system

Abstract

Provide a system. [Solution] a means for receiving emotional input from a user; means for selecting a predefined empathy message corresponding to the emotion input; a means for displaying the selected empathy message to the user; A system including:

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Children with autism spectrum disorder (ASD) and their families face many challenges in understanding emotions, providing appropriate learning resources, and addressing sensory sensitivities caused by environmental factors. Traditional support systems lack individualized optimization and real-time response, making them unable to effectively address these challenges. Therefore, there is a need for a system that can provide emotional empathy, learning support, and sensory sensitivities in an integrated manner, while achieving individualized optimization. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means. First, the system includes a means for receiving an input related to emotions from a user, and then a means for selecting a predefined empathy message corresponding to the emotional input. The system further includes a means for displaying the selected empathy message to the user. The system may also include a means for receiving an input requesting learning resources related to a specific topic, a means for selecting predefined learning resources corresponding to the topic, and a means for displaying the selected learning resources to the user. In addition, the system may also include a means for receiving an input related to environmental factors, selecting predefined hypersensitivity countermeasures corresponding to the input, and displaying the selected learning resources to the user, thereby realizing support for individual optimization.

[0006] "User" refers to children with autism spectrum disorder (ASD) and their families who use the system.

[0007] "Emotion input" refers to the act of a user inputting their current emotions into the system via text or voice.

[0008] An "empathetic message" is a predefined message that the system responds to in response to the input emotion, and has content that shows empathy and understanding for the user's emotion.

[0009] "Learning Resources" refers to information containing educational materials and links related to a particular topic.

[0010] "Topic input" refers to the act of a user entering a specific topic they want to learn about into the system using text or options.

[0011] "Environmental factors" refer to the sensory stimuli that users encounter in their daily lives (e.g., loud noises, bright lights, crowded places).

[0012] "Sensitivity solutions" refer to measures or suggestions that help users reduce their sensory sensitivity to specific environmental factors.

[0013] "Predefined" refers to data or information that is preset before the system operates. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

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

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families. The system is equipped with functions for emotional empathy, learning support, and sensory hypersensitivity management, enabling support optimized for individual situations.

[0036] Emotional empathy function

[0037] 1. The user inputs their emotion, for example, "sad" in text.

[0038] 2. The device receives this emotion input and invokes the emotion empathy function.

[0039] 3. The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[0040] Learning support functions

[0041] 1. The user enters the topic they want to learn about, for example, "science."

[0042] 2. The device receives this topic input and calls the learning support function.

[0043] 3. The device displays the resource selected by the learning support function to the user, for example, "Science experiment video here: [link]".

[0044] Sensory hypersensitivity prevention function

[0045] 1. The user inputs the current environmental factor, for example, "loud noise."

[0046] 2. The device receives this environmental factor input and calls the sensory hypersensitivity countermeasure function.

[0047] 3. The device displays the suggestion selected by the sensory hypersensitivity countermeasure function to the user: "Try earmuffs."

[0048] Specific examples

[0049] Example 1: Use of Emotional Empathy

[0050] When the user types "sad," the device invokes the emotion empathy function and displays the empathy message "Oh, that must be hard... Thanks for sharing your story." This allows the user to receive emotional support.

[0051] Example 2: Using the learning support function

[0052] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]." This allows the user to easily access the educational resources they need.

[0053] Example 3: Using the sensory hypersensitivity function

[0054] When a user requests measures to deal with "loud noise," the device will call the sensory hypersensitivity countermeasure function and display a suggestion such as "Try earmuffs," providing the user with an immediate solution to the environmental factor.

[0055] The system provides these functions through a user interface and is designed to reduce the difficulties faced by children with ASD and their families in their daily lives. By providing appropriate responses based on the input provided, users can receive personalized support.

[0056] The processing flow will be explained below.

[0057] Emotional empathy function

[0058] Step 1:

[0059] The user inputs their current emotion, for example, "sad."

[0060] Step 2:

[0061] The device receives the user's emotional input, which is in text format.

[0062] Step 3:

[0063] The device calls the emotion empathy function emotion_empathy(current_emotion) with the emotion input "sad" as an argument.

[0064] Step 4:

[0065] The device selects an empathy message from the dictionary empathy_responses that corresponds to the input emotion in the emotion empathy function. For example, for the emotion 'sad', "That must be tough... Thanks for sharing your story." is selected.

[0066] Step 5:

[0067] The terminal displays the selected sympathy message to the user.

[0068] Learning support functions

[0069] Step 1:

[0070] The user enters the topic they want to learn about, for example, "science."

[0071] Step 2:

[0072] The terminal receives the user's topic input, which is also in text format.

[0073] Step 3:

[0074] The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[0075] Step 4:

[0076] In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Here is a science experiment video: [Link]" is selected.

[0077] Step 5:

[0078] The terminal displays the selected learning resource to the user.

[0079] Sensory hypersensitivity prevention function

[0080] Step 1:

[0081] The user inputs a current environmental factor, for example, "loud noise."

[0082] Step 2:

[0083] The terminal receives the user's environmental factor input, which is also in text format.

[0084] Step 3:

[0085] The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[0086] Step 4:

[0087] The device selects a sensitivity solution from the sensitivity_solutions dictionary in the sensitivity solution function that corresponds to the input environmental factor. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[0088] Step 5:

[0089] The terminal displays the selected hypersensitivity measures to the user.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] There is a need for systems that can reduce the difficulties children with autism spectrum disorder (ASD) and their families face in their daily lives and provide individually optimized support. However, existing support systems are unable to provide integrated support for emotional empathy, learning support, and measures for sensory hypersensitivity, preventing users from receiving flexible support that can respond to a variety of situations.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for receiving an input regarding an emotion from a user, means for transmitting the emotion input to an emotion empathy function, means for activating the emotion empathy function and analyzing the emotion data, means for selecting and transmitting an empathy message to the user, and means for displaying the empathy message to the user, thereby enabling the user to receive emotional support and obtain an appropriate empathy message corresponding to each individual emotion.

[0095] The system further includes means for receiving input from a user requesting learning resources on a specific topic, means for sending the topic input to a learning support function, means for activating the learning support function and searching for resources related to the specified topic, means for selecting and sending the learning resources to the user, and means for displaying the learning resources to the user, thereby enabling the user to easily access resources related to the topic they wish to learn and receive support for their learning.

[0096] The device further includes means for receiving input regarding environmental factors from a user, means for transmitting the environmental factor input to a sensory hypersensitivity countermeasure function, means for activating the sensory hypersensitivity countermeasure function and searching for countermeasures for the specified environmental factor, means for selecting and transmitting the countermeasure to the user, and means for displaying the selected countermeasure to the user, thereby enabling the user to quickly obtain countermeasures for sensory hypersensitivity to the environment.

[0097] An "emotional empathy function" is an algorithm that analyzes a user's emotional input and selects an appropriate empathy message.

[0098] A "learning support function" is an algorithm for searching and selecting learning resources related to a topic specified by the user.

[0099] The "sensory hypersensitivity countermeasure function" is an algorithm for searching and selecting appropriate countermeasures for sensory hypersensitivity based on environmental factors entered by the user.

[0100] "Emotion data" is information about emotions entered by the user and expressed in text format.

[0101] A "sympathetic message" is a message that is displayed to the user based on the analyzed emotion data, and includes content that sympathizes with the user's emotions.

[0102] "Learning resources" is a general term for information, learning materials, links, etc. related to the topic that a user wants to learn.

[0103] "Environmental factors" are information that indicates the user's current surroundings and stimuli, and are expressed in text format.

[0104] "Sensitive measures" is a general term for countermeasures and suggestions for environmental factors entered by the user.

[0105] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families. The system has functions for emotional empathy, learning support, and sensory hypersensitivity treatment. The implementation methods for each function are described in detail below.

[0106] Emotional empathy function

[0107] 1. The user inputs their emotion, for example, "sad."

[0108] 2. The device receives this emotion input and sends it to the emotion empathy function.

[0109] This function is implemented using a natural language processing library (e.g., NLTK or spaCy).

[0110] 3. The server launches the emotion empathy function and analyzes the received emotion data.

[0111] 4. The server selects an empathetic message based on the results of sentiment analysis, for example, choosing from predefined messages to select a message like "That must be tough...Thanks for sharing your story."

[0112] 5. The server sends the selected empathy message to the device.

[0113] 6. The device displays the received sympathy message to the user.

[0114] For example, if a user types "sad," the device invokes the emotion empathy function, and the server selects and displays the message "That must be tough... Thanks for sharing your story."

[0115] Learning support functions

[0116] 1. The user enters the topic they want to learn about, for example, "science."

[0117] 2. The device receives the topic input and sends it to the learning support function.

[0118] The implementation of this function utilizes educational resource APIs (e.g., Khan Academy API).

[0119] 3. The server launches a learning support function to search for learning resources related to the specified topic.

[0120] 4. The server selects the most suitable learning resource from the search results, for example, "Here is a science experiment video: [link]".

[0121] 5. The server sends the selected learning resource to the terminal.

[0122] 6. The terminal displays the received resources to the user.

[0123] As a specific example, if a user requests a learning resource related to "science," the device calls the learning support function, and the server selects and displays the learning resource "Science experiment video here: [link]."

[0124] Sensory hypersensitivity prevention function

[0125] 1. The user inputs the current environmental factor, for example, "loud noise."

[0126] 2. The device receives input of environmental factors and sends it to the sensory hypersensitivity countermeasure function.

[0127] The implementation of this function utilizes the Customized Experience API.

[0128] 3. The server invokes the sensory hypersensitivity countermeasure function to search for countermeasures for the specified environmental factor.

[0129] 4. The server selects the best solution from the search results, for example, a suggestion such as "Try earmuffs."

[0130] 5. The server sends the selected measures to the device.

[0131] 6. The device displays the received measures to the user.

[0132] As a concrete example, if a user requests solutions to deal with "loud noises," the device calls a sensory hypersensitivity solution function, and the server selects and displays the suggestion "Try earmuffs."

[0133] Prompt Sentence Examples

[0134] "Use the Emotion Empathy feature. If I type 'sad', tell me how it responds."

[0135] "Please use the learning support feature. Can you tell me some resources about 'science'?"

[0136] "Please use the sensory sensitivity feature. What can I do about 'loud noises'?"

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Emotional empathy function

[0139] Step 1:

[0140] The user inputs their own emotion, for example, "sad."

[0141] Input: User emotion (e.g. "sad")

[0142] Output: Emotion input data

[0143] Step 2:

[0144] The device receives emotion input and prepares to send the emotion input data to the server.

[0145] Input: Emotion input data

[0146] Output: Input data ready to be sent to the server

[0147] Step 3:

[0148] The server receives the emotion input data and invokes the emotion empathy function, which analyzes the emotion data using a natural language processing library (e.g., NLTK or spaCy).

[0149] Input: Emotion input data

[0150] Output: Analysis results (e.g., appropriate response to the emotion "sad")

[0151] Step 4:

[0152] The server selects an empathetic message based on the analysis results, searching a database of predefined messages for an appropriate message (e.g., "That must be tough... Thanks for sharing your story.").

[0153] Input: Analysis results

[0154] Output: Selected empathy message

[0155] Step 5:

[0156] The server transmits the selected sympathy message to the terminal.

[0157] Input: Selected empathy message

[0158] Output: Message ready to be sent to the terminal

[0159] Step 6:

[0160] The terminal displays the received sympathy message to the user.

[0161] Input: Received sympathy message

[0162] Output: The message displayed to the user

[0163] Learning support functions

[0164] Step 1:

[0165] The user enters the topic they want to learn about, for example, "science."

[0166] Input: User topic (e.g. "Science")

[0167] Output: Topic input data

[0168] Step 2:

[0169] The terminal receives the topic input and prepares to send the topic input data to the server.

[0170] Input: Topic input data

[0171] Output: Input data ready to be sent to the server

[0172] Step 3:

[0173] The server receives the topic input data and launches the learning support function, which searches for related learning resources using an educational resource API (e.g., Khan Academy API).

[0174] Input: Topic input data

[0175] Output: Search results (e.g. "science experiment videos")

[0176] Step 4:

[0177] The server selects the most suitable learning resource from the search results.

[0178] Input: Search results

[0179] Output: The selected learning resource (e.g., "Here's a science experiment video: [link]")

[0180] Step 5:

[0181] The server transmits the selected learning resource to the terminal.

[0182] Input: Selected Learning Resources

[0183] Output: Resources ready to be sent to the device

[0184] Step 6:

[0185] The terminal displays the received learning resources to the user.

[0186] Input: Received learning resources

[0187] Output: The resource displayed to the user

[0188] Sensory hypersensitivity prevention function

[0189] Step 1:

[0190] The user inputs a current environmental factor, for example, "loud noise."

[0191] Input: User's environmental factors (e.g. "loud noise")

[0192] Output: Environmental factor input data

[0193] Step 2:

[0194] The terminal receives the environmental factor input and prepares to send the environmental factor input data to the server.

[0195] Input: Environmental factor input data

[0196] Output: Input data ready to be sent to the server

[0197] Step 3:

[0198] The server receives the environmental factor input data and invokes the sensory hypersensitivity countermeasure function, which uses the customized experience API to find the relevant countermeasure.

[0199] Input: Environmental factor input data

[0200] Output: Search results (e.g. "Try on some earmuffs.")

[0201] Step 4:

[0202] The server selects the most appropriate solution from the search results.

[0203] Input: Search results

[0204] Output: The selected measure (e.g., "Try earmuffs.")

[0205] Step 5:

[0206] The server transmits the selected measures to the terminal.

[0207] Input: Selected measures

[0208] Output: Measures ready to be sent to the device

[0209] Step 6:

[0210] The terminal displays the received measures to the user.

[0211] Input: Received measures

[0212] Output: The action displayed to the user

[0213] (Application example 1)

[0214] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] Children with autism spectrum disorder (ASD) and their families face many challenges when shopping and spending time in physical stores. They often have difficulty expressing their emotions and adapting to their environment, and are unable to receive appropriate support. As a result, their use of physical stores is limited, and stress in their daily lives increases.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0217] In this invention, the server includes means for receiving an input regarding an emotion from a user, means for selecting a predefined empathy message corresponding to the emotion input, means for displaying the selected empathy message to the user, means for providing a user interface for accepting emotion input from the user in a physical store, and means for displaying an appropriate empathy message for the user to receive emotion-based support in the physical store, thereby enabling children with ASD and their families to immediately receive appropriate support in the physical store.

[0218] "User" means an individual or family member who uses the System.

[0219] "Emotional input" refers to information such as text or voice that allows a user to express their emotional state.

[0220] "Empathetic messages" are predefined messages that are provided in response to the user's emotions.

[0221] "Displaying to the user" means that the system outputs selected information in a form that can be viewed by the user.

[0222] A "user interface" is an interactive screen equipped with input and display means for users to input emotions and obtain information.

[0223] A "brick and mortar store" is a physical commercial environment for selling goods.

[0224] "Emotion-based support" means providing appropriate messages and advice that take into account the user's emotional state.

[0225] The present invention provides a system that enables children with autism spectrum disorder (ASD) and their families to receive appropriate support immediately in a physical store. Specific embodiments of this system are described below.

[0226] System Configuration

[0227] The system is based on a smartphone application. The required hardware is a smartphone. The software includes a program developed using Python and web application frameworks such as Flask and Django.

[0228] Emotional empathy function

[0229] The user inputs their emotions into a smartphone app. For example, they might say, "I'm nervous." The system receives this emotion input and selects a predefined empathy message. The selected empathy message is displayed as, "It's natural to feel nervous. Let's take a moment to relax." This process allows the user to receive appropriate support for expressing their emotions in a physical store.

[0230] Learning support functions

[0231] The user inputs a request for learning resources related to a specific topic. For example, if the user selects "Store location," the system receives this input and selects a predefined learning resource. The selected resource is displayed as map information, indicating "The children's section is here." This process allows the user to easily obtain and understand information within a physical store.

[0232] Sensory hypersensitivity prevention function

[0233] The user inputs an environmental factor. For example, if the user types "glare," the system takes this input and selects a predefined hypersensitivity countermeasure. The selected countermeasure is displayed as a message: "Sunglasses are available. Please ask a member of staff if you need them." This process provides the user with an immediate way to address environmental factors within a physical store.

[0234] Specific examples

[0235] For example:

[0236] 1. Use cases for Emotional Empathy:

[0237] A user types "I'm nervous" into a smartphone app. The app responds, "It's natural to feel nervous. Take a break here."

[0238] 2. Example of using the learning support function:

[0239] When the user selects "Store location," the app displays map information and says, "This is the children's section."

[0240] 3. Examples of using the sensory sensitivity feature:

[0241] When a user types in "dazzling light," the app displays the message, "Sunglasses are available. Please ask a member of staff if you need them."

[0242] Prompt Sentence Examples

[0243] python

[0244] Emotion input example:

[0245] emotion = "I'm nervous"

[0246] response = support.get_response(emotion)

[0247] print(response)

[0248] Learning support examples:

[0249] topic = "location in store"

[0250] resource = guide.get_resource(topic)

[0251] print(resource)

[0252] Examples of measures to combat sensory hypersensitivity:

[0253] issue = "blinding light"

[0254] tip = support.get_tip(issue)

[0255] print(tip)

[0256] In this way, the system of the present invention allows children with autism spectrum disorder (ASD) and their families to receive appropriate support immediately in physical stores, thereby improving their shopping experience and the quality of their daily lives.

[0257] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0258] Step 1:

[0259] A user inputs information into a smartphone app about emotions, specific topics, or environmental factors. For example, the user might input "I'm nervous," "a location in a store," or "bright light." This input data is received by the device.

[0260] Step 2:

[0261] The terminal sends the input data received from the user to the server, and the system initiates the process to generate an appropriate response. Based on the type of input data, the terminal invokes the corresponding function.

[0262] Step 3:

[0263] The server analyzes the user's input data and uses a generative AI model to select appropriate empathetic messages, learning resources, or hypersensitivity measures. In this step, the input data is used as prompts for the AI ​​model, which generates the optimal response for each prompt. For example, in response to the emotional input "I'm nervous," the server selects the empathetic message "It's natural to feel nervous. Let's take a break here."

[0264] Step 4:

[0265] The server then sends the selected response back to the device, which may include a message of sympathy, a link to a learning resource, or suggestions for addressing the environmental factors.

[0266] Step 5:

[0267] The device displays the response received from the server to the user. Specifically, it is displayed as a message on the smartphone app screen. For example, if the user enters "I'm nervous," the device displays the message "It's natural to feel nervous. Take a break here."

[0268] Step 6:

[0269] The user can then review the displayed messages and resources and take action based on them. For example, they can follow the suggestion "Sunglasses available" presented as a hypersensitivity measure and ask the store staff for sunglasses.

[0270] At each step, data processing and calculations are performed based on the user's input, and the optimal response is generated and provided to the user, realizing a system that allows children with ASD and their families to receive appropriate support immediately within a physical store.

[0271] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0272] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families, with functions for emotional empathy, learning support, and sensory hypersensitivity treatment. In particular, the combination of an emotion engine enables more advanced emotion recognition and appropriate responses.

[0273] Emotional empathy function

[0274] 1. The user provides text or voice input to express an emotion, for example, "sad."

[0275] 2. The device receives the user's emotional input, which can be in text or voice format.

[0276] 3. The device calls the emotion engine and analyzes the input data.

[0277] 4. The emotion engine recognizes emotions based on the input data and generates emotion recognition results. For example, the input "sad" is recognized as the emotion "sad."

[0278] 5. The device receives the emotion recognition result and calls the emotion empathy function emotion_empathy(current_emotion) with the emotion recognition result “sad” as an argument.

[0279] 6. The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[0280] Learning support functions

[0281] 1. The user enters the topic they want to learn about, for example, "science."

[0282] 2. The terminal receives the user's topic input, which is in text format.

[0283] 3. The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[0284] 4. In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Science experiment videos are here: [Link]" is selected.

[0285] 5. The terminal displays the selected learning resource to the user.

[0286] Sensory hypersensitivity prevention function

[0287] 1. The user inputs the current environmental factor, for example, "loud noise."

[0288] 2. The terminal receives the user's environmental factor input, which is in text format.

[0289] 3. The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[0290] 4. The device selects a sensitivity solution corresponding to the input environmental factor from the sensitivity_solutions dictionary in the sensitivity solution function. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[0291] 5. The device displays the selected hypersensitivity measures to the user.

[0292] Specific examples

[0293] Example 1: Use of Emotional Empathy

[0294] When the user types "sad," the device invokes the emotion engine, recognizes the emotion "sad," and uses the emotion empathy function to display a message to the user saying, "That must be tough... Thanks for sharing your story."

[0295] Example 2: Using the learning support function

[0296] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]."

[0297] Example 3: Using the sensory hypersensitivity function

[0298] If the user requests a solution to deal with "loud noise," the device will call the sensory sensitivity solution function and display a suggestion: "Try earmuffs."

[0299] The system aims to alleviate the difficulties faced by users with ASD by using an emotion engine to recognize emotions based on user input and providing various support functions.

[0300] The processing flow will be explained below.

[0301] Emotional empathy function

[0302] Step 1:

[0303] The user provides text or voice input to express an emotion, for example, "sad."

[0304] Step 2:

[0305] The device receives the user's emotional input, which can be in text or voice format.

[0306] Step 3:

[0307] The device calls the emotion engine and analyzes the input data.

[0308] Step 4:

[0309] The emotion engine recognizes emotions based on the input data and generates emotion recognition results. For example, it recognizes the emotion "sad" from the input "sad."

[0310] Step 5:

[0311] The device receives the emotion recognition result and calls the emotion empathy function emotion_empathy(current_emotion) with the emotion recognition result "sad" as an argument.

[0312] Step 6:

[0313] The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[0314] Learning support functions

[0315] Step 1:

[0316] The user enters the topic they want to learn about, for example, "science."

[0317] Step 2:

[0318] The terminal receives the user's topic input, which is in text format.

[0319] Step 3:

[0320] The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[0321] Step 4:

[0322] In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Here is a science experiment video: [Link]" is selected.

[0323] Step 5:

[0324] The terminal displays the selected learning resource to the user.

[0325] Sensory hypersensitivity prevention function

[0326] Step 1:

[0327] The user inputs a current environmental factor, for example, "loud noise."

[0328] Step 2:

[0329] The terminal receives the user's environmental factor input, which is in text format.

[0330] Step 3:

[0331] The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[0332] Step 4:

[0333] The device selects a sensitivity solution from the sensitivity_solutions dictionary in the sensitivity solution function that corresponds to the input environmental factor. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[0334] Step 5:

[0335] The terminal displays the selected hypersensitivity measures to the user.

[0336] Example 2

[0337] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0338] To address the various challenges faced by children with autism spectrum disorder (ASD) and their families, appropriate support tailored to their individual needs is necessary. However, existing technologies lack comprehensive systems that address these challenges. This makes it difficult to provide effective support for users with ASD in recognizing their emotions, accessing appropriate learning resources, and addressing sensory sensitivities.

[0339] The identification process 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 means for receiving an input related to an emotion from a user, means for analyzing the emotion input and recognizing the emotion, means for selecting a predefined empathy message corresponding to the emotion recognition, means for receiving an input from a user requesting learning resources related to a specific topic, means for analyzing the topic input and selecting the learning resources, means for receiving an input related to environmental factors from a user, and means for analyzing the environmental factors and selecting a predefined hypersensitivity countermeasure. This makes it possible to comprehensively provide emotional empathy, learning support, and sensory hypersensitivity countermeasures for various challenges faced by users with ASD.

[0340] "User" refers to any individual or group of people who use the System, and specifically includes children with Autism Spectrum Disorder (ASD) and their families.

[0341] "Emotional input" refers to emotional expressions in text or speech that a user inputs into the system.

[0342] "Emotion engine" is a general term for software and hardware that analyzes emotional input from users and recognizes those emotions.

[0343] "Empathetic message" refers to a predefined response message that the system presents in response to the user's emotions.

[0344] "Learning resources" refers to information or content related to the topic a user wants to learn about, including, for example, text, videos, links, etc.

[0345] "Environmental factors" refer to elements and situations related to the user's current environment, such as "loud noises" and "bright light."

[0346] "Sensory sensitivity management" refers to suggestions and measures to reduce a user's sensitivity to environmental factors.

[0347] "Server" refers to the central processing unit of the system, which is responsible for receiving and analyzing input from users, and generating and providing appropriate responses.

[0348] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families, with functions of emotional empathy, learning support, and sensory hypersensitivity treatment. Each function of this system is described in detail below.

[0349] Emotional empathy function

[0350] 1. The user inputs text or voice to express an emotion into the device. Let's assume that the user inputs "sad" on a smartphone or PC.

[0351] 2. The device receives input from the user and, if there is voice input, converts it into text using the Google® Speech-to-Text API.

[0352] 3. The device calls the emotion engine, which uses IBM Watson® emotion recognition API to analyze the input data and recognize emotions.

[0353] 4. The emotion engine recognizes emotions based on the input data and returns the result in the form of something like "sad."

[0354] 5. The device executes the emotion empathy function emotion_empathy(current_emotion) and passes the emotion recognition result "sad" as an argument. The emotion empathy function selects an appropriate empathy message, such as "That must be tough... Thanks for sharing your story."

[0355] 6. The device displays the selected empathy message to the user, for example, "That must be tough... Thanks for sharing your story."

[0356] Learning support functions

[0357] 1. The user enters the topic they want to learn in text format into the terminal, for example, "science."

[0358] 2. The device receives the input and executes the learning support function learning_support(topic), passing "science" as the argument.

[0359] 3. The device selects an appropriate learning resource related to "science" from the dictionary learning_resources. For example, "Here is a science experiment video: [link]" is selected.

[0360] 4. The device displays the selected learning resource to the user.

[0361] Sensory hypersensitivity prevention function

[0362] 1. The user inputs the current environmental factor in text format into the terminal, for example, "loud noise."

[0363] 2. The device receives the input and executes the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor), passing "loud sound" as the argument.

[0364] 3. The device selects an appropriate solution to the "loud noise" sensitivity from the dictionary sensitivity_solutions, for example, "Try earmuffs."

[0365] 4. The device displays the selected measures to the user.

[0366] Specific examples

[0367] Example 1: Use of Emotional Empathy

[0368] When the user types "sad," the device invokes the emotion engine, recognizes the emotion "sad," and uses the emotion empathy function to display a message to the user saying, "That must be tough... Thanks for sharing your story."

[0369] Example 2: Using the learning support function

[0370] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]."

[0371] Example 3: Using the sensory hypersensitivity function

[0372] If the user requests a solution to deal with "loud noise," the device will call the sensory sensitivity solution function and display a suggestion: "Try earmuffs."

[0373] The system aims to alleviate the difficulties faced by users with ASD by utilizing an emotion engine and various functions based on user input.

[0374] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0375] Emotional empathy function

[0376] Step 1:

[0377] The user inputs text or voice to express their emotion into the device. The user uses the voice input function of their smartphone or PC to say "I'm sad."

[0378] Input: User's voice data

[0379] Output: Audio data

[0380] Specific operation: The user inputs voice into the microphone of their smartphone or PC.

[0381] Step 2:

[0382] The device receives input, and in the case of voice input, converts the speech to text using the Google Speech-to-Text API.

[0383] Input: Audio data

[0384] Output: Text data "Sad"

[0385] Specific behavior: The device sends the user's voice to the Google Speech-to-Text API and receives the text version of "sad."

[0386] Step 3:

[0387] The device calls the emotion engine to analyze the input data, using IBM Watson's emotion recognition API.

[0388] Input: Text data "sad"

[0389] Output: Emotion recognition result "sad"

[0390] Specific operation: The device sends the text "sad" to IBM Watson's emotion recognition API, which recognizes the emotion "sad" and returns it.

[0391] Step 4:

[0392] The device executes the emotion empathy function emotion_empathy(current_emotion) and passes the emotion recognition result "sad" as an argument. The empathy function selects an appropriate message.

[0393] Input: Emotion recognition result "sad"

[0394] Output: Empathetic message "That must be tough... Thanks for sharing your story."

[0395] Specific operation: The device executes the emotion empathy function, passing "sad" as an argument and selecting the appropriate message.

[0396] Step 5:

[0397] The terminal displays the selected sympathetic message to the user.

[0398] Input: Empathetic message "That must be tough... Thanks for sharing your story."

[0399] Output: An empathy message displayed on the user's screen

[0400] Specific operation: A sympathetic message is displayed on the device screen to provide feedback to the user.

[0401] Learning support functions

[0402] Step 1:

[0403] The user enters the topic they want to learn in text form into the terminal. The user uses the keyboard to type "science."

[0404] Input: topic "science"

[0405] Output: Text data "Science"

[0406] Specific operation: The user inputs a topic using the device keyboard.

[0407] Step 2:

[0408] The terminal receives the input. The input data is received as is.

[0409] Input: Text data "Science"

[0410] Output: Text data "Science"

[0411] Specific operation: The terminal receives input data from the user.

[0412] Step 3:

[0413] The terminal executes the learning support function learning_support(topic) and passes the topic "science" as an argument. The learning support function selects the corresponding resource.

[0414] Input: topic "science"

[0415] Output: Learning resource "Science experiment video here: [link]"

[0416] Specific operation: The terminal executes the learning support function and selects the learning resource corresponding to "science."

[0417] Step 4:

[0418] The terminal displays the selected learning resource to the user.

[0419] Input: Learning Resources "Here's a science experiment video: [link]"

[0420] Output: The learning resource displayed on the user's screen

[0421] Specific operation: Display learning resources on the device screen and provide them to the user.

[0422] Sensory hypersensitivity prevention function

[0423] Step 1:

[0424] The user inputs the current environmental factors in text format into the terminal. The user uses the keyboard to input "loud noise."

[0425] Input: Environmental factor "loud noise"

[0426] Output: Text data "Loud sound"

[0427] Specific operation: The user inputs the environmental factors using the keyboard of the device.

[0428] Step 2:

[0429] The terminal receives the input. The input data is received as is.

[0430] Input: Text data "loud sound"

[0431] Output: Text data "Loud sound"

[0432] Specific operation: The terminal receives input data from the user.

[0433] Step 3:

[0434] The device executes the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) and passes the environmental factor "loud noise" as an argument. The sensory sensitivity countermeasure function selects the corresponding countermeasure.

[0435] Input: Environmental factor "loud noise"

[0436] Output: Hypersensitivity Countermeasure "Try earmuffs."

[0437] Specific operation: The device executes the sensory hypersensitivity countermeasure function and selects the countermeasure corresponding to "loud noise."

[0438] Step 4:

[0439] The terminal displays the selected hypersensitivity measures to the user.

[0440] Input: Hypersensitivity Countermeasures "Try earmuffs."

[0441] Output: The hypersensitivity measures displayed on the user's screen

[0442] Specific operation: Display the hypersensitivity measures on the device screen and provide them to the user.

[0443] Through these steps, the system has the ability to comprehensively provide emotional empathy, learning support, and sensory hypersensitivity measures to users with ASD.

[0444] (Application example 2)

[0445] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0446] There is a need for a system that can effectively solve the various problems that users with autism spectrum disorder (ASD) face in their daily lives. Specifically, a comprehensive support platform that can seamlessly provide emotional understanding and empathy, learning support, and measures for sensory hypersensitivity is needed.

[0447] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving an input regarding emotions from a user, means for selecting a predefined empathy message corresponding to the emotion input, means for displaying the selected empathy message to the user, means for receiving an input from the user requesting learning resources on a specific topic, means for selecting predefined learning resources corresponding to the topic input, means for displaying the selected learning resources to the user, means for receiving an input regarding environmental factors from the user, means for selecting a predefined hypersensitivity countermeasure corresponding to the environmental factor, and means for displaying the selected hypersensitivity countermeasure to the user. This enables emotional empathy, learning support, and sensory hypersensitivity countermeasures.

[0448] "User" refers to the individual or family member who operates the system.

[0449] "Emotional input" refers to the act of a user inputting their emotional state into the system via text or voice.

[0450] A "predefined empathic message" refers to a message that is predefined as a response to a particular emotional state.

[0451] "Topical Learning Resources" refers to educational information and materials based on a particular subject designated by the User.

[0452] "Environmental factors" refer to the physical environmental conditions that the user currently faces.

[0453] "Hypersensitivity measures" refers to specific measures and suggestions to help users with sensory sensitivities to live comfortably.

[0454] "Emotion engine" refers to technology that analyzes a user's emotional input and recognizes a specific emotional state.

[0455] "Dynamic provision" refers to providing appropriate learning resources in real time in response to user requests.

[0456] "Suggestion functionality" refers to the functionality of the system that has the ability to suggest appropriate hypersensitivity countermeasures based on the user's input of environmental factors.

[0457] To implement this invention, we need a server with specific functions, a terminal, and an interface with the user. We will build a system with three main functions: emotional empathy, learning support, and measures against sensory hypersensitivity.

[0458] First, the server receives emotional input from the user. When the user inputs their emotions via text or voice, the server passes this input to the emotion engine for analysis. The emotion engine uses complex data analysis algorithms to recognize emotions from the input data. Based on this recognition result, it selects an appropriate message from predefined empathy messages and displays it to the user via the device.

[0459] The specific hardware used is a processor with a dedicated AI chip for emotion recognition, and the software uses a natural language processing model (e.g., BERT or GPT-3 (registered trademark)) to perform emotion analysis. The emotion recognition results are stored in a shared database, allowing for real-time processing on the server.

[0460] Next, we will explain the learning support function. When a user requests learning resources on a specific topic, the server receives this request and dynamically selects appropriate resources from the dictionary database. These resources are displayed on the terminal and provided for easy access by the user.

[0461] Specifically, it uses a learning management system (LMS) to manage educational content and links to a resource database stored there. It also includes algorithms for dynamically providing relevant resources based on user requests. It uses programming languages ​​such as Python and machine learning libraries (e.g., TENSORFLOW® and PyTorch) to select and present resources.

[0462] Finally, we will explain the sensory sensitivity countermeasure function. When the server receives input from the user about environmental factors, it analyzes this and selects an appropriate countermeasure from predefined sensitivity countermeasures. The selected countermeasure is displayed on the device to ensure the user's comfort.

[0463] This processing may involve IoT devices to utilize sensor data, such as a noise sensor to measure noise levels or a light sensor to measure light intensity. The data obtained from these sensors is sent in real time to a server for analysis, often using IoT frameworks such as Node-RED or MQTT.

[0464] Examples of concrete examples and prompts

[0465] 1. Emotional empathy function:

[0466] Example: If a user types "sad," the system displays the message "That must be hard... Thanks for sharing your story."

[0467] Prompt: "When the user enters the emotion 'sad', generate an appropriate empathetic message."

[0468] 2. Learning support functions:

[0469] Example: If a user requests learning resources related to "Science," you might display "Here are some science experiment videos: [Link]."

[0470] Prompt: "When a user asks for learning resources on the topic 'Science', generate the appropriate learning resources."

[0471] 3. Sensory hypersensitivity prevention function:

[0472] Example: If a user asks for advice on dealing with "loud noise," the system displays "Try earmuffs."

[0473] Prompt: "When a user requests a solution to a loud noise, generate an appropriate solution."

[0474] This system supports the daily lives of users with ASD through a variety of interfaces.

[0475] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0476] Step 1:

[0477] The user inputs their emotions into the system. This input can be in the form of text or voice. The input data is sent to the server via the terminal. For example, if the user inputs "sad," the text data is passed to the server.

[0478] Step 2:

[0479] The server sends the received emotion input data to the emotion engine. The emotion engine uses a natural language processing model to analyze the input data and generate emotion recognition results. Specifically, it uses generative AI models such as BERT and GPT-3 to analyze text data and recognize the emotion "sad" as "sad."

[0480] Step 3:

[0481] The server receives the emotion recognition results from the emotion engine. Based on the results, it selects a predefined empathy message. For example, to select the empathy message "That must have been difficult... Thank you for sharing your story" corresponding to the emotion "sad," it searches for and selects an appropriate message from the empathy message dictionary.

[0482] Step 4:

[0483] The server sends the selected sympathy message to the terminal, which then displays the message to the user, allowing the user to confirm the sympathy message from the server.

[0484] Step 5:

[0485] A user inputs a request for learning resources on a specific topic. For example, if a user requests resources on "science," the input in text format is sent to the server via the terminal.

[0486] Step 6:

[0487] The server sends the received topic input to the learning support function and selects an appropriate learning resource from the dictionary database. For example, it selects the learning resource "Science"-related "Science experiment video here: [Link]" for the topic "Science."

[0488] Step 7:

[0489] The server sends the selected learning resource to the terminal, which then displays it to the user, allowing the user to access the requested learning resource.

[0490] Step 8:

[0491] The user inputs information about environmental factors. For example, if the user requests measures to be taken against "loud noise," the text data is sent to the server via the terminal.

[0492] Step 9:

[0493] The server sends the received environmental factor input to the sensory hypersensitivity countermeasure function, which selects an appropriate hypersensitivity countermeasure from the dictionary database. For example, it selects "Try earmuffs" as a countermeasure for the environmental factor "loud noise."

[0494] Step 10:

[0495] The server transmits the selected hypersensitivity measures to the terminal, which then displays them to the user, allowing the user to confirm and implement specific measures against environmental factors.

[0496] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0497] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0498] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0499] [Second embodiment]

[0500] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0501] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0502] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0503] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0504] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0505] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0506] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0507] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0508] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0509] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0510] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0511] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0512] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families. The system is equipped with functions for emotional empathy, learning support, and sensory hypersensitivity management, enabling support optimized for individual situations.

[0513] Emotional empathy function

[0514] 1. The user inputs their emotion, for example, "sad" in text.

[0515] 2. The device receives this emotion input and invokes the emotion empathy function.

[0516] 3. The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[0517] Learning support functions

[0518] 1. The user enters the topic they want to learn about, for example, "science."

[0519] 2. The device receives this topic input and calls the learning support function.

[0520] 3. The device displays the resource selected by the learning support function to the user, for example, "Science experiment video here: [link]".

[0521] Sensory hypersensitivity prevention function

[0522] 1. The user inputs the current environmental factor, for example, "loud noise."

[0523] 2. The device receives this environmental factor input and calls the sensory hypersensitivity countermeasure function.

[0524] 3. The device displays the suggestion selected by the sensory hypersensitivity countermeasure function to the user: "Try earmuffs."

[0525] Specific examples

[0526] Example 1: Use of Emotional Empathy

[0527] When the user types "sad," the device invokes the emotion empathy function and displays the empathy message "Oh, that must be hard... Thanks for sharing your story." This allows the user to receive emotional support.

[0528] Example 2: Using the learning support function

[0529] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]." This allows the user to easily access the educational resources they need.

[0530] Example 3: Using the sensory hypersensitivity function

[0531] When a user requests measures to deal with "loud noise," the device will call the sensory hypersensitivity countermeasure function and display a suggestion such as "Try earmuffs," providing the user with an immediate solution to the environmental factor.

[0532] The system provides these functions through a user interface and is designed to reduce the difficulties faced by children with ASD and their families in their daily lives. By providing appropriate responses based on the input provided, users can receive personalized support.

[0533] The processing flow will be explained below.

[0534] Emotional empathy function

[0535] Step 1:

[0536] The user inputs their current emotion, for example, "sad."

[0537] Step 2:

[0538] The device receives the user's emotional input, which is in text format.

[0539] Step 3:

[0540] The device calls the emotion empathy function emotion_empathy(current_emotion) with the emotion input "sad" as an argument.

[0541] Step 4:

[0542] The device selects an empathy message from the dictionary empathy_responses that corresponds to the input emotion in the emotion empathy function. For example, for the emotion 'sad', "That must be tough... Thanks for sharing your story." is selected.

[0543] Step 5:

[0544] The terminal displays the selected sympathy message to the user.

[0545] Learning support functions

[0546] Step 1:

[0547] The user enters the topic they want to learn about, for example, "science."

[0548] Step 2:

[0549] The terminal receives the user's topic input, which is also in text format.

[0550] Step 3:

[0551] The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[0552] Step 4:

[0553] In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Here is a science experiment video: [Link]" is selected.

[0554] Step 5:

[0555] The terminal displays the selected learning resource to the user.

[0556] Sensory hypersensitivity prevention function

[0557] Step 1:

[0558] The user inputs a current environmental factor, for example, "loud noise."

[0559] Step 2:

[0560] The terminal receives the user's environmental factor input, which is also in text format.

[0561] Step 3:

[0562] The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[0563] Step 4:

[0564] The device selects a sensitivity solution from the sensitivity_solutions dictionary in the sensitivity solution function that corresponds to the input environmental factor. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[0565] Step 5:

[0566] The terminal displays the selected hypersensitivity measures to the user.

[0567] Example 1

[0568] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0569] There is a need for systems that can reduce the difficulties children with autism spectrum disorder (ASD) and their families face in their daily lives and provide individually optimized support. However, existing support systems are unable to provide integrated support for emotional empathy, learning support, and measures for sensory hypersensitivity, preventing users from receiving flexible support that can respond to a variety of situations.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0571] In this invention, the server includes means for receiving an input regarding an emotion from a user, means for transmitting the emotion input to an emotion empathy function, means for activating the emotion empathy function and analyzing the emotion data, means for selecting and transmitting an empathy message to the user, and means for displaying the empathy message to the user, thereby enabling the user to receive emotional support and obtain an appropriate empathy message corresponding to each individual emotion.

[0572] The system further includes means for receiving input from a user requesting learning resources on a specific topic, means for sending the topic input to a learning support function, means for activating the learning support function and searching for resources related to the specified topic, means for selecting and sending the learning resources to the user, and means for displaying the learning resources to the user, thereby enabling the user to easily access resources related to the topic they wish to learn and receive support for their learning.

[0573] The device further includes means for receiving input regarding environmental factors from a user, means for transmitting the environmental factor input to a sensory hypersensitivity countermeasure function, means for activating the sensory hypersensitivity countermeasure function and searching for countermeasures for the specified environmental factor, means for selecting and transmitting the countermeasure to the user, and means for displaying the selected countermeasure to the user, thereby enabling the user to quickly obtain countermeasures for sensory hypersensitivity to the environment.

[0574] An "emotional empathy function" is an algorithm that analyzes a user's emotional input and selects an appropriate empathy message.

[0575] A "learning support function" is an algorithm for searching and selecting learning resources related to a topic specified by the user.

[0576] The "sensory hypersensitivity countermeasure function" is an algorithm for searching and selecting appropriate countermeasures for sensory hypersensitivity based on environmental factors entered by the user.

[0577] "Emotion data" is information about emotions entered by the user and expressed in text format.

[0578] A "sympathetic message" is a message that is displayed to the user based on the analyzed emotion data, and includes content that sympathizes with the user's emotions.

[0579] "Learning resources" is a general term for information, learning materials, links, etc. related to the topic that a user wants to learn.

[0580] "Environmental factors" are information that indicates the user's current surroundings and stimuli, and are expressed in text format.

[0581] "Sensitive measures" is a general term for countermeasures and suggestions for environmental factors entered by the user.

[0582] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families. The system has functions for emotional empathy, learning support, and sensory hypersensitivity treatment. The implementation methods for each function are described in detail below.

[0583] Emotional empathy function

[0584] 1. The user inputs their emotion, for example, "sad."

[0585] 2. The device receives this emotion input and sends it to the emotion empathy function.

[0586] This function is implemented using a natural language processing library (e.g., NLTK or spaCy).

[0587] 3. The server launches the emotion empathy function and analyzes the received emotion data.

[0588] 4. The server selects an empathetic message based on the results of sentiment analysis, for example, choosing from predefined messages to select a message like "That must be tough...Thanks for sharing your story."

[0589] 5. The server sends the selected empathy message to the device.

[0590] 6. The device displays the received sympathy message to the user.

[0591] For example, if a user types "sad," the device invokes the emotion empathy function, and the server selects and displays the message "That must be tough... Thanks for sharing your story."

[0592] Learning support functions

[0593] 1. The user enters the topic they want to learn about, for example, "science."

[0594] 2. The device receives the topic input and sends it to the learning support function.

[0595] The implementation of this function utilizes educational resource APIs (e.g., Khan Academy API).

[0596] 3. The server launches a learning support function to search for learning resources related to the specified topic.

[0597] 4. The server selects the most suitable learning resource from the search results, for example, "Here is a science experiment video: [link]".

[0598] 5. The server sends the selected learning resource to the terminal.

[0599] 6. The terminal displays the received resources to the user.

[0600] As a specific example, if a user requests a learning resource related to "science," the device calls the learning support function, and the server selects and displays the learning resource "Science experiment video here: [link]."

[0601] Sensory hypersensitivity prevention function

[0602] 1. The user inputs the current environmental factor, for example, "loud noise."

[0603] 2. The device receives input of environmental factors and sends it to the sensory hypersensitivity countermeasure function.

[0604] The implementation of this function utilizes the Customized Experience API.

[0605] 3. The server invokes the sensory hypersensitivity countermeasure function to search for countermeasures for the specified environmental factor.

[0606] 4. The server selects the best solution from the search results, for example, a suggestion such as "Try earmuffs."

[0607] 5. The server sends the selected measures to the device.

[0608] 6. The device displays the received measures to the user.

[0609] As a concrete example, if a user requests solutions to deal with "loud noises," the device calls a sensory hypersensitivity solution function, and the server selects and displays the suggestion "Try earmuffs."

[0610] Prompt Sentence Examples

[0611] "Use the Emotion Empathy feature. If I type 'sad', tell me how it responds."

[0612] "Please use the learning support feature. Can you tell me some resources about 'science'?"

[0613] "Please use the sensory sensitivity feature. What can I do about 'loud noises'?"

[0614] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0615] Emotional empathy function

[0616] Step 1:

[0617] The user inputs their own emotion, for example, "sad."

[0618] Input: User emotion (e.g. "sad")

[0619] Output: Emotion input data

[0620] Step 2:

[0621] The device receives emotion input and prepares to send the emotion input data to the server.

[0622] Input: Emotion input data

[0623] Output: Input data ready to be sent to the server

[0624] Step 3:

[0625] The server receives the emotion input data and invokes the emotion empathy function, which analyzes the emotion data using a natural language processing library (e.g., NLTK or spaCy).

[0626] Input: Emotion input data

[0627] Output: Analysis results (e.g., appropriate response to the emotion "sad")

[0628] Step 4:

[0629] The server selects an empathetic message based on the analysis results, searching a database of predefined messages for an appropriate message (e.g., "That must be tough... Thanks for sharing your story.").

[0630] Input: Analysis results

[0631] Output: Selected empathy message

[0632] Step 5:

[0633] The server transmits the selected sympathy message to the terminal.

[0634] Input: Selected empathy message

[0635] Output: Message ready to be sent to the terminal

[0636] Step 6:

[0637] The terminal displays the received sympathy message to the user.

[0638] Input: Received sympathy message

[0639] Output: The message displayed to the user

[0640] Learning support functions

[0641] Step 1:

[0642] The user enters the topic they want to learn about, for example, "science."

[0643] Input: User topic (e.g. "Science")

[0644] Output: Topic input data

[0645] Step 2:

[0646] The terminal receives the topic input and prepares to send the topic input data to the server.

[0647] Input: Topic input data

[0648] Output: Input data ready to be sent to the server

[0649] Step 3:

[0650] The server receives the topic input data and launches the learning support function, which searches for related learning resources using an educational resource API (e.g., Khan Academy API).

[0651] Input: Topic input data

[0652] Output: Search results (e.g. "science experiment videos")

[0653] Step 4:

[0654] The server selects the most suitable learning resource from the search results.

[0655] Input: Search results

[0656] Output: The selected learning resource (e.g., "Here's a science experiment video: [link]")

[0657] Step 5:

[0658] The server transmits the selected learning resource to the terminal.

[0659] Input: Selected Learning Resources

[0660] Output: Resources ready to be sent to the device

[0661] Step 6:

[0662] The terminal displays the received learning resources to the user.

[0663] Input: Received learning resources

[0664] Output: The resource displayed to the user

[0665] Sensory hypersensitivity prevention function

[0666] Step 1:

[0667] The user inputs a current environmental factor, for example, "loud noise."

[0668] Input: User's environmental factors (e.g. "loud noise")

[0669] Output: Environmental factor input data

[0670] Step 2:

[0671] The terminal receives the environmental factor input and prepares to send the environmental factor input data to the server.

[0672] Input: Environmental factor input data

[0673] Output: Input data ready to be sent to the server

[0674] Step 3:

[0675] The server receives the environmental factor input data and invokes the sensory hypersensitivity countermeasure function, which uses the customized experience API to find the relevant countermeasure.

[0676] Input: Environmental factor input data

[0677] Output: Search results (e.g. "Try on some earmuffs.")

[0678] Step 4:

[0679] The server selects the most appropriate solution from the search results.

[0680] Input: Search results

[0681] Output: The selected measure (e.g., "Try earmuffs.")

[0682] Step 5:

[0683] The server transmits the selected measures to the terminal.

[0684] Input: Selected measures

[0685] Output: Measures ready to be sent to the device

[0686] Step 6:

[0687] The terminal displays the received measures to the user.

[0688] Input: Received measures

[0689] Output: The action displayed to the user

[0690] (Application example 1)

[0691] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0692] Children with autism spectrum disorder (ASD) and their families face many challenges when shopping and spending time in physical stores. They often have difficulty expressing their emotions and adapting to their environment, and are unable to receive appropriate support. As a result, their use of physical stores is limited, and stress in their daily lives increases.

[0693] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0694] In this invention, the server includes means for receiving an input regarding an emotion from a user, means for selecting a predefined empathy message corresponding to the emotion input, means for displaying the selected empathy message to the user, means for providing a user interface for accepting emotion input from the user in a physical store, and means for displaying an appropriate empathy message for the user to receive emotion-based support in the physical store, thereby enabling children with ASD and their families to immediately receive appropriate support in the physical store.

[0695] "User" means an individual or family member who uses the System.

[0696] "Emotional input" refers to information such as text or voice that allows a user to express their emotional state.

[0697] "Empathetic messages" are predefined messages that are provided in response to the user's emotions.

[0698] "Displaying to the user" means that the system outputs selected information in a form that can be viewed by the user.

[0699] A "user interface" is an interactive screen equipped with input and display means for users to input emotions and obtain information.

[0700] A "brick and mortar store" is a physical commercial environment for selling goods.

[0701] "Emotion-based support" means providing appropriate messages and advice that take into account the user's emotional state.

[0702] The present invention provides a system that enables children with autism spectrum disorder (ASD) and their families to receive appropriate support immediately in a physical store. Specific embodiments of this system are described below.

[0703] System Configuration

[0704] The system is based on a smartphone application. The required hardware is a smartphone. The software includes a program developed using Python and web application frameworks such as Flask and Django.

[0705] Emotional empathy function

[0706] The user inputs their emotions into a smartphone app. For example, they might say, "I'm nervous." The system receives this emotion input and selects a predefined empathy message. The selected empathy message is displayed as, "It's natural to feel nervous. Let's take a moment to relax." This process allows the user to receive appropriate support for expressing their emotions in a physical store.

[0707] Learning support functions

[0708] The user inputs a request for learning resources related to a specific topic. For example, if the user selects "Store location," the system receives this input and selects a predefined learning resource. The selected resource is displayed as map information, indicating "The children's section is here." This process allows the user to easily obtain and understand information within a physical store.

[0709] Sensory hypersensitivity prevention function

[0710] The user inputs an environmental factor. For example, if the user types "glare," the system takes this input and selects a predefined hypersensitivity countermeasure. The selected countermeasure is displayed as a message: "Sunglasses are available. Please ask a member of staff if you need them." This process provides the user with an immediate way to address environmental factors within a physical store.

[0711] Specific examples

[0712] For example:

[0713] 1. Use cases for Emotional Empathy:

[0714] A user types "I'm nervous" into a smartphone app. The app responds, "It's natural to feel nervous. Take a break here."

[0715] 2. Example of using the learning support function:

[0716] When the user selects "Store location," the app displays map information and says, "This is the children's section."

[0717] 3. Examples of using the sensory sensitivity feature:

[0718] When a user types in "dazzling light," the app displays the message, "Sunglasses are available. Please ask a member of staff if you need them."

[0719] Prompt Sentence Examples

[0720] python

[0721] Emotion input example:

[0722] emotion = "I'm nervous"

[0723] response = support.get_response(emotion)

[0724] print(response)

[0725] Learning support examples:

[0726] topic = "location in store"

[0727] resource = guide.get_resource(topic)

[0728] print(resource)

[0729] Examples of measures to combat sensory hypersensitivity:

[0730] issue = "blinding light"

[0731] tip = support.get_tip(issue)

[0732] print(tip)

[0733] In this way, the system of the present invention allows children with autism spectrum disorder (ASD) and their families to receive appropriate support immediately in physical stores, thereby improving their shopping experience and the quality of their daily lives.

[0734] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0735] Step 1:

[0736] A user inputs information into a smartphone app about emotions, specific topics, or environmental factors. For example, the user might input "I'm nervous," "a location in a store," or "bright light." This input data is received by the device.

[0737] Step 2:

[0738] The terminal sends the input data received from the user to the server, and the system initiates the process to generate an appropriate response. Based on the type of input data, the terminal invokes the corresponding function.

[0739] Step 3:

[0740] The server analyzes the user's input data and uses a generative AI model to select appropriate empathetic messages, learning resources, or hypersensitivity measures. In this step, the input data is used as prompts for the AI ​​model, which generates the optimal response for each prompt. For example, in response to the emotional input "I'm nervous," the server selects the empathetic message "It's natural to feel nervous. Let's take a break here."

[0741] Step 4:

[0742] The server then sends the selected response back to the device, which may include a message of sympathy, a link to a learning resource, or suggestions for addressing the environmental factors.

[0743] Step 5:

[0744] The device displays the response received from the server to the user. Specifically, it is displayed as a message on the smartphone app screen. For example, if the user enters "I'm nervous," the device displays the message "It's natural to feel nervous. Take a break here."

[0745] Step 6:

[0746] The user can then review the displayed messages and resources and take action based on them. For example, they can follow the suggestion "Sunglasses available" presented as a hypersensitivity measure and ask the store staff for sunglasses.

[0747] At each step, data processing and calculations are performed based on the user's input, and the optimal response is generated and provided to the user, realizing a system that allows children with ASD and their families to receive appropriate support immediately within a physical store.

[0748] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0749] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families, with functions for emotional empathy, learning support, and sensory hypersensitivity treatment. In particular, the combination of an emotion engine enables more advanced emotion recognition and appropriate responses.

[0750] Emotional empathy function

[0751] 1. The user provides text or voice input to express an emotion, for example, "sad."

[0752] 2. The device receives the user's emotional input, which can be in text or voice format.

[0753] 3. The device calls the emotion engine and analyzes the input data.

[0754] 4. The emotion engine recognizes emotions based on the input data and generates emotion recognition results. For example, the input "sad" is recognized as the emotion "sad."

[0755] 5. The device receives the emotion recognition result and calls the emotion empathy function emotion_empathy(current_emotion) with the emotion recognition result “sad” as an argument.

[0756] 6. The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[0757] Learning support functions

[0758] 1. The user enters the topic they want to learn about, for example, "science."

[0759] 2. The terminal receives the user's topic input, which is in text format.

[0760] 3. The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[0761] 4. In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Science experiment videos are here: [Link]" is selected.

[0762] 5. The terminal displays the selected learning resource to the user.

[0763] Sensory hypersensitivity prevention function

[0764] 1. The user inputs the current environmental factor, for example, "loud noise."

[0765] 2. The terminal receives the user's environmental factor input, which is in text format.

[0766] 3. The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[0767] 4. The device selects a sensitivity solution corresponding to the input environmental factor from the sensitivity_solutions dictionary in the sensitivity solution function. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[0768] 5. The device displays the selected hypersensitivity measures to the user.

[0769] Specific examples

[0770] Example 1: Use of Emotional Empathy

[0771] When the user types "sad," the device invokes the emotion engine, recognizes the emotion "sad," and uses the emotion empathy function to display a message to the user saying, "That must be tough... Thanks for sharing your story."

[0772] Example 2: Using the learning support function

[0773] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]."

[0774] Example 3: Using the sensory hypersensitivity function

[0775] If the user requests a solution to deal with "loud noise," the device will call the sensory sensitivity solution function and display a suggestion: "Try earmuffs."

[0776] The system aims to alleviate the difficulties faced by users with ASD by using an emotion engine to recognize emotions based on user input and providing various support functions.

[0777] The processing flow will be explained below.

[0778] Emotional empathy function

[0779] Step 1:

[0780] The user provides text or voice input to express an emotion, for example, "sad."

[0781] Step 2:

[0782] The device receives the user's emotional input, which can be in text or voice format.

[0783] Step 3:

[0784] The device calls the emotion engine and analyzes the input data.

[0785] Step 4:

[0786] The emotion engine recognizes emotions based on the input data and generates emotion recognition results. For example, it recognizes the emotion "sad" from the input "sad."

[0787] Step 5:

[0788] The device receives the emotion recognition result and calls the emotion empathy function emotion_empathy(current_emotion) with the emotion recognition result "sad" as an argument.

[0789] Step 6:

[0790] The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[0791] Learning support functions

[0792] Step 1:

[0793] The user enters the topic they want to learn about, for example, "science."

[0794] Step 2:

[0795] The terminal receives the user's topic input, which is in text format.

[0796] Step 3:

[0797] The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[0798] Step 4:

[0799] In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Here is a science experiment video: [Link]" is selected.

[0800] Step 5:

[0801] The terminal displays the selected learning resource to the user.

[0802] Sensory hypersensitivity prevention function

[0803] Step 1:

[0804] The user inputs a current environmental factor, for example, "loud noise."

[0805] Step 2:

[0806] The terminal receives the user's environmental factor input, which is in text format.

[0807] Step 3:

[0808] The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[0809] Step 4:

[0810] The device selects a sensitivity solution from the sensitivity_solutions dictionary in the sensitivity solution function that corresponds to the input environmental factor. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[0811] Step 5:

[0812] The terminal displays the selected hypersensitivity measures to the user.

[0813] Example 2

[0814] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0815] To address the various challenges faced by children with autism spectrum disorder (ASD) and their families, appropriate support tailored to their individual needs is necessary. However, existing technologies lack comprehensive systems that address these challenges. This makes it difficult to provide effective support for users with ASD in recognizing their emotions, accessing appropriate learning resources, and addressing sensory sensitivities.

[0816] The identification process 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 means for receiving an input related to an emotion from a user, means for analyzing the emotion input and recognizing the emotion, means for selecting a predefined empathy message corresponding to the emotion recognition, means for receiving an input from a user requesting learning resources related to a specific topic, means for analyzing the topic input and selecting the learning resources, means for receiving an input related to environmental factors from a user, and means for analyzing the environmental factors and selecting a predefined hypersensitivity countermeasure. This makes it possible to comprehensively provide emotional empathy, learning support, and sensory hypersensitivity countermeasures for various challenges faced by users with ASD.

[0817] "User" refers to any individual or group of people who use the System, and specifically includes children with Autism Spectrum Disorder (ASD) and their families.

[0818] "Emotional input" refers to emotional expressions in text or speech that a user inputs into the system.

[0819] "Emotion engine" is a general term for software and hardware that analyzes emotional input from users and recognizes those emotions.

[0820] "Empathetic message" refers to a predefined response message that the system presents in response to the user's emotions.

[0821] "Learning resources" refers to information or content related to the topic a user wants to learn about, including, for example, text, videos, links, etc.

[0822] "Environmental factors" refer to elements and situations related to the user's current environment, such as "loud noises" and "bright light."

[0823] "Sensory sensitivity management" refers to suggestions and measures to reduce a user's sensitivity to environmental factors.

[0824] "Server" refers to the central processing unit of the system, which is responsible for receiving and analyzing input from users, and generating and providing appropriate responses.

[0825] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families, with functions of emotional empathy, learning support, and sensory hypersensitivity treatment. Each function of this system is described in detail below.

[0826] Emotional empathy function

[0827] 1. The user inputs text or voice to express an emotion into the device. Let's assume that the user inputs "sad" on a smartphone or PC.

[0828] 2. The device receives input from the user and, if there is voice input, converts it to text using the Google Speech-to-Text API.

[0829] 3. The device calls the emotion engine, which uses IBM Watson's emotion recognition API to analyze the input data and recognize emotions.

[0830] 4. The emotion engine recognizes emotions based on the input data and returns the result in the form of something like "sad."

[0831] 5. The device executes the emotion empathy function emotion_empathy(current_emotion) and passes the emotion recognition result "sad" as an argument. The emotion empathy function selects an appropriate empathy message, such as "That must be tough... Thanks for sharing your story."

[0832] 6. The device displays the selected empathy message to the user, for example, "That must be tough... Thanks for sharing your story."

[0833] Learning support functions

[0834] 1. The user enters the topic they want to learn in text format into the terminal, for example, "science."

[0835] 2. The device receives the input and executes the learning support function learning_support(topic), passing "science" as the argument.

[0836] 3. The device selects an appropriate learning resource related to "science" from the dictionary learning_resources. For example, "Here is a science experiment video: [link]" is selected.

[0837] 4. The device displays the selected learning resource to the user.

[0838] Sensory hypersensitivity prevention function

[0839] 1. The user inputs the current environmental factor in text format into the terminal, for example, "loud noise."

[0840] 2. The device receives the input and executes the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor), passing "loud sound" as the argument.

[0841] 3. The device selects an appropriate solution to the "loud noise" sensitivity from the dictionary sensitivity_solutions, for example, "Try earmuffs."

[0842] 4. The device displays the selected measures to the user.

[0843] Specific examples

[0844] Example 1: Use of Emotional Empathy

[0845] When the user types "sad," the device invokes the emotion engine, recognizes the emotion "sad," and uses the emotion empathy function to display a message to the user saying, "That must be tough... Thanks for sharing your story."

[0846] Example 2: Using the learning support function

[0847] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]."

[0848] Example 3: Using the sensory hypersensitivity function

[0849] If the user requests a solution to deal with "loud noise," the device will call the sensory sensitivity solution function and display a suggestion: "Try earmuffs."

[0850] The system aims to alleviate the difficulties faced by users with ASD by utilizing an emotion engine and various functions based on user input.

[0851] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0852] Emotional empathy function

[0853] Step 1:

[0854] The user inputs text or voice to express their emotion into the device. The user uses the voice input function of their smartphone or PC to say "I'm sad."

[0855] Input: User's voice data

[0856] Output: Audio data

[0857] Specific operation: The user inputs voice into the microphone of their smartphone or PC.

[0858] Step 2:

[0859] The device receives input, and in the case of voice input, converts the speech to text using the Google Speech-to-Text API.

[0860] Input: Audio data

[0861] Output: Text data "Sad"

[0862] Specific behavior: The device sends the user's voice to the Google Speech-to-Text API and receives the text version of "sad."

[0863] Step 3:

[0864] The device calls the emotion engine to analyze the input data, using IBM Watson's emotion recognition API.

[0865] Input: Text data "sad"

[0866] Output: Emotion recognition result "sad"

[0867] Specific operation: The device sends the text "sad" to IBM Watson's emotion recognition API, which recognizes the emotion "sad" and returns it.

[0868] Step 4:

[0869] The device executes the emotion empathy function emotion_empathy(current_emotion) and passes the emotion recognition result "sad" as an argument. The empathy function selects an appropriate message.

[0870] Input: Emotion recognition result "sad"

[0871] Output: Empathetic message "That must be tough... Thanks for sharing your story."

[0872] Specific operation: The device executes the emotion empathy function, passing "sad" as an argument and selecting the appropriate message.

[0873] Step 5:

[0874] The terminal displays the selected sympathetic message to the user.

[0875] Input: Empathetic message "That must be tough... Thanks for sharing your story."

[0876] Output: An empathy message displayed on the user's screen

[0877] Specific operation: A sympathetic message is displayed on the device screen to provide feedback to the user.

[0878] Learning support functions

[0879] Step 1:

[0880] The user enters the topic they want to learn in text form into the terminal. The user uses the keyboard to type "science."

[0881] Input: topic "science"

[0882] Output: Text data "Science"

[0883] Specific operation: The user inputs a topic using the device keyboard.

[0884] Step 2:

[0885] The terminal receives the input. The input data is received as is.

[0886] Input: Text data "Science"

[0887] Output: Text data "Science"

[0888] Specific operation: The terminal receives input data from the user.

[0889] Step 3:

[0890] The terminal executes the learning support function learning_support(topic) and passes the topic "science" as an argument. The learning support function selects the corresponding resource.

[0891] Input: topic "science"

[0892] Output: Learning resource "Science experiment video here: [link]"

[0893] Specific operation: The terminal executes the learning support function and selects the learning resource corresponding to "science."

[0894] Step 4:

[0895] The terminal displays the selected learning resource to the user.

[0896] Input: Learning Resources "Here's a science experiment video: [link]"

[0897] Output: The learning resource displayed on the user's screen

[0898] Specific operation: Display learning resources on the device screen and provide them to the user.

[0899] Sensory hypersensitivity prevention function

[0900] Step 1:

[0901] The user inputs the current environmental factors in text format into the terminal. The user uses the keyboard to input "loud noise."

[0902] Input: Environmental factor "loud noise"

[0903] Output: Text data "Loud sound"

[0904] Specific operation: The user inputs the environmental factors using the keyboard of the device.

[0905] Step 2:

[0906] The terminal receives the input. The input data is received as is.

[0907] Input: Text data "loud sound"

[0908] Output: Text data "Loud sound"

[0909] Specific operation: The terminal receives input data from the user.

[0910] Step 3:

[0911] The device executes the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) and passes the environmental factor "loud noise" as an argument. The sensory sensitivity countermeasure function selects the corresponding countermeasure.

[0912] Input: Environmental factor "loud noise"

[0913] Output: Hypersensitivity Countermeasure "Try earmuffs."

[0914] Specific operation: The device executes the sensory hypersensitivity countermeasure function and selects the countermeasure corresponding to "loud noise."

[0915] Step 4:

[0916] The terminal displays the selected hypersensitivity measures to the user.

[0917] Input: Hypersensitivity Countermeasures "Try earmuffs."

[0918] Output: The hypersensitivity measures displayed on the user's screen

[0919] Specific operation: Display the hypersensitivity measures on the device screen and provide them to the user.

[0920] Through these steps, the system has the ability to comprehensively provide emotional empathy, learning support, and sensory hypersensitivity measures to users with ASD.

[0921] (Application example 2)

[0922] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0923] There is a need for a system that can effectively solve the various problems that users with autism spectrum disorder (ASD) face in their daily lives. Specifically, a comprehensive support platform that can seamlessly provide emotional understanding and empathy, learning support, and measures for sensory hypersensitivity is needed.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving an input regarding emotions from a user, means for selecting a predefined empathy message corresponding to the emotion input, means for displaying the selected empathy message to the user, means for receiving an input from the user requesting learning resources on a specific topic, means for selecting predefined learning resources corresponding to the topic input, means for displaying the selected learning resources to the user, means for receiving an input regarding environmental factors from the user, means for selecting a predefined hypersensitivity countermeasure corresponding to the environmental factor, and means for displaying the selected hypersensitivity countermeasure to the user. This enables emotional empathy, learning support, and sensory hypersensitivity countermeasures.

[0925] "User" refers to the individual or family member who operates the system.

[0926] "Emotional input" refers to the act of a user inputting their emotional state into the system via text or voice.

[0927] A "predefined empathic message" refers to a message that is predefined as a response to a particular emotional state.

[0928] "Topical Learning Resources" refers to educational information and materials based on a particular subject designated by the User.

[0929] "Environmental factors" refer to the physical environmental conditions that the user currently faces.

[0930] "Hypersensitivity measures" refers to specific measures and suggestions to help users with sensory sensitivities to live comfortably.

[0931] "Emotion engine" refers to technology that analyzes a user's emotional input and recognizes a specific emotional state.

[0932] "Dynamic provision" refers to providing appropriate learning resources in real time in response to user requests.

[0933] "Suggestion functionality" refers to the functionality of the system that has the ability to suggest appropriate hypersensitivity countermeasures based on the user's input of environmental factors.

[0934] To implement this invention, we need a server with specific functions, a terminal, and an interface with the user. We will build a system with three main functions: emotional empathy, learning support, and measures against sensory hypersensitivity.

[0935] First, the server receives emotional input from the user. When the user inputs their emotions via text or voice, the server passes this input to the emotion engine for analysis. The emotion engine uses complex data analysis algorithms to recognize emotions from the input data. Based on this recognition result, it selects an appropriate message from predefined empathy messages and displays it to the user via the device.

[0936] The specific hardware used is a processor with a dedicated AI chip for emotion recognition, and the software uses natural language processing models (such as BERT and GPT-3) to perform emotion analysis. The emotion recognition results are stored in a shared database, allowing for real-time processing on the server.

[0937] Next, we will explain the learning support function. When a user requests learning resources on a specific topic, the server receives this request and dynamically selects appropriate resources from the dictionary database. These resources are displayed on the terminal and provided for easy access by the user.

[0938] Specifically, it uses a learning management system (LMS) to manage educational content and links to a resource database stored there. It also includes algorithms for dynamically providing relevant resources based on user requests. It uses programming languages ​​such as Python and machine learning libraries (e.g., TensorFlow and PyTorch) to select and present resources.

[0939] Finally, we will explain the sensory sensitivity countermeasure function. When the server receives input from the user about environmental factors, it analyzes this and selects an appropriate countermeasure from predefined sensitivity countermeasures. The selected countermeasure is displayed on the device to ensure the user's comfort.

[0940] This processing may involve IoT devices to utilize sensor data, such as a noise sensor to measure noise levels or a light sensor to measure light intensity. The data obtained from these sensors is sent in real time to a server for analysis, often using IoT frameworks such as Node-RED or MQTT.

[0941] Examples of concrete examples and prompts

[0942] 1. Emotional empathy function:

[0943] Example: If a user types "sad," the system displays the message "That must be hard... Thanks for sharing your story."

[0944] Prompt: "When the user enters the emotion 'sad', generate an appropriate empathetic message."

[0945] 2. Learning support functions:

[0946] Example: If a user requests learning resources related to "Science," you might display "Here are some science experiment videos: [Link]."

[0947] Prompt: "When a user asks for learning resources on the topic 'Science', generate the appropriate learning resources."

[0948] 3. Sensory hypersensitivity prevention function:

[0949] Example: If a user asks for advice on dealing with "loud noise," the system displays "Try earmuffs."

[0950] Prompt: "When a user requests a solution to a loud noise, generate an appropriate solution."

[0951] This system supports the daily lives of users with ASD through a variety of interfaces.

[0952] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0953] Step 1:

[0954] The user inputs their emotions into the system. This input can be in the form of text or voice. The input data is sent to the server via the terminal. For example, if the user inputs "sad," the text data is passed to the server.

[0955] Step 2:

[0956] The server sends the received emotion input data to the emotion engine. The emotion engine uses a natural language processing model to analyze the input data and generate emotion recognition results. Specifically, it uses generative AI models such as BERT and GPT-3 to analyze text data and recognize the emotion "sad" as "sad."

[0957] Step 3:

[0958] The server receives the emotion recognition results from the emotion engine. Based on the results, it selects a predefined empathy message. For example, to select the empathy message "That must have been difficult... Thank you for sharing your story" corresponding to the emotion "sad," it searches for and selects an appropriate message from the empathy message dictionary.

[0959] Step 4:

[0960] The server sends the selected sympathy message to the terminal, which then displays the message to the user, allowing the user to confirm the sympathy message from the server.

[0961] Step 5:

[0962] A user inputs a request for learning resources on a specific topic. For example, if a user requests resources on "science," the input in text format is sent to the server via the terminal.

[0963] Step 6:

[0964] The server sends the received topic input to the learning support function and selects an appropriate learning resource from the dictionary database. For example, it selects the learning resource "Science"-related "Science experiment video here: [Link]" for the topic "Science."

[0965] Step 7:

[0966] The server sends the selected learning resource to the terminal, which then displays it to the user, allowing the user to access the requested learning resource.

[0967] Step 8:

[0968] The user inputs information about environmental factors. For example, if the user requests measures to be taken against "loud noise," the text data is sent to the server via the terminal.

[0969] Step 9:

[0970] The server sends the received environmental factor input to the sensory hypersensitivity countermeasure function, which selects an appropriate hypersensitivity countermeasure from the dictionary database. For example, it selects "Try earmuffs" as a countermeasure for the environmental factor "loud noise."

[0971] Step 10:

[0972] The server transmits the selected hypersensitivity measures to the terminal, which then displays them to the user, allowing the user to confirm and implement specific measures against environmental factors.

[0973] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0974] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0975] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0976] [Third embodiment]

[0977] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0978] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0979] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0980] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0981] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0982] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0983] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0984] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0985] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0986] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0987] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0988] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0989] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families. The system is equipped with functions for emotional empathy, learning support, and sensory hypersensitivity management, enabling support optimized for individual situations.

[0990] Emotional empathy function

[0991] 1. The user inputs their emotion, for example, "sad" in text.

[0992] 2. The device receives this emotion input and invokes the emotion empathy function.

[0993] 3. The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[0994] Learning support functions

[0995] 1. The user enters the topic they want to learn about, for example, "science."

[0996] 2. The device receives this topic input and calls the learning support function.

[0997] 3. The device displays the resource selected by the learning support function to the user, for example, "Science experiment video here: [link]".

[0998] Sensory hypersensitivity prevention function

[0999] 1. The user inputs the current environmental factor, for example, "loud noise."

[1000] 2. The device receives this environmental factor input and calls the sensory hypersensitivity countermeasure function.

[1001] 3. The device displays the suggestion selected by the sensory hypersensitivity countermeasure function to the user: "Try earmuffs."

[1002] Specific examples

[1003] Example 1: Use of Emotional Empathy

[1004] When the user types "sad," the device invokes the emotion empathy function and displays the empathy message "Oh, that must be hard... Thanks for sharing your story." This allows the user to receive emotional support.

[1005] Example 2: Using the learning support function

[1006] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]." This allows the user to easily access the educational resources they need.

[1007] Example 3: Using the sensory hypersensitivity function

[1008] When a user requests measures to deal with "loud noise," the device will call the sensory hypersensitivity countermeasure function and display a suggestion such as "Try earmuffs," providing the user with an immediate solution to the environmental factor.

[1009] The system provides these functions through a user interface and is designed to reduce the difficulties faced by children with ASD and their families in their daily lives. By providing appropriate responses based on the input provided, users can receive personalized support.

[1010] The processing flow will be explained below.

[1011] Emotional empathy function

[1012] Step 1:

[1013] The user inputs their current emotion, for example, "sad."

[1014] Step 2:

[1015] The device receives the user's emotional input, which is in text format.

[1016] Step 3:

[1017] The device calls the emotion empathy function emotion_empathy(current_emotion) with the emotion input "sad" as an argument.

[1018] Step 4:

[1019] The device selects an empathy message from the dictionary empathy_responses that corresponds to the input emotion in the emotion empathy function. For example, for the emotion 'sad', "That must be tough... Thanks for sharing your story." is selected.

[1020] Step 5:

[1021] The terminal displays the selected sympathy message to the user.

[1022] Learning support functions

[1023] Step 1:

[1024] The user enters the topic they want to learn about, for example, "science."

[1025] Step 2:

[1026] The terminal receives the user's topic input, which is also in text format.

[1027] Step 3:

[1028] The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[1029] Step 4:

[1030] In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Here is a science experiment video: [Link]" is selected.

[1031] Step 5:

[1032] The terminal displays the selected learning resource to the user.

[1033] Sensory hypersensitivity prevention function

[1034] Step 1:

[1035] The user inputs a current environmental factor, for example, "loud noise."

[1036] Step 2:

[1037] The terminal receives the user's environmental factor input, which is also in text format.

[1038] Step 3:

[1039] The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[1040] Step 4:

[1041] The device selects a sensitivity solution from the sensitivity_solutions dictionary in the sensitivity solution function that corresponds to the input environmental factor. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[1042] Step 5:

[1043] The terminal displays the selected hypersensitivity measures to the user.

[1044] Example 1

[1045] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1046] There is a need for systems that can reduce the difficulties children with autism spectrum disorder (ASD) and their families face in their daily lives and provide individually optimized support. However, existing support systems are unable to provide integrated support for emotional empathy, learning support, and measures for sensory hypersensitivity, preventing users from receiving flexible support that can respond to a variety of situations.

[1047] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1048] In this invention, the server includes means for receiving an input regarding an emotion from a user, means for transmitting the emotion input to an emotion empathy function, means for activating the emotion empathy function and analyzing the emotion data, means for selecting and transmitting an empathy message to the user, and means for displaying the empathy message to the user, thereby enabling the user to receive emotional support and obtain an appropriate empathy message corresponding to each individual emotion.

[1049] The system further includes means for receiving input from a user requesting learning resources on a specific topic, means for sending the topic input to a learning support function, means for activating the learning support function and searching for resources related to the specified topic, means for selecting and sending the learning resources to the user, and means for displaying the learning resources to the user, thereby enabling the user to easily access resources related to the topic they wish to learn and receive support for their learning.

[1050] The device further includes means for receiving input regarding environmental factors from a user, means for transmitting the environmental factor input to a sensory hypersensitivity countermeasure function, means for activating the sensory hypersensitivity countermeasure function and searching for countermeasures for the specified environmental factor, means for selecting and transmitting the countermeasure to the user, and means for displaying the selected countermeasure to the user, thereby enabling the user to quickly obtain countermeasures for sensory hypersensitivity to the environment.

[1051] An "emotional empathy function" is an algorithm that analyzes a user's emotional input and selects an appropriate empathy message.

[1052] A "learning support function" is an algorithm for searching and selecting learning resources related to a topic specified by the user.

[1053] The "sensory hypersensitivity countermeasure function" is an algorithm for searching and selecting appropriate countermeasures for sensory hypersensitivity based on environmental factors entered by the user.

[1054] "Emotion data" is information about emotions entered by the user and expressed in text format.

[1055] A "sympathetic message" is a message that is displayed to the user based on the analyzed emotion data, and includes content that sympathizes with the user's emotions.

[1056] "Learning resources" is a general term for information, learning materials, links, etc. related to the topic that a user wants to learn.

[1057] "Environmental factors" are information that indicates the user's current surroundings and stimuli, and are expressed in text format.

[1058] "Sensitive measures" is a general term for countermeasures and suggestions for environmental factors entered by the user.

[1059] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families. The system has functions for emotional empathy, learning support, and sensory hypersensitivity treatment. The implementation methods for each function are described in detail below.

[1060] Emotional empathy function

[1061] 1. The user inputs their emotion, for example, "sad."

[1062] 2. The device receives this emotion input and sends it to the emotion empathy function.

[1063] This function is implemented using a natural language processing library (e.g., NLTK or spaCy).

[1064] 3. The server launches the emotion empathy function and analyzes the received emotion data.

[1065] 4. The server selects an empathetic message based on the results of sentiment analysis, for example, choosing from predefined messages to select a message like "That must be tough...Thanks for sharing your story."

[1066] 5. The server sends the selected empathy message to the device.

[1067] 6. The device displays the received sympathy message to the user.

[1068] For example, if a user types "sad," the device invokes the emotion empathy function, and the server selects and displays the message "That must be tough... Thanks for sharing your story."

[1069] Learning support functions

[1070] 1. The user enters the topic they want to learn about, for example, "science."

[1071] 2. The device receives the topic input and sends it to the learning support function.

[1072] The implementation of this function utilizes educational resource APIs (e.g., Khan Academy API).

[1073] 3. The server launches a learning support function to search for learning resources related to the specified topic.

[1074] 4. The server selects the most suitable learning resource from the search results, for example, "Here is a science experiment video: [link]".

[1075] 5. The server sends the selected learning resource to the terminal.

[1076] 6. The terminal displays the received resources to the user.

[1077] As a specific example, if a user requests a learning resource related to "science," the device calls the learning support function, and the server selects and displays the learning resource "Science experiment video here: [link]."

[1078] Sensory hypersensitivity prevention function

[1079] 1. The user inputs the current environmental factor, for example, "loud noise."

[1080] 2. The device receives input of environmental factors and sends it to the sensory hypersensitivity countermeasure function.

[1081] The implementation of this function utilizes the Customized Experience API.

[1082] 3. The server invokes the sensory hypersensitivity countermeasure function to search for countermeasures for the specified environmental factor.

[1083] 4. The server selects the best solution from the search results, for example, a suggestion such as "Try earmuffs."

[1084] 5. The server sends the selected measures to the device.

[1085] 6. The device displays the received measures to the user.

[1086] As a concrete example, if a user requests solutions to deal with "loud noises," the device calls a sensory hypersensitivity solution function, and the server selects and displays the suggestion "Try earmuffs."

[1087] Prompt Sentence Examples

[1088] "Use the Emotion Empathy feature. If I type 'sad', tell me how it responds."

[1089] "Please use the learning support feature. Can you tell me some resources about 'science'?"

[1090] "Please use the sensory sensitivity feature. What can I do about 'loud noises'?"

[1091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1092] Emotional empathy function

[1093] Step 1:

[1094] The user inputs their own emotion, for example, "sad."

[1095] Input: User emotion (e.g. "sad")

[1096] Output: Emotion input data

[1097] Step 2:

[1098] The device receives emotion input and prepares to send the emotion input data to the server.

[1099] Input: Emotion input data

[1100] Output: Input data ready to be sent to the server

[1101] Step 3:

[1102] The server receives the emotion input data and invokes the emotion empathy function, which analyzes the emotion data using a natural language processing library (e.g., NLTK or spaCy).

[1103] Input: Emotion input data

[1104] Output: Analysis results (e.g., appropriate response to the emotion "sad")

[1105] Step 4:

[1106] The server selects an empathetic message based on the analysis results, searching a database of predefined messages for an appropriate message (e.g., "That must be tough... Thanks for sharing your story.").

[1107] Input: Analysis results

[1108] Output: Selected empathy message

[1109] Step 5:

[1110] The server transmits the selected sympathy message to the terminal.

[1111] Input: Selected empathy message

[1112] Output: Message ready to be sent to the terminal

[1113] Step 6:

[1114] The terminal displays the received sympathy message to the user.

[1115] Input: Received sympathy message

[1116] Output: The message displayed to the user

[1117] Learning support functions

[1118] Step 1:

[1119] The user enters the topic they want to learn about, for example, "science."

[1120] Input: User topic (e.g. "Science")

[1121] Output: Topic input data

[1122] Step 2:

[1123] The terminal receives the topic input and prepares to send the topic input data to the server.

[1124] Input: Topic input data

[1125] Output: Input data ready to be sent to the server

[1126] Step 3:

[1127] The server receives the topic input data and launches the learning support function, which searches for related learning resources using an educational resource API (e.g., Khan Academy API).

[1128] Input: Topic input data

[1129] Output: Search results (e.g. "science experiment videos")

[1130] Step 4:

[1131] The server selects the most suitable learning resource from the search results.

[1132] Input: Search results

[1133] Output: The selected learning resource (e.g., "Here's a science experiment video: [link]")

[1134] Step 5:

[1135] The server transmits the selected learning resource to the terminal.

[1136] Input: Selected Learning Resources

[1137] Output: Resources ready to be sent to the device

[1138] Step 6:

[1139] The terminal displays the received learning resources to the user.

[1140] Input: Received learning resources

[1141] Output: The resource displayed to the user

[1142] Sensory hypersensitivity prevention function

[1143] Step 1:

[1144] The user inputs a current environmental factor, for example, "loud noise."

[1145] Input: User's environmental factors (e.g. "loud noise")

[1146] Output: Environmental factor input data

[1147] Step 2:

[1148] The terminal receives the environmental factor input and prepares to send the environmental factor input data to the server.

[1149] Input: Environmental factor input data

[1150] Output: Input data ready to be sent to the server

[1151] Step 3:

[1152] The server receives the environmental factor input data and invokes the sensory hypersensitivity countermeasure function, which uses the customized experience API to find the relevant countermeasure.

[1153] Input: Environmental factor input data

[1154] Output: Search results (e.g. "Try on some earmuffs.")

[1155] Step 4:

[1156] The server selects the most appropriate solution from the search results.

[1157] Input: Search results

[1158] Output: The selected measure (e.g., "Try earmuffs.")

[1159] Step 5:

[1160] The server transmits the selected measures to the terminal.

[1161] Input: Selected measures

[1162] Output: Measures ready to be sent to the device

[1163] Step 6:

[1164] The terminal displays the received measures to the user.

[1165] Input: Received measures

[1166] Output: The action displayed to the user

[1167] (Application example 1)

[1168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1169] Children with autism spectrum disorder (ASD) and their families face many challenges when shopping and spending time in physical stores. They often have difficulty expressing their emotions and adapting to their environment, and are unable to receive appropriate support. As a result, their use of physical stores is limited, and stress in their daily lives increases.

[1170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1171] In this invention, the server includes means for receiving an input regarding an emotion from a user, means for selecting a predefined empathy message corresponding to the emotion input, means for displaying the selected empathy message to the user, means for providing a user interface for accepting emotion input from the user in a physical store, and means for displaying an appropriate empathy message for the user to receive emotion-based support in the physical store, thereby enabling children with ASD and their families to immediately receive appropriate support in the physical store.

[1172] "User" means an individual or family member who uses the System.

[1173] "Emotional input" refers to information such as text or voice that allows a user to express their emotional state.

[1174] "Empathetic messages" are predefined messages that are provided in response to the user's emotions.

[1175] "Displaying to the user" means that the system outputs selected information in a form that can be viewed by the user.

[1176] A "user interface" is an interactive screen equipped with input and display means for users to input emotions and obtain information.

[1177] A "brick and mortar store" is a physical commercial environment for selling goods.

[1178] "Emotion-based support" means providing appropriate messages and advice that take into account the user's emotional state.

[1179] The present invention provides a system that enables children with autism spectrum disorder (ASD) and their families to receive appropriate support immediately in a physical store. Specific embodiments of this system are described below.

[1180] System Configuration

[1181] The system is based on a smartphone application. The required hardware is a smartphone. The software includes a program developed using Python and web application frameworks such as Flask and Django.

[1182] Emotional empathy function

[1183] The user inputs their emotions into a smartphone app. For example, they might say, "I'm nervous." The system receives this emotion input and selects a predefined empathy message. The selected empathy message is displayed as, "It's natural to feel nervous. Let's take a moment to relax." This process allows the user to receive appropriate support for expressing their emotions in a physical store.

[1184] Learning support functions

[1185] The user inputs a request for learning resources related to a specific topic. For example, if the user selects "Store location," the system receives this input and selects a predefined learning resource. The selected resource is displayed as map information, indicating "The children's section is here." This process allows the user to easily obtain and understand information within a physical store.

[1186] Sensory hypersensitivity prevention function

[1187] The user inputs an environmental factor. For example, if the user types "glare," the system takes this input and selects a predefined hypersensitivity countermeasure. The selected countermeasure is displayed as a message: "Sunglasses are available. Please ask a member of staff if you need them." This process provides the user with an immediate way to address environmental factors within a physical store.

[1188] Specific examples

[1189] For example:

[1190] 1. Use cases for Emotional Empathy:

[1191] A user types "I'm nervous" into a smartphone app. The app responds, "It's natural to feel nervous. Take a break here."

[1192] 2. Example of using the learning support function:

[1193] When the user selects "Store location," the app displays map information and says, "This is the children's section."

[1194] 3. Examples of using the sensory sensitivity feature:

[1195] When a user types in "dazzling light," the app displays the message, "Sunglasses are available. Please ask a member of staff if you need them."

[1196] Prompt Sentence Examples

[1197] python

[1198] Emotion input example:

[1199] emotion = "I'm nervous"

[1200] response = support.get_response(emotion)

[1201] print(response)

[1202] Learning support examples:

[1203] topic = "location in store"

[1204] resource = guide.get_resource(topic)

[1205] print(resource)

[1206] Examples of measures to combat sensory hypersensitivity:

[1207] issue = "blinding light"

[1208] tip = support.get_tip(issue)

[1209] print(tip)

[1210] In this way, the system of the present invention allows children with autism spectrum disorder (ASD) and their families to receive appropriate support immediately in physical stores, thereby improving their shopping experience and the quality of their daily lives.

[1211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1212] Step 1:

[1213] A user inputs information into a smartphone app about emotions, specific topics, or environmental factors. For example, the user might input "I'm nervous," "a location in a store," or "bright light." This input data is received by the device.

[1214] Step 2:

[1215] The terminal sends the input data received from the user to the server, and the system initiates the process to generate an appropriate response. Based on the type of input data, the terminal invokes the corresponding function.

[1216] Step 3:

[1217] The server analyzes the user's input data and uses a generative AI model to select appropriate empathetic messages, learning resources, or hypersensitivity measures. In this step, the input data is used as prompts for the AI ​​model, which generates the optimal response for each prompt. For example, in response to the emotional input "I'm nervous," the server selects the empathetic message "It's natural to feel nervous. Let's take a break here."

[1218] Step 4:

[1219] The server then sends the selected response back to the device, which may include a message of sympathy, a link to a learning resource, or suggestions for addressing the environmental factors.

[1220] Step 5:

[1221] The device displays the response received from the server to the user. Specifically, it is displayed as a message on the smartphone app screen. For example, if the user enters "I'm nervous," the device displays the message "It's natural to feel nervous. Take a break here."

[1222] Step 6:

[1223] The user can then review the displayed messages and resources and take action based on them. For example, they can follow the suggestion "Sunglasses available" presented as a hypersensitivity measure and ask the store staff for sunglasses.

[1224] At each step, data processing and calculations are performed based on the user's input, and the optimal response is generated and provided to the user, realizing a system that allows children with ASD and their families to receive appropriate support immediately within a physical store.

[1225] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1226] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families, with functions for emotional empathy, learning support, and sensory hypersensitivity treatment. In particular, the combination of an emotion engine enables more advanced emotion recognition and appropriate responses.

[1227] Emotional empathy function

[1228] 1. The user provides text or voice input to express an emotion, for example, "sad."

[1229] 2. The device receives the user's emotional input, which can be in text or voice format.

[1230] 3. The device calls the emotion engine and analyzes the input data.

[1231] 4. The emotion engine recognizes emotions based on the input data and generates emotion recognition results. For example, the input "sad" is recognized as the emotion "sad."

[1232] 5. The device receives the emotion recognition result and calls the emotion empathy function emotion_empathy(current_emotion) with the emotion recognition result “sad” as an argument.

[1233] 6. The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[1234] Learning support functions

[1235] 1. The user enters the topic they want to learn about, for example, "science."

[1236] 2. The terminal receives the user's topic input, which is in text format.

[1237] 3. The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[1238] 4. In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Science experiment videos are here: [Link]" is selected.

[1239] 5. The terminal displays the selected learning resource to the user.

[1240] Sensory hypersensitivity prevention function

[1241] 1. The user inputs the current environmental factor, for example, "loud noise."

[1242] 2. The terminal receives the user's environmental factor input, which is in text format.

[1243] 3. The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[1244] 4. The device selects a sensitivity solution corresponding to the input environmental factor from the sensitivity_solutions dictionary in the sensitivity solution function. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[1245] 5. The device displays the selected hypersensitivity measures to the user.

[1246] Specific examples

[1247] Example 1: Use of Emotional Empathy

[1248] When the user types "sad," the device invokes the emotion engine, recognizes the emotion "sad," and uses the emotion empathy function to display a message to the user saying, "That must be tough... Thanks for sharing your story."

[1249] Example 2: Using the learning support function

[1250] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]."

[1251] Example 3: Using the sensory hypersensitivity function

[1252] If the user requests a solution to deal with "loud noise," the device will call the sensory sensitivity solution function and display a suggestion: "Try earmuffs."

[1253] The system aims to alleviate the difficulties faced by users with ASD by using an emotion engine to recognize emotions based on user input and providing various support functions.

[1254] The processing flow will be explained below.

[1255] Emotional empathy function

[1256] Step 1:

[1257] The user provides text or voice input to express an emotion, for example, "sad."

[1258] Step 2:

[1259] The device receives the user's emotional input, which can be in text or voice format.

[1260] Step 3:

[1261] The device calls the emotion engine and analyzes the input data.

[1262] Step 4:

[1263] The emotion engine recognizes emotions based on the input data and generates emotion recognition results. For example, it recognizes the emotion "sad" from the input "sad."

[1264] Step 5:

[1265] The device receives the emotion recognition result and calls the emotion empathy function emotion_empathy(current_emotion) with the emotion recognition result "sad" as an argument.

[1266] Step 6:

[1267] The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[1268] Learning support functions

[1269] Step 1:

[1270] The user enters the topic they want to learn about, for example, "science."

[1271] Step 2:

[1272] The terminal receives the user's topic input, which is in text format.

[1273] Step 3:

[1274] The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[1275] Step 4:

[1276] In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Here is a science experiment video: [Link]" is selected.

[1277] Step 5:

[1278] The terminal displays the selected learning resource to the user.

[1279] Sensory hypersensitivity prevention function

[1280] Step 1:

[1281] The user inputs a current environmental factor, for example, "loud noise."

[1282] Step 2:

[1283] The terminal receives the user's environmental factor input, which is in text format.

[1284] Step 3:

[1285] The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[1286] Step 4:

[1287] The device selects a sensitivity solution from the sensitivity_solutions dictionary in the sensitivity solution function that corresponds to the input environmental factor. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[1288] Step 5:

[1289] The terminal displays the selected hypersensitivity measures to the user.

[1290] Example 2

[1291] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1292] To address the various challenges faced by children with autism spectrum disorder (ASD) and their families, appropriate support tailored to their individual needs is necessary. However, existing technologies lack comprehensive systems that address these challenges. This makes it difficult to provide effective support for users with ASD in recognizing their emotions, accessing appropriate learning resources, and addressing sensory sensitivities.

[1293] The identification process 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 means for receiving an input related to an emotion from a user, means for analyzing the emotion input and recognizing the emotion, means for selecting a predefined empathy message corresponding to the emotion recognition, means for receiving an input from a user requesting learning resources related to a specific topic, means for analyzing the topic input and selecting the learning resources, means for receiving an input related to environmental factors from a user, and means for analyzing the environmental factors and selecting a predefined hypersensitivity countermeasure. This makes it possible to comprehensively provide emotional empathy, learning support, and sensory hypersensitivity countermeasures for various challenges faced by users with ASD.

[1294] "User" refers to any individual or group of people who use the System, and specifically includes children with Autism Spectrum Disorder (ASD) and their families.

[1295] "Emotional input" refers to emotional expressions in text or speech that a user inputs into the system.

[1296] "Emotion engine" is a general term for software and hardware that analyzes emotional input from users and recognizes those emotions.

[1297] "Empathetic message" refers to a predefined response message that the system presents in response to the user's emotions.

[1298] "Learning resources" refers to information or content related to the topic a user wants to learn about, including, for example, text, videos, links, etc.

[1299] "Environmental factors" refer to elements and situations related to the user's current environment, such as "loud noises" and "bright light."

[1300] "Sensory sensitivity management" refers to suggestions and measures to reduce a user's sensitivity to environmental factors.

[1301] "Server" refers to the central processing unit of the system, which is responsible for receiving and analyzing input from users, and generating and providing appropriate responses.

[1302] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families, with functions of emotional empathy, learning support, and sensory hypersensitivity treatment. Each function of this system is described in detail below.

[1303] Emotional empathy function

[1304] 1. The user inputs text or voice to express an emotion into the device. Let's assume that the user inputs "sad" on a smartphone or PC.

[1305] 2. The device receives input from the user and, if there is voice input, converts it to text using the Google Speech-to-Text API.

[1306] 3. The device calls the emotion engine, which uses IBM Watson's emotion recognition API to analyze the input data and recognize emotions.

[1307] 4. The emotion engine recognizes emotions based on the input data and returns the result in the form of something like "sad."

[1308] 5. The device executes the emotion empathy function emotion_empathy(current_emotion) and passes the emotion recognition result "sad" as an argument. The emotion empathy function selects an appropriate empathy message, such as "That must be tough... Thanks for sharing your story."

[1309] 6. The device displays the selected empathy message to the user, for example, "That must be tough... Thanks for sharing your story."

[1310] Learning support functions

[1311] 1. The user enters the topic they want to learn in text format into the terminal, for example, "science."

[1312] 2. The device receives the input and executes the learning support function learning_support(topic), passing "science" as the argument.

[1313] 3. The device selects an appropriate learning resource related to "science" from the dictionary learning_resources. For example, "Here is a science experiment video: [link]" is selected.

[1314] 4. The device displays the selected learning resource to the user.

[1315] Sensory hypersensitivity prevention function

[1316] 1. The user inputs the current environmental factor in text format into the terminal, for example, "loud noise."

[1317] 2. The device receives the input and executes the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor), passing "loud sound" as the argument.

[1318] 3. The device selects an appropriate solution to the "loud noise" sensitivity from the dictionary sensitivity_solutions, for example, "Try earmuffs."

[1319] 4. The device displays the selected measures to the user.

[1320] Specific examples

[1321] Example 1: Use of Emotional Empathy

[1322] When the user types "sad," the device invokes the emotion engine, recognizes the emotion "sad," and uses the emotion empathy function to display a message to the user saying, "That must be tough... Thanks for sharing your story."

[1323] Example 2: Using the learning support function

[1324] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]."

[1325] Example 3: Using the sensory hypersensitivity function

[1326] If the user requests a solution to deal with "loud noise," the device will call the sensory sensitivity solution function and display a suggestion: "Try earmuffs."

[1327] The system aims to alleviate the difficulties faced by users with ASD by utilizing an emotion engine and various functions based on user input.

[1328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1329] Emotional empathy function

[1330] Step 1:

[1331] The user inputs text or voice to express their emotion into the device. The user uses the voice input function of their smartphone or PC to say "I'm sad."

[1332] Input: User's voice data

[1333] Output: Audio data

[1334] Specific operation: The user inputs voice into the microphone of their smartphone or PC.

[1335] Step 2:

[1336] The device receives input, and in the case of voice input, converts the speech to text using the Google Speech-to-Text API.

[1337] Input: Audio data

[1338] Output: Text data "Sad"

[1339] Specific behavior: The device sends the user's voice to the Google Speech-to-Text API and receives the text version of "sad."

[1340] Step 3:

[1341] The device calls the emotion engine to analyze the input data, using IBM Watson's emotion recognition API.

[1342] Input: Text data "sad"

[1343] Output: Emotion recognition result "sad"

[1344] Specific operation: The device sends the text "sad" to IBM Watson's emotion recognition API, which recognizes the emotion "sad" and returns it.

[1345] Step 4:

[1346] The device executes the emotion empathy function emotion_empathy(current_emotion) and passes the emotion recognition result "sad" as an argument. The empathy function selects an appropriate message.

[1347] Input: Emotion recognition result "sad"

[1348] Output: Empathetic message "That must be tough... Thanks for sharing your story."

[1349] Specific operation: The device executes the emotion empathy function, passing "sad" as an argument and selecting the appropriate message.

[1350] Step 5:

[1351] The terminal displays the selected sympathetic message to the user.

[1352] Input: Empathetic message "That must be tough... Thanks for sharing your story."

[1353] Output: An empathy message displayed on the user's screen

[1354] Specific operation: A sympathetic message is displayed on the device screen to provide feedback to the user.

[1355] Learning support functions

[1356] Step 1:

[1357] The user enters the topic they want to learn in text form into the terminal. The user uses the keyboard to type "science."

[1358] Input: topic "science"

[1359] Output: Text data "Science"

[1360] Specific operation: The user inputs a topic using the device keyboard.

[1361] Step 2:

[1362] The terminal receives the input. The input data is received as is.

[1363] Input: Text data "Science"

[1364] Output: Text data "Science"

[1365] Specific operation: The terminal receives input data from the user.

[1366] Step 3:

[1367] The terminal executes the learning support function learning_support(topic) and passes the topic "science" as an argument. The learning support function selects the corresponding resource.

[1368] Input: topic "science"

[1369] Output: Learning resource "Science experiment video here: [link]"

[1370] Specific operation: The terminal executes the learning support function and selects the learning resource corresponding to "science."

[1371] Step 4:

[1372] The terminal displays the selected learning resource to the user.

[1373] Input: Learning Resources "Here's a science experiment video: [link]"

[1374] Output: The learning resource displayed on the user's screen

[1375] Specific operation: Display learning resources on the device screen and provide them to the user.

[1376] Sensory hypersensitivity prevention function

[1377] Step 1:

[1378] The user inputs the current environmental factors in text format into the terminal. The user uses the keyboard to input "loud noise."

[1379] Input: Environmental factor "loud noise"

[1380] Output: Text data "Loud sound"

[1381] Specific operation: The user inputs the environmental factors using the keyboard of the device.

[1382] Step 2:

[1383] The terminal receives the input. The input data is received as is.

[1384] Input: Text data "loud sound"

[1385] Output: Text data "Loud sound"

[1386] Specific operation: The terminal receives input data from the user.

[1387] Step 3:

[1388] The device executes the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) and passes the environmental factor "loud noise" as an argument. The sensory sensitivity countermeasure function selects the corresponding countermeasure.

[1389] Input: Environmental factor "loud noise"

[1390] Output: Hypersensitivity Countermeasure "Try earmuffs."

[1391] Specific operation: The device executes the sensory hypersensitivity countermeasure function and selects the countermeasure corresponding to "loud noise."

[1392] Step 4:

[1393] The terminal displays the selected hypersensitivity measures to the user.

[1394] Input: Hypersensitivity Countermeasures "Try earmuffs."

[1395] Output: The hypersensitivity measures displayed on the user's screen

[1396] Specific operation: Display the hypersensitivity measures on the device screen and provide them to the user.

[1397] Through these steps, the system has the ability to comprehensively provide emotional empathy, learning support, and sensory hypersensitivity measures to users with ASD.

[1398] (Application example 2)

[1399] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1400] There is a need for a system that can effectively solve the various problems that users with autism spectrum disorder (ASD) face in their daily lives. Specifically, a comprehensive support platform that can seamlessly provide emotional understanding and empathy, learning support, and measures for sensory hypersensitivity is needed.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving an input regarding emotions from a user, means for selecting a predefined empathy message corresponding to the emotion input, means for displaying the selected empathy message to the user, means for receiving an input from the user requesting learning resources on a specific topic, means for selecting predefined learning resources corresponding to the topic input, means for displaying the selected learning resources to the user, means for receiving an input regarding environmental factors from the user, means for selecting a predefined hypersensitivity countermeasure corresponding to the environmental factor, and means for displaying the selected hypersensitivity countermeasure to the user. This enables emotional empathy, learning support, and sensory hypersensitivity countermeasures.

[1402] "User" refers to the individual or family member who operates the system.

[1403] "Emotional input" refers to the act of a user inputting their emotional state into the system via text or voice.

[1404] A "predefined empathic message" refers to a message that is predefined as a response to a particular emotional state.

[1405] "Topical Learning Resources" refers to educational information and materials based on a particular subject designated by the User.

[1406] "Environmental factors" refer to the physical environmental conditions that the user currently faces.

[1407] "Hypersensitivity measures" refers to specific measures and suggestions to help users with sensory sensitivities to live comfortably.

[1408] "Emotion engine" refers to technology that analyzes a user's emotional input and recognizes a specific emotional state.

[1409] "Dynamic provision" refers to providing appropriate learning resources in real time in response to user requests.

[1410] "Suggestion functionality" refers to the functionality of the system that has the ability to suggest appropriate hypersensitivity countermeasures based on the user's input of environmental factors.

[1411] To implement this invention, we need a server with specific functions, a terminal, and an interface with the user. We will build a system with three main functions: emotional empathy, learning support, and measures against sensory hypersensitivity.

[1412] First, the server receives emotional input from the user. When the user inputs their emotions via text or voice, the server passes this input to the emotion engine for analysis. The emotion engine uses complex data analysis algorithms to recognize emotions from the input data. Based on this recognition result, it selects an appropriate message from predefined empathy messages and displays it to the user via the device.

[1413] The specific hardware used is a processor with a dedicated AI chip for emotion recognition, and the software uses natural language processing models (such as BERT and GPT-3) to perform emotion analysis. The emotion recognition results are stored in a shared database, allowing for real-time processing on the server.

[1414] Next, we will explain the learning support function. When a user requests learning resources on a specific topic, the server receives this request and dynamically selects appropriate resources from the dictionary database. These resources are displayed on the terminal and provided for easy access by the user.

[1415] Specifically, it uses a learning management system (LMS) to manage educational content and links to a resource database stored there. It also includes algorithms for dynamically providing relevant resources based on user requests. It uses programming languages ​​such as Python and machine learning libraries (e.g., TensorFlow and PyTorch) to select and present resources.

[1416] Finally, we will explain the sensory sensitivity countermeasure function. When the server receives input from the user about environmental factors, it analyzes this and selects an appropriate countermeasure from predefined sensitivity countermeasures. The selected countermeasure is displayed on the device to ensure the user's comfort.

[1417] This processing may involve IoT devices to utilize sensor data, such as a noise sensor to measure noise levels or a light sensor to measure light intensity. The data obtained from these sensors is sent in real time to a server for analysis, often using IoT frameworks such as Node-RED or MQTT.

[1418] Examples of concrete examples and prompts

[1419] 1. Emotional empathy function:

[1420] Example: If a user types "sad," the system displays the message "That must be hard... Thanks for sharing your story."

[1421] Prompt: "When the user enters the emotion 'sad', generate an appropriate empathetic message."

[1422] 2. Learning support functions:

[1423] Example: If a user requests learning resources related to "Science," you might display "Here are some science experiment videos: [Link]."

[1424] Prompt: "When a user asks for learning resources on the topic 'Science', generate the appropriate learning resources."

[1425] 3. Sensory hypersensitivity prevention function:

[1426] Example: If a user asks for advice on dealing with "loud noise," the system displays "Try earmuffs."

[1427] Prompt: "When a user requests a solution to a loud noise, generate an appropriate solution."

[1428] This system supports the daily lives of users with ASD through a variety of interfaces.

[1429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1430] Step 1:

[1431] The user inputs their emotions into the system. This input can be in the form of text or voice. The input data is sent to the server via the terminal. For example, if the user inputs "sad," the text data is passed to the server.

[1432] Step 2:

[1433] The server sends the received emotion input data to the emotion engine. The emotion engine uses a natural language processing model to analyze the input data and generate emotion recognition results. Specifically, it uses generative AI models such as BERT and GPT-3 to analyze text data and recognize the emotion "sad" as "sad."

[1434] Step 3:

[1435] The server receives the emotion recognition results from the emotion engine. Based on the results, it selects a predefined empathy message. For example, to select the empathy message "That must have been difficult... Thank you for sharing your story" corresponding to the emotion "sad," it searches for and selects an appropriate message from the empathy message dictionary.

[1436] Step 4:

[1437] The server sends the selected sympathy message to the terminal, which then displays the message to the user, allowing the user to confirm the sympathy message from the server.

[1438] Step 5:

[1439] A user inputs a request for learning resources on a specific topic. For example, if a user requests resources on "science," the input in text format is sent to the server via the terminal.

[1440] Step 6:

[1441] The server sends the received topic input to the learning support function and selects an appropriate learning resource from the dictionary database. For example, it selects the learning resource "Science"-related "Science experiment video here: [Link]" for the topic "Science."

[1442] Step 7:

[1443] The server sends the selected learning resource to the terminal, which then displays it to the user, allowing the user to access the requested learning resource.

[1444] Step 8:

[1445] The user inputs information about environmental factors. For example, if the user requests measures to be taken against "loud noise," the text data is sent to the server via the terminal.

[1446] Step 9:

[1447] The server sends the received environmental factor input to the sensory hypersensitivity countermeasure function, which selects an appropriate hypersensitivity countermeasure from the dictionary database. For example, it selects "Try earmuffs" as a countermeasure for the environmental factor "loud noise."

[1448] Step 10:

[1449] The server transmits the selected hypersensitivity measures to the terminal, which then displays them to the user, allowing the user to confirm and implement specific measures against environmental factors.

[1450] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1451] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1452] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1453] [Fourth embodiment]

[1454] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1455] 7, a 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.

[1456] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1457] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1458] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1459] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1460] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1461] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1462] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1463] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1464] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1465] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1466] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1467] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families. The system is equipped with functions for emotional empathy, learning support, and sensory hypersensitivity management, enabling support optimized for individual situations.

[1468] Emotional empathy function

[1469] 1. The user inputs their emotion, for example, "sad" in text.

[1470] 2. The device receives this emotion input and invokes the emotion empathy function.

[1471] 3. The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[1472] Learning support functions

[1473] 1. The user enters the topic they want to learn about, for example, "science."

[1474] 2. The device receives this topic input and calls the learning support function.

[1475] 3. The device displays the resource selected by the learning support function to the user, for example, "Science experiment video here: [link]".

[1476] Sensory hypersensitivity prevention function

[1477] 1. The user inputs the current environmental factor, for example, "loud noise."

[1478] 2. The device receives this environmental factor input and calls the sensory hypersensitivity countermeasure function.

[1479] 3. The device displays the suggestion selected by the sensory hypersensitivity countermeasure function to the user: "Try earmuffs."

[1480] Specific examples

[1481] Example 1: Use of Emotional Empathy

[1482] When the user types "sad," the device invokes the emotion empathy function and displays the empathy message "Oh, that must be hard... Thanks for sharing your story." This allows the user to receive emotional support.

[1483] Example 2: Using the learning support function

[1484] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]." This allows the user to easily access the educational resources they need.

[1485] Example 3: Using the sensory hypersensitivity function

[1486] When a user requests measures to deal with "loud noise," the device will call the sensory hypersensitivity countermeasure function and display a suggestion such as "Try earmuffs," providing the user with an immediate solution to the environmental factor.

[1487] The system provides these functions through a user interface and is designed to reduce the difficulties faced by children with ASD and their families in their daily lives. By providing appropriate responses based on the input provided, users can receive personalized support.

[1488] The processing flow will be explained below.

[1489] Emotional empathy function

[1490] Step 1:

[1491] The user inputs their current emotion, for example, "sad."

[1492] Step 2:

[1493] The device receives the user's emotional input, which is in text format.

[1494] Step 3:

[1495] The device calls the emotion empathy function emotion_empathy(current_emotion) with the emotion input "sad" as an argument.

[1496] Step 4:

[1497] The device selects an empathy message from the dictionary empathy_responses that corresponds to the input emotion in the emotion empathy function. For example, for the emotion 'sad', "That must be tough... Thanks for sharing your story." is selected.

[1498] Step 5:

[1499] The terminal displays the selected sympathy message to the user.

[1500] Learning support functions

[1501] Step 1:

[1502] The user enters the topic they want to learn about, for example, "science."

[1503] Step 2:

[1504] The terminal receives the user's topic input, which is also in text format.

[1505] Step 3:

[1506] The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[1507] Step 4:

[1508] In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Here is a science experiment video: [Link]" is selected.

[1509] Step 5:

[1510] The terminal displays the selected learning resource to the user.

[1511] Sensory hypersensitivity prevention function

[1512] Step 1:

[1513] The user inputs a current environmental factor, for example, "loud noise."

[1514] Step 2:

[1515] The terminal receives the user's environmental factor input, which is also in text format.

[1516] Step 3:

[1517] The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[1518] Step 4:

[1519] The device selects a sensitivity solution from the sensitivity_solutions dictionary in the sensitivity solution function that corresponds to the input environmental factor. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[1520] Step 5:

[1521] The terminal displays the selected hypersensitivity measures to the user.

[1522] Example 1

[1523] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1524] There is a need for systems that can reduce the difficulties children with autism spectrum disorder (ASD) and their families face in their daily lives and provide individually optimized support. However, existing support systems are unable to provide integrated support for emotional empathy, learning support, and measures for sensory hypersensitivity, preventing users from receiving flexible support that can respond to a variety of situations.

[1525] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1526] In this invention, the server includes means for receiving an input regarding an emotion from a user, means for transmitting the emotion input to an emotion empathy function, means for activating the emotion empathy function and analyzing the emotion data, means for selecting and transmitting an empathy message to the user, and means for displaying the empathy message to the user, thereby enabling the user to receive emotional support and obtain an appropriate empathy message corresponding to each individual emotion.

[1527] The system further includes means for receiving input from a user requesting learning resources on a specific topic, means for sending the topic input to a learning support function, means for activating the learning support function and searching for resources related to the specified topic, means for selecting and sending the learning resources to the user, and means for displaying the learning resources to the user, thereby enabling the user to easily access resources related to the topic they wish to learn and receive support for their learning.

[1528] The device further includes means for receiving input regarding environmental factors from a user, means for transmitting the environmental factor input to a sensory hypersensitivity countermeasure function, means for activating the sensory hypersensitivity countermeasure function and searching for countermeasures for the specified environmental factor, means for selecting and transmitting the countermeasure to the user, and means for displaying the selected countermeasure to the user, thereby enabling the user to quickly obtain countermeasures for sensory hypersensitivity to the environment.

[1529] An "emotional empathy function" is an algorithm that analyzes a user's emotional input and selects an appropriate empathy message.

[1530] A "learning support function" is an algorithm for searching and selecting learning resources related to a topic specified by the user.

[1531] The "sensory hypersensitivity countermeasure function" is an algorithm for searching and selecting appropriate countermeasures for sensory hypersensitivity based on environmental factors entered by the user.

[1532] "Emotion data" is information about emotions entered by the user and expressed in text format.

[1533] A "sympathetic message" is a message that is displayed to the user based on the analyzed emotion data, and includes content that sympathizes with the user's emotions.

[1534] "Learning resources" is a general term for information, learning materials, links, etc. related to the topic that a user wants to learn.

[1535] "Environmental factors" are information that indicates the user's current surroundings and stimuli, and are expressed in text format.

[1536] "Sensitive measures" is a general term for countermeasures and suggestions for environmental factors entered by the user.

[1537] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families. The system has functions for emotional empathy, learning support, and sensory hypersensitivity treatment. The implementation methods for each function are described in detail below.

[1538] Emotional empathy function

[1539] 1. The user inputs their emotion, for example, "sad."

[1540] 2. The device receives this emotion input and sends it to the emotion empathy function.

[1541] This function is implemented using a natural language processing library (e.g., NLTK or spaCy).

[1542] 3. The server launches the emotion empathy function and analyzes the received emotion data.

[1543] 4. The server selects an empathetic message based on the results of sentiment analysis, for example, choosing from predefined messages to select a message like "That must be tough...Thanks for sharing your story."

[1544] 5. The server sends the selected empathy message to the device.

[1545] 6. The device displays the received sympathy message to the user.

[1546] For example, if a user types "sad," the device invokes the emotion empathy function, and the server selects and displays the message "That must be tough... Thanks for sharing your story."

[1547] Learning support functions

[1548] 1. The user enters the topic they want to learn about, for example, "science."

[1549] 2. The device receives the topic input and sends it to the learning support function.

[1550] The implementation of this function utilizes educational resource APIs (e.g., Khan Academy API).

[1551] 3. The server launches a learning support function to search for learning resources related to the specified topic.

[1552] 4. The server selects the most suitable learning resource from the search results, for example, "Here is a science experiment video: [link]".

[1553] 5. The server sends the selected learning resource to the terminal.

[1554] 6. The terminal displays the received resources to the user.

[1555] As a specific example, if a user requests a learning resource related to "science," the device calls the learning support function, and the server selects and displays the learning resource "Science experiment video here: [link]."

[1556] Sensory hypersensitivity prevention function

[1557] 1. The user inputs the current environmental factor, for example, "loud noise."

[1558] 2. The device receives input of environmental factors and sends it to the sensory hypersensitivity countermeasure function.

[1559] The implementation of this function utilizes the Customized Experience API.

[1560] 3. The server invokes the sensory hypersensitivity countermeasure function to search for countermeasures for the specified environmental factor.

[1561] 4. The server selects the best solution from the search results, for example, a suggestion such as "Try earmuffs."

[1562] 5. The server sends the selected measures to the device.

[1563] 6. The device displays the received measures to the user.

[1564] As a concrete example, if a user requests solutions to deal with "loud noises," the device calls a sensory hypersensitivity solution function, and the server selects and displays the suggestion "Try earmuffs."

[1565] Prompt Sentence Examples

[1566] "Use the Emotion Empathy feature. If I type 'sad', tell me how it responds."

[1567] "Please use the learning support feature. Can you tell me some resources about 'science'?"

[1568] "Please use the sensory sensitivity feature. What can I do about 'loud noises'?"

[1569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1570] Emotional empathy function

[1571] Step 1:

[1572] The user inputs their own emotion, for example, "sad."

[1573] Input: User emotion (e.g. "sad")

[1574] Output: Emotion input data

[1575] Step 2:

[1576] The device receives emotion input and prepares to send the emotion input data to the server.

[1577] Input: Emotion input data

[1578] Output: Input data ready to be sent to the server

[1579] Step 3:

[1580] The server receives the emotion input data and invokes the emotion empathy function, which analyzes the emotion data using a natural language processing library (e.g., NLTK or spaCy).

[1581] Input: Emotion input data

[1582] Output: Analysis results (e.g., appropriate response to the emotion "sad")

[1583] Step 4:

[1584] The server selects an empathetic message based on the analysis results, searching a database of predefined messages for an appropriate message (e.g., "That must be tough... Thanks for sharing your story.").

[1585] Input: Analysis results

[1586] Output: Selected empathy message

[1587] Step 5:

[1588] The server transmits the selected sympathy message to the terminal.

[1589] Input: Selected empathy message

[1590] Output: Message ready to be sent to the terminal

[1591] Step 6:

[1592] The terminal displays the received sympathy message to the user.

[1593] Input: Received sympathy message

[1594] Output: The message displayed to the user

[1595] Learning support functions

[1596] Step 1:

[1597] The user enters the topic they want to learn about, for example, "science."

[1598] Input: User topic (e.g. "Science")

[1599] Output: Topic input data

[1600] Step 2:

[1601] The terminal receives the topic input and prepares to send the topic input data to the server.

[1602] Input: Topic input data

[1603] Output: Input data ready to be sent to the server

[1604] Step 3:

[1605] The server receives the topic input data and launches the learning support function, which searches for related learning resources using an educational resource API (e.g., Khan Academy API).

[1606] Input: Topic input data

[1607] Output: Search results (e.g. "science experiment videos")

[1608] Step 4:

[1609] The server selects the most suitable learning resource from the search results.

[1610] Input: Search results

[1611] Output: The selected learning resource (e.g., "Here's a science experiment video: [link]")

[1612] Step 5:

[1613] The server transmits the selected learning resource to the terminal.

[1614] Input: Selected Learning Resources

[1615] Output: Resources ready to be sent to the device

[1616] Step 6:

[1617] The terminal displays the received learning resources to the user.

[1618] Input: Received learning resources

[1619] Output: The resource displayed to the user

[1620] Sensory hypersensitivity prevention function

[1621] Step 1:

[1622] The user inputs a current environmental factor, for example, "loud noise."

[1623] Input: User's environmental factors (e.g. "loud noise")

[1624] Output: Environmental factor input data

[1625] Step 2:

[1626] The terminal receives the environmental factor input and prepares to send the environmental factor input data to the server.

[1627] Input: Environmental factor input data

[1628] Output: Input data ready to be sent to the server

[1629] Step 3:

[1630] The server receives the environmental factor input data and invokes the sensory hypersensitivity countermeasure function, which uses the customized experience API to find the relevant countermeasure.

[1631] Input: Environmental factor input data

[1632] Output: Search results (e.g. "Try on some earmuffs.")

[1633] Step 4:

[1634] The server selects the most appropriate solution from the search results.

[1635] Input: Search results

[1636] Output: The selected measure (e.g., "Try earmuffs.")

[1637] Step 5:

[1638] The server transmits the selected measures to the terminal.

[1639] Input: Selected measures

[1640] Output: Measures ready to be sent to the device

[1641] Step 6:

[1642] The terminal displays the received measures to the user.

[1643] Input: Received measures

[1644] Output: The action displayed to the user

[1645] (Application example 1)

[1646] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1647] Children with autism spectrum disorder (ASD) and their families face many challenges when shopping and spending time in physical stores. They often have difficulty expressing their emotions and adapting to their environment, and are unable to receive appropriate support. As a result, their use of physical stores is limited, and stress in their daily lives increases.

[1648] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1649] In this invention, the server includes means for receiving an input regarding an emotion from a user, means for selecting a predefined empathy message corresponding to the emotion input, means for displaying the selected empathy message to the user, means for providing a user interface for accepting emotion input from the user in a physical store, and means for displaying an appropriate empathy message for the user to receive emotion-based support in the physical store, thereby enabling children with ASD and their families to immediately receive appropriate support in the physical store.

[1650] "User" means an individual or family member who uses the System.

[1651] "Emotional input" refers to information such as text or voice that allows a user to express their emotional state.

[1652] "Empathetic messages" are predefined messages that are provided in response to the user's emotions.

[1653] "Displaying to the user" means that the system outputs selected information in a form that can be viewed by the user.

[1654] A "user interface" is an interactive screen equipped with input and display means for users to input emotions and obtain information.

[1655] A "brick and mortar store" is a physical commercial environment for selling goods.

[1656] "Emotion-based support" means providing appropriate messages and advice that take into account the user's emotional state.

[1657] The present invention provides a system that enables children with autism spectrum disorder (ASD) and their families to receive appropriate support immediately in a physical store. Specific embodiments of this system are described below.

[1658] System Configuration

[1659] The system is based on a smartphone application. The required hardware is a smartphone. The software includes a program developed using Python and web application frameworks such as Flask and Django.

[1660] Emotional empathy function

[1661] The user inputs their emotions into a smartphone app. For example, they might say, "I'm nervous." The system receives this emotion input and selects a predefined empathy message. The selected empathy message is displayed as, "It's natural to feel nervous. Let's take a moment to relax." This process allows the user to receive appropriate support for expressing their emotions in a physical store.

[1662] Learning support functions

[1663] The user inputs a request for learning resources related to a specific topic. For example, if the user selects "Store location," the system receives this input and selects a predefined learning resource. The selected resource is displayed as map information, indicating "The children's section is here." This process allows the user to easily obtain and understand information within a physical store.

[1664] Sensory hypersensitivity prevention function

[1665] The user inputs an environmental factor. For example, if the user types "glare," the system takes this input and selects a predefined hypersensitivity countermeasure. The selected countermeasure is displayed as a message: "Sunglasses are available. Please ask a member of staff if you need them." This process provides the user with an immediate way to address environmental factors within a physical store.

[1666] Specific examples

[1667] For example:

[1668] 1. Use cases for Emotional Empathy:

[1669] A user types "I'm nervous" into a smartphone app. The app responds, "It's natural to feel nervous. Take a break here."

[1670] 2. Example of using the learning support function:

[1671] When the user selects "Store location," the app displays map information and says, "This is the children's section."

[1672] 3. Examples of using the sensory sensitivity feature:

[1673] When a user types in "dazzling light," the app displays the message, "Sunglasses are available. Please ask a member of staff if you need them."

[1674] Prompt Sentence Examples

[1675] python

[1676] Emotion input example:

[1677] emotion = "I'm nervous"

[1678] response = support.get_response(emotion)

[1679] print(response)

[1680] Learning support examples:

[1681] topic = "location in store"

[1682] resource = guide.get_resource(topic)

[1683] print(resource)

[1684] Examples of measures to combat sensory hypersensitivity:

[1685] issue = "blinding light"

[1686] tip = support.get_tip(issue)

[1687] print(tip)

[1688] In this way, the system of the present invention allows children with autism spectrum disorder (ASD) and their families to receive appropriate support immediately in physical stores, thereby improving their shopping experience and the quality of their daily lives.

[1689] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1690] Step 1:

[1691] A user inputs information into a smartphone app about emotions, specific topics, or environmental factors. For example, the user might input "I'm nervous," "a location in a store," or "bright light." This input data is received by the device.

[1692] Step 2:

[1693] The terminal sends the input data received from the user to the server, and the system initiates the process to generate an appropriate response. Based on the type of input data, the terminal invokes the corresponding function.

[1694] Step 3:

[1695] The server analyzes the user's input data and uses a generative AI model to select appropriate empathetic messages, learning resources, or hypersensitivity measures. In this step, the input data is used as prompts for the AI ​​model, which generates the optimal response for each prompt. For example, in response to the emotional input "I'm nervous," the server selects the empathetic message "It's natural to feel nervous. Let's take a break here."

[1696] Step 4:

[1697] The server then sends the selected response back to the device, which may include a message of sympathy, a link to a learning resource, or suggestions for addressing the environmental factors.

[1698] Step 5:

[1699] The device displays the response received from the server to the user. Specifically, it is displayed as a message on the smartphone app screen. For example, if the user enters "I'm nervous," the device displays the message "It's natural to feel nervous. Take a break here."

[1700] Step 6:

[1701] The user can then review the displayed messages and resources and take action based on them. For example, they can follow the suggestion "Sunglasses available" presented as a hypersensitivity measure and ask the store staff for sunglasses.

[1702] At each step, data processing and calculations are performed based on the user's input, and the optimal response is generated and provided to the user, realizing a system that allows children with ASD and their families to receive appropriate support immediately within a physical store.

[1703] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1704] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families, with functions for emotional empathy, learning support, and sensory hypersensitivity treatment. In particular, the combination of an emotion engine enables more advanced emotion recognition and appropriate responses.

[1705] Emotional empathy function

[1706] 1. The user provides text or voice input to express an emotion, for example, "sad."

[1707] 2. The device receives the user's emotional input, which can be in text or voice format.

[1708] 3. The device calls the emotion engine and analyzes the input data.

[1709] 4. The emotion engine recognizes emotions based on the input data and generates emotion recognition results. For example, the input "sad" is recognized as the emotion "sad."

[1710] 5. The device receives the emotion recognition result and calls the emotion empathy function emotion_empathy(current_emotion) with the emotion recognition result “sad” as an argument.

[1711] 6. The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[1712] Learning support functions

[1713] 1. The user enters the topic they want to learn about, for example, "science."

[1714] 2. The terminal receives the user's topic input, which is in text format.

[1715] 3. The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[1716] 4. In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Science experiment videos are here: [Link]" is selected.

[1717] 5. The terminal displays the selected learning resource to the user.

[1718] Sensory hypersensitivity prevention function

[1719] 1. The user inputs the current environmental factor, for example, "loud noise."

[1720] 2. The terminal receives the user's environmental factor input, which is in text format.

[1721] 3. The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[1722] 4. The device selects a sensitivity solution corresponding to the input environmental factor from the sensitivity_solutions dictionary in the sensitivity solution function. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[1723] 5. The device displays the selected hypersensitivity measures to the user.

[1724] Specific examples

[1725] Example 1: Use of Emotional Empathy

[1726] When the user types "sad," the device invokes the emotion engine, recognizes the emotion "sad," and uses the emotion empathy function to display a message to the user saying, "That must be tough... Thanks for sharing your story."

[1727] Example 2: Using the learning support function

[1728] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]."

[1729] Example 3: Using the sensory hypersensitivity function

[1730] If the user requests a solution to deal with "loud noise," the device will call the sensory sensitivity solution function and display a suggestion: "Try earmuffs."

[1731] The system aims to alleviate the difficulties faced by users with ASD by using an emotion engine to recognize emotions based on user input and providing various support functions.

[1732] The processing flow will be explained below.

[1733] Emotional empathy function

[1734] Step 1:

[1735] The user provides text or voice input to express an emotion, for example, "sad."

[1736] Step 2:

[1737] The device receives the user's emotional input, which can be in text or voice format.

[1738] Step 3:

[1739] The device calls the emotion engine and analyzes the input data.

[1740] Step 4:

[1741] The emotion engine recognizes emotions based on the input data and generates emotion recognition results. For example, it recognizes the emotion "sad" from the input "sad."

[1742] Step 5:

[1743] The device receives the emotion recognition result and calls the emotion empathy function emotion_empathy(current_emotion) with the emotion recognition result "sad" as an argument.

[1744] Step 6:

[1745] The device displays the empathy message selected by the emotional empathy function to the user: "That must be tough... Thanks for sharing your story."

[1746] Learning support functions

[1747] Step 1:

[1748] The user enters the topic they want to learn about, for example, "science."

[1749] Step 2:

[1750] The terminal receives the user's topic input, which is in text format.

[1751] Step 3:

[1752] The device calls the learning support function learning_support(topic) with the topic input "science" as an argument.

[1753] Step 4:

[1754] In the learning support function, the device selects a learning resource corresponding to the input topic from the dictionary learning_resources. For example, for the topic 'science', "Here is a science experiment video: [Link]" is selected.

[1755] Step 5:

[1756] The terminal displays the selected learning resource to the user.

[1757] Sensory hypersensitivity prevention function

[1758] Step 1:

[1759] The user inputs a current environmental factor, for example, "loud noise."

[1760] Step 2:

[1761] The terminal receives the user's environmental factor input, which is in text format.

[1762] Step 3:

[1763] The device calls the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) with the environmental factor input "loud sound" as an argument.

[1764] Step 4:

[1765] The device selects a sensitivity solution from the sensitivity_solutions dictionary in the sensitivity solution function that corresponds to the input environmental factor. For example, "Try earmuffs" is selected for the environmental factor 'loud_noise'.

[1766] Step 5:

[1767] The terminal displays the selected hypersensitivity measures to the user.

[1768] Example 2

[1769] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1770] To address the various challenges faced by children with autism spectrum disorder (ASD) and their families, appropriate support tailored to their individual needs is necessary. However, existing technologies lack comprehensive systems that address these challenges. This makes it difficult to provide effective support for users with ASD in recognizing their emotions, accessing appropriate learning resources, and addressing sensory sensitivities.

[1771] The identification process 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 means for receiving an input related to an emotion from a user, means for analyzing the emotion input and recognizing the emotion, means for selecting a predefined empathy message corresponding to the emotion recognition, means for receiving an input from a user requesting learning resources related to a specific topic, means for analyzing the topic input and selecting the learning resources, means for receiving an input related to environmental factors from a user, and means for analyzing the environmental factors and selecting a predefined hypersensitivity countermeasure. This makes it possible to comprehensively provide emotional empathy, learning support, and sensory hypersensitivity countermeasures for various challenges faced by users with ASD.

[1772] "User" refers to any individual or group of people who use the System, and specifically includes children with Autism Spectrum Disorder (ASD) and their families.

[1773] "Emotional input" refers to emotional expressions in text or speech that a user inputs into the system.

[1774] "Emotion engine" is a general term for software and hardware that analyzes emotional input from users and recognizes those emotions.

[1775] "Empathetic message" refers to a predefined response message that the system presents in response to the user's emotions.

[1776] "Learning resources" refers to information or content related to the topic a user wants to learn about, including, for example, text, videos, links, etc.

[1777] "Environmental factors" refer to elements and situations related to the user's current environment, such as "loud noises" and "bright light."

[1778] "Sensory sensitivity management" refers to suggestions and measures to reduce a user's sensitivity to environmental factors.

[1779] "Server" refers to the central processing unit of the system, which is responsible for receiving and analyzing input from users, and generating and providing appropriate responses.

[1780] This invention provides a comprehensive AI platform to support children with autism spectrum disorder (ASD) and their families, with functions of emotional empathy, learning support, and sensory hypersensitivity treatment. Each function of this system is described in detail below.

[1781] Emotional empathy function

[1782] 1. The user inputs text or voice to express an emotion into the device. Let's assume that the user inputs "sad" on a smartphone or PC.

[1783] 2. The device receives input from the user and, if there is voice input, converts it to text using the Google Speech-to-Text API.

[1784] 3. The device calls the emotion engine, which uses IBM Watson's emotion recognition API to analyze the input data and recognize emotions.

[1785] 4. The emotion engine recognizes emotions based on the input data and returns the result in the form of something like "sad."

[1786] 5. The device executes the emotion empathy function emotion_empathy(current_emotion) and passes the emotion recognition result "sad" as an argument. The emotion empathy function selects an appropriate empathy message, such as "That must be tough... Thanks for sharing your story."

[1787] 6. The device displays the selected empathy message to the user, for example, "That must be tough... Thanks for sharing your story."

[1788] Learning support functions

[1789] 1. The user enters the topic they want to learn in text format into the terminal, for example, "science."

[1790] 2. The device receives the input and executes the learning support function learning_support(topic), passing "science" as the argument.

[1791] 3. The device selects an appropriate learning resource related to "science" from the dictionary learning_resources. For example, "Here is a science experiment video: [link]" is selected.

[1792] 4. The device displays the selected learning resource to the user.

[1793] Sensory hypersensitivity prevention function

[1794] 1. The user inputs the current environmental factor in text format into the terminal, for example, "loud noise."

[1795] 2. The device receives the input and executes the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor), passing "loud sound" as the argument.

[1796] 3. The device selects an appropriate solution to the "loud noise" sensitivity from the dictionary sensitivity_solutions, for example, "Try earmuffs."

[1797] 4. The device displays the selected measures to the user.

[1798] Specific examples

[1799] Example 1: Use of Emotional Empathy

[1800] When the user types "sad," the device invokes the emotion engine, recognizes the emotion "sad," and uses the emotion empathy function to display a message to the user saying, "That must be tough... Thanks for sharing your story."

[1801] Example 2: Using the learning support function

[1802] When a user requests a learning resource related to "science," the device calls the learning support function and displays the learning resource "Science experiment videos are available here: [Link]."

[1803] Example 3: Using the sensory hypersensitivity function

[1804] If the user requests a solution to deal with "loud noise," the device will call the sensory sensitivity solution function and display a suggestion: "Try earmuffs."

[1805] The system aims to alleviate the difficulties faced by users with ASD by utilizing an emotion engine and various functions based on user input.

[1806] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1807] Emotional empathy function

[1808] Step 1:

[1809] The user inputs text or voice to express their emotion into the device. The user uses the voice input function of their smartphone or PC to say "I'm sad."

[1810] Input: User's voice data

[1811] Output: Audio data

[1812] Specific operation: The user inputs voice into the microphone of their smartphone or PC.

[1813] Step 2:

[1814] The device receives input, and in the case of voice input, converts the speech to text using the Google Speech-to-Text API.

[1815] Input: Audio data

[1816] Output: Text data "Sad"

[1817] Specific behavior: The device sends the user's voice to the Google Speech-to-Text API and receives the text version of "sad."

[1818] Step 3:

[1819] The device calls the emotion engine to analyze the input data, using IBM Watson's emotion recognition API.

[1820] Input: Text data "sad"

[1821] Output: Emotion recognition result "sad"

[1822] Specific operation: The device sends the text "sad" to IBM Watson's emotion recognition API, which recognizes the emotion "sad" and returns it.

[1823] Step 4:

[1824] The device executes the emotion empathy function emotion_empathy(current_emotion) and passes the emotion recognition result "sad" as an argument. The empathy function selects an appropriate message.

[1825] Input: Emotion recognition result "sad"

[1826] Output: Empathetic message "That must be tough... Thanks for sharing your story."

[1827] Specific operation: The device executes the emotion empathy function, passing "sad" as an argument and selecting the appropriate message.

[1828] Step 5:

[1829] The terminal displays the selected sympathetic message to the user.

[1830] Input: Empathetic message "That must be tough... Thanks for sharing your story."

[1831] Output: An empathy message displayed on the user's screen

[1832] Specific operation: A sympathetic message is displayed on the device screen to provide feedback to the user.

[1833] Learning support functions

[1834] Step 1:

[1835] The user enters the topic they want to learn in text form into the terminal. The user uses the keyboard to type "science."

[1836] Input: topic "science"

[1837] Output: Text data "Science"

[1838] Specific operation: The user inputs a topic using the device keyboard.

[1839] Step 2:

[1840] The terminal receives the input. The input data is received as is.

[1841] Input: Text data "Science"

[1842] Output: Text data "Science"

[1843] Specific operation: The terminal receives input data from the user.

[1844] Step 3:

[1845] The terminal executes the learning support function learning_support(topic) and passes the topic "science" as an argument. The learning support function selects the corresponding resource.

[1846] Input: topic "science"

[1847] Output: Learning resource "Science experiment video here: [link]"

[1848] Specific operation: The terminal executes the learning support function and selects the learning resource corresponding to "science."

[1849] Step 4:

[1850] The terminal displays the selected learning resource to the user.

[1851] Input: Learning Resources "Here's a science experiment video: [link]"

[1852] Output: The learning resource displayed on the user's screen

[1853] Specific operation: Display learning resources on the device screen and provide them to the user.

[1854] Sensory hypersensitivity prevention function

[1855] Step 1:

[1856] The user inputs the current environmental factors in text format into the terminal. The user uses the keyboard to input "loud noise."

[1857] Input: Environmental factor "loud noise"

[1858] Output: Text data "Loud sound"

[1859] Specific operation: The user inputs the environmental factors using the keyboard of the device.

[1860] Step 2:

[1861] The terminal receives the input. The input data is received as is.

[1862] Input: Text data "loud sound"

[1863] Output: Text data "Loud sound"

[1864] Specific operation: The terminal receives input data from the user.

[1865] Step 3:

[1866] The device executes the sensory sensitivity countermeasure function sensory_sensitivity(environment_factor) and passes the environmental factor "loud noise" as an argument. The sensory sensitivity countermeasure function selects the corresponding countermeasure.

[1867] Input: Environmental factor "loud noise"

[1868] Output: Hypersensitivity Countermeasure "Try earmuffs."

[1869] Specific operation: The device executes the sensory hypersensitivity countermeasure function and selects the countermeasure corresponding to "loud noise."

[1870] Step 4:

[1871] The terminal displays the selected hypersensitivity measures to the user.

[1872] Input: Hypersensitivity Countermeasures "Try earmuffs."

[1873] Output: The hypersensitivity measures displayed on the user's screen

[1874] Specific operation: Display the hypersensitivity measures on the device screen and provide them to the user.

[1875] Through these steps, the system has the ability to comprehensively provide emotional empathy, learning support, and sensory hypersensitivity measures to users with ASD.

[1876] (Application example 2)

[1877] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1878] There is a need for a system that can effectively solve the various problems that users with autism spectrum disorder (ASD) face in their daily lives. Specifically, a comprehensive support platform that can seamlessly provide emotional understanding and empathy, learning support, and measures for sensory hypersensitivity is needed.

[1879] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving an input regarding emotions from a user, means for selecting a predefined empathy message corresponding to the emotion input, means for displaying the selected empathy message to the user, means for receiving an input from the user requesting learning resources on a specific topic, means for selecting predefined learning resources corresponding to the topic input, means for displaying the selected learning resources to the user, means for receiving an input regarding environmental factors from the user, means for selecting a predefined hypersensitivity countermeasure corresponding to the environmental factor, and means for displaying the selected hypersensitivity countermeasure to the user. This enables emotional empathy, learning support, and sensory hypersensitivity countermeasures.

[1880] "User" refers to the individual or family member who operates the system.

[1881] "Emotional input" refers to the act of a user inputting their emotional state into the system via text or voice.

[1882] A "predefined empathic message" refers to a message that is predefined as a response to a particular emotional state.

[1883] "Topical Learning Resources" refers to educational information and materials based on a particular subject designated by the User.

[1884] "Environmental factors" refer to the physical environmental conditions that the user currently faces.

[1885] "Hypersensitivity measures" refers to specific measures and suggestions to help users with sensory sensitivities to live comfortably.

[1886] "Emotion engine" refers to technology that analyzes a user's emotional input and recognizes a specific emotional state.

[1887] "Dynamic provision" refers to providing appropriate learning resources in real time in response to user requests.

[1888] "Suggestion functionality" refers to the functionality of the system that has the ability to suggest appropriate hypersensitivity countermeasures based on the user's input of environmental factors.

[1889] To implement this invention, we need a server with specific functions, a terminal, and an interface with the user. We will build a system with three main functions: emotional empathy, learning support, and measures against sensory hypersensitivity.

[1890] First, the server receives emotional input from the user. When the user inputs their emotions via text or voice, the server passes this input to the emotion engine for analysis. The emotion engine uses complex data analysis algorithms to recognize emotions from the input data. Based on this recognition result, it selects an appropriate message from predefined empathy messages and displays it to the user via the device.

[1891] The specific hardware used is a processor with a dedicated AI chip for emotion recognition, and the software uses natural language processing models (such as BERT and GPT-3) to perform emotion analysis. The emotion recognition results are stored in a shared database, allowing for real-time processing on the server.

[1892] Next, we will explain the learning support function. When a user requests learning resources on a specific topic, the server receives this request and dynamically selects appropriate resources from the dictionary database. These resources are displayed on the terminal and provided for easy access by the user.

[1893] Specifically, it uses a learning management system (LMS) to manage educational content and links to a resource database stored there. It also includes algorithms for dynamically providing relevant resources based on user requests. It uses programming languages ​​such as Python and machine learning libraries (e.g., TensorFlow and PyTorch) to select and present resources.

[1894] Finally, we will explain the sensory sensitivity countermeasure function. When the server receives input from the user about environmental factors, it analyzes this and selects an appropriate countermeasure from predefined sensitivity countermeasures. The selected countermeasure is displayed on the device to ensure the user's comfort.

[1895] This processing may involve IoT devices to utilize sensor data, such as a noise sensor to measure noise levels or a light sensor to measure light intensity. The data obtained from these sensors is sent in real time to a server for analysis, often using IoT frameworks such as Node-RED or MQTT.

[1896] Examples of concrete examples and prompts

[1897] 1. Emotional empathy function:

[1898] Example: If a user types "sad," the system displays the message "That must be hard... Thanks for sharing your story."

[1899] Prompt: "When the user enters the emotion 'sad', generate an appropriate empathetic message."

[1900] 2. Learning support functions:

[1901] Example: If a user requests learning resources related to "Science," you might display "Here are some science experiment videos: [Link]."

[1902] Prompt: "When a user asks for learning resources on the topic 'Science', generate the appropriate learning resources."

[1903] 3. Sensory hypersensitivity prevention function:

[1904] Example: If a user asks for advice on dealing with "loud noise," the system displays "Try earmuffs."

[1905] Prompt: "When a user requests a solution to a loud noise, generate an appropriate solution."

[1906] This system supports the daily lives of users with ASD through a variety of interfaces.

[1907] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1908] Step 1:

[1909] The user inputs their emotions into the system. This input can be in the form of text or voice. The input data is sent to the server via the terminal. For example, if the user inputs "sad," the text data is passed to the server.

[1910] Step 2:

[1911] The server sends the received emotion input data to the emotion engine. The emotion engine uses a natural language processing model to analyze the input data and generate emotion recognition results. Specifically, it uses generative AI models such as BERT and GPT-3 to analyze text data and recognize the emotion "sad" as "sad."

[1912] Step 3:

[1913] The server receives the emotion recognition results from the emotion engine. Based on the results, it selects a predefined empathy message. For example, to select the empathy message "That must have been difficult... Thank you for sharing your story" corresponding to the emotion "sad," it searches for and selects an appropriate message from the empathy message dictionary.

[1914] Step 4:

[1915] The server sends the selected sympathy message to the terminal, which then displays the message to the user, allowing the user to confirm the sympathy message from the server.

[1916] Step 5:

[1917] A user inputs a request for learning resources on a specific topic. For example, if a user requests resources on "science," the input in text format is sent to the server via the terminal.

[1918] Step 6:

[1919] The server sends the received topic input to the learning support function and selects an appropriate learning resource from the dictionary database. For example, it selects the learning resource "Science"-related "Science experiment video here: [Link]" for the topic "Science."

[1920] Step 7:

[1921] The server sends the selected learning resource to the terminal, which then displays it to the user, allowing the user to access the requested learning resource.

[1922] Step 8:

[1923] The user inputs information about environmental factors. For example, if the user requests measures to be taken against "loud noise," the text data is sent to the server via the terminal.

[1924] Step 9:

[1925] The server sends the received environmental factor input to the sensory hypersensitivity countermeasure function, which selects an appropriate hypersensitivity countermeasure from the dictionary database. For example, it selects "Try earmuffs" as a countermeasure for the environmental factor "loud noise."

[1926] Step 10:

[1927] The server transmits the selected hypersensitivity measures to the terminal, which then displays them to the user, allowing the user to confirm and implement specific measures against environmental factors.

[1928] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1929] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1930] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1931] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1932] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1933] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1934] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1935] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1936] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1937] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1938] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1939] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1940] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1941] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1942] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1943] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1944] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1945] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1946] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1947] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1948] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1949] The following is further disclosed regarding the above embodiment.

[1950] (Claim 1)

[1951] a means for receiving emotional input from a user;

[1952] means for selecting a predefined empathy message corresponding to the emotion input;

[1953] a means for displaying the selected empathy message to the user;

[1954] A system including:

[1955] (Claim 2)

[1956] means for receiving input from a user requesting learning resources on a particular topic;

[1957] a means for selecting predefined learning resources corresponding to the topic input;

[1958] a means for displaying the selected learning resource to the user;

[1959] 10. The system of claim 1, further comprising:

[1960] (Claim 3)

[1961] means for receiving input from a user regarding environmental factors;

[1962] a means of selecting predefined hypersensitivity measures in response to environmental factors;

[1963] means for displaying the selected hypersensitivity measure to the user;

[1964] 10. The system of claim 1, further comprising:

[1965] "Example 1"

[1966] (Claim 1)

[1967] a means for receiving emotional input from a user;

[1968] means for transmitting emotion input to an emotion empathy function;

[1969] means for activating an emotional empathy function and analyzing the emotional data;

[1970] means for selecting and sending empathetic messages to the user;

[1971] A means of displaying empathetic messages to users;

[1972] A system including:

[1973] (Claim 2)

[1974] means for receiving input from a user requesting learning resources on a particular topic;

[1975] means for sending topic input to a learning support function;

[1976] means for invoking learning support functions and retrieving resources related to a specified topic;

[1977] a means of selecting and sending learning resources to users;

[1978] a means of displaying learning resources to users;

[1979] 10. The system of claim 1, further comprising:

[1980] (Claim 3)

[1981] means for receiving input from a user regarding environmental factors;

[1982] means for transmitting environmental input to the sensory hypersensitivity countermeasure function;

[1983] means for invoking a sensory hypersensitivity countermeasure function to search for a countermeasure for a specified environmental factor;

[1984] a means for selecting and sending hypersensitivity measures to the user;

[1985] means for displaying the selected hypersensitivity measure to the user;

[1986] 10. The system of claim 1, further comprising:

[1987] "Application Example 1"

[1988] (Claim 1)

[1989] a means for receiving emotional input from a user;

[1990] means for selecting a predefined empathy message corresponding to the emotion input;

[1991] a means for displaying the selected empathy message to the user;

[1992] A means for providing a user interface for receiving emotion input from a user in a physical store;

[1993] A means to display appropriate empathetic messages to users in physical stores so that they can receive emotional support;

[1994] A system including:

[1995] (Claim 2)

[1996] 10. The system of claim 1, wherein the system receives input from a user requesting learning resources on a particular topic.

[1997] (Claim 3)

[1998] 10. The system of claim 1, wherein the system receives input from a user regarding environmental factors.

[1999] "Example 2: Combining Emotion Engines"

[2000] (Claim 1)

[2001] a means for receiving emotional input from a user;

[2002] means for analyzing emotion input and recognizing emotions;

[2003] a means for selecting a predefined empathy message corresponding to the emotion recognition;

[2004] a means for displaying the selected empathy message to the user;

[2005] A system including:

[2006] (Claim 2)

[2007] means for receiving input from a user requesting learning resources on a particular topic;

[2008] a means for parsing the topic input and selecting learning resources;

[2009] a means for displaying the selected learning resource to the user;

[2010] 10. The system of claim 1, further comprising:

[2011] (Claim 3)

[2012] means for receiving input from a user regarding environmental factors;

[2013] a means for analyzing environmental factors and selecting predefined hypersensitivity measures;

[2014] means for displaying the selected hypersensitivity measure to the user;

[2015] 10. The system of claim 1, further comprising:

[2016] "Application example 2 when combining emotion engines"

[2017] (Claim 1)

[2018] a means for receiving emotional input from a user;

[2019] means for selecting a predefined empathy message corresponding to the emotion input;

[2020] a means for displaying the selected empathy message to the user;

[2021] means for receiving input from a user requesting learning resources on a particular topic;

[2022] a means for selecting predefined learning resources corresponding to the topic input;

[2023] a means for displaying the selected learning resource to the user;

[2024] means for receiving input from a user regarding environmental factors;

[2025] a means of selecting predefined hypersensitivity measures in response to environmental factors;

[2026] means for displaying the selected hypersensitivity measure to the user;

[2027] A system including:

[2028] (Claim 2)

[2029] The system according to claim 1, wherein the system utilizes an emotion engine to recognize a user's emotions and generates an empathetic message based on the recognition result.

[2030] (Claim 3)

[2031] 10. The system of claim 1, wherein the system dynamically provides predefined learning resources for a topic specified by a user.

[2032] (Claim 4)

[2033] The system according to claim 1, further comprising a function for suggesting measures against hypersensitivity based on input of environmental factors from the user. [Explanation of symbols]

[2034] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for receiving emotional input from a user; means for selecting a predefined empathy message corresponding to the emotion input; a means for displaying the selected empathy message to the user; A system including:

2. means for receiving input from a user requesting learning resources on a particular topic; a means for selecting predefined learning resources corresponding to the topic input; a means for displaying the selected learning resource to the user; The system of claim 1 further comprising:

3. means for receiving input from a user regarding environmental factors; a means of selecting predefined hypersensitivity measures in response to environmental factors; means for displaying the selected hypersensitivity measure to the user; The system of claim 1 further comprising:

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