates the feelings and worries of children from their words and actions. For example, if a child says, "I don't want to go to school," the AI ​​estimates from that statement that the child may be having some kind of worry about school life.

[1357] "Example of form 2"

[1358] Furthermore, as a means of understanding situations where children compare themselves to others or are unable to express their true feelings due to concerns about what others think, the AI ​​analyzes children's words and actions to estimate their feelings and circumstances. For example, if a child says, "I'm no good compared to other kids," the AI ​​will estimate from that statement that the child may be struggling with self-esteem.

[1359] "Example of form 3"

[1360] Furthermore, as a means of providing encouragement and advice to children, the AI ​​generates optimal encouragement and advice according to the children's feelings and circumstances. For example, if the AI ​​estimates that a child is struggling with school life, it will generate an encouraging message for that child such as, "School life is tough, but I know you can overcome it."

[1361] The following describes the processing flow for each example of the form.

[1362] "Example of form 1"

[1363] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[1364] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate the children's feelings and concerns.

[1365] Step 3: The AI ​​generates encouragement and advice for the children based on their estimated feelings and concerns. This is done based on pre-set rules and learning models.

[1366] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[1367] "Example of form 2"

[1368] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[1369] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate situations where children are comparing themselves to others or are unable to express their true feelings due to concerns about what others think.

[1370] Step 3: The AI ​​generates encouragement and advice for the children based on the estimated situation. This is done based on pre-set rules and learning models.

[1371] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[1372] "Example of form 3"

[1373] Step 1: The AI ​​collects children's statements and actions in real time. This is done through input from the devices and platforms the children use.

[1374] Step 2: The collected statements and actions are analyzed by AI. The AI ​​uses natural language processing and machine learning techniques to estimate the children's feelings and situations.

[1375] Step 3: The AI ​​generates encouragement and advice for the children based on their estimated feelings and circumstances. This is done based on pre-set rules and learning models.

[1376] Step 4: The generated encouragement and advice are delivered to the children. This is done through the devices and platforms the children use.

[1377] (Example 1)

[1378] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1379] In today's educational environment, children often face interpersonal and emotional problems, and it is crucial to identify these issues early and provide appropriate support. However, accurately inferring children's feelings and problems from their words and actions is difficult, resulting in a lack of information for educators and support staff to respond appropriately.

[1380] 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.

[1381] In this invention, the server includes means for understanding interpersonal relationships and emotional problems in children's educational and daily environments, means for analyzing children's statements and actions to estimate their emotions and intentions, and means for providing the analysis results to educators and support staff. This makes it possible to grasp children's feelings and problems early and provide appropriate support and guidance.

[1382] The term "educational environment" refers to the physical and social environment in which children learn and grow, and includes schools, homes, and other similar settings.

[1383] "Everyday environment" refers to the environment that children encounter in their daily lives, including their home and community.

[1384] "Interpersonal relationships" refers to the human relationships that children form with others, including friendships and relationships with teachers.

[1385] "Emotional problems" refer to emotionally related issues that children experience, including anxiety and stress.

[1386] "Means of analyzing speech and actions" refers to techniques and methods for analyzing children's words and actions and inferring the emotions and intentions behind them.

[1387] "Means of inferring emotions and intentions" refers to techniques and methods for inferring children's feelings and intentions from their words and actions.

[1388] "Means of providing analysis results" refers to the technologies and methods for communicating the analyzed information to educators and support staff.

[1389] "Educators and supporters" refers to people who have a role in supporting children's learning and growth, and includes teachers, counselors, and others.

[1390] The following system is constructed as an embodiment of this invention.

[1391] The server generates a program to analyze the children's statements and actions. This program is designed to analyze the children's statements and actions using natural language processing techniques. Specifically, the server uses a cloud-based natural language processing API to analyze the text data and extract emotions and intentions. This API is provided by a common cloud service provider.

[1392] The devices are installed in classrooms and homes to collect children's speech as audio data. This audio data is sent to a server and converted into text data. The server analyzes the converted text data to estimate the children's feelings and problems.

[1393] Educators and support staff can view the analysis results provided by the server through a dedicated dashboard. This dashboard displays a list of children's emotional states and estimated problems. This allows users to provide appropriate support and guidance to the children.

[1394] For example, if a child says, "I don't want to go to school," the server analyzes this statement and estimates their anxiety and stress regarding school life. Users can then view this information on a dashboard and consider appropriate responses for their child.

[1395] An example of a prompt is, "If a child says they don't want to go to school, estimate the reason." Using this prompt, the server estimates the child's underlying concerns from their statements and generates information to provide appropriate support.

[1396] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1397] Step 1:

[1398] The devices are installed in classrooms and homes to collect children's speech as audio data. Specifically, they use microphones to record conversations and send that audio data to a server. The input is audio data, and the output is an audio file sent to the server.

[1399] Step 2:

[1400] The server converts the received audio data into text data. Specifically, it uses speech recognition software to convert the audio data into a string of characters. This process outputs the audio data as text data. The input is an audio file, and the output is text data.

[1401] Step 3:

[1402] The server analyzes text data using natural language processing techniques. Specifically, it uses a cloud-based natural language processing API to extract emotions and intentions from the text. This process yields information about emotions and intentions from the text data. The input is text data, and the output is the analysis results, including emotions and intentions.

[1403] Step 4:

[1404] The server estimates the children's feelings and problems based on the analysis results. Using a generative AI model, it identifies potential problems underlying their statements. This process reveals the children's underlying anxieties. The input is the analysis results, and the output is the estimated feelings and problems.

[1405] Step 5:

[1406] The server provides educators and support staff with estimated feelings and problems. Specifically, it displays the information through a dedicated dashboard. This dashboard lists each child's emotional state and estimated problems. The input is the estimated feelings and problems, and the output is the information displayed on the dashboard.

[1407] Step 6:

[1408] Educators and support staff, who are the users, can check the children's status through the dashboard and provide appropriate support and guidance. Specifically, they consider countermeasures for the children based on the information on the dashboard. The input is the information on the dashboard, and the output is the countermeasures taken by the educators and support staff.

[1409] (Application Example 1)

[1410] Next, we will describe Application Example 1 of Form 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".

[1411] In children's school and daily lives, there is a challenge in understanding the relationship and romantic problems they face, comparing themselves to others, and understanding situations where they cannot express their true feelings due to concern about what others think. Furthermore, there is a need to provide appropriate encouragement and advice tailored to the children's feelings and circumstances. In addition, it is necessary to promptly inform parents and educators of this information to enable appropriate intervention.

[1412] 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.

[1413] In this invention, the server includes means for analyzing children's words and actions in real time and notifying parents and educators, means for understanding children's concerns, and means for providing encouragement and advice to children. This makes it possible to quickly understand children's feelings and concerns and provide appropriate support.

[1414] "Methods for analyzing children's speech and actions in real time" refers to technologies that instantly acquire children's voices and movements as data and analyze their content using information processing equipment.

[1415] "Means of notifying parents and educators" refers to communication technologies that, based on analyzed information, quickly transmit important information about children's feelings and circumstances to parents and educators.

[1416] "Means for understanding children's worries" refers to information processing technology that identifies and understands the problems and feelings children are experiencing through their words and actions.

[1417] "Means of providing encouragement and advice" refers to information processing technology that generates and provides appropriate encouragement and advice according to the feelings and circumstances of children.

[1418] As a form of implementing the invention, this system has the function of analyzing children's speech and actions in real time and notifying parents and educators. The server converts children's speech into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text) and analyzes their actions using an image recognition API (e.g., Google Cloud Vision). This obtains data to estimate the children's feelings and concerns.

[1419] The server uses natural language processing libraries (e.g., NLTK, spaCy) to perform sentiment analysis on text data. Based on these analysis results, a generative AI model estimates the children's feelings and concerns and generates appropriate encouragement and advice. The generated information is sent to parents and educators via push notifications using communication technology.

[1420] For example, if a child says "I don't want to go to school," the server uses a speech recognition API to convert this statement into text, and a natural language processing library analyzes the negative emotion expressed by "I don't want to go." The generative AI model estimates that the child has problems related to school life and sends a notification to the parent saying, "Your child is not wanting to go to school. There may be a problem."

[1421] An example of a prompt question would be, "What kinds of feelings or worries might a child have if they say, 'I don't want to go to school'?"

[1422] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1423] Step 1:

[1424] The device captures the children's speech as audio data via a microphone. The captured audio data is then sent to a server.

[1425] Step 2:

[1426] The server uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the received audio data into text data. This process transforms the audio data into a format that can be parsed as text information.

[1427] Step 3:

[1428] The server performs sentiment analysis on text data using natural language processing libraries (e.g., NLTK, spaCy). It analyzes emotions and intentions from the input text data and generates data to estimate children's feelings and concerns.

[1429] Step 4:

[1430] The server uses a generative AI model to estimate children's feelings and worries based on the results of emotion analysis. Based on the estimation results, it generates appropriate encouragement and advice.

[1431] Step 5:

[1432] The server uses communication technology to send estimated results and generated encouragement and advice to parents and educators via push notifications. This allows parents and educators to understand their children's situation and provide the necessary support.

[1433] (Example 2)

[1434] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1435] Children often struggle to express their true feelings at school and in their daily lives because they are overly concerned with comparing themselves to others and the opinions of those around them. In such situations, it is difficult to accurately understand children's feelings and provide appropriate encouragement and advice. Furthermore, it is not easy to infer the emotions and circumstances behind children's words and actions.

[1436] 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.

[1437] In this invention, the server includes means for analyzing children's statements and actions and estimating the emotions and circumstances behind them; means for estimating the children's feelings based on the analyzed data and providing appropriate support information; and means for estimating emotions using a generative AI model. This makes it possible to accurately understand the children's feelings and provide appropriate encouragement and advice tailored to their individual circumstances.

[1438] "Living environment" refers to the physical and social environment in which children spend their days, such as school and home.

[1439] "Relationships" refers to the social connections and interactions that children build with others.

[1440] "Personal worries" refer to the internal problems and anxieties that children individually face.

[1441] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[1442] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new information or predictions from data.

[1443] "Inferring emotions" refers to inferring the emotions behind children's words and actions.

[1444] "Support information" refers to information such as encouragement and advice provided according to the feelings and circumstances of the children.

[1445] One embodiment of this invention is to provide a system that analyzes children's words and actions and estimates the emotions and circumstances behind them. A specific embodiment is shown below.

[1446] The server collects data on children's statements and actions. This data is obtained through the terminal as voice input or text input. For example, if a child says to the terminal, "I'm no good compared to other children," that voice data is sent to the server.

[1447] The server uses speech recognition software to convert the collected audio data into text data. Specifically, it can utilize APIs commonly used for speech recognition technology.

[1448] Next, the server analyzes the converted text data using natural language processing techniques. This analysis utilizes a generative AI model, which includes algorithms for understanding the context of natural language and estimating sentiment.

[1449] Based on the analysis, the server estimates the children's feelings and circumstances and generates appropriate support information. This support information is provided to the children as encouragement and advice.

[1450] As a concrete example, a user can input the following prompt message into the AI ​​model:

[1451] "What emotions might be at play when a child says, 'I'm no good compared to other kids'?"

[1452] "What kind of support do children need when they are worried about what others think of them?"

[1453] This allows users to gain a deeper understanding of children's feelings and obtain clues to provide appropriate support.

[1454] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1455] Step 1:

[1456] The server receives audio data from the terminal regarding the children's speech and actions. The user's spoken words into the terminal are input as audio data. This audio data is then sent to the server.

[1457] Step 2:

[1458] The server uses speech recognition software to convert the received audio data into text data. Specifically, it analyzes the audio data using a speech recognition API and outputs it as text data. This conversion transforms the audio information into text information.

[1459] Step 3:

[1460] The server analyzes the converted text data using natural language processing techniques. A generative AI model is used to understand the context of the text data and estimate emotions. This analysis outputs data that allows for the estimation of children's emotions and situations from the text data.

[1461] Step 4:

[1462] The server estimates the children's feelings and circumstances based on the analysis results. Using the output of the generative AI model, it estimates the emotions and worries the children may be experiencing. These estimation results serve as the basic data for generating appropriate support information for the children.

[1463] Step 5:

[1464] The server generates support information for the children based on the estimation results. Using a generation AI model, it generates encouragement and advice tailored to the children's feelings and circumstances. This support information is provided to the user through the terminal.

[1465] Step 6:

[1466] Users receive support information through their devices and provide appropriate support to the children. Based on this information, users can understand the children's feelings and offer necessary advice and encouragement.

[1467] (Application Example 2)

[1468] Next, we will describe application example 2 of form 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".

[1469] Modern children often struggle with self-esteem and are unable to express their true feelings due to excessive comparison with others and concern for how they are perceived in school and daily life. This situation can negatively impact children's mental health. However, there is a lack of means to identify these feelings and situations early and provide appropriate support. Therefore, there is a need for a system that analyzes children's words and actions, estimates their feelings, identifies their problems early, and provides appropriate encouragement and advice.

[1470] 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.

[1471] In this invention, the server includes means for transcribing children's speech into text using speech recognition technology and analyzing it using natural language processing technology, means for estimating the children's feelings using a generative AI model and notifying them of the results, and means for generating optimal encouragement and advice according to the children's feelings and circumstances. This makes it possible to accurately understand the children's feelings and provide appropriate support.

[1472] "School life" refers to the daily activities and experiences that children have while at school.

[1473] "Daily life" refers to the activities and experiences that children engage in on a daily basis at home and in their community.

[1474] "Human relationships" refers to the reciprocal relationships that children build with others.

[1475] "Romantic love" refers to the affection and feelings that children have for others.

[1476] "Worries" refer to the mental burdens and problems that children face.

[1477] "Comparison" refers to the act of children comparing themselves to others.

[1478] "True feelings" refer to the genuine emotions and thoughts that children feel in their hearts.

[1479] "Speech recognition technology" refers to the technology that converts speech into text data.

[1480] "Natural language processing technology" refers to the techn...

Claims

[Claim 1] Equipped with a processor and database, The aforementioned processor, It provides an interface for receiving input information from the user. The input information received through the interface is analyzed using natural language processing technology to estimate the user's psychological state in which they compare themselves to others, or their psychological state in which they are unable to express their true feelings due to concerns about the opinions of those around them. Based on the user's communication application or information retrieval tool identifier extracted from the input information, the user's past address history or related personal information is obtained from the database, thereby supplementing the input information with the information necessary for generating prompt sentences. The data is formatted by inserting the necessary information into the template using the completed input information. The user's identity is authenticated using the OAuth 2.0 protocol, and, conditional on successful authentication, a prompt message is generated that includes specific instructions for creating a change of address notice and instructions for generating encouragement corresponding to the estimated psychological state, and this message is input to the generation AI model. system.

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