System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving speech or text entered by a user during a communication; means for converting the entered speech to text; means for transmitting the text data to a server; means for parsing the text data to understand a user's request; means for retrieving information from a relational database based on the request; means for generating the retrieved information for providing to the user; and means for displaying or reciting the generated information to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] People with developmental disorders and neurodiversity characteristics can have difficulty accurately understanding and recording information during communication. In particular, they may miss important decisions or next steps in a conversation, or be unable to accurately grasp the other person's emotions or intentions. These challenges reduce efficiency in daily life and the workplace and increase stress in interpersonal relationships. The purpose of this invention is to provide a system that solves these communication challenges and enables users to accurately understand information and realize smooth conversations. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides the following means: A system including means for receiving voice or text input by a user during communication, means for converting the input voice into text, means for transmitting the text data to a server, means for analyzing the text data to understand the user's request, means for retrieving information from a relational database based on the request, and means for generating the retrieved information to provide to the user. Furthermore, the system is configured to include means for performing emotion analysis based on the analyzed data by the server and for inferring the other party's emotions and intentions, and is equipped with means for displaying or reading out to the user, thereby improving the accuracy and quality of communication.

[0006] "User" refers to an individual who uses the communication assistant system.

[0007] "Device" means a device through which a user can enter input or receive information, including, for example, a smartphone, tablet, or personal computer.

[0008] "Server" refers to a central processing unit that analyzes text data, obtains related data, generates information, etc.

[0009] "Voice input" refers to voice data provided by a user speaking into a terminal.

[0010] "Text input" refers to text data provided by a user entering characters into a terminal.

[0011] "Automatic speech recognition (ASR)" refers to the technology that converts voice data into text data.

[0012] "Natural language processing (NLP)" refers to artificial intelligence technology for analyzing text data and understanding its meaning and intent.

[0013] A "database" refers to a collection of data that allows information to be organized and retrieved.

[0014] "Sentiment analysis" refers to the technology of inferring emotions and intentions from text and audio data.

[0015] "Displaying information" refers to the device providing information visually to the user.

[0016] "Reading information aloud" refers to the device providing information to the user by voice. [Brief explanation of the drawings]

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

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

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

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

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

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

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0038] The present invention is a communication assistant system that helps users who are not good at communication to smoothly understand and record information. This system operates as follows.

[0039] First, the user inputs a question or request into the device by voice or text. For example, during a meeting, the user might say to the device, "When is the next meeting?" or type in text, "When is the next meeting?". As the user types, the device uses its voice recognition function to convert the voice into text data. This converted text data is then sent to the server.

[0040] The server analyzes the received text data using natural language processing (NLP) techniques. The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0041] The server then searches and retrieves the necessary information from relevant databases. For example, the server may retrieve the next meeting date and time from a meeting recording system. Based on this information, the server generates a response to provide to the user. The response may include not only the meeting date and time, but also the emotional reactions of the participants and other relevant information.

[0042] The server then sends the generated information back to the device, which then displays the received information to the user or reads it aloud. For example, the device might display the information "The next meeting is scheduled for next Tuesday at 2 PM" or read it aloud as "The next meeting is scheduled for next Tuesday at 2 PM." In this way, the user can keep track of important communication information without missing it.

[0043] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server analyzes it, retrieves the agenda from the previous meeting record, and sends back to the device, "The agenda for the next meeting will be a project progress report and discussion of new proposals." The device then displays this information to the user and reads it out loud if necessary. In this way, the user can accurately understand what they need to prepare for the next meeting.

[0044] As described above, the present invention helps users to grasp all necessary information during communication and accurately understand the intentions and feelings of the other person. It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] A user types a question or request into the device using voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0048] Step 2:

[0049] The device receives the voice and converts it into text using automatic speech recognition (ASR) technology. For example, converting the voice saying "When is the next meeting?" into text saying "When is the next meeting?"

[0050] Step 3:

[0051] The terminal sends the converted text data (or directly entered text data) to the server. For example, the text "When is the next meeting date?" is sent to the server.

[0052] Step 4:

[0053] The server uses natural language processing (NLP) technology to analyze the received text data. The server analyzes the text data and understands the user's request. For example, from the question "When is the next meeting?", the server understands that it should return the schedule information for the next meeting.

[0054] Step 5:

[0055] The server retrieves necessary information from a related database based on the analysis results, for example, the server retrieves the date and time of the next meeting from a conference recording system.

[0056] Step 6:

[0057] Based on the information obtained, the server generates a response to provide to the user, for example, "The next meeting is scheduled for next Tuesday at 2 p.m."

[0058] Step 7:

[0059] The server then performs sentiment analysis to analyze the other party's emotions and intentions, generating information such as "The participant's reaction is enthusiastic."

[0060] Step 8:

[0061] The server sends the generated information back to the device, for example, sending the generated reply text "The next meeting is scheduled for next Tuesday at 2pm" to the device.

[0062] Step 9:

[0063] The device displays the received information to the user or reads it aloud using speech synthesis technology. For example, the device may display "Your next meeting is scheduled for next Tuesday at 2 PM" on the screen or read aloud "Your next meeting is next Tuesday at 2 PM."

[0064] Step 10:

[0065] The user can review the displayed and spoken information to accurately understand the date of an upcoming meeting. For example, the user can add a meeting date to their calendar.

[0066] Through each of these steps, the system helps users accurately and quickly grasp the information they need, thereby enabling users who have communication difficulties in particular to communicate more smoothly in their daily lives and at work.

[0067] Example 1

[0068] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0069] In modern society, many users who are not good at communication have difficulty understanding and recording information smoothly. In particular, there are insufficient tools to enable efficient and stress-free communication for users with developmental disorders and neurodiversity characteristics. To solve this problem, a system is needed that can analyze user input, accurately understand their intention, and provide the necessary information.

[0070] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0071] In this invention, the server includes means for receiving voice or text input, means for converting voice to text, means for transmitting the text data to the server, means for analyzing the text data to understand the user's request, means for retrieving information from a relational database, means for generating the retrieved information, and means for displaying or reading out the generated information. This allows the server to instantly retrieve necessary information based on the user's prompt and provide it by display or voice. This enables efficient and stress-free communication, particularly for users with developmental disorders or neurodiversity.

[0072] "Voice or text input" refers to the voice or text data used by a user to communicate.

[0073] "Voice recognition" refers to the technology that analyzes the user's voice as a digital signal and converts it into corresponding text data.

[0074] "Text data" refers to character strings of information input by a user or generated by voice recognition technology.

[0075] "Server" refers to a computer system that is responsible for analyzing received text data and obtaining and providing related information.

[0076] "Natural language processing (NLP)" refers to technology for analyzing text data and understanding user requests and intent.

[0077] "Database" refers to a collection of data that stores related information and allows that information to be searched and retrieved as needed.

[0078] "Response" refers to the reply information to the user that the server generates after analysis and search.

[0079] "Display" refers to the act of presenting information as text or graphic data on a terminal display.

[0080] "Audio output" refers to the playback of text data generated by the terminal as audio and audibly conveying it to the user.

[0081] A "generative AI model" refers to an artificial intelligence model that can generatively create or process data for a specific task.

[0082] A "prompt" is a string of text that a user inputs to a generative AI model, containing commands or questions that the AI ​​immediately processes in response.

[0083] This invention is a Communication Assistant system that helps users smoothly understand and record information during communication, and is particularly useful for users who have difficulty communicating or who have developmental disabilities. This system analyzes voice and text input and provides the necessary information.

[0084] Hardware and software used

[0085] Hardware

[0086] Device: A device that receives voice or text input from a user, including computers, smartphones, tablets, etc.

[0087] Server: A computer system for data analysis and information retrieval.

[0088] software

[0089] Speech Recognition API: Software used to convert a user's speech into text. For example, a common speech recognition API is Google's Speech Recognition API.

[0090] Natural language processing (NLP) model: Software used to analyze text data and understand user requests and intent. For example, OpenAI's GPT-3 is used as a generative AI model.

[0091] Database: Stores related information and searches and retrieves it as needed. A common database system is MySQL.

[0092] Text-to-Speech Engine (TTS): Software that outputs generated text information as speech. For example, Microsoft's TTS engine is used.

[0093] System Operation

[0094] 1. User Input

[0095] A user speaks or texts a question or request into the device, for example, "When is our next meeting?" or texts "When is our next meeting?"

[0096] 2. Voice Recognition

[0097] The device uses Google's speech recognition API to convert voice input into text data. For example, voice input such as "When is the next meeting?" is converted into text data such as "When is the next meeting?"

[0098] 3. Sending text data

[0099] The terminal sends the converted text data to the server using an HTTP request.

[0100] 4. Text Data Analysis

[0101] The server uses OpenAI's GPT-3 model to analyze the text data, for example, "When is the next meeting?" to understand the intent of returning the schedule information for the next meeting.

[0102] 5. Searching for Information

[0103] The server queries the MySQL database to retrieve the next meeting date and time information from the associated meeting recording system.

[0104] 6. Generating the Response

[0105] The server uses the information it has received to generate a response, such as "The next meeting is scheduled for next Tuesday at 2pm", which includes the date, time and other relevant information.

[0106] 7. Transmission of Information

[0107] The server returns the generated response to the terminal as an HTTP response.

[0108] 8. Displaying or audibly outputting information

[0109] The device then displays the information to the user or reads it aloud, using Microsoft's TTS engine. For example, it might display "Our next meeting is next Tuesday at 2 PM" on the screen and simultaneously read it aloud.

[0110] Specific examples

[0111] Example 1

[0112] User prompt: "What's on the agenda next time?"

[0113] Conversion and sending on device: Converts speech to text and sends it to the server.

[0114] Analysis and data acquisition on the server: Search for agenda information from the previous meeting records and generate the following: "The next agenda will be a project progress report and consideration of new proposals."

[0115] Returning and displaying information: The device receives the response and displays or speaks it to the user.

[0116] In this way, the present invention allows users to grasp important communication information without missing anything and accurately understand the other person's intentions and feelings. This system is particularly useful for users with developmental disorders or neurodiversity, as it enables efficient and stress-free communication.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] User Input

[0120] Users can enter questions or requests into the device by voice or text, such as "When is the next meeting?" or "When is the next meeting?"

[0121] Input: User voice or text

[0122] Output: Raw data entered into the terminal

[0123] Step 2:

[0124] Voice Recognition

[0125] When using voice input, the device converts the voice into text data using a speech recognition API, such as Google's speech recognition API.

[0126] Input: raw user voice data

[0127] Data processing: Speech-to-text conversion using speech recognition API

[0128] Output: Text data

[0129] Step 3:

[0130] Sending text data

[0131] The terminal sends the converted text data to the server using an HTTP request.

[0132] Input: Text data

[0133] Data transmission: Data transmission via HTTP request

[0134] Output: Text data sent to the server

[0135] Step 4:

[0136] natural language analysis

[0137] The server analyzes the received text data using a natural language processing (NLP) model. For example, OpenAI's GPT-3 is used as a generative AI model. The server understands the user's intent from the text "When is the next meeting?"

[0138] Input: Text data

[0139] Data Computation: Semantic Analysis and Intention Understanding with Generative AI Models

[0140] Output: User intent as a result of analysis

[0141] Step 5:

[0142] Searching for information

[0143] Based on the analysis results, the server searches and retrieves the necessary information from the relevant database, for example, retrieving the date and time of the next meeting from a conference recording system.

[0144] Input: User Intent

[0145] Data retrieval: Querying a database

[0146] Output: Acquired meeting date and time information

[0147] Step 6:

[0148] Generating a response

[0149] The server uses the information it has obtained to generate a response to provide to the user, specifically, "The next meeting is next Tuesday at 2 p.m."

[0150] Input: Acquired meeting date and time information

[0151] Data generation: Generate a response message

[0152] Output: The generated response message

[0153] Step 7:

[0154] Sending information

[0155] The server returns the generated response message to the terminal, sending the data as an HTTP response.

[0156] Input: The generated response message

[0157] Data transmission: Data transmission via HTTP response

[0158] Output: Response message sent to the terminal

[0159] Step 8:

[0160] Display or speak information

[0161] The device then displays the received information to the user or reads it aloud, for example, using Microsoft's TTS engine to say, "Our next meeting is next Tuesday at 2 p.m.", or displays it on the screen.

[0162] Input: Received response message

[0163] Data output: TTS voice generation or display

[0164] Output: Information provided to the user (audio or text display)

[0165] (Application example 1)

[0166] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0167] In today's learning environment, learners with developmental disabilities and neurodiversity in particular face challenges in learning efficiently. Specifically, they face difficulties in obtaining quick and accurate answers when questions arise during learning, which negatively impacts their motivation and grades. Furthermore, general learning support systems lack the ability to properly understand emotions and intentions, making it impossible to provide support tailored to individual learners.

[0168] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0169] In this invention, the server includes: means for receiving voice or text input by a user during communication; means for converting the input voice into text; means for transmitting the text data to the server; means for analyzing the text data to understand a user's request; means for retrieving information from a related database based on the request; means for generating the retrieved information to provide to the user; means for displaying or reading out the generated information to the user; means for analyzing questions the user has while studying using natural language processing technology and generating answers; and means for searching and retrieving appropriate information from a learning resource database. This makes it possible to provide quick and accurate answers to specific questions faced by learners, thereby efficiently supporting the learning process of learners with neurodiversity characteristics, including developmental disabilities.

[0170] "User" means a person who uses the System.

[0171] "Communication" refers to the process of exchanging information through speech or text.

[0172] "Voice or text" refers to the form of information that a user inputs into a system.

[0173] "Means for converting voice to text" refers to a function that analyzes input voice data and converts it into corresponding text data.

[0174] "Text data" refers to information expressed in characters.

[0175] A "server" refers to a computer system that processes and stores data over a network.

[0176] "Means of analyzing and understanding user requests" refers to the function of analyzing received text data using natural language processing technology to understand the user's intentions and requests.

[0177] "Relational database" refers to a data storage system that stores information responsive to user requests.

[0178] "Means of obtaining information" refers to the process of searching and obtaining the required information from the relevant database.

[0179] "Means of generation" refers to the function of constructing a response to provide to the user based on the acquired information.

[0180] "Means for displaying or reading" refers to the ability to provide the generated information to the user visually or audibly.

[0181] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0182] "Learning resource database" refers to a database that stores information related to learning.

[0183] "Neurodiversity, including developmental disorders" refers to the concept of diverse cognitive and behavioral characteristics, particularly developmental disorders.

[0184] "Questions raised during learning" refers to questions or uncertainties that learners have during the learning process.

[0185] This invention is an interactive learning assistant system that helps users resolve questions that arise during learning. Its purpose is to provide efficient learning support, especially for learners with developmental disorders and neurodiversity.

[0186] The system consists of the following major hardware and software components:

[0187] Hardware

[0188] 1. User device: A mobile device such as a smartphone or head-mounted display that allows the user to input a question by voice or text and have it displayed or read aloud.

[0189] 2. Server: A computer system for performing advanced data analysis and information retrieval.

[0190] software

[0191] 1. Speech recognition software (speech_recognition): Converts user input into text data.

[0192] 2. Natural language processing software (spaCy): Used to analyze text data and understand user requirements.

[0193] 3. Generative AI model (Transformers Pipeline feature): Generates appropriate answers based on the user's question.

[0194] 4. Database system: Data storage for storing learning resources and searching and retrieving required information.

[0195] Processing Overview

[0196] 1. A question is input by voice or text from the user's device. For example, "What is an ionic bond?"

[0197] 2. Use speech recognition software to convert this speech data into text data. The converted text data will be "What is an ionic bond?"

[0198] 3. The converted text data is sent to the server.

[0199] 4. The server uses natural language processing software to analyze this text data and understand the user's request. From the analysis results, it is recognized that information related to "ionic bonds" is required.

[0200] 5. The server uses the generative AI model to generate an appropriate answer to the question, for example, "An ionic bond is a chemical bond in which atoms of different charges attract each other."

[0201] 6. If necessary, obtain additional information from database systems to complement your answers based on your analysis.

[0202] 7. The generated answer is sent to the user's device, where it can be displayed visually or read aloud.

[0203] This system enables efficient and stress-free learning, especially for learners with neurodiversity characteristics, including developmental disorders.

[0204] Examples of specific examples and prompts

[0205] Example 1:

[0206] If a student in a chemistry class asks, "What is an ionic bond?", they can input the question by voice. Speech recognition software converts the question into text and sends it to a server. It is analyzed using natural language processing technology, and a generative AI model generates the answer: "An ionic bond is a chemical bond in which atoms of different charges attract each other." The answer is then read aloud by the user's device and displayed on the screen.

[0207] Example prompt sentence:

[0208] When is the next meeting?

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] If a user has a question while studying, they can input it by voice or text into a mobile device such as a smartphone or head-mounted display. This input initiates the process.

[0212] Step 2:

[0213] The device receives input voice data and converts it into text data using speech recognition software (speech_recognition). In this case, the input is voice data and the output is the corresponding text data. For example, the speech "What is an ionic bond?" is converted into text.

[0214] Step 3:

[0215] The terminal sends the converted text data to the server. The input is the text data, and the output is the data sent to the server. The specific operation here is the process of sending data over the network.

[0216] Step 4:

[0217] The server analyzes the received text data using natural language processing software (spaCy). The input for the analysis is the text data, and the output is the user's request or intent as the analysis result. For example, the keyword "ionic bond" is extracted.

[0218] Step 5:

[0219] The server uses a generative AI model (Transformers' Pipeline function) to generate an appropriate answer to the user's question. The input is the keywords from the analysis results, and the output is the generated answer. Specifically, the generated text is "An ionic bond is a chemical bond in which atoms of different charges attract each other."

[0220] Step 6:

[0221] The server searches and retrieves additional information from the relevant database as needed. The input is the parsed keywords and the query, and the output is the information retrieved from the database. The specific operation is to execute a database query to retrieve data.

[0222] Step 7:

[0223] The server compiles the generated answer and the acquired information and sends it to the user terminal as a response. The input is the answer and additional information, and the output is the response data sent to the terminal.

[0224] Step 8:

[0225] The user device displays or reads out the received response data. The input is the response data sent from the server, and the output is visual or auditory information provided to the user. Specific operations include displaying the screen or outputting voice using a voice synthesis engine.

[0226] Through these steps, users can get quick and accurate answers to their questions.

[0227] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0228] This invention is a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. In particular, by combining it with an emotion engine, it analyzes the emotions of both the user and the other party, realizing richer communication.

[0229] First, the user inputs a question or request into the device by voice or text. For example, the user might say, "When is the next meeting?" or type, "When is the next meeting?". Once the user has input, the device uses its voice recognition function to convert the voice into text data. This converted text data is then sent to the server.

[0230] The server analyzes the received text data using natural language processing (NLP) techniques. The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0231] The server then searches and retrieves the necessary information from the relevant database. For example, the server retrieves the date and time of the next meeting from the conference recording system. Based on this information, the server generates a response to provide to the user. It also takes into account the emotional state of the user at the time of input. To do this, it uses an emotion engine to analyze emotions from the user's input voice or text. For example, if the user is feeling stressed, it provides appropriate feedback based on that information.

[0232] The server then uses an emotion engine to infer the other person's emotions and intentions based on the analyzed data. This process analyzes the context and tone of the conversation to understand the other person's emotional state. For example, if the other person has a positive reaction during a meeting, that information can be provided to the user, helping them understand the other person's emotions.

[0233] The server then sends the generated information back to the device. The device then displays the received information to the user or reads it aloud using speech synthesis technology. For example, the device may display the information "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded well" or read it aloud as "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded positively." In this way, the user can keep track of important communication information without missing anything.

[0234] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server then performs analysis, retrieves the agenda from the previous meeting record, and uses an emotion engine to take the user's emotional state into account. For example, if the user is feeling anxious, the server provides feedback based on that information. The device displays this information to the user and reads it aloud if necessary. For example, it provides the following information: "The next agenda will be a project progress report and discussion of new proposals. Please relax and focus." In this way, the user can accurately understand what to prepare for the next meeting, while also tracking their own emotions in the process.

[0235] As described above, the present invention helps users to grasp all necessary information during communication and accurately understand the intentions and feelings of the other person. It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0236] The processing flow will be explained below.

[0237] Step 1:

[0238] A user types a question or request into the device using voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0239] Step 2:

[0240] The device receives the voice and converts it into text using automatic speech recognition (ASR) technology. For example, converting the voice saying "When is the next meeting?" into text saying "When is the next meeting?"

[0241] Step 3:

[0242] The terminal sends the converted text data (or directly entered text data) to the server. For example, the text "When is the next meeting date?" is sent to the server.

[0243] Step 4:

[0244] When the device receives voice input, it uses an emotion engine to analyze emotions from the user's voice and sends the results to the server. For example, the emotion engine may detect stress from the user's speech and send that information to the server as "emotion data."

[0245] Step 5:

[0246] The server uses natural language processing (NLP) technology to analyze the received text data. The server analyzes the text data and understands the user's request. For example, from the question "When is the next meeting?", the server understands that it should return the schedule information for the next meeting.

[0247] Step 6:

[0248] The server retrieves necessary information from a related database based on the analysis results, for example, the server retrieves the date and time of the next meeting from a conference recording system.

[0249] Step 7:

[0250] Based on the information acquired by the server, a response is generated to be provided to the user, taking into account the user's emotional data. For example, a response such as "The next meeting is scheduled for next Tuesday at 2 p.m. Participants' reactions were positive."

[0251] Step 8:

[0252] The server then uses an emotion engine to infer the other person's emotions and intentions based on the analyzed data. This process analyzes the context and tone of the conversation to understand the other person's emotional state. For example, it generates information such as "The participant's response was very enthusiastic."

[0253] Step 9:

[0254] The server sends the generated information back to the device, for example, sending the generated reply text "The next meeting is scheduled for next Tuesday at 2pm. Participants' reactions were positive."

[0255] Step 10:

[0256] The device may display the received information to the user or read it aloud using speech synthesis technology. For example, the device may display "The next meeting is scheduled for next Tuesday at 2 PM" on the screen or read aloud "The next meeting is scheduled for next Tuesday at 2 PM. Participants responded positively."

[0257] Step 11:

[0258] By checking the displayed and spoken information, users can accurately understand the next meeting date and understand the other party's feelings. For example, users can add a meeting date to their calendar and prepare with confidence based on the positive reactions of participants.

[0259] Through each of these steps, the system helps users accurately and quickly grasp the information they need, thereby enabling users who have communication difficulties in particular to communicate more smoothly in their daily lives and at work.

[0260] Example 2

[0261] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0262] In modern society, users who struggle with communication, especially those with developmental disorders or neurodiversity, face challenges in understanding and recording information, as well as accurately grasping the emotions and intentions of others, in their daily lives and at work. Furthermore, there is a lack of mechanisms for smoothly understanding each other's emotions and intentions during communication and providing appropriate responses. There is a need to solve these challenges and support richer communication.

[0263] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0264] In this invention, the server includes a means for converting user input voice into text, a means for analyzing the text data to understand the user's request, and a means for generating a response based on emotion analysis, which allows the user to grasp all necessary information in communication and accurately understand the other party's intentions and emotions.

[0265] "User" means an entity that uses the system to provide information by voice or text input.

[0266] "Terminal" means a device or equipment that receives a user's voice or text input and transmits data to a server.

[0267] "Voice recognition function" refers to technology that converts a user's voice input into text data.

[0268] "Text data" refers to character string information converted by a voice recognition function.

[0269] A "server" is a central processing unit that receives text data and performs processes such as analysis, information acquisition, and sentiment analysis.

[0270] "Natural language processing (NLP) technology" is a technology for analyzing text data to understand user requests and intentions.

[0271] A "database" is an information collection system that stores, searches, and retrieves necessary information based on user requests.

[0272] An "emotion engine" is a technology that analyzes a user's emotional state from their voice or text input.

[0273] A "response" is a reaction or answer that a server generates in response to a user's request or intention and sends to a terminal.

[0274] "Speech synthesis technology" refers to technology that outputs text data as voice.

[0275] "Neurodiversity" refers to the concept and group of individuals with diverse neurological characteristics, including developmental disorders.

[0276] This invention provides a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. This system is realized by combining speech recognition functions, natural language processing technology, a database, an emotion engine, and speech synthesis technology.

[0277] When a user speaks or texts a question or request into the device, the device uses a speech recognition function to convert the speech into text data, typically using a speech recognition API (e.g., a voice recognition API). The converted text data is then sent to the server.

[0278] The server analyzes the received text data using natural language processing (NLP) techniques. Natural language processing is typically performed using a natural language processing API (e.g., a natural language processing API). The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0279] The server searches and retrieves the required information from a relevant database. Typically, it retrieves the information using a database API (e.g., a calendar API). For example, the server might retrieve the next meeting date and time from a meeting recording system. Based on this information, the server generates a response to serve to the user.

[0280] Furthermore, the server also takes into account the user's emotional state. To do this, it uses an emotion engine to analyze emotions from the user's input voice or text. For emotion analysis, an emotion analysis API (e.g., emotion analysis API) is generally used. For example, if the user is feeling stressed, the server can provide appropriate feedback based on that information.

[0281] The server infers the other person's emotions and intentions based on the analyzed data. It analyzes the context and tone of the conversation to understand the other person's emotional state. For example, if the other person has a positive reaction during a meeting, it will provide that information to the user, making it easier for the user to understand the other person's emotions.

[0282] The server sends the generated information back to the device. The device then displays the received information to the user or reads it aloud using speech synthesis technology (e.g., a speech synthesis API). For example, "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded well" may be displayed on the device or read aloud "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded positively." In this way, the user can keep track of important communication information without missing anything.

[0283] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server then performs analysis, retrieves the agenda from the previous meeting record, and uses an emotion engine to take the user's emotional state into account. For example, if the user is feeling anxious, the server provides feedback based on that information. The device displays this information to the user and reads it aloud if necessary. For example, it provides the following information: "The next agenda will be a project progress report and discussion of new proposals. Please relax and focus." In this way, the user can accurately understand what to prepare for the next meeting, while also tracking their own emotions in the process.

[0284] In this way, the present invention helps users grasp all necessary information during communication and accurately understand the other person's intentions and feelings.It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0285] Examples of prompts include:

[0286] When a user speaks to the device during a meeting, "When is the next meeting date?", the device performs speech recognition and sends the text to the server. The server then analyzes the text using NLP and retrieves date and time information from the meeting recording. It then uses an emotion engine to analyze the user's emotions and provides appropriate feedback. The device then displays this information and reads it out loud.

[0287] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0288] Step 1:

[0289] The user enters a question or request into the device by voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0290] Input: User voice or text data

[0291] Output: Voice data sent to the device's speech recognition engine or text data sent directly to the server

[0292] Step 2:

[0293] The device uses its speech recognition function to convert speech into text data. For speech recognition, a speech recognition API is used. For example, the speech "When is the next meeting?" is converted into text "When is the next meeting?"

[0294] Input: Audio data sent to the device

[0295] Output: Converted text data

[0296] Step 3:

[0297] The terminal transmits the generated text data to the server via the network.

[0298] Input: Converted text data

[0299] Output: Text data sent to the server

[0300] Step 4:

[0301] The server analyzes the received text data using natural language processing (NLP) technology. For natural language processing, a natural language processing API is used. The server analyzes the text data and understands the user's request. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0302] Input: Text data sent to the server

[0303] Output: Analysis information (user request intent)

[0304] Step 5:

[0305] The server searches and retrieves the required information from the relevant database, using a database API to retrieve the information. For example, the server retrieves the date and time of the next meeting from a conference recording system.

[0306] Input: Analysis information (user request intent)

[0307] Output: Retrieved data (next meeting date and time information)

[0308] Step 6:

[0309] The server uses an emotion engine to analyze emotions from the user's input voice or text. For emotion analysis, it uses an emotion analysis API. For example, if the user is feeling stressed, it provides appropriate feedback based on that information.

[0310] Input: User input voice or text data

[0311] Output: Parsed emotion information

[0312] Step 7:

[0313] The server generates a response based on the analysis information, acquired information, and emotional information. For example, it generates a response such as, "The next meeting is scheduled for next Tuesday at 2 p.m. Participants' responses were positive."

[0314] Input: Analysis information, Acquisition information, Emotion information

[0315] Output: The generated response

[0316] Step 8:

[0317] The server sends the generated response to the terminal, which then sends data to the terminal via the network.

[0318] Input: The generated response

[0319] Output: The response sent to the device

[0320] Step 9:

[0321] The device can then display the response to the user or read it aloud using speech synthesis technology. For example, it can read, "The next meeting is next Tuesday at 2 p.m. Participants responded positively."

[0322] Input: The response sent to the terminal

[0323] Output: The response that is displayed or spoken to the user

[0324] (Application example 2)

[0325] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0326] Users who struggle with communication, especially those with neurodivergent characteristics, often find it difficult to communicate effectively with staff and customers in physical stores. This problem also poses an obstacle to providing high-quality customer service. Furthermore, a lack of the ability to provide appropriate real-time responses during customer interactions can lead to lower customer satisfaction. The present invention aims to solve this problem and help users communicate effectively in physical stores.

[0327] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving voice or text input by a user during communication; means for converting the input voice into text; means for transmitting the text data to the server; means for analyzing the text data to understand the user's request; means for retrieving information from a relational database based on the request; means for generating the retrieved information to provide to the user; means for displaying or reading out the generated information to the user; means installed in a mobile or wearable device available to staff and providing appropriate responses to customers in real time when interacting with them; means for presenting appropriate answers to the customer's questions in real time to achieve high-quality customer service; and means for analyzing the customer's emotions and tone and suggesting appropriate responses. This enables users to communicate effectively even in physical stores.

[0328] A "means for receiving voice or text" is a means for receiving voice input or text input from a user through a device.

[0329] The "means for converting voice to text" refers to a means for converting input voice data into text data using voice recognition technology.

[0330] The "means for transmitting text data to a server" refers to a means for transmitting the converted text data to a server via a network.

[0331] "Means for understanding user requirements" refers to the means for analyzing text data and understanding the information the user is seeking and the intent behind the request.

[0332] "Means for obtaining information from a related database" refers to means for searching and obtaining necessary information from a database based on a user's request.

[0333] The "means for generating acquired information" refers to a means for generating a response to be provided to a user based on information acquired from a database.

[0334] "Means for displaying or reading aloud" refers to means for visually displaying the generated information to the user or reading it aloud using speech synthesis technology.

[0335] "Mobile or wearable device" is a general term for devices that can be carried or worn by staff.

[0336] "Means for providing appropriate responses to customers in real time" refers to means for providing immediate appropriate responses to customer questions or requests.

[0337] "Means for analyzing customer emotions and tone" refers to means for analyzing emotions from the content of what a customer says and the tone of their voice, and optimizing responses.

[0338] "High-quality customer service" refers to service that can respond quickly and appropriately to customer expectations and requests.

[0339] This invention is a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. In particular, by combining it with an emotion engine that analyzes the emotions of both the user and the other party, it realizes richer communication.

[0340] First, the user inputs a question or request into the device by voice or text. For example, if the user says, "Do you have this item in stock?", the device uses its voice recognition function to convert the voice into text data. The hardware used for this is smart glasses or a smartphone, and the speech recognition library is speech_recognition.

[0341] The converted text data is sent over the network to the server, which then analyzes the received text data using natural language processing (NLP) techniques to understand the user's request. NLP analysis uses the nltk and transformers libraries to analyze the user's question or request and understand their intent.

[0342] The server then searches and retrieves the required information from the relevant database, for example, referencing the product management system database to retrieve inventory information, and the retrieved information is quickly generated as a response by the server.

[0343] Additionally, an emotion engine can be used to analyze emotions from customer voice or text input. For example, if a customer is expressing anxiety, Transformers' emotion analysis model can be used to provide feedback based on that information.

[0344] The server then attempts to analyze the other person's emotions and intentions using an emotion engine, which evaluates the context and tone of the conversation to understand the other person's emotional state and generate an appropriate real-time response.

[0345] The generated information is sent back to the device and displayed to the user or read aloud using text-to-speech technology (e.g., google.cloud.texttospeech). For example, "This item is currently in stock."

[0346] Specific examples

[0347] When a customer asks, "Do you have this item in stock?", a staff member asks the smart glasses. The voice is converted into text using voice recognition and sent to the server. The server analyzes the text data, retrieves stock information from a database, and generates an appropriate response. If the customer's emotion analysis indicates anxiety, the device will read out loud, "We've checked. We currently have this item in stock, so don't worry."

[0348] Prompt Sentence Examples

[0349] "When a customer asks, 'Do you have this item in stock?' we check the inventory, and if it is in stock, we respond, 'We currently have it in stock.' If, based on sentiment analysis, the customer appears dissatisfied, we respond, 'We will check whether it is in stock immediately. Please wait a little longer.'"

[0350] In this way, the present invention helps users grasp important information and accurately understand the intentions and emotions of others. It is also designed to enable efficient and stress-free communication, especially for users with neurodiversity characteristics such as developmental disorders.

[0351] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0352] Step 1:

[0353] The terminal receives a user's voice or text input. For example, it receives an input such as "Do you have this item in stock?" through smart glasses or a smartphone, and acquires the input data. The input here is voice data or text data.

[0354] Step 2:

[0355] The device converts the received voice input into text data using its voice recognition function. At this time, it uses the speech_recognition library to convert the voice data into text data and output it as text data.

[0356] Step 3:

[0357] The converted text data is sent from the terminal to the server. Network communication technology is used to pass the text data to the server. The input here is the text data, and the output is the text data in the state it was sent to the server.

[0358] Step 4:

[0359] The server analyzes the received text data using natural language processing (NLP) techniques. NLP analysis uses the nltk and transformers libraries to analyze the context of the text data. As a result of the analysis, the user's request and intent are extracted.

[0360] Step 5:

[0361] After understanding the user's request, the server searches and retrieves the necessary information from the relevant database. For example, it retrieves product inventory information from the database of a product management system. The input is the user's request, and the output is the retrieved inventory information.

[0362] Step 6:

[0363] The server generates a response based on the information it has acquired. It generates a text response to fulfill the user's request, taking into account the customer's sentiment analysis results using an emotion engine if necessary. The generated response is a specific inventory confirmation message.

[0364] Step 7:

[0365] It uses an emotion engine to analyze customer emotions and tone. If the customer is anxious or dissatisfied, it analyzes their state and tailors the response to include appropriate feedback. The input is what the customer says, and the output is the analyzed emotion data.

[0366] Step 8:

[0367] The server sends the generated response back to the device. Based on the response received by the device, the information is displayed to the user or read aloud using speech synthesis technology. Here, google.cloud.texttospeech is used to convert text data into speech data. The input is the response text data, and the output is speech data.

[0368] Step 9:

[0369] The device displays or reads the information to the user, for example, "This item is currently in stock." Finally, the device provides the user with the information they requested in an appropriate format.

[0370] By following these steps, users will be able to communicate effectively in physical stores.

[0371] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0372] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0373] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0374] [Second embodiment]

[0375] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0376] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0377] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0378] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0379] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0380] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0381] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0382] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0383] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0384] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0385] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0386] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0387] The present invention is a communication assistant system that helps users who are not good at communication to smoothly understand and record information. This system operates as follows.

[0388] First, the user inputs a question or request into the device by voice or text. For example, during a meeting, the user might say to the device, "When is the next meeting?" or type in text, "When is the next meeting?". As the user types, the device uses its voice recognition function to convert the voice into text data. This converted text data is then sent to the server.

[0389] The server analyzes the received text data using natural language processing (NLP) techniques. The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0390] The server then searches and retrieves the necessary information from relevant databases. For example, the server may retrieve the next meeting date and time from a meeting recording system. Based on this information, the server generates a response to provide to the user. The response may include not only the meeting date and time, but also the emotional reactions of the participants and other relevant information.

[0391] The server then sends the generated information back to the device, which then displays the received information to the user or reads it aloud. For example, the device might display the information "The next meeting is scheduled for next Tuesday at 2 PM" or read it aloud as "The next meeting is scheduled for next Tuesday at 2 PM." In this way, the user can keep track of important communication information without missing it.

[0392] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server analyzes it, retrieves the agenda from the previous meeting record, and sends back to the device, "The agenda for the next meeting will be a project progress report and discussion of new proposals." The device then displays this information to the user and reads it out loud if necessary. In this way, the user can accurately understand what they need to prepare for the next meeting.

[0393] As described above, the present invention helps users to grasp all necessary information during communication and accurately understand the intentions and feelings of the other person. It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0394] The processing flow will be explained below.

[0395] Step 1:

[0396] A user types a question or request into the device using voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0397] Step 2:

[0398] The device receives the voice and converts it into text using automatic speech recognition (ASR) technology. For example, converting the voice saying "When is the next meeting?" into text saying "When is the next meeting?"

[0399] Step 3:

[0400] The terminal sends the converted text data (or directly entered text data) to the server. For example, the text "When is the next meeting date?" is sent to the server.

[0401] Step 4:

[0402] The server uses natural language processing (NLP) technology to analyze the received text data. The server analyzes the text data and understands the user's request. For example, from the question "When is the next meeting?", the server understands that it should return the schedule information for the next meeting.

[0403] Step 5:

[0404] The server retrieves necessary information from a related database based on the analysis results, for example, the server retrieves the date and time of the next meeting from a conference recording system.

[0405] Step 6:

[0406] Based on the information obtained, the server generates a response to provide to the user, for example, "The next meeting is scheduled for next Tuesday at 2 p.m."

[0407] Step 7:

[0408] The server then performs sentiment analysis to analyze the other party's emotions and intentions, generating information such as "The participant's reaction is enthusiastic."

[0409] Step 8:

[0410] The server sends the generated information back to the device, for example, sending the generated reply text "The next meeting is scheduled for next Tuesday at 2pm" to the device.

[0411] Step 9:

[0412] The device displays the received information to the user or reads it aloud using speech synthesis technology. For example, the device may display "Your next meeting is scheduled for next Tuesday at 2 PM" on the screen or read aloud "Your next meeting is next Tuesday at 2 PM."

[0413] Step 10:

[0414] The user can review the displayed and spoken information to accurately understand the date of an upcoming meeting. For example, the user can add a meeting date to their calendar.

[0415] Through each of these steps, the system helps users accurately and quickly grasp the information they need, thereby enabling users who have communication difficulties in particular to communicate more smoothly in their daily lives and at work.

[0416] Example 1

[0417] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0418] In modern society, many users who are not good at communication have difficulty understanding and recording information smoothly. In particular, there are insufficient tools to enable efficient and stress-free communication for users with developmental disorders and neurodiversity characteristics. To solve this problem, a system is needed that can analyze user input, accurately understand their intention, and provide the necessary information.

[0419] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0420] In this invention, the server includes means for receiving voice or text input, means for converting voice to text, means for transmitting the text data to the server, means for analyzing the text data to understand the user's request, means for retrieving information from a relational database, means for generating the retrieved information, and means for displaying or reading out the generated information. This allows the server to instantly retrieve necessary information based on the user's prompt and provide it by display or voice. This enables efficient and stress-free communication, particularly for users with developmental disorders or neurodiversity.

[0421] "Voice or text input" refers to the voice or text data used by a user to communicate.

[0422] "Voice recognition" refers to the technology that analyzes the user's voice as a digital signal and converts it into corresponding text data.

[0423] "Text data" refers to character strings of information input by a user or generated by voice recognition technology.

[0424] "Server" refers to a computer system that is responsible for analyzing received text data and obtaining and providing related information.

[0425] "Natural language processing (NLP)" refers to technology for analyzing text data and understanding user requests and intent.

[0426] "Database" refers to a collection of data that stores related information and allows that information to be searched and retrieved as needed.

[0427] "Response" refers to the reply information to the user that the server generates after analysis and search.

[0428] "Display" refers to the act of presenting information as text or graphic data on a terminal display.

[0429] "Audio output" refers to the playback of text data generated by the terminal as audio and audibly conveying it to the user.

[0430] A "generative AI model" refers to an artificial intelligence model that can generatively create or process data for a specific task.

[0431] A "prompt" is a string of text that a user inputs to a generative AI model, containing commands or questions that the AI ​​immediately processes in response.

[0432] This invention is a Communication Assistant system that helps users smoothly understand and record information during communication, and is particularly useful for users who have difficulty communicating or who have developmental disabilities. This system analyzes voice and text input and provides the necessary information.

[0433] Hardware and software used

[0434] Hardware

[0435] Device: A device that receives voice or text input from a user, including computers, smartphones, tablets, etc.

[0436] Server: A computer system for data analysis and information retrieval.

[0437] software

[0438] Speech Recognition API: Software used to convert a user's speech into text. For example, a common speech recognition API is Google's Speech Recognition API.

[0439] Natural language processing (NLP) model: Software used to analyze text data and understand user requests and intent. For example, OpenAI's GPT-3 is used as a generative AI model.

[0440] Database: Stores related information and searches and retrieves it as needed. A common database system is MySQL.

[0441] Text-to-Speech Engine (TTS): Software that outputs generated text information as speech. For example, Microsoft's TTS engine is used.

[0442] System Operation

[0443] 1. User Input

[0444] A user speaks or texts a question or request into the device, for example, "When is our next meeting?" or texts "When is our next meeting?"

[0445] 2. Voice Recognition

[0446] The device uses Google's speech recognition API to convert voice input into text data. For example, voice input such as "When is the next meeting?" is converted into text data such as "When is the next meeting?"

[0447] 3. Sending text data

[0448] The terminal sends the converted text data to the server using an HTTP request.

[0449] 4. Text Data Analysis

[0450] The server uses OpenAI's GPT-3 model to analyze the text data, for example, "When is the next meeting?" to understand the intent of returning the schedule information for the next meeting.

[0451] 5. Searching for Information

[0452] The server queries the MySQL database to retrieve the next meeting date and time information from the associated meeting recording system.

[0453] 6. Generating the Response

[0454] The server uses the information it has received to generate a response, such as "The next meeting is scheduled for next Tuesday at 2pm", which includes the date, time and other relevant information.

[0455] 7. Transmission of Information

[0456] The server returns the generated response to the terminal as an HTTP response.

[0457] 8. Displaying or audibly outputting information

[0458] The device then displays the information to the user or reads it aloud, using Microsoft's TTS engine. For example, it might display "Our next meeting is next Tuesday at 2 PM" on the screen and simultaneously read it aloud.

[0459] Specific examples

[0460] Example 1

[0461] User prompt: "What's on the agenda next time?"

[0462] Conversion and sending on device: Converts speech to text and sends it to the server.

[0463] Analysis and data acquisition on the server: Search for agenda information from the previous meeting records and generate the following: "The next agenda will be a project progress report and consideration of new proposals."

[0464] Returning and displaying information: The device receives the response and displays or speaks it to the user.

[0465] In this way, the present invention allows users to grasp important communication information without missing anything and accurately understand the other person's intentions and feelings. This system is particularly useful for users with developmental disorders or neurodiversity, as it enables efficient and stress-free communication.

[0466] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0467] Step 1:

[0468] User Input

[0469] Users can enter questions or requests into the device by voice or text, such as "When is the next meeting?" or "When is the next meeting?"

[0470] Input: User voice or text

[0471] Output: Raw data entered into the terminal

[0472] Step 2:

[0473] Voice Recognition

[0474] When using voice input, the device converts the voice into text data using a speech recognition API, such as Google's speech recognition API.

[0475] Input: raw user voice data

[0476] Data processing: Speech-to-text conversion using speech recognition API

[0477] Output: Text data

[0478] Step 3:

[0479] Sending text data

[0480] The terminal sends the converted text data to the server using an HTTP request.

[0481] Input: Text data

[0482] Data transmission: Data transmission via HTTP request

[0483] Output: Text data sent to the server

[0484] Step 4:

[0485] natural language analysis

[0486] The server analyzes the received text data using a natural language processing (NLP) model. For example, OpenAI's GPT-3 is used as a generative AI model. The server understands the user's intent from the text "When is the next meeting?"

[0487] Input: Text data

[0488] Data Computation: Semantic Analysis and Intention Understanding with Generative AI Models

[0489] Output: User intent as a result of analysis

[0490] Step 5:

[0491] Searching for information

[0492] Based on the analysis results, the server searches and retrieves the necessary information from the relevant database, for example, retrieving the date and time of the next meeting from a conference recording system.

[0493] Input: User Intent

[0494] Data retrieval: Querying a database

[0495] Output: Acquired meeting date and time information

[0496] Step 6:

[0497] Generating a response

[0498] The server uses the information it has obtained to generate a response to provide to the user, specifically, "The next meeting is next Tuesday at 2 p.m."

[0499] Input: Acquired meeting date and time information

[0500] Data generation: Generate a response message

[0501] Output: The generated response message

[0502] Step 7:

[0503] Sending information

[0504] The server returns the generated response message to the terminal, sending the data as an HTTP response.

[0505] Input: The generated response message

[0506] Data transmission: Data transmission via HTTP response

[0507] Output: Response message sent to the terminal

[0508] Step 8:

[0509] Display or speak information

[0510] The device then displays the received information to the user or reads it aloud, for example, using Microsoft's TTS engine to say, "Our next meeting is next Tuesday at 2 p.m.", or displays it on the screen.

[0511] Input: Received response message

[0512] Data output: TTS voice generation or display

[0513] Output: Information provided to the user (audio or text display)

[0514] (Application example 1)

[0515] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0516] In today's learning environment, learners with developmental disabilities and neurodiversity in particular face challenges in learning efficiently. Specifically, they face difficulties in obtaining quick and accurate answers when questions arise during learning, which negatively impacts their motivation and grades. Furthermore, general learning support systems lack the ability to properly understand emotions and intentions, making it impossible to provide support tailored to individual learners.

[0517] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0518] In this invention, the server includes: means for receiving voice or text input by a user during communication; means for converting the input voice into text; means for transmitting the text data to the server; means for analyzing the text data to understand a user's request; means for retrieving information from a related database based on the request; means for generating the retrieved information to provide to the user; means for displaying or reading out the generated information to the user; means for analyzing questions the user has while studying using natural language processing technology and generating answers; and means for searching and retrieving appropriate information from a learning resource database. This makes it possible to provide quick and accurate answers to specific questions faced by learners, thereby efficiently supporting the learning process of learners with neurodiversity characteristics, including developmental disabilities.

[0519] "User" means a person who uses the System.

[0520] "Communication" refers to the process of exchanging information through speech or text.

[0521] "Voice or text" refers to the form of information that a user inputs into a system.

[0522] "Means for converting voice to text" refers to a function that analyzes input voice data and converts it into corresponding text data.

[0523] "Text data" refers to information expressed in characters.

[0524] A "server" refers to a computer system that processes and stores data over a network.

[0525] "Means of analyzing and understanding user requests" refers to the function of analyzing received text data using natural language processing technology to understand the user's intentions and requests.

[0526] "Relational database" refers to a data storage system that stores information responsive to user requests.

[0527] "Means of obtaining information" refers to the process of searching and obtaining the required information from the relevant database.

[0528] "Means of generation" refers to the function of constructing a response to provide to the user based on the acquired information.

[0529] "Means for displaying or reading" refers to the ability to provide the generated information to the user visually or audibly.

[0530] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0531] "Learning resource database" refers to a database that stores information related to learning.

[0532] "Neurodiversity, including developmental disorders" refers to the concept of diverse cognitive and behavioral characteristics, particularly developmental disorders.

[0533] "Questions raised during learning" refers to questions or uncertainties that learners have during the learning process.

[0534] This invention is an interactive learning assistant system that helps users resolve questions that arise during learning. Its purpose is to provide efficient learning support, especially for learners with developmental disorders and neurodiversity.

[0535] The system consists of the following major hardware and software components:

[0536] Hardware

[0537] 1. User device: A mobile device such as a smartphone or head-mounted display that allows the user to input a question by voice or text and have it displayed or read aloud.

[0538] 2. Server: A computer system for performing advanced data analysis and information retrieval.

[0539] software

[0540] 1. Speech recognition software (speech_recognition): Converts user input into text data.

[0541] 2. Natural language processing software (spaCy): Used to analyze text data and understand user requirements.

[0542] 3. Generative AI model (Transformers Pipeline feature): Generates appropriate answers based on the user's question.

[0543] 4. Database system: Data storage for storing learning resources and searching and retrieving required information.

[0544] Processing Overview

[0545] 1. A question is input by voice or text from the user's device. For example, "What is an ionic bond?"

[0546] 2. Use speech recognition software to convert this speech data into text data. The converted text data will be "What is an ionic bond?"

[0547] 3. The converted text data is sent to the server.

[0548] 4. The server uses natural language processing software to analyze this text data and understand the user's request. From the analysis results, it is recognized that information related to "ionic bonds" is required.

[0549] 5. The server uses the generative AI model to generate an appropriate answer to the question, for example, "An ionic bond is a chemical bond in which atoms of different charges attract each other."

[0550] 6. If necessary, obtain additional information from database systems to complement your answers based on your analysis.

[0551] 7. The generated answer is sent to the user's device, where it can be displayed visually or read aloud.

[0552] This system enables efficient and stress-free learning, especially for learners with neurodiversity characteristics, including developmental disorders.

[0553] Examples of specific examples and prompts

[0554] Example 1:

[0555] If a student in a chemistry class asks, "What is an ionic bond?", they can input the question by voice. Speech recognition software converts the question into text and sends it to a server. It is analyzed using natural language processing technology, and a generative AI model generates the answer: "An ionic bond is a chemical bond in which atoms of different charges attract each other." The answer is then read aloud by the user's device and displayed on the screen.

[0556] Example prompt sentence:

[0557] When is the next meeting?

[0558] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0559] Step 1:

[0560] If a user has a question while studying, they can input it by voice or text into a mobile device such as a smartphone or head-mounted display. This input initiates the process.

[0561] Step 2:

[0562] The device receives input voice data and converts it into text data using speech recognition software (speech_recognition). In this case, the input is voice data and the output is the corresponding text data. For example, the speech "What is an ionic bond?" is converted into text.

[0563] Step 3:

[0564] The terminal sends the converted text data to the server. The input is the text data, and the output is the data sent to the server. The specific operation here is the process of sending data over the network.

[0565] Step 4:

[0566] The server analyzes the received text data using natural language processing software (spaCy). The input for the analysis is the text data, and the output is the user's request or intent as the analysis result. For example, the keyword "ionic bond" is extracted.

[0567] Step 5:

[0568] The server uses a generative AI model (Transformers' Pipeline function) to generate an appropriate answer to the user's question. The input is the keywords from the analysis results, and the output is the generated answer. Specifically, the generated text is "An ionic bond is a chemical bond in which atoms of different charges attract each other."

[0569] Step 6:

[0570] The server searches and retrieves additional information from the relevant database as needed. The input is the parsed keywords and the query, and the output is the information retrieved from the database. The specific operation is to execute a database query to retrieve data.

[0571] Step 7:

[0572] The server compiles the generated answer and the acquired information and sends it to the user terminal as a response. The input is the answer and additional information, and the output is the response data sent to the terminal.

[0573] Step 8:

[0574] The user device displays or reads out the received response data. The input is the response data sent from the server, and the output is visual or auditory information provided to the user. Specific operations include displaying the screen or outputting voice using a voice synthesis engine.

[0575] Through these steps, users can get quick and accurate answers to their questions.

[0576] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0577] This invention is a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. In particular, by combining it with an emotion engine, it analyzes the emotions of both the user and the other party, realizing richer communication.

[0578] First, the user inputs a question or request into the device by voice or text. For example, the user might say, "When is the next meeting?" or type, "When is the next meeting?". Once the user has input, the device uses its voice recognition function to convert the voice into text data. This converted text data is then sent to the server.

[0579] The server analyzes the received text data using natural language processing (NLP) techniques. The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0580] The server then searches and retrieves the necessary information from the relevant database. For example, the server retrieves the date and time of the next meeting from the conference recording system. Based on this information, the server generates a response to provide to the user. It also takes into account the emotional state of the user at the time of input. To do this, it uses an emotion engine to analyze emotions from the user's input voice or text. For example, if the user is feeling stressed, it provides appropriate feedback based on that information.

[0581] The server then uses an emotion engine to infer the other person's emotions and intentions based on the analyzed data. This process analyzes the context and tone of the conversation to understand the other person's emotional state. For example, if the other person has a positive reaction during a meeting, that information can be provided to the user, helping them understand the other person's emotions.

[0582] The server then sends the generated information back to the device. The device then displays the received information to the user or reads it aloud using speech synthesis technology. For example, the device may display the information "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded well" or read it aloud as "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded positively." In this way, the user can keep track of important communication information without missing anything.

[0583] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server then performs analysis, retrieves the agenda from the previous meeting record, and uses an emotion engine to take the user's emotional state into account. For example, if the user is feeling anxious, the server provides feedback based on that information. The device displays this information to the user and reads it aloud if necessary. For example, it provides the following information: "The next agenda will be a project progress report and discussion of new proposals. Please relax and focus." In this way, the user can accurately understand what to prepare for the next meeting, while also tracking their own emotions in the process.

[0584] As described above, the present invention helps users to grasp all necessary information during communication and accurately understand the intentions and feelings of the other person. It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0585] The processing flow will be explained below.

[0586] Step 1:

[0587] A user types a question or request into the device using voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0588] Step 2:

[0589] The device receives the voice and converts it into text using automatic speech recognition (ASR) technology. For example, converting the voice saying "When is the next meeting?" into text saying "When is the next meeting?"

[0590] Step 3:

[0591] The terminal sends the converted text data (or directly entered text data) to the server. For example, the text "When is the next meeting date?" is sent to the server.

[0592] Step 4:

[0593] When the device receives voice input, it uses an emotion engine to analyze emotions from the user's voice and sends the results to the server. For example, the emotion engine may detect stress from the user's speech and send that information to the server as "emotion data."

[0594] Step 5:

[0595] The server uses natural language processing (NLP) technology to analyze the received text data. The server analyzes the text data and understands the user's request. For example, from the question "When is the next meeting?", the server understands that it should return the schedule information for the next meeting.

[0596] Step 6:

[0597] The server retrieves necessary information from a related database based on the analysis results, for example, the server retrieves the date and time of the next meeting from a conference recording system.

[0598] Step 7:

[0599] Based on the information acquired by the server, a response is generated to be provided to the user, taking into account the user's emotional data. For example, a response such as "The next meeting is scheduled for next Tuesday at 2 p.m. Participants' reactions were positive."

[0600] Step 8:

[0601] The server then uses an emotion engine to infer the other person's emotions and intentions based on the analyzed data. This process analyzes the context and tone of the conversation to understand the other person's emotional state. For example, it generates information such as "The participant's response was very enthusiastic."

[0602] Step 9:

[0603] The server sends the generated information back to the device, for example, sending the generated reply text "The next meeting is scheduled for next Tuesday at 2pm. Participants' reactions were positive."

[0604] Step 10:

[0605] The device may display the received information to the user or read it aloud using speech synthesis technology. For example, the device may display "The next meeting is scheduled for next Tuesday at 2 PM" on the screen or read aloud "The next meeting is scheduled for next Tuesday at 2 PM. Participants responded positively."

[0606] Step 11:

[0607] By checking the displayed and spoken information, users can accurately understand the next meeting date and understand the other party's feelings. For example, users can add a meeting date to their calendar and prepare with confidence based on the positive reactions of participants.

[0608] Through each of these steps, the system helps users accurately and quickly grasp the information they need, thereby enabling users who have communication difficulties in particular to communicate more smoothly in their daily lives and at work.

[0609] Example 2

[0610] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0611] In modern society, users who struggle with communication, especially those with developmental disorders or neurodiversity, face challenges in understanding and recording information, as well as accurately grasping the emotions and intentions of others, in their daily lives and at work. Furthermore, there is a lack of mechanisms for smoothly understanding each other's emotions and intentions during communication and providing appropriate responses. There is a need to solve these challenges and support richer communication.

[0612] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0613] In this invention, the server includes a means for converting user input voice into text, a means for analyzing the text data to understand the user's request, and a means for generating a response based on emotion analysis, which allows the user to grasp all necessary information in communication and accurately understand the other party's intentions and emotions.

[0614] "User" means an entity that uses the system to provide information by voice or text input.

[0615] "Terminal" means a device or equipment that receives a user's voice or text input and transmits data to a server.

[0616] "Voice recognition function" refers to technology that converts a user's voice input into text data.

[0617] "Text data" refers to character string information converted by a voice recognition function.

[0618] A "server" is a central processing unit that receives text data and performs processes such as analysis, information acquisition, and sentiment analysis.

[0619] "Natural language processing (NLP) technology" is a technology for analyzing text data to understand user requests and intentions.

[0620] A "database" is an information collection system that stores, searches, and retrieves necessary information based on user requests.

[0621] An "emotion engine" is a technology that analyzes a user's emotional state from their voice or text input.

[0622] A "response" is a reaction or answer that a server generates in response to a user's request or intention and sends to a terminal.

[0623] "Speech synthesis technology" refers to technology that outputs text data as voice.

[0624] "Neurodiversity" refers to the concept and group of individuals with diverse neurological characteristics, including developmental disorders.

[0625] This invention provides a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. This system is realized by combining speech recognition functions, natural language processing technology, a database, an emotion engine, and speech synthesis technology.

[0626] When a user speaks or texts a question or request into the device, the device uses a speech recognition function to convert the speech into text data, typically using a speech recognition API (e.g., a voice recognition API). The converted text data is then sent to the server.

[0627] The server analyzes the received text data using natural language processing (NLP) techniques. Natural language processing is typically performed using a natural language processing API (e.g., a natural language processing API). The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0628] The server searches and retrieves the required information from a relevant database. Typically, it retrieves the information using a database API (e.g., a calendar API). For example, the server might retrieve the next meeting date and time from a meeting recording system. Based on this information, the server generates a response to serve to the user.

[0629] Furthermore, the server also takes into account the user's emotional state. To do this, it uses an emotion engine to analyze emotions from the user's input voice or text. For emotion analysis, an emotion analysis API (e.g., emotion analysis API) is generally used. For example, if the user is feeling stressed, the server can provide appropriate feedback based on that information.

[0630] The server infers the other person's emotions and intentions based on the analyzed data. It analyzes the context and tone of the conversation to understand the other person's emotional state. For example, if the other person has a positive reaction during a meeting, it will provide that information to the user, making it easier for the user to understand the other person's emotions.

[0631] The server sends the generated information back to the device. The device then displays the received information to the user or reads it aloud using speech synthesis technology (e.g., a speech synthesis API). For example, "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded well" may be displayed on the device or read aloud "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded positively." In this way, the user can keep track of important communication information without missing anything.

[0632] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server then performs analysis, retrieves the agenda from the previous meeting record, and uses an emotion engine to take the user's emotional state into account. For example, if the user is feeling anxious, the server provides feedback based on that information. The device displays this information to the user and reads it aloud if necessary. For example, it provides the following information: "The next agenda will be a project progress report and discussion of new proposals. Please relax and focus." In this way, the user can accurately understand what to prepare for the next meeting, while also tracking their own emotions in the process.

[0633] In this way, the present invention helps users grasp all necessary information during communication and accurately understand the other person's intentions and feelings.It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0634] Examples of prompts include:

[0635] When a user speaks to the device during a meeting, "When is the next meeting date?", the device performs speech recognition and sends the text to the server. The server then analyzes the text using NLP and retrieves date and time information from the meeting recording. It then uses an emotion engine to analyze the user's emotions and provides appropriate feedback. The device then displays this information and reads it out loud.

[0636] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0637] Step 1:

[0638] The user enters a question or request into the device by voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0639] Input: User voice or text data

[0640] Output: Voice data sent to the device's speech recognition engine or text data sent directly to the server

[0641] Step 2:

[0642] The device uses its speech recognition function to convert speech into text data. For speech recognition, a speech recognition API is used. For example, the speech "When is the next meeting?" is converted into text "When is the next meeting?"

[0643] Input: Audio data sent to the device

[0644] Output: Converted text data

[0645] Step 3:

[0646] The terminal transmits the generated text data to the server via the network.

[0647] Input: Converted text data

[0648] Output: Text data sent to the server

[0649] Step 4:

[0650] The server analyzes the received text data using natural language processing (NLP) technology. For natural language processing, a natural language processing API is used. The server analyzes the text data and understands the user's request. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0651] Input: Text data sent to the server

[0652] Output: Analysis information (user request intent)

[0653] Step 5:

[0654] The server searches and retrieves the required information from the relevant database. It retrieves the information using a database API. For example, the server retrieves the date and time of the next meeting from a conference recording system.

[0655] Input: Analysis information (user request intent)

[0656] Output: Retrieved data (next meeting date and time information)

[0657] Step 6:

[0658] The server uses an emotion engine to analyze emotions from the user's input voice or text. For emotion analysis, it uses an emotion analysis API. For example, if the user is feeling stressed, it provides appropriate feedback based on that information.

[0659] Input: User input voice or text data

[0660] Output: Parsed emotion information

[0661] Step 7:

[0662] The server generates a response based on the analysis information, acquired information, and emotional information. For example, it generates a response such as, "The next meeting is scheduled for next Tuesday at 2 p.m. Participants' responses were positive."

[0663] Input: Analysis information, Acquisition information, Emotion information

[0664] Output: The generated response

[0665] Step 8:

[0666] The server sends the generated response to the terminal, which then sends data to the terminal via the network.

[0667] Input: The generated response

[0668] Output: The response sent to the device

[0669] Step 9:

[0670] The device can then display the response to the user or read it aloud using speech synthesis technology. For example, it can read, "The next meeting is next Tuesday at 2 p.m. Participants responded positively."

[0671] Input: The response sent to the terminal

[0672] Output: The response that is displayed or spoken to the user

[0673] (Application example 2)

[0674] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0675] Users who struggle with communication, especially those with neurodivergent characteristics, often find it difficult to communicate effectively with staff and customers in physical stores. This problem also poses an obstacle to providing high-quality customer service. Furthermore, a lack of the ability to provide appropriate real-time responses during customer interactions can lead to lower customer satisfaction. The present invention aims to solve this problem and help users communicate effectively in physical stores.

[0676] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving voice or text input by a user during communication; means for converting the input voice into text; means for transmitting the text data to the server; means for analyzing the text data to understand the user's request; means for retrieving information from a relational database based on the request; means for generating the retrieved information to provide to the user; means for displaying or reading out the generated information to the user; means installed in a mobile or wearable device available to staff and providing appropriate responses to customers in real time when interacting with them; means for presenting appropriate answers to the customer's questions in real time to achieve high-quality customer service; and means for analyzing the customer's emotions and tone and suggesting appropriate responses. This enables users to communicate effectively even in physical stores.

[0677] A "means for receiving voice or text" is a means for receiving voice input or text input from a user through a device.

[0678] The "means for converting voice to text" refers to a means for converting input voice data into text data using voice recognition technology.

[0679] The "means for transmitting text data to a server" refers to a means for transmitting the converted text data to a server via a network.

[0680] "Means for understanding user requirements" refers to the means for analyzing text data and understanding the information the user is seeking and the intent behind the request.

[0681] "Means for obtaining information from a related database" refers to means for searching and obtaining necessary information from a database based on a user's request.

[0682] The "means for generating acquired information" refers to a means for generating a response to be provided to a user based on information acquired from a database.

[0683] "Means for displaying or reading aloud" refers to means for visually displaying the generated information to the user or reading it aloud using speech synthesis technology.

[0684] "Mobile or wearable device" is a general term for devices that can be carried or worn by staff.

[0685] "Means for providing appropriate responses to customers in real time" refers to means for providing immediate appropriate responses to customer questions or requests.

[0686] "Means for analyzing customer emotions and tone" refers to means for analyzing emotions from the content of what a customer says and the tone of their voice, and optimizing responses.

[0687] "High-quality customer service" refers to service that can respond quickly and appropriately to customer expectations and requests.

[0688] This invention is a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. In particular, by combining it with an emotion engine that analyzes the emotions of both the user and the other party, it realizes richer communication.

[0689] First, the user inputs a question or request into the device by voice or text. For example, if the user says, "Do you have this item in stock?", the device uses its voice recognition function to convert the voice into text data. The hardware used for this is smart glasses or a smartphone, and the speech recognition library is speech_recognition.

[0690] The converted text data is sent over the network to the server, which then analyzes the received text data using natural language processing (NLP) techniques to understand the user's request. NLP analysis uses the nltk and transformers libraries to analyze the user's question or request and understand their intent.

[0691] The server then searches and retrieves the required information from the relevant database, for example, referencing the product management system database to retrieve inventory information, and the retrieved information is quickly generated as a response by the server.

[0692] Additionally, an emotion engine can be used to analyze emotions from customer voice or text input. For example, if a customer is expressing anxiety, Transformers' emotion analysis model can be used to provide feedback based on that information.

[0693] The server then attempts to analyze the other person's emotions and intentions using an emotion engine, which evaluates the context and tone of the conversation to understand the other person's emotional state and generate an appropriate real-time response.

[0694] The generated information is sent back to the device and displayed to the user or read aloud using text-to-speech technology (e.g., google.cloud.texttospeech). For example, "This item is currently in stock."

[0695] Specific examples

[0696] When a customer asks, "Do you have this item in stock?", a staff member asks the smart glasses. The voice is converted into text using voice recognition and sent to the server. The server analyzes the text data, retrieves stock information from a database, and generates an appropriate response. If the customer's emotion analysis indicates anxiety, the device will read out loud, "We've checked. We currently have this item in stock, so don't worry."

[0697] Prompt Sentence Examples

[0698] "When a customer asks, 'Do you have this item in stock?' we check the inventory, and if it is in stock, we respond, 'We currently have it in stock.' If, based on sentiment analysis, the customer appears dissatisfied, we respond, 'We will check whether it is in stock immediately. Please wait a little longer.'"

[0699] In this way, the present invention helps users grasp important information and accurately understand the intentions and emotions of others. It is also designed to enable efficient and stress-free communication, especially for users with neurodiversity characteristics such as developmental disorders.

[0700] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0701] Step 1:

[0702] The terminal receives a user's voice or text input. For example, it receives an input such as "Do you have this item in stock?" through smart glasses or a smartphone, and acquires the input data. The input here is voice data or text data.

[0703] Step 2:

[0704] The device converts the received voice input into text data using its voice recognition function. At this time, it uses the speech_recognition library to convert the voice data into text data and output it as text data.

[0705] Step 3:

[0706] The converted text data is sent from the terminal to the server. Network communication technology is used to pass the text data to the server. The input here is the text data, and the output is the text data in the state it was sent to the server.

[0707] Step 4:

[0708] The server analyzes the received text data using natural language processing (NLP) techniques. NLP analysis uses the nltk and transformers libraries to analyze the context of the text data. As a result of the analysis, the user's request and intent are extracted.

[0709] Step 5:

[0710] After understanding the user's request, the server searches and retrieves the necessary information from the relevant database. For example, it retrieves product inventory information from the database of a product management system. The input is the user's request, and the output is the retrieved inventory information.

[0711] Step 6:

[0712] The server generates a response based on the information it has acquired. It generates a text response to fulfill the user's request, taking into account the customer's sentiment analysis results using an emotion engine if necessary. The generated response is a specific inventory confirmation message.

[0713] Step 7:

[0714] It uses an emotion engine to analyze customer emotions and tone. If the customer is anxious or dissatisfied, it analyzes their state and tailors the response to include appropriate feedback. The input is what the customer says, and the output is the analyzed emotion data.

[0715] Step 8:

[0716] The server sends the generated response back to the device. Based on the response received by the device, the information is displayed to the user or read aloud using speech synthesis technology. Here, google.cloud.texttospeech is used to convert text data into speech data. The input is the response text data, and the output is speech data.

[0717] Step 9:

[0718] The device displays or reads the information to the user, for example, "This item is currently in stock." Finally, the device provides the user with the information they requested in an appropriate format.

[0719] By following these steps, users will be able to communicate effectively in physical stores.

[0720] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0721] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0722] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0723] [Third embodiment]

[0724] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0725] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0726] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0727] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0728] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0729] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0730] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0731] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0732] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0733] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0734] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0735] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0736] The present invention is a communication assistant system that helps users who are not good at communication to smoothly understand and record information. This system operates as follows.

[0737] First, the user inputs a question or request into the device by voice or text. For example, during a meeting, the user might say to the device, "When is the next meeting?" or type in text, "When is the next meeting?". As the user types, the device uses its voice recognition function to convert the voice into text data. This converted text data is then sent to the server.

[0738] The server analyzes the received text data using natural language processing (NLP) techniques. The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0739] The server then searches and retrieves the necessary information from relevant databases. For example, the server may retrieve the next meeting date and time from a meeting recording system. Based on this information, the server generates a response to provide to the user. The response may include not only the meeting date and time, but also the emotional reactions of the participants and other relevant information.

[0740] The server then sends the generated information back to the device, which then displays the received information to the user or reads it aloud. For example, the device might display the information "The next meeting is scheduled for next Tuesday at 2 PM" or read it aloud as "The next meeting is scheduled for next Tuesday at 2 PM." In this way, the user can keep track of important communication information without missing it.

[0741] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server analyzes it, retrieves the agenda from the previous meeting record, and sends back to the device, "The agenda for the next meeting will be a project progress report and discussion of new proposals." The device then displays this information to the user and reads it out loud if necessary. In this way, the user can accurately understand what they need to prepare for the next meeting.

[0742] As described above, the present invention helps users to grasp all necessary information during communication and accurately understand the intentions and feelings of the other person. It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] A user types a question or request into the device using voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0746] Step 2:

[0747] The device receives the voice and converts it into text using automatic speech recognition (ASR) technology. For example, converting the voice saying "When is the next meeting?" into text saying "When is the next meeting?"

[0748] Step 3:

[0749] The terminal sends the converted text data (or directly entered text data) to the server. For example, the text "When is the next meeting date?" is sent to the server.

[0750] Step 4:

[0751] The server uses natural language processing (NLP) technology to analyze the received text data. The server analyzes the text data and understands the user's request. For example, from the question "When is the next meeting?", the server understands that it should return the schedule information for the next meeting.

[0752] Step 5:

[0753] The server retrieves necessary information from a related database based on the analysis results, for example, the server retrieves the date and time of the next meeting from a conference recording system.

[0754] Step 6:

[0755] Based on the information obtained, the server generates a response to provide to the user, for example, "The next meeting is scheduled for next Tuesday at 2 p.m."

[0756] Step 7:

[0757] The server then performs sentiment analysis to analyze the other party's emotions and intentions, generating information such as "The participant's reaction is enthusiastic."

[0758] Step 8:

[0759] The server sends the generated information back to the device, for example, sending the generated reply text "The next meeting is scheduled for next Tuesday at 2pm" to the device.

[0760] Step 9:

[0761] The device displays the received information to the user or reads it aloud using speech synthesis technology. For example, the device may display "Your next meeting is scheduled for next Tuesday at 2 PM" on the screen or read aloud "Your next meeting is next Tuesday at 2 PM."

[0762] Step 10:

[0763] The user can review the displayed and spoken information to accurately understand the date of an upcoming meeting. For example, the user can add a meeting date to their calendar.

[0764] Through each of these steps, the system helps users accurately and quickly grasp the information they need, thereby enabling users who have communication difficulties in particular to communicate more smoothly in their daily lives and at work.

[0765] Example 1

[0766] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0767] In modern society, many users who are not good at communication have difficulty understanding and recording information smoothly. In particular, there are insufficient tools to enable efficient and stress-free communication for users with developmental disorders and neurodiversity characteristics. To solve this problem, a system is needed that can analyze user input, accurately understand their intention, and provide the necessary information.

[0768] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0769] In this invention, the server includes means for receiving voice or text input, means for converting voice to text, means for transmitting the text data to the server, means for analyzing the text data to understand the user's request, means for retrieving information from a relational database, means for generating the retrieved information, and means for displaying or reading out the generated information. This allows the server to instantly retrieve necessary information based on the user's prompt and provide it by display or voice. This enables efficient and stress-free communication, particularly for users with developmental disorders or neurodiversity.

[0770] "Voice or text input" refers to the voice or text data used by a user to communicate.

[0771] "Voice recognition" refers to the technology that analyzes the user's voice as a digital signal and converts it into corresponding text data.

[0772] "Text data" refers to character strings of information input by a user or generated by voice recognition technology.

[0773] "Server" refers to a computer system that is responsible for analyzing received text data and obtaining and providing related information.

[0774] "Natural language processing (NLP)" refers to technology for analyzing text data and understanding user requests and intent.

[0775] "Database" refers to a collection of data that stores related information and allows that information to be searched and retrieved as needed.

[0776] "Response" refers to the reply information to the user that the server generates after analysis and search.

[0777] "Display" refers to the act of presenting information as text or graphic data on a terminal display.

[0778] "Audio output" refers to the playback of text data generated by the terminal as audio and audibly conveying it to the user.

[0779] A "generative AI model" refers to an artificial intelligence model that can generatively create or process data for a specific task.

[0780] A "prompt" is a string of text that a user inputs to a generative AI model, containing commands or questions that the AI ​​immediately processes in response.

[0781] This invention is a Communication Assistant system that helps users smoothly understand and record information during communication, and is particularly useful for users who have difficulty communicating or who have developmental disabilities. This system analyzes voice and text input and provides the necessary information.

[0782] Hardware and software used

[0783] Hardware

[0784] Device: A device that receives voice or text input from a user, including computers, smartphones, tablets, etc.

[0785] Server: A computer system for data analysis and information retrieval.

[0786] software

[0787] Speech Recognition API: Software used to convert a user's speech into text. For example, a common speech recognition API is Google's Speech Recognition API.

[0788] Natural language processing (NLP) model: Software used to analyze text data and understand user requests and intent. For example, OpenAI's GPT-3 is used as a generative AI model.

[0789] Database: Stores related information and searches and retrieves it as needed. A common database system is MySQL.

[0790] Text-to-Speech Engine (TTS): Software that outputs generated text information as speech. For example, Microsoft's TTS engine is used.

[0791] System Operation

[0792] 1. User Input

[0793] A user speaks or texts a question or request into the device, for example, "When is our next meeting?" or texts "When is our next meeting?"

[0794] 2. Voice Recognition

[0795] The device uses Google's speech recognition API to convert voice input into text data. For example, voice input such as "When is the next meeting?" is converted into text data such as "When is the next meeting?"

[0796] 3. Sending text data

[0797] The terminal sends the converted text data to the server using an HTTP request.

[0798] 4. Text Data Analysis

[0799] The server uses OpenAI's GPT-3 model to analyze the text data, for example, "When is the next meeting?" to understand the intent of returning the schedule information for the next meeting.

[0800] 5. Searching for Information

[0801] The server queries the MySQL database to retrieve the next meeting date and time information from the associated meeting recording system.

[0802] 6. Generating the Response

[0803] The server uses the information it has received to generate a response, such as "The next meeting is scheduled for next Tuesday at 2pm", which includes the date, time and other relevant information.

[0804] 7. Transmission of Information

[0805] The server returns the generated response to the terminal as an HTTP response.

[0806] 8. Displaying or audibly outputting information

[0807] The device then displays the information to the user or reads it aloud, using Microsoft's TTS engine. For example, it might display "Our next meeting is next Tuesday at 2 PM" on the screen and simultaneously read it aloud.

[0808] Specific examples

[0809] Example 1

[0810] User prompt: "What's on the agenda next time?"

[0811] Conversion and sending on device: Converts speech to text and sends it to the server.

[0812] Analysis and data acquisition on the server: Search for agenda information from the previous meeting records and generate the following: "The next agenda will be a project progress report and consideration of new proposals."

[0813] Returning and displaying information: The device receives the response and displays or speaks it to the user.

[0814] In this way, the present invention allows users to grasp important communication information without missing anything and accurately understand the other person's intentions and feelings. This system is particularly useful for users with developmental disorders or neurodiversity, as it enables efficient and stress-free communication.

[0815] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0816] Step 1:

[0817] User Input

[0818] Users can enter questions or requests into the device by voice or text, such as "When is the next meeting?" or "When is the next meeting?"

[0819] Input: User voice or text

[0820] Output: Raw data entered into the terminal

[0821] Step 2:

[0822] Voice Recognition

[0823] When using voice input, the device converts the voice into text data using a speech recognition API, such as Google's speech recognition API.

[0824] Input: raw user voice data

[0825] Data processing: Speech-to-text conversion using speech recognition API

[0826] Output: Text data

[0827] Step 3:

[0828] Sending text data

[0829] The terminal sends the converted text data to the server using an HTTP request.

[0830] Input: Text data

[0831] Data transmission: Data transmission via HTTP request

[0832] Output: Text data sent to the server

[0833] Step 4:

[0834] natural language analysis

[0835] The server analyzes the received text data using a natural language processing (NLP) model. For example, OpenAI's GPT-3 is used as a generative AI model. The server understands the user's intent from the text "When is the next meeting?"

[0836] Input: Text data

[0837] Data Computation: Semantic Analysis and Intention Understanding with Generative AI Models

[0838] Output: User intent as a result of analysis

[0839] Step 5:

[0840] Searching for information

[0841] Based on the analysis results, the server searches and retrieves the necessary information from the relevant database, for example, retrieving the date and time of the next meeting from a conference recording system.

[0842] Input: User Intent

[0843] Data retrieval: Querying a database

[0844] Output: Acquired meeting date and time information

[0845] Step 6:

[0846] Generating a response

[0847] The server uses the information it has obtained to generate a response to provide to the user, specifically, "The next meeting is next Tuesday at 2 p.m."

[0848] Input: Acquired meeting date and time information

[0849] Data generation: Generate a response message

[0850] Output: The generated response message

[0851] Step 7:

[0852] Sending information

[0853] The server returns the generated response message to the terminal, sending the data as an HTTP response.

[0854] Input: The generated response message

[0855] Data transmission: Data transmission via HTTP response

[0856] Output: Response message sent to the terminal

[0857] Step 8:

[0858] Display or speak information

[0859] The device then displays the received information to the user or reads it aloud, for example, using Microsoft's TTS engine to say, "Our next meeting is next Tuesday at 2 p.m.", or displays it on the screen.

[0860] Input: Received response message

[0861] Data output: TTS voice generation or display

[0862] Output: Information provided to the user (audio or text display)

[0863] (Application example 1)

[0864] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0865] In today's learning environment, learners with developmental disabilities and neurodiversity in particular face challenges in learning efficiently. Specifically, they face difficulties in obtaining quick and accurate answers when questions arise during learning, which negatively impacts their motivation and grades. Furthermore, general learning support systems lack the ability to properly understand emotions and intentions, making it impossible to provide support tailored to individual learners.

[0866] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0867] In this invention, the server includes: means for receiving voice or text input by a user during communication; means for converting the input voice into text; means for transmitting the text data to the server; means for analyzing the text data to understand a user's request; means for retrieving information from a related database based on the request; means for generating the retrieved information to provide to the user; means for displaying or reading out the generated information to the user; means for analyzing questions the user has while studying using natural language processing technology and generating answers; and means for searching and retrieving appropriate information from a learning resource database. This makes it possible to provide quick and accurate answers to specific questions faced by learners, thereby efficiently supporting the learning process of learners with neurodiversity characteristics, including developmental disabilities.

[0868] "User" means a person who uses the System.

[0869] "Communication" refers to the process of exchanging information through speech or text.

[0870] "Voice or text" refers to the form of information that a user inputs into a system.

[0871] "Means for converting voice to text" refers to a function that analyzes input voice data and converts it into corresponding text data.

[0872] "Text data" refers to information expressed in characters.

[0873] A "server" refers to a computer system that processes and stores data over a network.

[0874] "Means of analyzing and understanding user requests" refers to the function of analyzing received text data using natural language processing technology to understand the user's intentions and requests.

[0875] "Relational database" refers to a data storage system that stores information responsive to user requests.

[0876] "Means of obtaining information" refers to the process of searching and obtaining the required information from the relevant database.

[0877] "Means of generation" refers to the function of constructing a response to provide to the user based on the acquired information.

[0878] "Means for displaying or reading" refers to the ability to provide the generated information to the user visually or audibly.

[0879] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0880] "Learning resource database" refers to a database that stores information related to learning.

[0881] "Neurodiversity, including developmental disorders" refers to the concept of diverse cognitive and behavioral characteristics, particularly developmental disorders.

[0882] "Questions raised during learning" refers to questions or uncertainties that learners have during the learning process.

[0883] This invention is an interactive learning assistant system that helps users resolve questions that arise during learning. Its purpose is to provide efficient learning support, especially for learners with developmental disorders and neurodiversity.

[0884] The system consists of the following major hardware and software components:

[0885] Hardware

[0886] 1. User device: A mobile device such as a smartphone or head-mounted display that allows the user to input a question by voice or text and have it displayed or read aloud.

[0887] 2. Server: A computer system for performing advanced data analysis and information retrieval.

[0888] software

[0889] 1. Speech recognition software (speech_recognition): Converts user input into text data.

[0890] 2. Natural language processing software (spaCy): Used to analyze text data and understand user requirements.

[0891] 3. Generative AI model (Transformers Pipeline feature): Generates appropriate answers based on the user's question.

[0892] 4. Database system: Data storage for storing learning resources and searching and retrieving required information.

[0893] Processing Overview

[0894] 1. A question is input by voice or text from the user's device. For example, "What is an ionic bond?"

[0895] 2. Use speech recognition software to convert this speech data into text data. The converted text data will be "What is an ionic bond?"

[0896] 3. The converted text data is sent to the server.

[0897] 4. The server uses natural language processing software to analyze this text data and understand the user's request. From the analysis results, it is recognized that information related to "ionic bonds" is required.

[0898] 5. The server uses the generative AI model to generate an appropriate answer to the question, for example, "An ionic bond is a chemical bond in which atoms of different charges attract each other."

[0899] 6. If necessary, obtain additional information from database systems to complement your answers based on your analysis.

[0900] 7. The generated answer is sent to the user's device, where it can be displayed visually or read aloud.

[0901] This system enables efficient and stress-free learning, especially for learners with neurodiversity characteristics, including developmental disorders.

[0902] Examples of specific examples and prompts

[0903] Example 1:

[0904] If a student in a chemistry class asks, "What is an ionic bond?", they can input the question by voice. Speech recognition software converts the question into text and sends it to a server. It is analyzed using natural language processing technology, and a generative AI model generates the answer: "An ionic bond is a chemical bond in which atoms of different charges attract each other." The answer is then read aloud by the user's device and displayed on the screen.

[0905] Example prompt sentence:

[0906] When is the next meeting?

[0907] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0908] Step 1:

[0909] If a user has a question while studying, they can input it by voice or text into a mobile device such as a smartphone or head-mounted display. This input initiates the process.

[0910] Step 2:

[0911] The device receives input voice data and converts it into text data using speech recognition software (speech_recognition). In this case, the input is voice data and the output is the corresponding text data. For example, the speech "What is an ionic bond?" is converted into text.

[0912] Step 3:

[0913] The terminal sends the converted text data to the server. The input is the text data, and the output is the data sent to the server. The specific operation here is the process of sending data over the network.

[0914] Step 4:

[0915] The server analyzes the received text data using natural language processing software (spaCy). The input for the analysis is the text data, and the output is the user's request or intent as the analysis result. For example, the keyword "ionic bond" is extracted.

[0916] Step 5:

[0917] The server uses a generative AI model (Transformers' Pipeline function) to generate an appropriate answer to the user's question. The input is the keywords from the analysis results, and the output is the generated answer. Specifically, the generated text is "An ionic bond is a chemical bond in which atoms of different charges attract each other."

[0918] Step 6:

[0919] The server searches and retrieves additional information from the relevant database as needed. The input is the parsed keywords and the query, and the output is the information retrieved from the database. The specific operation is to execute a database query to retrieve data.

[0920] Step 7:

[0921] The server compiles the generated answer and the acquired information and sends it to the user terminal as a response. The input is the answer and additional information, and the output is the response data sent to the terminal.

[0922] Step 8:

[0923] The user device displays or reads out the received response data. The input is the response data sent from the server, and the output is visual or auditory information provided to the user. Specific operations include displaying the screen or outputting voice using a voice synthesis engine.

[0924] Through these steps, users can get quick and accurate answers to their questions.

[0925] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0926] This invention is a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. In particular, by combining it with an emotion engine, it analyzes the emotions of both the user and the other party, realizing richer communication.

[0927] First, the user inputs a question or request into the device by voice or text. For example, the user might say, "When is the next meeting?" or type, "When is the next meeting?". Once the user has input, the device uses its voice recognition function to convert the voice into text data. This converted text data is then sent to the server.

[0928] The server analyzes the received text data using natural language processing (NLP) techniques. The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0929] The server then searches and retrieves the necessary information from the relevant database. For example, the server retrieves the date and time of the next meeting from the conference recording system. Based on this information, the server generates a response to provide to the user. It also takes into account the emotional state of the user at the time of input. To do this, it uses an emotion engine to analyze emotions from the user's input voice or text. For example, if the user is feeling stressed, it provides appropriate feedback based on that information.

[0930] The server then uses an emotion engine to infer the other person's emotions and intentions based on the analyzed data. This process analyzes the context and tone of the conversation to understand the other person's emotional state. For example, if the other person has a positive reaction during a meeting, that information can be provided to the user, helping them understand the other person's emotions.

[0931] The server then sends the generated information back to the device. The device then displays the received information to the user or reads it aloud using speech synthesis technology. For example, the device may display the information "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded well" or read it aloud as "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded positively." In this way, the user can keep track of important communication information without missing anything.

[0932] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server then performs analysis, retrieves the agenda from the previous meeting record, and uses an emotion engine to take the user's emotional state into account. For example, if the user is feeling anxious, the server provides feedback based on that information. The device displays this information to the user and reads it aloud if necessary. For example, it provides the following information: "The next agenda will be a project progress report and discussion of new proposals. Please relax and focus." In this way, the user can accurately understand what to prepare for the next meeting, while also tracking their own emotions in the process.

[0933] As described above, the present invention helps users to grasp all necessary information during communication and accurately understand the intentions and feelings of the other person. It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0934] The processing flow will be explained below.

[0935] Step 1:

[0936] A user types a question or request into the device using voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0937] Step 2:

[0938] The device receives the voice and converts it into text using automatic speech recognition (ASR) technology. For example, converting the voice saying "When is the next meeting?" into text saying "When is the next meeting?"

[0939] Step 3:

[0940] The terminal sends the converted text data (or directly entered text data) to the server. For example, the text "When is the next meeting date?" is sent to the server.

[0941] Step 4:

[0942] When the device receives voice input, it uses an emotion engine to analyze emotions from the user's voice and sends the results to the server. For example, the emotion engine may detect stress from the user's speech and send that information to the server as "emotion data."

[0943] Step 5:

[0944] The server uses natural language processing (NLP) technology to analyze the received text data. The server analyzes the text data and understands the user's request. For example, from the question "When is the next meeting?", the server understands that it should return the schedule information for the next meeting.

[0945] Step 6:

[0946] The server retrieves necessary information from a related database based on the analysis results, for example, the server retrieves the date and time of the next meeting from a conference recording system.

[0947] Step 7:

[0948] Based on the information acquired by the server, a response is generated to be provided to the user, taking into account the user's emotional data. For example, a response such as "The next meeting is scheduled for next Tuesday at 2 p.m. Participants' reactions were positive."

[0949] Step 8:

[0950] The server then uses an emotion engine to infer the other person's emotions and intentions based on the analyzed data. This process analyzes the context and tone of the conversation to understand the other person's emotional state. For example, it generates information such as "The participant's response was very enthusiastic."

[0951] Step 9:

[0952] The server sends the generated information back to the device, for example, sending the generated reply text "The next meeting is scheduled for next Tuesday at 2pm. Participants' reactions were positive."

[0953] Step 10:

[0954] The device may display the received information to the user or read it aloud using speech synthesis technology. For example, the device may display "The next meeting is scheduled for next Tuesday at 2 PM" on the screen or read aloud "The next meeting is scheduled for next Tuesday at 2 PM. Participants responded positively."

[0955] Step 11:

[0956] By checking the displayed and spoken information, users can accurately understand the next meeting date and understand the other party's feelings. For example, users can add a meeting date to their calendar and prepare with confidence based on the positive reactions of participants.

[0957] Through each of these steps, the system helps users accurately and quickly grasp the information they need, thereby enabling users who have communication difficulties in particular to communicate more smoothly in their daily lives and at work.

[0958] Example 2

[0959] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0960] In modern society, users who struggle with communication, especially those with developmental disorders or neurodiversity, face challenges in understanding and recording information, as well as accurately grasping the emotions and intentions of others, in their daily lives and at work. Furthermore, there is a lack of mechanisms for smoothly understanding each other's emotions and intentions during communication and providing appropriate responses. There is a need to solve these challenges and support richer communication.

[0961] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0962] In this invention, the server includes a means for converting user input voice into text, a means for analyzing the text data to understand the user's request, and a means for generating a response based on emotion analysis, which allows the user to grasp all necessary information in communication and accurately understand the other party's intentions and emotions.

[0963] "User" means an entity that uses the system to provide information by voice or text input.

[0964] "Terminal" means a device or equipment that receives a user's voice or text input and transmits data to a server.

[0965] "Voice recognition function" refers to technology that converts a user's voice input into text data.

[0966] "Text data" refers to character string information converted by a voice recognition function.

[0967] A "server" is a central processing unit that receives text data and performs processes such as analysis, information acquisition, and sentiment analysis.

[0968] "Natural language processing (NLP) technology" is a technology for analyzing text data to understand user requests and intentions.

[0969] A "database" is an information collection system that stores, searches, and retrieves necessary information based on user requests.

[0970] An "emotion engine" is a technology that analyzes a user's emotional state from their voice or text input.

[0971] A "response" is a reaction or answer that a server generates in response to a user's request or intention and sends to a terminal.

[0972] "Speech synthesis technology" refers to technology that outputs text data as voice.

[0973] "Neurodiversity" refers to the concept and group of individuals with diverse neurological characteristics, including developmental disorders.

[0974] This invention provides a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. This system is realized by combining speech recognition functions, natural language processing technology, a database, an emotion engine, and speech synthesis technology.

[0975] When a user speaks or texts a question or request into the device, the device uses a speech recognition function to convert the speech into text data, typically using a speech recognition API (e.g., a voice recognition API). The converted text data is then sent to the server.

[0976] The server analyzes the received text data using natural language processing (NLP) techniques. Natural language processing is typically performed using a natural language processing API (e.g., a natural language processing API). The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[0977] The server searches and retrieves the required information from a relevant database. Typically, it retrieves the information using a database API (e.g., a calendar API). For example, the server might retrieve the next meeting date and time from a meeting recording system. Based on this information, the server generates a response to serve to the user.

[0978] Furthermore, the server also takes into account the user's emotional state. To do this, it uses an emotion engine to analyze emotions from the user's input voice or text. For emotion analysis, an emotion analysis API (e.g., emotion analysis API) is generally used. For example, if the user is feeling stressed, the server can provide appropriate feedback based on that information.

[0979] The server infers the other person's emotions and intentions based on the analyzed data. It analyzes the context and tone of the conversation to understand the other person's emotional state. For example, if the other person has a positive reaction during a meeting, it will provide that information to the user, making it easier for the user to understand the other person's emotions.

[0980] The server sends the generated information back to the device. The device then displays the received information to the user or reads it aloud using speech synthesis technology (e.g., a speech synthesis API). For example, "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded well" may be displayed on the device or read aloud "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded positively." In this way, the user can keep track of important communication information without missing anything.

[0981] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server then performs analysis, retrieves the agenda from the previous meeting record, and uses an emotion engine to take the user's emotional state into account. For example, if the user is feeling anxious, the server provides feedback based on that information. The device displays this information to the user and reads it aloud if necessary. For example, it provides the following information: "The next agenda will be a project progress report and discussion of new proposals. Please relax and focus." In this way, the user can accurately understand what to prepare for the next meeting, while also tracking their own emotions in the process.

[0982] In this way, the present invention helps users grasp all necessary information during communication and accurately understand the other person's intentions and feelings.It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[0983] Examples of prompts include:

[0984] When a user speaks to the device during a meeting, "When is the next meeting date?", the device performs speech recognition and sends the text to the server. The server then analyzes the text using NLP and retrieves date and time information from the meeting recording. It then uses an emotion engine to analyze the user's emotions and provides appropriate feedback. The device then displays this information and reads it out loud.

[0985] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0986] Step 1:

[0987] The user enters a question or request into the device by voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[0988] Input: User voice or text data

[0989] Output: Voice data sent to the device's speech recognition engine or text data sent directly to the server

[0990] Step 2:

[0991] The device uses its speech recognition function to convert speech into text data. For speech recognition, a speech recognition API is used. For example, the speech "When is the next meeting?" is converted into text "When is the next meeting?"

[0992] Input: Audio data sent to the device

[0993] Output: Converted text data

[0994] Step 3:

[0995] The terminal transmits the generated text data to the server via the network.

[0996] Input: Converted text data

[0997] Output: Text data sent to the server

[0998] Step 4:

[0999] The server analyzes the received text data using natural language processing (NLP) technology. For natural language processing, a natural language processing API is used. The server analyzes the text data and understands the user's request. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[1000] Input: Text data sent to the server

[1001] Output: Analysis information (user request intent)

[1002] Step 5:

[1003] The server searches and retrieves the required information from the relevant database, using a database API to retrieve the information. For example, the server retrieves the date and time of the next meeting from a conference recording system.

[1004] Input: Analysis information (user request intent)

[1005] Output: Retrieved data (next meeting date and time information)

[1006] Step 6:

[1007] The server uses an emotion engine to analyze emotions from the user's input voice or text. For emotion analysis, it uses an emotion analysis API. For example, if the user is feeling stressed, it provides appropriate feedback based on that information.

[1008] Input: User input voice or text data

[1009] Output: Parsed emotion information

[1010] Step 7:

[1011] The server generates a response based on the analysis information, acquired information, and emotional information. For example, it generates a response such as, "The next meeting is scheduled for next Tuesday at 2 p.m. Participants' responses were positive."

[1012] Input: Analysis information, Acquisition information, Emotion information

[1013] Output: The generated response

[1014] Step 8:

[1015] The server sends the generated response to the terminal, which then sends data to the terminal via the network.

[1016] Input: The generated response

[1017] Output: The response sent to the device

[1018] Step 9:

[1019] The device can then display the response to the user or read it aloud using speech synthesis technology. For example, it can read, "The next meeting is next Tuesday at 2 p.m. Participants responded positively."

[1020] Input: The response sent to the terminal

[1021] Output: The response that is displayed or spoken to the user

[1022] (Application example 2)

[1023] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1024] Users who struggle with communication, especially those with neurodivergent characteristics, often find it difficult to communicate effectively with staff and customers in physical stores. This problem also poses an obstacle to providing high-quality customer service. Furthermore, a lack of the ability to provide appropriate real-time responses during customer interactions can lead to lower customer satisfaction. The present invention aims to solve this problem and help users communicate effectively in physical stores.

[1025] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving voice or text input by a user during communication; means for converting the input voice into text; means for transmitting the text data to the server; means for analyzing the text data to understand the user's request; means for retrieving information from a relational database based on the request; means for generating the retrieved information to provide to the user; means for displaying or reading out the generated information to the user; means installed in a mobile or wearable device available to staff and providing appropriate responses to customers in real time when interacting with them; means for presenting appropriate answers to the customer's questions in real time to achieve high-quality customer service; and means for analyzing the customer's emotions and tone and suggesting appropriate responses. This enables users to communicate effectively even in physical stores.

[1026] A "means for receiving voice or text" is a means for receiving voice input or text input from a user through a device.

[1027] The "means for converting voice to text" refers to a means for converting input voice data into text data using voice recognition technology.

[1028] The "means for transmitting text data to a server" refers to a means for transmitting the converted text data to a server via a network.

[1029] "Means for understanding user requirements" refers to the means for analyzing text data and understanding the information the user is seeking and the intent behind the request.

[1030] "Means for obtaining information from a related database" refers to means for searching and obtaining necessary information from a database based on a user's request.

[1031] The "means for generating acquired information" refers to a means for generating a response to be provided to a user based on information acquired from a database.

[1032] "Means for displaying or reading aloud" refers to means for visually displaying the generated information to the user or reading it aloud using speech synthesis technology.

[1033] "Mobile or wearable device" is a general term for devices that can be carried or worn by staff.

[1034] "Means for providing appropriate responses to customers in real time" refers to means for providing immediate appropriate responses to customer questions or requests.

[1035] "Means for analyzing customer emotions and tone" refers to means for analyzing emotions from the content of what a customer says and the tone of their voice, and optimizing responses.

[1036] "High-quality customer service" refers to service that can respond quickly and appropriately to customer expectations and requests.

[1037] This invention is a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. In particular, by combining it with an emotion engine that analyzes the emotions of both the user and the other party, it realizes richer communication.

[1038] First, the user inputs a question or request into the device by voice or text. For example, if the user says, "Do you have this item in stock?", the device uses its voice recognition function to convert the voice into text data. The hardware used for this is smart glasses or a smartphone, and the speech recognition library is speech_recognition.

[1039] The converted text data is sent over the network to the server, which then analyzes the received text data using natural language processing (NLP) techniques to understand the user's request. NLP analysis uses the nltk and transformers libraries to analyze the user's question or request and understand their intent.

[1040] The server then searches and retrieves the required information from the relevant database, for example, referencing the product management system database to retrieve inventory information, and the retrieved information is quickly generated as a response by the server.

[1041] Additionally, an emotion engine can be used to analyze emotions from customer voice or text input. For example, if a customer is expressing anxiety, Transformers' emotion analysis model can be used to provide feedback based on that information.

[1042] The server then attempts to analyze the other person's emotions and intentions using an emotion engine, which evaluates the context and tone of the conversation to understand the other person's emotional state and generate an appropriate real-time response.

[1043] The generated information is sent back to the device and displayed to the user or read aloud using text-to-speech technology (e.g., google.cloud.texttospeech). For example, "This item is currently in stock."

[1044] Specific examples

[1045] When a customer asks, "Do you have this item in stock?", a staff member asks the smart glasses. The voice is converted into text using voice recognition and sent to the server. The server analyzes the text data, retrieves stock information from a database, and generates an appropriate response. If the customer's emotion analysis indicates anxiety, the device will read out loud, "We've checked. We currently have this item in stock, so don't worry."

[1046] Prompt Sentence Examples

[1047] "When a customer asks, 'Do you have this item in stock?' we check the inventory, and if it is in stock, we respond, 'We currently have it in stock.' If, based on sentiment analysis, the customer appears dissatisfied, we respond, 'We will check whether it is in stock immediately. Please wait a little longer.'"

[1048] In this way, the present invention helps users grasp important information and accurately understand the intentions and emotions of others. It is also designed to enable efficient and stress-free communication, especially for users with neurodiversity characteristics such as developmental disorders.

[1049] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1050] Step 1:

[1051] The terminal receives a user's voice or text input. For example, it receives an input such as "Do you have this item in stock?" through smart glasses or a smartphone, and acquires the input data. The input here is voice data or text data.

[1052] Step 2:

[1053] The device converts the received voice input into text data using its voice recognition function. At this time, it uses the speech_recognition library to convert the voice data into text data and output it as text data.

[1054] Step 3:

[1055] The converted text data is sent from the terminal to the server. Network communication technology is used to pass the text data to the server. The input here is the text data, and the output is the text data in the state it was sent to the server.

[1056] Step 4:

[1057] The server analyzes the received text data using natural language processing (NLP) techniques. NLP analysis uses the nltk and transformers libraries to analyze the context of the text data. As a result of the analysis, the user's request and intent are extracted.

[1058] Step 5:

[1059] After understanding the user's request, the server searches and retrieves the necessary information from the relevant database. For example, it retrieves product inventory information from the database of a product management system. The input is the user's request, and the output is the retrieved inventory information.

[1060] Step 6:

[1061] The server generates a response based on the information it has acquired. It generates a text response to fulfill the user's request, taking into account the customer's sentiment analysis results using an emotion engine if necessary. The generated response is a specific inventory confirmation message.

[1062] Step 7:

[1063] It uses an emotion engine to analyze customer emotions and tone. If the customer is anxious or dissatisfied, it analyzes their state and tailors the response to include appropriate feedback. The input is what the customer says, and the output is the analyzed emotion data.

[1064] Step 8:

[1065] The server sends the generated response back to the device. Based on the response received by the device, the information is displayed to the user or read aloud using speech synthesis technology. Here, google.cloud.texttospeech is used to convert text data into speech data. The input is the response text data, and the output is speech data.

[1066] Step 9:

[1067] The device displays or reads the information to the user, for example, "This item is currently in stock." Finally, the device provides the user with the information they requested in an appropriate format.

[1068] By following these steps, users will be able to communicate effectively in physical stores.

[1069] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1071] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1072] [Fourth embodiment]

[1073] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1074] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1075] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1076] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1077] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1078] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1079] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1080] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1081] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1082] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1084] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1085] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1086] The present invention is a communication assistant system that helps users who are not good at communication to smoothly understand and record information. This system operates as follows.

[1087] First, the user inputs a question or request into the device by voice or text. For example, during a meeting, the user might say to the device, "When is the next meeting?" or type in text, "When is the next meeting?". As the user types, the device uses its voice recognition function to convert the voice into text data. This converted text data is then sent to the server.

[1088] The server analyzes the received text data using natural language processing (NLP) techniques. The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[1089] The server then searches and retrieves the necessary information from relevant databases. For example, the server may retrieve the next meeting date and time from a meeting recording system. Based on this information, the server generates a response to provide to the user. The response may include not only the meeting date and time, but also the emotional reactions of the participants and other relevant information.

[1090] The server then sends the generated information back to the device, which then displays the received information to the user or reads it aloud. For example, the device might display the information "The next meeting is scheduled for next Tuesday at 2 PM" or read it aloud as "The next meeting is scheduled for next Tuesday at 2 PM." In this way, the user can keep track of important communication information without missing it.

[1091] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server analyzes it, retrieves the agenda from the previous meeting record, and sends back to the device, "The agenda for the next meeting will be a project progress report and discussion of new proposals." The device then displays this information to the user and reads it out loud if necessary. In this way, the user can accurately understand what they need to prepare for the next meeting.

[1092] As described above, the present invention helps users to grasp all necessary information during communication and accurately understand the intentions and feelings of the other person. It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[1093] The processing flow will be explained below.

[1094] Step 1:

[1095] A user types a question or request into the device using voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[1096] Step 2:

[1097] The device receives the voice and converts it into text using automatic speech recognition (ASR) technology. For example, converting the voice saying "When is the next meeting?" into text saying "When is the next meeting?"

[1098] Step 3:

[1099] The terminal sends the converted text data (or directly entered text data) to the server. For example, the text "When is the next meeting date?" is sent to the server.

[1100] Step 4:

[1101] The server uses natural language processing (NLP) technology to analyze the received text data. The server analyzes the text data and understands the user's request. For example, from the question "When is the next meeting?", the server understands that it should return the schedule information for the next meeting.

[1102] Step 5:

[1103] The server retrieves necessary information from a related database based on the analysis results, for example, the server retrieves the date and time of the next meeting from a conference recording system.

[1104] Step 6:

[1105] Based on the information obtained, the server generates a response to provide to the user, for example, "The next meeting is scheduled for next Tuesday at 2 p.m."

[1106] Step 7:

[1107] The server then performs sentiment analysis to analyze the other party's emotions and intentions, generating information such as "The participant's reaction is enthusiastic."

[1108] Step 8:

[1109] The server sends the generated information back to the device, for example, sending the generated reply text "The next meeting is scheduled for next Tuesday at 2pm" to the device.

[1110] Step 9:

[1111] The device displays the received information to the user or reads it aloud using speech synthesis technology. For example, the device may display "Your next meeting is scheduled for next Tuesday at 2 PM" on the screen or read aloud "Your next meeting is next Tuesday at 2 PM."

[1112] Step 10:

[1113] The user can review the displayed and spoken information to accurately understand the date of an upcoming meeting. For example, the user can add a meeting date to their calendar.

[1114] Through each of these steps, the system helps users accurately and quickly grasp the information they need, thereby enabling users who have communication difficulties in particular to communicate more smoothly in their daily lives and at work.

[1115] Example 1

[1116] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1117] In modern society, many users who are not good at communication have difficulty understanding and recording information smoothly. In particular, there are insufficient tools to enable efficient and stress-free communication for users with developmental disorders and neurodiversity characteristics. To solve this problem, a system is needed that can analyze user input, accurately understand their intention, and provide the necessary information.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1119] In this invention, the server includes means for receiving voice or text input, means for converting voice to text, means for transmitting the text data to the server, means for analyzing the text data to understand the user's request, means for retrieving information from a relational database, means for generating the retrieved information, and means for displaying or reading out the generated information. This allows the server to instantly retrieve necessary information based on the user's prompt and provide it by display or voice. This enables efficient and stress-free communication, particularly for users with developmental disorders or neurodiversity.

[1120] "Voice or text input" refers to the voice or text data used by a user to communicate.

[1121] "Voice recognition" refers to the technology that analyzes the user's voice as a digital signal and converts it into corresponding text data.

[1122] "Text data" refers to character strings of information input by a user or generated by voice recognition technology.

[1123] "Server" refers to a computer system that is responsible for analyzing received text data and obtaining and providing related information.

[1124] "Natural language processing (NLP)" refers to technology for analyzing text data and understanding user requests and intent.

[1125] "Database" refers to a collection of data that stores related information and allows that information to be searched and retrieved as needed.

[1126] "Response" refers to the reply information to the user that the server generates after analysis and search.

[1127] "Display" refers to the act of presenting information as text or graphic data on a terminal display.

[1128] "Audio output" refers to the playback of text data generated by the terminal as audio and audibly conveying it to the user.

[1129] A "generative AI model" refers to an artificial intelligence model that can generatively create or process data for a specific task.

[1130] A "prompt" is a string of text that a user inputs to a generative AI model, containing commands or questions that the AI ​​immediately processes in response.

[1131] This invention is a Communication Assistant system that helps users smoothly understand and record information during communication, and is particularly useful for users who have difficulty communicating or who have developmental disabilities. This system analyzes voice and text input and provides the necessary information.

[1132] Hardware and software used

[1133] Hardware

[1134] Device: A device that receives voice or text input from a user, including computers, smartphones, tablets, etc.

[1135] Server: A computer system for data analysis and information retrieval.

[1136] software

[1137] Speech Recognition API: Software used to convert a user's speech into text. For example, a common speech recognition API is Google's Speech Recognition API.

[1138] Natural language processing (NLP) model: Software used to analyze text data and understand user requests and intent. For example, OpenAI's GPT-3 is used as a generative AI model.

[1139] Database: Stores related information and searches and retrieves it as needed. A common database system is MySQL.

[1140] Text-to-Speech Engine (TTS): Software that outputs generated text information as speech. For example, Microsoft's TTS engine is used.

[1141] System Operation

[1142] 1. User Input

[1143] A user speaks or texts a question or request into the device, for example, "When is our next meeting?" or texts "When is our next meeting?"

[1144] 2. Voice Recognition

[1145] The device uses Google's speech recognition API to convert voice input into text data. For example, voice input such as "When is the next meeting?" is converted into text data such as "When is the next meeting?"

[1146] 3. Sending text data

[1147] The terminal sends the converted text data to the server using an HTTP request.

[1148] 4. Text Data Analysis

[1149] The server uses OpenAI's GPT-3 model to analyze the text data, for example, "When is the next meeting?" to understand the intent of returning the schedule information for the next meeting.

[1150] 5. Searching for Information

[1151] The server queries the MySQL database to retrieve the next meeting date and time information from the associated meeting recording system.

[1152] 6. Generating the Response

[1153] The server uses the information it has received to generate a response, such as "The next meeting is scheduled for next Tuesday at 2pm", which includes the date, time and other relevant information.

[1154] 7. Transmission of Information

[1155] The server returns the generated response to the terminal as an HTTP response.

[1156] 8. Displaying or audibly outputting information

[1157] The device then displays the information to the user or reads it aloud, using Microsoft's TTS engine. For example, it might display "Our next meeting is next Tuesday at 2 PM" on the screen and simultaneously read it aloud.

[1158] Specific examples

[1159] Example 1

[1160] User prompt: "What's on the agenda next time?"

[1161] Conversion and sending on device: Converts speech to text and sends it to the server.

[1162] Analysis and data acquisition on the server: Search for agenda information from the previous meeting records and generate the following: "The next agenda will be a project progress report and consideration of new proposals."

[1163] Returning and displaying information: The device receives the response and displays or speaks it to the user.

[1164] In this way, the present invention allows users to grasp important communication information without missing anything and accurately understand the other person's intentions and feelings. This system is particularly useful for users with developmental disorders or neurodiversity, as it enables efficient and stress-free communication.

[1165] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1166] Step 1:

[1167] User Input

[1168] Users can enter questions or requests into the device by voice or text, such as "When is the next meeting?" or "When is the next meeting?"

[1169] Input: User voice or text

[1170] Output: Raw data entered into the terminal

[1171] Step 2:

[1172] Voice Recognition

[1173] When using voice input, the device converts the voice into text data using a speech recognition API, such as Google's speech recognition API.

[1174] Input: raw user voice data

[1175] Data processing: Speech-to-text conversion using speech recognition API

[1176] Output: Text data

[1177] Step 3:

[1178] Sending text data

[1179] The terminal sends the converted text data to the server using an HTTP request.

[1180] Input: Text data

[1181] Data transmission: Data transmission via HTTP request

[1182] Output: Text data sent to the server

[1183] Step 4:

[1184] natural language analysis

[1185] The server analyzes the received text data using a natural language processing (NLP) model. For example, OpenAI's GPT-3 is used as a generative AI model. The server understands the user's intent from the text "When is the next meeting?"

[1186] Input: Text data

[1187] Data Computation: Semantic Analysis and Intention Understanding with Generative AI Models

[1188] Output: User intent as a result of analysis

[1189] Step 5:

[1190] Searching for information

[1191] Based on the analysis results, the server searches and retrieves the necessary information from the relevant database, for example, retrieving the date and time of the next meeting from a conference recording system.

[1192] Input: User Intent

[1193] Data retrieval: Querying a database

[1194] Output: Acquired meeting date and time information

[1195] Step 6:

[1196] Generating a response

[1197] The server uses the information it has obtained to generate a response to provide to the user, specifically, "The next meeting is next Tuesday at 2 p.m."

[1198] Input: Acquired meeting date and time information

[1199] Data generation: Generate a response message

[1200] Output: The generated response message

[1201] Step 7:

[1202] Sending information

[1203] The server returns the generated response message to the terminal, sending the data as an HTTP response.

[1204] Input: The generated response message

[1205] Data transmission: Data transmission via HTTP response

[1206] Output: Response message sent to the terminal

[1207] Step 8:

[1208] Display or speak information

[1209] The device then displays the received information to the user or reads it aloud, for example, using Microsoft's TTS engine to say, "Our next meeting is next Tuesday at 2 p.m.", or displays it on the screen.

[1210] Input: Received response message

[1211] Data output: TTS voice generation or display

[1212] Output: Information provided to the user (audio or text display)

[1213] (Application example 1)

[1214] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1215] In today's learning environment, learners with developmental disabilities and neurodiversity in particular face challenges in learning efficiently. Specifically, they face difficulties in obtaining quick and accurate answers when questions arise during learning, which negatively impacts their motivation and grades. Furthermore, general learning support systems lack the ability to properly understand emotions and intentions, making it impossible to provide support tailored to individual learners.

[1216] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1217] In this invention, the server includes: means for receiving voice or text input by a user during communication; means for converting the input voice into text; means for transmitting the text data to the server; means for analyzing the text data to understand a user's request; means for retrieving information from a related database based on the request; means for generating the retrieved information to provide to the user; means for displaying or reading out the generated information to the user; means for analyzing questions the user has while studying using natural language processing technology and generating answers; and means for searching and retrieving appropriate information from a learning resource database. This makes it possible to provide quick and accurate answers to specific questions faced by learners, thereby efficiently supporting the learning process of learners with neurodiversity characteristics, including developmental disabilities.

[1218] "User" means a person who uses the System.

[1219] "Communication" refers to the process of exchanging information through speech or text.

[1220] "Voice or text" refers to the form of information that a user inputs into a system.

[1221] "Means for converting voice to text" refers to a function that analyzes input voice data and converts it into corresponding text data.

[1222] "Text data" refers to information expressed in characters.

[1223] A "server" refers to a computer system that processes and stores data over a network.

[1224] "Means of analyzing and understanding user requests" refers to the function of analyzing received text data using natural language processing technology to understand the user's intentions and requests.

[1225] "Relational database" refers to a data storage system that stores information responsive to user requests.

[1226] "Means of obtaining information" refers to the process of searching and obtaining the required information from the relevant database.

[1227] "Means of generation" refers to the function of constructing a response to provide to the user based on the acquired information.

[1228] "Means for displaying or reading" refers to the ability to provide the generated information to the user visually or audibly.

[1229] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[1230] "Learning resource database" refers to a database that stores information related to learning.

[1231] "Neurodiversity, including developmental disorders" refers to the concept of diverse cognitive and behavioral characteristics, particularly developmental disorders.

[1232] "Questions raised during learning" refers to questions or uncertainties that learners have during the learning process.

[1233] This invention is an interactive learning assistant system that helps users resolve questions that arise during learning. Its purpose is to provide efficient learning support, especially for learners with developmental disorders and neurodiversity.

[1234] The system consists of the following major hardware and software components:

[1235] Hardware

[1236] 1. User device: A mobile device such as a smartphone or head-mounted display that allows the user to input a question by voice or text and have it displayed or read aloud.

[1237] 2. Server: A computer system for performing advanced data analysis and information retrieval.

[1238] software

[1239] 1. Speech recognition software (speech_recognition): Converts user input into text data.

[1240] 2. Natural language processing software (spaCy): Used to analyze text data and understand user requirements.

[1241] 3. Generative AI model (Transformers Pipeline feature): Generates appropriate answers based on the user's question.

[1242] 4. Database system: Data storage for storing learning resources and searching and retrieving required information.

[1243] Processing Overview

[1244] 1. A question is input by voice or text from the user's device. For example, "What is an ionic bond?"

[1245] 2. Use speech recognition software to convert this speech data into text data. The converted text data will be "What is an ionic bond?"

[1246] 3. The converted text data is sent to the server.

[1247] 4. The server uses natural language processing software to analyze this text data and understand the user's request. From the analysis results, it is recognized that information related to "ionic bonds" is required.

[1248] 5. The server uses the generative AI model to generate an appropriate answer to the question, for example, "An ionic bond is a chemical bond in which atoms of different charges attract each other."

[1249] 6. If necessary, obtain additional information from database systems to complement your answers based on your analysis.

[1250] 7. The generated answer is sent to the user's device, where it can be displayed visually or read aloud.

[1251] This system enables efficient and stress-free learning, especially for learners with neurodiversity characteristics, including developmental disorders.

[1252] Examples of specific examples and prompts

[1253] Example 1:

[1254] If a student in a chemistry class asks, "What is an ionic bond?", they can input the question by voice. Speech recognition software converts the question into text and sends it to a server. It is analyzed using natural language processing technology, and a generative AI model generates the answer: "An ionic bond is a chemical bond in which atoms of different charges attract each other." The answer is then read aloud by the user's device and displayed on the screen.

[1255] Example prompt sentence:

[1256] When is the next meeting?

[1257] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1258] Step 1:

[1259] If a user has a question while studying, they can input it by voice or text into a mobile device such as a smartphone or head-mounted display. This input initiates the process.

[1260] Step 2:

[1261] The device receives input voice data and converts it into text data using speech recognition software (speech_recognition). In this case, the input is voice data and the output is the corresponding text data. For example, the speech "What is an ionic bond?" is converted into text.

[1262] Step 3:

[1263] The terminal sends the converted text data to the server. The input is the text data, and the output is the data sent to the server. The specific operation here is the process of sending data over the network.

[1264] Step 4:

[1265] The server analyzes the received text data using natural language processing software (spaCy). The input for the analysis is the text data, and the output is the user's request or intent as the analysis result. For example, the keyword "ionic bond" is extracted.

[1266] Step 5:

[1267] The server uses a generative AI model (Transformers' Pipeline function) to generate an appropriate answer to the user's question. The input is the keywords from the analysis results, and the output is the generated answer. Specifically, the generated text is "An ionic bond is a chemical bond in which atoms of different charges attract each other."

[1268] Step 6:

[1269] The server searches and retrieves additional information from the relevant database as needed. The input is the parsed keywords and the query, and the output is the information retrieved from the database. The specific operation is to execute a database query to retrieve data.

[1270] Step 7:

[1271] The server compiles the generated answer and the acquired information and sends it to the user terminal as a response. The input is the answer and additional information, and the output is the response data sent to the terminal.

[1272] Step 8:

[1273] The user device displays or reads out the received response data. The input is the response data sent from the server, and the output is visual or auditory information provided to the user. Specific operations include displaying the screen or outputting voice using a voice synthesis engine.

[1274] Through these steps, users can get quick and accurate answers to their questions.

[1275] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1276] This invention is a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. In particular, by combining it with an emotion engine, it analyzes the emotions of both the user and the other party, realizing richer communication.

[1277] First, the user inputs a question or request into the device by voice or text. For example, the user might say, "When is the next meeting?" or type, "When is the next meeting?". Once the user has input, the device uses its voice recognition function to convert the voice into text data. This converted text data is then sent to the server.

[1278] The server analyzes the received text data using natural language processing (NLP) techniques. The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[1279] The server then searches and retrieves the necessary information from the relevant database. For example, the server retrieves the date and time of the next meeting from the conference recording system. Based on this information, the server generates a response to provide to the user. It also takes into account the emotional state of the user at the time of input. To do this, it uses an emotion engine to analyze emotions from the user's input voice or text. For example, if the user is feeling stressed, it provides appropriate feedback based on that information.

[1280] The server then uses an emotion engine to infer the other person's emotions and intentions based on the analyzed data. This process analyzes the context and tone of the conversation to understand the other person's emotional state. For example, if the other person has a positive reaction during a meeting, that information can be provided to the user, helping them understand the other person's emotions.

[1281] The server then sends the generated information back to the device. The device then displays the received information to the user or reads it aloud using speech synthesis technology. For example, the device may display the information "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded well" or read it aloud as "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded positively." In this way, the user can keep track of important communication information without missing anything.

[1282] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server then performs analysis, retrieves the agenda from the previous meeting record, and uses an emotion engine to take the user's emotional state into account. For example, if the user is feeling anxious, the server provides feedback based on that information. The device displays this information to the user and reads it aloud if necessary. For example, it provides the following information: "The next agenda will be a project progress report and discussion of new proposals. Please relax and focus." In this way, the user can accurately understand what to prepare for the next meeting, while also tracking their own emotions in the process.

[1283] As described above, the present invention helps users to grasp all necessary information during communication and accurately understand the intentions and feelings of the other person. It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[1284] The processing flow will be explained below.

[1285] Step 1:

[1286] A user types a question or request into the device using voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[1287] Step 2:

[1288] The device receives the voice and converts it into text using automatic speech recognition (ASR) technology. For example, converting the voice saying "When is the next meeting?" into text saying "When is the next meeting?"

[1289] Step 3:

[1290] The terminal sends the converted text data (or directly entered text data) to the server. For example, the text "When is the next meeting date?" is sent to the server.

[1291] Step 4:

[1292] When the device receives voice input, it uses an emotion engine to analyze emotions from the user's voice and sends the results to the server. For example, the emotion engine may detect stress from the user's speech and send that information to the server as "emotion data."

[1293] Step 5:

[1294] The server uses natural language processing (NLP) technology to analyze the received text data. The server analyzes the text data and understands the user's request. For example, from the question "When is the next meeting?", the server understands that it should return the schedule information for the next meeting.

[1295] Step 6:

[1296] The server retrieves necessary information from a related database based on the analysis results, for example, the server retrieves the date and time of the next meeting from a conference recording system.

[1297] Step 7:

[1298] Based on the information acquired by the server, a response is generated to be provided to the user, taking into account the user's emotional data. For example, a response such as "The next meeting is scheduled for next Tuesday at 2 p.m. Participants' reactions were positive."

[1299] Step 8:

[1300] The server then uses an emotion engine to infer the other person's emotions and intentions based on the analyzed data. This process analyzes the context and tone of the conversation to understand the other person's emotional state. For example, it generates information such as "The participant's response was very enthusiastic."

[1301] Step 9:

[1302] The server sends the generated information back to the device, for example, sending the generated reply text "The next meeting is scheduled for next Tuesday at 2pm. Participants' reactions were positive."

[1303] Step 10:

[1304] The device may display the received information to the user or read it aloud using speech synthesis technology. For example, the device may display "The next meeting is scheduled for next Tuesday at 2 PM" on the screen or read aloud "The next meeting is scheduled for next Tuesday at 2 PM. Participants responded positively."

[1305] Step 11:

[1306] By checking the displayed and spoken information, users can accurately understand the next meeting date and understand the other party's feelings. For example, users can add a meeting date to their calendar and prepare with confidence based on the positive reactions of participants.

[1307] Through each of these steps, the system helps users accurately and quickly grasp the information they need, thereby enabling users who have communication difficulties in particular to communicate more smoothly in their daily lives and at work.

[1308] Example 2

[1309] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1310] In modern society, users who struggle with communication, especially those with developmental disorders or neurodiversity, face challenges in understanding and recording information, as well as accurately grasping the emotions and intentions of others, in their daily lives and at work. Furthermore, there is a lack of mechanisms for smoothly understanding each other's emotions and intentions during communication and providing appropriate responses. There is a need to solve these challenges and support richer communication.

[1311] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1312] In this invention, the server includes a means for converting user input voice into text, a means for analyzing the text data to understand the user's request, and a means for generating a response based on emotion analysis, which allows the user to grasp all necessary information in communication and accurately understand the other party's intentions and emotions.

[1313] "User" means an entity that uses the system to provide information by voice or text input.

[1314] "Terminal" means a device or equipment that receives a user's voice or text input and transmits data to a server.

[1315] "Voice recognition function" refers to technology that converts a user's voice input into text data.

[1316] "Text data" refers to character string information converted by a voice recognition function.

[1317] A "server" is a central processing unit that receives text data and performs processes such as analysis, information acquisition, and sentiment analysis.

[1318] "Natural language processing (NLP) technology" is a technology for analyzing text data to understand user requests and intentions.

[1319] A "database" is an information collection system that stores, searches, and retrieves necessary information based on user requests.

[1320] An "emotion engine" is a technology that analyzes a user's emotional state from their voice or text input.

[1321] A "response" is a reaction or answer that a server generates in response to a user's request or intention and sends to a terminal.

[1322] "Speech synthesis technology" refers to technology that outputs text data as voice.

[1323] "Neurodiversity" refers to the concept and group of individuals with diverse neurological characteristics, including developmental disorders.

[1324] This invention provides a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. This system is realized by combining speech recognition functions, natural language processing technology, a database, an emotion engine, and speech synthesis technology.

[1325] When a user speaks or texts a question or request into the device, the device uses a speech recognition function to convert the speech into text data, typically using a speech recognition API (e.g., a voice recognition API). The converted text data is then sent to the server.

[1326] The server analyzes the received text data using natural language processing (NLP) techniques. Natural language processing is typically performed using a natural language processing API (e.g., a natural language processing API). The server analyzes the text data to understand the user's request. In this analysis process, the server analyzes the user's question or request to understand their intent. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[1327] The server searches and retrieves the required information from a relevant database. Typically, it retrieves the information using a database API (e.g., a calendar API). For example, the server might retrieve the next meeting date and time from a meeting recording system. Based on this information, the server generates a response to serve to the user.

[1328] Furthermore, the server also takes into account the user's emotional state. To do this, it uses an emotion engine to analyze emotions from the user's input voice or text. For emotion analysis, an emotion analysis API (e.g., emotion analysis API) is generally used. For example, if the user is feeling stressed, the server can provide appropriate feedback based on that information.

[1329] The server infers the other person's emotions and intentions based on the analyzed data. It analyzes the context and tone of the conversation to understand the other person's emotional state. For example, if the other person has a positive reaction during a meeting, it will provide that information to the user, making it easier for the user to understand the other person's emotions.

[1330] The server sends the generated information back to the device. The device then displays the received information to the user or reads it aloud using speech synthesis technology (e.g., a speech synthesis API). For example, "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded well" may be displayed on the device or read aloud "The next meeting is scheduled for next Tuesday at 2 p.m. Participants responded positively." In this way, the user can keep track of important communication information without missing anything.

[1331] For example, if a user asks "What's on the agenda for the next meeting?" during a meeting, the device converts this speech into text and sends it to the server. The server then performs analysis, retrieves the agenda from the previous meeting record, and uses an emotion engine to take the user's emotional state into account. For example, if the user is feeling anxious, the server provides feedback based on that information. The device displays this information to the user and reads it aloud if necessary. For example, it provides the following information: "The next agenda will be a project progress report and discussion of new proposals. Please relax and focus." In this way, the user can accurately understand what to prepare for the next meeting, while also tracking their own emotions in the process.

[1332] In this way, the present invention helps users grasp all necessary information during communication and accurately understand the other person's intentions and feelings.It is also designed to enable users with developmental disorders and neurodiversity characteristics to communicate efficiently and with less stress.

[1333] Examples of prompts include:

[1334] When a user speaks to the device during a meeting, "When is the next meeting date?", the device performs speech recognition and sends the text to the server. The server then analyzes the text using NLP and retrieves date and time information from the meeting recording. It then uses an emotion engine to analyze the user's emotions and provides appropriate feedback. The device then displays this information and reads it out loud.

[1335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1336] Step 1:

[1337] The user enters a question or request into the device by voice or text. For example, the user might say, "When is our next meeting?" or type, "When is our next meeting?"

[1338] Input: User voice or text data

[1339] Output: Voice data sent to the device's speech recognition engine or text data sent directly to the server

[1340] Step 2:

[1341] The device uses its speech recognition function to convert speech into text data. For speech recognition, a speech recognition API is used. For example, the speech "When is the next meeting?" is converted into text "When is the next meeting?"

[1342] Input: Audio data sent to the device

[1343] Output: Converted text data

[1344] Step 3:

[1345] The terminal transmits the generated text data to the server via the network.

[1346] Input: Converted text data

[1347] Output: Text data sent to the server

[1348] Step 4:

[1349] The server analyzes the received text data using natural language processing (NLP) technology. For natural language processing, a natural language processing API is used. The server analyzes the text data and understands the user's request. For example, from the request "When is the next meeting?", the server understands that it should return schedule information about the next meeting.

[1350] Input: Text data sent to the server

[1351] Output: Analysis information (user request intent)

[1352] Step 5:

[1353] The server searches and retrieves the required information from the relevant database, using a database API to retrieve the information. For example, the server retrieves the date and time of the next meeting from a conference recording system.

[1354] Input: Analysis information (user request intent)

[1355] Output: Retrieved data (next meeting date and time information)

[1356] Step 6:

[1357] The server uses an emotion engine to analyze emotions from the user's input voice or text. For emotion analysis, it uses an emotion analysis API. For example, if the user is feeling stressed, it provides appropriate feedback based on that information.

[1358] Input: User input voice or text data

[1359] Output: Parsed emotion information

[1360] Step 7:

[1361] The server generates a response based on the analysis information, acquired information, and emotional information. For example, it generates a response such as, "The next meeting is scheduled for next Tuesday at 2 p.m. Participants' responses were positive."

[1362] Input: Analysis information, Acquisition information, Emotion information

[1363] Output: The generated response

[1364] Step 8:

[1365] The server sends the generated response to the terminal, which then sends data to the terminal via the network.

[1366] Input: The generated response

[1367] Output: The response sent to the device

[1368] Step 9:

[1369] The device can then display the response to the user or read it aloud using speech synthesis technology. For example, it can read, "The next meeting is next Tuesday at 2 p.m. Participants responded positively."

[1370] Input: The response sent to the terminal

[1371] Output: The response that is displayed or spoken to the user

[1372] (Application example 2)

[1373] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1374] Users who struggle with communication, especially those with neurodivergent characteristics, often find it difficult to communicate effectively with staff and customers in physical stores. This problem also poses an obstacle to providing high-quality customer service. Furthermore, a lack of the ability to provide appropriate real-time responses during customer interactions can lead to lower customer satisfaction. The present invention aims to solve this problem and help users communicate effectively in physical stores.

[1375] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving voice or text input by a user during communication; means for converting the input voice into text; means for transmitting the text data to the server; means for analyzing the text data to understand the user's request; means for retrieving information from a relational database based on the request; means for generating the retrieved information to provide to the user; means for displaying or reading out the generated information to the user; means installed in a mobile or wearable device available to staff and providing appropriate responses to customers in real time when interacting with them; means for presenting appropriate answers to the customer's questions in real time to achieve high-quality customer service; and means for analyzing the customer's emotions and tone and suggesting appropriate responses. This enables users to communicate effectively even in physical stores.

[1376] A "means for receiving voice or text" is a means for receiving voice input or text input from a user through a device.

[1377] The "means for converting voice to text" refers to a means for converting input voice data into text data using voice recognition technology.

[1378] The "means for transmitting text data to a server" refers to a means for transmitting the converted text data to a server via a network.

[1379] "Means for understanding user requirements" refers to the means for analyzing text data and understanding the information the user is seeking and the intent behind the request.

[1380] "Means for obtaining information from a related database" refers to means for searching and obtaining necessary information from a database based on a user's request.

[1381] The "means for generating acquired information" refers to a means for generating a response to be provided to a user based on information acquired from a database.

[1382] "Means for displaying or reading aloud" refers to means for visually displaying the generated information to the user or reading it aloud using speech synthesis technology.

[1383] "Mobile or wearable device" is a general term for devices that can be carried or worn by staff.

[1384] "Means for providing appropriate responses to customers in real time" refers to means for providing immediate appropriate responses to customer questions or requests.

[1385] "Means for analyzing customer emotions and tone" refers to means for analyzing emotions from the content of what a customer says and the tone of their voice, and optimizing responses.

[1386] "High-quality customer service" refers to service that can respond quickly and appropriately to customer expectations and requests.

[1387] This invention is a Communication Assistant system that helps users who are not good at communication to smoothly understand and record information. In particular, by combining it with an emotion engine that analyzes the emotions of both the user and the other party, it realizes richer communication.

[1388] First, the user inputs a question or request into the device by voice or text. For example, if the user says, "Do you have this item in stock?", the device uses its voice recognition function to convert the voice into text data. The hardware used for this is smart glasses or a smartphone, and the speech recognition library is speech_recognition.

[1389] The converted text data is sent over the network to the server, which then analyzes the received text data using natural language processing (NLP) techniques to understand the user's request. NLP analysis uses the nltk and transformers libraries to analyze the user's question or request and understand their intent.

[1390] The server then searches and retrieves the required information from the relevant database, for example, referencing the product management system database to retrieve inventory information, and the retrieved information is quickly generated as a response by the server.

[1391] Additionally, an emotion engine can be used to analyze emotions from customer voice or text input. For example, if a customer is expressing anxiety, Transformers' emotion analysis model can be used to provide feedback based on that information.

[1392] The server then attempts to analyze the other person's emotions and intentions using an emotion engine, which evaluates the context and tone of the conversation to understand the other person's emotional state and generate an appropriate real-time response.

[1393] The generated information is sent back to the device and displayed to the user or read aloud using text-to-speech technology (e.g., google.cloud.texttospeech). For example, "This item is currently in stock."

[1394] Specific examples

[1395] When a customer asks, "Do you have this item in stock?", a staff member asks the smart glasses. The voice is converted into text using voice recognition and sent to the server. The server analyzes the text data, retrieves stock information from a database, and generates an appropriate response. If the customer's emotion analysis indicates anxiety, the device will read out loud, "We've checked. We currently have this item in stock, so don't worry."

[1396] Prompt Sentence Examples

[1397] "When a customer asks, 'Do you have this item in stock?' we check the inventory, and if it is in stock, we respond, 'We currently have it in stock.' If, based on sentiment analysis, the customer appears dissatisfied, we respond, 'We will check whether it is in stock immediately. Please wait a little longer.'"

[1398] In this way, the present invention helps users grasp important information and accurately understand the intentions and emotions of others. It is also designed to enable efficient and stress-free communication, especially for users with neurodiversity characteristics such as developmental disorders.

[1399] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1400] Step 1:

[1401] The terminal receives a user's voice or text input. For example, it receives an input such as "Do you have this item in stock?" through smart glasses or a smartphone, and acquires the input data. The input here is voice data or text data.

[1402] Step 2:

[1403] The device converts the received voice input into text data using its voice recognition function. At this time, it uses the speech_recognition library to convert the voice data into text data and output it as text data.

[1404] Step 3:

[1405] The converted text data is sent from the terminal to the server. Network communication technology is used to pass the text data to the server. The input here is the text data, and the output is the text data in the state it was sent to the server.

[1406] Step 4:

[1407] The server analyzes the received text data using natural language processing (NLP) techniques. NLP analysis uses the nltk and transformers libraries to analyze the context of the text data. As a result of the analysis, the user's request and intent are extracted.

[1408] Step 5:

[1409] After understanding the user's request, the server searches and retrieves the necessary information from the relevant database. For example, it retrieves product inventory information from the database of a product management system. The input is the user's request, and the output is the retrieved inventory information.

[1410] Step 6:

[1411] The server generates a response based on the information it has acquired. It generates a text response to fulfill the user's request, taking into account the customer's sentiment analysis results using an emotion engine if necessary. The generated response is a specific inventory confirmation message.

[1412] Step 7:

[1413] It uses an emotion engine to analyze customer emotions and tone. If the customer is anxious or dissatisfied, it analyzes their state and tailors the response to include appropriate feedback. The input is what the customer says, and the output is the analyzed emotion data.

[1414] Step 8:

[1415] The server sends the generated response back to the device. Based on the response received by the device, the information is displayed to the user or read aloud using speech synthesis technology. Here, google.cloud.texttospeech is used to convert text data into speech data. The input is the response text data, and the output is speech data.

[1416] Step 9:

[1417] The device displays or reads the information to the user, for example, "This item is currently in stock." Finally, the device provides the user with the information they requested in an appropriate format.

[1418] By following these steps, users will be able to communicate effectively in physical stores.

[1419] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1420] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1421] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1422] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1423] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1424] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1425] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1426] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1427] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1428] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1429] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1430] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1431] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1432] 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.

[1433] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1434] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1435] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1436] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1437] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1438] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1439] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1440] The following is further disclosed regarding the above embodiment.

[1441] (Claim 1)

[1442] a means of receiving voice or text input by the user during communication;

[1443] means for converting the input speech into text;

[1444] means for transmitting the text data to a server;

[1445] means for analyzing the text data to understand a user's request;

[1446] means for retrieving information from a relational database based on said request;

[1447] means for generating the acquired information for providing it to a user;

[1448] means for displaying or reading the generated information to a user;

[1449] A system including:

[1450] (Claim 2)

[1451] 2. The system according to claim 1, further comprising means for performing emotion analysis based on the data analyzed by said server and for inferring the emotions and intentions of the other party.

[1452] (Claim 3)

[1453] 10. The system of claim 1, designed to assist the user with neurodiversity characteristics, including developmental disabilities.

[1454] "Example 1"

[1455] (Claim 1)

[1456] a means of receiving voice or text input by the user during communication;

[1457] means for converting the input speech into text;

[1458] means for transmitting the text data to a server;

[1459] means for analyzing the text data to understand a user's request;

[1460] means for retrieving information from a relational database based on said request;

[1461] means for generating the acquired information for providing it to a user;

[1462] means for displaying or reading the generated information to a user;

[1463] A system including:

[1464] (Claim 2)

[1465] The system according to claim 1, further comprising means for performing natural language processing based on the data analyzed by the server to recognize a user's request and understand their intention.

[1466] (Claim 3)

[1467] 2. The system according to claim 1, further comprising means for performing emotion analysis based on the data analyzed by said server and for inferring the emotions and intentions of the other party.

[1468] (Claim 4)

[1469] 10. The system of claim 1, designed to assist the user with neurodiversity characteristics, including developmental disabilities.

[1470] (Claim 5)

[1471] The system of claim 1, further comprising means for utilizing a generative AI model to analyze the text data and for obtaining information based on a user prompt.

[1472] "Application Example 1"

[1473] (Claim 1)

[1474] a means of receiving voice or text input by the user during communication;

[1475] means for converting the input speech into text;

[1476] means for transmitting the text data to a server;

[1477] means for analyzing the text data to understand a user's request;

[1478] means for retrieving information from a relational database based on said request;

[1479] means for generating the acquired information for providing it to a user;

[1480] means for displaying or reading the generated information to a user;

[1481] A means to analyze questions users have while studying using natural language processing technology and generate answers;

[1482] a means of searching and retrieving appropriate information from a learning resource database;

[1483] A system including:

[1484] (Claim 2)

[1485] 2. The system according to claim 1, further comprising means for performing emotion analysis based on the data analyzed by said server and for inferring the emotions and intentions of the other party.

[1486] (Claim 3)

[1487] 10. The system of claim 1, designed to assist the user with neurodiversity characteristics, including developmental disabilities.

[1488] "Example 2: Combining Emotion Engines"

[1489] (Claim 1)

[1490] a means of receiving voice or text input by the user during communication;

[1491] means for converting the input speech into text;

[1492] means for transmitting the text data to a server;

[1493] means for analyzing the text data to understand a user's request;

[1494] means for retrieving information from a relational database based on said request;

[1495] means for generating the acquired information for providing it to a user;

[1496] means for displaying or reading the generated information to a user;

[1497] means for analyzing the user's emotions;

[1498] means for generating a response based on the sentiment analysis;

[1499] A system including:

[1500] (Claim 2)

[1501] 2. The system according to claim 1, further comprising means for performing emotion analysis based on the data analyzed by said server and for inferring the emotions and intentions of the other party.

[1502] (Claim 3)

[1503] 10. The system of claim 1, designed to assist the user with neurodiversity characteristics, including developmental disabilities.

[1504] "Application example 2 when combining emotion engines"

[1505] (Claim 1)

[1506] a means of receiving voice or text input by the user during communication;

[1507] means for converting the input speech into text;

[1508] means for transmitting the text data to a server;

[1509] means for analyzing the text data to understand a user's request;

[1510] means for retrieving information from a relational database based on said request;

[1511] means for generating the acquired information for providing it to a user;

[1512] means for displaying or reading the generated information to a user;

[1513] A means installed on a mobile or wearable device available to staff to provide appropriate responses to customers in real time at the time of interaction;

[1514] A means for providing appropriate answers to the customer's questions in real time to realize high-quality customer service;

[1515] means for analyzing the customer's sentiment and tone and suggesting appropriate responses;

[1516] A system including:

[1517] (Claim 2)

[1518] 2. The system according to claim 1, further comprising means for performing emotion analysis based on the data analyzed by said server and for inferring the emotions and intentions of the other party.

[1519] (Claim 3)

[1520] 10. The system of claim 1, designed to assist the user with neurodiversity characteristics, including developmental disabilities. [Explanation of symbols]

[1521] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of receiving voice or text input by the user during communication; means for converting the input speech into text; means for transmitting the text data to a server; means for analyzing the text data to understand a user's request; means for retrieving information from a relational database based on said request; means for generating the acquired information for providing it to a user; means for displaying or reading the generated information to a user; A system including:

2. The system according to claim 1, further comprising means for performing emotion analysis based on the data analyzed by said server and for inferring the emotions and intentions of the other party.

3. 10. The system of claim 1, designed to assist the user with neurodiversity characteristics, including developmental disabilities.

Images