system

Abstract

We provide the system. [Solution] A means for receiving input text data, A natural language processing means that analyzes the aforementioned text data to identify emotions and intentions, Means for modifying or supplementing input text data based on identified emotions or intentions, A system including means for outputting modified or supplemented text data.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the number of people who have difficulty expressing their feelings and thoughts well in words due to intellectual disabilities and the like has been increasing. There is a need for support to enable such people to communicate smoothly, but current technologies have problems in accurately interpreting the feelings and intentions of users and expressing them in an easy-to-understand manner. It is an object of the present invention to provide a system that can address such problems and enable users to easily and accurately express their feelings and thoughts.

Means for Solving the Problems

[0005] The present invention solves the above problems by the following means.

[0006] The system includes means for receiving input text data, natural language processing means for analyzing the text data and identifying emotions and intentions, means for modifying or supplementing the input text data based on the identified emotions and intentions, and means for outputting the modified or supplemented text data. Furthermore, the natural language processing means includes means for splitting the input text data and tagging parts of speech, and means for automatically generating appropriate sentences based on the analysis of emotions and intentions, thereby enabling support for accurately conveying the user's feelings.

[0007] "Input text data" refers to the text information that a user enters into their device to express their feelings or thoughts.

[0008] "Means of receiving" refers to a component that has the function of receiving text data entered by the user on a terminal or server.

[0009] "Means of analysis" are components that perform a process to analyze input text data and understand its grammar and meaning.

[0010] "Means for identifying emotions and intentions" are components that perform the process of identifying a user's emotions (e.g., joy, sadness, anger, etc.) and intentions (e.g., wanting to ask a question, wanting to make a request, etc.) from text data.

[0011] "Natural language processing means" refers to components that use technologies for analyzing and understanding natural language text.

[0012] "Means of modification or supplementation" refers to components that perform the process of modifying or adding information to input text data based on the analysis results in order to make it clearer and easier to understand.

[0013] "Outputting means" refers to components used to display modified or supplemented text data to a user or to communicate with other systems.

[0014] "Splitting" is the process of breaking down input text data into its smallest meaningful units, such as words or phrases.

[0015] "Part-of-speech tagging" is the process of labeling each divided word with its grammatical role (noun, verb, adjective, etc.).

[0016] "Means of automatic generation" refers to components that execute a process to automatically construct new and appropriate sentences based on the results of analyzing emotions and intentions. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention is a system that enables individuals with intellectual disabilities or other conditions to express their feelings and thoughts clearly and to support smooth communication. The embodiments of this invention will be described in detail below.

[0039] System Configuration

[0040] This system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication network that connects these two.

[0041] Overall Operation Overview

[0042] 1. The user enters their feelings.

[0043] Users input their feelings and thoughts into the device. For example, they might input a sentence like, "I'm tired today, but I feel a sense of accomplishment."

[0044] 2. The terminal receives input and sends it to the server.

[0045] The terminal receives text data entered by the user and sends it to the server. The data is encoded in an appropriate format, such as JSON.

[0046] 3. The server analyzes and identifies emotions and intentions.

[0047] The server analyzes the received data, uses natural language processing to segment the text, and tags each word by its part of speech. Then, it uses sentiment analysis algorithms to identify emotions and intentions such as "tired" or "feeling accomplished."

[0048] 4. The server corrects and completes the text.

[0049] Based on identified emotions and intentions, the server modifies or completes the entered text. For example, it might be transformed into a clearer sentence such as, "I'm very tired today, but I feel a great sense of accomplishment."

[0050] 5. The server sends the corrected text back to the terminal.

[0051] The server returns the corrected or completed text to the terminal.

[0052] 6. The device displays organized text.

[0053] The device displays the received message to the user, allowing the user to confirm it. This enables the user to communicate their intentions to others more accurately.

[0054] Specific examples

[0055] User scenario:

[0056] User: After returning home from school, they type into their device, "I want to hang out with my friends, but I don't know how to ask them."

[0057] Terminal: Receives user input and sends it to the server.

[0058] server:

[0059] 1. Analyze the input text and identify two intentions: "I want to hang out with my friends" and "I don't know how to ask them out."

[0060] 2. Based on these intentions, revise and supplement the sentence to: "I want to hang out with my friends, but I don't know how to ask them."

[0061] Server: Sends the corrected text back to the terminal.

[0062] Terminal: Displays the corrected text to the user.

[0063] User: Check the message and contact your friend appropriately.

[0064] This system helps users express their feelings and thoughts accurately, thereby improving their quality of life.

[0065] The following describes the processing flow.

[0066] Step 1:

[0067] Users input their feelings and thoughts into the device. They can input free-form text, including individual words and short sentences.

[0068] Step 2:

[0069] The terminal receives the entered text. For processing, the text data is converted to an appropriate format, such as JSON format.

[0070] Step 3:

[0071] The device sends text data to the server. The data transmission is performed using an HTTP request.

[0072] Step 4:

[0073] The server receives the HTTP request and extracts the text data for analysis. It then verifies that the data is in the correct format.

[0074] Step 5:

[0075] The server uses a natural language processing (NLP) engine to analyze text data. This analysis includes word segmentation and part-of-speech tagging.

[0076] Step 6:

[0077] The server applies sentiment analysis algorithms to identify the emotions and intentions contained in the text data. For example, it can identify intentions such as "I want to hang out with my friends" or "I don't know how to ask them out."

[0078] Step 7:

[0079] The server modifies or completes the input text data based on the results of identifying emotions and intentions. For example, it might generate a more polished sentence like, "I want to hang out with my friends, but I don't know how to ask them."

[0080] Step 8:

[0081] The server generates the corrected or completed text data as a response in JSON format.

[0082] Step 9:

[0083] The server sends a response to the terminal. This transmission occurs via an HTTP response.

[0084] Step 10:

[0085] The terminal receives a response from the server and retrieves the corrected or completed text data.

[0086] Step 11:

[0087] The device displays corrected or completed text data to the user. The output is displayed on the screen in a user-friendly format.

[0088] Step 12:

[0089] Users can review the organized text displayed and share it with others as needed. For example, they might use it to contact a friend.

[0090] (Example 1)

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

[0092] This invention relates to a system for supporting individuals with intellectual disabilities and other conditions in clearly expressing their emotions and thoughts and communicating smoothly. Conventional systems sometimes made it difficult for users to accurately express their emotions and intentions, leading to communication problems. The challenge is to solve this problem and enable users to communicate their emotions and thoughts simply and clearly.

[0093] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0094] In this invention, the server includes means for receiving text data containing emotions and intentions entered by a user; means for encoding the text data and transferring it via a communication network; means for analyzing the received text data and identifying emotions and intentions using a natural language processing system; means for using a generative AI model to modify or supplement the input text data based on the identified emotions and intentions; means for sending the modified or supplemented text data back to the terminal via the communication network to notify the user; and means for displaying the returned text data to the user. This makes it easier for users to clearly express their emotions and intentions and enables smooth communication with others.

[0095] A "user" refers to an individual who uses a system to express their own emotions or intentions.

[0096] "Text data" refers to data consisting of natural language sentences and words entered by the user.

[0097] A "terminal" refers to a device used by a user to input text data and communicate with a server. Specifically, this includes smartphones, tablets, and personal computers.

[0098] A "server" refers to a computing device that analyzes received text data and uses a generative AI model to correct or complete it.

[0099] A "communication network" refers to the network infrastructure used to send and receive data between terminals and servers. Specifically, it refers to the internet and local networks.

[0100] "Encoding" refers to the process of converting information, such as text data, into a specific format (for example, JSON format).

[0101] "Natural language processing methods" refer to technical means for analyzing text data and identifying emotions and intentions. Specifically, this involves processes such as text segmentation and part-of-speech tagging.

[0102] A "sentiment analysis algorithm" refers to an algorithm used to identify a user's emotions and intentions from text data.

[0103] A "generative AI model" refers to an artificial intelligence model that generates new text based on input text data. Specifically, it refers to models such as GPT-3 (registered trademark).

[0104] "Correction and supplementation" refers to the process of transforming input text data into a clearer and more understandable form based on identified emotions and intentions.

[0105] "Display" refers to showing the corrected or completed text data on the device's screen in a format that the user can review.

[0106] This invention relates to a system that enables individuals with intellectual disabilities to express their feelings and thoughts clearly and communicate smoothly. The system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication network connecting the two.

[0107] 1. System Configuration

[0108] This system is comprised of the following hardware and software.

[0109] Terminal: Input devices such as smartphones, tablets, and personal computers.

[0110] Server: A computing device that performs analysis and data processing. Specifically, it uses Python libraries such as NLTK and spaCy, the TextBlob sentiment analysis library, and the GPT-3 generative AI model.

[0111] Communication network: Internet or local network.

[0112] 2. System Operation

[0113] Text data entered by the user into the device is first encoded into JSON format by the device and sent to the server. The server analyzes the received text data, uses natural language processing tools (NLTK and spaCy) to segment the text, and tags each word with its part of speech. Furthermore, it uses a sentiment analysis library (TextBlob) to identify emotions and intentions such as "tired" or "feeling accomplished."

[0114] Based on identified emotions and intentions, the server uses a generative AI model (GPT-3) to modify or supplement the text data. The modified text is then re-encoded in JSON format and sent back to the terminal via the communication network. The terminal decodes the received modified data and displays it to the user.

[0115] 3. Specific examples

[0116] For example, if a user enters "I want to hang out with my friends, but I don't know how to invite them," the following process will be executed:

[0117] 1. The user enters "I want to hang out with my friends, but I don't know how to invite them" into their device.

[0118] 2. The terminal receives input and sends it to the server.

[0119] 3. The server analyzes the text data and identifies two intentions: "I want to play with my friends" and "I don't know how to invite them."

[0120] 4. The server enters the following prompt into the generated AI model:

[0121] The user typed, "I want to hang out with my friends, but I don't know how to ask them." Please rephrase this sentence to be more specific and easier to understand:

[0122] 5. The AI ​​generation model generates the corrected text: "I want to hang out with my friends, but I don't know how to ask them."

[0123] 6. The server returns the corrected text to the terminal.

[0124] 7. The device displays the corrected text to the user.

[0125] In this way, users can express their feelings and thoughts more clearly and communicate more smoothly with others.

[0126] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0127] Step 1:

[0128] Users enter text data that includes emotions and intentions.

[0129] The user inputs text data expressing their feelings and thoughts into their device (smartphone, tablet, or PC). For example, they might input, "I'm tired today, but I feel a sense of accomplishment."

[0130] Input: Text input by the user.

[0131] Output: Input text data ("I'm tired today, but I feel a sense of accomplishment").

[0132] Step 2:

[0133] The terminal receives input and sends it to the server.

[0134] The terminal receives user input, encodes it in JSON format, and sends it.

[0135] Specific operation: The terminal encodes the data in the following format:

[0136] json

[0137] {

[0138] "text": "I'm tired today, but I feel a sense of accomplishment."

[0139] }

[0140] The encoded data is then sent to the server.

[0141] Input: Text data entered by the user.

[0142] Output: Text data encoded in JSON format.

[0143] Step 3:

[0144] The server receives and analyzes the input data.

[0145] The server parses the JSON-formatted text data received from the terminal. It uses natural language processing tools (NLTK, spaCy) to segment the text and tag its parts of speech.

[0146] Specific operation: The server parses the text as follows:

[0147] python

[0148] import spacy

[0149] nlp = spacy.load("ja_core_news_md")

[0150] doc = nlp("I'm tired today, but I feel a sense of accomplishment")

[0151] for token in doc:

[0152] print(token.text, token.pos_)

[0153] This allows us to identify words and their parts of speech, such as "today," "tired," "but," "sense of accomplishment," "is," and "there is."

[0154] Input: Encoded text data.

[0155] Output: Analysis results (text data tagged with parts of speech).

[0156] Step 4:

[0157] The server identifies emotions and intentions.

[0158] The server uses an emotion analysis algorithm (e.g., TextBlob) to identify emotions and intentions from the analysis results.

[0159] Specific operation: The server analyzes emotions as follows:

[0160] python

[0161] from textblob import TextBlob

[0162] analysis = TextBlob("I'm tired today, but I feel a sense of accomplishment")

[0163] sentiment = analysis.sentiment

[0164] The analysis results identify emotions such as "tired" and "a sense of accomplishment."

[0165] Input: Text data tagged with parts of speech.

[0166] Output: Identified emotions and intentions.

[0167] Step 5:

[0168] The server uses a generated AI model to modify or supplement text data.

[0169] The server inputs prompt text into the generative AI model (GPT-3) and modifies or completes the text data.

[0170] Specific action: The server will enter the following prompt:

[0171] The user typed, "I'm tired today, but I feel a sense of accomplishment." Please revise this sentence to make it clearer and easier to understand:

[0172] The generative AI model outputs the corrected text "I'm very tired today, but I feel a great sense of accomplishment" as its response.

[0173] Input: Identified emotion and intention, prompt sentence.

[0174] Output: Modified text data.

[0175] Step 6:

[0176] The server sends the corrected text data back to the terminal.

[0177] The server encodes the modified text data into JSON format and sends it back to the terminal.

[0178] Specific operation: The server encodes the modified text data in the following format:

[0179] json

[0180] {

[0181] "text": "I'm very tired today, but I feel a great sense of accomplishment."

[0182] }

[0183] The encoded data is then sent to the terminal.

[0184] Input: Modified text data.

[0185] Output: Modified text data encoded in JSON format.

[0186] Step 7:

[0187] The device receives the corrected text data and displays it to the user.

[0188] The terminal decodes the JSON data received from the server and displays it to the user.

[0189] Specific action: The terminal performs the decoding process and displays the following text on the screen:

[0190] I'm very tired today, but I feel a great sense of accomplishment.

[0191] Input: Modified text data encoded in JSON format.

[0192] Output: The corrected text data displayed to the user.

[0193] (Application Example 1)

[0194] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0195] This initiative aims to address the difficulties individuals with intellectual disabilities face in clearly expressing their feelings and thoughts and communicating safely with those around them. Furthermore, it supports guardians and caregivers in taking appropriate actions quickly, thereby ensuring the safety and security of individuals with intellectual disabilities.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0197] In this invention, the server includes means for receiving input text data, means for natural language processing, means for modifying or supplementing the input text data based on emotions and intentions, means for outputting the modified or supplemented text data, means for notifying the user of anomalies to ensure user safety, and means for generating safety confirmation messages and providing feedback to the user. This makes it possible to ensure the user's safety and peace of mind by clearly expressing the user's feelings and thoughts and providing appropriate feedback.

[0198] "Means for receiving text data" refers to a device or software that electronically receives text data entered by a user.

[0199] "Natural language processing means" refers to algorithms and software used to analyze received text data and identify emotions and intentions.

[0200] "Means for modifying or supplementing input text data" refers to devices or software that transform the original text data into an appropriate form based on identified emotions or intentions.

[0201] "Means for outputting corrected or supplemented text data" refers to a device or software for presenting corrected or supplemented text data to a user.

[0202] "Means of notifying of abnormalities" refers to devices or software that detect unusual situations or emotions and notify guardians or caregivers in order to ensure the user's safety.

[0203] "Means for generating safety confirmation messages and providing feedback" refers to devices or software that provide safety-related feedback and prompt necessary actions based on user input.

[0204] This invention is a system that enables individuals with intellectual disabilities to express their feelings and thoughts clearly and communicate safely with those around them. The system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication means that connects these two.

[0205] System Configuration

[0206] 1. Terminal

[0207] This is a device for users to input their feelings and circumstances. Smartphones and smart glasses are used as terminals. The terminal has an input interface, allowing users to input text data.

[0208] 2. Server

[0209] The system analyzes received text data to identify emotions and intentions, and then modifies or supplements the data accordingly. Furthermore, it detects anomalies as needed and notifies parents and caregivers. It also generates safety confirmation messages and provides feedback. The server has natural language processing libraries (e.g., TextBlob) and communication libraries (e.g., requests) installed.

[0210] Overall Operation Overview

[0211] 1. The user enters their feelings or situation.

[0212] The user enters their current feelings or situation into the device. For example, they might enter a sentence like, "I'm in the park right now, but I feel a little scared."

[0213] 2. The terminal receives input and sends it to the server.

[0214] The terminal receives text data entered by the user and sends it to the server. The data is encoded in JSON format.

[0215] 3. The server analyzes and identifies emotions and intentions.

[0216] To analyze the received data, the server uses natural language processing to segment the text data and tag it by part of speech. Then, it uses sentiment analysis algorithms to identify emotions such as "scared" and situations such as "being in a park."

[0217] 4. The server modifies and supplements the content based on emotions and circumstances, and sends notifications to confirm safety.

[0218] Based on identified emotions and circumstances, the system modifies or completes the entered text. Furthermore, it uses means to notify parents or caregivers of the situation. For example, it might generate a message such as, "I'm in the park right now, and I'm feeling a little scared. Please move to a safe place or ask for help."

[0219] 5. The server sends the corrected text and feedback back to the terminal.

[0220] The server returns the corrected or supplemented text and a security confirmation message to the terminal.

[0221] 6. The device displays organized text and feedback.

[0222] The device displays received text and messages to the user, enabling the user to respond appropriately.

[0223] Specific example

[0224] User scenario:

[0225] User: Feeling uneasy in the park, they type "I'm in the park right now, and I feel a little scared" into their device.

[0226] Terminal: Receives user input and sends it to the server.

[0227] server:

[0228] 1. Analyze the input text to identify the emotions and situations: "scared" and "in a park."

[0229] 2. Based on these feelings and circumstances, revise and complete the sentence to generate the feedback: "I'm in the park right now, and I'm feeling a little scared. I should move to a safe place or ask for help."

[0230] Server: Sends the corrected text and feedback back to the terminal.

[0231] Terminal: Displays the corrected text and feedback to the user.

[0232] User: Review the feedback and take appropriate action.

[0233] Example of a prompt

[0234] If a user enters "I'm in the park right now, but I feel a little scared," the app performs sentiment analysis and detects anxiety. The server then provides feedback such as, "Ask someone for help or move to a safe place."

[0235] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0236] Step 1:

[0237] The user enters their feelings and situation.

[0238] The user uses their device to input their current feelings and situation. Specifically, they enter text data such as, "I'm in the park right now, but I feel a little scared," into the input field. This input is done using the device's keyboard or voice input function.

[0239] Step 2:

[0240] The terminal receives input and sends it to the server.

[0241] The terminal receives text data entered by the user. This received text data is encoded into a format such as JSON and sent to the server. Here, the input is the text data entered by the user, and the output is the encoded data.

[0242] Step 3:

[0243] The server analyzes the text data to identify emotions and intentions.

[0244] The server uses a natural language processing library (e.g., TextBlob) to analyze the received text data. The server splits the text data and tags it by part of speech. Then, it applies a sentiment analysis algorithm to identify the emotion "scared" and the intention "in the park." This transforms the input text data into data tagged with emotion and intention.

[0245] Step 4:

[0246] The server modifies and supplements text data based on emotions and context.

[0247] The server modifies or completes the input text data based on identified emotions and intentions. For example, it might be rewritten to say, "I'm in a park right now, and I'm feeling a little scared. I should move to a safe place or ask for help." The input for this step is emotion and intention tagged data, and the output is the modified or completed text data.

[0248] Step 5:

[0249] The server detects an anomaly and notifies parents or caregivers.

[0250] The server detects anomalies based on emotions and circumstances. For example, if the emotion "fear" is detected, it notifies parents or caregivers via email or text message. The input for this step is the emotion analysis result, and the output is the notification message.

[0251] Step 6:

[0252] The server generates the corrected text and sends it back to the terminal.

[0253] The server returns the corrected or completed text to the terminal. This returned data is encoded in an appropriate format for the user. The input for this step is the corrected or completed text data, and the output is the encoded data.

[0254] Step 7:

[0255] The device displays organized text and feedback.

[0256] The terminal displays the corrected or completed text data received from the server to the user. The user reviews the displayed data and takes appropriate action. The input for this step is the encoded data returned from the server, and the output is the organized text and feedback displayed to the user.

[0257] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0258] This invention is a system that combines an emotion engine that recognizes the user's emotions in order to enable individuals with intellectual disabilities or other conditions to express their feelings and thoughts clearly and to communicate smoothly. Embodiments of this invention will be described in detail below.

[0259] System Configuration

[0260] This system consists of a terminal used by the user, a server that analyzes and processes text data, an emotion engine that recognizes the user's emotions, and a communication network that connects the two.

[0261] Overall Operation Overview

[0262] 1. The user enters their feelings.

[0263] The user inputs their feelings and thoughts into the device. For example, they might input a sentence like, "I'm tired today, but I feel a sense of accomplishment."

[0264] 2. The terminal receives input and sends it to the server.

[0265] The terminal receives text data entered by the user and sends it to the server. The data is encoded in an appropriate format, such as JSON.

[0266] 3. The server analyzes and utilizes the emotion engine.

[0267] The server analyzes the received data, uses natural language processing (NLP) techniques to segment the text, and tags each word with its part of speech.

[0268] The system utilizes an emotion engine to recognize the user's emotions and intentions from the input text. The analysis uses algorithms that identify the user's emotions in real time.

[0269] 4. Modify and supplement the text based on sentiment analysis.

[0270] Based on identified emotions and intentions, the server modifies or completes the entered text. For example, "I'm very tired today, but I feel a great sense of accomplishment."

[0271] 5. The server sends the corrected text back to the terminal.

[0272] The server returns the modified or completed text to the terminal.

[0273] 6. The terminal displays the organized text

[0274] The terminal displays the received text to the user so that the user can confirm it. As a result, the user can convey their intentions to others more accurately.

[0275] Specific example

[0276] User scenario:

[0277] User: Returns home from school and enters "I want to play with my friends, but I don't know how to invite them" into the terminal.

[0278] Terminal: Receives the user's input and sends it to the server.

[0279] Server:

[0280] 1. Analyze the input text to identify two intentions: "want to play with friends" and "don't know how to invite".

[0281] 2. Use the emotion engine to analyze the user's emotions in real time and refer to the past emotion history to more accurately identify the emotions.

[0282] 3. Based on these intentions, modify and complete the text to "I want to play with my friends, but I don't know how to invite them."

[0283] Server: Returns the modified text to the terminal.

[0284] Terminal: Displays the modified text to the user.

[0285] User: Confirms the text and appropriately contacts friends.

[0286] This system utilizes an emotion engine to more accurately understand the user's emotions and support appropriate communication based on that. As a result, the user can accurately express their feelings and thoughts, improving the quality of life.

[0287] The processing flow will be described below.

[0288] Step 1:

[0289] The user inputs their feelings and thoughts into the terminal. For example, they input text such as "I want to play with my friends, but I don't know how to invite them."

[0290] Step 2:

[0291] The terminal receives the input text. It converts the received text data into an appropriate format such as JSON format.

[0292] Step 3:

[0293] The terminal sends the formatted text data to the server. The data transmission is carried out using an HTTP request.

[0294] Step 4:

[0295] The server receives the HTTP request and extracts the text data for analysis. It checks that the data is in the correct format.

[0296] Step 5:

[0297] The server analyzes the text data using a natural language processing (NLP) engine. The analysis includes the following steps:

[0298] The text data is split into words (word segmentation).

[0299] Each word is tagged with a part-of-speech tag (part-of-speech tagging).

[0300] Step 6:

[0301] The server uses the emotion engine to identify the user's emotion and intention from the text data. Specifically, the following processes are performed:

[0302] The emotion engine extracts emotion keywords (e.g., tired, happy) from the input text data.

[0303] Refer to past emotion data to confirm the current emotional state.

[0304] Step 7:

[0305] Based on the identified emotion and intention, the server modifies or complements the input text data. For example, it generates a sentence like "I want to play with my friends, but I don't know how to invite them."

[0306] Step 8:

[0307] The server constructs the modified or complemented text data as a response in JSON format.

[0308] Step 9:

[0309] The server sends the response to the terminal. The sending is performed via an HTTP response.

[0310] Step 10:

[0311] The terminal receives the response from the server and extracts the modified or complemented text data.

[0312] Step 11:

[0313] The terminal displays the modified or complemented text data to the user. For example, "I want to play with my friends, but I don't know how to invite them." is displayed on the screen.

[0314] Step 12:

[0315] The user reviews the organized text displayed and communicates it to others as needed. For example, they can appropriately contact a friend.

[0316] (Example 2)

[0317] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0318] The aim is to solve the problem that individuals with intellectual disabilities and other conditions face in accurately expressing their emotions and thoughts and engaging in smooth communication. Conventional technologies have been insufficient in analyzing input text data and recognizing emotions, making it difficult to generate text that accurately reflects the user's intentions and feelings. As a result, users have been unable to improve the quality of their communication and have difficulty having satisfying conversations.

[0319] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0320] In this invention, the server includes means for encoding input text data and transmitting it over a network, means for dividing the text data and tagging parts of speech, means for identifying emotions and intentions using an emotion recognition engine, and means for modifying or supplementing the text data based on the identified emotions and intentions. This makes it possible to modify and supplement text that accurately reflects the user's emotions and intentions based on the input information.

[0321] "Text data" refers to data in the form of text that users input, including their emotions and intentions.

[0322] "Means of receiving" refers to the means by which a terminal receives and processes text data entered by the user.

[0323] "Encoding" refers to a method of converting received text data into an appropriate data format (e.g., JSON format) so that it can be transmitted over a network.

[0324] "Network" refers to the entire communication infrastructure used to send and receive data between terminals and servers, and includes the internet and local area networks (LANs).

[0325] The "method of splitting" refers to a method of dividing the input text data into individual words and tagging each word with its part of speech.

[0326] "Part-of-speech tagging" is the process of assigning a part of speech (noun, verb, adjective, etc.) to each of the divided words.

[0327] An "emotion recognition engine" is a program that includes an algorithm to identify a user's emotions (positive, negative, etc.) and intentions from input text data.

[0328] "Means for identifying emotions and intentions" refers to methods that use an emotion recognition engine to analyze and identify a user's emotional state and intentions from text data.

[0329] "Means of correction or supplementation" refers to means of modifying the original text data or supplementing missing information to properly organize a text, based on identified sentiments or intentions.

[0330] "Output method" refers to a means of sending the corrected or supplemented text data back to the terminal and displaying it to the user.

[0331] "Real-time processing methods" refer to methods for performing text data analysis and sentiment recognition instantly and without delay.

[0332] "Means for automatically generating appropriate text" refers to means of generating natural and accurate text based on analyzed and corrected text data and providing it to the user.

[0333] This invention is a system for enabling individuals with intellectual disabilities to express their emotions and intentions more accurately and to communicate more smoothly. This system includes techniques for appropriately analyzing input text data, recognizing emotions, and modifying or supplementing the text data based on those emotions. Embodiments of this invention are described in detail below.

[0334] System Configuration

[0335] The system consists of the following hardware and software:

[0336] Hardware:

[0337] Devices: Smartphones, tablets, personal computers, etc.

[0338] Servers: Cloud servers and physical servers

[0339] software:

[0340] Natural Language Processing (NLP) libraries: spaCy, NLTK, etc.

[0341] Emotion recognition engine: Emotion analysis tool, such as IBM Watson® Tone Analyzer.

[0342] Communication network: Internet or local area network (LAN)

[0343] Specific Operation Overview

[0344] 1. The user inputs their feelings or thoughts into the device. For example, they might input, "I want to hang out with my friends, but I don't know how to ask them."

[0345] 2. The terminal receives the input text data, encodes it in JSON format, and sends it to the server.

[0346] 3. The server analyzes the received text data using a natural language processing library, splitting the text into individual words and tagging them by part of speech.

[0347] 4. The server uses an emotion recognition engine to identify the user's emotions and intentions from the text data. Specifically, it identifies emotions such as positive and negative.

[0348] 5. The server modifies or completes the entered text data based on the identified emotions or intentions. For example, it might modify it to "I want to hang out with my friends, but I don't know how to ask them."

[0349] 6. The server sends the corrected or completed text data back to the terminal.

[0350] 7. The device displays the corrected text to the user, allowing the user to review it.

[0351] Examples of specific actions

[0352] The following are specific scenarios in which users may use the system:

[0353] User: After returning home from school, they type "I want to hang out with my friends, but I don't know how to ask them" into their device.

[0354] Terminal: Receives the input text data, encodes it, and sends it to the server.

[0355] server:

[0356] The text is analyzed, split into individual words, and then tagged with parts of speech.

[0357] Using an emotion recognition engine, the system identifies intentions such as "I want to hang out with my friends" and "I don't know how to ask them," and then identifies the emotions behind those intentions.

[0358] Revise the text to "I want to hang out with my friends, but I don't know how to ask them."

[0359] Server: Sends the corrected text data back to the terminal.

[0360] Terminal: Displays the corrected text to the user.

[0361] User: Check the displayed message and contact your friend appropriately.

[0362] Example of a prompt

[0363] The following are examples of prompts used to test emotion recognition engines and natural language processing technologies:

[0364] Prompt message:

[0365] The user typed, "I want to hang out with my friends, but I don't know how to ask them." Analyze this text, recognize the sentiment, and correct / complete it as follows: "I want to hang out with my friends, but I don't know how to ask them."

[0366] In this way, the system can analyze the user's emotions and intentions, and support accurate communication.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The user enters text.

[0370] The user inputs their feelings and thoughts into the device. For example, they might input, "I want to hang out with my friends, but I don't know how to ask them."

[0371] Input: User's text input

[0372] Output: Input text data on the terminal

[0373] Specific action: The user enters their feelings or thoughts in text format into an input form on the device.

[0374] Step 2:

[0375] The terminal receives input, encodes it, and sends it to the server.

[0376] The terminal receives text data entered by the user, encodes it into an appropriate format such as JSON, and sends it to the server.

[0377] Input: User's text input data

[0378] Output: Encoded text data (Example: {"text": "I want to hang out with my friends, but I don't know how to ask them"})

[0379] Specific operation: The terminal program receives the input text, encodes it, and sends it to the server via the communication network.

[0380] Step 3:

[0381] The server parses the text data.

[0382] The server analyzes the received text data using a natural language processing library. It divides the text into words and tags each word with its part of speech.

[0383] Input: Encoded text data

[0384] Output: Split and part-of-speech tagged data (e.g., "friend / noun with / particle want to play / verb but / conjunction how to invite / noun but / particle don't know / verb")

[0385] Specific operation: The server parses the received JSON data and uses a natural language processing library to split and tag parts of speech.

[0386] Step 4:

[0387] The server uses an emotion recognition engine to identify emotions and intentions.

[0388] The server uses an emotion recognition engine to identify the user's emotions and intentions from the segmented text data. It identifies emotions such as positive and negative.

[0389] Input: Split and part-of-speech tagged data

[0390] Output: Data with identified emotions and intentions (e.g., "Positive / I want to hang out with friends," "Negative / I don't know how to ask them out")

[0391] Specific operation: The emotion recognition engine analyzes text data to identify the user's emotions and intentions in real time. Specifically, it uses tools such as IBM Watson Tone Analyzer.

[0392] Step 5:

[0393] The server modifies and completes the text.

[0394] The server modifies or completes the entered text data based on identified emotions and intentions. For example, it might revise it to "I want to hang out with my friends, but I don't know how to ask them."

[0395] Input: Data with identified emotions and intentions

[0396] Output: Modified or completed text data (e.g., "I want to hang out with my friends, but I don't know how to ask them.")

[0397] Specific operation: The server processes the input text by correcting and completing it in a natural way based on the identified emotions and intentions.

[0398] Step 6:

[0399] The server sends the corrected text back to the terminal.

[0400] The server re-encodes the corrected or completed text data in JSON format or another suitable format and sends it back to the terminal.

[0401] Input: Modified or completed text data

[0402] Output: Encoded data sent to the terminal (e.g., {"text": "I want to hang out with my friends, but I don't know how to ask them"})

[0403] Specific operation: The server encodes the modified text data and sends it to the terminal via the communication network.

[0404] Step 7:

[0405] The device displays the corrected text to the user.

[0406] The terminal displays the received, corrected text data to the user. The user can then review the displayed text.

[0407] Input: Encoded modified text data

[0408] Output: The corrected text displayed on the device (Example: "I want to hang out with my friends, but I don't know how to ask them.")

[0409] Specific action: The corrected text data is displayed on the device's screen and presented in a user-friendly format.

[0410] (Application Example 2)

[0411] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0412] There is a problem in that employees with intellectual disabilities have difficulty adequately communicating their physical condition and feelings within the factory. As a result, appropriate support and feedback may not be provided promptly by factory managers. Furthermore, there is a lack of effective means to accurately understand employees' emotions and intentions and to facilitate smooth communication based on that understanding.

[0413] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving input string data, means for analyzing the string data and identifying emotions and intentions, means for modifying or supplementing the input string data based on the identified emotions and intentions, means for outputting the modified or supplemented string data, means for the user to input their feelings and physical condition into a terminal, means for supplementing the text based on the analysis results and displaying it to the user, and means for sending alerts to the administrator as needed. This makes it possible for employees with intellectual disabilities to appropriately communicate their physical condition and emotions, enabling smooth communication and prompt support within the factory.

[0414] "Input string data" refers to text data that users input using their devices, including information such as their feelings and physical condition.

[0415] "Natural language processing means" refers to computer tools that analyze string data entered by a user and identify emotions and intentions from it. Specifically, this includes means for splitting text data and tagging parts of speech.

[0416] "Emotions and intentions" refer to the user's feelings and thoughts, extracted from the string data entered by the user.

[0417] "Means of modification or supplementation" refers to computational means that transform input string data into an appropriate form or provide additional information based on identified emotions or intentions.

[0418] "Modified or completed string data" refers to text data that has been appropriately modified or completed from the original input based on the results of sentiment and intent analysis.

[0419] "Means for users to input their feelings and physical condition into a device" refers to methods that allow employees with intellectual disabilities to input their feelings and physical condition in text using devices such as tablets or smart glasses.

[0420] "Means of completing and displaying text to the user based on analysis results" refers to means of displaying text data that has been modified or completed based on the analysis results of emotions and intentions to the user.

[0421] "Means of sending alerts to administrators" refers to means of issuing warnings or notifications to factory administrators based on the results of emotion and intent analysis, as needed.

[0422] The present invention is a system that helps employees with intellectual disabilities appropriately communicate their physical condition and feelings within a factory. The system includes means for receiving input string data, natural language processing means for analyzing the string data and identifying emotions and intentions, means for modifying or completing the string data based on the identified emotions and intentions, means for outputting the modified or completed data, means for inputting feelings and physical condition using a terminal, means for completing sentences based on the analysis results and displaying them to the user, and means for sending alerts to administrators as needed.

[0423] Hardware and software to be used

[0424] The following hardware and software will be used to implement the system.

[0425] Hardware:

[0426] Device (tablet or smart glasses): A device used by the user to input their feelings and physical condition.

[0427] Factory robot: The main device that runs the emotional support program.

[0428] software:

[0429] Python: The main programming language used.

[0430] TextBlob: Used as a natural language processing library, it performs basic text analysis and part-of-speech tagging.

[0431] Requests: A library for sending HTTP requests.

[0432] Emotion recognition engine: Executes an algorithm to identify the user's emotions from text data.

[0433] System operation

[0434] 1. The user enters their feelings and physical condition:

[0435] Users input their feelings and physical condition as text data using a tablet or smart glasses.

[0436] 2. The terminal receives input and sends it to the server:

[0437] The terminal receives the entered string data and sends it to the server.

[0438] 3. The server analyzes the text and identifies sentiment and intent:

[0439] The server uses an emotion recognition engine to analyze the text and identify the user's emotions and intentions.

[0440] The analysis involves using TextBlob to segment the input data and then tagging it with parts of speech.

[0441] 4. Modify and supplement the text based on the analysis results:

[0442] Based on identified emotions and intentions, the server modifies or supplements the original input data.

[0443] 5. Send the corrected text back to the device and display it:

[0444] The server sends the corrected text back to the terminal, which then displays it to the user.

[0445] Users can review the corrected text.

[0446] 6. Send an alert to the administrator if necessary:

[0447] If necessary, an alert will be sent to the factory manager based on the analysis results.

[0448] Specific example

[0449] If a user types "I'm tired today," the system analyzes it and displays the completed text "(I feel tired)" to the user. It can also send an alert to administrators that "an employee is tired."

[0450] Example of a prompt

[0451] Example input: I'm tired today.

[0452] Example output: I am tired today (I feel tired).

[0453] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0454] Step 1:

[0455] The user inputs their feelings and physical condition. The user inputs their feelings and physical condition in text format into a tablet or smart glasses. For example, they might input a sentence like, "I'm tired today." The input in this step is text data entered by the user into the device, and the output is that same text data.

[0456] Step 2:

[0457] The terminal receives input and sends it to the server. The terminal receives the string data entered by the user, encodes it into an appropriate format such as JSON, and sends it to the server. In this step, the input is the text data entered by the user, and the output is the encoded text data sent to the server.

[0458] Step 3:

[0459] The server analyzes the text to identify emotions and intentions. The server uses an emotion recognition engine to analyze the received string data and identify the user's emotions and intentions. Specifically, it uses a natural language processing library (TextBlob) to segment the text data and tag it with parts of speech. As a result of the analysis, it identifies an emotion such as "tired." The input for this step is text data sent from the terminal, and the output is the analysis result that identifies emotions and intentions.

[0460] Step 4:

[0461] The text is modified or completed based on the analysis results. The server generates modified or completed text based on the identified emotions or intentions. For example, if the emotion "tired" is identified, the original input is completed to "I am tired today (I feel tired)." The input for this step is the analysis results and the original text data, and the output is the modified or completed text data.

[0462] Step 5:

[0463] The corrected text is returned to the terminal and displayed. The server returns the corrected or completed text data to the terminal. The terminal displays the received corrected or completed text data to the user. The user reviews and uses it appropriately. The input for this step is the corrected or completed text data, and the output is the text data displayed on the terminal.

[0464] Step 6:

[0465] The server sends alerts to the administrator as needed. Based on the analysis results, the server sends warnings and notifications to the administrator when necessary. For example, if a user is unwell, the server notifies the administrator of this information. The input for this step is the analysis results, and the output is the alert information sent to the administrator.

[0466] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0467] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0468] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0469] [Second Embodiment]

[0470] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0471] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0472] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0473] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0474] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0475] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0476] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0477] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0478] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0479] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0480] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0481] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0482] This invention is a system that enables individuals with intellectual disabilities or other conditions to express their feelings and thoughts clearly and to support smooth communication. The embodiments of this invention will be described in detail below.

[0483] System Configuration

[0484] This system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication network that connects these two.

[0485] Overall Operation Overview

[0486] 1. The user enters their feelings.

[0487] Users input their feelings and thoughts into the device. For example, they might input a sentence like, "I'm tired today, but I feel a sense of accomplishment."

[0488] 2. The terminal receives input and sends it to the server.

[0489] The terminal receives text data entered by the user and sends it to the server. The data is encoded in an appropriate format, such as JSON.

[0490] 3. The server analyzes and identifies emotions and intentions.

[0491] The server analyzes the received data, uses natural language processing to segment the text, and tags each word by its part of speech. Then, it uses sentiment analysis algorithms to identify emotions and intentions such as "tired" or "feeling accomplished."

[0492] 4. The server corrects and completes the text.

[0493] Based on identified emotions and intentions, the server modifies or completes the entered text. For example, it might be transformed into a clearer sentence such as, "I'm very tired today, but I feel a great sense of accomplishment."

[0494] 5. The server sends the corrected text back to the terminal.

[0495] The server returns the corrected or completed text to the terminal.

[0496] 6. The device displays organized text.

[0497] The device displays the received message to the user, allowing the user to confirm it. This enables the user to communicate their intentions to others more accurately.

[0498] Specific examples

[0499] User scenario:

[0500] User: After returning home from school, they type into their device, "I want to hang out with my friends, but I don't know how to ask them."

[0501] Terminal: Receives user input and sends it to the server.

[0502] server:

[0503] 1. Analyze the input text and identify two intentions: "I want to hang out with my friends" and "I don't know how to ask them out."

[0504] 2. Based on these intentions, revise and supplement the sentence to: "I want to hang out with my friends, but I don't know how to ask them."

[0505] Server: Sends the corrected text back to the terminal.

[0506] Terminal: Displays the corrected text to the user.

[0507] User: Check the message and contact your friend appropriately.

[0508] This system helps users express their feelings and thoughts accurately, thereby improving their quality of life.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] Users input their feelings and thoughts into the device. They can input free-form text, including individual words and short sentences.

[0512] Step 2:

[0513] The terminal receives the entered text. For processing, the text data is converted to an appropriate format, such as JSON format.

[0514] Step 3:

[0515] The device sends text data to the server. The data transmission is performed using an HTTP request.

[0516] Step 4:

[0517] The server receives the HTTP request and extracts the text data for analysis. It then verifies that the data is in the correct format.

[0518] Step 5:

[0519] The server uses a natural language processing (NLP) engine to analyze text data. This analysis includes word segmentation and part-of-speech tagging.

[0520] Step 6:

[0521] The server applies sentiment analysis algorithms to identify the emotions and intentions contained in the text data. For example, it can identify intentions such as "I want to hang out with my friends" or "I don't know how to ask them out."

[0522] Step 7:

[0523] The server modifies or completes the input text data based on the results of identifying emotions and intentions. For example, it might generate a more polished sentence like, "I want to hang out with my friends, but I don't know how to ask them."

[0524] Step 8:

[0525] The server generates the corrected or completed text data as a response in JSON format.

[0526] Step 9:

[0527] The server sends a response to the terminal. This transmission occurs via an HTTP response.

[0528] Step 10:

[0529] The terminal receives a response from the server and retrieves the corrected or completed text data.

[0530] Step 11:

[0531] The device displays corrected or completed text data to the user. The output is displayed on the screen in a user-friendly format.

[0532] Step 12:

[0533] Users can review the organized text displayed and share it with others as needed. For example, they might use it to contact a friend.

[0534] (Example 1)

[0535] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0536] This invention relates to a system for supporting individuals with intellectual disabilities and other conditions in clearly expressing their emotions and thoughts and communicating smoothly. Conventional systems sometimes made it difficult for users to accurately express their emotions and intentions, leading to communication problems. The challenge is to solve this problem and enable users to communicate their emotions and thoughts simply and clearly.

[0537] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0538] In this invention, the server includes means for receiving text data containing emotions and intentions entered by a user; means for encoding the text data and transferring it via a communication network; means for analyzing the received text data and identifying emotions and intentions using a natural language processing system; means for using a generative AI model to modify or supplement the input text data based on the identified emotions and intentions; means for sending the modified or supplemented text data back to the terminal via the communication network to notify the user; and means for displaying the returned text data to the user. This makes it easier for users to clearly express their emotions and intentions and enables smooth communication with others.

[0539] A "user" refers to an individual who uses a system to express their own emotions or intentions.

[0540] "Text data" refers to data consisting of natural language sentences and words entered by the user.

[0541] A "terminal" refers to a device used by a user to input text data and communicate with a server. Specifically, this includes smartphones, tablets, and personal computers.

[0542] A "server" refers to a computing device that analyzes received text data and uses a generative AI model to correct or complete it.

[0543] A "communication network" refers to the network infrastructure used to send and receive data between terminals and servers. Specifically, it refers to the internet and local networks.

[0544] "Encoding" refers to the process of converting information, such as text data, into a specific format (for example, JSON format).

[0545] "Natural language processing methods" refer to technical means for analyzing text data and identifying emotions and intentions. Specifically, this involves processes such as text segmentation and part-of-speech tagging.

[0546] A "sentiment analysis algorithm" refers to an algorithm used to identify a user's emotions and intentions from text data.

[0547] A "generative AI model" refers to an artificial intelligence model that generates new text based on input text data. Specifically, it refers to models such as GPT-3.

[0548] "Correction and supplementation" refers to the process of transforming input text data into a clearer and more understandable form based on identified emotions and intentions.

[0549] "Display" refers to showing the corrected or completed text data on the device's screen in a format that the user can review.

[0550] This invention relates to a system that enables individuals with intellectual disabilities to express their feelings and thoughts clearly and communicate smoothly. The system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication network connecting the two.

[0551] 1. System Configuration

[0552] This system is comprised of the following hardware and software.

[0553] Terminal: Input devices such as smartphones, tablets, and personal computers.

[0554] Server: A computing device that performs analysis and data processing. Specifically, it uses Python libraries such as NLTK and spaCy, the TextBlob sentiment analysis library, and the GPT-3 generative AI model.

[0555] Communication network: Internet or local network.

[0556] 2. System Operation

[0557] Text data entered by the user into the device is first encoded into JSON format by the device and sent to the server. The server analyzes the received text data, uses natural language processing tools (NLTK and spaCy) to segment the text, and tags each word with its part of speech. Furthermore, it uses a sentiment analysis library (TextBlob) to identify emotions and intentions such as "tired" or "feeling accomplished."

[0558] Based on identified emotions and intentions, the server uses a generative AI model (GPT-3) to modify or supplement the text data. The modified text is then re-encoded in JSON format and sent back to the terminal via the communication network. The terminal decodes the received modified data and displays it to the user.

[0559] 3. Specific examples

[0560] For example, if a user enters "I want to hang out with my friends, but I don't know how to invite them," the following process will be executed:

[0561] 1. The user enters "I want to hang out with my friends, but I don't know how to invite them" into their device.

[0562] 2. The terminal receives input and sends it to the server.

[0563] 3. The server analyzes the text data and identifies two intentions: "I want to play with my friends" and "I don't know how to invite them."

[0564] 4. The server enters the following prompt into the generated AI model:

[0565] The user typed, "I want to hang out with my friends, but I don't know how to ask them." Please rephrase this sentence to be more specific and easier to understand:

[0566] 5. The AI ​​generation model generates the corrected text: "I want to hang out with my friends, but I don't know how to ask them."

[0567] 6. The server returns the corrected text to the terminal.

[0568] 7. The device displays the corrected text to the user.

[0569] In this way, users can express their feelings and thoughts more clearly and communicate more smoothly with others.

[0570] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0571] Step 1:

[0572] Users enter text data that includes emotions and intentions.

[0573] The user inputs text data expressing their feelings and thoughts into their device (smartphone, tablet, or PC). For example, they might input, "I'm tired today, but I feel a sense of accomplishment."

[0574] Input: Text input by the user.

[0575] Output: Input text data ("I'm tired today, but I feel a sense of accomplishment").

[0576] Step 2:

[0577] The terminal receives input and sends it to the server.

[0578] The terminal receives user input, encodes it in JSON format, and sends it.

[0579] Specific operation: The terminal encodes the data in the following format:

[0580] json

[0581] {

[0582] "text": "I'm tired today, but I feel a sense of accomplishment."

[0583] }

[0584] The encoded data is then sent to the server.

[0585] Input: Text data entered by the user.

[0586] Output: Text data encoded in JSON format.

[0587] Step 3:

[0588] The server receives and analyzes the input data.

[0589] The server parses the JSON-formatted text data received from the terminal. It uses natural language processing tools (NLTK, spaCy) to segment the text and tag its parts of speech.

[0590] Specific operation: The server parses the text as follows:

[0591] python

[0592] import spacy

[0593] nlp = spacy.load("ja_core_news_md")

[0594] doc = nlp("I'm tired today, but I feel a sense of accomplishment")

[0595] for token in doc:

[0596] print(token.text, token.pos_)

[0597] This allows us to identify words and their parts of speech, such as "today," "tired," "but," "sense of accomplishment," "is," and "there is."

[0598] Input: Encoded text data.

[0599] Output: Analysis results (text data tagged with parts of speech).

[0600] Step 4:

[0601] The server identifies emotions and intentions.

[0602] The server uses an emotion analysis algorithm (e.g., TextBlob) to identify emotions and intentions from the analysis results.

[0603] Specific operation: The server analyzes emotions as follows:

[0604] python

[0605] from textblob import TextBlob

[0606] analysis = TextBlob("I'm tired today, but I feel a sense of accomplishment")

[0607] sentiment = analysis.sentiment

[0608] The analysis results identify emotions such as "tired" and "a sense of accomplishment."

[0609] Input: Text data tagged with parts of speech.

[0610] Output: Identified emotions and intentions.

[0611] Step 5:

[0612] The server uses a generated AI model to modify or supplement text data.

[0613] The server inputs prompt text into the generative AI model (GPT-3) and modifies or completes the text data.

[0614] Specific action: The server will enter the following prompt:

[0615] The user typed, "I'm tired today, but I feel a sense of accomplishment." Please revise this sentence to make it clearer and easier to understand:

[0616] The generative AI model outputs the corrected text "I'm very tired today, but I feel a great sense of accomplishment" as its response.

[0617] Input: Identified emotion and intention, prompt sentence.

[0618] Output: Modified text data.

[0619] Step 6:

[0620] The server sends the corrected text data back to the terminal.

[0621] The server encodes the modified text data into JSON format and sends it back to the terminal.

[0622] Specific operation: The server encodes the modified text data in the following format:

[0623] json

[0624] {

[0625] "text": "I'm very tired today, but I feel a great sense of accomplishment."

[0626] }

[0627] The encoded data is then sent to the terminal.

[0628] Input: Modified text data.

[0629] Output: Modified text data encoded in JSON format.

[0630] Step 7:

[0631] The device receives the corrected text data and displays it to the user.

[0632] The terminal decodes the JSON data received from the server and displays it to the user.

[0633] Specific action: The terminal performs the decoding process and displays the following text on the screen:

[0634] I'm very tired today, but I feel a great sense of accomplishment.

[0635] Input: Modified text data encoded in JSON format.

[0636] Output: The corrected text data displayed to the user.

[0637] (Application Example 1)

[0638] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0639] This initiative aims to address the difficulties individuals with intellectual disabilities face in clearly expressing their feelings and thoughts and communicating safely with those around them. Furthermore, it supports guardians and caregivers in taking appropriate actions quickly, thereby ensuring the safety and security of individuals with intellectual disabilities.

[0640] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0641] In this invention, the server includes means for receiving input text data, means for natural language processing, means for modifying or supplementing the input text data based on emotions and intentions, means for outputting the modified or supplemented text data, means for notifying the user of anomalies to ensure user safety, and means for generating safety confirmation messages and providing feedback to the user. This makes it possible to ensure the user's safety and peace of mind by clearly expressing the user's feelings and thoughts and providing appropriate feedback.

[0642] "Means for receiving text data" refers to a device or software that electronically receives text data entered by a user.

[0643] "Natural language processing means" refers to algorithms and software used to analyze received text data and identify emotions and intentions.

[0644] "Means for modifying or supplementing input text data" refers to devices or software that transform the original text data into an appropriate form based on identified emotions or intentions.

[0645] "Means for outputting corrected or supplemented text data" refers to a device or software for presenting corrected or supplemented text data to a user.

[0646] "Means of notifying of abnormalities" refers to devices or software that detect unusual situations or emotions and notify guardians or caregivers in order to ensure the user's safety.

[0647] "Means for generating safety confirmation messages and providing feedback" refers to devices or software that provide safety-related feedback and prompt necessary actions based on user input.

[0648] This invention is a system that enables individuals with intellectual disabilities to express their feelings and thoughts clearly and communicate safely with those around them. The system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication means that connects these two.

[0649] System Configuration

[0650] 1. Terminal

[0651] This is a device for users to input their feelings and circumstances. Smartphones and smart glasses are used as terminals. The terminal has an input interface, allowing users to input text data.

[0652] 2. Server

[0653] The system analyzes received text data to identify emotions and intentions, and then modifies or supplements the data accordingly. Furthermore, it detects anomalies as needed and notifies parents and caregivers. It also generates safety confirmation messages and provides feedback. The server has natural language processing libraries (e.g., TextBlob) and communication libraries (e.g., requests) installed.

[0654] Overall Operation Overview

[0655] 1. The user enters their feelings or situation.

[0656] The user enters their current feelings or situation into the device. For example, they might enter a sentence like, "I'm in the park right now, but I feel a little scared."

[0657] 2. The terminal receives input and sends it to the server.

[0658] The terminal receives text data entered by the user and sends it to the server. The data is encoded in JSON format.

[0659] 3. The server analyzes and identifies emotions and intentions.

[0660] To analyze the received data, the server uses natural language processing to segment the text data and tag it by part of speech. Then, it uses sentiment analysis algorithms to identify emotions such as "scared" and situations such as "being in a park."

[0661] 4. The server modifies and supplements the content based on emotions and circumstances, and sends notifications to confirm safety.

[0662] Based on identified emotions and circumstances, the system modifies or completes the entered text. Furthermore, it uses means to notify parents or caregivers of the situation. For example, it might generate a message such as, "I'm in the park right now, and I'm feeling a little scared. Please move to a safe place or ask for help."

[0663] 5. The server sends the corrected text and feedback back to the terminal.

[0664] The server returns the corrected or supplemented text and a security confirmation message to the terminal.

[0665] 6. The device displays organized text and feedback.

[0666] The device displays received text and messages to the user, enabling the user to respond appropriately.

[0667] Specific example

[0668] User scenario:

[0669] User: Feeling uneasy in the park, they type "I'm in the park right now, and I feel a little scared" into their device.

[0670] Terminal: Receives user input and sends it to the server.

[0671] server:

[0672] 1. Analyze the input text to identify the emotions and situations: "scared" and "in a park."

[0673] 2. Based on these feelings and circumstances, revise and complete the sentence to generate the feedback: "I'm in the park right now, and I'm feeling a little scared. I should move to a safe place or ask for help."

[0674] Server: Sends the corrected text and feedback back to the terminal.

[0675] Terminal: Displays the corrected text and feedback to the user.

[0676] User: Review the feedback and take appropriate action.

[0677] Example of a prompt

[0678] If a user enters "I'm in the park right now, but I feel a little scared," the app performs sentiment analysis and detects anxiety. The server then provides feedback such as, "Ask someone for help or move to a safe place."

[0679] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0680] Step 1:

[0681] The user enters their feelings and situation.

[0682] The user uses their device to input their current feelings and situation. Specifically, they enter text data such as, "I'm in the park right now, but I feel a little scared," into the input field. This input is done using the device's keyboard or voice input function.

[0683] Step 2:

[0684] The terminal receives input and sends it to the server.

[0685] The terminal receives text data entered by the user. This received text data is encoded into a format such as JSON and sent to the server. Here, the input is the text data entered by the user, and the output is the encoded data.

[0686] Step 3:

[0687] The server analyzes the text data to identify emotions and intentions.

[0688] The server uses a natural language processing library (e.g., TextBlob) to analyze the received text data. The server splits the text data and tags it by part of speech. Then, it applies a sentiment analysis algorithm to identify the emotion "scared" and the intention "in the park." This transforms the input text data into data tagged with emotion and intention.

[0689] Step 4:

[0690] The server modifies and supplements text data based on emotions and context.

[0691] The server modifies or completes the input text data based on identified emotions and intentions. For example, it might be rewritten to say, "I'm in a park right now, and I'm feeling a little scared. I should move to a safe place or ask for help." The input for this step is emotion and intention tagged data, and the output is the modified or completed text data.

[0692] Step 5:

[0693] The server detects an anomaly and notifies parents or caregivers.

[0694] The server detects anomalies based on emotions and circumstances. For example, if the emotion "fear" is detected, it notifies parents or caregivers via email or text message. The input for this step is the emotion analysis result, and the output is the notification message.

[0695] Step 6:

[0696] The server generates the corrected text and sends it back to the terminal.

[0697] The server returns the corrected or completed text to the terminal. This returned data is encoded in an appropriate format for the user. The input for this step is the corrected or completed text data, and the output is the encoded data.

[0698] Step 7:

[0699] The device displays organized text and feedback.

[0700] The terminal displays the corrected or completed text data received from the server to the user. The user reviews the displayed data and takes appropriate action. The input for this step is the encoded data returned from the server, and the output is the organized text and feedback displayed to the user.

[0701] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0702] This invention is a system that combines an emotion engine that recognizes the user's emotions in order to enable individuals with intellectual disabilities or other conditions to express their feelings and thoughts clearly and to communicate smoothly. Embodiments of this invention will be described in detail below.

[0703] System Configuration

[0704] This system consists of a terminal used by the user, a server that analyzes and processes text data, an emotion engine that recognizes the user's emotions, and a communication network that connects the two.

[0705] Overall Operation Overview

[0706] 1. The user enters their feelings.

[0707] The user inputs their feelings and thoughts into the device. For example, they might input a sentence like, "I'm tired today, but I feel a sense of accomplishment."

[0708] 2. The terminal receives input and sends it to the server.

[0709] The terminal receives text data entered by the user and sends it to the server. The data is encoded in an appropriate format, such as JSON.

[0710] 3. The server analyzes and utilizes the emotion engine.

[0711] The server analyzes the received data, uses natural language processing (NLP) techniques to segment the text, and tags each word with its part of speech.

[0712] The system utilizes an emotion engine to recognize the user's emotions and intentions from the input text. The analysis uses algorithms that identify the user's emotions in real time.

[0713] 4. Modify and supplement the text based on sentiment analysis.

[0714] Based on identified emotions and intentions, the server modifies or completes the entered text. For example, "I'm very tired today, but I feel a great sense of accomplishment."

[0715] 5. The server sends the corrected text back to the terminal.

[0716] The server returns the corrected or completed text to the terminal.

[0717] 6. The device displays organized text.

[0718] The device displays the received message to the user, allowing the user to confirm it. This enables the user to communicate their intentions to others more accurately.

[0719] Specific examples

[0720] User scenario:

[0721] User: After returning home from school, they type into their device, "I want to hang out with my friends, but I don't know how to ask them."

[0722] Terminal: Receives user input and sends it to the server.

[0723] server:

[0724] 1. Analyze the input text and identify two intentions: "I want to hang out with my friends" and "I don't know how to ask them out."

[0725] 2. Use an emotion engine to analyze the user's emotions in real time and refer to past emotion history to more accurately identify emotions.

[0726] 3. Based on these intentions, revise and supplement the sentence to: "I want to hang out with my friends, but I don't know how to ask them."

[0727] Server: Sends the corrected text back to the terminal.

[0728] Terminal: Displays the corrected text to the user.

[0729] User: Check the message and contact your friend appropriately.

[0730] This system utilizes an emotion engine to more accurately understand users' emotions and support appropriate communication based on those emotions. This allows users to express their feelings and thoughts precisely, improving their quality of life.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The user enters their feelings or thoughts into the device. For example, they might enter text like, "I want to hang out with my friends, but I don't know how to ask them."

[0734] Step 2:

[0735] The terminal receives the entered text. The received text data is converted to an appropriate format, such as JSON format.

[0736] Step 3:

[0737] The terminal sends formatted text data to the server. Data transmission is performed using an HTTP request.

[0738] Step 4:

[0739] The server receives the HTTP request and extracts the text data for parsing. It then verifies that the data is in the correct format.

[0740] Step 5:

[0741] The server uses a natural language processing (NLP) engine to analyze the text data. The analysis includes the following steps:

[0742] This process divides text data into individual words (word segmentation).

[0743] Add part-of-speech tags to each word (part-of-speech tagging).

[0744] Step 6:

[0745] The server uses an emotion engine to identify the user's emotions and intentions from text data. Specifically, the following processes are performed:

[0746] The emotion engine extracts emotional keywords (e.g., tired, happy) from the input text data.

[0747] Refer to past emotional data to check your current emotional state.

[0748] Step 7:

[0749] The server modifies or completes the input text data based on identified emotions and intentions. For example, it might generate a sentence like, "I want to hang out with my friends, but I don't know how to ask them."

[0750] Step 8:

[0751] The server constructs the modified or completed text data as a response in JSON format.

[0752] Step 9:

[0753] The server sends a response to the terminal. This transmission occurs via an HTTP response.

[0754] Step 10:

[0755] The terminal receives a response from the server and retrieves the corrected or completed text data.

[0756] Step 11:

[0757] The device displays corrected or completed text data to the user. For example, "I want to hang out with my friends, but I don't know how to ask them" might appear on the screen.

[0758] Step 12:

[0759] The user reviews the organized text displayed and communicates it to others as needed. For example, they can appropriately contact a friend.

[0760] (Example 2)

[0761] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0762] The aim is to solve the problem that individuals with intellectual disabilities and other conditions face in accurately expressing their emotions and thoughts and engaging in smooth communication. Conventional technologies have been insufficient in analyzing input text data and recognizing emotions, making it difficult to generate text that accurately reflects the user's intentions and feelings. As a result, users have been unable to improve the quality of their communication and have difficulty having satisfying conversations.

[0763] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0764] In this invention, the server includes means for encoding input text data and transmitting it over a network, means for dividing the text data and tagging parts of speech, means for identifying emotions and intentions using an emotion recognition engine, and means for modifying or supplementing the text data based on the identified emotions and intentions. This makes it possible to modify and supplement text that accurately reflects the user's emotions and intentions based on the input information.

[0765] "Text data" refers to data in the form of text that users input, including their emotions and intentions.

[0766] "Means of receiving" refers to the means by which a terminal receives and processes text data entered by the user.

[0767] "Encoding" refers to a method of converting received text data into an appropriate data format (e.g., JSON format) so that it can be transmitted over a network.

[0768] "Network" refers to the entire communication infrastructure used to send and receive data between terminals and servers, and includes the internet and local area networks (LANs).

[0769] The "method of splitting" refers to a method of dividing the input text data into individual words and tagging each word with its part of speech.

[0770] "Part-of-speech tagging" is the process of assigning a part of speech (noun, verb, adjective, etc.) to each of the divided words.

[0771] An "emotion recognition engine" is a program that includes an algorithm to identify a user's emotions (positive, negative, etc.) and intentions from input text data.

[0772] "Means for identifying emotions and intentions" refers to methods that use an emotion recognition engine to analyze and identify a user's emotional state and intentions from text data.

[0773] "Means of correction or supplementation" refers to means of modifying the original text data or supplementing missing information to properly organize a text, based on identified sentiments or intentions.

[0774] "Output method" refers to a means of sending the corrected or supplemented text data back to the terminal and displaying it to the user.

[0775] "Real-time processing methods" refer to methods for performing text data analysis and sentiment recognition instantly and without delay.

[0776] "Means for automatically generating appropriate text" refers to means of generating natural and accurate text based on analyzed and corrected text data and providing it to the user.

[0777] This invention is a system for enabling individuals with intellectual disabilities to express their emotions and intentions more accurately and to communicate more smoothly. This system includes techniques for appropriately analyzing input text data, recognizing emotions, and modifying or supplementing the text data based on those emotions. Embodiments of this invention are described in detail below.

[0778] System Configuration

[0779] The system consists of the following hardware and software:

[0780] Hardware:

[0781] Devices: Smartphones, tablets, personal computers, etc.

[0782] Servers: Cloud servers and physical servers

[0783] software:

[0784] Natural Language Processing (NLP) libraries: spaCy, NLTK, etc.

[0785] Emotion recognition engine: Emotion analysis tool, such as IBM Watson Tone Analyzer.

[0786] Communication network: Internet or local area network (LAN)

[0787] Specific Operation Overview

[0788] 1. The user inputs their feelings or thoughts into the device. For example, they might input, "I want to hang out with my friends, but I don't know how to ask them."

[0789] 2. The terminal receives the input text data, encodes it in JSON format, and sends it to the server.

[0790] 3. The server analyzes the received text data using a natural language processing library, splitting the text into individual words and tagging them by part of speech.

[0791] 4. The server uses an emotion recognition engine to identify the user's emotions and intentions from the text data. Specifically, it identifies emotions such as positive and negative.

[0792] 5. The server modifies or completes the entered text data based on the identified emotions or intentions. For example, it might modify it to "I want to hang out with my friends, but I don't know how to ask them."

[0793] 6. The server sends the corrected or completed text data back to the terminal.

[0794] 7. The device displays the corrected text to the user, allowing the user to review it.

[0795] Examples of specific actions

[0796] The following are specific scenarios in which users may use the system:

[0797] User: After returning home from school, they type "I want to hang out with my friends, but I don't know how to ask them" into their device.

[0798] Terminal: Receives the input text data, encodes it, and sends it to the server.

[0799] server:

[0800] The text is analyzed, split into individual words, and then tagged with parts of speech.

[0801] Using an emotion recognition engine, the system identifies intentions such as "I want to hang out with my friends" and "I don't know how to ask them," and then identifies the emotions behind those intentions.

[0802] Revise the text to "I want to hang out with my friends, but I don't know how to ask them."

[0803] Server: Sends the corrected text data back to the terminal.

[0804] Terminal: Displays the corrected text to the user.

[0805] User: Check the displayed message and contact your friend appropriately.

[0806] Example of a prompt

[0807] The following are examples of prompts used to test emotion recognition engines and natural language processing technologies:

[0808] Prompt message:

[0809] The user typed, "I want to hang out with my friends, but I don't know how to ask them." Analyze this text, recognize the sentiment, and correct / complete it as follows: "I want to hang out with my friends, but I don't know how to ask them."

[0810] In this way, the system can analyze the user's emotions and intentions, and support accurate communication.

[0811] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0812] Step 1:

[0813] The user enters text.

[0814] The user inputs their feelings and thoughts into the device. For example, they might input, "I want to hang out with my friends, but I don't know how to ask them."

[0815] Input: User's text input

[0816] Output: Input text data on the terminal

[0817] Specific action: The user enters their feelings or thoughts in text format into an input form on the device.

[0818] Step 2:

[0819] The terminal receives input, encodes it, and sends it to the server.

[0820] The terminal receives text data entered by the user, encodes it into an appropriate format such as JSON, and sends it to the server.

[0821] Input: User's text input data

[0822] Output: Encoded text data (Example: {"text": "I want to hang out with my friends, but I don't know how to ask them"})

[0823] Specific operation: The terminal program receives the input text, encodes it, and sends it to the server via the communication network.

[0824] Step 3:

[0825] The server parses the text data.

[0826] The server analyzes the received text data using a natural language processing library. It divides the text into words and tags each word with its part of speech.

[0827] Input: Encoded text data

[0828] Output: Split and part-of-speech tagged data (e.g., "friend / noun with / particle want to play / verb but / conjunction how to invite / noun but / particle don't know / verb")

[0829] Specific operation: The server parses the received JSON data and uses a natural language processing library to split and tag parts of speech.

[0830] Step 4:

[0831] The server uses an emotion recognition engine to identify emotions and intentions.

[0832] The server uses an emotion recognition engine to identify the user's emotions and intentions from the segmented text data. It identifies emotions such as positive and negative.

[0833] Input: Split and part-of-speech tagged data

[0834] Output: Data with identified emotions and intentions (e.g., "Positive / I want to hang out with friends," "Negative / I don't know how to ask them out")

[0835] Specific operation: The emotion recognition engine analyzes text data to identify the user's emotions and intentions in real time. Specifically, it uses tools such as IBM Watson Tone Analyzer.

[0836] Step 5:

[0837] The server modifies and completes the text.

[0838] The server modifies or completes the entered text data based on identified emotions and intentions. For example, it might revise it to "I want to hang out with my friends, but I don't know how to ask them."

[0839] Input: Data with identified emotions and intentions

[0840] Output: Modified or completed text data (e.g., "I want to hang out with my friends, but I don't know how to ask them.")

[0841] Specific operation: The server processes the input text by correcting and completing it in a natural way based on the identified emotions and intentions.

[0842] Step 6:

[0843] The server sends the corrected text back to the terminal.

[0844] The server re-encodes the corrected or completed text data in JSON format or another suitable format and sends it back to the terminal.

[0845] Input: Modified or completed text data

[0846] Output: Encoded data sent to the terminal (e.g., {"text": "I want to hang out with my friends, but I don't know how to ask them"})

[0847] Specific operation: The server encodes the modified text data and sends it to the terminal via the communication network.

[0848] Step 7:

[0849] The device displays the corrected text to the user.

[0850] The terminal displays the received, corrected text data to the user. The user can then review the displayed text.

[0851] Input: Encoded modified text data

[0852] Output: The corrected text displayed on the device (Example: "I want to hang out with my friends, but I don't know how to ask them.")

[0853] Specific action: The corrected text data is displayed on the device's screen and presented in a user-friendly format.

[0854] (Application Example 2)

[0855] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0856] There is a problem in that employees with intellectual disabilities have difficulty adequately communicating their physical condition and feelings within the factory. As a result, appropriate support and feedback may not be provided promptly by factory managers. Furthermore, there is a lack of effective means to accurately understand employees' emotions and intentions and to facilitate smooth communication based on that understanding.

[0857] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving input string data, means for analyzing the string data and identifying emotions and intentions, means for modifying or supplementing the input string data based on the identified emotions and intentions, means for outputting the modified or supplemented string data, means for the user to input their feelings and physical condition into a terminal, means for supplementing the text based on the analysis results and displaying it to the user, and means for sending alerts to the administrator as needed. This makes it possible for employees with intellectual disabilities to appropriately communicate their physical condition and emotions, enabling smooth communication and prompt support within the factory.

[0858] "Input string data" refers to text data that users input using their devices, including information such as their feelings and physical condition.

[0859] "Natural language processing means" refers to computer tools that analyze string data entered by a user and identify emotions and intentions from it. Specifically, this includes means for splitting text data and tagging parts of speech.

[0860] "Emotions and intentions" refer to the user's feelings and thoughts, extracted from the string data entered by the user.

[0861] "Means of modification or supplementation" refers to computational means that transform input string data into an appropriate form or provide additional information based on identified emotions or intentions.

[0862] "Modified or completed string data" refers to text data that has been appropriately modified or completed from the original input based on the results of sentiment and intent analysis.

[0863] "Means for users to input their feelings and physical condition into a device" refers to methods that allow employees with intellectual disabilities to input their feelings and physical condition in text using devices such as tablets or smart glasses.

[0864] "Means of completing and displaying text to the user based on analysis results" refers to means of displaying text data that has been modified or completed based on the analysis results of emotions and intentions to the user.

[0865] "Means of sending alerts to administrators" refers to means of issuing warnings or notifications to factory administrators based on the results of emotion and intent analysis, as needed.

[0866] The present invention is a system that helps employees with intellectual disabilities appropriately communicate their physical condition and feelings within a factory. The system includes means for receiving input string data, natural language processing means for analyzing the string data and identifying emotions and intentions, means for modifying or completing the string data based on the identified emotions and intentions, means for outputting the modified or completed data, means for inputting feelings and physical condition using a terminal, means for completing sentences based on the analysis results and displaying them to the user, and means for sending alerts to administrators as needed.

[0867] Hardware and software to be used

[0868] The following hardware and software will be used to implement the system.

[0869] Hardware:

[0870] Device (tablet or smart glasses): A device used by the user to input their feelings and physical condition.

[0871] Factory robot: The main device that runs the emotional support program.

[0872] software:

[0873] Python: The main programming language used.

[0874] TextBlob: Used as a natural language processing library, it performs basic text analysis and part-of-speech tagging.

[0875] Requests: A library for sending HTTP requests.

[0876] Emotion recognition engine: Executes an algorithm to identify the user's emotions from text data.

[0877] System operation

[0878] 1. The user enters their feelings and physical condition:

[0879] Users input their feelings and physical condition as text data using a tablet or smart glasses.

[0880] 2. The terminal receives input and sends it to the server:

[0881] The terminal receives the entered string data and sends it to the server.

[0882] 3. The server analyzes the text and identifies sentiment and intent:

[0883] The server uses an emotion recognition engine to analyze the text and identify the user's emotions and intentions.

[0884] The analysis involves using TextBlob to segment the input data and then tagging it with parts of speech.

[0885] 4. Modify and supplement the text based on the analysis results:

[0886] Based on identified emotions and intentions, the server modifies or supplements the original input data.

[0887] 5. Send the corrected text back to the device and display it:

[0888] The server sends the corrected text back to the terminal, which then displays it to the user.

[0889] Users can review the corrected text.

[0890] 6. Send an alert to the administrator if necessary:

[0891] If necessary, an alert will be sent to the factory manager based on the analysis results.

[0892] Specific example

[0893] If a user types "I'm tired today," the system analyzes it and displays the completed text "(I feel tired)" to the user. It can also send an alert to administrators that "an employee is tired."

[0894] Example of a prompt

[0895] Example input: I'm tired today.

[0896] Example output: I am tired today (I feel tired).

[0897] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0898] Step 1:

[0899] The user inputs their feelings and physical condition. The user inputs their feelings and physical condition in text format into a tablet or smart glasses. For example, they might input a sentence like, "I'm tired today." The input in this step is text data entered by the user into the device, and the output is that same text data.

[0900] Step 2:

[0901] The terminal receives input and sends it to the server. The terminal receives the string data entered by the user, encodes it into an appropriate format such as JSON, and sends it to the server. In this step, the input is the text data entered by the user, and the output is the encoded text data sent to the server.

[0902] Step 3:

[0903] The server analyzes the text to identify emotions and intentions. The server uses an emotion recognition engine to analyze the received string data and identify the user's emotions and intentions. Specifically, it uses a natural language processing library (TextBlob) to segment the text data and tag it with parts of speech. As a result of the analysis, it identifies an emotion such as "tired." The input for this step is text data sent from the terminal, and the output is the analysis result that identifies emotions and intentions.

[0904] Step 4:

[0905] The text is modified or completed based on the analysis results. The server generates modified or completed text based on the identified emotions or intentions. For example, if the emotion "tired" is identified, the original input is completed to "I am tired today (I feel tired)." The input for this step is the analysis results and the original text data, and the output is the modified or completed text data.

[0906] Step 5:

[0907] The corrected text is returned to the terminal and displayed. The server returns the corrected or completed text data to the terminal. The terminal displays the received corrected or completed text data to the user. The user reviews and uses it appropriately. The input for this step is the corrected or completed text data, and the output is the text data displayed on the terminal.

[0908] Step 6:

[0909] The server sends alerts to the administrator as needed. Based on the analysis results, the server sends warnings and notifications to the administrator when necessary. For example, if a user is unwell, the server notifies the administrator of this information. The input for this step is the analysis results, and the output is the alert information sent to the administrator.

[0910] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0911] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0912] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0913] [Third Embodiment]

[0914] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0915] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0916] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0917] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0918] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0919] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0920] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0921] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0922] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0923] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0924] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0925] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0926] This invention is a system that enables individuals with intellectual disabilities or other conditions to express their feelings and thoughts clearly and to support smooth communication. The embodiments of this invention will be described in detail below.

[0927] System Configuration

[0928] This system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication network that connects these two.

[0929] Overall Operation Overview

[0930] 1. The user enters their feelings.

[0931] Users input their feelings and thoughts into the device. For example, they might input a sentence like, "I'm tired today, but I feel a sense of accomplishment."

[0932] 2. The terminal receives input and sends it to the server.

[0933] The terminal receives text data entered by the user and sends it to the server. The data is encoded in an appropriate format, such as JSON.

[0934] 3. The server analyzes and identifies emotions and intentions.

[0935] The server analyzes the received data, uses natural language processing to segment the text, and tags each word by its part of speech. Then, it uses sentiment analysis algorithms to identify emotions and intentions such as "tired" or "feeling accomplished."

[0936] 4. The server corrects and completes the text.

[0937] Based on identified emotions and intentions, the server modifies or completes the entered text. For example, it might be transformed into a clearer sentence such as, "I'm very tired today, but I feel a great sense of accomplishment."

[0938] 5. The server sends the corrected text back to the terminal.

[0939] The server returns the corrected or completed text to the terminal.

[0940] 6. The device displays organized text.

[0941] The device displays the received message to the user, allowing the user to confirm it. This enables the user to communicate their intentions to others more accurately.

[0942] Specific examples

[0943] User scenario:

[0944] User: After returning home from school, they type into their device, "I want to hang out with my friends, but I don't know how to ask them."

[0945] Terminal: Receives user input and sends it to the server.

[0946] server:

[0947] 1. Analyze the input text and identify two intentions: "I want to hang out with my friends" and "I don't know how to ask them out."

[0948] 2. Based on these intentions, revise and supplement the sentence to: "I want to hang out with my friends, but I don't know how to ask them."

[0949] Server: Sends the corrected text back to the terminal.

[0950] Terminal: Displays the corrected text to the user.

[0951] User: Check the message and contact your friend appropriately.

[0952] This system helps users express their feelings and thoughts accurately, thereby improving their quality of life.

[0953] The following describes the processing flow.

[0954] Step 1:

[0955] Users input their feelings and thoughts into the device. They can input free-form text, including individual words and short sentences.

[0956] Step 2:

[0957] The terminal receives the entered text. For processing, the text data is converted to an appropriate format, such as JSON format.

[0958] Step 3:

[0959] The device sends text data to the server. The data transmission is performed using an HTTP request.

[0960] Step 4:

[0961] The server receives the HTTP request and extracts the text data for analysis. It then verifies that the data is in the correct format.

[0962] Step 5:

[0963] The server uses a natural language processing (NLP) engine to analyze text data. This analysis includes word segmentation and part-of-speech tagging.

[0964] Step 6:

[0965] The server applies sentiment analysis algorithms to identify the emotions and intentions contained in the text data. For example, it can identify intentions such as "I want to hang out with my friends" or "I don't know how to ask them out."

[0966] Step 7:

[0967] The server modifies or completes the input text data based on the results of identifying emotions and intentions. For example, it might generate a more polished sentence like, "I want to hang out with my friends, but I don't know how to ask them."

[0968] Step 8:

[0969] The server generates the corrected or completed text data as a response in JSON format.

[0970] Step 9:

[0971] The server sends a response to the terminal. This transmission occurs via an HTTP response.

[0972] Step 10:

[0973] The terminal receives a response from the server and retrieves the corrected or completed text data.

[0974] Step 11:

[0975] The device displays corrected or completed text data to the user. The output is displayed on the screen in a user-friendly format.

[0976] Step 12:

[0977] Users can review the organized text displayed and share it with others as needed. For example, they might use it to contact a friend.

[0978] (Example 1)

[0979] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0980] This invention relates to a system for supporting individuals with intellectual disabilities and other conditions in clearly expressing their emotions and thoughts and communicating smoothly. Conventional systems sometimes made it difficult for users to accurately express their emotions and intentions, leading to communication problems. The challenge is to solve this problem and enable users to communicate their emotions and thoughts simply and clearly.

[0981] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0982] In this invention, the server includes means for receiving text data containing emotions and intentions entered by a user; means for encoding the text data and transferring it via a communication network; means for analyzing the received text data and identifying emotions and intentions using a natural language processing system; means for using a generative AI model to modify or supplement the input text data based on the identified emotions and intentions; means for sending the modified or supplemented text data back to the terminal via the communication network to notify the user; and means for displaying the returned text data to the user. This makes it easier for users to clearly express their emotions and intentions and enables smooth communication with others.

[0983] A "user" refers to an individual who uses a system to express their own emotions or intentions.

[0984] "Text data" refers to data consisting of natural language sentences and words entered by the user.

[0985] A "terminal" refers to a device used by a user to input text data and communicate with a server. Specifically, this includes smartphones, tablets, and personal computers.

[0986] A "server" refers to a computing device that analyzes received text data and uses a generative AI model to correct or complete it.

[0987] A "communication network" refers to the network infrastructure used to send and receive data between terminals and servers. Specifically, it refers to the internet and local networks.

[0988] "Encoding" refers to the process of converting information, such as text data, into a specific format (for example, JSON format).

[0989] "Natural language processing methods" refer to technical means for analyzing text data and identifying emotions and intentions. Specifically, this involves processes such as text segmentation and part-of-speech tagging.

[0990] A "sentiment analysis algorithm" refers to an algorithm used to identify a user's emotions and intentions from text data.

[0991] A "generative AI model" refers to an artificial intelligence model that generates new text based on input text data. Specifically, it refers to models such as GPT-3.

[0992] "Correction and supplementation" refers to the process of transforming input text data into a clearer and more understandable form based on identified emotions and intentions.

[0993] "Display" refers to showing the corrected or completed text data on the device's screen in a format that the user can review.

[0994] This invention relates to a system that enables individuals with intellectual disabilities to express their feelings and thoughts clearly and communicate smoothly. The system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication network connecting the two.

[0995] 1. System Configuration

[0996] This system is comprised of the following hardware and software.

[0997] Terminal: Input devices such as smartphones, tablets, and personal computers.

[0998] Server: A computing device that performs analysis and data processing. Specifically, it uses Python libraries such as NLTK and spaCy, the TextBlob sentiment analysis library, and the GPT-3 generative AI model.

[0999] Communication network: Internet or local network.

[1000] 2. System Operation

[1001] Text data entered by the user into the device is first encoded into JSON format by the device and sent to the server. The server analyzes the received text data, uses natural language processing tools (NLTK and spaCy) to segment the text, and tags each word with its part of speech. Furthermore, it uses a sentiment analysis library (TextBlob) to identify emotions and intentions such as "tired" or "feeling accomplished."

[1002] Based on identified emotions and intentions, the server uses a generative AI model (GPT-3) to modify or supplement the text data. The modified text is then re-encoded in JSON format and sent back to the terminal via the communication network. The terminal decodes the received modified data and displays it to the user.

[1003] 3. Specific examples

[1004] For example, if a user enters "I want to hang out with my friends, but I don't know how to invite them," the following process will be executed:

[1005] 1. The user enters "I want to hang out with my friends, but I don't know how to invite them" into their device.

[1006] 2. The terminal receives input and sends it to the server.

[1007] 3. The server analyzes the text data and identifies two intentions: "I want to play with my friends" and "I don't know how to invite them."

[1008] 4. The server enters the following prompt into the generated AI model:

[1009] The user typed, "I want to hang out with my friends, but I don't know how to ask them." Please rephrase this sentence to be more specific and easier to understand:

[1010] 5. The AI ​​generation model generates the corrected text: "I want to hang out with my friends, but I don't know how to ask them."

[1011] 6. The server returns the corrected text to the terminal.

[1012] 7. The device displays the corrected text to the user.

[1013] In this way, users can express their feelings and thoughts more clearly and communicate more smoothly with others.

[1014] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1015] Step 1:

[1016] Users enter text data that includes emotions and intentions.

[1017] The user inputs text data expressing their feelings and thoughts into their device (smartphone, tablet, or PC). For example, they might input, "I'm tired today, but I feel a sense of accomplishment."

[1018] Input: Text input by the user.

[1019] Output: Input text data ("I'm tired today, but I feel a sense of accomplishment").

[1020] Step 2:

[1021] The terminal receives input and sends it to the server.

[1022] The terminal receives user input, encodes it in JSON format, and sends it.

[1023] Specific operation: The terminal encodes the data in the following format:

[1024] json

[1025] {

[1026] "text": "I'm tired today, but I feel a sense of accomplishment."

[1027] }

[1028] The encoded data is then sent to the server.

[1029] Input: Text data entered by the user.

[1030] Output: Text data encoded in JSON format.

[1031] Step 3:

[1032] The server receives and analyzes the input data.

[1033] The server parses the JSON-formatted text data received from the terminal. It uses natural language processing tools (NLTK, spaCy) to segment the text and tag its parts of speech.

[1034] Specific operation: The server parses the text as follows:

[1035] python

[1036] import spacy

[1037] nlp = spacy.load("ja_core_news_md")

[1038] doc = nlp("I'm tired today, but I feel a sense of accomplishment")

[1039] for token in doc:

[1040] print(token.text, token.pos_)

[1041] This allows us to identify words and their parts of speech, such as "today," "tired," "but," "sense of accomplishment," "is," and "there is."

[1042] Input: Encoded text data.

[1043] Output: Analysis results (text data tagged with parts of speech).

[1044] Step 4:

[1045] The server identifies emotions and intentions.

[1046] The server uses an emotion analysis algorithm (e.g., TextBlob) to identify emotions and intentions from the analysis results.

[1047] Specific operation: The server analyzes emotions as follows:

[1048] python

[1049] from textblob import TextBlob

[1050] analysis = TextBlob("I'm tired today, but I feel a sense of accomplishment")

[1051] sentiment = analysis.sentiment

[1052] The analysis results identify emotions such as "tired" and "a sense of accomplishment."

[1053] Input: Text data tagged with parts of speech.

[1054] Output: Identified emotions and intentions.

[1055] Step 5:

[1056] The server uses a generated AI model to modify or supplement text data.

[1057] The server inputs prompt text into the generative AI model (GPT-3) and modifies or completes the text data.

[1058] Specific action: The server will enter the following prompt:

[1059] The user typed, "I'm tired today, but I feel a sense of accomplishment." Please revise this sentence to make it clearer and easier to understand:

[1060] The generative AI model outputs the corrected text "I'm very tired today, but I feel a great sense of accomplishment" as its response.

[1061] Input: Identified emotion and intention, prompt sentence.

[1062] Output: Modified text data.

[1063] Step 6:

[1064] The server sends the corrected text data back to the terminal.

[1065] The server encodes the modified text data into JSON format and sends it back to the terminal.

[1066] Specific operation: The server encodes the modified text data in the following format:

[1067] json

[1068] {

[1069] "text": "I'm very tired today, but I feel a great sense of accomplishment."

[1070] }

[1071] The encoded data is then sent to the terminal.

[1072] Input: Modified text data.

[1073] Output: Modified text data encoded in JSON format.

[1074] Step 7:

[1075] The device receives the corrected text data and displays it to the user.

[1076] The terminal decodes the JSON data received from the server and displays it to the user.

[1077] Specific action: The terminal performs the decoding process and displays the following text on the screen:

[1078] I'm very tired today, but I feel a great sense of accomplishment.

[1079] Input: Modified text data encoded in JSON format.

[1080] Output: The corrected text data displayed to the user.

[1081] (Application Example 1)

[1082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1083] This initiative aims to address the difficulties individuals with intellectual disabilities face in clearly expressing their feelings and thoughts and communicating safely with those around them. Furthermore, it supports guardians and caregivers in taking appropriate actions quickly, thereby ensuring the safety and security of individuals with intellectual disabilities.

[1084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1085] In this invention, the server includes means for receiving input text data, means for natural language processing, means for modifying or supplementing the input text data based on emotions and intentions, means for outputting the modified or supplemented text data, means for notifying the user of anomalies to ensure user safety, and means for generating safety confirmation messages and providing feedback to the user. This makes it possible to ensure the user's safety and peace of mind by clearly expressing the user's feelings and thoughts and providing appropriate feedback.

[1086] "Means for receiving text data" refers to a device or software that electronically receives text data entered by a user.

[1087] "Natural language processing means" refers to algorithms and software used to analyze received text data and identify emotions and intentions.

[1088] "Means for modifying or supplementing input text data" refers to devices or software that transform the original text data into an appropriate form based on identified emotions or intentions.

[1089] "Means for outputting corrected or supplemented text data" refers to a device or software for presenting corrected or supplemented text data to a user.

[1090] "Means of notifying of abnormalities" refers to devices or software that detect unusual situations or emotions and notify guardians or caregivers in order to ensure the user's safety.

[1091] "Means for generating safety confirmation messages and providing feedback" refers to devices or software that provide safety-related feedback and prompt necessary actions based on user input.

[1092] This invention is a system that enables individuals with intellectual disabilities to express their feelings and thoughts clearly and communicate safely with those around them. The system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication means that connects these two.

[1093] System Configuration

[1094] 1. Terminal

[1095] This is a device for users to input their feelings and circumstances. Smartphones and smart glasses are used as terminals. The terminal has an input interface, allowing users to input text data.

[1096] 2. Server

[1097] The system analyzes received text data to identify emotions and intentions, and then modifies or supplements the data accordingly. Furthermore, it detects anomalies as needed and notifies parents and caregivers. It also generates safety confirmation messages and provides feedback. The server has natural language processing libraries (e.g., TextBlob) and communication libraries (e.g., requests) installed.

[1098] Overall Operation Overview

[1099] 1. The user enters their feelings or situation.

[1100] The user enters their current feelings or situation into the device. For example, they might enter a sentence like, "I'm in the park right now, but I feel a little scared."

[1101] 2. The terminal receives input and sends it to the server.

[1102] The terminal receives text data entered by the user and sends it to the server. The data is encoded in JSON format.

[1103] 3. The server analyzes and identifies emotions and intentions.

[1104] To analyze the received data, the server uses natural language processing to segment the text data and tag it by part of speech. Then, it uses sentiment analysis algorithms to identify emotions such as "scared" and situations such as "being in a park."

[1105] 4. The server modifies and supplements the content based on emotions and circumstances, and sends notifications to confirm safety.

[1106] Based on identified emotions and circumstances, the system modifies or completes the entered text. Furthermore, it uses means to notify parents or caregivers of the situation. For example, it might generate a message such as, "I'm in the park right now, and I'm feeling a little scared. Please move to a safe place or ask for help."

[1107] 5. The server sends the corrected text and feedback back to the terminal.

[1108] The server returns the corrected or supplemented text and a security confirmation message to the terminal.

[1109] 6. The device displays organized text and feedback.

[1110] The device displays received text and messages to the user, enabling the user to respond appropriately.

[1111] Specific example

[1112] User scenario:

[1113] User: Feeling uneasy in the park, they type "I'm in the park right now, and I feel a little scared" into their device.

[1114] Terminal: Receives user input and sends it to the server.

[1115] server:

[1116] 1. Analyze the input text to identify the emotions and situations: "scared" and "in a park."

[1117] 2. Based on these feelings and circumstances, revise and complete the sentence to generate the feedback: "I'm in the park right now, and I'm feeling a little scared. I should move to a safe place or ask for help."

[1118] Server: Sends the corrected text and feedback back to the terminal.

[1119] Terminal: Displays the corrected text and feedback to the user.

[1120] User: Review the feedback and take appropriate action.

[1121] Example of a prompt

[1122] If a user enters "I'm in the park right now, but I feel a little scared," the app performs sentiment analysis and detects anxiety. The server then provides feedback such as, "Ask someone for help or move to a safe place."

[1123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1124] Step 1:

[1125] The user enters their feelings and situation.

[1126] The user uses their device to input their current feelings and situation. Specifically, they enter text data such as, "I'm in the park right now, but I feel a little scared," into the input field. This input is done using the device's keyboard or voice input function.

[1127] Step 2:

[1128] The terminal receives input and sends it to the server.

[1129] The terminal receives text data entered by the user. This received text data is encoded into a format such as JSON and sent to the server. Here, the input is the text data entered by the user, and the output is the encoded data.

[1130] Step 3:

[1131] The server analyzes the text data to identify emotions and intentions.

[1132] The server uses a natural language processing library (e.g., TextBlob) to analyze the received text data. The server splits the text data and tags it by part of speech. Then, it applies a sentiment analysis algorithm to identify the emotion "scared" and the intention "in the park." This transforms the input text data into data tagged with emotion and intention.

[1133] Step 4:

[1134] The server modifies and supplements text data based on emotions and context.

[1135] The server modifies or completes the input text data based on identified emotions and intentions. For example, it might be rewritten to say, "I'm in a park right now, and I'm feeling a little scared. I should move to a safe place or ask for help." The input for this step is emotion and intention tagged data, and the output is the modified or completed text data.

[1136] Step 5:

[1137] The server detects an anomaly and notifies parents or caregivers.

[1138] The server detects anomalies based on emotions and circumstances. For example, if the emotion "fear" is detected, it notifies parents or caregivers via email or text message. The input for this step is the emotion analysis result, and the output is the notification message.

[1139] Step 6:

[1140] The server generates the corrected text and sends it back to the terminal.

[1141] The server returns the corrected or completed text to the terminal. This returned data is encoded in an appropriate format for the user. The input for this step is the corrected or completed text data, and the output is the encoded data.

[1142] Step 7:

[1143] The device displays organized text and feedback.

[1144] The terminal displays the corrected or completed text data received from the server to the user. The user reviews the displayed data and takes appropriate action. The input for this step is the encoded data returned from the server, and the output is the organized text and feedback displayed to the user.

[1145] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1146] This invention is a system that combines an emotion engine that recognizes the user's emotions in order to enable individuals with intellectual disabilities or other conditions to express their feelings and thoughts clearly and to communicate smoothly. Embodiments of this invention will be described in detail below.

[1147] System Configuration

[1148] This system consists of a terminal used by the user, a server that analyzes and processes text data, an emotion engine that recognizes the user's emotions, and a communication network that connects the two.

[1149] Overall Operation Overview

[1150] 1. The user enters their feelings.

[1151] The user inputs their feelings and thoughts into the device. For example, they might input a sentence like, "I'm tired today, but I feel a sense of accomplishment."

[1152] 2. The terminal receives input and sends it to the server.

[1153] The terminal receives text data entered by the user and sends it to the server. The data is encoded in an appropriate format, such as JSON.

[1154] 3. The server analyzes and utilizes the emotion engine.

[1155] The server analyzes the received data, uses natural language processing (NLP) techniques to segment the text, and tags each word with its part of speech.

[1156] The system utilizes an emotion engine to recognize the user's emotions and intentions from the input text. The analysis uses algorithms that identify the user's emotions in real time.

[1157] 4. Modify and supplement the text based on sentiment analysis.

[1158] Based on identified emotions and intentions, the server modifies or completes the entered text. For example, "I'm very tired today, but I feel a great sense of accomplishment."

[1159] 5. The server sends the corrected text back to the terminal.

[1160] The server returns the corrected or completed text to the terminal.

[1161] 6. The device displays organized text.

[1162] The device displays the received message to the user, allowing the user to confirm it. This enables the user to communicate their intentions to others more accurately.

[1163] Specific examples

[1164] User scenario:

[1165] User: After returning home from school, they type into their device, "I want to hang out with my friends, but I don't know how to ask them."

[1166] Terminal: Receives user input and sends it to the server.

[1167] server:

[1168] 1. Analyze the input text and identify two intentions: "I want to hang out with my friends" and "I don't know how to ask them out."

[1169] 2. Use an emotion engine to analyze the user's emotions in real time and refer to past emotion history to more accurately identify emotions.

[1170] 3. Based on these intentions, revise and supplement the sentence to: "I want to hang out with my friends, but I don't know how to ask them."

[1171] Server: Sends the corrected text back to the terminal.

[1172] Terminal: Displays the corrected text to the user.

[1173] User: Check the message and contact your friend appropriately.

[1174] This system utilizes an emotion engine to more accurately understand users' emotions and support appropriate communication based on those emotions. This allows users to express their feelings and thoughts precisely, improving their quality of life.

[1175] The following describes the processing flow.

[1176] Step 1:

[1177] The user enters their feelings or thoughts into the device. For example, they might enter text like, "I want to hang out with my friends, but I don't know how to ask them."

[1178] Step 2:

[1179] The terminal receives the entered text. The received text data is converted to an appropriate format, such as JSON format.

[1180] Step 3:

[1181] The terminal sends formatted text data to the server. Data transmission is performed using an HTTP request.

[1182] Step 4:

[1183] The server receives the HTTP request and extracts the text data for parsing. It then verifies that the data is in the correct format.

[1184] Step 5:

[1185] The server uses a natural language processing (NLP) engine to analyze the text data. The analysis includes the following steps:

[1186] This process divides text data into individual words (word segmentation).

[1187] Add part-of-speech tags to each word (part-of-speech tagging).

[1188] Step 6:

[1189] The server uses an emotion engine to identify the user's emotions and intentions from text data. Specifically, the following processes are performed:

[1190] The emotion engine extracts emotional keywords (e.g., tired, happy) from the input text data.

[1191] Refer to past emotional data to check your current emotional state.

[1192] Step 7:

[1193] The server modifies or completes the input text data based on identified emotions and intentions. For example, it might generate a sentence like, "I want to hang out with my friends, but I don't know how to ask them."

[1194] Step 8:

[1195] The server constructs the modified or completed text data as a response in JSON format.

[1196] Step 9:

[1197] The server sends a response to the terminal. This transmission occurs via an HTTP response.

[1198] Step 10:

[1199] The terminal receives a response from the server and retrieves the corrected or completed text data.

[1200] Step 11:

[1201] The device displays corrected or completed text data to the user. For example, "I want to hang out with my friends, but I don't know how to ask them" might appear on the screen.

[1202] Step 12:

[1203] The user reviews the organized text displayed and communicates it to others as needed. For example, they can appropriately contact a friend.

[1204] (Example 2)

[1205] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1206] The aim is to solve the problem that individuals with intellectual disabilities and other conditions face in accurately expressing their emotions and thoughts and engaging in smooth communication. Conventional technologies have been insufficient in analyzing input text data and recognizing emotions, making it difficult to generate text that accurately reflects the user's intentions and feelings. As a result, users have been unable to improve the quality of their communication and have difficulty having satisfying conversations.

[1207] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1208] In this invention, the server includes means for encoding input text data and transmitting it over a network, means for dividing the text data and tagging parts of speech, means for identifying emotions and intentions using an emotion recognition engine, and means for modifying or supplementing the text data based on the identified emotions and intentions. This makes it possible to modify and supplement text that accurately reflects the user's emotions and intentions based on the input information.

[1209] "Text data" refers to data in the form of text that users input, including their emotions and intentions.

[1210] "Means of receiving" refers to the means by which a terminal receives and processes text data entered by the user.

[1211] "Encoding" refers to a method of converting received text data into an appropriate data format (e.g., JSON format) so that it can be transmitted over a network.

[1212] "Network" refers to the entire communication infrastructure used to send and receive data between terminals and servers, and includes the internet and local area networks (LANs).

[1213] The "method of splitting" refers to a method of dividing the input text data into individual words and tagging each word with its part of speech.

[1214] "Part-of-speech tagging" is the process of assigning a part of speech (noun, verb, adjective, etc.) to each of the divided words.

[1215] An "emotion recognition engine" is a program that includes an algorithm to identify a user's emotions (positive, negative, etc.) and intentions from input text data.

[1216] "Means for identifying emotions and intentions" refers to methods that use an emotion recognition engine to analyze and identify a user's emotional state and intentions from text data.

[1217] "Means of correction or supplementation" refers to means of modifying the original text data or supplementing missing information to properly organize a text, based on identified sentiments or intentions.

[1218] "Output method" refers to a means of sending the corrected or supplemented text data back to the terminal and displaying it to the user.

[1219] "Real-time processing methods" refer to methods for performing text data analysis and sentiment recognition instantly and without delay.

[1220] "Means for automatically generating appropriate text" refers to means of generating natural and accurate text based on analyzed and corrected text data and providing it to the user.

[1221] This invention is a system for enabling individuals with intellectual disabilities to express their emotions and intentions more accurately and to communicate more smoothly. This system includes techniques for appropriately analyzing input text data, recognizing emotions, and modifying or supplementing the text data based on those emotions. Embodiments of this invention are described in detail below.

[1222] System Configuration

[1223] The system consists of the following hardware and software:

[1224] Hardware:

[1225] Devices: Smartphones, tablets, personal computers, etc.

[1226] Servers: Cloud servers and physical servers

[1227] software:

[1228] Natural Language Processing (NLP) libraries: spaCy, NLTK, etc.

[1229] Emotion recognition engine: Emotion analysis tool, such as IBM Watson Tone Analyzer.

[1230] Communication network: Internet or local area network (LAN)

[1231] Specific Operation Overview

[1232] 1. The user inputs their feelings or thoughts into the device. For example, they might input, "I want to hang out with my friends, but I don't know how to ask them."

[1233] 2. The terminal receives the input text data, encodes it in JSON format, and sends it to the server.

[1234] 3. The server analyzes the received text data using a natural language processing library, splitting the text into individual words and tagging them by part of speech.

[1235] 4. The server uses an emotion recognition engine to identify the user's emotions and intentions from the text data. Specifically, it identifies emotions such as positive and negative.

[1236] 5. The server modifies or completes the entered text data based on the identified emotions or intentions. For example, it might modify it to "I want to hang out with my friends, but I don't know how to ask them."

[1237] 6. The server sends the corrected or completed text data back to the terminal.

[1238] 7. The device displays the corrected text to the user, allowing the user to review it.

[1239] Examples of specific actions

[1240] The following are specific scenarios in which users may use the system:

[1241] User: After returning home from school, they type "I want to hang out with my friends, but I don't know how to ask them" into their device.

[1242] Terminal: Receives the input text data, encodes it, and sends it to the server.

[1243] server:

[1244] The text is analyzed, split into individual words, and then tagged with parts of speech.

[1245] Using an emotion recognition engine, the system identifies intentions such as "I want to hang out with my friends" and "I don't know how to ask them," and then identifies the emotions behind those intentions.

[1246] Revise the text to "I want to hang out with my friends, but I don't know how to ask them."

[1247] Server: Sends the corrected text data back to the terminal.

[1248] Terminal: Displays the corrected text to the user.

[1249] User: Check the displayed message and contact your friend appropriately.

[1250] Example of a prompt

[1251] The following are examples of prompts used to test emotion recognition engines and natural language processing technologies:

[1252] Prompt message:

[1253] The user typed, "I want to hang out with my friends, but I don't know how to ask them." Analyze this text, recognize the sentiment, and correct / complete it as follows: "I want to hang out with my friends, but I don't know how to ask them."

[1254] In this way, the system can analyze the user's emotions and intentions, and support accurate communication.

[1255] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1256] Step 1:

[1257] The user enters text.

[1258] The user inputs their feelings and thoughts into the device. For example, they might input, "I want to hang out with my friends, but I don't know how to ask them."

[1259] Input: User's text input

[1260] Output: Input text data on the terminal

[1261] Specific action: The user enters their feelings or thoughts in text format into an input form on the device.

[1262] Step 2:

[1263] The terminal receives input, encodes it, and sends it to the server.

[1264] The terminal receives text data entered by the user, encodes it into an appropriate format such as JSON, and sends it to the server.

[1265] Input: User's text input data

[1266] Output: Encoded text data (Example: {"text": "I want to hang out with my friends, but I don't know how to ask them"})

[1267] Specific operation: The terminal program receives the input text, encodes it, and sends it to the server via the communication network.

[1268] Step 3:

[1269] The server parses the text data.

[1270] The server analyzes the received text data using a natural language processing library. It divides the text into words and tags each word with its part of speech.

[1271] Input: Encoded text data

[1272] Output: Split and part-of-speech tagged data (e.g., "friend / noun with / particle want to play / verb but / conjunction how to invite / noun but / particle don't know / verb")

[1273] Specific operation: The server parses the received JSON data and uses a natural language processing library to split and tag parts of speech.

[1274] Step 4:

[1275] The server uses an emotion recognition engine to identify emotions and intentions.

[1276] The server uses an emotion recognition engine to identify the user's emotions and intentions from the segmented text data. It identifies emotions such as positive and negative.

[1277] Input: Split and part-of-speech tagged data

[1278] Output: Data with identified emotions and intentions (e.g., "Positive / I want to hang out with friends," "Negative / I don't know how to ask them out")

[1279] Specific operation: The emotion recognition engine analyzes text data to identify the user's emotions and intentions in real time. Specifically, it uses tools such as IBM Watson Tone Analyzer.

[1280] Step 5:

[1281] The server modifies and completes the text.

[1282] The server modifies or completes the entered text data based on identified emotions and intentions. For example, it might revise it to "I want to hang out with my friends, but I don't know how to ask them."

[1283] Input: Data with identified emotions and intentions

[1284] Output: Modified or completed text data (e.g., "I want to hang out with my friends, but I don't know how to ask them.")

[1285] Specific operation: The server processes the input text by correcting and completing it in a natural way based on the identified emotions and intentions.

[1286] Step 6:

[1287] The server sends the corrected text back to the terminal.

[1288] The server re-encodes the corrected or completed text data in JSON format or another suitable format and sends it back to the terminal.

[1289] Input: Modified or completed text data

[1290] Output: Encoded data sent to the terminal (e.g., {"text": "I want to hang out with my friends, but I don't know how to ask them"})

[1291] Specific operation: The server encodes the modified text data and sends it to the terminal via the communication network.

[1292] Step 7:

[1293] The device displays the corrected text to the user.

[1294] The terminal displays the received, corrected text data to the user. The user can then review the displayed text.

[1295] Input: Encoded modified text data

[1296] Output: The corrected text displayed on the device (Example: "I want to hang out with my friends, but I don't know how to ask them.")

[1297] Specific action: The corrected text data is displayed on the device's screen and presented in a user-friendly format.

[1298] (Application Example 2)

[1299] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1300] There is a problem in that employees with intellectual disabilities have difficulty adequately communicating their physical condition and feelings within the factory. As a result, appropriate support and feedback may not be provided promptly by factory managers. Furthermore, there is a lack of effective means to accurately understand employees' emotions and intentions and to facilitate smooth communication based on that understanding.

[1301] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving input string data, means for analyzing the string data and identifying emotions and intentions, means for modifying or supplementing the input string data based on the identified emotions and intentions, means for outputting the modified or supplemented string data, means for the user to input their feelings and physical condition into a terminal, means for supplementing the text based on the analysis results and displaying it to the user, and means for sending alerts to the administrator as needed. This makes it possible for employees with intellectual disabilities to appropriately communicate their physical condition and emotions, enabling smooth communication and prompt support within the factory.

[1302] "Input string data" refers to text data that users input using their devices, including information such as their feelings and physical condition.

[1303] "Natural language processing means" refers to computer tools that analyze string data entered by a user and identify emotions and intentions from it. Specifically, this includes means for splitting text data and tagging parts of speech.

[1304] "Emotions and intentions" refer to the user's feelings and thoughts, extracted from the string data entered by the user.

[1305] "Means of modification or supplementation" refers to computational means that transform input string data into an appropriate form or provide additional information based on identified emotions or intentions.

[1306] "Modified or completed string data" refers to text data that has been appropriately modified or completed from the original input based on the results of sentiment and intent analysis.

[1307] "Means for users to input their feelings and physical condition into a device" refers to methods that allow employees with intellectual disabilities to input their feelings and physical condition in text using devices such as tablets or smart glasses.

[1308] "Means of completing and displaying text to the user based on analysis results" refers to means of displaying text data that has been modified or completed based on the analysis results of emotions and intentions to the user.

[1309] "Means of sending alerts to administrators" refers to means of issuing warnings or notifications to factory administrators based on the results of emotion and intent analysis, as needed.

[1310] The present invention is a system that helps employees with intellectual disabilities appropriately communicate their physical condition and feelings within a factory. The system includes means for receiving input string data, natural language processing means for analyzing the string data and identifying emotions and intentions, means for modifying or completing the string data based on the identified emotions and intentions, means for outputting the modified or completed data, means for inputting feelings and physical condition using a terminal, means for completing sentences based on the analysis results and displaying them to the user, and means for sending alerts to administrators as needed.

[1311] Hardware and software to be used

[1312] The following hardware and software will be used to implement the system.

[1313] Hardware:

[1314] Device (tablet or smart glasses): A device used by the user to input their feelings and physical condition.

[1315] Factory robot: The main device that runs the emotional support program.

[1316] software:

[1317] Python: The main programming language used.

[1318] TextBlob: Used as a natural language processing library, it performs basic text analysis and part-of-speech tagging.

[1319] Requests: A library for sending HTTP requests.

[1320] Emotion recognition engine: Executes an algorithm to identify the user's emotions from text data.

[1321] System operation

[1322] 1. The user enters their feelings and physical condition:

[1323] Users input their feelings and physical condition as text data using a tablet or smart glasses.

[1324] 2. The terminal receives input and sends it to the server:

[1325] The terminal receives the entered string data and sends it to the server.

[1326] 3. The server analyzes the text and identifies sentiment and intent:

[1327] The server uses an emotion recognition engine to analyze the text and identify the user's emotions and intentions.

[1328] The analysis involves using TextBlob to segment the input data and then tagging it with parts of speech.

[1329] 4. Modify and supplement the text based on the analysis results:

[1330] Based on identified emotions and intentions, the server modifies or supplements the original input data.

[1331] 5. Send the corrected text back to the device and display it:

[1332] The server sends the corrected text back to the terminal, which then displays it to the user.

[1333] Users can review the corrected text.

[1334] 6. Send an alert to the administrator if necessary:

[1335] If necessary, an alert will be sent to the factory manager based on the analysis results.

[1336] Specific example

[1337] If a user types "I'm tired today," the system analyzes it and displays the completed text "(I feel tired)" to the user. It can also send an alert to administrators that "an employee is tired."

[1338] Example of a prompt

[1339] Example input: I'm tired today.

[1340] Example output: I am tired today (I feel tired).

[1341] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1342] Step 1:

[1343] The user inputs their feelings and physical condition. The user inputs their feelings and physical condition in text format into a tablet or smart glasses. For example, they might input a sentence like, "I'm tired today." The input in this step is text data entered by the user into the device, and the output is that same text data.

[1344] Step 2:

[1345] The terminal receives input and sends it to the server. The terminal receives the string data entered by the user, encodes it into an appropriate format such as JSON, and sends it to the server. In this step, the input is the text data entered by the user, and the output is the encoded text data sent to the server.

[1346] Step 3:

[1347] The server analyzes the text to identify emotions and intentions. The server uses an emotion recognition engine to analyze the received string data and identify the user's emotions and intentions. Specifically, it uses a natural language processing library (TextBlob) to segment the text data and tag it with parts of speech. As a result of the analysis, it identifies an emotion such as "tired." The input for this step is text data sent from the terminal, and the output is the analysis result that identifies emotions and intentions.

[1348] Step 4:

[1349] The text is modified or completed based on the analysis results. The server generates modified or completed text based on the identified emotions or intentions. For example, if the emotion "tired" is identified, the original input is completed to "I am tired today (I feel tired)." The input for this step is the analysis results and the original text data, and the output is the modified or completed text data.

[1350] Step 5:

[1351] The corrected text is returned to the terminal and displayed. The server returns the corrected or completed text data to the terminal. The terminal displays the received corrected or completed text data to the user. The user reviews and uses it appropriately. The input for this step is the corrected or completed text data, and the output is the text data displayed on the terminal.

[1352] Step 6:

[1353] The server sends alerts to the administrator as needed. Based on the analysis results, the server sends warnings and notifications to the administrator when necessary. For example, if a user is unwell, the server notifies the administrator of this information. The input for this step is the analysis results, and the output is the alert information sent to the administrator.

[1354] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1355] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1356] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1357] [Fourth Embodiment]

[1358] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1359] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1360] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1361] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1362] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1363] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1364] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1365] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1366] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1367] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1368] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1369] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1370] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1371] This invention is a system that enables individuals with intellectual disabilities or other conditions to express their feelings and thoughts clearly and to support smooth communication. The embodiments of this invention will be described in detail below.

[1372] System Configuration

[1373] This system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication network that connects these two.

[1374] Overall Operation Overview

[1375] 1. The user enters their feelings.

[1376] Users input their feelings and thoughts into the device. For example, they might input a sentence like, "I'm tired today, but I feel a sense of accomplishment."

[1377] 2. The terminal receives input and sends it to the server.

[1378] The terminal receives text data entered by the user and sends it to the server. The data is encoded in an appropriate format, such as JSON.

[1379] 3. The server analyzes and identifies emotions and intentions.

[1380] The server analyzes the received data, uses natural language processing to segment the text, and tags each word by its part of speech. Then, it uses sentiment analysis algorithms to identify emotions and intentions such as "tired" or "feeling accomplished."

[1381] 4. The server corrects and completes the text.

[1382] Based on identified emotions and intentions, the server modifies or completes the entered text. For example, it might be transformed into a clearer sentence such as, "I'm very tired today, but I feel a great sense of accomplishment."

[1383] 5. The server sends the corrected text back to the terminal.

[1384] The server returns the corrected or completed text to the terminal.

[1385] 6. The device displays organized text.

[1386] The device displays the received message to the user, allowing the user to confirm it. This enables the user to communicate their intentions to others more accurately.

[1387] Specific examples

[1388] User scenario:

[1389] User: After returning home from school, they type into their device, "I want to hang out with my friends, but I don't know how to ask them."

[1390] Terminal: Receives user input and sends it to the server.

[1391] server:

[1392] 1. Analyze the input text and identify two intentions: "I want to hang out with my friends" and "I don't know how to ask them out."

[1393] 2. Based on these intentions, revise and supplement the sentence to: "I want to hang out with my friends, but I don't know how to ask them."

[1394] Server: Sends the corrected text back to the terminal.

[1395] Terminal: Displays the corrected text to the user.

[1396] User: Check the message and contact your friend appropriately.

[1397] This system helps users express their feelings and thoughts accurately, thereby improving their quality of life.

[1398] The following describes the processing flow.

[1399] Step 1:

[1400] Users input their feelings and thoughts into the device. They can input free-form text, including individual words and short sentences.

[1401] Step 2:

[1402] The terminal receives the entered text. For processing, the text data is converted to an appropriate format, such as JSON format.

[1403] Step 3:

[1404] The device sends text data to the server. The data transmission is performed using an HTTP request.

[1405] Step 4:

[1406] The server receives the HTTP request and extracts the text data for analysis. It then verifies that the data is in the correct format.

[1407] Step 5:

[1408] The server uses a natural language processing (NLP) engine to analyze text data. This analysis includes word segmentation and part-of-speech tagging.

[1409] Step 6:

[1410] The server applies sentiment analysis algorithms to identify the emotions and intentions contained in the text data. For example, it can identify intentions such as "I want to hang out with my friends" or "I don't know how to ask them out."

[1411] Step 7:

[1412] The server modifies or completes the input text data based on the results of identifying emotions and intentions. For example, it might generate a more polished sentence like, "I want to hang out with my friends, but I don't know how to ask them."

[1413] Step 8:

[1414] The server generates the corrected or completed text data as a response in JSON format.

[1415] Step 9:

[1416] The server sends a response to the terminal. This transmission occurs via an HTTP response.

[1417] Step 10:

[1418] The terminal receives a response from the server and retrieves the corrected or completed text data.

[1419] Step 11:

[1420] The device displays corrected or completed text data to the user. The output is displayed on the screen in a user-friendly format.

[1421] Step 12:

[1422] Users can review the organized text displayed and share it with others as needed. For example, they might use it to contact a friend.

[1423] (Example 1)

[1424] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1425] This invention relates to a system for supporting individuals with intellectual disabilities and other conditions in clearly expressing their emotions and thoughts and communicating smoothly. Conventional systems sometimes made it difficult for users to accurately express their emotions and intentions, leading to communication problems. The challenge is to solve this problem and enable users to communicate their emotions and thoughts simply and clearly.

[1426] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1427] In this invention, the server includes means for receiving text data containing emotions and intentions entered by a user; means for encoding the text data and transferring it via a communication network; means for analyzing the received text data and identifying emotions and intentions using a natural language processing system; means for using a generative AI model to modify or supplement the input text data based on the identified emotions and intentions; means for sending the modified or supplemented text data back to the terminal via the communication network to notify the user; and means for displaying the returned text data to the user. This makes it easier for users to clearly express their emotions and intentions and enables smooth communication with others.

[1428] A "user" refers to an individual who uses a system to express their own emotions or intentions.

[1429] "Text data" refers to data consisting of natural language sentences and words entered by the user.

[1430] A "terminal" refers to a device used by a user to input text data and communicate with a server. Specifically, this includes smartphones, tablets, and personal computers.

[1431] A "server" refers to a computing device that analyzes received text data and uses a generative AI model to correct or complete it.

[1432] A "communication network" refers to the network infrastructure used to send and receive data between terminals and servers. Specifically, it refers to the internet and local networks.

[1433] "Encoding" refers to the process of converting information, such as text data, into a specific format (for example, JSON format).

[1434] "Natural language processing methods" refer to technical means for analyzing text data and identifying emotions and intentions. Specifically, this involves processes such as text segmentation and part-of-speech tagging.

[1435] A "sentiment analysis algorithm" refers to an algorithm used to identify a user's emotions and intentions from text data.

[1436] A "generative AI model" refers to an artificial intelligence model that generates new text based on input text data. Specifically, it refers to models such as GPT-3.

[1437] "Correction and supplementation" refers to the process of transforming input text data into a clearer and more understandable form based on identified emotions and intentions.

[1438] "Display" refers to showing the corrected or completed text data on the device's screen in a format that the user can review.

[1439] This invention relates to a system that enables individuals with intellectual disabilities to express their feelings and thoughts clearly and communicate smoothly. The system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication network connecting the two.

[1440] 1. System Configuration

[1441] This system is comprised of the following hardware and software.

[1442] Terminal: Input devices such as smartphones, tablets, and personal computers.

[1443] Server: A computing device that performs analysis and data processing. Specifically, it uses Python libraries such as NLTK and spaCy, the TextBlob sentiment analysis library, and the GPT-3 generative AI model.

[1444] Communication network: Internet or local network.

[1445] 2. System Operation

[1446] Text data entered by the user into the device is first encoded into JSON format by the device and sent to the server. The server analyzes the received text data, uses natural language processing tools (NLTK and spaCy) to segment the text, and tags each word with its part of speech. Furthermore, it uses a sentiment analysis library (TextBlob) to identify emotions and intentions such as "tired" or "feeling accomplished."

[1447] Based on identified emotions and intentions, the server uses a generative AI model (GPT-3) to modify or supplement the text data. The modified text is then re-encoded in JSON format and sent back to the terminal via the communication network. The terminal decodes the received modified data and displays it to the user.

[1448] 3. Specific examples

[1449] For example, if a user enters "I want to hang out with my friends, but I don't know how to invite them," the following process will be executed:

[1450] 1. The user enters "I want to hang out with my friends, but I don't know how to invite them" into their device.

[1451] 2. The terminal receives input and sends it to the server.

[1452] 3. The server analyzes the text data and identifies two intentions: "I want to play with my friends" and "I don't know how to invite them."

[1453] 4. The server enters the following prompt into the generated AI model:

[1454] The user typed, "I want to hang out with my friends, but I don't know how to ask them." Please rephrase this sentence to be more specific and easier to understand:

[1455] 5. The AI ​​generation model generates the corrected text: "I want to hang out with my friends, but I don't know how to ask them."

[1456] 6. The server returns the corrected text to the terminal.

[1457] 7. The device displays the corrected text to the user.

[1458] In this way, users can express their feelings and thoughts more clearly and communicate more smoothly with others.

[1459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1460] Step 1:

[1461] Users enter text data that includes emotions and intentions.

[1462] The user inputs text data expressing their feelings and thoughts into their device (smartphone, tablet, or PC). For example, they might input, "I'm tired today, but I feel a sense of accomplishment."

[1463] Input: Text input by the user.

[1464] Output: Input text data ("I'm tired today, but I feel a sense of accomplishment").

[1465] Step 2:

[1466] The terminal receives input and sends it to the server.

[1467] The terminal receives user input, encodes it in JSON format, and sends it.

[1468] Specific operation: The terminal encodes the data in the following format:

[1469] json

[1470] {

[1471] "text": "I'm tired today, but I feel a sense of accomplishment."

[1472] }

[1473] The encoded data is then sent to the server.

[1474] Input: Text data entered by the user.

[1475] Output: Text data encoded in JSON format.

[1476] Step 3:

[1477] The server receives and analyzes the input data.

[1478] The server parses the JSON-formatted text data received from the terminal. It uses natural language processing tools (NLTK, spaCy) to segment the text and tag its parts of speech.

[1479] Specific operation: The server parses the text as follows:

[1480] python

[1481] import spacy

[1482] nlp = spacy.load("ja_core_news_md")

[1483] doc = nlp("I'm tired today, but I feel a sense of accomplishment")

[1484] for token in doc:

[1485] print(token.text, token.pos_)

[1486] This allows us to identify words and their parts of speech, such as "today," "tired," "but," "sense of accomplishment," "is," and "there is."

[1487] Input: Encoded text data.

[1488] Output: Analysis results (text data tagged with parts of speech).

[1489] Step 4:

[1490] The server identifies emotions and intentions.

[1491] The server uses an emotion analysis algorithm (e.g., TextBlob) to identify emotions and intentions from the analysis results.

[1492] Specific operation: The server analyzes emotions as follows:

[1493] python

[1494] from textblob import TextBlob

[1495] analysis = TextBlob("I'm tired today, but I feel a sense of accomplishment")

[1496] sentiment = analysis.sentiment

[1497] The analysis results identify emotions such as "tired" and "a sense of accomplishment."

[1498] Input: Text data tagged with parts of speech.

[1499] Output: Identified emotions and intentions.

[1500] Step 5:

[1501] The server uses a generated AI model to modify or supplement text data.

[1502] The server inputs prompt text into the generative AI model (GPT-3) and modifies or completes the text data.

[1503] Specific action: The server will enter the following prompt:

[1504] The user typed, "I'm tired today, but I feel a sense of accomplishment." Please revise this sentence to make it clearer and easier to understand:

[1505] The generative AI model outputs the corrected text "I'm very tired today, but I feel a great sense of accomplishment" as its response.

[1506] Input: Identified emotion and intention, prompt sentence.

[1507] Output: Modified text data.

[1508] Step 6:

[1509] The server sends the corrected text data back to the terminal.

[1510] The server encodes the modified text data into JSON format and sends it back to the terminal.

[1511] Specific operation: The server encodes the modified text data in the following format:

[1512] json

[1513] {

[1514] "text": "I'm very tired today, but I feel a great sense of accomplishment."

[1515] }

[1516] The encoded data is then sent to the terminal.

[1517] Input: Modified text data.

[1518] Output: Modified text data encoded in JSON format.

[1519] Step 7:

[1520] The device receives the corrected text data and displays it to the user.

[1521] The terminal decodes the JSON data received from the server and displays it to the user.

[1522] Specific action: The terminal performs the decoding process and displays the following text on the screen:

[1523] I'm very tired today, but I feel a great sense of accomplishment.

[1524] Input: Modified text data encoded in JSON format.

[1525] Output: The corrected text data displayed to the user.

[1526] (Application Example 1)

[1527] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1528] This initiative aims to address the difficulties individuals with intellectual disabilities face in clearly expressing their feelings and thoughts and communicating safely with those around them. Furthermore, it supports guardians and caregivers in taking appropriate actions quickly, thereby ensuring the safety and security of individuals with intellectual disabilities.

[1529] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1530] In this invention, the server includes means for receiving input text data, means for natural language processing, means for modifying or supplementing the input text data based on emotions and intentions, means for outputting the modified or supplemented text data, means for notifying the user of anomalies to ensure user safety, and means for generating safety confirmation messages and providing feedback to the user. This makes it possible to ensure the user's safety and peace of mind by clearly expressing the user's feelings and thoughts and providing appropriate feedback.

[1531] "Means for receiving text data" refers to a device or software that electronically receives text data entered by a user.

[1532] "Natural language processing means" refers to algorithms and software used to analyze received text data and identify emotions and intentions.

[1533] "Means for modifying or supplementing input text data" refers to devices or software that transform the original text data into an appropriate form based on identified emotions or intentions.

[1534] "Means for outputting corrected or supplemented text data" refers to a device or software for presenting corrected or supplemented text data to a user.

[1535] "Means of notifying of abnormalities" refers to devices or software that detect unusual situations or emotions and notify guardians or caregivers in order to ensure the user's safety.

[1536] "Means for generating safety confirmation messages and providing feedback" refers to devices or software that provide safety-related feedback and prompt necessary actions based on user input.

[1537] This invention is a system that enables individuals with intellectual disabilities to express their feelings and thoughts clearly and communicate safely with those around them. The system consists of a terminal used by the user, a server that analyzes and processes text data, and a communication means that connects these two.

[1538] System Configuration

[1539] 1. Terminal

[1540] This is a device for users to input their feelings and circumstances. Smartphones and smart glasses are used as terminals. The terminal has an input interface, allowing users to input text data.

[1541] 2. Server

[1542] The system analyzes received text data to identify emotions and intentions, and then modifies or supplements the data accordingly. Furthermore, it detects anomalies as needed and notifies parents and caregivers. It also generates safety confirmation messages and provides feedback. The server has natural language processing libraries (e.g., TextBlob) and communication libraries (e.g., requests) installed.

[1543] Overall Operation Overview

[1544] 1. The user enters their feelings or situation.

[1545] The user enters their current feelings or situation into the device. For example, they might enter a sentence like, "I'm in the park right now, but I feel a little scared."

[1546] 2. The terminal receives input and sends it to the server.

[1547] The terminal receives text data entered by the user and sends it to the server. The data is encoded in JSON format.

[1548] 3. The server analyzes and identifies emotions and intentions.

[1549] To analyze the received data, the server uses natural language processing to segment the text data and tag it by part of speech. Then, it uses sentiment analysis algorithms to identify emotions such as "scared" and situations such as "being in a park."

[1550] 4. The server modifies and supplements the content based on emotions and circumstances, and sends notifications to confirm safety.

[1551] Based on identified emotions and circumstances, the system modifies or completes the entered text. Furthermore, it uses means to notify parents or caregivers of the situation. For example, it might generate a message such as, "I'm in the park right now, and I'm feeling a little scared. Please move to a safe place or ask for help."

[1552] 5. The server sends the corrected text and feedback back to the terminal.

[1553] The server returns the corrected or supplemented text and a security confirmation message to the terminal.

[1554] 6. The device displays organized text and feedback.

[1555] The device displays received text and messages to the user, enabling the user to respond appropriately.

[1556] Specific example

[1557] User scenario:

[1558] User: Feeling uneasy in the park, they type "I'm in the park right now, and I feel a little scared" into their device.

[1559] Terminal: Receives user input and sends it to the server.

[1560] server:

[1561] 1. Analyze the input text to identify the emotions and situations: "scared" and "in a park."

[1562] 2. Based on these feelings and circumstances, revise and complete the sentence to generate the feedback: "I'm in the park right now, and I'm feeling a little scared. I should move to a safe place or ask for help."

[1563] Server: Sends the corrected text and feedback back to the terminal.

[1564] Terminal: Displays the corrected text and feedback to the user.

[1565] User: Review the feedback and take appropriate action.

[1566] Example of a prompt

[1567] If a user enters "I'm in the park right now, but I feel a little scared," the app performs sentiment analysis and detects anxiety. The server then provides feedback such as, "Ask someone for help or move to a safe place."

[1568] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1569] Step 1:

[1570] The user enters their feelings and situation.

[1571] The user uses their device to input their current feelings and situation. Specifically, they enter text data such as, "I'm in the park right now, but I feel a little scared," into the input field. This input is done using the device's keyboard or voice input function.

[1572] Step 2:

[1573] The terminal receives input and sends it to the server.

[1574] The terminal receives text data entered by the user. This received text data is encoded into a format such as JSON and sent to the server. Here, the input is the text data entered by the user, and the output is the encoded data.

[1575] Step 3:

[1576] The server analyzes the text data to identify emotions and intentions.

[1577] The server uses a natural language processing library (e.g., TextBlob) to analyze the received text data. The server splits the text data and tags it by part of speech. Then, it applies a sentiment analysis algorithm to identify the emotion "scared" and the intention "in the park." This transforms the input text data into data tagged with emotion and intention.

[1578] Step 4:

[1579] The server modifies and supplements text data based on emotions and context.

[1580] The server modifies or completes the input text data based on identified emotions and intentions. For example, it might be rewritten to say, "I'm in a park right now, and I'm feeling a little scared. I should move to a safe place or ask for help." The input for this step is emotion and intention tagged data, and the output is the modified or completed text data.

[1581] Step 5:

[1582] The server detects an anomaly and notifies parents or caregivers.

[1583] The server detects anomalies based on emotions and circumstances. For example, if the emotion "fear" is detected, it notifies parents or caregivers via email or text message. The input for this step is the emotion analysis result, and the output is the notification message.

[1584] Step 6:

[1585] The server generates the corrected text and sends it back to the terminal.

[1586] The server returns the corrected or completed text to the terminal. This returned data is encoded in an appropriate format for the user. The input for this step is the corrected or completed text data, and the output is the encoded data.

[1587] Step 7:

[1588] The device displays organized text and feedback.

[1589] The terminal displays the corrected or completed text data received from the server to the user. The user reviews the displayed data and takes appropriate action. The input for this step is the encoded data returned from the server, and the output is the organized text and feedback displayed to the user.

[1590] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1591] This invention is a system that combines an emotion engine that recognizes the user's emotions in order to enable individuals with intellectual disabilities or other conditions to express their feelings and thoughts clearly and to communicate smoothly. Embodiments of this invention will be described in detail below.

[1592] System Configuration

[1593] This system consists of a terminal used by the user, a server that analyzes and processes text data, an emotion engine that recognizes the user's emotions, and a communication network that connects the two.

[1594] Overall Operation Overview

[1595] 1. The user enters their feelings.

[1596] The user inputs their feelings and thoughts into the device. For example, they might input a sentence like, "I'm tired today, but I feel a sense of accomplishment."

[1597] 2. The terminal receives input and sends it to the server.

[1598] The terminal receives text data entered by the user and sends it to the server. The data is encoded in an appropriate format, such as JSON.

[1599] 3. The server analyzes and utilizes the emotion engine.

[1600] The server analyzes the received data, uses natural language processing (NLP) techniques to segment the text, and tags each word with its part of speech.

[1601] The system utilizes an emotion engine to recognize the user's emotions and intentions from the input text. The analysis uses algorithms that identify the user's emotions in real time.

[1602] 4. Modify and supplement the text based on sentiment analysis.

[1603] Based on identified emotions and intentions, the server modifies or completes the entered text. For example, "I'm very tired today, but I feel a great sense of accomplishment."

[1604] 5. The server sends the corrected text back to the terminal.

[1605] The server returns the corrected or completed text to the terminal.

[1606] 6. The device displays organized text.

[1607] The device displays the received message to the user, allowing the user to confirm it. This enables the user to communicate their intentions to others more accurately.

[1608] Specific examples

[1609] User scenario:

[1610] User: After returning home from school, they type into their device, "I want to hang out with my friends, but I don't know how to ask them."

[1611] Terminal: Receives user input and sends it to the server.

[1612] server:

[1613] 1. Analyze the input text and identify two intentions: "I want to hang out with my friends" and "I don't know how to ask them out."

[1614] 2. Use an emotion engine to analyze the user's emotions in real time and refer to past emotion history to more accurately identify emotions.

[1615] 3. Based on these intentions, revise and supplement the sentence to: "I want to hang out with my friends, but I don't know how to ask them."

[1616] Server: Sends the corrected text back to the terminal.

[1617] Terminal: Displays the corrected text to the user.

[1618] User: Check the message and contact your friend appropriately.

[1619] This system utilizes an emotion engine to more accurately understand users' emotions and support appropriate communication based on those emotions. This allows users to express their feelings and thoughts precisely, improving their quality of life.

[1620] The following describes the processing flow.

[1621] Step 1:

[1622] The user enters their feelings or thoughts into the device. For example, they might enter text like, "I want to hang out with my friends, but I don't know how to ask them."

[1623] Step 2:

[1624] The terminal receives the entered text. The received text data is converted to an appropriate format, such as JSON format.

[1625] Step 3:

[1626] The terminal sends formatted text data to the server. Data transmission is performed using an HTTP request.

[1627] Step 4:

[1628] The server receives the HTTP request and extracts the text data for parsing. It then verifies that the data is in the correct format.

[1629] Step 5:

[1630] The server uses a natural language processing (NLP) engine to analyze the text data. The analysis includes the following steps:

[1631] This process divides text data into individual words (word segmentation).

[1632] Add part-of-speech tags to each word (part-of-speech tagging).

[1633] Step 6:

[1634] The server uses an emotion engine to identify the user's emotions and intentions from text data. Specifically, the following processes are performed:

[1635] The emotion engine extracts emotional keywords (e.g., tired, happy) from the input text data.

[1636] Refer to past emotional data to check your current emotional state.

[1637] Step 7:

[1638] The server modifies or completes the input text data based on identified emotions and intentions. For example, it might generate a sentence like, "I want to hang out with my friends, but I don't know how to ask them."

[1639] Step 8:

[1640] The server constructs the modified or completed text data as a response in JSON format.

[1641] Step 9:

[1642] The server sends a response to the terminal. This transmission occurs via an HTTP response.

[1643] Step 10:

[1644] The terminal receives a response from the server and retrieves the corrected or completed text data.

[1645] Step 11:

[1646] The device displays corrected or completed text data to the user. For example, "I want to hang out with my friends, but I don't know how to ask them" might appear on the screen.

[1647] Step 12:

[1648] The user reviews the organized text displayed and communicates it to others as needed. For example, they can appropriately contact a friend.

[1649] (Example 2)

[1650] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1651] The aim is to solve the problem that individuals with intellectual disabilities and other conditions face in accurately expressing their emotions and thoughts and engaging in smooth communication. Conventional technologies have been insufficient in analyzing input text data and recognizing emotions, making it difficult to generate text that accurately reflects the user's intentions and feelings. As a result, users have been unable to improve the quality of their communication and have difficulty having satisfying conversations.

[1652] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1653] In this invention, the server includes means for encoding input text data and transmitting it over a network, means for dividing the text data and tagging parts of speech, means for identifying emotions and intentions using an emotion recognition engine, and means for modifying or supplementing the text data based on the identified emotions and intentions. This makes it possible to modify and supplement text that accurately reflects the user's emotions and intentions based on the input information.

[1654] "Text data" refers to data in the form of text that users input, including their emotions and intentions.

[1655] "Means of receiving" refers to the means by which a terminal receives and processes text data entered by the user.

[1656] "Encoding" refers to a method of converting received text data into an appropriate data format (e.g., JSON format) so that it can be transmitted over a network.

[1657] "Network" refers to the entire communication infrastructure used to send and receive data between terminals and servers, and includes the internet and local area networks (LANs).

[1658] The "method of splitting" refers to a method of dividing the input text data into individual words and tagging each word with its part of speech.

[1659] "Part-of-speech tagging" is the process of assigning a part of speech (noun, verb, adjective, etc.) to each of the divided words.

[1660] An "emotion recognition engine" is a program that includes an algorithm to identify a user's emotions (positive, negative, etc.) and intentions from input text data.

[1661] "Means for identifying emotions and intentions" refers to methods that use an emotion recognition engine to analyze and identify a user's emotional state and intentions from text data.

[1662] "Means of correction or supplementation" refers to means of modifying the original text data or supplementing missing information to properly organize a text, based on identified sentiments or intentions.

[1663] "Output method" refers to a means of sending the corrected or supplemented text data back to the terminal and displaying it to the user.

[1664] "Real-time processing methods" refer to methods for performing text data analysis and sentiment recognition instantly and without delay.

[1665] "Means for automatically generating appropriate text" refers to means of generating natural and accurate text based on analyzed and corrected text data and providing it to the user.

[1666] This invention is a system for enabling individuals with intellectual disabilities to express their emotions and intentions more accurately and to communicate more smoothly. This system includes techniques for appropriately analyzing input text data, recognizing emotions, and modifying or supplementing the text data based on those emotions. Embodiments of this invention are described in detail below.

[1667] System Configuration

[1668] The system consists of the following hardware and software:

[1669] Hardware:

[1670] Devices: Smartphones, tablets, personal computers, etc.

[1671] Servers: Cloud servers and physical servers

[1672] software:

[1673] Natural Language Processing (NLP) libraries: spaCy, NLTK, etc.

[1674] Emotion recognition engine: Emotion analysis tool, such as IBM Watson Tone Analyzer.

[1675] Communication network: Internet or local area network (LAN)

[1676] Specific Operation Overview

[1677] 1. The user inputs their feelings or thoughts into the device. For example, they might input, "I want to hang out with my friends, but I don't know how to ask them."

[1678] 2. The terminal receives the input text data, encodes it in JSON format, and sends it to the server.

[1679] 3. The server analyzes the received text data using a natural language processing library, splitting the text into individual words and tagging them by part of speech.

[1680] 4. The server uses an emotion recognition engine to identify the user's emotions and intentions from the text data. Specifically, it identifies emotions such as positive and negative.

[1681] 5. The server modifies or completes the entered text data based on the identified emotions or intentions. For example, it might modify it to "I want to hang out with my friends, but I don't know how to ask them."

[1682] 6. The server sends the corrected or completed text data back to the terminal.

[1683] 7. The device displays the corrected text to the user, allowing the user to review it.

[1684] Examples of specific actions

[1685] The following are specific scenarios in which users may use the system:

[1686] User: After returning home from school, they type "I want to hang out with my friends, but I don't know how to ask them" into their device.

[1687] Terminal: Receives the input text data, encodes it, and sends it to the server.

[1688] server:

[1689] The text is analyzed, split into individual words, and then tagged with parts of speech.

[1690] Using an emotion recognition engine, the system identifies intentions such as "I want to hang out with my friends" and "I don't know how to ask them," and then identifies the emotions behind those intentions.

[1691] Revise the text to "I want to hang out with my friends, but I don't know how to ask them."

[1692] Server: Sends the corrected text data back to the terminal.

[1693] Terminal: Displays the corrected text to the user.

[1694] User: Check the displayed message and contact your friend appropriately.

[1695] Example of a prompt

[1696] The following are examples of prompts used to test emotion recognition engines and natural language processing technologies:

[1697] Prompt message:

[1698] The user typed, "I want to hang out with my friends, but I don't know how to ask them." Analyze this text, recognize the sentiment, and correct / complete it as follows: "I want to hang out with my friends, but I don't know how to ask them."

[1699] In this way, the system can analyze the user's emotions and intentions, and support accurate communication.

[1700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1701] Step 1:

[1702] The user enters text.

[1703] The user inputs their feelings and thoughts into the device. For example, they might input, "I want to hang out with my friends, but I don't know how to ask them."

[1704] Input: User's text input

[1705] Output: Input text data on the terminal

[1706] Specific action: The user enters their feelings or thoughts in text format into an input form on the device.

[1707] Step 2:

[1708] The terminal receives input, encodes it, and sends it to the server.

[1709] The terminal receives text data entered by the user, encodes it into an appropriate format such as JSON, and sends it to the server.

[1710] Input: User's text input data

[1711] Output: Encoded text data (Example: {"text": "I want to hang out with my friends, but I don't know how to ask them"})

[1712] Specific operation: The terminal program receives the input text, encodes it, and sends it to the server via the communication network.

[1713] Step 3:

[1714] The server parses the text data.

[1715] The server analyzes the received text data using a natural language processing library. It divides the text into words and tags each word with its part of speech.

[1716] Input: Encoded text data

[1717] Output: Split and part-of-speech tagged data (e.g., "friend / noun with / particle want to play / verb but / conjunction how to invite / noun but / particle don't know / verb")

[1718] Specific operation: The server parses the received JSON data and uses a natural language processing library to split and tag parts of speech.

[1719] Step 4:

[1720] The server uses an emotion recognition engine to identify emotions and intentions.

[1721] The server uses an emotion recognition engine to identify the user's emotions and intentions from the segmented text data. It identifies emotions such as positive and negative.

[1722] Input: Split and part-of-speech tagged data

[1723] Output: Data with identified emotions and intentions (e.g., "Positive / I want to hang out with friends," "Negative / I don't know how to ask them out")

[1724] Specific operation: The emotion recognition engine analyzes text data to identify the user's emotions and intentions in real time. Specifically, it uses tools such as IBM Watson Tone Analyzer.

[1725] Step 5:

[1726] The server modifies and completes the text.

[1727] The server modifies or completes the entered text data based on identified emotions and intentions. For example, it might revise it to "I want to hang out with my friends, but I don't know how to ask them."

[1728] Input: Data with identified emotions and intentions

[1729] Output: Modified or completed text data (e.g., "I want to hang out with my friends, but I don't know how to ask them.")

[1730] Specific operation: The server processes the input text by correcting and completing it in a natural way based on the identified emotions and intentions.

[1731] Step 6:

[1732] The server sends the corrected text back to the terminal.

[1733] The server re-encodes the corrected or completed text data in JSON format or another suitable format and sends it back to the terminal.

[1734] Input: Modified or completed text data

[1735] Output: Encoded data sent to the terminal (e.g., {"text": "I want to hang out with my friends, but I don't know how to ask them"})

[1736] Specific operation: The server encodes the modified text data and sends it to the terminal via the communication network.

[1737] Step 7:

[1738] The device displays the corrected text to the user.

[1739] The terminal displays the received, corrected text data to the user. The user can then review the displayed text.

[1740] Input: Encoded modified text data

[1741] Output: The corrected text displayed on the device (Example: "I want to hang out with my friends, but I don't know how to ask them.")

[1742] Specific action: The corrected text data is displayed on the device's screen and presented in a user-friendly format.

[1743] (Application Example 2)

[1744] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1745] There is a problem in that employees with intellectual disabilities have difficulty adequately communicating their physical condition and feelings within the factory. As a result, appropriate support and feedback may not be provided promptly by factory managers. Furthermore, there is a lack of effective means to accurately understand employees' emotions and intentions and to facilitate smooth communication based on that understanding.

[1746] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving input string data, means for analyzing the string data and identifying emotions and intentions, means for modifying or supplementing the input string data based on the identified emotions and intentions, means for outputting the modified or supplemented string data, means for the user to input their feelings and physical condition into a terminal, means for supplementing the text based on the analysis results and displaying it to the user, and means for sending alerts to the administrator as needed. This makes it possible for employees with intellectual disabilities to appropriately communicate their physical condition and emotions, enabling smooth communication and prompt support within the factory.

[1747] "Input string data" refers to text data that users input using their devices, including information such as their feelings and physical condition.

[1748] "Natural language processing means" refers to computer tools that analyze string data entered by a user and identify emotions and intentions from it. Specifically, this includes means for splitting text data and tagging parts of speech.

[1749] "Emotions and intentions" refer to the user's feelings and thoughts, extracted from the string data entered by the user.

[1750] "Means of modification or supplementation" refers to computational means that transform input string data into an appropriate form or provide additional information based on identified emotions or intentions.

[1751] "Modified or completed string data" refers to text data that has been appropriately modified or completed from the original input based on the results of sentiment and intent analysis.

[1752] "Means for users to input their feelings and physical condition into a device" refers to methods that allow employees with intellectual disabilities to input their feelings and physical condition in text using devices such as tablets or smart glasses.

[1753] "Means of completing and displaying text to the user based on analysis results" refers to means of displaying text data that has been modified or completed based on the analysis results of emotions and intentions to the user.

[1754] "Means of sending alerts to administrators" refers to means of issuing warnings or notifications to factory administrators based on the results of emotion and intent analysis, as needed.

[1755] The present invention is a system that helps employees with intellectual disabilities appropriately communicate their physical condition and feelings within a factory. The system includes means for receiving input string data, natural language processing means for analyzing the string data and identifying emotions and intentions, means for modifying or completing the string data based on the identified emotions and intentions, means for outputting the modified or completed data, means for inputting feelings and physical condition using a terminal, means for completing sentences based on the analysis results and displaying them to the user, and means for sending alerts to administrators as needed.

[1756] Hardware and software to be used

[1757] The following hardware and software will be used to implement the system.

[1758] Hardware:

[1759] Device (tablet or smart glasses): A device used by the user to input their feelings and physical condition.

[1760] Factory robot: The main device that runs the emotional support program.

[1761] software:

[1762] Python: The main programming language used.

[1763] TextBlob: Used as a natural language processing library, it performs basic text analysis and part-of-speech tagging.

[1764] Requests: A library for sending HTTP requests.

[1765] Emotion recognition engine: Executes an algorithm to identify the user's emotions from text data.

[1766] System operation

[1767] 1. The user enters their feelings and physical condition:

[1768] Users input their feelings and physical condition as text data using a tablet or smart glasses.

[1769] 2. The terminal receives input and sends it to the server:

[1770] The terminal receives the entered string data and sends it to the server.

[1771] 3. The server analyzes the text and identifies sentiment and intent:

[1772] The server uses an emotion recognition engine to analyze the text and identify the user's emotions and intentions.

[1773] The analysis involves using TextBlob to segment the input data and then tagging it with parts of speech.

[1774] 4. Modify and supplement the text based on the analysis results:

[1775] Based on identified emotions and intentions, the server modifies or supplements the original input data.

[1776] 5. Send the corrected text back to the device and display it:

[1777] The server sends the corrected text back to the terminal, which then displays it to the user.

[1778] Users can review the corrected text.

[1779] 6. Send an alert to the administrator if necessary:

[1780] If necessary, an alert will be sent to the factory manager based on the analysis results.

[1781] Specific example

[1782] If a user types "I'm tired today," the system analyzes it and displays the completed text "(I feel tired)" to the user. It can also send an alert to administrators that "an employee is tired."

[1783] Example of a prompt

[1784] Example input: I'm tired today.

[1785] Example output: I am tired today (I feel tired).

[1786] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1787] Step 1:

[1788] The user inputs their feelings and physical condition. The user inputs their feelings and physical condition in text format into a tablet or smart glasses. For example, they might input a sentence like, "I'm tired today." The input in this step is text data entered by the user into the device, and the output is that same text data.

[1789] Step 2:

[1790] The terminal receives input and sends it to the server. The terminal receives the string data entered by the user, encodes it into an appropriate format such as JSON, and sends it to the server. In this step, the input is the text data entered by the user, and the output is the encoded text data sent to the server.

[1791] Step 3:

[1792] The server analyzes the text to identify emotions and intentions. The server uses an emotion recognition engine to analyze the received string data and identify the user's emotions and intentions. Specifically, it uses a natural language processing library (TextBlob) to segment the text data and tag it with parts of speech. As a result of the analysis, it identifies an emotion such as "tired." The input for this step is text data sent from the terminal, and the output is the analysis result that identifies emotions and intentions.

[1793] Step 4:

[1794] The text is modified or completed based on the analysis results. The server generates modified or completed text based on the identified emotions or intentions. For example, if the emotion "tired" is identified, the original input is completed to "I am tired today (I feel tired)." The input for this step is the analysis results and the original text data, and the output is the modified or completed text data.

[1795] Step 5:

[1796] The corrected text is returned to the terminal and displayed. The server returns the corrected or completed text data to the terminal. The terminal displays the received corrected or completed text data to the user. The user reviews and uses it appropriately. The input for this step is the corrected or completed text data, and the output is the text data displayed on the terminal.

[1797] Step 6:

[1798] The server sends alerts to the administrator as needed. Based on the analysis results, the server sends warnings and notifications to the administrator when necessary. For example, if a user is unwell, the server notifies the administrator of this information. The input for this step is the analysis results, and the output is the alert information sent to the administrator.

[1799] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1800] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1801] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1802] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1803] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1804] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1805] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1806] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1807] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1808] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1809] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1810] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1811] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1812] 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.

[1813] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1814] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1815] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1816] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1817] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1818] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1819] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1820] The following is further disclosed regarding the embodiments described above.

[1821] Understood. Below is a draft of the patent claims regarding the distinctive features of the system.

[1822] (Claim 1)

[1823] A means for receiving input text data,

[1824] A natural language processing means that analyzes the aforementioned text data to identify emotions and intentions,

[1825] Means for modifying or supplementing input text data based on identified emotions or intentions,

[1826] A system including means for outputting modified or supplemented text data.

[1827] (Claim 2)

[1828] The system according to claim 1, wherein the natural language processing means includes means for splitting input text data and performing part-of-speech tagging.

[1829] (Claim 3)

[1830] The system according to claim 1, comprising means for automatically generating appropriate text based on the analysis of emotions and intentions.

[1831] "Example 1"

[1832] (Claim 1)

[1833] A means of receiving text data that includes emotions and intentions entered by the user,

[1834] Means for encoding the aforementioned text data and transferring it via a communication network,

[1835] A natural language processing method for analyzing received text data and identifying emotions and intentions,

[1836] A means of using a generative AI model to modify or complete input text data based on identified emotions and intentions,

[1837] A means of sending corrected or supplemented text data back to the terminal via a communication network to notify the user,

[1838] A means of displaying the returned text data to the user,

[1839] A system that includes this.

[1840] (Claim 2)

[1841] The system according to claim 1, wherein the natural language processing means includes means for splitting input text data and tagging it with parts of speech.

[1842] (Claim 3)

[1843] The system according to claim 1, wherein the generating AI model includes means for automatically generating appropriate text based on the analysis of emotions and intentions.

[1844] "Application Example 1"

[1845] (Claim 1)

[1846] A means for receiving input text data,

[1847] A natural language processing means that analyzes the aforementioned text data to identify emotions and intentions,

[1848] Means for modifying or supplementing input text data based on identified emotions or intentions,

[1849] A means of outputting corrected or completed text data,

[1850] A means of notifying users of anomalies to ensure their safety,

[1851] A means of generating safety confirmation messages for users and providing feedback.

[1852] A system that includes this.

[1853] (Claim 2)

[1854] The system according to claim 1, wherein the natural language processing means includes means for splitting input text data and performing part-of-speech tagging.

[1855] (Claim 3)

[1856] The system according to claim 1, comprising means for automatically generating appropriate text based on the analysis of emotions and intentions.

[1857] "Example 2 of combining an emotion engine"

[1858] (Claim 1)

[1859] A means for receiving input text data,

[1860] Means for encoding the aforementioned text data and transmitting it to a server via a network,

[1861] A means for splitting the aforementioned text data and tagging it with parts of speech,

[1862] A means of identifying emotions and intentions from text data using an emotion recognition engine,

[1863] Means for modifying or supplementing text data entered based on identified emotions or intentions,

[1864] A system including means for outputting modified or supplemented text data.

[1865] (Claim 2)

[1866] The system according to claim 1, comprising means for processing data in real time based on the results of analyzing input text data.

[1867] (Claim 3)

[1868] The system according to claim 1, comprising means for automatically generating appropriate text based on analyzed text data and displaying it to the user.

[1869] "Application example 2 when combining with an emotional engine"

[1870] (Claim 1)

[1871] A means for receiving input string data,

[1872] A natural language processing means that analyzes the aforementioned string data and identifies emotions and intentions,

[1873] Means for modifying or supplementing input string data based on identified emotions or intentions,

[1874] A means for outputting modified or completed string data,

[1875] A means for users to input their feelings and physical condition into the device,

[1876] A means of completing the text based on the analysis results and displaying it to the user,

[1877] A system that includes a means of sending alerts to the administrator as needed.

[1878] (Claim 2)

[1879] The system according to claim 1, wherein the natural language processing means includes means for splitting input string data and performing part-of-speech tagging.

[1880] (Claim 3)

[1881] The system according to claim 1, comprising means for automatically generating appropriate text based on the analysis of emotions and intentions. [Explanation of symbols]

[1882] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving input text data, A natural language processing means that analyzes the aforementioned text data to identify emotions and intentions, Means for modifying or supplementing input text data based on identified emotions or intentions, A system including means for outputting modified or supplemented text data.

2. The system according to claim 1, wherein the natural language processing means includes means for splitting input text data and performing part-of-speech tagging.

3. The system according to claim 1, comprising means for automatically generating appropriate text based on the analysis of emotions and intentions.

Images