system

Abstract

We provide the system. [Solution] An input method for entering basic information and characteristic information of a child, A communication means that receives data transmitted from the input means and passes the data to the analysis platform, An analytical means for identifying the type of child using an analytical platform, A generation means that generates suggestions for appropriate approaches and verbal communication based on the type of child identified by the analysis means, A communication means for transmitting the generated proposal to an output means, A system including a display means that displays information to the user via an output means.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the number of children with developmental disabilities is increasing, and the childcare burden on each family is also increasing. Since developmental disabilities are different for each individual, the optimal childcare methods and approaches vary widely, and there is a problem that it is difficult for parents and educators to respond effectively. In addition, the opportunity to receive appropriate professional support is limited, and the lack of knowledge about specific approaches in daily life is a serious problem for many families. There is a need for a system that solves these problems and effectively supports the childcare of children with developmental disabilities.

Means for Solving the Problems

[0005] The present invention provides a system that reduces the burden of raising children with developmental disabilities and provides individualized support, comprising: input means for inputting basic information and characteristic information of a child; communication means for receiving data transmitted from the input means and passing the data to an analysis platform; analysis means for identifying the child's type using the analysis platform; generation means for generating suggestions for appropriate approaches and communication based on the child's type identified by the analysis means; communication means for transmitting the generated suggestions to an output means; and display means for displaying the suggestions to the user on the output means. Furthermore, by including means for collecting user feedback, transmitting the collected feedback to a server using the communication means, and having the server optimize the suggestions using the feedback, the system continuously improves the effectiveness of the suggestions and provides more realistic support. In addition, by including analysis means using an AI model that identifies the type best suited to the child's characteristics, even ordinary parents without advanced expertise can implement appropriate parenting methods.

[0006] "Input method" refers to the interface that allows users to input basic and characteristic information about their child into the application.

[0007] "Communication means" refers to a device or software that has the function of transmitting input data to a server via a network and receiving data from the server.

[0008] "Analysis infrastructure" refers to the hardware and software infrastructure used to perform advanced analysis based on received data.

[0009] "Analysis means" refers to a device or software that has the function of analyzing input data, classifying children's characteristics based on specific patterns or types, and providing results derived based on that information.

[0010] "Generation means" refers to a device or software that has the function of automatically generating appropriate approaches and suggestions for communication to the user based on the analysis results.

[0011] "Display means" refers to an interface for visually presenting the generated proposals to the user.

[0012] A "feedback collection method" refers to an interface for users to input feedback about the approaches they have taken and the results they have achieved.

[0013] "Optimization means" refers to a device or software that has the function of automatically improving the content of future suggestions based on the collected feedback.

[0014] An "AI model" refers to a mathematical model that uses machine learning algorithms to recognize specific patterns or types based on large amounts of data, and provides analytical results based on new data. [Brief explanation of the drawing]

[0015] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] The present invention is a system for effectively supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, and optimization means.

[0037] Overall system configuration

[0038] This system consists of the following main elements:

[0039] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[0040] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[0041] 3. Analysis method: Installed on the server, it analyzes the input data to identify the type based on the child's characteristics.

[0042] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[0043] 5. Display means: An interface for displaying the generated suggestions to the user.

[0044] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[0045] Program processing

[0046] The program for this system operates as follows:

[0047] 1. Enter the child's information

[0048] The user opens the application and enters the child's name, age, gender, diagnosis information for developmental disorders, and characteristics (e.g., anxiety in social situations, inattention, etc.).

[0049] The user reviews the input and presses the "Submit" button.

[0050] 2. Sending data

[0051] The terminal sends the entered information to the server via the internet.

[0052] 3. Analysis of the child's personality type

[0053] The server passes the received data to the analysis platform, which uses a database and AI models to identify the child's type based on their characteristics. For example, ASD (Autism Spectrum Disorder) or ADHD (Attention Deficit Hyperactivity Disorder).

[0054] 4. Generating an appropriate approach

[0055] Based on the identified type, the server automatically generates specific approaches and verbal suggestions using existing knowledge and AI models in the database. For example, for a child with ASD, it might suggest "praising specific behaviors" and "giving short, clear instructions."

[0056] 5. Submitting a proposal

[0057] The server sends the generated proposal to the terminal.

[0058] 6. Display of Proposal

[0059] The device analyzes the received suggestions and displays them in the user interface. Users can review the suggestions and try out the approaches in their daily lives.

[0060] 7. Gathering Feedback

[0061] Users provide feedback on the approaches they actually took and the results. For example, they might write, "When I praised a specific action, the child responded well."

[0062] 8. Submit feedback

[0063] The device sends feedback data to the server.

[0064] 9. Optimizing the proposal

[0065] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[0066] Specific example

[0067] Example 1: A child with autism spectrum disorder

[0068] The user enters the child's information: "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[0069] The device sends this information to the server.

[0070] The server analyzes the data and identifies the child's type as "ASD".

[0071] The server generates suggestions such as "give instructions in short, clear language" and "praise specific actions."

[0072] The device displays the suggestion in the user interface.

[0073] Users try out the suggestions in their daily lives and provide feedback on the results.

[0074] The server uses feedback to update the AI ​​model and optimize future suggestions.

[0075] In this way, this system provides individualized support for children with developmental disabilities and reduces the burden of childcare.

[0076] *The processing steps will be explained in detail upon request.

[0077] The following describes the processing flow.

[0078] Step 1:

[0079] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: for example, social anxiety, inattention, etc.).

[0080] Step 2:

[0081] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[0082] Step 3:

[0083] The terminal organizes the confirmed data and generates an HTTP request to send to the server.

[0084] Step 4:

[0085] The device sends the HTTP request it generates to the server via the internet.

[0086] Step 5:

[0087] The server saves the received data to the database.

[0088] Step 6:

[0089] The server passes the received data to the analysis platform, which uses a database and AI models to analyze the data and identify types based on the child's characteristics.

[0090] Step 7:

[0091] The server processes the analysis results to automatically generate appropriate approaches and suggestions for communication. This is done using a database and AI models within the server.

[0092] Step 8:

[0093] The server converts the generated proposal into a data format such as JSON and generates an HTTP response to send to the client.

[0094] Step 9:

[0095] The server sends the generated HTTP response to the terminal via the internet.

[0096] Step 10:

[0097] The terminal analyzes the suggestion data received from the server and formats it into a human-readable format.

[0098] Step 11:

[0099] The terminal displays the analysis results in the application's user interface. For example, it may be presented as a short text or a chart.

[0100] Step 12:

[0101] Users view the displayed suggestions and try to implement specific approaches in their daily lives.

[0102] Step 13:

[0103] The application receives feedback on the approaches taken by the user and the results they achieved.

[0104] Step 14:

[0105] The user reviews the input and confirms the feedback by pressing the "Submit" button.

[0106] Step 15:

[0107] The system organizes the feedback data from the identified terminal and generates an HTTP request to send it to the server.

[0108] Step 16:

[0109] The device sends the HTTP request it generates to the server via the internet.

[0110] Step 17:

[0111] The server saves the feedback data it receives to the database.

[0112] Step 18:

[0113] The server analyzes the feedback data and uses an AI model to perform update processing to optimize future suggestions.

[0114] This allows the system to continuously learn from user feedback and provide more effective suggestions.

[0115] (Example 1)

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

[0117] Raising children with developmental disabilities requires appropriate support methods and communication approaches based on individual characteristics, but finding specific methods is considered difficult. Furthermore, there is a lack of mechanisms to properly collect user feedback and incorporate it into future suggestions, which is necessary for continuously optimizing effective support methods. A system is needed to address these problems.

[0118] 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.

[0119] In this invention, the server includes an input means for inputting basic information and characteristic information of a child; a communication means for receiving data transmitted from the input means and passing the data to an analysis platform; an analysis means for identifying the child's type using the analysis platform; a generation means for generating appropriate approaches and suggestions for communication based on the child's type identified by the analysis means; a communication means for transmitting the generated suggestions to an output means; a display means for displaying the suggestions to the user on the output means; a feedback input means for allowing the user to confirm the generated suggestions, take actions based on the suggestions, and input the results as feedback; and an optimization means for receiving the feedback data and optimizing the next suggestions. This enables individualized support for children with developmental disabilities, reduces the burden of childcare, and allows for continuous optimization of support methods.

[0120] "Basic information about a child" refers to basic personal information such as the child's name, age, gender, and information about any diagnosed developmental disorders.

[0121] "Characteristic information" refers to information that includes a child's specific behaviors, personality, and problematic characteristics (e.g., anxiety or inattention in social situations).

[0122] "Input method" refers to an interface for users to input basic information and characteristic information about their children, and includes applications for smartphones and tablets.

[0123] A "communication means" is a network interface used to transmit and receive input data to and from other devices and systems.

[0124] An "analysis platform" is a set of systems that includes a database and AI models for analyzing received data.

[0125] "Analysis means" refers to the process and software used to identify a child's type using an analysis platform.

[0126] "Generation means" refers to software and processes for automatically generating appropriate approaches and suggestions for communication based on analysis results.

[0127] "Output means" refers to a display or other display device for providing the generated proposal to the user.

[0128] "Display means" refers to an interface for displaying proposals on a user interface, and includes the screens of smartphones and tablets.

[0129] A "feedback input method" is an interface that allows users to take action based on a suggestion and input the results as feedback.

[0130] "Optimization means" refers to software and processes for analyzing feedback data and optimizing the next proposal.

[0131] An "AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and perform predictions and classifications.

[0132] This invention is a system for effectively supporting the upbringing of children with developmental disabilities. This system is a combination of hardware and software and includes the following main elements:

[0133] Overall system configuration

[0134] 1. Input method

[0135] This is an interface for users to input basic and characteristic information about their children. Specifically, it refers to the form input fields in applications installed on smartphones and tablets. For example, a user opens the application and enters the child's name, age, gender, diagnosis information for developmental disorders, and characteristics.

[0136] 2. Means of communication

[0137] This is an interface for a terminal to send data entered into it to a server via the internet. The data is sent in a standard data format such as JSON.

[0138] 3. Analysis method

[0139] The server passes the received data to the analysis platform, which uses a database and a generative AI model to identify the child's type based on their characteristics. This analysis platform utilizes existing diagnostic criteria and characteristic classification information for developmental disorders stored in the database.

[0140] 4. Generation means

[0141] Based on the identified child type, the server automatically generates appropriate approaches and conversation suggestions using a generative AI model. This generation method utilizes existing knowledge in the database and predictions from the AI ​​model to create specific suggestions.

[0142] 5. Output means and display means

[0143] The server sends the generated suggestions to the terminal, which then displays them on the user interface. This display method can be a smartphone or tablet screen. The user can then review the displayed suggestions and try them out in their daily life.

[0144] 6. Feedback Input Methods

[0145] This is an interface for users to take action based on suggestions and input the results as feedback. The feedback is implemented as form input to record the results of specific actions and the child's reactions.

[0146] 7. Optimization methods

[0147] The server analyzes the received feedback data and updates the generated AI model to optimize the next proposal. This improves the accuracy of the proposals and continuously provides more effective support.

[0148] Specific example

[0149] The following is a concrete example of the system.

[0150] Example 1: A child with autism spectrum disorder

[0151] The user enters the child's information: "Name: Taro, Age: 6 years old, Gender: Male, Diagnosis: ASD, Characteristics: Social anxiety, Language delay".

[0152] The device sends this information to the server. The server analyzes the data and identifies the child's type as "ASD". The server generates suggestions such as "give instructions in short, clear language" and "praise specific behaviors".

[0153] The device displays suggestions in the user interface. The user tries out the suggestions in their daily life and provides feedback on the results. The server uses the feedback to update the generating AI model and optimize suggestions for future use.

[0154] Example of a prompt

[0155] The following are prompt statements as concrete examples for a generative AI model.

[0156] This system suggests appropriate approaches based on your child's characteristics and diagnosis. Please enter the following information.

[0157] 1. Name:

[0158] 2. Age:

[0159] 3. Gender:

[0160] 4. Diagnostic information for developmental disorders (e.g., ASD, ADHD):

[0161] 5. Characteristics (e.g., social anxiety, inattentiveness, language delay):

[0162] Based on the input information, we will propose a specific approach.

[0163] for example:

[0164] Name: Taro, Age: 6 years old, Gender: Male, Diagnosis: ASD, Characteristics: Social anxiety, language delay

[0165] In this way, the present invention provides individualized support for children with developmental disabilities and reduces the burden of childcare.

[0166] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0167] System program processing flow

[0168] Step 1:

[0169] The user opens the application and enters the child's information (name, age, gender, diagnosis of developmental disability, and characteristics).

[0170] Input: Child's name, age, gender, diagnosis information and characteristics of developmental disorders

[0171] Data processing or calculation: The data is formatted when the user enters information into the input fields and presses the "Submit" button.

[0172] Output: JSON data of the input information

[0173] Step 2:

[0174] The terminal converts the entered information into JSON format and sends it to the server via the internet.

[0175] Input: JSON data generated in Step 1

[0176] Data processing or calculation: Format the data as JSON and generate an HTTP request.

[0177] Output: Formatted JSON data is sent to the server.

[0178] Step 3:

[0179] The server passes the received data to the analysis platform, which uses a database and a generative AI model to identify the child's type based on their characteristics.

[0180] Input: JSON data sent from the device

[0181] Data processing or computation: Data analysis using database lookups and generative AI models (e.g., classification of ASD and its characteristics)

[0182] Output: Type identification results based on child characteristics

[0183] Step 4:

[0184] The server uses a generation mechanism to generate appropriate approaches and suggestions for communication based on the type of child it identifies.

[0185] Input: Information on the trait type obtained in Step 3

[0186] Data processing or computation: Generate specific suggestions from existing knowledge in the database or from generative AI models.

[0187] Output: A list of generated suggestions (e.g., give instructions in short, clear language, praise specific actions)

[0188] Step 5:

[0189] The server sends the generated proposal to the terminal.

[0190] Input: List of suggestions generated in Step 4

[0191] Data processing or calculation: Convert the proposed data to JSON format and send it as an HTTP request.

[0192] Output: Proposal data is sent to the terminal.

[0193] Step 6:

[0194] The terminal analyzes the received suggestions and displays them in the user interface.

[0195] Input: List of suggestion data received from the server

[0196] Data processing or calculation: Parsing JSON data and converting it to a display format for the user interface.

[0197] Output: The user will see a suggestion.

[0198] Step 7:

[0199] Users take action based on the suggestions and input the results as feedback.

[0200] Input: Results of actions taken based on the suggestion (e.g., the child responded well when instructions were given in short, clear language).

[0201] Data processing or calculation: Enter feedback data into the input form and submit.

[0202] Output: Feedback data in JSON format

[0203] Step 8:

[0204] The device sends feedback data to the server.

[0205] Input: Feedback data entered in Step 7

[0206] Data processing or calculation: Convert feedback data to JSON format and generate an HTTP request.

[0207] Output: Feedback data is sent to the server.

[0208] Step 9:

[0209] The server analyzes the feedback data, updates the generating AI model, and optimizes the next proposal.

[0210] Input: Feedback data sent from the device

[0211] Data processing or computation: Training and updating generative AI models using feedback data.

[0212] Output: Optimized proposed model and proposed data to be used in subsequent iterations.

[0213] The above describes the processing flow of the system program.

[0214] (Application Example 1)

[0215] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0216] In modern society, it is extremely difficult for parents of children with developmental disabilities to provide appropriate support for their children in daily life and while shopping. In particular, in crowded stores, quick responses tailored to the child's characteristics are required, but conventional technology cannot monitor a child's behavior in real time and provide suggestions at the appropriate time. As a result, the burden on parents increases, and there is a problem that children do not receive the appropriate support they need.

[0217] 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.

[0218] In this invention, the server includes input means for inputting basic information and characteristic information of a child; communication means for receiving data transmitted from the input means and passing the data to an analysis platform; analysis means for identifying the child's type using the analysis platform; generation means for generating appropriate approaches and suggestions for communication based on the child's type identified by the analysis means; communication means for transmitting the generated suggestions to an output means; display means for displaying them to the user on the output means; monitoring means for monitoring the child's behavior in real time within the store and providing behavioral suggestions at the appropriate time; and notification means for providing suggestion notifications according to the situation. As a result, parents can receive appropriate support in real time within the store, enabling effective support for children.

[0219] The "input method" refers to an interface for inputting basic information and characteristic information about a child.

[0220] "Communication means" refers to means for receiving data transmitted from input means and passing it to the analysis platform, and means for transmitting the generated proposals to output means.

[0221] An "analysis platform" is a foundation for analyzing input data and identifying types based on the characteristics of children.

[0222] "Analysis means" refers to methods for identifying a child's type using an analysis platform.

[0223] "Generating means" refers to means for generating appropriate approaches and suggestions for communication based on the type of child identified by the analysis means.

[0224] "Output means" refers to an interface for displaying the generated suggestions to the user.

[0225] "Display means" refers to means for displaying suggestions to the user using output means.

[0226] "Monitoring measures" refer to methods for monitoring children's behavior in real time within a store and providing appropriate behavioral suggestions at the right time.

[0227] "Notification means" refers to the means of providing proposals and notifications appropriate to the situation.

[0228] "Feedback collection methods" refer to means of collecting feedback from users.

[0229] "Optimization methods" refer to the means used to optimize the proposed content based on the collected feedback.

[0230] A "server" is a central processing system for data management and proposal generation, including analysis and generation methods.

[0231] An "AI model" is an artificial intelligence model used to identify the type best suited to a child's characteristics.

[0232] This invention relates to a system for effectively supporting the upbringing of children with developmental disabilities. This system is primarily configured to provide real-time monitoring of children's behavior within a store and to offer appropriate behavioral suggestions at the right time.

[0233] Overall system configuration

[0234] The system consists of the following main elements:

[0235] 1. Input method: An interface for users to input basic and characteristic information about their child. Specifically, this is an input form in a smartphone or tablet application.

[0236] 2. Communication method: An interface for sending input data to a server and receiving analysis results and suggestions from the server. For example, data is sent and received via the internet.

[0237] 3. Analysis Method: This method uses an analysis platform within the server to analyze the input data and identify the type based on the child's characteristics. A specific example is the use of an AI model (such as TENSORFLOW®).

[0238] 4. Generation means: A means for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[0239] 5. Display means: An interface for displaying the generated suggestions to the user. Specifically, the suggested content is displayed on the smartphone screen.

[0240] 6. Monitoring methods: These are means of monitoring children's behavior in real time within the store and providing appropriate behavioral suggestions at the right time. Examples include behavioral monitoring using cameras and sensors.

[0241] 7. Notification methods: Means for providing situation-appropriate notification suggestions. Specifically, this involves utilizing the notification function of smartphones.

[0242] Program processing

[0243] The server receives data transmitted from the input means and passes it to the analysis platform. The analysis platform analyzes the received data and uses an AI model to identify the type best suited to the child's characteristics. Subsequently, the generation means generates appropriate suggestions based on the identified type and transmits them to the output means via the communication means. The output means displays the suggestions on the smartphone screen and provides them to the user.

[0244] Specific example

[0245] The following is a specific example of its use. In this example, the child's basic information and characteristics are as follows:

[0246] Example of a prompt

[0247] Name: Taro

[0248] Age: 6 years old

[0249] Gender: Male

[0250] Diagnosis: ASD

[0251] Characteristics: Social anxiety, language delay

[0252] When a user enters the above information through the application and presses the "Submit" button, the data is sent from the device to the server. The server passes this data to an analysis platform, which uses an AI model to identify the child's type. For example, in this case, "ASD" would be identified. Based on this data, the server generates suggestions such as "give instructions in short, clear language" and "praise specific behaviors," and sends them back to the device.

[0253] Furthermore, the store's monitoring system (cameras and sensors) monitors children's behavior in real time and notifies the user of appropriate suggestions based on specific situations (for example, situations where social anxiety increases). The user receives a notification on their smartphone, tries the suggested action, and provides feedback on the results. This feedback is sent back to the server and used to optimize suggestions for future visits.

[0254] This system allows users to receive appropriate support in real time within the store, enabling an effective approach to children with developmental disabilities.

[0255] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0256] Step 1:

[0257] The user enters basic and characteristic information about their child. Specifically, they use a smartphone or tablet application to fill in forms with information such as name, age, gender, diagnosis, and characteristics (e.g., social anxiety, language delay). This entered data is sent from the device to the server when the user presses the "Submit" button. The input is in text format, and the output is data sent to the server.

[0258] Step 2:

[0259] The terminal sends the entered data to the server via the internet. The server receives the data and passes it to the analysis platform. The input is the basic and characteristic information of the child entered by the user, and the output is that this information is passed to the analysis platform.

[0260] Step 3:

[0261] The server uses an analysis platform to analyze the input data and uses an AI model to identify the child's type based on their characteristics. In this step, data such as name, age, gender, diagnosis, and characteristics are passed to the AI ​​model as prompts, and the output is the child's type (e.g., ASD or ADHD). Data processing includes pre-processing the input data into a format suitable for the AI ​​model.

[0262] Step 4:

[0263] Based on the identified type, the server uses a generation method to generate appropriate approach and communication suggestions. In this step, the suggestions are created using existing knowledge bases and AI models based on the analysis results, and the suggestions are output in text format. The input is the child's type and related data, and the output is the suggested content.

[0264] Step 5:

[0265] The server sends the generated proposal back to the terminal using a communication method. The terminal receives the proposal and displays it to the user through a display device. Specifically, the proposal is displayed as text on the smartphone screen. The input is the proposal sent from the server, and the output is the proposal displayed on the user's smartphone screen.

[0266] Step 6:

[0267] The terminal uses monitoring devices to monitor children's behavior in real time within the store. This is done using cameras and sensors. In this step, data from cameras and sensors is collected in real time based on user data and transmitted to a server. The input is detection data from the monitoring device, and the output is real-time data transmission to the server.

[0268] Step 7:

[0269] The server analyzes the monitoring data received in real time and generates suggestion notifications tailored to the situation. For example, when a child takes a specific action, it generates the most appropriate suggestion to address that situation and sends it as a notification to the user's smartphone. The input is monitoring data, and the output is a notification to the user.

[0270] Step 8:

[0271] The user executes a suggestion and provides feedback on the result to the application. This feedback includes information such as which suggestion was executed and the child's reaction. The input is the feedback information, and the output is its saving or transmission.

[0272] Step 9:

[0273] The device sends feedback data to the server. The server analyzes the received feedback data and optimizes the AI ​​model. In this step, the feedback data is used to improve future suggestions and enhance the effectiveness of the suggestions provided to the user. The input is the feedback data, and the output is the optimized suggestions.

[0274] 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.

[0275] This invention is a system for effectively supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. Its detailed configuration and processing flow are described below.

[0276] Overall system configuration

[0277] This system consists of the following main elements:

[0278] 1. Input means: An interface for the user to input basic information and characteristic information of a child. For example, the form input fields of applications on smartphones or tablets.

[0279] 2. Communication means: An interface for sending the input data to the server and receiving analysis results and suggestions from the server.

[0280] 3. Analysis means: Installed in the server, it analyzes the input data to identify the type based on the characteristics of the child.

[0281] 4. Generation means: Based on the type identified by the analysis means, it automatically generates appropriate approaches and suggestions for conversation.

[0282] 5. Display means: An interface for displaying the generated suggestions to the user.

[0283] 6. Optimization means: It collects feedback from the user and improves the content of suggestions for subsequent times.

[0284] 7. Emotion engine: A function for recognizing emotions from the user's input information, voice, and expressions, and reflecting the analysis results in the suggestions.

[0285] Program processing

[0286] The program of this system operates as follows.

[0287] 1. Input of child and user information

[0288] The user opens the application and inputs the basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, attention deficit, etc.) of the child.

[0289] The user confirms the input content and determines the input data by pressing the "Send" button.

[0290] 2. Sending and receiving data

[0291] The terminal generates an HTTP request to send the confirmed data to the server, and the server receives it.

[0292] 3. Analysis of the child's personality type

[0293] The server passes the received data to the analysis platform, which analyzes the data using a database and AI models to identify types based on the child's characteristics.

[0294] 4. Recognition and reflection of emotions

[0295] The emotion engine analyzes user input information and recognizes the user's emotional state (stress, joy, fatigue, etc.).

[0296] Based on recognized emotional information, the system reflects the user's emotional state in its suggestions for approaches and communication with children.

[0297] 5. Generating an appropriate approach

[0298] Based on the analysis results and the emotion engine's recognition results, the server automatically generates specific approaches and suggestions for communication using existing knowledge and AI models in the database. For example, in the case of a child with ASD, it might create suggestions such as "praise specific behaviors" or "give short, clear instructions."

[0299] 6. Submitting and displaying proposals

[0300] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[0301] The terminal analyzes the received suggestion data, formats it into a human-readable format, and displays it on the user interface. For example, it might be presented as short sentences or diagrams.

[0302] The user checks the proposal and tries that approach in actual daily life.

[0303] 7. Feedback Collection and Optimization

[0304] The user inputs feedback on the approach taken and its results into the application. For example, input "When praising specific actions, the child's reaction was good."

[0305] The terminal sends the finalized feedback data to the server, and the server receives and stores it in the database.

[0306] The server analyzes the feedback data and uses the AI model to optimize the content of future proposals. This continuously provides more effective approaches.

[0307] Specific Example

[0308] Example 1: A child with autism spectrum disorder

[0309] The user inputs the child's information "Name: A, Age: 6 years old, Characteristics: Social anxiety, Speech delay".

[0310] The terminal sends this information to the server.

[0311] The server analyzes the data and identifies the child's type as "ASD".

[0312] The emotion engine analyzes the user's voice and expression and recognizes that the user is tired.

[0313] The server creates proposals such as "Give instructions in short and clear words" and "Praise specific actions", and adds a way of greeting that takes into account the user's fatigue.

[0314] The terminal displays the proposal on the user interface.

[0315] Users try out the suggestions in their daily lives and provide feedback on the results.

[0316] The server uses feedback to update the AI ​​model and optimize future suggestions.

[0317] In this way, this system enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[0321] Step 2:

[0322] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[0323] Step 3:

[0324] The terminal organizes the confirmed data and generates an HTTP request to send to the server.

[0325] Step 4:

[0326] The device sends the HTTP request it generates to the server via the internet.

[0327] Step 5:

[0328] The server saves the received data to the database and prepares it to be passed to the analysis platform.

[0329] Step 6:

[0330] The server passes the data to the analysis platform, which uses a database and AI models to identify the child's type based on their characteristics. At this stage, diagnoses such as ASD (Autism Spectrum Disorder) and ADHD (Attention Deficit Hyperactivity Disorder) are made.

[0331] Step 7:

[0332] The emotion engine analyzes user input information and, if possible, user voice and facial expression data. This analysis recognizes the user's emotional state (e.g., stress, joy, fatigue).

[0333] Step 8:

[0334] Based on the analysis results and the emotion engine's recognition results, the server automatically generates specific approaches and suggestions for communication using existing knowledge and AI models in the database. For example, for a child with ASD, suggestions such as "praise specific behaviors" and "give short, clear instructions" are generated. These suggestions also take into account the user's emotional state.

[0335] Step 9:

[0336] The server converts the generated proposal into a data format such as JSON and generates an HTTP response to send to the client.

[0337] Step 10:

[0338] The server sends the generated HTTP response to the terminal via the internet.

[0339] Step 11:

[0340] The terminal analyzes the suggestion data received from the server and formats it into a human-readable format. For example, it can be presented as short sentences or diagrams.

[0341] Step 12:

[0342] The device displays the analysis results in the application's user interface. This allows the user to review the suggestions and try out the approach in their daily life.

[0343] Step 13:

[0344] Users provide feedback on the approaches they implemented and the results. For example, they might write, "When I praised specific behaviors, the child responded well."

[0345] Step 14:

[0346] The user reviews the input and confirms the feedback by pressing the "Submit" button.

[0347] Step 15:

[0348] The system organizes the feedback data from the identified terminal and generates an HTTP request to send it to the server.

[0349] Step 16:

[0350] The device sends the HTTP request it generates to the server via the internet.

[0351] Step 17:

[0352] The server saves the feedback data it receives to the database.

[0353] Step 18:

[0354] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This allows the system to continuously learn and provide more effective suggestions.

[0355] This system, which incorporates an emotion engine, enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[0356] (Example 2)

[0357] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0358] In raising children with developmental disabilities, there is a lack of means to provide individually tailored approaches and suggestions for communication. Furthermore, existing systems fail to consider the user's emotional state, resulting in uniform suggestions that make it difficult to alleviate the user's emotional burden. Additionally, there is insufficient mechanism for effectively collecting feedback and incorporating the results into future suggestions.

[0359] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting basic information and characteristic information of a child; a communication means for receiving data transmitted from the input means and passing the data to an analysis platform; an analysis means for identifying the type of child using the analysis platform; a means for recognizing the user's emotional state using an emotion engine; a generation means for generating appropriate approach and communication suggestions based on the recognition results of the analysis means and the emotion engine; a communication means for transmitting the generated suggestions to an output means; and a display means for displaying the suggestions to the user on the output means. This makes it possible to provide individual approaches according to the child's characteristics and the parent's emotional state, and to optimize suggestions for subsequent visits based on feedback.

[0360] An "input method" is an interface for users to input basic information and characteristic information about their child.

[0361] A "communication means" is an interface for receiving data transmitted from an input means and passing it on to the analysis platform.

[0362] "Analysis means" refers to the means of identifying a child's type using an analysis platform.

[0363] An "emotion engine" is an engine that recognizes the user's emotional state from their voice and facial expressions.

[0364] "Generation means" refers to means for automatically generating appropriate approaches and suggestions for communication based on the recognition results of the analysis means and the emotion engine.

[0365] "Output means" refers to an interface for displaying the generated suggestions to the user.

[0366] An "AI model" is an artificial intelligence model used to identify the type best suited to a child's characteristics.

[0367] A "feedback mechanism" is a means of collecting feedback from users and sending it to a server.

[0368] "Optimization methods" refer to means of optimizing the content of future proposals using the collected feedback.

[0369] This invention is a system for effectively supporting the upbringing of children with developmental disabilities. The system includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. The detailed configuration and processing flow of the system are described below.

[0370] Overall system configuration

[0371] This system consists of the following main elements:

[0372] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[0373] 2. Communication method: This is an interface for sending input data to the server and receiving analysis results and suggestions from the server.

[0374] 3. Analysis Methods: These are methods installed on the server to analyze the input data and identify types based on the characteristics of the children. For example, machine learning models or databases may be used.

[0375] 4. Generation means: A means for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[0376] 5. Display means: This is an interface for displaying the generated suggestions to the user.

[0377] 6. Optimization methods: These are means of collecting user feedback and improving the content of future suggestions.

[0378] 7. Emotion Engine: This engine recognizes emotions from user input, voice, and facial expressions, and incorporates the analysis results into the suggestions.

[0379] Program processing

[0380] The program for this system operates as follows:

[0381] Entering information about children and users

[0382] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[0383] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[0384] Sending and receiving data

[0385] The terminal generates an HTTP request based on the input data and sends it to the server.

[0386] The server receives an HTTP request and prepares the input data to be passed to the analysis platform.

[0387] Analysis of children's types

[0388] The server passes the received data to the analysis platform. This analysis platform includes machine learning models (e.g., TensorFlow or PyTorch).

[0389] The server compares the information with existing data in the database and uses an AI model to identify the child's type based on their characteristics (e.g., autism spectrum disorder, social anxiety disorder, etc.).

[0390] Recognition and reflection of emotions

[0391] The emotion engine analyzes the user's voice and facial expressions. This uses speech recognition software and facial recognition software (e.g., OpenFace, DeepFace, etc.).

[0392] The emotion engine identifies the user's emotional state (stress, joy, fatigue, etc.) based on the analysis results.

[0393] The server receives this sentiment information and incorporates it into the generation of the next suggestion.

[0394] Generating an appropriate method

[0395] The server generates suggestions for appropriate approaches and responses based on the analysis results and the emotion engine's recognition results.

[0396] Using a generation tool, suggestions such as "praise specific behaviors" and "give short, clear instructions" can be automatically created for children with ASD.

[0397] Submitting and displaying proposals

[0398] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[0399] The terminal analyzes the received data and organizes it for display in the user interface. For example, it displays it as short sentences or charts.

[0400] Users review the displayed information and then act upon it in their daily lives.

[0401] Gathering and optimizing feedback

[0402] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[0403] The device sends feedback data to the server.

[0404] The server receives the feedback data and saves it to the database.

[0405] The server analyzes the collected feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[0406] Specific example

[0407] Example 1: A child with autism spectrum disorder

[0408] The user opens the app and enters "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[0409] The device sends an HTTP request containing this information to the server.

[0410] The server receives the data and uses an analysis platform and AI model to identify the child's specific type as "ASD".

[0411] The emotion engine analyzes the user's voice data and recognizes that the user is tired.

[0412] The server generates suggestions such as "give instructions in short, clear language" and "praise specific actions," including methods of verbal communication that take user fatigue into consideration.

[0413] The device analyzes the generated suggestions and displays them in the UI as short sentences or diagrams.

[0414] Users implement the suggested actions and input the results as feedback into the app.

[0415] The device sends feedback data to the server, which receives it, saves it to a database, and updates the AI ​​model.

[0416] In this way, this system provides individualized support for children with developmental disabilities and also reduces the mental burden on users. Furthermore, since the generated suggestions are adjusted according to the user's emotional state, more personalized support is provided.

[0417] Example of a prompt

[0418] "Please advise on an appropriate approach for a 6-year-old child with autism spectrum disorder. Their emotional state is one of exhaustion."

[0419] "Please suggest effective ways to talk to children who have social anxiety."

[0420] This system allows users to obtain approaches that are tailored to the child's characteristics and individual emotional state.

[0421] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0422] Step 1:

[0423] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[0424] Input: Basic information and characteristics of the child

[0425] Data processing: Data collection through input forms

[0426] Output: Confirmed input data

[0427] Step 2:

[0428] The terminal generates an HTTP request based on the confirmed input data and sends it to the server.

[0429] Input: Confirmed input data

[0430] Data processing: Generation of HTTP requests

[0431] Output: HTTP request

[0432] Step 3:

[0433] The server receives an HTTP request and prepares the input data to be passed to the analysis platform.

[0434] Input: HTTP Request

[0435] Data processing: Preparing data for transfer to the analysis platform.

[0436] Output: Data ready for analysis

[0437] Step 4:

[0438] The server passes data to the analysis platform, which then analyzes the data using machine learning models (e.g., TensorFlow or PyTorch).

[0439] Input: Data ready for analysis

[0440] Data processing: Data analysis (using machine learning models)

[0441] Output: Analysis results (type based on child's characteristics)

[0442] Step 5:

[0443] The emotion engine analyzes the user's voice and facial expressions to recognize the user's emotional state (stress, joy, fatigue, etc.). This uses voice recognition software and facial recognition software (e.g., OpenFace, DeepFace, etc.).

[0444] Input: User's voice and facial expression data

[0445] Data processing: Analysis of voice and facial expression data

[0446] Output: User's emotional state

[0447] Step 6:

[0448] The server receives the analysis results and emotional state, and generates suggestions for appropriate approaches and responses. An AI model is used as the generation method in this process.

[0449] Input: Analysis results, user's emotional state

[0450] Data processing: Generation of proposed data (using an AI model)

[0451] Output: Generated proposals

[0452] Step 7:

[0453] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[0454] Input: Generated proposal

[0455] Data processing: Conversion to a data format (e.g., JSON format)

[0456] Output: Converted proposed data

[0457] Step 8:

[0458] The terminal analyzes the received data and prepares it for display in the user interface. It is displayed as short texts or charts.

[0459] Input: Converted suggestion data

[0460] Data processing: Conversion to a format suitable for UI.

[0461] Output: Data displayed in the user interface

[0462] Step 9:

[0463] Users review the displayed content and then implement that approach in their actual daily lives.

[0464] Input: Suggestions displayed in the UI

[0465] Data processing: None (User's actual actions)

[0466] Output: None

[0467] Step 10:

[0468] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[0469] Input: Feedback Information

[0470] Data processing: Collection of feedback information

[0471] Output: Confirmed feedback data

[0472] Step 11:

[0473] The device sends feedback data to the server.

[0474] Input: Confirmed feedback data

[0475] Data processing: Generation of HTTP requests (feedback data)

[0476] Output: HTTP request for feedback data

[0477] Step 12:

[0478] The server receives the feedback data and saves it to the database.

[0479] Input: HTTP request for feedback data

[0480] Data processing: Saving to a database

[0481] Output: Saved feedback data

[0482] Step 13:

[0483] The server analyzes the collected feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[0484] Input: Saved feedback data

[0485] Data processing: Analysis of feedback data and updating of AI models.

[0486] Output: Optimized proposed model

[0487] These processing steps allow the system to provide a personalized approach tailored to the user's emotional state and optimize future suggestions based on feedback.

[0488] (Application Example 2)

[0489] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0490] Supporting the care of children with developmental disabilities requires tailored approaches to various characteristics and situations, and general approaches have limited effectiveness. Furthermore, in physical stores, appropriate responses are needed based on the specific circumstances at hand, but current systems lack sufficient support for this. Therefore, a system is needed that provides real-time, appropriate responses, enabling users to respond immediately on the spot.

[0491] 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.

[0492] In this invention, the server includes input means for inputting basic and characteristic information of a child, means for optimizing the generated suggestions so that they are applied to specific situations in a physical store, and means for generating suggestions to be applied within the physical store using a generation AI model. This makes it possible to provide timely suggestions for appropriate approaches and conversations according to specific situations within the physical store.

[0493] "Basic information and characteristics of the child" includes data such as the child's name, age, and gender, as well as diagnostic information for developmental disorders and specific characteristics, such as social anxiety or inattention.

[0494] An "input method" is an interface that allows users to input data through devices such as smartphones and tablets.

[0495] A "communication method" is an interface for sending input data to a server and receiving analysis results and suggestions from the server.

[0496] "Analysis means" refers to a mechanism installed within the server that identifies a child's type based on their characteristics, using the input data.

[0497] The "generation means" is a mechanism for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[0498] "Output means" refers to an interface for displaying or notifying the user of the generated suggestions.

[0499] "Display means" refers to screens or displays used to visually present generated suggestions to the user.

[0500] "Means for applying generated proposals to users in physical stores" refers to means of interaction that enable users to actually implement proposals generated in a physical store environment.

[0501] "Means for optimizing generated suggestions to be applicable to specific situations in physical stores" refers to methods for selecting and customizing generated suggestions to suit the specific circumstances of physical stores and providing them to users.

[0502] A "means of collecting feedback" is an interface for users to input and record information about the approaches they have tried and the results of those approaches.

[0503] "Means for optimizing suggestions" refers to a mechanism that updates the AI ​​model based on collected feedback, making future suggestions more effective.

[0504] An "AI model" is a machine learning algorithm used to identify the type best suited to a child's characteristics.

[0505] A "generative AI model" is an algorithm that automatically generates actionable suggestions and prompts based on specific situations and user input data.

[0506] A "prompt message" is a sentence containing instructions or advice for a user in a specific situation, generated using a generative AI model.

[0507] Modes for carrying out the invention

[0508] This invention is a system for supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. Its detailed configuration and processing flow are described below.

[0509] Overall system configuration

[0510] This system consists of the following main elements:

[0511] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, using a form input field in a smartphone or tablet application.

[0512] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[0513] 3. Analysis method: Installed on a server, it analyzes the input data to identify a type based on the child's characteristics.

[0514] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[0515] 5. Display means: An interface for displaying the generated suggestions to the user.

[0516] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[0517] 7. Emotion Engine: Recognizes emotions from user input information, voice, and facial expressions, and reflects the analysis results in suggestions.

[0518] Program processing

[0519] 1. Collecting user input:

[0520] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[0521] The terminal confirms the information it has received and sends the data to the server by pressing the send button.

[0522] 2. Sending and receiving data:

[0523] The server receives the input data via an HTTP request.

[0524] 3. Data Analysis:

[0525] The server receives the data and passes it to the analysis platform, which then analyzes the data using a database and AI models (such as TensorFlow or BERT).

[0526] The emotion engine recognizes emotions from the user's input and voice, and incorporates that information into the analysis.

[0527] 4. Proposal generation:

[0528] Based on the analysis results, the server uses knowledge from the database and AI models to generate specific approaches and suggestions for communication.

[0529] For example, it generates suggestions such as "praise specific actions" and "give short, clear instructions."

[0530] 5. Display of proposals:

[0531] The server sends the generated suggestions to the terminal, and the terminal displays those suggestions in the user interface.

[0532] The user reviews the proposal and tries out the approach in a physical store.

[0533] 6. Gathering feedback and optimizing:

[0534] The application receives feedback on the approaches taken by the user and the results they achieved.

[0535] The device sends confirmed feedback data to the server, which receives it and stores it in a database.

[0536] The server analyzes the feedback data and uses an AI model to optimize future suggestions.

[0537] Specific example

[0538] For example, let's discuss how to handle a situation where a child panics at the checkout counter.

[0539] 1. The user provides voice input about the register status in the application.

[0540] 2. The server analyzes the data and generates suggestions that are "brief, clear, and shift attention elsewhere."

[0541] 3. The smartphone screen displays the message, "Try saying in a loud, slow voice, 'When this is over, let's go buy our favorite snacks.'"

[0542] 4. The user implements the suggested method, and the child regains their composure.

[0543] 5. The user provides feedback on the results to the application, and the server uses this data to improve future suggestions.

[0544] Example of a prompt

[0545] "If a child with a developmental disability panics at the checkout counter, please suggest how to briefly and clearly explain the situation and redirect their attention."

[0546] In this way, this system enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The user opens the application and enters basic information about the child (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.). The device collects the input data and generates an HTTP request to send this data to the server. The input data is packaged in JSON format.

[0550] Step 2:

[0551] The server receives an HTTP request sent from the terminal and passes the input data to the analysis platform. The server first checks the data integrity to ensure that no missing or invalid data is included. The verified data is then input into the analysis platform.

[0552] Step 3:

[0553] The server uses an analysis platform to analyze the input data and identify the child's type based on their characteristics. Specifically, it processes the data using TensorFlow or BERT models and applies an AI model to classify the child's characteristics. The analysis result outputs the child's type (e.g., ASD, ADHD).

[0554] Step 4:

[0555] The server uses an emotion engine to recognize the user's emotional state (e.g., stress, fatigue, joy) from user input information and voice data. The emotion engine uses voice analysis and natural language processing techniques to identify the user's current emotional state. The analysis results output the user's emotional state.

[0556] Step 5:

[0557] Based on the analysis results and the emotion engine's output, the server uses knowledge and AI models in the database to generate specific approaches and suggestions for communication. For example, it uses the BERT model to analyze past cases and feedback, automatically generating suggestions such as "praise specific actions" and "give short, clear instructions." The generated suggestions are stored as data in JSON format.

[0558] Step 6:

[0559] The server generates an HTTP response to send the generated suggestions to the terminal and sends it to the terminal. The terminal interprets the received suggestion data and displays it visually in the user interface. This display may be in the form of text or icons on a smartphone screen, for example.

[0560] Step 7:

[0561] Users review the suggestions displayed in the application and try them out in a physical store. They then input feedback into the application regarding their experience trying the suggestions and their child's reaction. This feedback data is then sent back from the device to the server.

[0562] Step 8:

[0563] The server analyzes the feedback data it receives and uses an AI model to optimize future suggestions. Specifically, it stores the feedback data in a database and uses an AI model (e.g., TensorFlow) to compare and analyze it with past data to update the suggestions.

[0564] Examples of prompt statements include:

[0565] "If a child with a developmental disability panics at the checkout counter, please suggest how to briefly and clearly explain the situation and redirect their attention."

[0566] 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.

[0567] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0568] 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.

[0569] [Second Embodiment]

[0570] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0582] The present invention is a system for effectively supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, and optimization means.

[0583] Overall system configuration

[0584] This system consists of the following main elements:

[0585] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[0586] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[0587] 3. Analysis method: Installed on the server, it analyzes the input data to identify the type based on the child's characteristics.

[0588] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[0589] 5. Display means: An interface for displaying the generated suggestions to the user.

[0590] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[0591] Program processing

[0592] The program for this system operates as follows:

[0593] 1. Enter the child's information

[0594] The user opens the application and enters the child's name, age, gender, diagnosis information for developmental disorders, and characteristics (e.g., anxiety in social situations, inattention, etc.).

[0595] The user reviews the input and presses the "Submit" button.

[0596] 2. Sending data

[0597] The terminal sends the entered information to the server via the internet.

[0598] 3. Analysis of the child's personality type

[0599] The server passes the received data to the analysis platform, which uses a database and AI models to identify the child's type based on their characteristics. For example, ASD (Autism Spectrum Disorder) or ADHD (Attention Deficit Hyperactivity Disorder).

[0600] 4. Generating an appropriate approach

[0601] Based on the identified type, the server automatically generates specific approaches and verbal suggestions using existing knowledge and AI models in the database. For example, for a child with ASD, it might suggest "praising specific behaviors" and "giving short, clear instructions."

[0602] 5. Submitting a proposal

[0603] The server sends the generated proposal to the terminal.

[0604] 6. Display of Proposal

[0605] The device analyzes the received suggestions and displays them in the user interface. Users can review the suggestions and try out the approaches in their daily lives.

[0606] 7. Gathering Feedback

[0607] Users provide feedback on the approaches they actually took and the results. For example, they might write, "When I praised a specific action, the child responded well."

[0608] 8. Submit feedback

[0609] The device sends feedback data to the server.

[0610] 9. Optimizing the proposal

[0611] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[0612] Specific example

[0613] Example 1: A child with autism spectrum disorder

[0614] The user enters the child's information: "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[0615] The device sends this information to the server.

[0616] The server analyzes the data and identifies the child's type as "ASD".

[0617] The server generates suggestions such as "give instructions in short, clear language" and "praise specific actions."

[0618] The device displays the suggestion in the user interface.

[0619] Users try out the suggestions in their daily lives and provide feedback on the results.

[0620] The server uses feedback to update the AI ​​model and optimize future suggestions.

[0621] In this way, this system provides individualized support for children with developmental disabilities and reduces the burden of childcare.

[0622] *The processing steps will be explained in detail upon request.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: for example, social anxiety, inattention, etc.).

[0626] Step 2:

[0627] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[0628] Step 3:

[0629] The terminal organizes the confirmed data and generates an HTTP request to send to the server.

[0630] Step 4:

[0631] The device sends the HTTP request it generates to the server via the internet.

[0632] Step 5:

[0633] The server saves the received data to the database.

[0634] Step 6:

[0635] The server passes the received data to the analysis platform, which uses a database and AI models to analyze the data and identify types based on the child's characteristics.

[0636] Step 7:

[0637] The server processes the analysis results to automatically generate appropriate approaches and suggestions for communication. This is done using a database and AI models within the server.

[0638] Step 8:

[0639] The server converts the generated proposal into a data format such as JSON and generates an HTTP response to send to the client.

[0640] Step 9:

[0641] The server sends the generated HTTP response to the terminal via the internet.

[0642] Step 10:

[0643] The terminal analyzes the suggestion data received from the server and formats it into a human-readable format.

[0644] Step 11:

[0645] The terminal displays the analysis results in the application's user interface. For example, it may be presented as a short text or a chart.

[0646] Step 12:

[0647] Users view the displayed suggestions and try to implement specific approaches in their daily lives.

[0648] Step 13:

[0649] The application receives feedback on the approaches taken by the user and the results they achieved.

[0650] Step 14:

[0651] The user reviews the input and confirms the feedback by pressing the "Submit" button.

[0652] Step 15:

[0653] The system organizes the feedback data from the identified terminal and generates an HTTP request to send it to the server.

[0654] Step 16:

[0655] The device sends the HTTP request it generates to the server via the internet.

[0656] Step 17:

[0657] The server saves the feedback data it receives to the database.

[0658] Step 18:

[0659] The server analyzes the feedback data and uses an AI model to perform update processing to optimize future suggestions.

[0660] This allows the system to continuously learn from user feedback and provide more effective suggestions.

[0661] (Example 1)

[0662] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0663] Raising children with developmental disabilities requires appropriate support methods and communication approaches based on individual characteristics, but finding specific methods is considered difficult. Furthermore, there is a lack of mechanisms to properly collect user feedback and incorporate it into future suggestions, which is necessary for continuously optimizing effective support methods. A system is needed to address these problems.

[0664] 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.

[0665] In this invention, the server includes an input means for inputting basic information and characteristic information of a child; a communication means for receiving data transmitted from the input means and passing the data to an analysis platform; an analysis means for identifying the child's type using the analysis platform; a generation means for generating appropriate approaches and suggestions for communication based on the child's type identified by the analysis means; a communication means for transmitting the generated suggestions to an output means; a display means for displaying the suggestions to the user on the output means; a feedback input means for allowing the user to confirm the generated suggestions, take actions based on the suggestions, and input the results as feedback; and an optimization means for receiving the feedback data and optimizing the next suggestions. This enables individualized support for children with developmental disabilities, reduces the burden of childcare, and allows for continuous optimization of support methods.

[0666] "Basic information about a child" refers to basic personal information such as the child's name, age, gender, and information about any diagnosed developmental disorders.

[0667] "Characteristic information" refers to information that includes a child's specific behaviors, personality, and problematic characteristics (e.g., anxiety or inattention in social situations).

[0668] "Input method" refers to an interface for users to input basic information and characteristic information about their children, and includes applications for smartphones and tablets.

[0669] A "communication means" is a network interface used to transmit and receive input data to and from other devices and systems.

[0670] An "analysis platform" is a set of systems that includes a database and AI models for analyzing received data.

[0671] "Analysis means" refers to the process and software used to identify a child's type using an analysis platform.

[0672] "Generation means" refers to software and processes for automatically generating appropriate approaches and suggestions for communication based on analysis results.

[0673] "Output means" refers to a display or other display device for providing the generated proposal to the user.

[0674] "Display means" refers to an interface for displaying proposals on a user interface, and includes the screens of smartphones and tablets.

[0675] A "feedback input method" is an interface that allows users to take action based on a suggestion and input the results as feedback.

[0676] "Optimization means" refers to software and processes for analyzing feedback data and optimizing the next proposal.

[0677] An "AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and perform predictions and classifications.

[0678] This invention is a system for effectively supporting the upbringing of children with developmental disabilities. This system is a combination of hardware and software and includes the following main elements:

[0679] Overall system configuration

[0680] 1. Input method

[0681] This is an interface for users to input basic and characteristic information about their children. Specifically, it refers to the form input fields in applications installed on smartphones and tablets. For example, a user opens the application and enters the child's name, age, gender, diagnosis information for developmental disorders, and characteristics.

[0682] 2. Means of communication

[0683] This is an interface for a terminal to send data entered into it to a server via the internet. The data is sent in a standard data format such as JSON.

[0684] 3. Analysis method

[0685] The server passes the received data to the analysis platform, which uses a database and a generative AI model to identify the child's type based on their characteristics. This analysis platform utilizes existing diagnostic criteria and characteristic classification information for developmental disorders stored in the database.

[0686] 4. Generation means

[0687] Based on the identified child type, the server automatically generates appropriate approaches and conversation suggestions using a generative AI model. This generation method utilizes existing knowledge in the database and predictions from the AI ​​model to create specific suggestions.

[0688] 5. Output means and display means

[0689] The server sends the generated suggestions to the terminal, which then displays them on the user interface. This display method can be a smartphone or tablet screen. The user can then review the displayed suggestions and try them out in their daily life.

[0690] 6. Feedback Input Methods

[0691] This is an interface for users to take action based on suggestions and input the results as feedback. The feedback is implemented as form input to record the results of specific actions and the child's reactions.

[0692] 7. Optimization methods

[0693] The server analyzes the received feedback data and updates the generated AI model to optimize the next proposal. This improves the accuracy of the proposals and continuously provides more effective support.

[0694] Specific example

[0695] The following is a concrete example of the system.

[0696] Example 1: A child with autism spectrum disorder

[0697] The user enters the child's information: "Name: Taro, Age: 6 years old, Gender: Male, Diagnosis: ASD, Characteristics: Social anxiety, Language delay".

[0698] The device sends this information to the server. The server analyzes the data and identifies the child's type as "ASD". The server generates suggestions such as "give instructions in short, clear language" and "praise specific behaviors".

[0699] The device displays suggestions in the user interface. The user tries out the suggestions in their daily life and provides feedback on the results. The server uses the feedback to update the generating AI model and optimize suggestions for future use.

[0700] Example of a prompt

[0701] The following are prompt statements as concrete examples for a generative AI model.

[0702] This system suggests appropriate approaches based on your child's characteristics and diagnosis. Please enter the following information.

[0703] 1. Name:

[0704] 2. Age:

[0705] 3. Gender:

[0706] 4. Diagnostic information for developmental disorders (e.g., ASD, ADHD):

[0707] 5. Characteristics (e.g., social anxiety, inattentiveness, language delay):

[0708] Based on the input information, we will propose a specific approach.

[0709] for example:

[0710] Name: Taro, Age: 6 years old, Gender: Male, Diagnosis: ASD, Characteristics: Social anxiety, language delay

[0711] In this way, the present invention provides individualized support for children with developmental disabilities and reduces the burden of childcare.

[0712] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0713] System program processing flow

[0714] Step 1:

[0715] The user opens the application and enters the child's information (name, age, gender, diagnosis of developmental disability, and characteristics).

[0716] Input: Child's name, age, gender, diagnosis information and characteristics of developmental disorders

[0717] Data processing or calculation: The data is formatted when the user enters information into the input fields and presses the "Submit" button.

[0718] Output: JSON data of the input information

[0719] Step 2:

[0720] The terminal converts the entered information into JSON format and sends it to the server via the internet.

[0721] Input: JSON data generated in Step 1

[0722] Data processing or calculation: Format the data as JSON and generate an HTTP request.

[0723] Output: Formatted JSON data is sent to the server.

[0724] Step 3:

[0725] The server passes the received data to the analysis platform, which uses a database and a generative AI model to identify the child's type based on their characteristics.

[0726] Input: JSON data sent from the device

[0727] Data processing or computation: Data analysis using database lookups and generative AI models (e.g., classification of ASD and its characteristics)

[0728] Output: Type identification results based on child characteristics

[0729] Step 4:

[0730] The server uses a generation mechanism to generate appropriate approaches and suggestions for communication based on the type of child it identifies.

[0731] Input: Information on the trait type obtained in Step 3

[0732] Data processing or computation: Generate specific suggestions from existing knowledge in the database or from generative AI models.

[0733] Output: A list of generated suggestions (e.g., give instructions in short, clear language, praise specific actions)

[0734] Step 5:

[0735] The server sends the generated proposal to the terminal.

[0736] Input: List of suggestions generated in Step 4

[0737] Data processing or calculation: Convert the proposed data to JSON format and send it as an HTTP request.

[0738] Output: Proposal data is sent to the terminal.

[0739] Step 6:

[0740] The terminal analyzes the received suggestions and displays them in the user interface.

[0741] Input: List of suggestion data received from the server

[0742] Data processing or calculation: Parsing JSON data and converting it to a display format for the user interface.

[0743] Output: The user will see a suggestion.

[0744] Step 7:

[0745] Users take action based on the suggestions and input the results as feedback.

[0746] Input: Results of actions taken based on the suggestion (e.g., the child responded well when instructions were given in short, clear language).

[0747] Data processing or calculation: Enter feedback data into the input form and submit.

[0748] Output: Feedback data in JSON format

[0749] Step 8:

[0750] The device sends feedback data to the server.

[0751] Input: Feedback data entered in Step 7

[0752] Data processing or calculation: Convert feedback data to JSON format and generate an HTTP request.

[0753] Output: Feedback data is sent to the server.

[0754] Step 9:

[0755] The server analyzes the feedback data, updates the generating AI model, and optimizes the next proposal.

[0756] Input: Feedback data sent from the device

[0757] Data processing or computation: Training and updating generative AI models using feedback data.

[0758] Output: Optimized proposed model and proposed data to be used in subsequent iterations.

[0759] The above describes the processing flow of the system program.

[0760] (Application Example 1)

[0761] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0762] In modern society, it is extremely difficult for parents of children with developmental disabilities to provide appropriate support for their children in daily life and while shopping. In particular, in crowded stores, quick responses tailored to the child's characteristics are required, but conventional technology cannot monitor a child's behavior in real time and provide suggestions at the appropriate time. As a result, the burden on parents increases, and there is a problem that children do not receive the appropriate support they need.

[0763] 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.

[0764] In this invention, the server includes input means for inputting basic information and characteristic information of a child; communication means for receiving data transmitted from the input means and passing the data to an analysis platform; analysis means for identifying the child's type using the analysis platform; generation means for generating appropriate approaches and suggestions for communication based on the child's type identified by the analysis means; communication means for transmitting the generated suggestions to an output means; display means for displaying them to the user on the output means; monitoring means for monitoring the child's behavior in real time within the store and providing behavioral suggestions at the appropriate time; and notification means for providing suggestion notifications according to the situation. As a result, parents can receive appropriate support in real time within the store, enabling effective support for children.

[0765] The "input method" refers to an interface for inputting basic information and characteristic information about a child.

[0766] "Communication means" refers to means for receiving data transmitted from input means and passing it to the analysis platform, and means for transmitting the generated proposals to output means.

[0767] An "analysis platform" is a foundation for analyzing input data and identifying types based on the characteristics of children.

[0768] "Analysis means" refers to methods for identifying a child's type using an analysis platform.

[0769] "Generating means" refers to means for generating appropriate approaches and suggestions for communication based on the type of child identified by the analysis means.

[0770] "Output means" refers to an interface for displaying the generated suggestions to the user.

[0771] "Display means" refers to means for displaying suggestions to the user using output means.

[0772] "Monitoring measures" refer to methods for monitoring children's behavior in real time within a store and providing appropriate behavioral suggestions at the right time.

[0773] "Notification means" refers to the means of providing proposals and notifications appropriate to the situation.

[0774] "Feedback collection methods" refer to means of collecting feedback from users.

[0775] "Optimization methods" refer to the means used to optimize the proposed content based on the collected feedback.

[0776] A "server" is a central processing system for data management and proposal generation, including analysis and generation methods.

[0777] An "AI model" is an artificial intelligence model used to identify the type best suited to a child's characteristics.

[0778] This invention relates to a system for effectively supporting the upbringing of children with developmental disabilities. This system is primarily configured to provide real-time monitoring of children's behavior within a store and to offer appropriate behavioral suggestions at the right time.

[0779] Overall system configuration

[0780] The system consists of the following main elements:

[0781] 1. Input method: An interface for users to input basic and characteristic information about their child. Specifically, this is an input form in a smartphone or tablet application.

[0782] 2. Communication method: An interface for sending input data to a server and receiving analysis results and suggestions from the server. For example, data is sent and received via the internet.

[0783] 3. Analysis Method: This method uses an analysis platform on the server to analyze the input data and identify the type based on the child's characteristics. A specific example is the use of an AI model (such as TensorFlow).

[0784] 4. Generation means: A means for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[0785] 5. Display means: An interface for displaying the generated suggestions to the user. Specifically, the suggested content is displayed on the smartphone screen.

[0786] 6. Monitoring methods: These are means of monitoring children's behavior in real time within the store and providing appropriate behavioral suggestions at the right time. Examples include behavioral monitoring using cameras and sensors.

[0787] 7. Notification methods: Means for providing situation-appropriate notification suggestions. Specifically, this involves utilizing the notification function of smartphones.

[0788] Program processing

[0789] The server receives data transmitted from the input means and passes it to the analysis platform. The analysis platform analyzes the received data and uses an AI model to identify the type best suited to the child's characteristics. Subsequently, the generation means generates appropriate suggestions based on the identified type and transmits them to the output means via the communication means. The output means displays the suggestions on the smartphone screen and provides them to the user.

[0790] Specific example

[0791] The following is a specific example of its use. In this example, the child's basic information and characteristics are as follows:

[0792] Example of a prompt

[0793] Name: Taro

[0794] Age: 6 years old

[0795] Gender: Male

[0796] Diagnosis: ASD

[0797] Characteristics: Social anxiety, language delay

[0798] When a user enters the above information through the application and presses the "Submit" button, the data is sent from the device to the server. The server passes this data to an analysis platform, which uses an AI model to identify the child's type. For example, in this case, "ASD" would be identified. Based on this data, the server generates suggestions such as "give instructions in short, clear language" and "praise specific behaviors," and sends them back to the device.

[0799] Furthermore, the store's monitoring system (cameras and sensors) monitors children's behavior in real time and notifies the user of appropriate suggestions based on specific situations (for example, situations where social anxiety increases). The user receives a notification on their smartphone, tries the suggested action, and provides feedback on the results. This feedback is sent back to the server and used to optimize suggestions for future visits.

[0800] This system allows users to receive appropriate support in real time within the store, enabling an effective approach to children with developmental disabilities.

[0801] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0802] Step 1:

[0803] The user enters basic and characteristic information about their child. Specifically, they use a smartphone or tablet application to fill in forms with information such as name, age, gender, diagnosis, and characteristics (e.g., social anxiety, language delay). This entered data is sent from the device to the server when the user presses the "Submit" button. The input is in text format, and the output is data sent to the server.

[0804] Step 2:

[0805] The terminal sends the entered data to the server via the internet. The server receives the data and passes it to the analysis platform. The input is the basic and characteristic information of the child entered by the user, and the output is that this information is passed to the analysis platform.

[0806] Step 3:

[0807] The server uses an analysis platform to analyze the input data and uses an AI model to identify the child's type based on their characteristics. In this step, data such as name, age, gender, diagnosis, and characteristics are passed to the AI ​​model as prompts, and the output is the child's type (e.g., ASD or ADHD). Data processing includes pre-processing the input data into a format suitable for the AI ​​model.

[0808] Step 4:

[0809] Based on the identified type, the server uses a generation method to generate appropriate approach and communication suggestions. In this step, the suggestions are created using existing knowledge bases and AI models based on the analysis results, and the suggestions are output in text format. The input is the child's type and related data, and the output is the suggested content.

[0810] Step 5:

[0811] The server sends the generated proposal back to the terminal using a communication method. The terminal receives the proposal and displays it to the user through a display device. Specifically, the proposal is displayed as text on the smartphone screen. The input is the proposal sent from the server, and the output is the proposal displayed on the user's smartphone screen.

[0812] Step 6:

[0813] The terminal uses monitoring devices to monitor children's behavior in real time within the store. This is done using cameras and sensors. In this step, data from cameras and sensors is collected in real time based on user data and transmitted to a server. The input is detection data from the monitoring device, and the output is real-time data transmission to the server.

[0814] Step 7:

[0815] The server analyzes the monitoring data received in real time and generates suggestion notifications tailored to the situation. For example, when a child takes a specific action, it generates the most appropriate suggestion to address that situation and sends it as a notification to the user's smartphone. The input is monitoring data, and the output is a notification to the user.

[0816] Step 8:

[0817] The user executes a suggestion and provides feedback on the result to the application. This feedback includes information such as which suggestion was executed and the child's reaction. The input is the feedback information, and the output is its saving or transmission.

[0818] Step 9:

[0819] The device sends feedback data to the server. The server analyzes the received feedback data and optimizes the AI ​​model. In this step, the feedback data is used to improve future suggestions and enhance the effectiveness of the suggestions provided to the user. The input is the feedback data, and the output is the optimized suggestions.

[0820] 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.

[0821] This invention is a system for effectively supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. Its detailed configuration and processing flow are described below.

[0822] Overall system configuration

[0823] This system consists of the following main elements:

[0824] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[0825] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[0826] 3. Analysis method: Installed on the server, it analyzes the input data to identify the type based on the child's characteristics.

[0827] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[0828] 5. Display means: An interface for displaying the generated suggestions to the user.

[0829] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[0830] 7. Emotion Engine: A function that recognizes emotions from user input information, voice, and facial expressions, and reflects the analysis results in suggestions.

[0831] Program processing

[0832] The program for this system operates as follows:

[0833] 1. Enter information about children and users.

[0834] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[0835] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[0836] 2. Sending and receiving data

[0837] The terminal generates an HTTP request to send the confirmed data to the server, and the server receives it.

[0838] 3. Analysis of the child's personality type

[0839] The server passes the received data to the analysis platform, which analyzes the data using a database and AI models to identify types based on the child's characteristics.

[0840] 4. Recognition and reflection of emotions

[0841] The emotion engine analyzes user input information and recognizes the user's emotional state (stress, joy, fatigue, etc.).

[0842] Based on recognized emotional information, the system reflects the user's emotional state in its suggestions for approaches and communication with children.

[0843] 5. Generating an appropriate approach

[0844] Based on the analysis results and the emotion engine's recognition results, the server automatically generates specific approaches and suggestions for communication using existing knowledge and AI models in the database. For example, in the case of a child with ASD, it might create suggestions such as "praise specific behaviors" or "give short, clear instructions."

[0845] 6. Submitting and displaying proposals

[0846] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[0847] The terminal analyzes the received suggestion data, formats it into a human-readable format, and displays it on the user interface. For example, it might be presented as short sentences or diagrams.

[0848] Users review the suggestions and try out the approaches in their daily lives.

[0849] 7. Gathering and optimizing feedback

[0850] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[0851] The device sends confirmed feedback data to the server, which receives it and stores it in a database.

[0852] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[0853] Specific example

[0854] Example 1: A child with autism spectrum disorder

[0855] The user enters the child's information: "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[0856] The device sends this information to the server.

[0857] The server analyzes the data and identifies the child's type as "ASD".

[0858] The emotion engine analyzes the user's voice and facial expressions to recognize when the user is tired.

[0859] The server will generate suggestions such as "give instructions in short, clear language" and "praise specific actions," adding ways to communicate with users that take their fatigue into consideration.

[0860] The device displays the suggestion in the user interface.

[0861] Users try out the suggestions in their daily lives and provide feedback on the results.

[0862] The server uses feedback to update the AI ​​model and optimize future suggestions.

[0863] In this way, this system enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[0864] The following describes the processing flow.

[0865] Step 1:

[0866] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[0867] Step 2:

[0868] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[0869] Step 3:

[0870] The terminal organizes the confirmed data and generates an HTTP request to send to the server.

[0871] Step 4:

[0872] The device sends the HTTP request it generates to the server via the internet.

[0873] Step 5:

[0874] The server saves the received data to the database and prepares it to be passed to the analysis platform.

[0875] Step 6:

[0876] The server passes the data to the analysis platform, which uses a database and AI models to identify the child's type based on their characteristics. At this stage, diagnoses such as ASD (Autism Spectrum Disorder) and ADHD (Attention Deficit Hyperactivity Disorder) are made.

[0877] Step 7:

[0878] The emotion engine analyzes user input information and, if possible, user voice and facial expression data. This analysis recognizes the user's emotional state (e.g., stress, joy, fatigue).

[0879] Step 8:

[0880] Based on the analysis results and the emotion engine's recognition results, the server automatically generates specific approaches and suggestions for communication using existing knowledge and AI models in the database. For example, for a child with ASD, suggestions such as "praise specific behaviors" and "give short, clear instructions" are generated. These suggestions also take into account the user's emotional state.

[0881] Step 9:

[0882] The server converts the generated proposal into a data format such as JSON and generates an HTTP response to send to the client.

[0883] Step 10:

[0884] The server sends the generated HTTP response to the terminal via the internet.

[0885] Step 11:

[0886] The terminal analyzes the suggestion data received from the server and formats it into a human-readable format. For example, it can be presented as short sentences or diagrams.

[0887] Step 12:

[0888] The device displays the analysis results in the application's user interface. This allows the user to review the suggestions and try out the approach in their daily life.

[0889] Step 13:

[0890] Users provide feedback on the approaches they implemented and the results. For example, they might write, "When I praised specific behaviors, the child responded well."

[0891] Step 14:

[0892] The user reviews the input and confirms the feedback by pressing the "Submit" button.

[0893] Step 15:

[0894] The system organizes the feedback data from the identified terminal and generates an HTTP request to send it to the server.

[0895] Step 16:

[0896] The device sends the HTTP request it generates to the server via the internet.

[0897] Step 17:

[0898] The server saves the feedback data it receives to the database.

[0899] Step 18:

[0900] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This allows the system to continuously learn and provide more effective suggestions.

[0901] This system, which incorporates an emotion engine, enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[0902] (Example 2)

[0903] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0904] In raising children with developmental disabilities, there is a lack of means to provide individually tailored approaches and suggestions for communication. Furthermore, existing systems fail to consider the user's emotional state, resulting in uniform suggestions that make it difficult to alleviate the user's emotional burden. Additionally, there is insufficient mechanism for effectively collecting feedback and incorporating the results into future suggestions.

[0905] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting basic information and characteristic information of a child; a communication means for receiving data transmitted from the input means and passing the data to an analysis platform; an analysis means for identifying the type of child using the analysis platform; a means for recognizing the user's emotional state using an emotion engine; a generation means for generating appropriate approach and communication suggestions based on the recognition results of the analysis means and the emotion engine; a communication means for transmitting the generated suggestions to an output means; and a display means for displaying the suggestions to the user on the output means. This makes it possible to provide individual approaches according to the child's characteristics and the parent's emotional state, and to optimize suggestions for subsequent visits based on feedback.

[0906] An "input method" is an interface for users to input basic information and characteristic information about their child.

[0907] A "communication means" is an interface for receiving data transmitted from an input means and passing it on to the analysis platform.

[0908] "Analysis means" refers to the means of identifying a child's type using an analysis platform.

[0909] An "emotion engine" is an engine that recognizes the user's emotional state from their voice and facial expressions.

[0910] "Generation means" refers to means for automatically generating appropriate approaches and suggestions for communication based on the recognition results of the analysis means and the emotion engine.

[0911] "Output means" refers to an interface for displaying the generated suggestions to the user.

[0912] An "AI model" is an artificial intelligence model used to identify the type best suited to a child's characteristics.

[0913] A "feedback mechanism" is a means of collecting feedback from users and sending it to a server.

[0914] "Optimization methods" refer to means of optimizing the content of future proposals using the collected feedback.

[0915] This invention is a system for effectively supporting the upbringing of children with developmental disabilities. The system includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. The detailed configuration and processing flow of the system are described below.

[0916] Overall system configuration

[0917] This system consists of the following main elements:

[0918] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[0919] 2. Communication method: This is an interface for sending input data to the server and receiving analysis results and suggestions from the server.

[0920] 3. Analysis Methods: These are methods installed on the server to analyze the input data and identify types based on the characteristics of the children. For example, machine learning models or databases may be used.

[0921] 4. Generation means: A means for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[0922] 5. Display means: This is an interface for displaying the generated suggestions to the user.

[0923] 6. Optimization methods: These are means of collecting user feedback and improving the content of future suggestions.

[0924] 7. Emotion Engine: This engine recognizes emotions from user input, voice, and facial expressions, and incorporates the analysis results into the suggestions.

[0925] Program processing

[0926] The program for this system operates as follows:

[0927] Entering information about children and users

[0928] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[0929] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[0930] Sending and receiving data

[0931] The terminal generates an HTTP request based on the input data and sends it to the server.

[0932] The server receives an HTTP request and prepares the input data to be passed to the analysis platform.

[0933] Analysis of children's types

[0934] The server passes the received data to the analysis platform. This analysis platform includes machine learning models (e.g., TensorFlow or PyTorch).

[0935] The server compares the information with existing data in the database and uses an AI model to identify the child's type based on their characteristics (e.g., autism spectrum disorder, social anxiety disorder, etc.).

[0936] Recognition and reflection of emotions

[0937] The emotion engine analyzes the user's voice and facial expressions. This uses speech recognition software and facial recognition software (e.g., OpenFace, DeepFace, etc.).

[0938] The emotion engine identifies the user's emotional state (stress, joy, fatigue, etc.) based on the analysis results.

[0939] The server receives this sentiment information and incorporates it into the generation of the next suggestion.

[0940] Generating an appropriate method

[0941] The server generates suggestions for appropriate approaches and responses based on the analysis results and the emotion engine's recognition results.

[0942] Using a generation tool, suggestions such as "praise specific behaviors" and "give short, clear instructions" can be automatically created for children with ASD.

[0943] Submitting and displaying proposals

[0944] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[0945] The terminal analyzes the received data and organizes it for display in the user interface. For example, it displays it as short sentences or charts.

[0946] Users review the displayed information and then act upon it in their daily lives.

[0947] Gathering and optimizing feedback

[0948] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[0949] The device sends feedback data to the server.

[0950] The server receives the feedback data and saves it to the database.

[0951] The server analyzes the collected feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[0952] Specific example

[0953] Example 1: A child with autism spectrum disorder

[0954] The user opens the app and enters "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[0955] The device sends an HTTP request containing this information to the server.

[0956] The server receives the data and uses an analysis platform and AI model to identify the child's specific type as "ASD".

[0957] The emotion engine analyzes the user's voice data and recognizes that the user is tired.

[0958] The server generates suggestions such as "give instructions in short, clear language" and "praise specific actions," including methods of verbal communication that take user fatigue into consideration.

[0959] The device analyzes the generated suggestions and displays them in the UI as short sentences or diagrams.

[0960] Users implement the suggested actions and input the results as feedback into the app.

[0961] The device sends feedback data to the server, which receives it, saves it to a database, and updates the AI ​​model.

[0962] In this way, this system provides individualized support for children with developmental disabilities and also reduces the mental burden on users. Furthermore, since the generated suggestions are adjusted according to the user's emotional state, more personalized support is provided.

[0963] Example of a prompt

[0964] "Please advise on an appropriate approach for a 6-year-old child with autism spectrum disorder. Their emotional state is one of exhaustion."

[0965] "Please suggest effective ways to talk to children who have social anxiety."

[0966] This system allows users to obtain approaches that are tailored to the child's characteristics and individual emotional state.

[0967] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0968] Step 1:

[0969] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[0970] Input: Basic information and characteristics of the child

[0971] Data processing: Data collection through input forms

[0972] Output: Confirmed input data

[0973] Step 2:

[0974] The terminal generates an HTTP request based on the confirmed input data and sends it to the server.

[0975] Input: Confirmed input data

[0976] Data processing: Generation of HTTP requests

[0977] Output: HTTP request

[0978] Step 3:

[0979] The server receives an HTTP request and prepares the input data to be passed to the analysis platform.

[0980] Input: HTTP Request

[0981] Data processing: Preparing data for transfer to the analysis platform.

[0982] Output: Data ready for analysis

[0983] Step 4:

[0984] The server passes data to the analysis platform, which then analyzes the data using machine learning models (e.g., TensorFlow or PyTorch).

[0985] Input: Data ready for analysis

[0986] Data processing: Data analysis (using machine learning models)

[0987] Output: Analysis results (type based on child's characteristics)

[0988] Step 5:

[0989] The emotion engine analyzes the user's voice and facial expressions to recognize the user's emotional state (stress, joy, fatigue, etc.). This uses voice recognition software and facial recognition software (e.g., OpenFace, DeepFace, etc.).

[0990] Input: User's voice and facial expression data

[0991] Data processing: Analysis of voice and facial expression data

[0992] Output: User's emotional state

[0993] Step 6:

[0994] The server receives the analysis results and emotional state, and generates suggestions for appropriate approaches and responses. An AI model is used as the generation method in this process.

[0995] Input: Analysis results, user's emotional state

[0996] Data processing: Generation of proposed data (using an AI model)

[0997] Output: Generated proposals

[0998] Step 7:

[0999] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[1000] Input: Generated proposal

[1001] Data processing: Conversion to a data format (e.g., JSON format)

[1002] Output: Converted proposed data

[1003] Step 8:

[1004] The terminal analyzes the received data and prepares it for display in the user interface. It is displayed as short texts or charts.

[1005] Input: Converted suggestion data

[1006] Data processing: Conversion to a format suitable for UI.

[1007] Output: Data displayed in the user interface

[1008] Step 9:

[1009] Users review the displayed content and then implement that approach in their actual daily lives.

[1010] Input: Suggestions displayed in the UI

[1011] Data processing: None (User's actual actions)

[1012] Output: None

[1013] Step 10:

[1014] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[1015] Input: Feedback Information

[1016] Data processing: Collection of feedback information

[1017] Output: Confirmed feedback data

[1018] Step 11:

[1019] The device sends feedback data to the server.

[1020] Input: Confirmed feedback data

[1021] Data processing: Generation of HTTP requests (feedback data)

[1022] Output: HTTP request for feedback data

[1023] Step 12:

[1024] The server receives the feedback data and saves it to the database.

[1025] Input: HTTP request for feedback data

[1026] Data processing: Saving to a database

[1027] Output: Saved feedback data

[1028] Step 13:

[1029] The server analyzes the collected feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[1030] Input: Saved feedback data

[1031] Data processing: Analysis of feedback data and updating of AI models.

[1032] Output: Optimized proposed model

[1033] These processing steps allow the system to provide a personalized approach tailored to the user's emotional state and optimize future suggestions based on feedback.

[1034] (Application Example 2)

[1035] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1036] Supporting the care of children with developmental disabilities requires tailored approaches to various characteristics and situations, and general approaches have limited effectiveness. Furthermore, in physical stores, appropriate responses are needed based on the specific circumstances at hand, but current systems lack sufficient support for this. Therefore, a system is needed that provides real-time, appropriate responses, enabling users to respond immediately on the spot.

[1037] 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.

[1038] In this invention, the server includes input means for inputting basic and characteristic information of a child, means for optimizing the generated suggestions so that they are applied to specific situations in a physical store, and means for generating suggestions to be applied within the physical store using a generation AI model. This makes it possible to provide timely suggestions for appropriate approaches and conversations according to specific situations within the physical store.

[1039] "Basic information and characteristics of the child" includes data such as the child's name, age, and gender, as well as diagnostic information for developmental disorders and specific characteristics, such as social anxiety or inattention.

[1040] An "input method" is an interface that allows users to input data through devices such as smartphones and tablets.

[1041] A "communication method" is an interface for sending input data to a server and receiving analysis results and suggestions from the server.

[1042] "Analysis means" refers to a mechanism installed within the server that identifies a child's type based on their characteristics, using the input data.

[1043] The "generation means" is a mechanism for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[1044] "Output means" refers to an interface for displaying or notifying the user of the generated suggestions.

[1045] "Display means" refers to screens or displays used to visually present generated suggestions to the user.

[1046] "Means for applying generated proposals to users in physical stores" refers to means of interaction that enable users to actually implement proposals generated in a physical store environment.

[1047] "Means for optimizing generated suggestions to be applicable to specific situations in physical stores" refers to methods for selecting and customizing generated suggestions to suit the specific circumstances of physical stores and providing them to users.

[1048] A "means of collecting feedback" is an interface for users to input and record information about the approaches they have tried and the results of those approaches.

[1049] "Means for optimizing suggestions" refers to a mechanism that updates the AI ​​model based on collected feedback, making future suggestions more effective.

[1050] An "AI model" is a machine learning algorithm used to identify the type best suited to a child's characteristics.

[1051] A "generative AI model" is an algorithm that automatically generates actionable suggestions and prompts based on specific situations and user input data.

[1052] A "prompt message" is a sentence containing instructions or advice for a user in a specific situation, generated using a generative AI model.

[1053] Modes for carrying out the invention

[1054] This invention is a system for supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. Its detailed configuration and processing flow are described below.

[1055] Overall system configuration

[1056] This system consists of the following main elements:

[1057] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, using a form input field in a smartphone or tablet application.

[1058] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[1059] 3. Analysis method: Installed on a server, it analyzes the input data to identify a type based on the child's characteristics.

[1060] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[1061] 5. Display means: An interface for displaying the generated suggestions to the user.

[1062] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[1063] 7. Emotion Engine: Recognizes emotions from user input information, voice, and facial expressions, and reflects the analysis results in suggestions.

[1064] Program processing

[1065] 1. Collecting user input:

[1066] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[1067] The terminal confirms the information it has received and sends the data to the server by pressing the send button.

[1068] 2. Sending and receiving data:

[1069] The server receives the input data via an HTTP request.

[1070] 3. Data Analysis:

[1071] The server receives the data and passes it to the analysis platform, which then analyzes the data using a database and AI models (such as TensorFlow or BERT).

[1072] The emotion engine recognizes emotions from the user's input and voice, and incorporates that information into the analysis.

[1073] 4. Proposal generation:

[1074] Based on the analysis results, the server uses knowledge from the database and AI models to generate specific approaches and suggestions for communication.

[1075] For example, it generates suggestions such as "praise specific actions" and "give short, clear instructions."

[1076] 5. Display of proposals:

[1077] The server sends the generated suggestions to the terminal, and the terminal displays those suggestions in the user interface.

[1078] The user reviews the proposal and tries out the approach in a physical store.

[1079] 6. Gathering feedback and optimizing:

[1080] The application receives feedback on the approaches taken by the user and the results they achieved.

[1081] The device sends confirmed feedback data to the server, which receives it and stores it in a database.

[1082] The server analyzes the feedback data and uses an AI model to optimize future suggestions.

[1083] Specific example

[1084] For example, let's discuss how to handle a situation where a child panics at the checkout counter.

[1085] 1. The user provides voice input about the register status in the application.

[1086] 2. The server analyzes the data and generates suggestions that are "brief, clear, and shift attention elsewhere."

[1087] 3. The smartphone screen displays the message, "Try saying in a loud, slow voice, 'When this is over, let's go buy our favorite snacks.'"

[1088] 4. The user implements the suggested method, and the child regains their composure.

[1089] 5. The user provides feedback on the results to the application, and the server uses this data to improve future suggestions.

[1090] Example of a prompt

[1091] "If a child with a developmental disability panics at the checkout counter, please suggest how to briefly and clearly explain the situation and redirect their attention."

[1092] In this way, this system enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[1093] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1094] Step 1:

[1095] The user opens the application and enters basic information about the child (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.). The device collects the input data and generates an HTTP request to send this data to the server. The input data is packaged in JSON format.

[1096] Step 2:

[1097] The server receives an HTTP request sent from the terminal and passes the input data to the analysis platform. The server first checks the data integrity to ensure that no missing or invalid data is included. The verified data is then input into the analysis platform.

[1098] Step 3:

[1099] The server uses an analysis platform to analyze the input data and identify the child's type based on their characteristics. Specifically, it processes the data using TensorFlow or BERT models and applies an AI model to classify the child's characteristics. The analysis result outputs the child's type (e.g., ASD, ADHD).

[1100] Step 4:

[1101] The server uses an emotion engine to recognize the user's emotional state (e.g., stress, fatigue, joy) from user input information and voice data. The emotion engine uses voice analysis and natural language processing techniques to identify the user's current emotional state. The analysis results output the user's emotional state.

[1102] Step 5:

[1103] Based on the analysis results and the emotion engine's output, the server uses knowledge and AI models in the database to generate specific approaches and suggestions for communication. For example, it uses the BERT model to analyze past cases and feedback, automatically generating suggestions such as "praise specific actions" and "give short, clear instructions." The generated suggestions are stored as data in JSON format.

[1104] Step 6:

[1105] The server generates an HTTP response to send the generated suggestions to the terminal and sends it to the terminal. The terminal interprets the received suggestion data and displays it visually in the user interface. This display may be in the form of text or icons on a smartphone screen, for example.

[1106] Step 7:

[1107] Users review the suggestions displayed in the application and try them out in a physical store. They then input feedback into the application regarding their experience trying the suggestions and their child's reaction. This feedback data is then sent back from the device to the server.

[1108] Step 8:

[1109] The server analyzes the feedback data it receives and uses an AI model to optimize future suggestions. Specifically, it stores the feedback data in a database and uses an AI model (e.g., TensorFlow) to compare and analyze it with past data to update the suggestions.

[1110] Examples of prompt statements include:

[1111] "If a child with a developmental disability panics at the checkout counter, please suggest how to briefly and clearly explain the situation and redirect their attention."

[1112] 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.

[1113] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1114] 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.

[1115] [Third Embodiment]

[1116] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1117] 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.

[1118] 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).

[1119] 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.

[1120] 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.

[1121] 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).

[1122] 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.

[1123] 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.

[1124] 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.

[1125] 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.

[1126] 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.

[1127] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1128] The present invention is a system for effectively supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, and optimization means.

[1129] Overall system configuration

[1130] This system consists of the following main elements:

[1131] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[1132] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[1133] 3. Analysis method: Installed on the server, it analyzes the input data to identify the type based on the child's characteristics.

[1134] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[1135] 5. Display means: An interface for displaying the generated suggestions to the user.

[1136] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[1137] Program processing

[1138] The program for this system operates as follows:

[1139] 1. Enter the child's information

[1140] The user opens the application and enters the child's name, age, gender, diagnosis information for developmental disorders, and characteristics (e.g., anxiety in social situations, inattention, etc.).

[1141] The user reviews the input and presses the "Submit" button.

[1142] 2. Sending data

[1143] The terminal sends the entered information to the server via the internet.

[1144] 3. Analysis of the child's personality type

[1145] The server passes the received data to the analysis platform, which uses a database and AI models to identify the child's type based on their characteristics. For example, ASD (Autism Spectrum Disorder) or ADHD (Attention Deficit Hyperactivity Disorder).

[1146] 4. Generating an appropriate approach

[1147] Based on the identified type, the server automatically generates specific approaches and verbal suggestions using existing knowledge and AI models in the database. For example, for a child with ASD, it might suggest "praising specific behaviors" and "giving short, clear instructions."

[1148] 5. Submitting a proposal

[1149] The server sends the generated proposal to the terminal.

[1150] 6. Display of Proposal

[1151] The device analyzes the received suggestions and displays them in the user interface. Users can review the suggestions and try out the approaches in their daily lives.

[1152] 7. Gathering Feedback

[1153] Users provide feedback on the approaches they actually took and the results. For example, they might write, "When I praised a specific action, the child responded well."

[1154] 8. Submit feedback

[1155] The device sends feedback data to the server.

[1156] 9. Optimizing the proposal

[1157] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[1158] Specific example

[1159] Example 1: A child with autism spectrum disorder

[1160] The user enters the child's information: "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[1161] The device sends this information to the server.

[1162] The server analyzes the data and identifies the child's type as "ASD".

[1163] The server generates suggestions such as "give instructions in short, clear language" and "praise specific actions."

[1164] The device displays the suggestion in the user interface.

[1165] Users try out the suggestions in their daily lives and provide feedback on the results.

[1166] The server uses feedback to update the AI ​​model and optimize future suggestions.

[1167] In this way, this system provides individualized support for children with developmental disabilities and reduces the burden of childcare.

[1168] *The processing steps will be explained in detail upon request.

[1169] The following describes the processing flow.

[1170] Step 1:

[1171] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: for example, social anxiety, inattention, etc.).

[1172] Step 2:

[1173] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[1174] Step 3:

[1175] The terminal organizes the confirmed data and generates an HTTP request to send to the server.

[1176] Step 4:

[1177] The device sends the HTTP request it generates to the server via the internet.

[1178] Step 5:

[1179] The server saves the received data to the database.

[1180] Step 6:

[1181] The server passes the received data to the analysis platform, which uses a database and AI models to analyze the data and identify types based on the child's characteristics.

[1182] Step 7:

[1183] The server processes the analysis results to automatically generate appropriate approaches and suggestions for communication. This is done using a database and AI models within the server.

[1184] Step 8:

[1185] The server converts the generated proposal into a data format such as JSON and generates an HTTP response to send to the client.

[1186] Step 9:

[1187] The server sends the generated HTTP response to the terminal via the internet.

[1188] Step 10:

[1189] The terminal analyzes the suggestion data received from the server and formats it into a human-readable format.

[1190] Step 11:

[1191] The terminal displays the analysis results in the application's user interface. For example, it may be presented as a short text or a chart.

[1192] Step 12:

[1193] Users view the displayed suggestions and try to implement specific approaches in their daily lives.

[1194] Step 13:

[1195] The application receives feedback on the approaches taken by the user and the results they achieved.

[1196] Step 14:

[1197] The user reviews the input and confirms the feedback by pressing the "Submit" button.

[1198] Step 15:

[1199] The system organizes the feedback data from the identified terminal and generates an HTTP request to send it to the server.

[1200] Step 16:

[1201] The device sends the HTTP request it generates to the server via the internet.

[1202] Step 17:

[1203] The server saves the feedback data it receives to the database.

[1204] Step 18:

[1205] The server analyzes the feedback data and uses an AI model to perform update processing to optimize future suggestions.

[1206] This allows the system to continuously learn from user feedback and provide more effective suggestions.

[1207] (Example 1)

[1208] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1209] Raising children with developmental disabilities requires appropriate support methods and communication approaches based on individual characteristics, but finding specific methods is considered difficult. Furthermore, there is a lack of mechanisms to properly collect user feedback and incorporate it into future suggestions, which is necessary for continuously optimizing effective support methods. A system is needed to address these problems.

[1210] 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.

[1211] In this invention, the server includes an input means for inputting basic information and characteristic information of a child; a communication means for receiving data transmitted from the input means and passing the data to an analysis platform; an analysis means for identifying the child's type using the analysis platform; a generation means for generating appropriate approaches and suggestions for communication based on the child's type identified by the analysis means; a communication means for transmitting the generated suggestions to an output means; a display means for displaying the suggestions to the user on the output means; a feedback input means for allowing the user to confirm the generated suggestions, take actions based on the suggestions, and input the results as feedback; and an optimization means for receiving the feedback data and optimizing the next suggestions. This enables individualized support for children with developmental disabilities, reduces the burden of childcare, and allows for continuous optimization of support methods.

[1212] "Basic information about a child" refers to basic personal information such as the child's name, age, gender, and information about any diagnosed developmental disorders.

[1213] "Characteristic information" refers to information that includes a child's specific behaviors, personality, and problematic characteristics (e.g., anxiety or inattention in social situations).

[1214] "Input method" refers to an interface for users to input basic information and characteristic information about their children, and includes applications for smartphones and tablets.

[1215] A "communication means" is a network interface used to transmit and receive input data to and from other devices and systems.

[1216] An "analysis platform" is a set of systems that includes a database and AI models for analyzing received data.

[1217] "Analysis means" refers to the process and software used to identify a child's type using an analysis platform.

[1218] "Generation means" refers to software and processes for automatically generating appropriate approaches and suggestions for communication based on analysis results.

[1219] "Output means" refers to a display or other display device for providing the generated proposal to the user.

[1220] "Display means" refers to an interface for displaying proposals on a user interface, and includes the screens of smartphones and tablets.

[1221] A "feedback input method" is an interface that allows users to take action based on a suggestion and input the results as feedback.

[1222] "Optimization means" refers to software and processes for analyzing feedback data and optimizing the next proposal.

[1223] An "AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and perform predictions and classifications.

[1224] This invention is a system for effectively supporting the upbringing of children with developmental disabilities. This system is a combination of hardware and software and includes the following main elements:

[1225] Overall system configuration

[1226] 1. Input method

[1227] This is an interface for users to input basic and characteristic information about their children. Specifically, it refers to the form input fields in applications installed on smartphones and tablets. For example, a user opens the application and enters the child's name, age, gender, diagnosis information for developmental disorders, and characteristics.

[1228] 2. Means of communication

[1229] This is an interface for a terminal to send data entered into it to a server via the internet. The data is sent in a standard data format such as JSON.

[1230] 3. Analysis method

[1231] The server passes the received data to the analysis platform, which uses a database and a generative AI model to identify the child's type based on their characteristics. This analysis platform utilizes existing diagnostic criteria and characteristic classification information for developmental disorders stored in the database.

[1232] 4. Generation means

[1233] Based on the identified child type, the server automatically generates appropriate approaches and conversation suggestions using a generative AI model. This generation method utilizes existing knowledge in the database and predictions from the AI ​​model to create specific suggestions.

[1234] 5. Output means and display means

[1235] The server sends the generated suggestions to the terminal, which then displays them on the user interface. This display method can be a smartphone or tablet screen. The user can then review the displayed suggestions and try them out in their daily life.

[1236] 6. Feedback Input Methods

[1237] This is an interface for users to take action based on suggestions and input the results as feedback. The feedback is implemented as form input to record the results of specific actions and the child's reactions.

[1238] 7. Optimization methods

[1239] The server analyzes the received feedback data and updates the generated AI model to optimize the next proposal. This improves the accuracy of the proposals and continuously provides more effective support.

[1240] Specific example

[1241] The following is a concrete example of the system.

[1242] Example 1: A child with autism spectrum disorder

[1243] The user enters the child's information: "Name: Taro, Age: 6 years old, Gender: Male, Diagnosis: ASD, Characteristics: Social anxiety, Language delay".

[1244] The device sends this information to the server. The server analyzes the data and identifies the child's type as "ASD". The server generates suggestions such as "give instructions in short, clear language" and "praise specific behaviors".

[1245] The device displays suggestions in the user interface. The user tries out the suggestions in their daily life and provides feedback on the results. The server uses the feedback to update the generating AI model and optimize suggestions for future use.

[1246] Example of a prompt

[1247] The following are prompt statements as concrete examples for a generative AI model.

[1248] This system suggests appropriate approaches based on your child's characteristics and diagnosis. Please enter the following information.

[1249] 1. Name:

[1250] 2. Age:

[1251] 3. Gender:

[1252] 4. Diagnostic information for developmental disorders (e.g., ASD, ADHD):

[1253] 5. Characteristics (e.g., social anxiety, inattentiveness, language delay):

[1254] Based on the input information, we will propose a specific approach.

[1255] for example:

[1256] Name: Taro, Age: 6 years old, Gender: Male, Diagnosis: ASD, Characteristics: Social anxiety, language delay

[1257] In this way, the present invention provides individualized support for children with developmental disabilities and reduces the burden of childcare.

[1258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1259] System program processing flow

[1260] Step 1:

[1261] The user opens the application and enters the child's information (name, age, gender, diagnosis of developmental disability, and characteristics).

[1262] Input: Child's name, age, gender, diagnosis information and characteristics of developmental disorders

[1263] Data processing or calculation: The data is formatted when the user enters information into the input fields and presses the "Submit" button.

[1264] Output: JSON data of the input information

[1265] Step 2:

[1266] The terminal converts the entered information into JSON format and sends it to the server via the internet.

[1267] Input: JSON data generated in Step 1

[1268] Data processing or calculation: Format the data as JSON and generate an HTTP request.

[1269] Output: Formatted JSON data is sent to the server.

[1270] Step 3:

[1271] The server passes the received data to the analysis platform, which uses a database and a generative AI model to identify the child's type based on their characteristics.

[1272] Input: JSON data sent from the device

[1273] Data processing or computation: Data analysis using database lookups and generative AI models (e.g., classification of ASD and its characteristics)

[1274] Output: Type identification results based on child characteristics

[1275] Step 4:

[1276] The server uses a generation mechanism to generate appropriate approaches and suggestions for communication based on the type of child it identifies.

[1277] Input: Information on the trait type obtained in Step 3

[1278] Data processing or computation: Generate specific suggestions from existing knowledge in the database or from generative AI models.

[1279] Output: A list of generated suggestions (e.g., give instructions in short, clear language, praise specific actions)

[1280] Step 5:

[1281] The server sends the generated proposal to the terminal.

[1282] Input: List of suggestions generated in Step 4

[1283] Data processing or calculation: Convert the proposed data to JSON format and send it as an HTTP request.

[1284] Output: Proposal data is sent to the terminal.

[1285] Step 6:

[1286] The terminal analyzes the received suggestions and displays them in the user interface.

[1287] Input: List of suggestion data received from the server

[1288] Data processing or calculation: Parsing JSON data and converting it to a display format for the user interface.

[1289] Output: The user will see a suggestion.

[1290] Step 7:

[1291] Users take action based on the suggestions and input the results as feedback.

[1292] Input: Results of actions taken based on the suggestion (e.g., the child responded well when instructions were given in short, clear language).

[1293] Data processing or calculation: Enter feedback data into the input form and submit.

[1294] Output: Feedback data in JSON format

[1295] Step 8:

[1296] The device sends feedback data to the server.

[1297] Input: Feedback data entered in Step 7

[1298] Data processing or calculation: Convert feedback data to JSON format and generate an HTTP request.

[1299] Output: Feedback data is sent to the server.

[1300] Step 9:

[1301] The server analyzes the feedback data, updates the generating AI model, and optimizes the next proposal.

[1302] Input: Feedback data sent from the device

[1303] Data processing or computation: Training and updating generative AI models using feedback data.

[1304] Output: Optimized proposed model and proposed data to be used in subsequent iterations.

[1305] The above describes the processing flow of the system program.

[1306] (Application Example 1)

[1307] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1308] In modern society, it is extremely difficult for parents of children with developmental disabilities to provide appropriate support for their children in daily life and while shopping. In particular, in crowded stores, quick responses tailored to the child's characteristics are required, but conventional technology cannot monitor a child's behavior in real time and provide suggestions at the appropriate time. As a result, the burden on parents increases, and there is a problem that children do not receive the appropriate support they need.

[1309] 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.

[1310] In this invention, the server includes input means for inputting basic information and characteristic information of a child; communication means for receiving data transmitted from the input means and passing the data to an analysis platform; analysis means for identifying the child's type using the analysis platform; generation means for generating appropriate approaches and suggestions for communication based on the child's type identified by the analysis means; communication means for transmitting the generated suggestions to an output means; display means for displaying them to the user on the output means; monitoring means for monitoring the child's behavior in real time within the store and providing behavioral suggestions at the appropriate time; and notification means for providing suggestion notifications according to the situation. As a result, parents can receive appropriate support in real time within the store, enabling effective support for children.

[1311] The "input method" refers to an interface for inputting basic information and characteristic information about a child.

[1312] "Communication means" refers to means for receiving data transmitted from input means and passing it to the analysis platform, and means for transmitting the generated proposals to output means.

[1313] An "analysis platform" is a foundation for analyzing input data and identifying types based on the characteristics of children.

[1314] "Analysis means" refers to methods for identifying a child's type using an analysis platform.

[1315] "Generating means" refers to means for generating appropriate approaches and suggestions for communication based on the type of child identified by the analysis means.

[1316] "Output means" refers to an interface for displaying the generated suggestions to the user.

[1317] "Display means" refers to means for displaying suggestions to the user using output means.

[1318] "Monitoring measures" refer to methods for monitoring children's behavior in real time within a store and providing appropriate behavioral suggestions at the right time.

[1319] "Notification means" refers to the means of providing proposals and notifications appropriate to the situation.

[1320] "Feedback collection methods" refer to means of collecting feedback from users.

[1321] "Optimization methods" refer to the means used to optimize the proposed content based on the collected feedback.

[1322] A "server" is a central processing system for data management and proposal generation, including analysis and generation methods.

[1323] An "AI model" is an artificial intelligence model used to identify the type best suited to a child's characteristics.

[1324] This invention relates to a system for effectively supporting the upbringing of children with developmental disabilities. This system is primarily configured to provide real-time monitoring of children's behavior within a store and to offer appropriate behavioral suggestions at the right time.

[1325] Overall system configuration

[1326] The system consists of the following main elements:

[1327] 1. Input method: An interface for users to input basic and characteristic information about their child. Specifically, this is an input form in a smartphone or tablet application.

[1328] 2. Communication method: An interface for sending input data to a server and receiving analysis results and suggestions from the server. For example, data is sent and received via the internet.

[1329] 3. Analysis Method: This method uses an analysis platform on the server to analyze the input data and identify the type based on the child's characteristics. A specific example is the use of an AI model (such as TensorFlow).

[1330] 4. Generation means: A means for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[1331] 5. Display means: An interface for displaying the generated suggestions to the user. Specifically, the suggested content is displayed on the smartphone screen.

[1332] 6. Monitoring methods: These are means of monitoring children's behavior in real time within the store and providing appropriate behavioral suggestions at the right time. Examples include behavioral monitoring using cameras and sensors.

[1333] 7. Notification methods: Means for providing situation-appropriate notification suggestions. Specifically, this involves utilizing the notification function of smartphones.

[1334] Program processing

[1335] The server receives data transmitted from the input means and passes it to the analysis platform. The analysis platform analyzes the received data and uses an AI model to identify the type best suited to the child's characteristics. Subsequently, the generation means generates appropriate suggestions based on the identified type and transmits them to the output means via the communication means. The output means displays the suggestions on the smartphone screen and provides them to the user.

[1336] Specific example

[1337] The following is a specific example of its use. In this example, the child's basic information and characteristics are as follows:

[1338] Example of a prompt

[1339] Name: Taro

[1340] Age: 6 years old

[1341] Gender: Male

[1342] Diagnosis: ASD

[1343] Characteristics: Social anxiety, language delay

[1344] When a user enters the above information through the application and presses the "Submit" button, the data is sent from the device to the server. The server passes this data to an analysis platform, which uses an AI model to identify the child's type. For example, in this case, "ASD" would be identified. Based on this data, the server generates suggestions such as "give instructions in short, clear language" and "praise specific behaviors," and sends them back to the device.

[1345] Furthermore, the store's monitoring system (cameras and sensors) monitors children's behavior in real time and notifies the user of appropriate suggestions based on specific situations (for example, situations where social anxiety increases). The user receives a notification on their smartphone, tries the suggested action, and provides feedback on the results. This feedback is sent back to the server and used to optimize suggestions for future visits.

[1346] This system allows users to receive appropriate support in real time within the store, enabling an effective approach to children with developmental disabilities.

[1347] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1348] Step 1:

[1349] The user enters basic and characteristic information about their child. Specifically, they use a smartphone or tablet application to fill in forms with information such as name, age, gender, diagnosis, and characteristics (e.g., social anxiety, language delay). This entered data is sent from the device to the server when the user presses the "Submit" button. The input is in text format, and the output is data sent to the server.

[1350] Step 2:

[1351] The terminal sends the entered data to the server via the internet. The server receives the data and passes it to the analysis platform. The input is the basic and characteristic information of the child entered by the user, and the output is that this information is passed to the analysis platform.

[1352] Step 3:

[1353] The server uses an analysis platform to analyze the input data and uses an AI model to identify the child's type based on their characteristics. In this step, data such as name, age, gender, diagnosis, and characteristics are passed to the AI ​​model as prompts, and the output is the child's type (e.g., ASD or ADHD). Data processing includes pre-processing the input data into a format suitable for the AI ​​model.

[1354] Step 4:

[1355] Based on the identified type, the server uses a generation method to generate appropriate approach and communication suggestions. In this step, the suggestions are created using existing knowledge bases and AI models based on the analysis results, and the suggestions are output in text format. The input is the child's type and related data, and the output is the suggested content.

[1356] Step 5:

[1357] The server sends the generated proposal back to the terminal using a communication method. The terminal receives the proposal and displays it to the user through a display device. Specifically, the proposal is displayed as text on the smartphone screen. The input is the proposal sent from the server, and the output is the proposal displayed on the user's smartphone screen.

[1358] Step 6:

[1359] The terminal uses monitoring devices to monitor children's behavior in real time within the store. This is done using cameras and sensors. In this step, data from cameras and sensors is collected in real time based on user data and transmitted to a server. The input is detection data from the monitoring device, and the output is real-time data transmission to the server.

[1360] Step 7:

[1361] The server analyzes the monitoring data received in real time and generates suggestion notifications tailored to the situation. For example, when a child takes a specific action, it generates the most appropriate suggestion to address that situation and sends it as a notification to the user's smartphone. The input is monitoring data, and the output is a notification to the user.

[1362] Step 8:

[1363] The user executes a suggestion and provides feedback on the result to the application. This feedback includes information such as which suggestion was executed and the child's reaction. The input is the feedback information, and the output is its saving or transmission.

[1364] Step 9:

[1365] The device sends feedback data to the server. The server analyzes the received feedback data and optimizes the AI ​​model. In this step, the feedback data is used to improve future suggestions and enhance the effectiveness of the suggestions provided to the user. The input is the feedback data, and the output is the optimized suggestions.

[1366] 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.

[1367] This invention is a system for effectively supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. Its detailed configuration and processing flow are described below.

[1368] Overall system configuration

[1369] This system consists of the following main elements:

[1370] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[1371] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[1372] 3. Analysis method: Installed on the server, it analyzes the input data to identify the type based on the child's characteristics.

[1373] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[1374] 5. Display means: An interface for displaying the generated suggestions to the user.

[1375] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[1376] 7. Emotion Engine: A function that recognizes emotions from user input information, voice, and facial expressions, and reflects the analysis results in suggestions.

[1377] Program processing

[1378] The program for this system operates as follows:

[1379] 1. Enter information about children and users.

[1380] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[1381] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[1382] 2. Sending and receiving data

[1383] The terminal generates an HTTP request to send the confirmed data to the server, and the server receives it.

[1384] 3. Analysis of the child's personality type

[1385] The server passes the received data to the analysis platform, which analyzes the data using a database and AI models to identify types based on the child's characteristics.

[1386] 4. Recognition and reflection of emotions

[1387] The emotion engine analyzes user input information and recognizes the user's emotional state (stress, joy, fatigue, etc.).

[1388] Based on recognized emotional information, the system reflects the user's emotional state in its suggestions for approaches and communication with children.

[1389] 5. Generating an appropriate approach

[1390] Based on the analysis results and the emotion engine's recognition results, the server automatically generates specific approaches and suggestions for communication using existing knowledge and AI models in the database. For example, in the case of a child with ASD, it might create suggestions such as "praise specific behaviors" or "give short, clear instructions."

[1391] 6. Submitting and displaying proposals

[1392] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[1393] The terminal analyzes the received suggestion data, formats it into a human-readable format, and displays it on the user interface. For example, it might be presented as short sentences or diagrams.

[1394] Users review the suggestions and try out the approaches in their daily lives.

[1395] 7. Gathering and optimizing feedback

[1396] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[1397] The device sends confirmed feedback data to the server, which receives it and stores it in a database.

[1398] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[1399] Specific example

[1400] Example 1: A child with autism spectrum disorder

[1401] The user enters the child's information: "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[1402] The device sends this information to the server.

[1403] The server analyzes the data and identifies the child's type as "ASD".

[1404] The emotion engine analyzes the user's voice and facial expressions to recognize when the user is tired.

[1405] The server will generate suggestions such as "give instructions in short, clear language" and "praise specific actions," adding ways to communicate with users that take their fatigue into consideration.

[1406] The device displays the suggestion in the user interface.

[1407] Users try out the suggestions in their daily lives and provide feedback on the results.

[1408] The server uses feedback to update the AI ​​model and optimize future suggestions.

[1409] In this way, this system enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[1410] The following describes the processing flow.

[1411] Step 1:

[1412] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[1413] Step 2:

[1414] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[1415] Step 3:

[1416] The terminal organizes the confirmed data and generates an HTTP request to send to the server.

[1417] Step 4:

[1418] The device sends the HTTP request it generates to the server via the internet.

[1419] Step 5:

[1420] The server saves the received data to the database and prepares it to be passed to the analysis platform.

[1421] Step 6:

[1422] The server passes the data to the analysis platform, which uses a database and AI models to identify the child's type based on their characteristics. At this stage, diagnoses such as ASD (Autism Spectrum Disorder) and ADHD (Attention Deficit Hyperactivity Disorder) are made.

[1423] Step 7:

[1424] The emotion engine analyzes user input information and, if possible, user voice and facial expression data. This analysis recognizes the user's emotional state (e.g., stress, joy, fatigue).

[1425] Step 8:

[1426] Based on the analysis results and the emotion engine's recognition results, the server automatically generates specific approaches and suggestions for communication using existing knowledge and AI models in the database. For example, for a child with ASD, suggestions such as "praise specific behaviors" and "give short, clear instructions" are generated. These suggestions also take into account the user's emotional state.

[1427] Step 9:

[1428] The server converts the generated proposal into a data format such as JSON and generates an HTTP response to send to the client.

[1429] Step 10:

[1430] The server sends the generated HTTP response to the terminal via the internet.

[1431] Step 11:

[1432] The terminal analyzes the suggestion data received from the server and formats it into a human-readable format. For example, it can be presented as short sentences or diagrams.

[1433] Step 12:

[1434] The device displays the analysis results in the application's user interface. This allows the user to review the suggestions and try out the approach in their daily life.

[1435] Step 13:

[1436] Users provide feedback on the approaches they implemented and the results. For example, they might write, "When I praised specific behaviors, the child responded well."

[1437] Step 14:

[1438] The user reviews the input and confirms the feedback by pressing the "Submit" button.

[1439] Step 15:

[1440] The system organizes the feedback data from the identified terminal and generates an HTTP request to send it to the server.

[1441] Step 16:

[1442] The device sends the HTTP request it generates to the server via the internet.

[1443] Step 17:

[1444] The server saves the feedback data it receives to the database.

[1445] Step 18:

[1446] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This allows the system to continuously learn and provide more effective suggestions.

[1447] This system, which incorporates an emotion engine, enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[1448] (Example 2)

[1449] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1450] In raising children with developmental disabilities, there is a lack of means to provide individually tailored approaches and suggestions for communication. Furthermore, existing systems fail to consider the user's emotional state, resulting in uniform suggestions that make it difficult to alleviate the user's emotional burden. Additionally, there is insufficient mechanism for effectively collecting feedback and incorporating the results into future suggestions.

[1451] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting basic information and characteristic information of a child; a communication means for receiving data transmitted from the input means and passing the data to an analysis platform; an analysis means for identifying the type of child using the analysis platform; a means for recognizing the user's emotional state using an emotion engine; a generation means for generating appropriate approach and communication suggestions based on the recognition results of the analysis means and the emotion engine; a communication means for transmitting the generated suggestions to an output means; and a display means for displaying the suggestions to the user on the output means. This makes it possible to provide individual approaches according to the child's characteristics and the parent's emotional state, and to optimize suggestions for subsequent visits based on feedback.

[1452] An "input method" is an interface for users to input basic information and characteristic information about their child.

[1453] A "communication means" is an interface for receiving data transmitted from an input means and passing it on to the analysis platform.

[1454] "Analysis means" refers to the means of identifying a child's type using an analysis platform.

[1455] An "emotion engine" is an engine that recognizes the user's emotional state from their voice and facial expressions.

[1456] "Generation means" refers to means for automatically generating appropriate approaches and suggestions for communication based on the recognition results of the analysis means and the emotion engine.

[1457] "Output means" refers to an interface for displaying the generated suggestions to the user.

[1458] An "AI model" is an artificial intelligence model used to identify the type best suited to a child's characteristics.

[1459] A "feedback mechanism" is a means of collecting feedback from users and sending it to a server.

[1460] "Optimization methods" refer to means of optimizing the content of future proposals using the collected feedback.

[1461] This invention is a system for effectively supporting the upbringing of children with developmental disabilities. The system includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. The detailed configuration and processing flow of the system are described below.

[1462] Overall system configuration

[1463] This system consists of the following main elements:

[1464] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[1465] 2. Communication method: This is an interface for sending input data to the server and receiving analysis results and suggestions from the server.

[1466] 3. Analysis Methods: These are methods installed on the server to analyze the input data and identify types based on the characteristics of the children. For example, machine learning models or databases may be used.

[1467] 4. Generation means: A means for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[1468] 5. Display means: This is an interface for displaying the generated suggestions to the user.

[1469] 6. Optimization methods: These are means of collecting user feedback and improving the content of future suggestions.

[1470] 7. Emotion Engine: This engine recognizes emotions from user input, voice, and facial expressions, and incorporates the analysis results into the suggestions.

[1471] Program processing

[1472] The program for this system operates as follows:

[1473] Entering information about children and users

[1474] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[1475] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[1476] Sending and receiving data

[1477] The terminal generates an HTTP request based on the input data and sends it to the server.

[1478] The server receives an HTTP request and prepares the input data to be passed to the analysis platform.

[1479] Analysis of children's types

[1480] The server passes the received data to the analysis platform. This analysis platform includes machine learning models (e.g., TensorFlow or PyTorch).

[1481] The server compares the information with existing data in the database and uses an AI model to identify the child's type based on their characteristics (e.g., autism spectrum disorder, social anxiety disorder, etc.).

[1482] Recognition and reflection of emotions

[1483] The emotion engine analyzes the user's voice and facial expressions. This uses speech recognition software and facial recognition software (e.g., OpenFace, DeepFace, etc.).

[1484] The emotion engine identifies the user's emotional state (stress, joy, fatigue, etc.) based on the analysis results.

[1485] The server receives this sentiment information and incorporates it into the generation of the next suggestion.

[1486] Generating an appropriate method

[1487] The server generates suggestions for appropriate approaches and responses based on the analysis results and the emotion engine's recognition results.

[1488] Using a generation tool, suggestions such as "praise specific behaviors" and "give short, clear instructions" can be automatically created for children with ASD.

[1489] Submitting and displaying proposals

[1490] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[1491] The terminal analyzes the received data and organizes it for display in the user interface. For example, it displays it as short sentences or charts.

[1492] Users review the displayed information and then act upon it in their daily lives.

[1493] Gathering and optimizing feedback

[1494] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[1495] The device sends feedback data to the server.

[1496] The server receives the feedback data and saves it to the database.

[1497] The server analyzes the collected feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[1498] Specific example

[1499] Example 1: A child with autism spectrum disorder

[1500] The user opens the app and enters "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[1501] The device sends an HTTP request containing this information to the server.

[1502] The server receives the data and uses an analysis platform and AI model to identify the child's specific type as "ASD".

[1503] The emotion engine analyzes the user's voice data and recognizes that the user is tired.

[1504] The server generates suggestions such as "give instructions in short, clear language" and "praise specific actions," including methods of verbal communication that take user fatigue into consideration.

[1505] The device analyzes the generated suggestions and displays them in the UI as short sentences or diagrams.

[1506] Users implement the suggested actions and input the results as feedback into the app.

[1507] The device sends feedback data to the server, which receives it, saves it to a database, and updates the AI ​​model.

[1508] In this way, this system provides individualized support for children with developmental disabilities and also reduces the mental burden on users. Furthermore, since the generated suggestions are adjusted according to the user's emotional state, more personalized support is provided.

[1509] Example of a prompt

[1510] "Please advise on an appropriate approach for a 6-year-old child with autism spectrum disorder. Their emotional state is one of exhaustion."

[1511] "Please suggest effective ways to talk to children who have social anxiety."

[1512] This system allows users to obtain approaches that are tailored to the child's characteristics and individual emotional state.

[1513] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1514] Step 1:

[1515] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[1516] Input: Basic information and characteristics of the child

[1517] Data processing: Data collection through input forms

[1518] Output: Confirmed input data

[1519] Step 2:

[1520] The terminal generates an HTTP request based on the confirmed input data and sends it to the server.

[1521] Input: Confirmed input data

[1522] Data processing: Generation of HTTP requests

[1523] Output: HTTP request

[1524] Step 3:

[1525] The server receives an HTTP request and prepares the input data to be passed to the analysis platform.

[1526] Input: HTTP Request

[1527] Data processing: Preparing data for transfer to the analysis platform.

[1528] Output: Data ready for analysis

[1529] Step 4:

[1530] The server passes data to the analysis platform, which then analyzes the data using machine learning models (e.g., TensorFlow or PyTorch).

[1531] Input: Data ready for analysis

[1532] Data processing: Data analysis (using machine learning models)

[1533] Output: Analysis results (type based on child's characteristics)

[1534] Step 5:

[1535] The emotion engine analyzes the user's voice and facial expressions to recognize the user's emotional state (stress, joy, fatigue, etc.). This uses voice recognition software and facial recognition software (e.g., OpenFace, DeepFace, etc.).

[1536] Input: User's voice and facial expression data

[1537] Data processing: Analysis of voice and facial expression data

[1538] Output: User's emotional state

[1539] Step 6:

[1540] The server receives the analysis results and emotional state, and generates suggestions for appropriate approaches and responses. An AI model is used as the generation method in this process.

[1541] Input: Analysis results, user's emotional state

[1542] Data processing: Generation of proposed data (using an AI model)

[1543] Output: Generated proposals

[1544] Step 7:

[1545] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[1546] Input: Generated proposal

[1547] Data processing: Conversion to a data format (e.g., JSON format)

[1548] Output: Converted proposed data

[1549] Step 8:

[1550] The terminal analyzes the received data and prepares it for display in the user interface. It is displayed as short texts or charts.

[1551] Input: Converted suggestion data

[1552] Data processing: Conversion to a format suitable for UI.

[1553] Output: Data displayed in the user interface

[1554] Step 9:

[1555] Users review the displayed content and then implement that approach in their actual daily lives.

[1556] Input: Suggestions displayed in the UI

[1557] Data processing: None (User's actual actions)

[1558] Output: None

[1559] Step 10:

[1560] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[1561] Input: Feedback Information

[1562] Data processing: Collection of feedback information

[1563] Output: Confirmed feedback data

[1564] Step 11:

[1565] The device sends feedback data to the server.

[1566] Input: Confirmed feedback data

[1567] Data processing: Generation of HTTP requests (feedback data)

[1568] Output: HTTP request for feedback data

[1569] Step 12:

[1570] The server receives the feedback data and saves it to the database.

[1571] Input: HTTP request for feedback data

[1572] Data processing: Saving to a database

[1573] Output: Saved feedback data

[1574] Step 13:

[1575] The server analyzes the collected feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[1576] Input: Saved feedback data

[1577] Data processing: Analysis of feedback data and updating of AI models.

[1578] Output: Optimized proposed model

[1579] These processing steps allow the system to provide a personalized approach tailored to the user's emotional state and optimize future suggestions based on feedback.

[1580] (Application Example 2)

[1581] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1582] Supporting the care of children with developmental disabilities requires tailored approaches to various characteristics and situations, and general approaches have limited effectiveness. Furthermore, in physical stores, appropriate responses are needed based on the specific circumstances at hand, but current systems lack sufficient support for this. Therefore, a system is needed that provides real-time, appropriate responses, enabling users to respond immediately on the spot.

[1583] 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.

[1584] In this invention, the server includes input means for inputting basic and characteristic information of a child, means for optimizing the generated suggestions so that they are applied to specific situations in a physical store, and means for generating suggestions to be applied within the physical store using a generation AI model. This makes it possible to provide timely suggestions for appropriate approaches and conversations according to specific situations within the physical store.

[1585] "Basic information and characteristics of the child" includes data such as the child's name, age, and gender, as well as diagnostic information for developmental disorders and specific characteristics, such as social anxiety or inattention.

[1586] An "input method" is an interface that allows users to input data through devices such as smartphones and tablets.

[1587] A "communication method" is an interface for sending input data to a server and receiving analysis results and suggestions from the server.

[1588] "Analysis means" refers to a mechanism installed within the server that identifies a child's type based on their characteristics, using the input data.

[1589] The "generation means" is a mechanism for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[1590] "Output means" refers to an interface for displaying or notifying the user of the generated suggestions.

[1591] "Display means" refers to screens or displays used to visually present generated suggestions to the user.

[1592] "Means for applying generated proposals to users in physical stores" refers to means of interaction that enable users to actually implement proposals generated in a physical store environment.

[1593] "Means for optimizing generated suggestions to be applicable to specific situations in physical stores" refers to methods for selecting and customizing generated suggestions to suit the specific circumstances of physical stores and providing them to users.

[1594] A "means of collecting feedback" is an interface for users to input and record information about the approaches they have tried and the results of those approaches.

[1595] "Means for optimizing suggestions" refers to a mechanism that updates the AI ​​model based on collected feedback, making future suggestions more effective.

[1596] An "AI model" is a machine learning algorithm used to identify the type best suited to a child's characteristics.

[1597] A "generative AI model" is an algorithm that automatically generates actionable suggestions and prompts based on specific situations and user input data.

[1598] A "prompt message" is a sentence containing instructions or advice for a user in a specific situation, generated using a generative AI model.

[1599] Modes for carrying out the invention

[1600] This invention is a system for supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. Its detailed configuration and processing flow are described below.

[1601] Overall system configuration

[1602] This system consists of the following main elements:

[1603] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, using a form input field in a smartphone or tablet application.

[1604] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[1605] 3. Analysis method: Installed on a server, it analyzes the input data to identify a type based on the child's characteristics.

[1606] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[1607] 5. Display means: An interface for displaying the generated suggestions to the user.

[1608] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[1609] 7. Emotion Engine: Recognizes emotions from user input information, voice, and facial expressions, and reflects the analysis results in suggestions.

[1610] Program processing

[1611] 1. Collecting user input:

[1612] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[1613] The terminal confirms the information it has received and sends the data to the server by pressing the send button.

[1614] 2. Sending and receiving data:

[1615] The server receives the input data via an HTTP request.

[1616] 3. Data Analysis:

[1617] The server receives the data and passes it to the analysis platform, which then analyzes the data using a database and AI models (such as TensorFlow or BERT).

[1618] The emotion engine recognizes emotions from the user's input and voice, and incorporates that information into the analysis.

[1619] 4. Proposal generation:

[1620] Based on the analysis results, the server uses knowledge from the database and AI models to generate specific approaches and suggestions for communication.

[1621] For example, it generates suggestions such as "praise specific actions" and "give short, clear instructions."

[1622] 5. Display of proposals:

[1623] The server sends the generated suggestions to the terminal, and the terminal displays those suggestions in the user interface.

[1624] The user reviews the proposal and tries out the approach in a physical store.

[1625] 6. Gathering feedback and optimizing:

[1626] The application receives feedback on the approaches taken by the user and the results they achieved.

[1627] The device sends confirmed feedback data to the server, which receives it and stores it in a database.

[1628] The server analyzes the feedback data and uses an AI model to optimize future suggestions.

[1629] Specific example

[1630] For example, let's discuss how to handle a situation where a child panics at the checkout counter.

[1631] 1. The user provides voice input about the register status in the application.

[1632] 2. The server analyzes the data and generates suggestions that are "brief, clear, and shift attention elsewhere."

[1633] 3. The smartphone screen displays the message, "Try saying in a loud, slow voice, 'When this is over, let's go buy our favorite snacks.'"

[1634] 4. The user implements the suggested method, and the child regains their composure.

[1635] 5. The user provides feedback on the results to the application, and the server uses this data to improve future suggestions.

[1636] Example of a prompt

[1637] "If a child with a developmental disability panics at the checkout counter, please suggest how to briefly and clearly explain the situation and redirect their attention."

[1638] In this way, this system enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[1639] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1640] Step 1:

[1641] The user opens the application and enters basic information about the child (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.). The device collects the input data and generates an HTTP request to send this data to the server. The input data is packaged in JSON format.

[1642] Step 2:

[1643] The server receives an HTTP request sent from the terminal and passes the input data to the analysis platform. The server first checks the data integrity to ensure that no missing or invalid data is included. The verified data is then input into the analysis platform.

[1644] Step 3:

[1645] The server uses an analysis platform to analyze the input data and identify the child's type based on their characteristics. Specifically, it processes the data using TensorFlow or BERT models and applies an AI model to classify the child's characteristics. The analysis result outputs the child's type (e.g., ASD, ADHD).

[1646] Step 4:

[1647] The server uses an emotion engine to recognize the user's emotional state (e.g., stress, fatigue, joy) from user input information and voice data. The emotion engine uses voice analysis and natural language processing techniques to identify the user's current emotional state. The analysis results output the user's emotional state.

[1648] Step 5:

[1649] Based on the analysis results and the emotion engine's output, the server uses knowledge and AI models in the database to generate specific approaches and suggestions for communication. For example, it uses the BERT model to analyze past cases and feedback, automatically generating suggestions such as "praise specific actions" and "give short, clear instructions." The generated suggestions are stored as data in JSON format.

[1650] Step 6:

[1651] The server generates an HTTP response to send the generated suggestions to the terminal and sends it to the terminal. The terminal interprets the received suggestion data and displays it visually in the user interface. This display may be in the form of text or icons on a smartphone screen, for example.

[1652] Step 7:

[1653] Users review the suggestions displayed in the application and try them out in a physical store. They then input feedback into the application regarding their experience trying the suggestions and their child's reaction. This feedback data is then sent back from the device to the server.

[1654] Step 8:

[1655] The server analyzes the feedback data it receives and uses an AI model to optimize future suggestions. Specifically, it stores the feedback data in a database and uses an AI model (e.g., TensorFlow) to compare and analyze it with past data to update the suggestions.

[1656] Examples of prompt statements include:

[1657] "If a child with a developmental disability panics at the checkout counter, please suggest how to briefly and clearly explain the situation and redirect their attention."

[1658] 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.

[1659] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1660] 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.

[1661] [Fourth Embodiment]

[1662] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1663] 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.

[1664] 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).

[1665] 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.

[1666] 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.

[1667] 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).

[1668] 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.

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1675] The present invention is a system for effectively supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, and optimization means.

[1676] Overall system configuration

[1677] This system consists of the following main elements:

[1678] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[1679] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[1680] 3. Analysis method: Installed on the server, it analyzes the input data to identify the type based on the child's characteristics.

[1681] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[1682] 5. Display means: An interface for displaying the generated suggestions to the user.

[1683] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[1684] Program processing

[1685] The program for this system operates as follows:

[1686] 1. Enter the child's information

[1687] The user opens the application and enters the child's name, age, gender, diagnosis information for developmental disorders, and characteristics (e.g., anxiety in social situations, inattention, etc.).

[1688] The user reviews the input and presses the "Submit" button.

[1689] 2. Sending data

[1690] The terminal sends the entered information to the server via the internet.

[1691] 3. Analysis of the child's personality type

[1692] The server passes the received data to the analysis platform, which uses a database and AI models to identify the child's type based on their characteristics. For example, ASD (Autism Spectrum Disorder) or ADHD (Attention Deficit Hyperactivity Disorder).

[1693] 4. Generating an appropriate approach

[1694] Based on the identified type, the server automatically generates specific approaches and verbal suggestions using existing knowledge and AI models in the database. For example, for a child with ASD, it might suggest "praising specific behaviors" and "giving short, clear instructions."

[1695] 5. Submitting a proposal

[1696] The server sends the generated proposal to the terminal.

[1697] 6. Display of Proposal

[1698] The device analyzes the received suggestions and displays them in the user interface. Users can review the suggestions and try out the approaches in their daily lives.

[1699] 7. Gathering Feedback

[1700] Users provide feedback on the approaches they actually took and the results. For example, they might write, "When I praised a specific action, the child responded well."

[1701] 8. Submit feedback

[1702] The device sends feedback data to the server.

[1703] 9. Optimizing the proposal

[1704] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[1705] Specific example

[1706] Example 1: A child with autism spectrum disorder

[1707] The user enters the child's information: "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[1708] The device sends this information to the server.

[1709] The server analyzes the data and identifies the child's type as "ASD".

[1710] The server generates suggestions such as "give instructions in short, clear language" and "praise specific actions."

[1711] The device displays the suggestion in the user interface.

[1712] Users try out the suggestions in their daily lives and provide feedback on the results.

[1713] The server uses feedback to update the AI ​​model and optimize future suggestions.

[1714] In this way, this system provides individualized support for children with developmental disabilities and reduces the burden of childcare.

[1715] *The processing steps will be explained in detail upon request.

[1716] The following describes the processing flow.

[1717] Step 1:

[1718] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: for example, social anxiety, inattention, etc.).

[1719] Step 2:

[1720] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[1721] Step 3:

[1722] The terminal organizes the confirmed data and generates an HTTP request to send to the server.

[1723] Step 4:

[1724] The device sends the HTTP request it generates to the server via the internet.

[1725] Step 5:

[1726] The server saves the received data to the database.

[1727] Step 6:

[1728] The server passes the received data to the analysis platform, which uses a database and AI models to analyze the data and identify types based on the child's characteristics.

[1729] Step 7:

[1730] The server processes the analysis results to automatically generate appropriate approaches and suggestions for communication. This is done using a database and AI models within the server.

[1731] Step 8:

[1732] The server converts the generated proposal into a data format such as JSON and generates an HTTP response to send to the client.

[1733] Step 9:

[1734] The server sends the generated HTTP response to the terminal via the internet.

[1735] Step 10:

[1736] The terminal analyzes the suggestion data received from the server and formats it into a human-readable format.

[1737] Step 11:

[1738] The terminal displays the analysis results in the application's user interface. For example, it may be presented as a short text or a chart.

[1739] Step 12:

[1740] Users view the displayed suggestions and try to implement specific approaches in their daily lives.

[1741] Step 13:

[1742] The application receives feedback on the approaches taken by the user and the results they achieved.

[1743] Step 14:

[1744] The user reviews the input and confirms the feedback by pressing the "Submit" button.

[1745] Step 15:

[1746] The system organizes the feedback data from the identified terminal and generates an HTTP request to send it to the server.

[1747] Step 16:

[1748] The device sends the HTTP request it generates to the server via the internet.

[1749] Step 17:

[1750] The server saves the feedback data it receives to the database.

[1751] Step 18:

[1752] The server analyzes the feedback data and uses an AI model to perform update processing to optimize future suggestions.

[1753] This allows the system to continuously learn from user feedback and provide more effective suggestions.

[1754] (Example 1)

[1755] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1756] Raising children with developmental disabilities requires appropriate support methods and communication approaches based on individual characteristics, but finding specific methods is considered difficult. Furthermore, there is a lack of mechanisms to properly collect user feedback and incorporate it into future suggestions, which is necessary for continuously optimizing effective support methods. A system is needed to address these problems.

[1757] 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.

[1758] In this invention, the server includes an input means for inputting basic information and characteristic information of a child; a communication means for receiving data transmitted from the input means and passing the data to an analysis platform; an analysis means for identifying the child's type using the analysis platform; a generation means for generating appropriate approaches and suggestions for communication based on the child's type identified by the analysis means; a communication means for transmitting the generated suggestions to an output means; a display means for displaying the suggestions to the user on the output means; a feedback input means for allowing the user to confirm the generated suggestions, take actions based on the suggestions, and input the results as feedback; and an optimization means for receiving the feedback data and optimizing the next suggestions. This enables individualized support for children with developmental disabilities, reduces the burden of childcare, and allows for continuous optimization of support methods.

[1759] "Basic information about a child" refers to basic personal information such as the child's name, age, gender, and information about any diagnosed developmental disorders.

[1760] "Characteristic information" refers to information that includes a child's specific behaviors, personality, and problematic characteristics (e.g., anxiety or inattention in social situations).

[1761] "Input method" refers to an interface for users to input basic information and characteristic information about their children, and includes applications for smartphones and tablets.

[1762] A "communication means" is a network interface used to transmit and receive input data to and from other devices and systems.

[1763] An "analysis platform" is a set of systems that includes a database and AI models for analyzing received data.

[1764] "Analysis means" refers to the process and software used to identify a child's type using an analysis platform.

[1765] "Generation means" refers to software and processes for automatically generating appropriate approaches and suggestions for communication based on analysis results.

[1766] "Output means" refers to a display or other display device for providing the generated proposal to the user.

[1767] "Display means" refers to an interface for displaying proposals on a user interface, and includes the screens of smartphones and tablets.

[1768] A "feedback input method" is an interface that allows users to take action based on a suggestion and input the results as feedback.

[1769] "Optimization means" refers to software and processes for analyzing feedback data and optimizing the next proposal.

[1770] An "AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and perform predictions and classifications.

[1771] This invention is a system for effectively supporting the upbringing of children with developmental disabilities. This system is a combination of hardware and software and includes the following main elements:

[1772] Overall system configuration

[1773] 1. Input method

[1774] This is an interface for users to input basic and characteristic information about their children. Specifically, it refers to the form input fields in applications installed on smartphones and tablets. For example, a user opens the application and enters the child's name, age, gender, diagnosis information for developmental disorders, and characteristics.

[1775] 2. Means of communication

[1776] This is an interface for a terminal to send data entered into it to a server via the internet. The data is sent in a standard data format such as JSON.

[1777] 3. Analysis method

[1778] The server passes the received data to the analysis platform, which uses a database and a generative AI model to identify the child's type based on their characteristics. This analysis platform utilizes existing diagnostic criteria and characteristic classification information for developmental disorders stored in the database.

[1779] 4. Generation means

[1780] Based on the identified child type, the server automatically generates appropriate approaches and conversation suggestions using a generative AI model. This generation method utilizes existing knowledge in the database and predictions from the AI ​​model to create specific suggestions.

[1781] 5. Output means and display means

[1782] The server sends the generated suggestions to the terminal, which then displays them on the user interface. This display method can be a smartphone or tablet screen. The user can then review the displayed suggestions and try them out in their daily life.

[1783] 6. Feedback Input Methods

[1784] This is an interface for users to take action based on suggestions and input the results as feedback. The feedback is implemented as form input to record the results of specific actions and the child's reactions.

[1785] 7. Optimization methods

[1786] The server analyzes the received feedback data and updates the generated AI model to optimize the next proposal. This improves the accuracy of the proposals and continuously provides more effective support.

[1787] Specific example

[1788] The following is a concrete example of the system.

[1789] Example 1: A child with autism spectrum disorder

[1790] The user enters the child's information: "Name: Taro, Age: 6 years old, Gender: Male, Diagnosis: ASD, Characteristics: Social anxiety, Language delay".

[1791] The device sends this information to the server. The server analyzes the data and identifies the child's type as "ASD". The server generates suggestions such as "give instructions in short, clear language" and "praise specific behaviors".

[1792] The device displays suggestions in the user interface. The user tries out the suggestions in their daily life and provides feedback on the results. The server uses the feedback to update the generating AI model and optimize suggestions for future use.

[1793] Example of a prompt

[1794] The following are prompt statements as concrete examples for a generative AI model.

[1795] This system suggests appropriate approaches based on your child's characteristics and diagnosis. Please enter the following information.

[1796] 1. Name:

[1797] 2. Age:

[1798] 3. Gender:

[1799] 4. Diagnostic information for developmental disorders (e.g., ASD, ADHD):

[1800] 5. Characteristics (e.g., social anxiety, inattentiveness, language delay):

[1801] Based on the input information, we will propose a specific approach.

[1802] for example:

[1803] Name: Taro, Age: 6 years old, Gender: Male, Diagnosis: ASD, Characteristics: Social anxiety, language delay

[1804] In this way, the present invention provides individualized support for children with developmental disabilities and reduces the burden of childcare.

[1805] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1806] System program processing flow

[1807] Step 1:

[1808] The user opens the application and enters the child's information (name, age, gender, diagnosis of developmental disability, and characteristics).

[1809] Input: Child's name, age, gender, diagnosis information and characteristics of developmental disorders

[1810] Data processing or calculation: The data is formatted when the user enters information into the input fields and presses the "Submit" button.

[1811] Output: JSON data of the input information

[1812] Step 2:

[1813] The terminal converts the entered information into JSON format and sends it to the server via the internet.

[1814] Input: JSON data generated in Step 1

[1815] Data processing or calculation: Format the data as JSON and generate an HTTP request.

[1816] Output: Formatted JSON data is sent to the server.

[1817] Step 3:

[1818] The server passes the received data to the analysis platform, which uses a database and a generative AI model to identify the child's type based on their characteristics.

[1819] Input: JSON data sent from the device

[1820] Data processing or computation: Data analysis using database lookups and generative AI models (e.g., classification of ASD and its characteristics)

[1821] Output: Type identification results based on child characteristics

[1822] Step 4:

[1823] The server uses a generation mechanism to generate appropriate approaches and suggestions for communication based on the type of child it identifies.

[1824] Input: Information on the trait type obtained in Step 3

[1825] Data processing or computation: Generate specific suggestions from existing knowledge in the database or from generative AI models.

[1826] Output: A list of generated suggestions (e.g., give instructions in short, clear language, praise specific actions)

[1827] Step 5:

[1828] The server sends the generated proposal to the terminal.

[1829] Input: List of suggestions generated in Step 4

[1830] Data processing or calculation: Convert the proposed data to JSON format and send it as an HTTP request.

[1831] Output: Proposal data is sent to the terminal.

[1832] Step 6:

[1833] The terminal analyzes the received suggestions and displays them in the user interface.

[1834] Input: List of suggestion data received from the server

[1835] Data processing or calculation: Parsing JSON data and converting it to a display format for the user interface.

[1836] Output: The user will see a suggestion.

[1837] Step 7:

[1838] Users take action based on the suggestions and input the results as feedback.

[1839] Input: Results of actions taken based on the suggestion (e.g., the child responded well when instructions were given in short, clear language).

[1840] Data processing or calculation: Enter feedback data into the input form and submit.

[1841] Output: Feedback data in JSON format

[1842] Step 8:

[1843] The device sends feedback data to the server.

[1844] Input: Feedback data entered in Step 7

[1845] Data processing or calculation: Convert feedback data to JSON format and generate an HTTP request.

[1846] Output: Feedback data is sent to the server.

[1847] Step 9:

[1848] The server analyzes the feedback data, updates the generating AI model, and optimizes the next proposal.

[1849] Input: Feedback data sent from the device

[1850] Data processing or computation: Training and updating generative AI models using feedback data.

[1851] Output: Optimized proposed model and proposed data to be used in subsequent iterations.

[1852] The above describes the processing flow of the system program.

[1853] (Application Example 1)

[1854] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1855] In modern society, it is extremely difficult for parents of children with developmental disabilities to provide appropriate support for their children in daily life and while shopping. In particular, in crowded stores, quick responses tailored to the child's characteristics are required, but conventional technology cannot monitor a child's behavior in real time and provide suggestions at the appropriate time. As a result, the burden on parents increases, and there is a problem that children do not receive the appropriate support they need.

[1856] 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.

[1857] In this invention, the server includes input means for inputting basic information and characteristic information of a child; communication means for receiving data transmitted from the input means and passing the data to an analysis platform; analysis means for identifying the child's type using the analysis platform; generation means for generating appropriate approaches and suggestions for communication based on the child's type identified by the analysis means; communication means for transmitting the generated suggestions to an output means; display means for displaying them to the user on the output means; monitoring means for monitoring the child's behavior in real time within the store and providing behavioral suggestions at the appropriate time; and notification means for providing suggestion notifications according to the situation. As a result, parents can receive appropriate support in real time within the store, enabling effective support for children.

[1858] The "input method" refers to an interface for inputting basic information and characteristic information about a child.

[1859] "Communication means" refers to means for receiving data transmitted from input means and passing it to the analysis platform, and means for transmitting the generated proposals to output means.

[1860] An "analysis platform" is a foundation for analyzing input data and identifying types based on the characteristics of children.

[1861] "Analysis means" refers to methods for identifying a child's type using an analysis platform.

[1862] "Generating means" refers to means for generating appropriate approaches and suggestions for communication based on the type of child identified by the analysis means.

[1863] "Output means" refers to an interface for displaying the generated suggestions to the user.

[1864] "Display means" refers to means for displaying suggestions to the user using output means.

[1865] "Monitoring measures" refer to methods for monitoring children's behavior in real time within a store and providing appropriate behavioral suggestions at the right time.

[1866] "Notification means" refers to the means of providing proposals and notifications appropriate to the situation.

[1867] "Feedback collection methods" refer to means of collecting feedback from users.

[1868] "Optimization methods" refer to the means used to optimize the proposed content based on the collected feedback.

[1869] A "server" is a central processing system for data management and proposal generation, including analysis and generation methods.

[1870] An "AI model" is an artificial intelligence model used to identify the type best suited to a child's characteristics.

[1871] This invention relates to a system for effectively supporting the upbringing of children with developmental disabilities. This system is primarily configured to provide real-time monitoring of children's behavior within a store and to offer appropriate behavioral suggestions at the right time.

[1872] Overall system configuration

[1873] The system consists of the following main elements:

[1874] 1. Input method: An interface for users to input basic and characteristic information about their child. Specifically, this is an input form in a smartphone or tablet application.

[1875] 2. Communication method: An interface for sending input data to a server and receiving analysis results and suggestions from the server. For example, data is sent and received via the internet.

[1876] 3. Analysis Method: This method uses an analysis platform on the server to analyze the input data and identify the type based on the child's characteristics. A specific example is the use of an AI model (such as TensorFlow).

[1877] 4. Generation means: A means for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[1878] 5. Display means: An interface for displaying the generated suggestions to the user. Specifically, the suggested content is displayed on the smartphone screen.

[1879] 6. Monitoring methods: These are means of monitoring children's behavior in real time within the store and providing appropriate behavioral suggestions at the right time. Examples include behavioral monitoring using cameras and sensors.

[1880] 7. Notification methods: Means for providing situation-appropriate notification suggestions. Specifically, this involves utilizing the notification function of smartphones.

[1881] Program processing

[1882] The server receives data transmitted from the input means and passes it to the analysis platform. The analysis platform analyzes the received data and uses an AI model to identify the type best suited to the child's characteristics. Subsequently, the generation means generates appropriate suggestions based on the identified type and transmits them to the output means via the communication means. The output means displays the suggestions on the smartphone screen and provides them to the user.

[1883] Specific example

[1884] The following is a specific example of its use. In this example, the child's basic information and characteristics are as follows:

[1885] Example of a prompt

[1886] Name: Taro

[1887] Age: 6 years old

[1888] Gender: Male

[1889] Diagnosis: ASD

[1890] Characteristics: Social anxiety, language delay

[1891] When a user enters the above information through the application and presses the "Submit" button, the data is sent from the device to the server. The server passes this data to an analysis platform, which uses an AI model to identify the child's type. For example, in this case, "ASD" would be identified. Based on this data, the server generates suggestions such as "give instructions in short, clear language" and "praise specific behaviors," and sends them back to the device.

[1892] Furthermore, the store's monitoring system (cameras and sensors) monitors children's behavior in real time and notifies the user of appropriate suggestions based on specific situations (for example, situations where social anxiety increases). The user receives a notification on their smartphone, tries the suggested action, and provides feedback on the results. This feedback is sent back to the server and used to optimize suggestions for future visits.

[1893] This system allows users to receive appropriate support in real time within the store, enabling an effective approach to children with developmental disabilities.

[1894] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1895] Step 1:

[1896] The user enters basic and characteristic information about their child. Specifically, they use a smartphone or tablet application to fill in forms with information such as name, age, gender, diagnosis, and characteristics (e.g., social anxiety, language delay). This entered data is sent from the device to the server when the user presses the "Submit" button. The input is in text format, and the output is data sent to the server.

[1897] Step 2:

[1898] The terminal sends the entered data to the server via the internet. The server receives the data and passes it to the analysis platform. The input is the basic and characteristic information of the child entered by the user, and the output is that this information is passed to the analysis platform.

[1899] Step 3:

[1900] The server uses an analysis platform to analyze the input data and uses an AI model to identify the child's type based on their characteristics. In this step, data such as name, age, gender, diagnosis, and characteristics are passed to the AI ​​model as prompts, and the output is the child's type (e.g., ASD or ADHD). Data processing includes pre-processing the input data into a format suitable for the AI ​​model.

[1901] Step 4:

[1902] Based on the identified type, the server uses a generation method to generate appropriate approach and communication suggestions. In this step, the suggestions are created using existing knowledge bases and AI models based on the analysis results, and the suggestions are output in text format. The input is the child's type and related data, and the output is the suggested content.

[1903] Step 5:

[1904] The server sends the generated proposal back to the terminal using a communication method. The terminal receives the proposal and displays it to the user through a display device. Specifically, the proposal is displayed as text on the smartphone screen. The input is the proposal sent from the server, and the output is the proposal displayed on the user's smartphone screen.

[1905] Step 6:

[1906] The terminal uses monitoring devices to monitor children's behavior in real time within the store. This is done using cameras and sensors. In this step, data from cameras and sensors is collected in real time based on user data and transmitted to a server. The input is detection data from the monitoring device, and the output is real-time data transmission to the server.

[1907] Step 7:

[1908] The server analyzes the monitoring data received in real time and generates suggestion notifications tailored to the situation. For example, when a child takes a specific action, it generates the most appropriate suggestion to address that situation and sends it as a notification to the user's smartphone. The input is monitoring data, and the output is a notification to the user.

[1909] Step 8:

[1910] The user executes a suggestion and provides feedback on the result to the application. This feedback includes information such as which suggestion was executed and the child's reaction. The input is the feedback information, and the output is its saving or transmission.

[1911] Step 9:

[1912] The device sends feedback data to the server. The server analyzes the received feedback data and optimizes the AI ​​model. In this step, the feedback data is used to improve future suggestions and enhance the effectiveness of the suggestions provided to the user. The input is the feedback data, and the output is the optimized suggestions.

[1913] 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.

[1914] This invention is a system for effectively supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. Its detailed configuration and processing flow are described below.

[1915] Overall system configuration

[1916] This system consists of the following main elements:

[1917] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[1918] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[1919] 3. Analysis method: Installed on the server, it analyzes the input data to identify the type based on the child's characteristics.

[1920] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[1921] 5. Display means: An interface for displaying the generated suggestions to the user.

[1922] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[1923] 7. Emotion Engine: A function that recognizes emotions from user input information, voice, and facial expressions, and reflects the analysis results in suggestions.

[1924] Program processing

[1925] The program for this system operates as follows:

[1926] 1. Enter information about children and users.

[1927] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[1928] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[1929] 2. Sending and receiving data

[1930] The terminal generates an HTTP request to send the confirmed data to the server, and the server receives it.

[1931] 3. Analysis of the child's personality type

[1932] The server passes the received data to the analysis platform, which analyzes the data using a database and AI models to identify types based on the child's characteristics.

[1933] 4. Recognition and reflection of emotions

[1934] The emotion engine analyzes user input information and recognizes the user's emotional state (stress, joy, fatigue, etc.).

[1935] Based on recognized emotional information, the system reflects the user's emotional state in its suggestions for approaches and communication with children.

[1936] 5. Generating an appropriate approach

[1937] Based on the analysis results and the emotion engine's recognition results, the server automatically generates specific approaches and suggestions for communication using existing knowledge and AI models in the database. For example, in the case of a child with ASD, it might create suggestions such as "praise specific behaviors" or "give short, clear instructions."

[1938] 6. Submitting and displaying proposals

[1939] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[1940] The terminal analyzes the received suggestion data, formats it into a human-readable format, and displays it on the user interface. For example, it might be presented as short sentences or diagrams.

[1941] Users review the suggestions and try out the approaches in their daily lives.

[1942] 7. Gathering and optimizing feedback

[1943] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[1944] The device sends confirmed feedback data to the server, which receives it and stores it in a database.

[1945] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[1946] Specific example

[1947] Example 1: A child with autism spectrum disorder

[1948] The user enters the child's information: "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[1949] The device sends this information to the server.

[1950] The server analyzes the data and identifies the child's type as "ASD".

[1951] The emotion engine analyzes the user's voice and facial expressions to recognize when the user is tired.

[1952] The server will generate suggestions such as "give instructions in short, clear language" and "praise specific actions," adding ways to communicate with users that take their fatigue into consideration.

[1953] The device displays the suggestion in the user interface.

[1954] Users try out the suggestions in their daily lives and provide feedback on the results.

[1955] The server uses feedback to update the AI ​​model and optimize future suggestions.

[1956] In this way, this system enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[1957] The following describes the processing flow.

[1958] Step 1:

[1959] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[1960] Step 2:

[1961] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[1962] Step 3:

[1963] The terminal organizes the confirmed data and generates an HTTP request to send to the server.

[1964] Step 4:

[1965] The device sends the HTTP request it generates to the server via the internet.

[1966] Step 5:

[1967] The server saves the received data to the database and prepares it to be passed to the analysis platform.

[1968] Step 6:

[1969] The server passes the data to the analysis platform, which uses a database and AI models to identify the child's type based on their characteristics. At this stage, diagnoses such as ASD (Autism Spectrum Disorder) and ADHD (Attention Deficit Hyperactivity Disorder) are made.

[1970] Step 7:

[1971] The emotion engine analyzes user input information and, if possible, user voice and facial expression data. This analysis recognizes the user's emotional state (e.g., stress, joy, fatigue).

[1972] Step 8:

[1973] Based on the analysis results and the emotion engine's recognition results, the server automatically generates specific approaches and suggestions for communication using existing knowledge and AI models in the database. For example, for a child with ASD, suggestions such as "praise specific behaviors" and "give short, clear instructions" are generated. These suggestions also take into account the user's emotional state.

[1974] Step 9:

[1975] The server converts the generated proposal into a data format such as JSON and generates an HTTP response to send to the client.

[1976] Step 10:

[1977] The server sends the generated HTTP response to the terminal via the internet.

[1978] Step 11:

[1979] The terminal analyzes the suggestion data received from the server and formats it into a human-readable format. For example, it can be presented as short sentences or diagrams.

[1980] Step 12:

[1981] The device displays the analysis results in the application's user interface. This allows the user to review the suggestions and try out the approach in their daily life.

[1982] Step 13:

[1983] Users provide feedback on the approaches they implemented and the results. For example, they might write, "When I praised specific behaviors, the child responded well."

[1984] Step 14:

[1985] The user reviews the input and confirms the feedback by pressing the "Submit" button.

[1986] Step 15:

[1987] The system organizes the feedback data from the identified terminal and generates an HTTP request to send it to the server.

[1988] Step 16:

[1989] The device sends the HTTP request it generates to the server via the internet.

[1990] Step 17:

[1991] The server saves the feedback data it receives to the database.

[1992] Step 18:

[1993] The server analyzes the feedback data and uses an AI model to optimize future suggestions. This allows the system to continuously learn and provide more effective suggestions.

[1994] This system, which incorporates an emotion engine, enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[1995] (Example 2)

[1996] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1997] In raising children with developmental disabilities, there is a lack of means to provide individually tailored approaches and suggestions for communication. Furthermore, existing systems fail to consider the user's emotional state, resulting in uniform suggestions that make it difficult to alleviate the user's emotional burden. Additionally, there is insufficient mechanism for effectively collecting feedback and incorporating the results into future suggestions.

[1998] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting basic information and characteristic information of a child; a communication means for receiving data transmitted from the input means and passing the data to an analysis platform; an analysis means for identifying the type of child using the analysis platform; a means for recognizing the user's emotional state using an emotion engine; a generation means for generating appropriate approach and communication suggestions based on the recognition results of the analysis means and the emotion engine; a communication means for transmitting the generated suggestions to an output means; and a display means for displaying the suggestions to the user on the output means. This makes it possible to provide individual approaches according to the child's characteristics and the parent's emotional state, and to optimize suggestions for subsequent visits based on feedback.

[1999] An "input method" is an interface for users to input basic information and characteristic information about their child.

[2000] A "communication means" is an interface for receiving data transmitted from an input means and passing it on to the analysis platform.

[2001] "Analysis means" refers to the means of identifying a child's type using an analysis platform.

[2002] An "emotion engine" is an engine that recognizes the user's emotional state from their voice and facial expressions.

[2003] "Generation means" refers to means for automatically generating appropriate approaches and suggestions for communication based on the recognition results of the analysis means and the emotion engine.

[2004] "Output means" refers to an interface for displaying the generated suggestions to the user.

[2005] An "AI model" is an artificial intelligence model used to identify the type best suited to a child's characteristics.

[2006] A "feedback mechanism" is a means of collecting feedback from users and sending it to a server.

[2007] "Optimization methods" refer to means of optimizing the content of future proposals using the collected feedback.

[2008] This invention is a system for effectively supporting the upbringing of children with developmental disabilities. The system includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. The detailed configuration and processing flow of the system are described below.

[2009] Overall system configuration

[2010] This system consists of the following main elements:

[2011] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, a form input field in a smartphone or tablet application.

[2012] 2. Communication method: This is an interface for sending input data to the server and receiving analysis results and suggestions from the server.

[2013] 3. Analysis Methods: These are methods installed on the server to analyze the input data and identify types based on the characteristics of the children. For example, machine learning models or databases may be used.

[2014] 4. Generation means: A means for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[2015] 5. Display means: This is an interface for displaying the generated suggestions to the user.

[2016] 6. Optimization methods: These are means of collecting user feedback and improving the content of future suggestions.

[2017] 7. Emotion Engine: This engine recognizes emotions from user input, voice, and facial expressions, and incorporates the analysis results into the suggestions.

[2018] Program processing

[2019] The program for this system operates as follows:

[2020] Entering information about children and users

[2021] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[2022] The user confirms the entered information and finalizes the data by pressing the "Submit" button.

[2023] Sending and receiving data

[2024] The terminal generates an HTTP request based on the input data and sends it to the server.

[2025] The server receives an HTTP request and prepares the input data to be passed to the analysis platform.

[2026] Analysis of children's types

[2027] The server passes the received data to the analysis platform. This analysis platform includes machine learning models (e.g., TensorFlow or PyTorch).

[2028] The server compares the information with existing data in the database and uses an AI model to identify the child's type based on their characteristics (e.g., autism spectrum disorder, social anxiety disorder, etc.).

[2029] Recognition and reflection of emotions

[2030] The emotion engine analyzes the user's voice and facial expressions. This uses speech recognition software and facial recognition software (e.g., OpenFace, DeepFace, etc.).

[2031] The emotion engine identifies the user's emotional state (stress, joy, fatigue, etc.) based on the analysis results.

[2032] The server receives this sentiment information and incorporates it into the generation of the next suggestion.

[2033] Generating an appropriate method

[2034] The server generates suggestions for appropriate approaches and responses based on the analysis results and the emotion engine's recognition results.

[2035] Using a generation tool, suggestions such as "praise specific behaviors" and "give short, clear instructions" can be automatically created for children with ASD.

[2036] Submitting and displaying proposals

[2037] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[2038] The terminal analyzes the received data and organizes it for display in the user interface. For example, it displays it as short sentences or charts.

[2039] Users review the displayed information and then act upon it in their daily lives.

[2040] Gathering and optimizing feedback

[2041] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[2042] The device sends feedback data to the server.

[2043] The server receives the feedback data and saves it to the database.

[2044] The server analyzes the collected feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[2045] Specific example

[2046] Example 1: A child with autism spectrum disorder

[2047] The user opens the app and enters "Name: A, Age: 6 years old, Characteristics: Social anxiety, language delay".

[2048] The device sends an HTTP request containing this information to the server.

[2049] The server receives the data and uses an analysis platform and AI model to identify the child's specific type as "ASD".

[2050] The emotion engine analyzes the user's voice data and recognizes that the user is tired.

[2051] The server generates suggestions such as "give instructions in short, clear language" and "praise specific actions," including methods of verbal communication that take user fatigue into consideration.

[2052] The device analyzes the generated suggestions and displays them in the UI as short sentences or diagrams.

[2053] Users implement the suggested actions and input the results as feedback into the app.

[2054] The device sends feedback data to the server, which receives it, saves it to a database, and updates the AI ​​model.

[2055] In this way, this system provides individualized support for children with developmental disabilities and also reduces the mental burden on users. Furthermore, since the generated suggestions are adjusted according to the user's emotional state, more personalized support is provided.

[2056] Example of a prompt

[2057] "Please advise on an appropriate approach for a 6-year-old child with autism spectrum disorder. Their emotional state is one of exhaustion."

[2058] "Please suggest effective ways to talk to children who have social anxiety."

[2059] This system allows users to obtain approaches that are tailored to the child's characteristics and individual emotional state.

[2060] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2061] Step 1:

[2062] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[2063] Input: Basic information and characteristics of the child

[2064] Data processing: Data collection through input forms

[2065] Output: Confirmed input data

[2066] Step 2:

[2067] The terminal generates an HTTP request based on the confirmed input data and sends it to the server.

[2068] Input: Confirmed input data

[2069] Data processing: Generation of HTTP requests

[2070] Output: HTTP request

[2071] Step 3:

[2072] The server receives an HTTP request and prepares the input data to be passed to the analysis platform.

[2073] Input: HTTP Request

[2074] Data processing: Preparing data for transfer to the analysis platform.

[2075] Output: Data ready for analysis

[2076] Step 4:

[2077] The server passes data to the analysis platform, which then analyzes the data using machine learning models (e.g., TensorFlow or PyTorch).

[2078] Input: Data ready for analysis

[2079] Data processing: Data analysis (using machine learning models)

[2080] Output: Analysis results (type based on child's characteristics)

[2081] Step 5:

[2082] The emotion engine analyzes the user's voice and facial expressions to recognize the user's emotional state (stress, joy, fatigue, etc.). This uses voice recognition software and facial recognition software (e.g., OpenFace, DeepFace, etc.).

[2083] Input: User's voice and facial expression data

[2084] Data processing: Analysis of voice and facial expression data

[2085] Output: User's emotional state

[2086] Step 6:

[2087] The server receives the analysis results and emotional state, and generates suggestions for appropriate approaches and responses. An AI model is used as the generation method in this process.

[2088] Input: Analysis results, user's emotional state

[2089] Data processing: Generation of proposed data (using an AI model)

[2090] Output: Generated proposals

[2091] Step 7:

[2092] The server converts the generated proposal into a data format such as JSON and sends it to the terminal.

[2093] Input: Generated proposal

[2094] Data processing: Conversion to a data format (e.g., JSON format)

[2095] Output: Converted proposed data

[2096] Step 8:

[2097] The terminal analyzes the received data and prepares it for display in the user interface. It is displayed as short texts or charts.

[2098] Input: Converted suggestion data

[2099] Data processing: Conversion to a format suitable for UI.

[2100] Output: Data displayed in the user interface

[2101] Step 9:

[2102] Users review the displayed content and then implement that approach in their actual daily lives.

[2103] Input: Suggestions displayed in the UI

[2104] Data processing: None (User's actual actions)

[2105] Output: None

[2106] Step 10:

[2107] The user inputs feedback into the application about the approach they took and its results. For example, they might input, "When I praised a specific action, the child responded well."

[2108] Input: Feedback Information

[2109] Data processing: Collection of feedback information

[2110] Output: Confirmed feedback data

[2111] Step 11:

[2112] The device sends feedback data to the server.

[2113] Input: Confirmed feedback data

[2114] Data processing: Generation of HTTP requests (feedback data)

[2115] Output: HTTP request for feedback data

[2116] Step 12:

[2117] The server receives the feedback data and saves it to the database.

[2118] Input: HTTP request for feedback data

[2119] Data processing: Saving to a database

[2120] Output: Saved feedback data

[2121] Step 13:

[2122] The server analyzes the collected feedback data and uses an AI model to optimize future suggestions. This ensures that more effective approaches are continuously provided.

[2123] Input: Saved feedback data

[2124] Data processing: Analysis of feedback data and updating of AI models.

[2125] Output: Optimized proposed model

[2126] These processing steps allow the system to provide a personalized approach tailored to the user's emotional state and optimize future suggestions based on feedback.

[2127] (Application Example 2)

[2128] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2129] Supporting the care of children with developmental disabilities requires tailored approaches to various characteristics and situations, and general approaches have limited effectiveness. Furthermore, in physical stores, appropriate responses are needed based on the specific circumstances at hand, but current systems lack sufficient support for this. Therefore, a system is needed that provides real-time, appropriate responses, enabling users to respond immediately on the spot.

[2130] 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.

[2131] In this invention, the server includes input means for inputting basic and characteristic information of a child, means for optimizing the generated suggestions so that they are applied to specific situations in a physical store, and means for generating suggestions to be applied within the physical store using a generation AI model. This makes it possible to provide timely suggestions for appropriate approaches and conversations according to specific situations within the physical store.

[2132] "Basic information and characteristics of the child" includes data such as the child's name, age, and gender, as well as diagnostic information for developmental disorders and specific characteristics, such as social anxiety or inattention.

[2133] An "input method" is an interface that allows users to input data through devices such as smartphones and tablets.

[2134] A "communication method" is an interface for sending input data to a server and receiving analysis results and suggestions from the server.

[2135] "Analysis means" refers to a mechanism installed within the server that identifies a child's type based on their characteristics, using the input data.

[2136] The "generation means" is a mechanism for automatically generating appropriate approaches and suggestions for communication based on the type identified by the analysis means.

[2137] "Output means" refers to an interface for displaying or notifying the user of the generated suggestions.

[2138] "Display means" refers to screens or displays used to visually present generated suggestions to the user.

[2139] "Means for applying generated proposals to users in physical stores" refers to means of interaction that enable users to actually implement proposals generated in a physical store environment.

[2140] "Means for optimizing generated suggestions to be applicable to specific situations in physical stores" refers to methods for selecting and customizing generated suggestions to suit the specific circumstances of physical stores and providing them to users.

[2141] A "means of collecting feedback" is an interface for users to input and record information about the approaches they have tried and the results of those approaches.

[2142] "Means for optimizing suggestions" refers to a mechanism that updates the AI ​​model based on collected feedback, making future suggestions more effective.

[2143] An "AI model" is a machine learning algorithm used to identify the type best suited to a child's characteristics.

[2144] A "generative AI model" is an algorithm that automatically generates actionable suggestions and prompts based on specific situations and user input data.

[2145] A "prompt message" is a sentence containing instructions or advice for a user in a specific situation, generated using a generative AI model.

[2146] Modes for carrying out the invention

[2147] This invention is a system for supporting the upbringing of children with developmental disabilities, and includes input means, communication means, analysis means, generation means, display means, optimization means, and an emotion engine. Its detailed configuration and processing flow are described below.

[2148] Overall system configuration

[2149] This system consists of the following main elements:

[2150] 1. Input method: An interface for users to input basic and characteristic information about their child. For example, using a form input field in a smartphone or tablet application.

[2151] 2. Communication method: An interface for sending input data to the server and receiving analysis results and suggestions from the server.

[2152] 3. Analysis method: Installed on a server, it analyzes the input data to identify a type based on the child's characteristics.

[2153] 4. Generation method: Based on the type identified by the analysis method, appropriate approaches and suggestions for communication are automatically generated.

[2154] 5. Display means: An interface for displaying the generated suggestions to the user.

[2155] 6. Optimization measures: Collect user feedback and improve the suggestions for future updates.

[2156] 7. Emotion Engine: Recognizes emotions from user input information, voice, and facial expressions, and reflects the analysis results in suggestions.

[2157] Program processing

[2158] 1. Collecting user input:

[2159] The user opens the application and enters the child's basic information (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.).

[2160] The terminal confirms the information it has received and sends the data to the server by pressing the send button.

[2161] 2. Sending and receiving data:

[2162] The server receives the input data via an HTTP request.

[2163] 3. Data Analysis:

[2164] The server receives the data and passes it to the analysis platform, which then analyzes the data using a database and AI models (such as TensorFlow or BERT).

[2165] The emotion engine recognizes emotions from the user's input and voice, and incorporates that information into the analysis.

[2166] 4. Proposal generation:

[2167] Based on the analysis results, the server uses knowledge from the database and AI models to generate specific approaches and suggestions for communication.

[2168] For example, it generates suggestions such as "praise specific actions" and "give short, clear instructions."

[2169] 5. Display of proposals:

[2170] The server sends the generated suggestions to the terminal, and the terminal displays those suggestions in the user interface.

[2171] The user reviews the proposal and tries out the approach in a physical store.

[2172] 6. Gathering feedback and optimizing:

[2173] The application receives feedback on the approaches taken by the user and the results they achieved.

[2174] The device sends confirmed feedback data to the server, which receives it and stores it in a database.

[2175] The server analyzes the feedback data and uses an AI model to optimize future suggestions.

[2176] Specific example

[2177] For example, let's discuss how to handle a situation where a child panics at the checkout counter.

[2178] 1. The user provides voice input about the register status in the application.

[2179] 2. The server analyzes the data and generates suggestions that are "brief, clear, and shift attention elsewhere."

[2180] 3. The smartphone screen displays the message, "Try saying in a loud, slow voice, 'When this is over, let's go buy our favorite snacks.'"

[2181] 4. The user implements the suggested method, and the child regains their composure.

[2182] 5. The user provides feedback on the results to the application, and the server uses this data to improve future suggestions.

[2183] Example of a prompt

[2184] "If a child with a developmental disability panics at the checkout counter, please suggest how to briefly and clearly explain the situation and redirect their attention."

[2185] In this way, this system enables individualized support for children with developmental disabilities and reduces the mental burden on users.

[2186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2187] Step 1:

[2188] The user opens the application and enters basic information about the child (name, age, gender) and characteristic information (diagnosis information and characteristics of developmental disorders: social anxiety, inattention, etc.). The device collects the input data and generates an HTTP request to send this data to the server. The input data is packaged in JSON format.

[2189] Step 2:

[2190] The server receives an HTTP request sent from the terminal and passes the input data to the analysis platform. The server first checks the data integrity to ensure that no missing or invalid data is included. The verified data is then input into the analysis platform.

[2191] Step 3:

[2192] The server uses an analysis platform to analyze the input data and identify the child's type based on their characteristics. Specifically, it processes the data using TensorFlow or BERT models and applies an AI model to classify the child's characteristics. The analysis result outputs the child's type (e.g., ASD, ADHD).

[2193] Step 4:

[2194] The server uses an emotion engine to recognize the user's emotional state (e.g., stress, fatigue, joy) from user input information and voice data. The emotion engine uses voice analysis and natural language processing techniques to identify the user's current emotional state. The analysis results output the user's emotional state.

[2195] Step 5:

[2196] Based on the analysis results and the emotion engine's output, the server uses knowledge and AI models in the database to generate specific approaches and suggestions for communication. For example, it uses the BERT model to analyze past cases and feedback, automatically generating suggestions such as "praise specific actions" and "give short, clear instructions." The generated suggestions are stored as data in JSON format.

[2197] Step 6:

[2198] The server generates an HTTP response to send the generated suggestions to the terminal and sends it to the terminal. The terminal interprets the received suggestion data and displays it visually in the user interface. This display may be in the form of text or icons on a smartphone screen, for example.

[2199] Step 7:

[2200] Users review the suggestions displayed in the application and try them out in a physical store. They then input feedback into the application regarding their experience trying the suggestions and their child's reaction. This feedback data is then sent back from the device to the server.

[2201] Step 8:

[2202] The server analyzes the feedback data it receives and uses an AI model to optimize future suggestions. Specifically, it stores the feedback data in a database and uses an AI model (e.g., TensorFlow) to compare and analyze it with past data to update the suggestions.

[2203] Examples of prompt statements include:

[2204] "If a child with a developmental disability panics at the checkout counter, please suggest how to briefly and clearly explain the situation and redirect their attention."

[2205] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2206] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2207] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2208] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2209] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2210] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2211] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2212] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it result...

Claims

1. An input method for entering basic information and characteristic information of a child, A communication means that receives data transmitted from the input means and passes the data to the analysis platform, An analytical means for identifying the type of child using an analytical platform, A generation means that generates suggestions for appropriate approaches and verbal communication based on the type of child identified by the analysis means, A communication means for transmitting the generated proposal to an output means, A system including a display means that displays information to the user via an output means.

2. Means of collecting user feedback, A means for transmitting the collected feedback to a server using the communication means, The system according to claim 1, which includes means for the server to optimize the proposal using the feedback.

3. The system according to claim 1, comprising an analysis means using an AI model to identify the type best suited to the characteristics of a child.

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