system

Abstract

We provide the system. [Solution] A means for the user to input symptoms using voice or push tones, A means for converting input data into text data using a speech recognition engine, A method for analyzing converted text data using a natural language processing engine to determine symptoms and urgency, A means of listing appropriate medical institutions based on the assessment results and guiding the user through that list using voice guidance, A means of automatically contacting the medical institution that was referred, A means of sending follow-up messages and surveys to users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In case of sudden injury or illness, it is difficult to quickly find an appropriate medical institution. Also, in many situations, it is difficult to appropriately judge symptoms and the degree of urgency and act immediately. As a result, appropriate medical treatment may be delayed, which may have an adverse effect on the patient's health condition. In the conventional telephone consultation desk, it may take time to obtain advice from an expert, so a prompt response is required.

Means for Solving the Problems

[0005] This invention provides means for a user to input symptoms via voice or touch tone, and for that data to be converted into text data using a speech recognition engine. Furthermore, it provides means for analyzing the converted text data using a natural language processing engine to determine the symptoms and their urgency. Based on the analysis results, it includes means for listing appropriate medical institutions and guiding the user through this list via voice guidance. In addition, it includes means for automatically contacting the listed medical institutions and for sending follow-up messages and questionnaires to the user. This enables the provision of rapid and accurate medical assistance and allows for a quick response to the user's health condition.

[0006] A "user" refers to a person who uses this system to seek medical assistance.

[0007] A "speech recognition engine" refers to a component that possesses the technology and functionality to convert speech data into text data.

[0008] A "natural language processing engine" refers to a component that possesses the technology and functions to analyze text data, understand and classify its content, and generate appropriate responses.

[0009] "Symptoms" refer to physical or mental abnormalities or discomforts that the user experiences.

[0010] "Urgency" refers to an indicator that shows the severity of the symptoms reported by the user and whether immediate medical attention is required.

[0011] "Medical institutions" refer to facilities such as hospitals, clinics, and medical offices that provide treatment for illnesses and injuries.

[0012] "Voice guidance" refers to a method by which a system uses voice to provide instructions and information to the user.

[0013] A "follow-up message" refers to a message or survey that a system sends to a user for subsequent confirmation or evaluation.

[0014] "Automatic telephone connection" refers to the function of the system to automatically connect a call to a medical institution based on the user's selection.

[0015] "Analysis result" refers to the information obtained by the natural language processing engine analyzing text data.

[0016] "Engine" refers to software or hardware for executing specific functions or processes.

Brief Description of the Drawings

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0025] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0038] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and quickly guides and contacts the appropriate medical institution according to the urgency of the situation.

[0039] System components

[0040] This system includes the following main components:

[0041] 1. User

[0042] In an emergency, users can access the system using their telephone and input their symptoms via voice or touch tone.

[0043] 2. Server

[0044] It detects incoming calls and activates the automated answering system.

[0045] The system uses a speech recognition engine to convert audio data into text, which is then analyzed by a natural language processing engine.

[0046] Based on the analysis results, the system lists appropriate medical institutions and guides users to them via voice guidance.

[0047] Based on user selection, the system automatically connects users to medical institutions.

[0048] A follow-up message or survey will be sent after a certain period of time.

[0049] Program processing

[0050] The program in this system performs the following series of processes.

[0051] 1. User call and symptom input

[0052] When a user calls a specific number, the server activates an automated response system.

[0053] The server will announce via voice, "Please tell us what problem you are experiencing."

[0054] Users can input symptoms such as "I have a severe stomach ache" by voice, or use push tones if necessary.

[0055] 2. Analysis and Diagnosis of Symptoms

[0056] The server sends the received audio data to the speech recognition engine, where it is converted into text data.

[0057] Next, the server sends the converted text data to a natural language processing engine to determine the symptoms and urgency.

[0058] 3. Guidance and Information

[0059] Based on the analysis results, the server lists appropriate medical institutions and guides the user through the list using voice guidance.

[0060] The server then asks the user, "Shall we contact the nearby XX Hospital right now?"

[0061] 4. Automatic telephone connection

[0062] When the user answers "yes," the server activates the automatic phone connection function and makes a direct call from the user's device to the medical institution (e.g., XX Hospital).

[0063] 5. Follow-up

[0064] The server will send messages or surveys to follow up on the user's status after a certain period of time.

[0065] Users provide feedback on the service by responding to these messages and surveys.

[0066] Specific example

[0067] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[0068] User: Call 7119.

[0069] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[0070] User: "I have a severe stomach ache," entered via voice input.

[0071] Server: Converts speech data into text using a speech recognition engine.

[0072] Server: Analyzes using a natural language processing engine to determine urgency.

[0073] Server: Lists appropriate medical institutions and suggests, "Shall we contact the nearest XX Hospital immediately?"

[0074] User: "Yes," they replied.

[0075] Server: Executes automated telephone connections to medical institutions.

[0076] User: Contact XX Hospital directly to receive appropriate medical care.

[0077] In this way, this system supports users in receiving prompt and appropriate medical assistance in emergencies.

[0078] The following describes the processing flow.

[0079] Step 1:

[0080] User: In an emergency, use your phone to call a specific number (e.g., 7119).

[0081] Step 2:

[0082] Server: Detects an incoming call and activates the automated answering system. It then instructs the user via voice, "Please tell us what problem you are experiencing."

[0083] Step 3:

[0084] User: Input symptoms by voice, such as "I have a severe stomach ache," or use push tones if necessary.

[0085] Step 4:

[0086] Server: Sends the input voice data to the speech recognition engine and converts it into text data.

[0087] Step 5:

[0088] Server: Sends the converted text data to a natural language processing engine for analysis. Through the analysis, it determines the symptoms and urgency.

[0089] Step 6:

[0090] Server: Based on the analysis results, it lists appropriate medical institutions (e.g., nearby emergency hospitals) and guides the user through this list using voice guidance.

[0091] Step 7:

[0092] Server: The server suggests to the user, "Would you like to contact the nearest XX Hospital immediately?" and provides options.

[0093] Step 8:

[0094] User: Responds with "Yes".

[0095] Step 9:

[0096] Server: Based on the user's response, retrieve the phone number for the selected medical institution (e.g., XX Hospital).

[0097] Step 10:

[0098] Server: Activates the automatic telephone connection function and makes a direct call from the user's terminal to the medical institution (○○ Hospital).

[0099] Step 11:

[0100] Terminal: The user's phone is automatically connected to XX Hospital.

[0101] Step 12:

[0102] Server: After a certain period of time, the server creates and sends surveys and follow-up messages to the user to check on their status.

[0103] Step 13:

[0104] User: Provide feedback based on the surveys and messages received.

[0105] Through the steps outlined above, users can receive prompt and accurate guidance to appropriate medical institutions, and then contact those institutions directly to receive the necessary care.

[0106] (Example 1)

[0107] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0108] Current medical support systems have problems in providing a quick and appropriate response to sudden injuries and illnesses. Misunderstandings and delays are common when users describe their symptoms, and it is often difficult to smoothly guide or contact appropriate medical institutions according to the urgency of the situation. Furthermore, the mechanisms for follow-up according to the user's situation are insufficient. A system is needed that solves these problems and allows users to receive quick and appropriate medical support.

[0109] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0110] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for listing appropriate medical institutions based on the determination results and guiding the user through the list with voice guidance; means for automatically contacting the indicated medical institutions; means for sending follow-up messages and questionnaires to the user; means for suggesting to the user contact the nearest medical institution with voice guidance; means for coordinating with the server to execute the above means; and means for collecting responses from the user to follow-up messages. This enables the user to receive prompt and appropriate medical assistance in an emergency.

[0111] A "user" is a person who calls the system of the present invention and inputs their symptoms using voice or touch tones.

[0112] "Means of input" refers to the methods or devices by which a user communicates symptoms to the system via voice or touch tone, and usually refers to telephones or smartphones.

[0113] A "speech recognition engine" is software or algorithms used to convert speech data into text data.

[0114] "Text data" refers to character information converted from speech data by a speech recognition engine.

[0115] A "natural language processing engine" is software or algorithms that analyze text data to determine symptoms and urgency.

[0116] The "means for determining symptoms and urgency" refer to a method that uses a natural language processing engine to understand the content of symptom information from the user and evaluate its urgency.

[0117] A "medical institution" refers to a place that provides medical services, such as a general hospital or clinic.

[0118] "Listing" refers to selecting appropriate medical institutions based on the analysis results and compiling them into a list.

[0119] "Voice guidance" refers to a system that provides guidance and instructions to users via voice, and usually refers to voice playback by an automated response system.

[0120] "Automated methods" refer to functions or mechanisms that allow a system to automatically process tasks without user intervention.

[0121] A "follow-up message" is a confirmation or survey message sent after a certain period of time to check on the user's status.

[0122] "Means of collaboration" refers to the methods and protocols by which a server communicates with other systems or databases to obtain or transmit necessary information.

[0123] "Means of collecting responses" refer to functions for collecting user feedback and survey responses, which typically include SMS or dedicated web forms.

[0124] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and promptly guides and contacts the appropriate medical institution according to its urgency. The system of this invention includes the following main components:

[0125] 1. User

[0126] In emergencies, users access the system using a telephone and input their symptoms via voice or touch-tone input. Users can use a standard landline or mobile phone.

[0127] 2. Terminal

[0128] This refers to a user's phone or smartphone, and is a means of inputting voice or touch-tone signals into the system.

[0129] 3. Server

[0130] The server has the following roles:

[0131] Incoming call detection and activation of the automated answering system: When the server detects an incoming call, it activates the automated answering system (e.g., Asterisk PBX) and provides a voice message saying, "Please tell us what problem you are experiencing."

[0132] Speech recognition of audio data: The server sends the audio data received from the user to a speech recognition engine (e.g., Google® Speech-to-Text API) to convert the audio data into text data.

[0133] Text data analysis and urgency assessment: The converted text data is analyzed using a natural language processing engine (e.g., Python's NLTK library) to determine the symptoms and urgency level.

[0134] Listing and guiding users to medical institutions: Based on the analysis results, the server lists appropriate medical institutions and guides the user through voice guidance. For example, it might suggest to the user, "Would you like to contact the nearby XX Hospital now?"

[0135] Automated phone connection: When the user answers "yes," the server activates the automated phone connection function (e.g., Twilio API) and makes a call to the selected healthcare provider from the user's device.

[0136] Follow-up: After a certain period of time, the server will send a follow-up message or survey to the user to check on their status. This follow-up message will be sent using an SMS sending API (e.g., Twilio SMS API).

[0137] Specific example

[0138] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[0139] User: Make a phone call to the specified phone number (e.g., 7119).

[0140] Server: The automated response system receives the call and instructs the user, "Please tell us what problem you are experiencing."

[0141] User: "I have a severe stomach ache," is entered via voice input.

[0142] Server: Converts audio data to text using the Google Speech-to-Text API.

[0143] Server: The converted text is analyzed using Python's NLTK library to determine its urgency.

[0144] Server: Lists appropriate medical facilities (e.g., nearby XX Hospital) and suggests, "Shall we contact XX Hospital immediately?"

[0145] User: Responds with "Yes".

[0146] Server: Uses the Twilio API to make phone calls to XX Hospital from the user's device.

[0147] Server: After a certain period of time, send a follow-up message to the user using the Twilio SMS API.

[0148] This system will enable users to receive prompt and appropriate medical assistance in emergencies.

[0149] Example of a prompt

[0150] The following are examples of prompts to input into the generating AI model.

[0151] I suddenly started experiencing severe abdominal pain this afternoon. Could you please tell me what I should do?

[0152] In this way, the present invention provides prompt and appropriate medical support based on the user's symptoms and urgency, creating an environment in which users can respond with peace of mind even in emergencies.

[0153] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0154] Step 1:

[0155] User call and symptom input

[0156] When a user experiences sudden symptoms, they call a designated phone number (e.g., 7119). This results in a phone call being generated as input.

[0157] The server detects an incoming call and activates an automated answering system (e.g., Asterisk PBX). Here, it receives the incoming call signal as input and activates the automated answering system as output.

[0158] The server plays an audio message asking the user, "Please tell us what problem you are experiencing." This initiates the voice guidance process, which then takes the user's input as input.

[0159] The user can describe their symptoms by voice, for example, "I have a severe stomach ache," or by using push tones if necessary. This will result in the user's voice data being used as input.

[0160] Step 2:

[0161] Speech recognition of audio data

[0162] The server processes the audio data received from the user. The audio data received as input is the subject of processing.

[0163] The server sends the audio data to a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio into text data. This allows the speech recognition engine to process the audio data into text data.

[0164] The server receives text data (e.g., "I have a severe stomach ache") as output from the speech recognition engine.

[0165] Step 3:

[0166] Natural language analysis of symptoms and assessment of urgency.

[0167] The server sends the converted text data to a natural language processing engine (e.g., Python's NLTK library) for analysis. Text data is the input to be processed.

[0168] The server uses a natural language processing engine to analyze text data and extract keywords (e.g., "severe stomach ache"). This is how keyword extraction and analysis are performed.

[0169] The server compares the extracted keywords with predefined urgency rules to determine the urgency of the symptoms. This processes the urgency data, and the urgency determination result is output.

[0170] Step 4:

[0171] Listing and guidance for medical institutions

[0172] The server lists appropriate medical institutions from its database (e.g., PostgreSQL) based on the assessment result. The urgency assessment result is the input that is processed.

[0173] The server uses an audio guidance system to provide users with information such as the names of listed medical institutions. This results in a list of medical institutions being output.

[0174] The server then prompts the user with the question, "Would you like to contact the nearby XX Hospital immediately?" This triggers an automated voice guidance message for the user.

[0175] Step 5:

[0176] Automated telephone connection

[0177] The user responds with "Yes". The user's response is received as input.

[0178] The server analyzes the user's responses and activates an automated telephone connection function (e.g., Twilio API) to the medical institution selected from the user's device. This means the user's response data is the input, and the telephone connection is the output.

[0179] The server places a phone call from the user's terminal to a medical institution (e.g., XX Hospital). This initiates the actual phone connection.

[0180] Step 6:

[0181] Follow-up

[0182] The server sends follow-up messages or surveys to the user after a certain period of time. The timing of the follow-up action is set as an input.

[0183] The server uses an SMS sending API (e.g., Twilio SMS API) to send a follow-up message. This results in the follow-up message being sent to the user as output.

[0184] Users respond to follow-up messages and surveys, thereby obtaining user response data as input.

[0185] In this way, by executing each processing step sequentially, the system of the present invention helps users receive prompt and appropriate medical assistance in emergency situations.

[0186] (Application Example 1)

[0187] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0188] Receiving prompt and appropriate medical assistance in the event of a sudden injury or illness is crucial. However, it is often difficult to quickly and reliably identify accessible medical facilities and transport patients appropriately, especially if the illness occurs at home or while out. Furthermore, elderly individuals and those with disabilities often find it difficult to reach medical facilities on their own. Therefore, there is a need to develop automated systems that address these challenges and provide prompt and appropriate medical assistance.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0190] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for listing appropriate medical institutions based on the determination results and guiding the user through the list with voice guidance; means for automatically contacting the listed medical institutions; means for sending follow-up messages and questionnaires to the user; and means for coordinating with an autonomous vehicle to automatically transport the user to an appropriate medical institution. This enables the user to receive prompt and appropriate medical assistance in an emergency.

[0191] A "user" refers to a person who uses the system to input symptoms and receive medical support.

[0192] "Voice or push-tone" refers to the method a user uses to input their symptoms into the system.

[0193] A "speech recognition engine" refers to the technology that converts speech data into text data.

[0194] "Text data" refers to data in the form of text converted by a speech recognition engine, which can be analyzed by the system.

[0195] A "natural language processing engine" refers to a technology that analyzes text data and determines symptoms and urgency based on its content.

[0196] "Symptoms and urgency" refers to information indicating the content of the symptoms entered by the user and the urgent need for them.

[0197] "Voice guidance" refers to a system function that provides users with voice guidance and instructions.

[0198] "Medical institutions" refer to medical facilities such as hospitals, clinics, and medical offices.

[0199] A "follow-up message" refers to a message sent after the initial response to check on the user's status.

[0200] A "survey" refers to a set of questions used to collect feedback on the evaluation of medical services and the user's situation.

[0201] An "autonomous vehicle" refers to a vehicle that can automatically operate and travel to a designated location.

[0202] "Transportation" refers to the act of using an autonomous vehicle to transport a user to an appropriate medical facility.

[0203] This invention relates to an autonomous vehicle collaboration system that provides rapid and appropriate medical assistance for sudden injuries or illnesses. The system receives symptom input from the user via voice or push-tone, analyzes the input, and takes appropriate action according to the urgency of the situation.

[0204] System Overview

[0205] The system consists of the following elements:

[0206] 1. User terminal: Functions as a smartphone application and provides an interface for the user to input symptoms using voice or touch tones.

[0207] 2. Server: The server analyzes voice data from the user using a speech recognition engine (Google Cloud Speech-to-Text API) and a natural language processing engine (IBM Watson® Natural Language Understanding) to determine the symptoms and their urgency.

[0208] 3. Autonomous vehicles: These vehicles transport users to appropriate medical facilities and configure the transport route in conjunction with a navigation system (e.g., Waymo's API).

[0209] 4. Follow-up system: Send follow-up messages and surveys to users to help them evaluate and improve the service.

[0210] Specific processing of the program

[0211] 1. User symptom input: The user launches the application from their smartphone and inputs their symptoms using voice or touch tones. In the case of voice input, the user describes the symptoms in a format such as "I have severe chest pain."

[0212] 2. Speech Recognition and Text Conversion: User voice data is converted into text data using the Google Cloud Speech-to-Text API.

[0213] 3. Text data analysis: The converted text data is analyzed by IBM Watson Natural Language Understanding to determine the urgency of the symptoms and the appropriate medical institution.

[0214] 4. Listing and Guiding Users to Medical Institutions: Based on the analysis results, appropriate medical institutions will be listed and guided to the user via voice guidance through the application.

[0215] 5. Arranging an autonomous vehicle: Once the user approves the transfer, the server uses Waymo's API to instruct the autonomous vehicle to navigate and transport the user to the designated medical facility.

[0216] 6. Follow-up: After the user arrives at the medical facility, follow-up messages and questionnaires are sent to confirm the quality of service.

[0217] Hardware and software to use

[0218] Speech recognition engine: Google Cloud Speech-to-Text API

[0219] Natural Language Processing Engine: IBM Watson Natural Language Understanding

[0220] Autonomous vehicle navigation system: Waymo API-enabled navigation

[0221] Specific example

[0222] For example, if a user suddenly experiences severe chest pain at home, it would work as follows:

[0223] The user launches the app on their smartphone and uses voice input to say, "I have severe chest pain."

[0224] The server converts the audio data into text and analyzes it using a natural language processing engine.

[0225] The system assesses the urgency of the situation and lists the most suitable nearby medical facilities.

[0226] After the user receives instructions and responds with "yes," an autonomous vehicle is dispatched to transport the user to a medical facility.

[0227] Upon arrival at the medical facility, a follow-up message is sent to confirm the user's condition.

[0228] Example of a prompt

[0229] "I want to develop a media mobile assistance app that arranges transportation to the nearest medical facility in case of severe chest pain. This app will use the Google Cloud Speech-to-Text API to convert speech to text, IBM Watson Natural Language Understanding to determine the urgency of the symptoms, and Waymo's API to connect with the navigation system of an autonomous vehicle. Please provide a complete program."

[0230] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0231] Step 1:

[0232] The user launches the application from their smartphone. The user inputs their symptoms using voice or touch tones. For example, the user might voice input, "I have severe chest pain." This input data is saved within the application as voice data.

[0233] Step 2:

[0234] The server receives the user's voice data. It sends the received voice data to the Google Cloud Speech-to-Text API, where it is converted into text data. By sending this audio data to the API, the audio data is returned as text data.

[0235] Step 3:

[0236] The server sends the converted text data to IBM Watson Natural Language Understanding for analysis. The analysis determines the urgency of the symptoms, and the result is returned as text data. The server receives this analysis result and understands the nature and urgency of the symptoms.

[0237] Step 4:

[0238] The server creates a list of appropriate medical institutions based on the analysis results. It lists the most suitable medical institutions from a database of medical institutions based on location information and the symptoms they can treat. This list is stored as internal data.

[0239] Step 5:

[0240] The server provides the user with a voice guidance message listing appropriate medical facilities. For example, it might say, "We will now begin transporting you to the nearest XX Hospital. Is that alright?" This voice guidance is transmitted to the user's terminal as audio data.

[0241] Step 6:

[0242] The user responds with "yes" to the voice guidance. The user's device sends this response as audio data to the server. The server then sends this audio data back to the Google Cloud Speech-to-Text API, where it is converted into text data.

[0243] Step 7:

[0244] The server analyzes the user's response and initiates contact with a medical facility and dispatch of an autonomous vehicle. Using the Waymo API, it inputs the location information of the destination medical facility into the navigation system and dispatches a vehicle. As a result, a notification that the transfer has begun is displayed on the user's device.

[0245] Step 8:

[0246] The user boards an autonomous vehicle. The vehicle automatically transports the user to a designated medical facility according to the navigation system. During transport, the vehicle's location information and transport status are transmitted to a server in real time for monitoring.

[0247] Step 9:

[0248] After the user arrives at the medical facility, the server sends follow-up messages and questionnaires to the user's device. The user responds to these messages and enters information confirming their condition and evaluating the service. This data is sent to the server and used to improve future services.

[0249] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0250] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and quickly guides and contacts the appropriate medical institution according to its urgency. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves a more sophisticated response.

[0251] System components

[0252] This system includes the following main components:

[0253] 1. User

[0254] In an emergency, users can access the system using their telephone and input their symptoms via voice or touch tone.

[0255] 2. Server

[0256] It detects incoming calls and activates the automated answering system.

[0257] The system uses a speech recognition engine to convert audio data into text, which is then analyzed by a natural language processing engine.

[0258] An emotion engine is used to analyze the user's emotions from voice data.

[0259] Based on the analysis results (symptoms, urgency, and user's emotions), the system lists appropriate medical institutions and guides the user to them via voice guidance.

[0260] Based on user selection, the system automatically connects users to medical institutions.

[0261] A follow-up message or survey will be sent after a certain period of time.

[0262] Program processing

[0263] The program in this system performs the following series of processes.

[0264] 1. User call and symptom input

[0265] When a user calls a specific number, the server activates an automated response system.

[0266] The server prompts the user with a voice message saying, "Please tell us what symptoms you are experiencing," and encourages them to enter their symptoms.

[0267] Users input symptoms such as "I have a severe stomach ache" using voice. Push-tone input is also available if needed.

[0268] 2. Analysis of symptoms and emotions

[0269] The server sends the input voice data to the speech recognition engine, where it is converted into text data.

[0270] Next, the server sends the converted text data to a natural language processing engine to determine the symptoms and urgency.

[0271] Furthermore, the voice data is sent to an emotion engine to analyze the user's emotions (e.g., stress, anxiety, anger, etc.).

[0272] 3. Guidance and Information

[0273] Based on the analysis results of symptoms and emotions, the server lists appropriate medical institutions.

[0274] The server guides the user with voice guidance: "I will guide you to the nearest emergency hospital. Do you want to call the nearest ○○ Hospital immediately now?"

[0275] Based on the results of the emotion engine, the tone and content of the voice guidance are adjusted, and relaxation guidance is also provided to enhance the user's sense of security.

[0276] 4. Automatic telephone connection

[0277] When the user answers "yes", the server activates the automatic telephone connection function and makes a call directly to the medical institution (e.g., ○○ Hospital) from the user's terminal.

[0278] 5. Follow-up

[0279] After a certain period of time, the server creates messages and questionnaires for following up on the user's situation and sends them to the user.

[0280] The user provides feedback on the service by responding to these messages and questionnaires.

[0281] Specific example

[0282] For example, when the user suddenly feels severe abdominal pain at home, this system functions as follows.

[0283] User: Call 7119.

[0284] Server: The automatic response system guides with "Please tell me your symptoms."

[0285] User: Input by voice "I have severe abdominal pain."

[0286] Server: Convert voice data into text using a voice recognition engine.

[0287] Server: Analyze using a natural language processing engine and determine the urgency.

[0288] Server: Analyze the voice data using an emotion engine and recognize the user's emotion (e.g., anxiety).

[0289] Server: List up appropriate medical institutions, guide with "Do you want to connect to the nearest XX hospital immediately?" and also provide relaxation guidance with "Relax and take a deep breath."

[0290] User: Respond with "Yes".

[0291] Server: Make an automatic phone connection to the medical institution.

[0292] User: Contact XX hospital directly and receive appropriate medical care.

[0293] In this way, this system is designed to support the user to receive prompt and appropriate medical assistance in case of emergency and enhance the user's sense of security.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] User: In case of emergency, use the phone to call a specific number (e.g., 7119).

[0297] Step 2:

[0298] Server: Detect the incoming call and activate the automatic response system. Instruct the user verbally with "Please tell me your symptoms."

[0299] Step 3:

[0300] User: Input symptoms such as "severe abdominal pain" by voice. Or use push tones as needed.

[0301] Step 4:

[0302] Server: Send the input voice data to the voice recognition engine and convert it into text data.

[0303] Step 5:

[0304] Server: Send the converted text data to the natural language processing engine for analysis. Through the analysis, determine the symptoms and urgency.

[0305] Step 6:

[0306] Server: At the same time, send the input voice data to the emotion engine to analyze the user's emotions (e.g., stress, anxiety, anger, etc.).

[0307] Step 7:

[0308] Server: Based on the analysis results of the natural language processing engine and the emotion engine (symptoms, urgency, emotions), list appropriate medical institutions (e.g., nearby emergency hospitals).

[0309] Step 8:

[0310] Server: Guide the user by voice with "I will guide you to the nearby emergency hospital. Do you want to contact the nearest XX Hospital immediately?" Also, based on the results of the emotion engine, add voice tones and relaxation instructions according to the user's emotions.

[0311] Step 9:

[0312] User: Respond with "Yes".

[0313] Step 10:

[0314] Server: Based on the user's response, retrieve the phone number for the selected medical institution (e.g., XX Hospital).

[0315] Step 11:

[0316] Server: Activates the automatic telephone connection function and makes a direct call from the user's terminal to the medical institution (○○ Hospital).

[0317] Step 12:

[0318] Terminal: The user's phone is automatically connected to XX Hospital.

[0319] Step 13:

[0320] Server: After a certain period of time, it creates and sends a survey or follow-up message to the user to check on their status.

[0321] Step 14:

[0322] User: Provide feedback based on the surveys and messages received.

[0323] Through the steps outlined above, users can receive prompt and accurate guidance to appropriate medical institutions, and then contact those institutions directly to receive the necessary care. Furthermore, the introduction of an emotion engine enables responses tailored to the user's emotional state, thereby increasing the user's sense of security and trust.

[0324] (Example 2)

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

[0326] Conventional medical support systems often fail to adequately consider the user's emotions and urgency during the process of connecting them to a suitable medical institution quickly. This can lead to increased anxiety and stress, potentially delaying appropriate medical assistance. Furthermore, there were problems with providing guidance on emergency first aid and ensuring smooth telephone connections with medical institutions. Additionally, follow-up care lacked consideration for the user's emotional well-being.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for analyzing the user's emotions using an emotion recognition engine based on the determination results and voice data; means for listing appropriate medical institutions based on the analysis results and guiding the user through the list with voice guidance; means for automatically contacting the guided medical institutions; means for adjusting the tone and content of the voice guidance based on the user's emotional state to provide relaxation guidance; and means for sending follow-up messages and questionnaires to the user. This enables the user to receive prompt and appropriate medical assistance in emergencies and allows for responses that take the user's emotions into consideration.

[0329] A "user" refers to someone who uses the system to input their symptoms and request medical assistance.

[0330] "Means of inputting symptoms by voice or touch tone" refers to a method by which a user provides information about their symptoms to the system using voice or telephone key input.

[0331] A "speech recognition engine" refers to software or hardware that has the function of converting input speech data into text data.

[0332] "Text data" refers to data that has been converted by a speech recognition engine and expressed as a string of characters.

[0333] A "natural language processing engine" refers to software or hardware that analyzes text data and has the function of determining the content and urgency of symptoms.

[0334] "Urgency" refers to a scale that indicates how urgent the user's symptoms are.

[0335] An "emotion recognition engine" refers to software or hardware that has the function of analyzing a user's emotional state from voice data or input data.

[0336] "Medical institutions" refer to facilities that provide medical services, such as hospitals and clinics.

[0337] "Voice guidance" refers to a method of providing users with guidance information via voice.

[0338] "Automated methods" refer to methods in which the system performs processing automatically without requiring user intervention.

[0339] "Relaxation guidance" refers to audio guidance designed to alleviate user tension and anxiety.

[0340] "Follow-up messages and questionnaires" refer to messages and questionnaires sent by the system after a user has received medical assistance, for the purpose of checking the user's condition and evaluating the service.

[0341] "Medical support" refers to providing advice, guidance, or arranging treatment for illness or injury.

[0342] This invention relates to a system for providing rapid and appropriate medical assistance in the event of sudden injury or illness. This system includes the following main components:

[0343] 1. User

[0344] In an emergency, the user accesses the system using their phone and enters their symptoms using voice or touch tones. The symptom information entered by the user forms the basis for subsequent analysis and processing.

[0345] 2. Terminal

[0346] The user's phone or other device initiates a call to the system and receives the user's voice input or touch-tone input through that call.

[0347] 3. Server

[0348] The server is the core of the system and performs the following tasks:

[0349] Automated response system

[0350] When an incoming call is detected, the automated response system is activated and instructs the user to "Please tell us what problem you are experiencing."

[0351] Speech recognition engine

[0352] The speech recognition engine converts the voice data entered by the user into text data. Specifically, it uses "Google Cloud Speech-to-Text".

[0353] Natural Language Processing Engine

[0354] The natural language processing engine analyzes the text data converted by the speech recognition engine to determine the symptoms and urgency. Here, we use the "Google Cloud Natural Language API".

[0355] Emotion recognition engine

[0356] The emotion recognition engine analyzes the user's emotions from voice data. Specifically, it uses "IBM Watson Tone Analyzer."

[0357] Voice guidance

[0358] The voice guidance system directs users to appropriate medical facilities based on analysis results. Furthermore, it adjusts the tone and content of the voice guidance based on the user's emotional state, providing relaxation guidance as well.

[0359] Automatic telephone connection function

[0360] When a user requests to connect to a medical institution, the automated telephone connection function is activated, and the user's device makes a call to the nearest medical institution.

[0361] Follow-up

[0362] After a certain period of time, the server generates and sends follow-up messages or questionnaires to the user to check on their status.

[0363] Specific example

[0364] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[0365] User: First, the user calls 7119.

[0366] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[0367] User: "I have a severe stomach ache," is entered via voice input.

[0368] Server: Uses a speech recognition engine (Google Cloud Speech-to-Text) to convert speech data into text.

[0369] Server: The converted text data is analyzed using a natural language processing engine (Google Cloud Natural Language API) to determine the symptoms and urgency.

[0370] Server: Furthermore, the server analyzes the voice data using an emotion recognition engine (IBM Watson Tone Analyzer) to recognize the user's emotions (e.g., anxiety).

[0371] Server: Lists appropriate medical facilities and provides voice guidance such as, "Would you like to contact the nearest XX Hospital now?" It also offers relaxation guidance such as, "Relax and take a deep breath."

[0372] User: Responds with "Yes".

[0373] Server: Activates the automatic phone connection function and places a call from the user's terminal to XX Hospital.

[0374] User: Contact XX Hospital directly to receive appropriate medical care.

[0375] Server: After a certain period of time, it sends follow-up messages or surveys to the user.

[0376] User: Respond to follow-up messages and surveys, and provide feedback.

[0377] Example of a prompt

[0378] The following are examples of prompts to input into a generative AI model:

[0379] "Please explain how users who call 7119 and report sudden abdominal pain can receive medical assistance. Please explain in detail how this system works when a user experiences sudden, severe abdominal pain at home."

[0380] By using this prompt, the AI ​​model can generate text that explains the specific processing flow of the system. This system allows users to receive prompt and appropriate medical assistance in emergencies, and enables emotionally sensitive responses.

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Program processing steps

[0383] Step 1:

[0384] The server detects the user's call. When a user dials a specific phone number, the server detects the incoming call and activates the automated response system. The server plays a voice message to the user asking, "Please tell us what problem you are experiencing."

[0385] Input: User's phone call

[0386] Data processing: Incoming call detection, voice message playback

[0387] Output: Voice message from the system to the user.

[0388] Step 2:

[0389] The user enters their symptoms using voice or touch tones. The user can enter their symptoms by voice, such as "I have a severe stomach ache," or by entering the corresponding number using touch tones.

[0390] Input: User voice input or push-tone input

[0391] Data processing: Acquisition of user voice, acquisition of push tone input

[0392] Output: Voice data or push-tone data

[0393] Step 3:

[0394] The server sends the voice data to the speech recognition engine to convert it into text data. The speech recognition engine (Google Cloud Speech-to-Text) is used to convert the user's voice data into text.

[0395] Input: User's voice data

[0396] Data processing: Conversion processing using a speech recognition engine.

[0397] Output: Text data

[0398] Step 4:

[0399] The server sends text data to a natural language processing engine to analyze symptoms and urgency. The natural language processing engine (Google Cloud Natural Language API) is used to analyze the text data and determine the symptoms and their urgency.

[0400] Input: Text data

[0401] Data processing: Analysis and processing using a natural language processing engine.

[0402] Output: Analysis results of symptoms and urgency

[0403] Step 5:

[0404] The server sends voice data to the emotion recognition engine, which analyzes the user's emotions. The emotion recognition engine (IBM Watson Tone Analyzer) is used to analyze the user's emotions from the voice data.

[0405] Input: Audio data

[0406] Data processing: Analysis and processing using an emotion recognition engine.

[0407] Output: Emotion analysis results

[0408] Step 6:

[0409] The server lists appropriate medical institutions based on the analysis results and guides the user through voice guidance. Based on the analysis of symptoms, urgency, and emotions, the server selects the most suitable medical institutions from the database and creates a list. Next, it provides voice guidance such as, "We will guide you to nearby emergency hospitals. Shall we contact the nearest XX Hospital now?" Furthermore, taking into consideration the user's emotional state, it also provides relaxation guidance such as, "Please relax and take a deep breath."

[0410] Input: Symptom and urgency analysis results, emotion analysis results

[0411] Data processing: Database lookup, voice guidance generation.

[0412] Output: List of medical facilities, voice guidance

[0413] Step 7:

[0414] The user responds to the voice guidance and requests to connect to a medical institution. When the user responds with "yes," the server receives the response.

[0415] Input: User response data

[0416] Data processing: Analysis of response data

[0417] Output: User response result

[0418] Step 8:

[0419] The server activates the automatic telephone connection function and places a call from the user's terminal to a medical institution. Based on the response, the server activates the automatic telephone connection function again and places a call from the user's terminal to the nearest medical institution.

[0420] Input: User response result

[0421] Data processing: Activation of automatic connection function

[0422] Output: Telephone connection to a medical institution

[0423] Step 9:

[0424] The server sends follow-up messages and surveys to the user after a certain period of time. To check on the user's status and collect feedback, the server generates follow-up messages and surveys and sends them via the specified method (email or SMS).

[0425] Input: User response result

[0426] Data processing: Generating messages and surveys

[0427] Output: Follow-up message, survey

[0428] Through these steps, the system is designed to ensure that users receive prompt and appropriate medical assistance in emergencies. Furthermore, it provides relaxation guidance that takes the user's emotions into consideration, enhancing their sense of security.

[0429] (Application Example 2)

[0430] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0431] Currently, there are limited means of receiving prompt and appropriate medical assistance in the event of a sudden illness or injury while in an autonomous vehicle. Furthermore, the technology to analyze the user's symptoms and emotions and appropriately set the autonomous vehicle's route is insufficient. Therefore, there are challenges in quickly arriving at the appropriate medical facility in an emergency and enhancing the user's sense of security.

[0432] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input symptoms by voice or push tone, means for converting the input data into text data using a speech recognition engine, means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency, means for listing appropriate medical institutions based on the determination result and guiding the user to the list with voice guidance, means for automatically contacting the guided medical institutions, means for sending follow-up messages and questionnaires to the user, and means for analyzing the user's symptoms and emotions in the autonomous vehicle with a speech recognition engine and an emotion analysis engine, and setting or resetting the route of the autonomous vehicle according to the urgency. This makes it possible for the user to receive prompt and appropriate medical assistance in an emergency, and furthermore, by appropriately setting the route of the autonomous vehicle, the user's sense of security can be enhanced.

[0433] Definitions of important words

[0434] A "user" is a person who uses the system to input symptoms using voice or push tones.

[0435] A "speech recognition engine" is a device that converts input speech data into text data.

[0436] A "natural language processing engine" is a technology that analyzes text data to determine symptoms and urgency.

[0437] "Voice guidance" is a system that provides users with a list of appropriate medical facilities via voice.

[0438] The "emotion analysis engine" analyzes the user's emotions from their voice data.

[0439] A "medical institution" is a facility that provides medical services for health problems and injuries.

[0440] An "autonomous vehicle" is a vehicle that operates automatically using programs and sensors, without requiring human intervention.

[0441] A "follow-up message" is a message sent to a user after medical care to check on their condition and request feedback.

[0442] A "survey" is a questionnaire used to collect feedback and opinions from users.

[0443] "Route setting" is the process of determining the optimal route for an autonomous vehicle to reach a specific destination.

[0444] "Urgency" is a criterion that indicates how quickly medical attention is needed for the user's symptoms.

[0445] "Push tones" are sounds generated by pressing the number buttons on a telephone, and are a means of inputting information into a system.

[0446] "Text data" refers to character information converted from audio data.

[0447] A "server" is a computer system that performs speech recognition, natural language processing, sentiment analysis, and data management.

[0448] invention specification

[0449] System Overview

[0450] This invention is a system that provides rapid and appropriate medical assistance to passengers who experience sudden illness or injury in an autonomous vehicle. The user inputs symptoms via voice or touch-tone input, which are then analyzed using a voice recognition engine and a natural language processing engine. Furthermore, an emotion analysis engine is used to analyze the user's emotions, and the system guides them to the appropriate medical facility according to the urgency of the situation. This allows the autonomous vehicle to reconfigure its route to the optimal one, enhancing the user's sense of security.

[0451] Hardware and software used

[0452] Hardware: Audio input device (microphone), audio output device (speaker), control system for autonomous vehicles, server

[0453] Software: Speech recognition engines (e.g., Google Cloud Speech-to-Text), natural language processing engines (e.g., NLTK and SpaCy), sentiment analysis engines (e.g., EmotionRecognizer), map information provision APIs (e.g., Google Maps API)

[0454] Data processing

[0455] 1. Voice input:

[0456] The user inputs their symptoms by voice. For example, they might say, "I have severe chest pain."

[0457] 2. Speech recognition and analysis:

[0458] The server sends the audio data to the speech recognition engine, where it is converted into text data.

[0459] This text data is analyzed using a natural language processing engine to determine the symptoms and their urgency.

[0460] 3. Emotion analysis:

[0461] The server sends the voice data to an emotion analysis engine, which analyzes the user's emotional state (e.g., anxiety, stress).

[0462] 4. Guidance to medical facilities and route planning:

[0463] Based on the urgency and sentiment analysis results, the server identifies the appropriate medical facility and guides the user via voice guidance.

[0464] If necessary, the autonomous vehicle's navigation system will reset the route.

[0465] 5. Automated telephone connection and follow-up:

[0466] When the user responds with "yes," the server automatically connects to the medical institution.

[0467] After a certain period of time, follow-up messages or surveys will be sent to the user.

[0468] Specific example

[0469] For example, suppose a user suddenly experiences chest pain while in an autonomous vehicle. The user inputs, "I have severe chest pain." The server recognizes this voice, converts it to text, and then analyzes it using a natural language processing engine to determine the urgency level is high. The emotion analysis engine then detects anxiety and provides a relaxation message such as, "Please relax, we will be there shortly." Next, the server guides the user to an appropriate medical facility and resets the autonomous vehicle's route.

[0470] Example of a prompt

[0471] User's voice message regarding symptoms: "I have severe chest pain."

[0472] Symptoms analyzed: "Severe chest pain"

[0473] Analyzed urgency level: "High"

[0474] Analyzed emotion: "anxiety"

[0475] The nearest hospital that was found: "〇〇 Hospital"

[0476] In this way, the present invention is a system that enables users to receive prompt and appropriate medical assistance in emergencies, and further improves user safety and peace of mind by appropriately controlling autonomous vehicles.

[0477] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0478] Program processing steps

[0479] Processing flow

[0480] Step 1:

[0481] The user inputs their symptoms using voice.

[0482] Specific action: The user experiences severe chest pain while inside the autonomous vehicle and describes the symptom by saying, "I have severe chest pain."

[0483] Input: User's voice data.

[0484] Output: Send as audio data to the server.

[0485] Step 2:

[0486] The server sends the audio data to the speech recognition engine, where it is converted into text data.

[0487] Specific operation: The server uses a speech recognition engine to convert "I have severe chest pain" into text data.

[0488] Input: Audio data.

[0489] Output: Text data.

[0490] Step 3:

[0491] The server analyzes the text data using a natural language processing engine to determine the symptoms and urgency.

[0492] Specific operation: The server inputs the text data "I have severe chest pain" into a natural language processing engine, which determines the symptom to be "chest pain" and the urgency to be "high".

[0493] Input: Text data.

[0494] Output: Symptom and urgency assessment results.

[0495] Step 4:

[0496] The server sends the voice data to an emotion analysis engine, which then analyzes the user's emotions.

[0497] Specific operation: The server analyzes the audio data using an emotion analysis engine and detects the emotional state of "anxiety."

[0498] Input: Audio data.

[0499] Output: Emotion analysis results.

[0500] Step 5:

[0501] The server identifies the appropriate medical facility based on the urgency and sentiment analysis results, and guides the user through voice guidance.

[0502] Specific operation: The server checks the medical institution database, lists the nearest medical institutions, and provides voice guidance to the user saying, "We will guide you to the nearest XX Hospital."

[0503] Input: Symptoms, urgency, and sentiment analysis results.

[0504] Output: List of medical facilities, voice guidance.

[0505] Step 6:

[0506] If necessary, the autonomous vehicle's navigation system will reset the route.

[0507] Specific operation: The server instructs the autonomous vehicle's navigation system to set an emergency route and reconfigure the optimal path.

[0508] Input: Location information of the most suitable medical facility.

[0509] Output: Reconfigured route information.

[0510] Step 7:

[0511] The system automatically connects users to medical institutions based on their responses.

[0512] Specific operation: When the user responds with "yes," the server automatically connects to the medical institution by phone.

[0513] Input: User response.

[0514] Output: Telephone connection to a medical institution.

[0515] Step 8:

[0516] After a certain period of time, follow-up messages or surveys will be sent to the user.

[0517] Specific operation: The server creates and sends follow-up messages and surveys to the user.

[0518] Input: A certain amount of time has elapsed.

[0519] Output: Follow-up messages and surveys.

[0520] In this way, each processing step allows the system to automatically perform a series of processes, from receiving voice input from the user to guiding them to a medical facility, determining the urgency of the situation, and resetting the route of the autonomous vehicle.

[0521] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0522] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0523] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0524] [Second Embodiment]

[0525] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0526] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0527] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0528] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0529] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0530] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0531] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0532] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0533] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0534] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0535] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0536] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0537] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and quickly guides and contacts the appropriate medical institution according to the urgency of the situation.

[0538] System components

[0539] This system includes the following main components:

[0540] 1. User

[0541] In an emergency, users can access the system using their telephone and input their symptoms via voice or touch tone.

[0542] 2. Server

[0543] It detects incoming calls and activates the automated answering system.

[0544] The audio data is converted to text using a speech recognition engine, and then analyzed using a natural language processing engine.

[0545] Based on the analysis results, the system lists appropriate medical institutions and guides users to them via voice guidance.

[0546] Based on user selection, the system automatically connects users to medical institutions.

[0547] A follow-up message or survey will be sent after a certain period of time.

[0548] Program processing

[0549] The program in this system performs the following series of processes.

[0550] 1. User call and symptom input

[0551] When a user calls a specific number, the server activates an automated response system.

[0552] The server will announce via voice, "Please tell us what problem you are experiencing."

[0553] Users can input symptoms such as "I have a severe stomach ache" by voice, or use push tones if necessary.

[0554] 2. Analysis and Diagnosis of Symptoms

[0555] The server sends the received audio data to the speech recognition engine, where it is converted into text data.

[0556] Next, the server sends the converted text data to a natural language processing engine to determine the symptoms and urgency.

[0557] 3. Guidance and Information

[0558] Based on the analysis results, the server lists appropriate medical institutions and guides the user through the list using voice guidance.

[0559] The server then asks the user, "Shall we contact the nearby XX Hospital right now?"

[0560] 4. Automatic telephone connection

[0561] When the user answers "yes," the server activates the automatic phone connection function and makes a direct call from the user's device to the medical institution (e.g., XX Hospital).

[0562] 5. Follow-up

[0563] The server will send messages or surveys to follow up on the user's status after a certain period of time.

[0564] Users provide feedback on the service by responding to these messages and surveys.

[0565] Specific example

[0566] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[0567] User: Call 7119.

[0568] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[0569] User: "I have a severe stomach ache," entered via voice input.

[0570] Server: Converts speech data into text using a speech recognition engine.

[0571] Server: Analyzes using a natural language processing engine to determine urgency.

[0572] Server: Lists appropriate medical institutions and suggests, "Shall we contact the nearest XX Hospital immediately?"

[0573] User: "Yes," they replied.

[0574] Server: Executes automated telephone connections to medical institutions.

[0575] User: Contact XX Hospital directly to receive appropriate medical care.

[0576] In this way, this system supports users in receiving prompt and appropriate medical assistance in emergencies.

[0577] The following describes the processing flow.

[0578] Step 1:

[0579] User: In an emergency, use your phone to call a specific number (e.g., 7119).

[0580] Step 2:

[0581] Server: Detects an incoming call and activates the automated answering system. It then instructs the user via voice, "Please tell us what problem you are experiencing."

[0582] Step 3:

[0583] User: Input symptoms by voice, such as "I have a severe stomach ache," or use push tones if necessary.

[0584] Step 4:

[0585] Server: Sends the input voice data to the speech recognition engine and converts it into text data.

[0586] Step 5:

[0587] Server: Sends the converted text data to a natural language processing engine for analysis. Through the analysis, it determines the symptoms and urgency.

[0588] Step 6:

[0589] Server: Based on the analysis results, it lists appropriate medical institutions (e.g., nearby emergency hospitals) and guides the user through this list using voice guidance.

[0590] Step 7:

[0591] Server: The server suggests to the user, "Would you like to contact the nearest XX Hospital immediately?" and provides options.

[0592] Step 8:

[0593] User: Responds with "Yes".

[0594] Step 9:

[0595] Server: Based on the user's response, retrieve the phone number for the selected medical institution (e.g., XX Hospital).

[0596] Step 10:

[0597] Server: Activates the automatic telephone connection function and makes a direct call from the user's terminal to the medical institution (○○ Hospital).

[0598] Step 11:

[0599] Terminal: The user's phone is automatically connected to XX Hospital.

[0600] Step 12:

[0601] Server: After a certain period of time, the server creates and sends surveys and follow-up messages to the user to check on their status.

[0602] Step 13:

[0603] User: Provide feedback based on the surveys and messages received.

[0604] Through the steps outlined above, users can receive prompt and accurate guidance to appropriate medical institutions, and then contact those institutions directly to receive the necessary care.

[0605] (Example 1)

[0606] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0607] Current medical support systems have problems in providing a quick and appropriate response to sudden injuries and illnesses. Misunderstandings and delays are common when users describe their symptoms, and it is often difficult to smoothly guide or contact appropriate medical institutions according to the urgency of the situation. Furthermore, the mechanisms for follow-up according to the user's situation are insufficient. A system is needed that solves these problems and allows users to receive quick and appropriate medical support.

[0608] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0609] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for listing appropriate medical institutions based on the determination results and guiding the user through the list with voice guidance; means for automatically contacting the guided medical institutions; means for sending follow-up messages and questionnaires to the user; means for suggesting to the user contact the nearest medical institution with voice guidance; means for coordinating with the server to execute the above means; and means for collecting responses from the user to follow-up messages. This enables the user to receive prompt and appropriate medical assistance in an emergency.

[0610] A "user" is a person who calls the system of the present invention and inputs their symptoms using voice or touch tones.

[0611] "Means of input" refers to the methods or devices by which a user communicates symptoms to the system via voice or touch tone, and usually refers to telephones or smartphones.

[0612] A "speech recognition engine" is software or algorithms used to convert speech data into text data.

[0613] "Text data" refers to character information converted from speech data by a speech recognition engine.

[0614] A "natural language processing engine" is software or algorithms that analyze text data to determine symptoms and urgency.

[0615] "Methods for determining symptoms and urgency" refers to a method that uses a natural language processing engine to understand the content of symptom information from the user and evaluate its urgency.

[0616] A "medical institution" refers to a place that provides medical services, such as a general hospital or clinic.

[0617] "Listing" refers to selecting appropriate medical institutions based on the analysis results and compiling them into a list.

[0618] "Voice guidance" refers to a system that provides guidance and instructions to users via voice, and usually refers to voice playback by an automated response system.

[0619] "Automated methods" refer to functions or mechanisms that allow a system to automatically process tasks without user intervention.

[0620] A "follow-up message" is a confirmation or survey message sent after a certain period of time to check on the user's status.

[0621] "Means of collaboration" refers to the methods and protocols by which a server communicates with other systems or databases to obtain or transmit necessary information.

[0622] "Means of collecting responses" refer to functions for collecting user feedback and survey responses, which typically include SMS or dedicated web forms.

[0623] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and promptly guides and contacts the appropriate medical institution according to its urgency. The system of this invention includes the following main components:

[0624] 1. User

[0625] In emergencies, users access the system using a telephone and input their symptoms via voice or touch-tone input. Users can use a standard landline or mobile phone.

[0626] 2. Terminal

[0627] This refers to a user's phone or smartphone, and is a means of inputting voice or touch-tone signals into the system.

[0628] 3. Server

[0629] The server has the following roles:

[0630] Incoming call detection and activation of the automated answering system: When the server detects an incoming call, it activates the automated answering system (e.g., Asterisk PBX) and provides a voice message saying, "Please tell us what problem you are experiencing."

[0631] Speech recognition of audio data: The server sends the audio data received from the user to a speech recognition engine (e.g., Google Speech-to-Text API) and converts the audio data into text data.

[0632] Text data analysis and urgency assessment: The converted text data is analyzed using a natural language processing engine (e.g., Python's NLTK library) to determine the symptoms and urgency level.

[0633] Listing and guiding users to medical institutions: Based on the analysis results, the server lists appropriate medical institutions and guides users to them via voice guidance. For example, it might suggest to the user, "Would you like to contact the nearby XX Hospital now?"

[0634] Automated phone connection: When the user answers "yes," the server activates the automated phone connection function (e.g., Twilio API) and makes a call to the selected healthcare provider from the user's device.

[0635] Follow-up: After a certain period of time, the server will send a follow-up message or survey to the user to check on their status. This follow-up message will be sent using an SMS sending API (e.g., Twilio SMS API).

[0636] Specific example

[0637] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[0638] User: Make a phone call to the specified phone number (e.g., 7119).

[0639] Server: The automated response system receives the call and instructs the user, "Please tell us what problem you are experiencing."

[0640] User: "I have a severe stomach ache," is entered via voice input.

[0641] Server: Converts audio data to text using the Google Speech-to-Text API.

[0642] Server: The converted text is analyzed using Python's NLTK library to determine its urgency.

[0643] Server: Lists appropriate medical facilities (e.g., nearby XX Hospital) and suggests, "Shall we contact XX Hospital immediately?"

[0644] User: Responds with "Yes".

[0645] Server: Uses the Twilio API to make phone calls to XX Hospital from the user's device.

[0646] Server: After a certain period of time, send a follow-up message to the user using the Twilio SMS API.

[0647] This system will enable users to receive prompt and appropriate medical assistance in emergencies.

[0648] Example of a prompt

[0649] The following are examples of prompts to input into the generating AI model.

[0650] I suddenly started experiencing severe abdominal pain this afternoon. Could you please tell me what I should do?

[0651] In this way, the present invention provides prompt and appropriate medical support based on the user's symptoms and urgency, creating an environment in which users can respond with peace of mind even in emergencies.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] User call and symptom input

[0655] When a user experiences sudden symptoms, they call a designated phone number (e.g., 7119). This results in a phone call being generated as input.

[0656] The server detects an incoming call and activates an automated answering system (e.g., Asterisk PBX). Here, it receives the incoming call signal as input and activates the automated answering system as output.

[0657] The server plays an audio message asking the user, "Please tell us what problem you are experiencing." This initiates the voice guidance process, which then takes the user's input as input.

[0658] The user can describe their symptoms by voice, for example, "I have a severe stomach ache," or by using push tones if necessary. This will result in the user's voice data being used as input.

[0659] Step 2:

[0660] Speech recognition of audio data

[0661] The server processes the audio data received from the user. The audio data received as input is the subject of processing.

[0662] The server sends the audio data to a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio into text data. This allows the speech recognition engine to process the audio data into text data.

[0663] The server receives text data (e.g., "I have a severe stomach ache") as output from the speech recognition engine.

[0664] Step 3:

[0665] Natural language analysis of symptoms and assessment of urgency.

[0666] The server sends the converted text data to a natural language processing engine (e.g., Python's NLTK library) for analysis. Text data is the input to be processed.

[0667] The server uses a natural language processing engine to analyze text data and extract keywords (e.g., "severe stomach ache"). This is how keyword extraction and analysis are performed.

[0668] The server compares the extracted keywords with predefined urgency rules to determine the urgency of the symptoms. This processes the urgency data, and the urgency determination result is output.

[0669] Step 4:

[0670] Listing and guidance for medical institutions

[0671] The server lists appropriate medical institutions from its database (e.g., PostgreSQL) based on the assessment result. The urgency assessment result is the input to be processed.

[0672] The server uses an audio guidance system to provide users with information such as the names of listed medical institutions. This results in a list of medical institutions being output.

[0673] The server then prompts the user with the question, "Would you like to contact the nearby XX Hospital immediately?" This triggers an automated voice guidance message for the user.

[0674] Step 5:

[0675] Automated telephone connection

[0676] The user responds with "Yes". The user's response is received as input.

[0677] The server analyzes the user's responses and activates an automated telephone connection function (e.g., Twilio API) to the medical institution selected from the user's device. This means the user's response data is the input, and the telephone connection is the output.

[0678] The server places a phone call from the user's terminal to a medical institution (e.g., XX Hospital). This initiates the actual phone connection.

[0679] Step 6:

[0680] Follow-up

[0681] The server sends follow-up messages or surveys to the user after a certain period of time. The timing of the follow-up action is set as an input.

[0682] The server uses an SMS sending API (e.g., Twilio SMS API) to send a follow-up message. This results in the follow-up message being sent to the user as output.

[0683] Users respond to follow-up messages and surveys, thereby obtaining user response data as input.

[0684] In this way, by executing each processing step sequentially, the system of the present invention helps users receive prompt and appropriate medical assistance in emergency situations.

[0685] (Application Example 1)

[0686] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0687] Receiving prompt and appropriate medical assistance in the event of a sudden injury or illness is crucial. However, it is often difficult to quickly and reliably identify accessible medical facilities and transport patients appropriately, especially if the illness occurs at home or while out. Furthermore, elderly individuals and those with disabilities often find it difficult to reach medical facilities on their own. Therefore, there is a need to develop automated systems that address these challenges and provide prompt and appropriate medical assistance.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0689] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for listing appropriate medical institutions based on the determination results and guiding the user through the list with voice guidance; means for automatically contacting the listed medical institutions; means for sending follow-up messages and questionnaires to the user; and means for coordinating with an autonomous vehicle to automatically transport the user to an appropriate medical institution. This enables the user to receive prompt and appropriate medical assistance in an emergency.

[0690] A "user" refers to a person who uses the system to input symptoms and receive medical support.

[0691] "Voice or push-tone" refers to the method a user uses to input their symptoms into the system.

[0692] A "speech recognition engine" refers to the technology that converts speech data into text data.

[0693] "Text data" refers to data in the form of text converted by a speech recognition engine, which can be analyzed by the system.

[0694] A "natural language processing engine" refers to a technology that analyzes text data and determines symptoms and urgency based on its content.

[0695] "Symptoms and urgency" refers to information indicating the content of the symptoms entered by the user and the urgent need for them.

[0696] "Voice guidance" refers to a system function that provides users with voice guidance and instructions.

[0697] "Medical institutions" refer to medical facilities such as hospitals, clinics, and medical offices.

[0698] A "follow-up message" refers to a message sent after the initial response to check on the user's status.

[0699] A "survey" refers to a set of questions used to collect feedback on the evaluation of medical services and the user's situation.

[0700] An "autonomous vehicle" refers to a vehicle that can automatically operate and travel to a designated location.

[0701] "Transportation" refers to the act of using an autonomous vehicle to transport a user to an appropriate medical facility.

[0702] This invention relates to an autonomous vehicle collaboration system that provides rapid and appropriate medical assistance for sudden injuries or illnesses. The system receives symptom input from the user via voice or push-tone, analyzes the input, and takes appropriate action according to the urgency of the situation.

[0703] System Overview

[0704] The system consists of the following elements:

[0705] 1. User terminal: Functions as a smartphone application and provides an interface for the user to input symptoms using voice or touch tones.

[0706] 2. Server: Uses a speech recognition engine (Google Cloud Speech-to-Text API) and a natural language processing engine (IBM Watson Natural Language Understanding) to analyze voice data from the user and determine the symptoms and their urgency.

[0707] 3. Autonomous vehicles: These vehicles transport users to appropriate medical facilities and configure the transport route in conjunction with a navigation system (e.g., Waymo's API).

[0708] 4. Follow-up system: Send follow-up messages and surveys to users to help them evaluate and improve the service.

[0709] Specific processing of the program

[0710] 1. User symptom input: The user launches the application from their smartphone and inputs their symptoms using voice or touch tones. In the case of voice input, the user describes the symptoms in a format such as "I have severe chest pain."

[0711] 2. Speech Recognition and Text Conversion: User voice data is converted into text data using the Google Cloud Speech-to-Text API.

[0712] 3. Text data analysis: The converted text data is analyzed by IBM Watson Natural Language Understanding to determine the urgency of the symptoms and the appropriate medical institution.

[0713] 4. Listing and Guiding Users to Medical Institutions: Based on the analysis results, appropriate medical institutions will be listed and guided to the user via voice guidance through the application.

[0714] 5. Arranging an autonomous vehicle: Once the user approves the transfer, the server uses Waymo's API to instruct the autonomous vehicle to navigate and transport the user to the designated medical facility.

[0715] 6. Follow-up: After the user arrives at the medical facility, follow-up messages and questionnaires are sent to confirm the quality of service.

[0716] Hardware and software to use

[0717] Speech recognition engine: Google Cloud Speech-to-Text API

[0718] Natural Language Processing Engine: IBM Watson Natural Language Understanding

[0719] Autonomous vehicle navigation system: Waymo API-enabled navigation

[0720] Specific example

[0721] For example, if a user suddenly experiences severe chest pain at home, it would work as follows:

[0722] The user launches the app on their smartphone and uses voice input to say, "I have severe chest pain."

[0723] The server converts the audio data into text and analyzes it using a natural language processing engine.

[0724] The system assesses the urgency of the situation and lists the most suitable nearby medical facilities.

[0725] After the user receives instructions and responds with "yes," an autonomous vehicle is dispatched to transport the user to a medical facility.

[0726] Upon arrival at the medical facility, a follow-up message is sent to confirm the user's condition.

[0727] Example of a prompt

[0728] "I want to develop a media mobile assistance app that arranges transportation to the nearest medical facility in case of severe chest pain. This app will use the Google Cloud Speech-to-Text API to convert speech to text, IBM Watson Natural Language Understanding to determine the urgency of the symptoms, and Waymo's API to connect with the navigation system of an autonomous vehicle. Please provide a complete program."

[0729] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0730] Step 1:

[0731] The user launches the application from their smartphone. The user inputs their symptoms using voice or touch tones. For example, the user might voice input, "I have severe chest pain." This input data is saved within the application as voice data.

[0732] Step 2:

[0733] The server receives the user's voice data. It sends the received voice data to the Google Cloud Speech-to-Text API, where it is converted into text data. By sending this audio data to the API, the audio data is returned as text data.

[0734] Step 3:

[0735] The server sends the converted text data to IBM Watson Natural Language Understanding for analysis. The analysis determines the urgency of the symptoms, and the result is returned as text data. The server receives this analysis result and understands the nature and urgency of the symptoms.

[0736] Step 4:

[0737] The server creates a list of appropriate medical institutions based on the analysis results. It lists the most suitable medical institutions from a database of medical institutions based on location information and the symptoms they can treat. This list is stored as internal data.

[0738] Step 5:

[0739] The server provides the user with a voice guidance message listing appropriate medical facilities. For example, it might say, "We will now begin transporting you to the nearest XX Hospital. Is that alright?" This voice guidance is transmitted to the user's terminal as audio data.

[0740] Step 6:

[0741] The user responds with "yes" to the voice guidance. The user's device sends this response as audio data to the server. The server then sends this audio data back to the Google Cloud Speech-to-Text API, where it is converted into text data.

[0742] Step 7:

[0743] The server analyzes the user's response and initiates contact with a medical facility and dispatch of an autonomous vehicle. Using the Waymo API, it inputs the location information of the destination medical facility into the navigation system and dispatches a vehicle. As a result, a notification that the transfer has begun is displayed on the user's device.

[0744] Step 8:

[0745] The user boards an autonomous vehicle. The vehicle automatically transports the user to a designated medical facility according to the navigation system. During transport, the vehicle's location information and transport status are transmitted to a server in real time for monitoring.

[0746] Step 9:

[0747] After the user arrives at the medical facility, the server sends follow-up messages and questionnaires to the user's device. The user responds to these messages and enters information confirming their condition and evaluating the service. This data is sent to the server and used to improve future services.

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

[0749] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and quickly guides and contacts the appropriate medical institution according to its urgency. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves a more sophisticated response.

[0750] System components

[0751] This system includes the following main components:

[0752] 1. User

[0753] In an emergency, users can access the system using their telephone and input their symptoms via voice or touch tone.

[0754] 2. Server

[0755] It detects incoming calls and activates the automated answering system.

[0756] The audio data is converted to text using a speech recognition engine, and then analyzed using a natural language processing engine.

[0757] An emotion engine is used to analyze the user's emotions from voice data.

[0758] Based on the analysis results (symptoms, urgency, and user's emotions), the system lists appropriate medical institutions and guides the user to them via voice guidance.

[0759] Based on user selection, the system automatically connects users to medical institutions.

[0760] A follow-up message or survey will be sent after a certain period of time.

[0761] Program processing

[0762] The program in this system performs the following series of processes.

[0763] 1. User call and symptom input

[0764] When a user calls a specific number, the server activates an automated response system.

[0765] The server prompts the user with a voice message saying, "Please tell us what symptoms you are experiencing," and encourages them to enter their symptoms.

[0766] Users input symptoms such as "I have a severe stomach ache" using voice. Push-tone input is also available if needed.

[0767] 2. Analysis of symptoms and emotions

[0768] The server sends the input voice data to the speech recognition engine, where it is converted into text data.

[0769] Next, the server sends the converted text data to a natural language processing engine to determine the symptoms and urgency.

[0770] Furthermore, the voice data is sent to an emotion engine to analyze the user's emotions (e.g., stress, anxiety, anger, etc.).

[0771] 3. Guidance and Information

[0772] Based on the analysis of symptoms and emotions, the server lists appropriate medical institutions.

[0773] The server will guide the user via voice guidance, saying, "We will guide you to nearby emergency hospitals. Would you like to contact the nearest XX Hospital immediately?"

[0774] Based on the results of the emotion engine, the tone and content of the voice guidance are adjusted, and relaxation guidance is also provided to enhance the user's sense of security.

[0775] 4. Automatic telephone connection

[0776] When the user answers "yes," the server activates the automatic phone connection function and directly calls the medical institution (e.g., XX Hospital) from the user's device.

[0777] 5. Follow-up

[0778] After a certain period of time, the server creates and sends messages or questionnaires to the user to follow up on their situation.

[0779] Users provide feedback on the service by responding to these messages and surveys.

[0780] Specific example

[0781] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[0782] User: Call 7119.

[0783] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[0784] User: "I have a severe stomach ache," entered via voice input.

[0785] Server: Converts speech data into text using a speech recognition engine.

[0786] Server: Analyzes using a natural language processing engine to determine urgency.

[0787] Server: Analyzes voice data using an emotion engine to recognize the user's emotions (e.g., anxiety).

[0788] Server: Lists appropriate medical facilities and asks, "Would you like to contact the nearest XX Hospital now?" and also provides relaxation guidance such as, "Relax and take a deep breath."

[0789] User: "Yes," they replied.

[0790] Server: Executes automated telephone connections to medical institutions.

[0791] User: Contact XX Hospital directly to receive appropriate medical care.

[0792] In this way, the system is designed to support users in receiving prompt and appropriate medical assistance in emergencies, thereby increasing their sense of security.

[0793] The following describes the processing flow.

[0794] Step 1:

[0795] User: In an emergency, use your phone to call a specific number (e.g., 7119).

[0796] Step 2:

[0797] Server: Detects an incoming call and activates the automated answering system. It then instructs the user via voice, "Please tell us what problem you are experiencing."

[0798] Step 3:

[0799] User: Input symptoms by voice, such as "I have a severe stomach ache," or use push tones if necessary.

[0800] Step 4:

[0801] Server: Sends the input voice data to the speech recognition engine and converts it into text data.

[0802] Step 5:

[0803] Server: Sends the converted text data to a natural language processing engine for analysis. Through the analysis, it determines the symptoms and urgency.

[0804] Step 6:

[0805] Server: Simultaneously sends the input voice data to the emotion engine to analyze the user's emotions (e.g., stress, anxiety, anger, etc.).

[0806] Step 7:

[0807] Server: Based on the analysis results (symptoms, urgency, emotion) from the natural language processing engine and emotion engine, it lists appropriate medical facilities (e.g., nearby emergency hospitals).

[0808] Step 8:

[0809] Server: The server provides voice guidance to the user, saying, "We will guide you to nearby emergency hospitals. Would you like to contact the nearest XX Hospital now?" It also adds voice tones and relaxation instructions tailored to the user's emotions, based on the results of the emotion engine.

[0810] Step 9:

[0811] User: Responds with "Yes".

[0812] Step 10:

[0813] Server: Based on the user's response, retrieve the phone number for the selected medical institution (e.g., XX Hospital).

[0814] Step 11:

[0815] Server: Activates the automatic telephone connection function and makes a direct call from the user's terminal to the medical institution (○○ Hospital).

[0816] Step 12:

[0817] Terminal: The user's phone is automatically connected to XX Hospital.

[0818] Step 13:

[0819] Server: After a certain period of time, it creates and sends a survey or follow-up message to the user to check on their status.

[0820] Step 14:

[0821] User: Provide feedback based on the surveys and messages received.

[0822] Through the steps outlined above, users can receive prompt and accurate guidance to appropriate medical institutions, and then contact those institutions directly to receive the necessary care. Furthermore, the introduction of an emotion engine enables responses tailored to the user's emotional state, thereby increasing the user's sense of security and trust.

[0823] (Example 2)

[0824] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0825] Conventional medical support systems often fail to adequately consider the user's emotions and urgency during the process of connecting them to a suitable medical institution quickly. This can lead to increased anxiety and stress, potentially delaying appropriate medical assistance. Furthermore, there were problems with providing guidance on emergency first aid and ensuring smooth telephone connections with medical institutions. Additionally, follow-up care lacked consideration for the user's emotional well-being.

[0826] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0827] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for analyzing the user's emotions using an emotion recognition engine based on the determination results and voice data; means for listing appropriate medical institutions based on the analysis results and guiding the user through the list with voice guidance; means for automatically contacting the guided medical institutions; means for adjusting the tone and content of the voice guidance based on the user's emotional state to provide relaxation guidance; and means for sending follow-up messages and questionnaires to the user. This enables the user to receive prompt and appropriate medical assistance in emergencies and allows for responses that take the user's emotions into consideration.

[0828] A "user" refers to someone who uses the system to input their symptoms and request medical assistance.

[0829] "Means of inputting symptoms by voice or touch tone" refers to a method for users to provide information about their symptoms to the system using voice or phone key input.

[0830] A "speech recognition engine" refers to software or hardware that has the function of converting input speech data into text data.

[0831] "Text data" refers to data that has been converted by a speech recognition engine and expressed as a string of characters.

[0832] A "natural language processing engine" refers to software or hardware that analyzes text data and has the function of determining the content and urgency of symptoms.

[0833] "Urgency" refers to a scale that indicates how urgent the user's symptoms are.

[0834] An "emotion recognition engine" refers to software or hardware that has the function of analyzing a user's emotional state from voice data or input data.

[0835] "Medical institutions" refer to facilities that provide medical services, such as hospitals and clinics.

[0836] "Voice guidance" refers to a method of providing users with guidance information via voice.

[0837] "Automated methods" refer to methods in which the system performs processing automatically without requiring user intervention.

[0838] "Relaxation guidance" refers to audio guidance designed to alleviate user tension and anxiety.

[0839] "Follow-up messages and questionnaires" refer to messages and questionnaires sent by the system after a user has received medical assistance, for the purpose of checking the user's condition and evaluating the service.

[0840] "Medical support" refers to providing advice, guidance, or arranging treatment for illness or injury.

[0841] This invention relates to a system for providing rapid and appropriate medical assistance in the event of sudden injury or illness. This system includes the following main components:

[0842] 1. User

[0843] In an emergency, the user accesses the system using their telephone and enters their symptoms using voice or touch tones. The symptom information entered by the user forms the basis for subsequent analysis and processing.

[0844] 2. Terminal

[0845] The user's phone or other device initiates a call to the system and receives the user's voice input or touch-tone input through that call.

[0846] 3. Server

[0847] The server is the core of the system and performs the following tasks:

[0848] Automated response system

[0849] When an incoming call is detected, the automated response system is activated and instructs the user to "Please tell us what problem you are experiencing."

[0850] Speech recognition engine

[0851] The speech recognition engine converts the voice data entered by the user into text data. Specifically, it uses "Google Cloud Speech-to-Text".

[0852] Natural Language Processing Engine

[0853] The natural language processing engine analyzes the text data converted by the speech recognition engine to determine the symptoms and urgency. Here, we use the "Google Cloud Natural Language API".

[0854] Emotion recognition engine

[0855] The emotion recognition engine analyzes the user's emotions from voice data. Specifically, it uses "IBM Watson Tone Analyzer."

[0856] Voice guidance

[0857] The voice guidance system directs users to appropriate medical facilities based on analysis results. Furthermore, it adjusts the tone and content of the voice guidance based on the user's emotional state, providing relaxation guidance as well.

[0858] Automatic telephone connection function

[0859] When a user requests to connect to a medical institution, the automated telephone connection function is activated, and the user's device makes a call to the nearest medical institution.

[0860] Follow-up

[0861] After a certain period of time, the server generates and sends follow-up messages or questionnaires to the user to check on their status.

[0862] Specific example

[0863] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[0864] User: First, the user calls 7119.

[0865] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[0866] User: "I have a severe stomach ache," is entered via voice input.

[0867] Server: Uses a speech recognition engine (Google Cloud Speech-to-Text) to convert speech data into text.

[0868] Server: The converted text data is analyzed using a natural language processing engine (Google Cloud Natural Language API) to determine the symptoms and urgency.

[0869] Server: Furthermore, the server analyzes the voice data using an emotion recognition engine (IBM Watson Tone Analyzer) to recognize the user's emotions (e.g., anxiety).

[0870] Server: Lists appropriate medical facilities and provides voice guidance such as, "Would you like to contact the nearest XX Hospital now?" It also offers relaxation guidance such as, "Relax and take a deep breath."

[0871] User: Responds with "Yes".

[0872] Server: Activates the automatic phone connection function and places a call from the user's terminal to XX Hospital.

[0873] User: Contact XX Hospital directly to receive appropriate medical care.

[0874] Server: After a certain period of time, it sends follow-up messages or surveys to the user.

[0875] User: Respond to follow-up messages and surveys, and provide feedback.

[0876] Example of a prompt

[0877] The following are examples of prompts to input into a generative AI model:

[0878] "Please explain how users who call 7119 and report sudden abdominal pain can receive medical assistance. Please explain in detail how this system works when a user experiences sudden, severe abdominal pain at home."

[0879] By using this prompt, the generating AI model can produce text that explains the specific processing flow of the system. This system allows users to receive prompt and appropriate medical assistance in emergencies, and enables emotionally sensitive responses.

[0880] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0881] Program processing steps

[0882] Step 1:

[0883] The server detects the user's call. When a user dials a specific phone number, the server detects the incoming call and activates the automated response system. The server plays a voice message to the user asking, "Please tell us what problem you are experiencing."

[0884] Input: User's phone call

[0885] Data processing: Incoming call detection, voice message playback

[0886] Output: Voice message from the system to the user.

[0887] Step 2:

[0888] The user enters their symptoms using voice or touch tones. The user can enter their symptoms by voice, such as "I have a severe stomach ache," or by using touch tones to enter the corresponding number.

[0889] Input: User voice input or push-tone input

[0890] Data processing: Acquisition of user voice, acquisition of push tone input

[0891] Output: Voice data or push-tone data

[0892] Step 3:

[0893] The server sends the voice data to the speech recognition engine to convert it into text data. The speech recognition engine (Google Cloud Speech-to-Text) is used to convert the user's voice data into text.

[0894] Input: User's voice data

[0895] Data processing: Conversion processing using a speech recognition engine.

[0896] Output: Text data

[0897] Step 4:

[0898] The server sends text data to a natural language processing engine to analyze symptoms and urgency. The natural language processing engine (Google Cloud Natural Language API) is used to analyze the text data and determine the symptoms and their urgency.

[0899] Input: Text data

[0900] Data processing: Analysis and processing using a natural language processing engine.

[0901] Output: Analysis results of symptoms and urgency

[0902] Step 5:

[0903] The server sends voice data to the emotion recognition engine, which analyzes the user's emotions. The emotion recognition engine (IBM Watson Tone Analyzer) is used to analyze the user's emotions from the voice data.

[0904] Input: Audio data

[0905] Data processing: Analysis processing using an emotion recognition engine.

[0906] Output: Emotion analysis results

[0907] Step 6:

[0908] The server lists appropriate medical institutions based on the analysis results and guides the user through voice guidance. Based on the analysis of symptoms, urgency, and emotions, the server selects the most suitable medical institutions from the database and creates a list. Next, it provides voice guidance such as, "We will guide you to nearby emergency hospitals. Shall we contact the nearest XX Hospital now?" Furthermore, taking into consideration the user's emotional state, it also provides relaxation guidance such as, "Please relax and take a deep breath."

[0909] Input: Symptom and urgency analysis results, emotion analysis results

[0910] Data processing: Database lookup, voice guidance generation.

[0911] Output: List of medical facilities, voice guidance

[0912] Step 7:

[0913] The user responds to the voice guidance and requests to connect to a medical institution. When the user responds with "yes," the server receives the response.

[0914] Input: User response data

[0915] Data processing: Analysis of response data

[0916] Output: User response result

[0917] Step 8:

[0918] The server activates the automatic telephone connection function and places a call from the user's terminal to a medical institution. Based on the response, the server activates the automatic telephone connection function again and places a call from the user's terminal to the nearest medical institution.

[0919] Input: User response result

[0920] Data processing: Activation of automatic connection function

[0921] Output: Telephone connection to a medical institution

[0922] Step 9:

[0923] The server sends follow-up messages and surveys to the user after a certain period of time. To check on the user's status and collect feedback, the server generates follow-up messages and surveys and sends them via the specified method (email or SMS).

[0924] Input: User response result

[0925] Data processing: Generating messages and surveys

[0926] Output: Follow-up message, survey

[0927] Through these steps, the system is designed to ensure that users receive prompt and appropriate medical assistance in emergencies. Furthermore, it provides relaxation guidance that takes the user's emotions into consideration, enhancing their sense of security.

[0928] (Application Example 2)

[0929] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0930] Currently, there are limited means of receiving prompt and appropriate medical assistance in the event of a sudden illness or injury while in an autonomous vehicle. Furthermore, the technology to analyze the user's symptoms and emotions and appropriately set the autonomous vehicle's route is insufficient. Therefore, there are challenges in quickly arriving at the appropriate medical facility in an emergency and enhancing the user's sense of security.

[0931] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input symptoms by voice or push tone, means for converting the input data into text data using a speech recognition engine, means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency, means for listing appropriate medical institutions based on the determination result and guiding the user to the list with voice guidance, means for automatically contacting the guided medical institutions, means for sending follow-up messages and questionnaires to the user, and means for analyzing the user's symptoms and emotions in the autonomous vehicle with a speech recognition engine and an emotion analysis engine, and setting or resetting the route of the autonomous vehicle according to the urgency. This makes it possible for the user to receive prompt and appropriate medical assistance in an emergency, and furthermore, by appropriately setting the route of the autonomous vehicle, the user's sense of security can be enhanced.

[0932] Definitions of important words

[0933] A "user" is a person who uses the system to input symptoms using voice or push tones.

[0934] A "speech recognition engine" is a device that converts input speech data into text data.

[0935] A "natural language processing engine" is a technology that analyzes text data to determine symptoms and urgency.

[0936] "Voice guidance" is a system that provides users with a list of appropriate medical facilities via voice.

[0937] The "emotion analysis engine" analyzes the user's emotions from their voice data.

[0938] A "medical institution" is a facility that provides medical services for health problems and injuries.

[0939] An "autonomous vehicle" is a vehicle that operates automatically using programs and sensors, without requiring human intervention.

[0940] A "follow-up message" is a message sent to a user after medical care to check on their condition and request feedback.

[0941] A "survey" is a questionnaire used to collect feedback and opinions from users.

[0942] "Route setting" is the process of determining the optimal route for an autonomous vehicle to reach a specific destination.

[0943] "Urgency" is a criterion that indicates how quickly medical attention is needed for the user's symptoms.

[0944] "Push tones" are sounds generated by pressing the number buttons on a telephone, and are a means of inputting information into a system.

[0945] "Text data" refers to character information converted from audio data.

[0946] A "server" is a computer system that performs speech recognition, natural language processing, sentiment analysis, and data management.

[0947] invention specification

[0948] System Overview

[0949] This invention is a system that provides rapid and appropriate medical assistance to passengers who experience sudden illness or injury in an autonomous vehicle. The user inputs symptoms via voice or touch-tone input, which are then analyzed using a voice recognition engine and a natural language processing engine. Furthermore, an emotion analysis engine is used to analyze the user's emotions, and the system guides them to the appropriate medical facility according to the urgency of the situation. This allows the autonomous vehicle to reconfigure its route to the optimal one, enhancing the user's sense of security.

[0950] Hardware and software used

[0951] Hardware: Audio input device (microphone), audio output device (speaker), control system for autonomous vehicles, server

[0952] Software: Speech recognition engines (e.g., Google Cloud Speech-to-Text), natural language processing engines (e.g., NLTK and SpaCy), sentiment analysis engines (e.g., EmotionRecognizer), map information provision APIs (e.g., Google Maps API)

[0953] Data processing

[0954] 1. Voice input:

[0955] The user inputs their symptoms by voice. For example, they might say, "I have severe chest pain."

[0956] 2. Speech recognition and analysis:

[0957] The server sends the audio data to the speech recognition engine, where it is converted into text data.

[0958] This text data is analyzed using a natural language processing engine to determine the symptoms and their urgency.

[0959] 3. Emotion analysis:

[0960] The server sends the voice data to an emotion analysis engine, which analyzes the user's emotional state (e.g., anxiety, stress).

[0961] 4. Guidance to medical facilities and route planning:

[0962] Based on the urgency and sentiment analysis results, the server identifies the appropriate medical facility and guides the user via voice guidance.

[0963] If necessary, the autonomous vehicle's navigation system will reset the route.

[0964] 5. Automated telephone connection and follow-up:

[0965] When the user responds with "yes," the server automatically connects to the medical institution.

[0966] After a certain period of time, follow-up messages or surveys will be sent to the user.

[0967] Specific example

[0968] For example, suppose a user suddenly experiences chest pain while in an autonomous vehicle. The user inputs, "I have severe chest pain." The server recognizes this voice, converts it to text, and then analyzes it using a natural language processing engine to determine the urgency level is high. The emotion analysis engine then detects anxiety and provides a relaxation message such as, "Please relax, we will be there shortly." Next, the server guides the user to an appropriate medical facility and resets the autonomous vehicle's route.

[0969] Example of a prompt

[0970] User's voice message regarding symptoms: "I have severe chest pain."

[0971] Symptoms analyzed: "Severe chest pain"

[0972] Analyzed urgency level: "High"

[0973] Analyzed emotion: "anxiety"

[0974] The nearest hospital that was found: "〇〇 Hospital"

[0975] In this way, the present invention is a system that enables users to receive prompt and appropriate medical assistance in emergencies, and further improves user safety and peace of mind by appropriately controlling autonomous vehicles.

[0976] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0977] Program processing steps

[0978] Processing flow

[0979] Step 1:

[0980] The user inputs the symptoms using voice.

[0981] Specific action: The user experiences severe chest pain while inside the autonomous vehicle and describes the symptom by saying, "I have severe chest pain."

[0982] Input: User's voice data.

[0983] Output: Send as audio data to the server.

[0984] Step 2:

[0985] The server sends the audio data to the speech recognition engine, where it is converted into text data.

[0986] Specific operation: The server uses a speech recognition engine to convert "I have severe chest pain" into text data.

[0987] Input: Audio data.

[0988] Output: Text data.

[0989] Step 3:

[0990] The server analyzes the text data using a natural language processing engine to determine the symptoms and urgency.

[0991] Specific operation: The server inputs the text data "I have severe chest pain" into a natural language processing engine, which determines the symptom to be "chest pain" and the urgency to be "high".

[0992] Input: Text data.

[0993] Output: Symptom and urgency assessment results.

[0994] Step 4:

[0995] The server sends the voice data to an emotion analysis engine, which then analyzes the user's emotions.

[0996] Specific operation: The server analyzes the audio data using an emotion analysis engine and detects the emotional state of "anxiety."

[0997] Input: Audio data.

[0998] Output: Emotion analysis results.

[0999] Step 5:

[1000] The server identifies the appropriate medical facility based on the urgency and sentiment analysis results, and guides the user through voice guidance.

[1001] Specific operation: The server checks the medical institution database, lists the nearest medical institutions, and provides voice guidance to the user saying, "We will guide you to the nearest XX Hospital."

[1002] Input: Symptoms, urgency, and sentiment analysis results.

[1003] Output: List of medical facilities, voice guidance.

[1004] Step 6:

[1005] If necessary, the autonomous vehicle's navigation system will reset the route.

[1006] Specific operation: The server instructs the autonomous vehicle's navigation system to set an emergency route and reconfigure the optimal path.

[1007] Input: Location information of the most suitable medical facility.

[1008] Output: Reconfigured route information.

[1009] Step 7:

[1010] The system automatically connects users to medical institutions based on their responses.

[1011] Specific operation: When the user responds with "yes," the server automatically connects to the medical institution by phone.

[1012] Input: User response.

[1013] Output: Telephone connection to a medical institution.

[1014] Step 8:

[1015] After a certain period of time, follow-up messages or surveys will be sent to the user.

[1016] Specific operation: The server creates and sends follow-up messages and surveys to the user.

[1017] Input: A certain amount of time has elapsed.

[1018] Output: Follow-up messages and surveys.

[1019] In this way, each processing step allows the system to automatically perform a series of processes, from receiving voice input from the user to guiding them to a medical facility, determining the urgency of the situation, and resetting the route of the autonomous vehicle.

[1020] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1021] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1022] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1023] [Third Embodiment]

[1024] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1025] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1027] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1028] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1029] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1031] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1032] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1034] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1036] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and quickly guides and contacts the appropriate medical institution according to the urgency of the situation.

[1037] System components

[1038] This system includes the following main components:

[1039] 1. User

[1040] In an emergency, users can access the system using their telephone and input their symptoms via voice or touch tone.

[1041] 2. Server

[1042] It detects incoming calls and activates the automated answering system.

[1043] The audio data is converted to text using a speech recognition engine, and then analyzed using a natural language processing engine.

[1044] Based on the analysis results, the system lists appropriate medical institutions and guides users to them via voice guidance.

[1045] Based on user selection, the system automatically connects users to medical institutions.

[1046] A follow-up message or survey will be sent after a certain period of time.

[1047] Program processing

[1048] The program in this system performs the following series of processes.

[1049] 1. User call and symptom input

[1050] When a user calls a specific number, the server activates an automated response system.

[1051] The server will announce via voice, "Please tell us what problem you are experiencing."

[1052] Users can input symptoms such as "I have a severe stomach ache" by voice, or use push tones if necessary.

[1053] 2. Analysis and Diagnosis of Symptoms

[1054] The server sends the received audio data to the speech recognition engine, where it is converted into text data.

[1055] Next, the server sends the converted text data to a natural language processing engine to determine the symptoms and urgency.

[1056] 3. Guidance and Information

[1057] Based on the analysis results, the server lists appropriate medical institutions and guides the user through the list using voice guidance.

[1058] The server then asks the user, "Shall we contact the nearby XX Hospital right now?"

[1059] 4. Automatic telephone connection

[1060] When the user answers "yes," the server activates the automatic phone connection function and makes a direct call from the user's device to the medical institution (e.g., XX Hospital).

[1061] 5. Follow-up

[1062] The server will send messages or surveys to follow up on the user's status after a certain period of time.

[1063] Users provide feedback on the service by responding to these messages and surveys.

[1064] Specific example

[1065] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[1066] User: Call 7119.

[1067] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[1068] User: "I have a severe stomach ache," entered via voice input.

[1069] Server: Converts speech data into text using a speech recognition engine.

[1070] Server: Analyzes using a natural language processing engine to determine urgency.

[1071] Server: Lists appropriate medical institutions and suggests, "Shall we contact the nearest XX Hospital immediately?"

[1072] User: "Yes," they replied.

[1073] Server: Executes automated telephone connections to medical institutions.

[1074] User: Contact XX Hospital directly to receive appropriate medical care.

[1075] In this way, this system supports users in receiving prompt and appropriate medical assistance in emergencies.

[1076] The following describes the processing flow.

[1077] Step 1:

[1078] User: In an emergency, use your phone to call a specific number (e.g., 7119).

[1079] Step 2:

[1080] Server: Detects an incoming call and activates the automated answering system. It then instructs the user via voice, "Please tell us what problem you are experiencing."

[1081] Step 3:

[1082] User: Input symptoms by voice, such as "I have a severe stomach ache," or use push tones if necessary.

[1083] Step 4:

[1084] Server: Sends the input voice data to the speech recognition engine and converts it into text data.

[1085] Step 5:

[1086] Server: Sends the converted text data to a natural language processing engine for analysis. Through the analysis, it determines the symptoms and urgency.

[1087] Step 6:

[1088] Server: Based on the analysis results, it lists appropriate medical institutions (e.g., nearby emergency hospitals) and guides the user through this list using voice guidance.

[1089] Step 7:

[1090] Server: The server suggests to the user, "Would you like to contact the nearest XX Hospital immediately?" and provides options.

[1091] Step 8:

[1092] User: Responds with "Yes".

[1093] Step 9:

[1094] Server: Based on the user's response, retrieve the phone number for the selected medical institution (e.g., XX Hospital).

[1095] Step 10:

[1096] Server: Activates the automatic telephone connection function and makes a direct call from the user's terminal to the medical institution (○○ Hospital).

[1097] Step 11:

[1098] Terminal: The user's phone is automatically connected to XX Hospital.

[1099] Step 12:

[1100] Server: After a certain period of time, the server creates and sends surveys and follow-up messages to the user to check on their status.

[1101] Step 13:

[1102] User: Provide feedback based on the surveys and messages received.

[1103] Through the steps outlined above, users can receive prompt and accurate guidance to appropriate medical institutions, and then contact those institutions directly to receive the necessary care.

[1104] (Example 1)

[1105] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1106] Current medical support systems have problems in providing a quick and appropriate response to sudden injuries and illnesses. Misunderstandings and delays are common when users describe their symptoms, and it is often difficult to smoothly guide or contact appropriate medical institutions according to the urgency of the situation. Furthermore, the mechanisms for follow-up according to the user's situation are insufficient. A system is needed that solves these problems and allows users to receive quick and appropriate medical support.

[1107] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1108] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for listing appropriate medical institutions based on the determination results and guiding the user through the list with voice guidance; means for automatically contacting the guided medical institutions; means for sending follow-up messages and questionnaires to the user; means for suggesting to the user contact the nearest medical institution with voice guidance; means for coordinating with the server to execute the above means; and means for collecting responses from the user to follow-up messages. This enables the user to receive prompt and appropriate medical assistance in an emergency.

[1109] A "user" is a person who calls the system of the present invention and inputs their symptoms using voice or touch tones.

[1110] "Means of input" refers to the methods or devices by which a user communicates symptoms to the system via voice or touch tone, and usually refers to telephones or smartphones.

[1111] A "speech recognition engine" is software or algorithms used to convert speech data into text data.

[1112] "Text data" refers to character information converted from speech data by a speech recognition engine.

[1113] A "natural language processing engine" is software or algorithms that analyze text data to determine symptoms and urgency.

[1114] "Methods for determining symptoms and urgency" refers to a method that uses a natural language processing engine to understand the content of symptom information from the user and evaluate its urgency.

[1115] A "medical institution" refers to a place that provides medical services, such as a general hospital or clinic.

[1116] "Listing" refers to selecting appropriate medical institutions based on the analysis results and compiling them into a list.

[1117] "Voice guidance" refers to a system that provides guidance and instructions to users via voice, and usually refers to voice playback by an automated response system.

[1118] "Automated methods" refer to functions or mechanisms that allow a system to automatically process tasks without user intervention.

[1119] A "follow-up message" is a confirmation or survey message sent after a certain period of time to check on the user's status.

[1120] "Means of collaboration" refers to the methods and protocols by which a server communicates with other systems or databases to obtain or transmit necessary information.

[1121] "Means of collecting responses" refer to functions for collecting user feedback and survey responses, which typically include SMS or dedicated web forms.

[1122] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and promptly guides and contacts the appropriate medical institution according to its urgency. The system of this invention includes the following main components:

[1123] 1. User

[1124] In emergencies, users access the system using a telephone and input their symptoms via voice or touch-tone input. Users can use a standard landline or mobile phone.

[1125] 2. Terminal

[1126] This refers to a user's phone or smartphone, and is a means of inputting voice or touch-tone signals into the system.

[1127] 3. Server

[1128] The server has the following roles:

[1129] Incoming call detection and activation of the automated answering system: When the server detects an incoming call, it activates the automated answering system (e.g., Asterisk PBX) and provides a voice message saying, "Please tell us what problem you are experiencing."

[1130] Speech recognition of audio data: The server sends the audio data received from the user to a speech recognition engine (e.g., Google Speech-to-Text API) and converts the audio data into text data.

[1131] Text data analysis and urgency assessment: The converted text data is analyzed using a natural language processing engine (e.g., Python's NLTK library) to determine the symptoms and urgency level.

[1132] Listing and guiding users to medical institutions: Based on the analysis results, the server lists appropriate medical institutions and guides users to them via voice guidance. For example, it might suggest to the user, "Would you like to contact the nearby XX Hospital now?"

[1133] Automated phone connection: When the user answers "yes," the server activates the automated phone connection function (e.g., Twilio API) and makes a call to the selected healthcare provider from the user's device.

[1134] Follow-up: After a certain period of time, the server will send a follow-up message or survey to the user to check on their status. This follow-up message will be sent using an SMS sending API (e.g., Twilio SMS API).

[1135] Specific example

[1136] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[1137] User: Make a phone call to the specified phone number (e.g., 7119).

[1138] Server: The automated response system receives the call and instructs the user, "Please tell us what problem you are experiencing."

[1139] User: "I have a severe stomach ache," is entered via voice input.

[1140] Server: Converts audio data to text using the Google Speech-to-Text API.

[1141] Server: The converted text is analyzed using Python's NLTK library to determine its urgency.

[1142] Server: Lists appropriate medical facilities (e.g., nearby XX Hospital) and suggests, "Shall we contact XX Hospital immediately?"

[1143] User: Responds with "Yes".

[1144] Server: Uses the Twilio API to make phone calls to XX Hospital from the user's device.

[1145] Server: After a certain period of time, send a follow-up message to the user using the Twilio SMS API.

[1146] This system will enable users to receive prompt and appropriate medical assistance in emergencies.

[1147] Example of a prompt

[1148] The following are examples of prompts to input into the generating AI model.

[1149] I suddenly started experiencing severe abdominal pain this afternoon. Could you please tell me what I should do?

[1150] In this way, the present invention provides prompt and appropriate medical support based on the user's symptoms and urgency, creating an environment in which users can respond with peace of mind even in emergencies.

[1151] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1152] Step 1:

[1153] User call and symptom input

[1154] When a user experiences sudden symptoms, they call a designated phone number (e.g., 7119). This results in a phone call being generated as input.

[1155] The server detects an incoming call and activates an automated answering system (e.g., Asterisk PBX). Here, it receives the incoming call signal as input and activates the automated answering system as output.

[1156] The server plays an audio message asking the user, "Please tell us what problem you are experiencing." This initiates the voice guidance process, which then takes the user's input as input.

[1157] The user can describe their symptoms by voice, for example, "I have a severe stomach ache," or by using push tones if necessary. This will result in the user's voice data being used as input.

[1158] Step 2:

[1159] Speech recognition of audio data

[1160] The server processes the audio data received from the user. The audio data received as input is the subject of processing.

[1161] The server sends the audio data to a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio into text data. This allows the speech recognition engine to process the audio data into text data.

[1162] The server receives text data (e.g., "I have a severe stomach ache") as output from the speech recognition engine.

[1163] Step 3:

[1164] Natural language analysis of symptoms and assessment of urgency.

[1165] The server sends the converted text data to a natural language processing engine (e.g., Python's NLTK library) for analysis. Text data is the input to be processed.

[1166] The server uses a natural language processing engine to analyze text data and extract keywords (e.g., "severe stomach ache"). This is how keyword extraction and analysis are performed.

[1167] The server compares the extracted keywords with predefined urgency rules to determine the urgency of the symptoms. This processes the urgency data, and the urgency determination result is output.

[1168] Step 4:

[1169] Listing and guidance for medical institutions

[1170] The server lists appropriate medical institutions from its database (e.g., PostgreSQL) based on the assessment result. The urgency assessment result is the input to be processed.

[1171] The server uses an audio guidance system to provide users with information such as the names of listed medical institutions. This results in a list of medical institutions being output.

[1172] The server then prompts the user with the question, "Would you like to contact the nearby XX Hospital immediately?" This triggers an automated voice guidance message for the user.

[1173] Step 5:

[1174] Automated telephone connection

[1175] The user responds with "Yes". The user's response is received as input.

[1176] The server analyzes the user's responses and activates an automated telephone connection function (e.g., Twilio API) to the medical institution selected from the user's device. This means the user's response data is the input, and the telephone connection is the output.

[1177] The server places a phone call from the user's terminal to a medical institution (e.g., XX Hospital). This initiates the actual phone connection.

[1178] Step 6:

[1179] Follow-up

[1180] The server sends follow-up messages or surveys to the user after a certain period of time. The timing of the follow-up action is set as an input.

[1181] The server uses an SMS sending API (e.g., Twilio SMS API) to send a follow-up message. This results in the follow-up message being sent to the user as output.

[1182] Users respond to follow-up messages and surveys, thereby obtaining user response data as input.

[1183] In this way, by executing each processing step sequentially, the system of the present invention helps users receive prompt and appropriate medical assistance in emergency situations.

[1184] (Application Example 1)

[1185] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1186] Receiving prompt and appropriate medical assistance in the event of a sudden injury or illness is crucial. However, it is often difficult to quickly and reliably identify accessible medical facilities and transport patients appropriately, especially if the illness occurs at home or while out. Furthermore, elderly individuals and those with disabilities often find it difficult to reach medical facilities on their own. Therefore, there is a need to develop automated systems that address these challenges and provide prompt and appropriate medical assistance.

[1187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1188] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for listing appropriate medical institutions based on the determination results and guiding the user through the list with voice guidance; means for automatically contacting the listed medical institutions; means for sending follow-up messages and questionnaires to the user; and means for coordinating with an autonomous vehicle to automatically transport the user to an appropriate medical institution. This enables the user to receive prompt and appropriate medical assistance in an emergency.

[1189] A "user" refers to a person who uses the system to input symptoms and receive medical support.

[1190] "Voice or push-tone" refers to the method a user uses to input their symptoms into the system.

[1191] A "speech recognition engine" refers to the technology that converts speech data into text data.

[1192] "Text data" refers to data in the form of text converted by a speech recognition engine, which can be analyzed by the system.

[1193] A "natural language processing engine" refers to a technology that analyzes text data and determines symptoms and urgency based on its content.

[1194] "Symptoms and urgency" refers to information indicating the content of the symptoms entered by the user and the urgent need for them.

[1195] "Voice guidance" refers to a system function that provides users with voice guidance and instructions.

[1196] "Medical institutions" refer to medical facilities such as hospitals, clinics, and medical offices.

[1197] A "follow-up message" refers to a message sent after the initial response to check on the user's status.

[1198] A "survey" refers to a set of questions used to collect feedback on the evaluation of medical services and the user's situation.

[1199] An "autonomous vehicle" refers to a vehicle that can automatically operate and travel to a designated location.

[1200] "Transportation" refers to the act of using an autonomous vehicle to transport a user to an appropriate medical facility.

[1201] This invention relates to an autonomous vehicle collaboration system that provides rapid and appropriate medical assistance for sudden injuries or illnesses. The system receives symptom input from the user via voice or push-tone, analyzes the input, and takes appropriate action according to the urgency of the situation.

[1202] System Overview

[1203] The system consists of the following elements:

[1204] 1. User terminal: Functions as a smartphone application and provides an interface for the user to input symptoms using voice or touch tones.

[1205] 2. Server: Uses a speech recognition engine (Google Cloud Speech-to-Text API) and a natural language processing engine (IBM Watson Natural Language Understanding) to analyze voice data from the user and determine the symptoms and their urgency.

[1206] 3. Autonomous vehicles: These vehicles transport users to appropriate medical facilities and configure the transport route in conjunction with a navigation system (e.g., Waymo's API).

[1207] 4. Follow-up system: Send follow-up messages and surveys to users to help them evaluate and improve the service.

[1208] Specific processing of the program

[1209] 1. User symptom input: The user launches the application from their smartphone and inputs their symptoms using voice or touch tones. In the case of voice input, the user describes the symptoms in a format such as "I have severe chest pain."

[1210] 2. Speech Recognition and Text Conversion: User voice data is converted into text data using the Google Cloud Speech-to-Text API.

[1211] 3. Text data analysis: The converted text data is analyzed by IBM Watson Natural Language Understanding to determine the urgency of the symptoms and the appropriate medical institution.

[1212] 4. Listing and Guiding Users to Medical Institutions: Based on the analysis results, appropriate medical institutions will be listed and guided to the user via voice guidance through the application.

[1213] 5. Arranging an autonomous vehicle: Once the user approves the transfer, the server uses Waymo's API to instruct the autonomous vehicle to navigate and transport the user to the designated medical facility.

[1214] 6. Follow-up: After the user arrives at the medical facility, follow-up messages and questionnaires are sent to confirm the quality of service.

[1215] Hardware and software to use

[1216] Speech recognition engine: Google Cloud Speech-to-Text API

[1217] Natural Language Processing Engine: IBM Watson Natural Language Understanding

[1218] Autonomous vehicle navigation system: Waymo API-enabled navigation

[1219] Specific example

[1220] For example, if a user suddenly experiences severe chest pain at home, it would work as follows:

[1221] The user launches the app on their smartphone and uses voice input to say, "I have severe chest pain."

[1222] The server converts the audio data into text and analyzes it using a natural language processing engine.

[1223] The system assesses the urgency of the situation and lists the most suitable nearby medical facilities.

[1224] After the user receives instructions and responds with "yes," an autonomous vehicle is dispatched to transport the user to a medical facility.

[1225] Upon arrival at the medical facility, a follow-up message is sent to confirm the user's condition.

[1226] Example of a prompt

[1227] "I want to develop a media mobile assistance app that arranges transportation to the nearest medical facility in case of severe chest pain. This app will use the Google Cloud Speech-to-Text API to convert speech to text, IBM Watson Natural Language Understanding to determine the urgency of the symptoms, and Waymo's API to connect with the navigation system of an autonomous vehicle. Please provide a complete program."

[1228] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1229] Step 1:

[1230] The user launches the application from their smartphone. The user inputs their symptoms using voice or touch tones. For example, the user might voice input, "I have severe chest pain." This input data is saved within the application as voice data.

[1231] Step 2:

[1232] The server receives the user's voice data. It sends the received voice data to the Google Cloud Speech-to-Text API, where it is converted into text data. By sending this audio data to the API, the audio data is returned as text data.

[1233] Step 3:

[1234] The server sends the converted text data to IBM Watson Natural Language Understanding for analysis. The analysis determines the urgency of the symptoms, and the result is returned as text data. The server receives this analysis result and understands the nature and urgency of the symptoms.

[1235] Step 4:

[1236] The server creates a list of appropriate medical institutions based on the analysis results. It lists the most suitable medical institutions from a database of medical institutions based on location information and the symptoms they can treat. This list is stored as internal data.

[1237] Step 5:

[1238] The server provides the user with a voice guidance message listing appropriate medical facilities. For example, it might say, "We will now begin transporting you to the nearest XX Hospital. Is that alright?" This voice guidance is transmitted to the user's terminal as audio data.

[1239] Step 6:

[1240] The user responds with "yes" to the voice guidance. The user's device sends this response as audio data to the server. The server then sends this audio data back to the Google Cloud Speech-to-Text API, where it is converted into text data.

[1241] Step 7:

[1242] The server analyzes the user's response and initiates contact with a medical facility and dispatch of an autonomous vehicle. Using the Waymo API, it inputs the location information of the destination medical facility into the navigation system and dispatches a vehicle. As a result, a notification that the transfer has begun is displayed on the user's device.

[1243] Step 8:

[1244] The user boards an autonomous vehicle. The vehicle automatically transports the user to a designated medical facility according to the navigation system. During transport, the vehicle's location information and transport status are transmitted to a server in real time for monitoring.

[1245] Step 9:

[1246] After the user arrives at the medical facility, the server sends follow-up messages and questionnaires to the user's device. The user responds to these messages and enters information confirming their condition and evaluating the service. This data is sent to the server and used to improve future services.

[1247] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1248] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and quickly guides and contacts the appropriate medical institution according to its urgency. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves a more sophisticated response.

[1249] System components

[1250] This system includes the following main components:

[1251] 1. User

[1252] In an emergency, users can access the system using their telephone and input their symptoms via voice or touch tone.

[1253] 2. Server

[1254] It detects incoming calls and activates the automated answering system.

[1255] The audio data is converted to text using a speech recognition engine, and then analyzed using a natural language processing engine.

[1256] An emotion engine is used to analyze the user's emotions from voice data.

[1257] Based on the analysis results (symptoms, urgency, and user's emotions), the system lists appropriate medical institutions and guides the user to them via voice guidance.

[1258] Based on user selection, the system automatically connects users to medical institutions.

[1259] A follow-up message or survey will be sent after a certain period of time.

[1260] Program processing

[1261] The program in this system performs the following series of processes.

[1262] 1. User call and symptom input

[1263] When a user calls a specific number, the server activates an automated response system.

[1264] The server prompts the user with a voice message saying, "Please tell us what symptoms you are experiencing," and encourages them to enter their symptoms.

[1265] Users input symptoms such as "I have a severe stomach ache" using voice. Push-tone input is also available if needed.

[1266] 2. Analysis of symptoms and emotions

[1267] The server sends the input voice data to the speech recognition engine, where it is converted into text data.

[1268] Next, the server sends the converted text data to a natural language processing engine to determine the symptoms and urgency.

[1269] Furthermore, the voice data is sent to an emotion engine to analyze the user's emotions (e.g., stress, anxiety, anger, etc.).

[1270] 3. Guidance and Information

[1271] Based on the analysis of symptoms and emotions, the server lists appropriate medical institutions.

[1272] The server will guide the user via voice guidance, saying, "We will guide you to nearby emergency hospitals. Would you like to contact the nearest XX Hospital immediately?"

[1273] Based on the results of the emotion engine, the tone and content of the voice guidance are adjusted, and relaxation guidance is also provided to enhance the user's sense of security.

[1274] 4. Automatic telephone connection

[1275] When the user answers "yes," the server activates the automatic phone connection function and directly calls the medical institution (e.g., XX Hospital) from the user's device.

[1276] 5. Follow-up

[1277] After a certain period of time, the server creates and sends messages or questionnaires to the user to follow up on their situation.

[1278] Users provide feedback on the service by responding to these messages and surveys.

[1279] Specific example

[1280] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[1281] User: Call 7119.

[1282] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[1283] User: "I have a severe stomach ache," entered via voice input.

[1284] Server: Converts speech data into text using a speech recognition engine.

[1285] Server: Analyzes using a natural language processing engine to determine urgency.

[1286] Server: Analyzes voice data using an emotion engine to recognize the user's emotions (e.g., anxiety).

[1287] Server: Lists appropriate medical facilities and asks, "Would you like to contact the nearest XX Hospital now?" and also provides relaxation guidance such as, "Relax and take a deep breath."

[1288] User: "Yes," they replied.

[1289] Server: Executes automated telephone connections to medical institutions.

[1290] User: Contact XX Hospital directly to receive appropriate medical care.

[1291] In this way, the system is designed to support users in receiving prompt and appropriate medical assistance in emergencies, thereby increasing their sense of security.

[1292] The following describes the processing flow.

[1293] Step 1:

[1294] User: In an emergency, use your phone to call a specific number (e.g., 7119).

[1295] Step 2:

[1296] Server: Detects an incoming call and activates the automated answering system. It then instructs the user via voice, "Please tell us what problem you are experiencing."

[1297] Step 3:

[1298] User: Input symptoms by voice, such as "I have a severe stomach ache," or use push tones if necessary.

[1299] Step 4:

[1300] Server: Sends the input voice data to the speech recognition engine and converts it into text data.

[1301] Step 5:

[1302] Server: Sends the converted text data to a natural language processing engine for analysis. Through the analysis, it determines the symptoms and urgency.

[1303] Step 6:

[1304] Server: Simultaneously sends the input voice data to the emotion engine to analyze the user's emotions (e.g., stress, anxiety, anger, etc.).

[1305] Step 7:

[1306] Server: Based on the analysis results (symptoms, urgency, emotion) from the natural language processing engine and emotion engine, it lists appropriate medical facilities (e.g., nearby emergency hospitals).

[1307] Step 8:

[1308] Server: The server provides voice guidance to the user, saying, "We will guide you to nearby emergency hospitals. Would you like to contact the nearest XX Hospital now?" It also adds voice tones and relaxation instructions tailored to the user's emotions, based on the results of the emotion engine.

[1309] Step 9:

[1310] User: Responds with "Yes".

[1311] Step 10:

[1312] Server: Based on the user's response, retrieve the phone number for the selected medical institution (e.g., XX Hospital).

[1313] Step 11:

[1314] Server: Activates the automatic telephone connection function and makes a direct call from the user's terminal to the medical institution (○○ Hospital).

[1315] Step 12:

[1316] Terminal: The user's phone is automatically connected to XX Hospital.

[1317] Step 13:

[1318] Server: After a certain period of time, it creates and sends a survey or follow-up message to the user to check on their status.

[1319] Step 14:

[1320] User: Provide feedback based on the surveys and messages received.

[1321] Through the steps outlined above, users can receive prompt and accurate guidance to appropriate medical institutions, and then contact those institutions directly to receive the necessary care. Furthermore, the introduction of an emotion engine enables responses tailored to the user's emotional state, thereby increasing the user's sense of security and trust.

[1322] (Example 2)

[1323] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1324] Conventional medical support systems often fail to adequately consider the user's emotions and urgency during the process of connecting them to a suitable medical institution quickly. This can lead to increased anxiety and stress, potentially delaying appropriate medical assistance. Furthermore, there were problems with providing guidance on emergency first aid and ensuring smooth telephone connections with medical institutions. Additionally, follow-up care lacked consideration for the user's emotional well-being.

[1325] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1326] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for analyzing the user's emotions using an emotion recognition engine based on the determination results and voice data; means for listing appropriate medical institutions based on the analysis results and guiding the user through the list with voice guidance; means for automatically contacting the guided medical institutions; means for adjusting the tone and content of the voice guidance based on the user's emotional state to provide relaxation guidance; and means for sending follow-up messages and questionnaires to the user. This enables the user to receive prompt and appropriate medical assistance in emergencies and allows for responses that take the user's emotions into consideration.

[1327] A "user" refers to someone who uses the system to input their symptoms and request medical assistance.

[1328] "Means of inputting symptoms by voice or touch tone" refers to a method for users to provide information about their symptoms to the system using voice or phone key input.

[1329] A "speech recognition engine" refers to software or hardware that has the function of converting input speech data into text data.

[1330] "Text data" refers to data that has been converted by a speech recognition engine and expressed as a string of characters.

[1331] A "natural language processing engine" refers to software or hardware that analyzes text data and has the function of determining the content and urgency of symptoms.

[1332] "Urgency" refers to a scale that indicates how urgent the user's symptoms are.

[1333] An "emotion recognition engine" refers to software or hardware that has the function of analyzing a user's emotional state from voice data or input data.

[1334] "Medical institutions" refer to facilities that provide medical services, such as hospitals and clinics.

[1335] "Voice guidance" refers to a method of providing users with guidance information via voice.

[1336] "Automated methods" refer to methods in which the system performs processing automatically without requiring user intervention.

[1337] "Relaxation guidance" refers to audio guidance designed to alleviate user tension and anxiety.

[1338] "Follow-up messages and questionnaires" refer to messages and questionnaires sent by the system after a user has received medical assistance, for the purpose of checking the user's condition and evaluating the service.

[1339] "Medical support" refers to providing advice, guidance, or arranging treatment for illness or injury.

[1340] This invention relates to a system for providing rapid and appropriate medical assistance in the event of sudden injury or illness. This system includes the following main components:

[1341] 1. User

[1342] In an emergency, the user accesses the system using their telephone and enters their symptoms using voice or touch tones. The symptom information entered by the user forms the basis for subsequent analysis and processing.

[1343] 2. Terminal

[1344] The user's phone or other device initiates a call to the system and receives the user's voice input or touch-tone input through that call.

[1345] 3. Server

[1346] The server is the core of the system and performs the following tasks:

[1347] Automated response system

[1348] When an incoming call is detected, the automated response system is activated and instructs the user to "Please tell us what problem you are experiencing."

[1349] Speech recognition engine

[1350] The speech recognition engine converts the voice data entered by the user into text data. Specifically, it uses "Google Cloud Speech-to-Text".

[1351] Natural Language Processing Engine

[1352] The natural language processing engine analyzes the text data converted by the speech recognition engine to determine the symptoms and urgency. Here, we use the "Google Cloud Natural Language API".

[1353] Emotion recognition engine

[1354] The emotion recognition engine analyzes the user's emotions from voice data. Specifically, it uses "IBM Watson Tone Analyzer."

[1355] Voice guidance

[1356] The voice guidance system directs users to appropriate medical facilities based on analysis results. Furthermore, it adjusts the tone and content of the voice guidance based on the user's emotional state, providing relaxation guidance as well.

[1357] Automatic telephone connection function

[1358] When a user requests to connect to a medical institution, the automated telephone connection function is activated, and the user's device makes a call to the nearest medical institution.

[1359] Follow-up

[1360] After a certain period of time, the server generates and sends follow-up messages or questionnaires to the user to check on their status.

[1361] Specific example

[1362] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[1363] User: First, the user calls 7119.

[1364] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[1365] User: "I have a severe stomach ache," is entered via voice input.

[1366] Server: Uses a speech recognition engine (Google Cloud Speech-to-Text) to convert speech data into text.

[1367] Server: The converted text data is analyzed using a natural language processing engine (Google Cloud Natural Language API) to determine the symptoms and urgency.

[1368] Server: Furthermore, the server analyzes the voice data using an emotion recognition engine (IBM Watson Tone Analyzer) to recognize the user's emotions (e.g., anxiety).

[1369] Server: Lists appropriate medical facilities and provides voice guidance such as, "Would you like to contact the nearest XX Hospital now?" It also offers relaxation guidance such as, "Relax and take a deep breath."

[1370] User: Responds with "Yes".

[1371] Server: Activates the automatic phone connection function and places a call from the user's terminal to XX Hospital.

[1372] User: Contact XX Hospital directly to receive appropriate medical care.

[1373] Server: After a certain period of time, it sends follow-up messages or surveys to the user.

[1374] User: Respond to follow-up messages and surveys, and provide feedback.

[1375] Example of a prompt

[1376] The following are examples of prompts to input into a generative AI model:

[1377] "Please explain how users who call 7119 and report sudden abdominal pain can receive medical assistance. Please explain in detail how this system works when a user experiences sudden, severe abdominal pain at home."

[1378] By using this prompt, the generating AI model can produce text that explains the specific processing flow of the system. This system allows users to receive prompt and appropriate medical assistance in emergencies, and enables emotionally sensitive responses.

[1379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1380] Program processing steps

[1381] Step 1:

[1382] The server detects the user's call. When a user dials a specific phone number, the server detects the incoming call and activates the automated response system. The server plays a voice message to the user asking, "Please tell us what problem you are experiencing."

[1383] Input: User's phone call

[1384] Data processing: Incoming call detection, voice message playback

[1385] Output: Voice message from the system to the user.

[1386] Step 2:

[1387] The user enters their symptoms using voice or touch tones. The user can enter their symptoms by voice, such as "I have a severe stomach ache," or by using touch tones to enter the corresponding number.

[1388] Input: User voice input or push-tone input

[1389] Data processing: Acquisition of user voice, acquisition of push tone input

[1390] Output: Voice data or push-tone data

[1391] Step 3:

[1392] The server sends the voice data to the speech recognition engine to convert it into text data. The speech recognition engine (Google Cloud Speech-to-Text) is used to convert the user's voice data into text.

[1393] Input: User's voice data

[1394] Data processing: Conversion processing using a speech recognition engine.

[1395] Output: Text data

[1396] Step 4:

[1397] The server sends text data to a natural language processing engine to analyze symptoms and urgency. The natural language processing engine (Google Cloud Natural Language API) is used to analyze the text data and determine the symptoms and their urgency.

[1398] Input: Text data

[1399] Data processing: Analysis and processing using a natural language processing engine.

[1400] Output: Analysis results of symptoms and urgency

[1401] Step 5:

[1402] The server sends voice data to the emotion recognition engine, which analyzes the user's emotions. The emotion recognition engine (IBM Watson Tone Analyzer) is used to analyze the user's emotions from the voice data.

[1403] Input: Audio data

[1404] Data processing: Analysis processing using an emotion recognition engine.

[1405] Output: Emotion analysis results

[1406] Step 6:

[1407] The server lists appropriate medical institutions based on the analysis results and guides the user through voice guidance. Based on the analysis of symptoms, urgency, and emotions, the server selects the most suitable medical institutions from the database and creates a list. Next, it provides voice guidance such as, "We will guide you to nearby emergency hospitals. Shall we contact the nearest XX Hospital now?" Furthermore, taking into consideration the user's emotional state, it also provides relaxation guidance such as, "Please relax and take a deep breath."

[1408] Input: Symptom and urgency analysis results, emotion analysis results

[1409] Data processing: Database lookup, voice guidance generation.

[1410] Output: List of medical facilities, voice guidance

[1411] Step 7:

[1412] The user responds to the voice guidance and requests to connect to a medical institution. When the user responds with "yes," the server receives the response.

[1413] Input: User response data

[1414] Data processing: Analysis of response data

[1415] Output: User response result

[1416] Step 8:

[1417] The server activates the automatic telephone connection function and places a call from the user's terminal to a medical institution. Based on the response, the server activates the automatic telephone connection function again and places a call from the user's terminal to the nearest medical institution.

[1418] Input: User response result

[1419] Data processing: Activation of automatic connection function

[1420] Output: Telephone connection to a medical institution

[1421] Step 9:

[1422] The server sends follow-up messages and surveys to the user after a certain period of time. To check on the user's status and collect feedback, the server generates follow-up messages and surveys and sends them via the specified method (email or SMS).

[1423] Input: User response result

[1424] Data processing: Generating messages and surveys

[1425] Output: Follow-up message, survey

[1426] Through these steps, the system is designed to ensure that users receive prompt and appropriate medical assistance in emergencies. Furthermore, it provides relaxation guidance that takes the user's emotions into consideration, enhancing their sense of security.

[1427] (Application Example 2)

[1428] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1429] Currently, there are limited means of receiving prompt and appropriate medical assistance in the event of a sudden illness or injury while in an autonomous vehicle. Furthermore, the technology to analyze the user's symptoms and emotions and appropriately set the autonomous vehicle's route is insufficient. Therefore, there are challenges in quickly arriving at the appropriate medical facility in an emergency and enhancing the user's sense of security.

[1430] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input symptoms by voice or push tone, means for converting the input data into text data using a speech recognition engine, means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency, means for listing appropriate medical institutions based on the determination result and guiding the user to the list with voice guidance, means for automatically contacting the guided medical institutions, means for sending follow-up messages and questionnaires to the user, and means for analyzing the user's symptoms and emotions in the autonomous vehicle with a speech recognition engine and an emotion analysis engine, and setting or resetting the route of the autonomous vehicle according to the urgency. This makes it possible for the user to receive prompt and appropriate medical assistance in an emergency, and furthermore, by appropriately setting the route of the autonomous vehicle, the user's sense of security can be enhanced.

[1431] Definitions of important words

[1432] A "user" is a person who uses the system to input symptoms using voice or push tones.

[1433] A "speech recognition engine" is a device that converts input speech data into text data.

[1434] A "natural language processing engine" is a technology that analyzes text data to determine symptoms and urgency.

[1435] "Voice guidance" is a system that provides users with a list of appropriate medical facilities via voice.

[1436] The "emotion analysis engine" analyzes the user's emotions from their voice data.

[1437] A "medical institution" is a facility that provides medical services for health problems and injuries.

[1438] An "autonomous vehicle" is a vehicle that operates automatically using programs and sensors, without requiring human intervention.

[1439] A "follow-up message" is a message sent to a user after medical care to check on their condition and request feedback.

[1440] A "survey" is a questionnaire used to collect feedback and opinions from users.

[1441] "Route setting" is the process of determining the optimal route for an autonomous vehicle to reach a specific destination.

[1442] "Urgency" is a criterion that indicates how quickly medical attention is needed for the user's symptoms.

[1443] "Push tones" are sounds generated by pressing the number buttons on a telephone, and are a means of inputting information into a system.

[1444] "Text data" refers to character information converted from audio data.

[1445] A "server" is a computer system that performs speech recognition, natural language processing, sentiment analysis, and data management.

[1446] invention specification

[1447] System Overview

[1448] This invention is a system that provides rapid and appropriate medical assistance to passengers who experience sudden illness or injury in an autonomous vehicle. The user inputs symptoms via voice or touch-tone input, which are then analyzed using a voice recognition engine and a natural language processing engine. Furthermore, an emotion analysis engine is used to analyze the user's emotions, and the system guides them to the appropriate medical facility according to the urgency of the situation. This allows the autonomous vehicle to reconfigure its route to the optimal one, enhancing the user's sense of security.

[1449] Hardware and software used

[1450] Hardware: Audio input device (microphone), audio output device (speaker), control system for autonomous vehicles, server

[1451] Software: Speech recognition engines (e.g., Google Cloud Speech-to-Text), natural language processing engines (e.g., NLTK and SpaCy), sentiment analysis engines (e.g., EmotionRecognizer), map information provision APIs (e.g., Google Maps API)

[1452] Data processing

[1453] 1. Voice input:

[1454] The user inputs their symptoms by voice. For example, they might say, "I have severe chest pain."

[1455] 2. Speech recognition and analysis:

[1456] The server sends the audio data to the speech recognition engine, where it is converted into text data.

[1457] This text data is analyzed using a natural language processing engine to determine the symptoms and their urgency.

[1458] 3. Emotion analysis:

[1459] The server sends the voice data to an emotion analysis engine, which analyzes the user's emotional state (e.g., anxiety, stress).

[1460] 4. Guidance to medical facilities and route planning:

[1461] Based on the urgency and sentiment analysis results, the server identifies the appropriate medical facility and guides the user via voice guidance.

[1462] If necessary, the autonomous vehicle's navigation system will reset the route.

[1463] 5. Automated telephone connection and follow-up:

[1464] When the user responds with "yes," the server automatically connects to the medical institution.

[1465] After a certain period of time, follow-up messages or surveys will be sent to the user.

[1466] Specific example

[1467] For example, suppose a user suddenly experiences chest pain while in an autonomous vehicle. The user inputs, "I have severe chest pain." The server recognizes this voice, converts it to text, and then analyzes it using a natural language processing engine to determine the urgency level is high. The emotion analysis engine then detects anxiety and provides a relaxation message such as, "Please relax, we will be there shortly." Next, the server guides the user to an appropriate medical facility and resets the autonomous vehicle's route.

[1468] Example of a prompt

[1469] User's voice message regarding symptoms: "I have severe chest pain."

[1470] Symptoms analyzed: "Severe chest pain"

[1471] Analyzed urgency level: "High"

[1472] Analyzed emotion: "anxiety"

[1473] The nearest hospital that was found: "〇〇 Hospital"

[1474] In this way, the present invention is a system that enables users to receive prompt and appropriate medical assistance in emergencies, and further improves user safety and peace of mind by appropriately controlling autonomous vehicles.

[1475] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1476] Program processing steps

[1477] Processing flow

[1478] Step 1:

[1479] The user inputs the symptoms using voice.

[1480] Specific action: The user experiences severe chest pain while inside the autonomous vehicle and describes the symptom by saying, "I have severe chest pain."

[1481] Input: User's voice data.

[1482] Output: Send as audio data to the server.

[1483] Step 2:

[1484] The server sends the audio data to the speech recognition engine, where it is converted into text data.

[1485] Specific operation: The server uses a speech recognition engine to convert "I have severe chest pain" into text data.

[1486] Input: Audio data.

[1487] Output: Text data.

[1488] Step 3:

[1489] The server analyzes the text data using a natural language processing engine to determine the symptoms and urgency.

[1490] Specific operation: The server inputs the text data "I have severe chest pain" into a natural language processing engine, which determines the symptom to be "chest pain" and the urgency to be "high".

[1491] Input: Text data.

[1492] Output: Symptom and urgency assessment results.

[1493] Step 4:

[1494] The server sends the voice data to an emotion analysis engine, which then analyzes the user's emotions.

[1495] Specific operation: The server analyzes the audio data using an emotion analysis engine and detects the emotional state of "anxiety."

[1496] Input: Audio data.

[1497] Output: Emotion analysis results.

[1498] Step 5:

[1499] The server identifies the appropriate medical facility based on the urgency and sentiment analysis results, and guides the user through voice guidance.

[1500] Specific operation: The server checks the medical institution database, lists the nearest medical institutions, and provides voice guidance to the user saying, "We will guide you to the nearest XX Hospital."

[1501] Input: Symptoms, urgency, and sentiment analysis results.

[1502] Output: List of medical facilities, voice guidance.

[1503] Step 6:

[1504] If necessary, the autonomous vehicle's navigation system will reset the route.

[1505] Specific operation: The server instructs the autonomous vehicle's navigation system to set an emergency route and reconfigure the optimal path.

[1506] Input: Location information of the most suitable medical facility.

[1507] Output: Reconfigured route information.

[1508] Step 7:

[1509] The system automatically connects users to medical institutions based on their responses.

[1510] Specific operation: When the user responds with "yes," the server automatically connects to the medical institution by phone.

[1511] Input: User response.

[1512] Output: Telephone connection to a medical institution.

[1513] Step 8:

[1514] After a certain period of time, follow-up messages or surveys will be sent to the user.

[1515] Specific operation: The server creates and sends follow-up messages and surveys to the user.

[1516] Input: A certain amount of time has elapsed.

[1517] Output: Follow-up messages and surveys.

[1518] In this way, each processing step allows the system to automatically perform a series of processes, from receiving voice input from the user to guiding them to a medical facility, determining the urgency of the situation, and resetting the route of the autonomous vehicle.

[1519] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1520] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1521] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1522] [Fourth Embodiment]

[1523] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1524] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1525] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1526] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1527] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1528] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1529] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1530] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1531] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1532] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1533] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1534] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1535] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1536] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and quickly guides and contacts the appropriate medical institution according to the urgency of the situation.

[1537] System components

[1538] This system includes the following main components:

[1539] 1. User

[1540] In an emergency, users can access the system using their telephone and input their symptoms via voice or touch tone.

[1541] 2. Server

[1542] It detects incoming calls and activates the automated answering system.

[1543] The audio data is converted to text using a speech recognition engine, and then analyzed using a natural language processing engine.

[1544] Based on the analysis results, the system lists appropriate medical institutions and guides users to them via voice guidance.

[1545] Based on user selection, the system automatically connects users to medical institutions.

[1546] A follow-up message or survey will be sent after a certain period of time.

[1547] Program processing

[1548] The program in this system performs the following series of processes.

[1549] 1. User call and symptom input

[1550] When a user calls a specific number, the server activates an automated response system.

[1551] The server will announce via voice, "Please tell us what problem you are experiencing."

[1552] Users can input symptoms such as "I have a severe stomach ache" by voice, or use push tones if necessary.

[1553] 2. Analysis and Diagnosis of Symptoms

[1554] The server sends the received audio data to the speech recognition engine, where it is converted into text data.

[1555] Next, the server sends the converted text data to a natural language processing engine to determine the symptoms and urgency.

[1556] 3. Guidance and Information

[1557] Based on the analysis results, the server lists appropriate medical institutions and guides the user through the list using voice guidance.

[1558] The server then asks the user, "Shall we contact the nearby XX Hospital right now?"

[1559] 4. Automatic telephone connection

[1560] When the user answers "yes," the server activates the automatic phone connection function and makes a direct call from the user's device to the medical institution (e.g., XX Hospital).

[1561] 5. Follow-up

[1562] The server will send messages or surveys to follow up on the user's status after a certain period of time.

[1563] Users provide feedback on the service by responding to these messages and surveys.

[1564] Specific example

[1565] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[1566] User: Call 7119.

[1567] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[1568] User: "I have a severe stomach ache," entered via voice input.

[1569] Server: Converts speech data into text using a speech recognition engine.

[1570] Server: Analyzes using a natural language processing engine to determine urgency.

[1571] Server: Lists appropriate medical institutions and suggests, "Shall we contact the nearest XX Hospital immediately?"

[1572] User: "Yes," they replied.

[1573] Server: Executes automated telephone connections to medical institutions.

[1574] User: Contact XX Hospital directly to receive appropriate medical care.

[1575] In this way, this system supports users in receiving prompt and appropriate medical assistance in emergencies.

[1576] The following describes the processing flow.

[1577] Step 1:

[1578] User: In an emergency, use your phone to call a specific number (e.g., 7119).

[1579] Step 2:

[1580] Server: Detects an incoming call and activates the automated answering system. It then instructs the user via voice, "Please tell us what problem you are experiencing."

[1581] Step 3:

[1582] User: Input symptoms by voice, such as "I have a severe stomach ache," or use push tones if necessary.

[1583] Step 4:

[1584] Server: Sends the input voice data to the speech recognition engine and converts it into text data.

[1585] Step 5:

[1586] Server: Sends the converted text data to a natural language processing engine for analysis. Through the analysis, it determines the symptoms and urgency.

[1587] Step 6:

[1588] Server: Based on the analysis results, it lists appropriate medical institutions (e.g., nearby emergency hospitals) and guides the user through this list using voice guidance.

[1589] Step 7:

[1590] Server: The server suggests to the user, "Would you like to contact the nearest XX Hospital immediately?" and provides options.

[1591] Step 8:

[1592] User: Responds with "Yes".

[1593] Step 9:

[1594] Server: Based on the user's response, retrieve the phone number for the selected medical institution (e.g., XX Hospital).

[1595] Step 10:

[1596] Server: Activates the automatic telephone connection function and makes a direct call from the user's terminal to the medical institution (○○ Hospital).

[1597] Step 11:

[1598] Terminal: The user's phone is automatically connected to XX Hospital.

[1599] Step 12:

[1600] Server: After a certain period of time, the server creates and sends surveys and follow-up messages to the user to check on their status.

[1601] Step 13:

[1602] User: Provide feedback based on the surveys and messages received.

[1603] Through the steps outlined above, users can receive prompt and accurate guidance to appropriate medical institutions, and then contact those institutions directly to receive the necessary care.

[1604] (Example 1)

[1605] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1606] Current medical support systems have problems in providing a quick and appropriate response to sudden injuries and illnesses. Misunderstandings and delays are common when users describe their symptoms, and it is often difficult to smoothly guide or contact appropriate medical institutions according to the urgency of the situation. Furthermore, the mechanisms for follow-up according to the user's situation are insufficient. A system is needed that solves these problems and allows users to receive quick and appropriate medical support.

[1607] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1608] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for listing appropriate medical institutions based on the determination results and guiding the user through the list with voice guidance; means for automatically contacting the guided medical institutions; means for sending follow-up messages and questionnaires to the user; means for suggesting to the user contact the nearest medical institution with voice guidance; means for coordinating with the server to execute the above means; and means for collecting responses from the user to follow-up messages. This enables the user to receive prompt and appropriate medical assistance in an emergency.

[1609] A "user" is a person who calls the system of the present invention and inputs their symptoms using voice or touch tones.

[1610] "Means of input" refers to the methods or devices by which a user communicates symptoms to the system via voice or touch tone, and usually refers to telephones or smartphones.

[1611] A "speech recognition engine" is software or algorithms used to convert speech data into text data.

[1612] "Text data" refers to character information converted from speech data by a speech recognition engine.

[1613] A "natural language processing engine" is software or algorithms that analyze text data to determine symptoms and urgency.

[1614] "Methods for determining symptoms and urgency" refers to a method that uses a natural language processing engine to understand the content of symptom information from the user and evaluate its urgency.

[1615] A "medical institution" refers to a place that provides medical services, such as a general hospital or clinic.

[1616] "Listing" refers to selecting appropriate medical institutions based on the analysis results and compiling them into a list.

[1617] "Voice guidance" refers to a system that provides guidance and instructions to users via voice, and usually refers to voice playback by an automated response system.

[1618] "Automated methods" refer to functions or mechanisms that allow a system to automatically process tasks without user intervention.

[1619] A "follow-up message" is a confirmation or survey message sent after a certain period of time to check on the user's status.

[1620] "Means of collaboration" refers to the methods and protocols by which a server communicates with other systems or databases to obtain or transmit necessary information.

[1621] "Means of collecting responses" refer to functions for collecting user feedback and survey responses, which typically include SMS or dedicated web forms.

[1622] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and promptly guides and contacts the appropriate medical institution according to its urgency. The system of this invention includes the following main components:

[1623] 1. User

[1624] In emergencies, users access the system using a telephone and input their symptoms via voice or touch-tone input. Users can use a standard landline or mobile phone.

[1625] 2. Terminal

[1626] This refers to a user's phone or smartphone, and is a means of inputting voice or touch-tone signals into the system.

[1627] 3. Server

[1628] The server has the following roles:

[1629] Incoming call detection and activation of the automated answering system: When the server detects an incoming call, it activates the automated answering system (e.g., Asterisk PBX) and provides a voice message saying, "Please tell us what problem you are experiencing."

[1630] Speech recognition of audio data: The server sends the audio data received from the user to a speech recognition engine (e.g., Google Speech-to-Text API) and converts the audio data into text data.

[1631] Text data analysis and urgency assessment: The converted text data is analyzed using a natural language processing engine (e.g., Python's NLTK library) to determine the symptoms and urgency level.

[1632] Listing and guiding users to medical institutions: Based on the analysis results, the server lists appropriate medical institutions and guides users to them via voice guidance. For example, it might suggest to the user, "Would you like to contact the nearby XX Hospital now?"

[1633] Automated phone connection: When the user answers "yes," the server activates the automated phone connection function (e.g., Twilio API) and makes a call to the selected healthcare provider from the user's device.

[1634] Follow-up: After a certain period of time, the server will send a follow-up message or survey to the user to check on their status. This follow-up message will be sent using an SMS sending API (e.g., Twilio SMS API).

[1635] Specific example

[1636] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[1637] User: Make a phone call to the specified phone number (e.g., 7119).

[1638] Server: The automated response system receives the call and instructs the user, "Please tell us what problem you are experiencing."

[1639] User: "I have a severe stomach ache," is entered via voice input.

[1640] Server: Converts audio data to text using the Google Speech-to-Text API.

[1641] Server: The converted text is analyzed using Python's NLTK library to determine its urgency.

[1642] Server: Lists appropriate medical facilities (e.g., nearby XX Hospital) and suggests, "Shall we contact XX Hospital immediately?"

[1643] User: Responds with "Yes".

[1644] Server: Uses the Twilio API to make phone calls to XX Hospital from the user's device.

[1645] Server: After a certain period of time, send a follow-up message to the user using the Twilio SMS API.

[1646] This system will enable users to receive prompt and appropriate medical assistance in emergencies.

[1647] Example of a prompt

[1648] The following are examples of prompts to input into the generating AI model.

[1649] I suddenly started experiencing severe abdominal pain this afternoon. Could you please tell me what I should do?

[1650] In this way, the present invention provides prompt and appropriate medical support based on the user's symptoms and urgency, creating an environment in which users can respond with peace of mind even in emergencies.

[1651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1652] Step 1:

[1653] User call and symptom input

[1654] When a user experiences sudden symptoms, they call a designated phone number (e.g., 7119). This results in a phone call being generated as input.

[1655] The server detects an incoming call and activates an automated answering system (e.g., Asterisk PBX). Here, it receives the incoming call signal as input and activates the automated answering system as output.

[1656] The server plays an audio message asking the user, "Please tell us what problem you are experiencing." This initiates the voice guidance process, which then takes the user's input as input.

[1657] The user can describe their symptoms by voice, for example, "I have a severe stomach ache," or by using push tones if necessary. This will result in the user's voice data being used as input.

[1658] Step 2:

[1659] Speech recognition of audio data

[1660] The server processes the audio data received from the user. The audio data received as input is the subject of processing.

[1661] The server sends the audio data to a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio into text data. This allows the speech recognition engine to process the audio data into text data.

[1662] The server receives text data (e.g., "I have a severe stomach ache") as output from the speech recognition engine.

[1663] Step 3:

[1664] Natural language analysis of symptoms and assessment of urgency.

[1665] The server sends the converted text data to a natural language processing engine (e.g., Python's NLTK library) for analysis. Text data is the input to be processed.

[1666] The server uses a natural language processing engine to analyze text data and extract keywords (e.g., "severe stomach ache"). This is how keyword extraction and analysis are performed.

[1667] The server compares the extracted keywords with predefined urgency rules to determine the urgency of the symptoms. This processes the urgency data, and the urgency determination result is output.

[1668] Step 4:

[1669] Listing and guidance for medical institutions

[1670] The server lists appropriate medical institutions from its database (e.g., PostgreSQL) based on the assessment result. The urgency assessment result is the input to be processed.

[1671] The server uses an audio guidance system to provide users with information such as the names of listed medical institutions. This results in a list of medical institutions being output.

[1672] The server then prompts the user with the question, "Would you like to contact the nearby XX Hospital immediately?" This triggers an automated voice guidance message for the user.

[1673] Step 5:

[1674] Automated telephone connection

[1675] The user responds with "Yes". The user's response is received as input.

[1676] The server analyzes the user's responses and activates an automated telephone connection function (e.g., Twilio API) to the medical institution selected from the user's device. This means the user's response data is the input, and the telephone connection is the output.

[1677] The server places a phone call from the user's terminal to a medical institution (e.g., XX Hospital). This initiates the actual phone connection.

[1678] Step 6:

[1679] Follow-up

[1680] The server sends follow-up messages or surveys to the user after a certain period of time. The timing of the follow-up action is set as an input.

[1681] The server uses an SMS sending API (e.g., Twilio SMS API) to send a follow-up message. This results in the follow-up message being sent to the user as output.

[1682] Users respond to follow-up messages and surveys, thereby obtaining user response data as input.

[1683] In this way, by executing each processing step sequentially, the system of the present invention helps users receive prompt and appropriate medical assistance in emergency situations.

[1684] (Application Example 1)

[1685] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1686] Receiving prompt and appropriate medical assistance in the event of a sudden injury or illness is crucial. However, it is often difficult to quickly and reliably identify accessible medical facilities and transport patients appropriately, especially if the illness occurs at home or while out. Furthermore, elderly individuals and those with disabilities often find it difficult to reach medical facilities on their own. Therefore, there is a need to develop automated systems that address these challenges and provide prompt and appropriate medical assistance.

[1687] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1688] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for listing appropriate medical institutions based on the determination results and guiding the user through the list with voice guidance; means for automatically contacting the listed medical institutions; means for sending follow-up messages and questionnaires to the user; and means for coordinating with an autonomous vehicle to automatically transport the user to an appropriate medical institution. This enables the user to receive prompt and appropriate medical assistance in an emergency.

[1689] A "user" refers to a person who uses the system to input symptoms and receive medical support.

[1690] "Voice or push-tone" refers to the method a user uses to input their symptoms into the system.

[1691] A "speech recognition engine" refers to the technology that converts speech data into text data.

[1692] "Text data" refers to data in the form of text converted by a speech recognition engine, which can be analyzed by the system.

[1693] A "natural language processing engine" refers to a technology that analyzes text data and determines symptoms and urgency based on its content.

[1694] "Symptoms and urgency" refers to information indicating the content of the symptoms entered by the user and the urgent need for them.

[1695] "Voice guidance" refers to a system function that provides users with voice guidance and instructions.

[1696] "Medical institutions" refer to medical facilities such as hospitals, clinics, and medical offices.

[1697] A "follow-up message" refers to a message sent after the initial response to check on the user's status.

[1698] A "survey" refers to a set of questions used to collect feedback on the evaluation of medical services and the user's situation.

[1699] An "autonomous vehicle" refers to a vehicle that can automatically operate and travel to a designated location.

[1700] "Transportation" refers to the act of using an autonomous vehicle to transport a user to an appropriate medical facility.

[1701] This invention relates to an autonomous vehicle collaboration system that provides rapid and appropriate medical assistance for sudden injuries or illnesses. The system receives symptom input from the user via voice or push-tone, analyzes the input, and takes appropriate action according to the urgency of the situation.

[1702] System Overview

[1703] The system consists of the following elements:

[1704] 1. User terminal: Functions as a smartphone application and provides an interface for the user to input symptoms using voice or touch tones.

[1705] 2. Server: Uses a speech recognition engine (Google Cloud Speech-to-Text API) and a natural language processing engine (IBM Watson Natural Language Understanding) to analyze voice data from the user and determine the symptoms and their urgency.

[1706] 3. Autonomous vehicles: These vehicles transport users to appropriate medical facilities and configure the transport route in conjunction with a navigation system (e.g., Waymo's API).

[1707] 4. Follow-up system: Send follow-up messages and surveys to users to help them evaluate and improve the service.

[1708] Specific processing of the program

[1709] 1. User symptom input: The user launches the application from their smartphone and inputs their symptoms using voice or touch tones. In the case of voice input, the user describes the symptoms in a format such as "I have severe chest pain."

[1710] 2. Speech Recognition and Text Conversion: User voice data is converted into text data using the Google Cloud Speech-to-Text API.

[1711] 3. Text data analysis: The converted text data is analyzed by IBM Watson Natural Language Understanding to determine the urgency of the symptoms and the appropriate medical institution.

[1712] 4. Listing and Guiding Users to Medical Institutions: Based on the analysis results, appropriate medical institutions will be listed and guided to the user via voice guidance through the application.

[1713] 5. Arranging an autonomous vehicle: Once the user approves the transfer, the server uses Waymo's API to instruct the autonomous vehicle to navigate and transport the user to the designated medical facility.

[1714] 6. Follow-up: After the user arrives at the medical facility, follow-up messages and questionnaires are sent to confirm the quality of service.

[1715] Hardware and software to use

[1716] Speech recognition engine: Google Cloud Speech-to-Text API

[1717] Natural Language Processing Engine: IBM Watson Natural Language Understanding

[1718] Autonomous vehicle navigation system: Waymo API-enabled navigation

[1719] Specific example

[1720] For example, if a user suddenly experiences severe chest pain at home, it would work as follows:

[1721] The user launches the app on their smartphone and uses voice input to say, "I have severe chest pain."

[1722] The server converts the audio data into text and analyzes it using a natural language processing engine.

[1723] The system assesses the urgency of the situation and lists the most suitable nearby medical facilities.

[1724] After the user receives instructions and responds with "yes," an autonomous vehicle is dispatched to transport the user to a medical facility.

[1725] Upon arrival at the medical facility, a follow-up message is sent to confirm the user's condition.

[1726] Example of a prompt

[1727] "I want to develop a media mobile assistance app that arranges transportation to the nearest medical facility in case of severe chest pain. This app will use the Google Cloud Speech-to-Text API to convert speech to text, IBM Watson Natural Language Understanding to determine the urgency of the symptoms, and Waymo's API to connect with the navigation system of an autonomous vehicle. Please provide a complete program."

[1728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1729] Step 1:

[1730] The user launches the application from their smartphone. The user inputs their symptoms using voice or touch tones. For example, the user might voice input, "I have severe chest pain." This input data is saved within the application as voice data.

[1731] Step 2:

[1732] The server receives the user's voice data. It sends the received voice data to the Google Cloud Speech-to-Text API, where it is converted into text data. By sending this audio data to the API, the audio data is returned as text data.

[1733] Step 3:

[1734] The server sends the converted text data to IBM Watson Natural Language Understanding for analysis. The analysis determines the urgency of the symptoms, and the result is returned as text data. The server receives this analysis result and understands the nature and urgency of the symptoms.

[1735] Step 4:

[1736] The server creates a list of appropriate medical institutions based on the analysis results. It lists the most suitable medical institutions from a database of medical institutions based on location information and the symptoms they can treat. This list is stored as internal data.

[1737] Step 5:

[1738] The server provides the user with a voice guidance message listing appropriate medical facilities. For example, it might say, "We will now begin transporting you to the nearest XX Hospital. Is that alright?" This voice guidance is transmitted to the user's terminal as audio data.

[1739] Step 6:

[1740] The user responds with "yes" to the voice guidance. The user's device sends this response as audio data to the server. The server then sends this audio data back to the Google Cloud Speech-to-Text API, where it is converted into text data.

[1741] Step 7:

[1742] The server analyzes the user's response and initiates contact with a medical facility and dispatch of an autonomous vehicle. Using the Waymo API, it inputs the location information of the destination medical facility into the navigation system and dispatches a vehicle. As a result, a notification that the transfer has begun is displayed on the user's device.

[1743] Step 8:

[1744] The user boards an autonomous vehicle. The vehicle automatically transports the user to a designated medical facility according to the navigation system. During transport, the vehicle's location information and transport status are transmitted to a server in real time for monitoring.

[1745] Step 9:

[1746] After the user arrives at the medical facility, the server sends follow-up messages and questionnaires to the user's device. The user responds to these messages and enters information confirming their condition and evaluating the service. This data is sent to the server and used to improve future services.

[1747] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1748] This invention relates to a system that provides rapid and appropriate medical support for sudden injuries or illnesses. This system analyzes symptom information entered by the user via voice or touch tone and quickly guides and contacts the appropriate medical institution according to its urgency. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves a more sophisticated response.

[1749] System components

[1750] This system includes the following main components:

[1751] 1. User

[1752] In an emergency, users can access the system using their telephone and input their symptoms via voice or touch tone.

[1753] 2. Server

[1754] It detects incoming calls and activates the automated answering system.

[1755] The audio data is converted to text using a speech recognition engine, and then analyzed using a natural language processing engine.

[1756] An emotion engine is used to analyze the user's emotions from voice data.

[1757] Based on the analysis results (symptoms, urgency, and user's emotions), the system lists appropriate medical institutions and guides the user to them via voice guidance.

[1758] Based on user selection, the system automatically connects users to medical institutions.

[1759] A follow-up message or survey will be sent after a certain period of time.

[1760] Program processing

[1761] The program in this system performs the following series of processes.

[1762] 1. User call and symptom input

[1763] When a user calls a specific number, the server activates an automated response system.

[1764] The server prompts the user with a voice message saying, "Please tell us what symptoms you are experiencing," and encourages them to enter their symptoms.

[1765] Users input symptoms such as "I have a severe stomach ache" using voice. Push-tone input is also available if needed.

[1766] 2. Analysis of symptoms and emotions

[1767] The server sends the input voice data to the speech recognition engine, where it is converted into text data.

[1768] Next, the server sends the converted text data to a natural language processing engine to determine the symptoms and urgency.

[1769] Furthermore, the voice data is sent to an emotion engine to analyze the user's emotions (e.g., stress, anxiety, anger, etc.).

[1770] 3. Guidance and Information

[1771] Based on the analysis of symptoms and emotions, the server lists appropriate medical institutions.

[1772] The server will guide the user via voice guidance, saying, "We will guide you to nearby emergency hospitals. Would you like to contact the nearest XX Hospital immediately?"

[1773] Based on the results of the emotion engine, the tone and content of the voice guidance are adjusted, and relaxation guidance is also provided to enhance the user's sense of security.

[1774] 4. Automatic telephone connection

[1775] When the user answers "yes," the server activates the automatic phone connection function and directly calls the medical institution (e.g., XX Hospital) from the user's device.

[1776] 5. Follow-up

[1777] After a certain period of time, the server creates and sends messages or questionnaires to the user to follow up on their situation.

[1778] Users provide feedback on the service by responding to these messages and surveys.

[1779] Specific example

[1780] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[1781] User: Call 7119.

[1782] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[1783] User: "I have a severe stomach ache," entered via voice input.

[1784] Server: Converts speech data into text using a speech recognition engine.

[1785] Server: Analyzes using a natural language processing engine to determine urgency.

[1786] Server: Analyzes voice data using an emotion engine to recognize the user's emotions (e.g., anxiety).

[1787] Server: Lists appropriate medical facilities and asks, "Would you like to contact the nearest XX Hospital now?" and also provides relaxation guidance such as, "Relax and take a deep breath."

[1788] User: "Yes," they replied.

[1789] Server: Executes automated telephone connections to medical institutions.

[1790] User: Contact XX Hospital directly to receive appropriate medical care.

[1791] In this way, the system is designed to support users in receiving prompt and appropriate medical assistance in emergencies, thereby increasing their sense of security.

[1792] The following describes the processing flow.

[1793] Step 1:

[1794] User: In an emergency, use your phone to call a specific number (e.g., 7119).

[1795] Step 2:

[1796] Server: Detects an incoming call and activates the automated answering system. It then instructs the user via voice, "Please tell us what problem you are experiencing."

[1797] Step 3:

[1798] User: Input symptoms by voice, such as "I have a severe stomach ache," or use push tones if necessary.

[1799] Step 4:

[1800] Server: Sends the input voice data to the speech recognition engine and converts it into text data.

[1801] Step 5:

[1802] Server: Sends the converted text data to a natural language processing engine for analysis. Through the analysis, it determines the symptoms and urgency.

[1803] Step 6:

[1804] Server: Simultaneously sends the input voice data to the emotion engine to analyze the user's emotions (e.g., stress, anxiety, anger, etc.).

[1805] Step 7:

[1806] Server: Based on the analysis results (symptoms, urgency, emotion) from the natural language processing engine and emotion engine, it lists appropriate medical facilities (e.g., nearby emergency hospitals).

[1807] Step 8:

[1808] Server: The server provides voice guidance to the user, saying, "We will guide you to nearby emergency hospitals. Would you like to contact the nearest XX Hospital now?" It also adds voice tones and relaxation instructions tailored to the user's emotions, based on the results of the emotion engine.

[1809] Step 9:

[1810] User: Responds with "Yes".

[1811] Step 10:

[1812] Server: Based on the user's response, retrieve the phone number for the selected medical institution (e.g., XX Hospital).

[1813] Step 11:

[1814] Server: Activates the automatic telephone connection function and makes a direct call from the user's terminal to the medical institution (○○ Hospital).

[1815] Step 12:

[1816] Terminal: The user's phone is automatically connected to XX Hospital.

[1817] Step 13:

[1818] Server: After a certain period of time, it creates and sends a survey or follow-up message to the user to check on their status.

[1819] Step 14:

[1820] User: Provide feedback based on the surveys and messages received.

[1821] Through the steps outlined above, users can receive prompt and accurate guidance to appropriate medical institutions, and then contact those institutions directly to receive the necessary care. Furthermore, the introduction of an emotion engine enables responses tailored to the user's emotional state, thereby increasing the user's sense of security and trust.

[1822] (Example 2)

[1823] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1824] Conventional medical support systems often fail to adequately consider the user's emotions and urgency during the process of connecting them to a suitable medical institution quickly. This can lead to increased anxiety and stress, potentially delaying appropriate medical assistance. Furthermore, there were problems with providing guidance on emergency first aid and ensuring smooth telephone connections with medical institutions. Additionally, follow-up care lacked consideration for the user's emotional well-being.

[1825] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1826] In this invention, the server includes means for the user to input symptoms by voice or push tone; means for converting the input data into text data using a speech recognition engine; means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency; means for analyzing the user's emotions using an emotion recognition engine based on the determination results and voice data; means for listing appropriate medical institutions based on the analysis results and guiding the user through the list with voice guidance; means for automatically contacting the guided medical institutions; means for adjusting the tone and content of the voice guidance based on the user's emotional state to provide relaxation guidance; and means for sending follow-up messages and questionnaires to the user. This enables the user to receive prompt and appropriate medical assistance in emergencies and allows for responses that take the user's emotions into consideration.

[1827] A "user" refers to someone who uses the system to input their symptoms and request medical assistance.

[1828] "Means of inputting symptoms by voice or touch tone" refers to a method for users to provide information about their symptoms to the system using voice or phone key input.

[1829] A "speech recognition engine" refers to software or hardware that has the function of converting input speech data into text data.

[1830] "Text data" refers to data that has been converted by a speech recognition engine and expressed as a string of characters.

[1831] A "natural language processing engine" refers to software or hardware that analyzes text data and has the function of determining the content and urgency of symptoms.

[1832] "Urgency" refers to a scale that indicates how urgent the user's symptoms are.

[1833] An "emotion recognition engine" refers to software or hardware that has the function of analyzing a user's emotional state from voice data or input data.

[1834] "Medical institutions" refer to facilities that provide medical services, such as hospitals and clinics.

[1835] "Voice guidance" refers to a method of providing users with guidance information via voice.

[1836] "Automated methods" refer to methods in which the system performs processing automatically without requiring user intervention.

[1837] "Relaxation guidance" refers to audio guidance designed to alleviate user tension and anxiety.

[1838] "Follow-up messages and questionnaires" refer to messages and questionnaires sent by the system after a user has received medical assistance, for the purpose of checking the user's condition and evaluating the service.

[1839] "Medical support" refers to providing advice, guidance, or arranging treatment for illness or injury.

[1840] This invention relates to a system for providing rapid and appropriate medical assistance in the event of sudden injury or illness. This system includes the following main components:

[1841] 1. User

[1842] In an emergency, the user accesses the system using their telephone and enters their symptoms using voice or touch tones. The symptom information entered by the user forms the basis for subsequent analysis and processing.

[1843] 2. Terminal

[1844] The user's phone or other device initiates a call to the system and receives the user's voice input or touch-tone input through that call.

[1845] 3. Server

[1846] The server is the core of the system and performs the following tasks:

[1847] Automated response system

[1848] When an incoming call is detected, the automated response system is activated and instructs the user to "Please tell us what problem you are experiencing."

[1849] Speech recognition engine

[1850] The speech recognition engine converts the voice data entered by the user into text data. Specifically, it uses "Google Cloud Speech-to-Text".

[1851] Natural Language Processing Engine

[1852] The natural language processing engine analyzes the text data converted by the speech recognition engine to determine the symptoms and urgency. Here, we use the "Google Cloud Natural Language API".

[1853] Emotion recognition engine

[1854] The emotion recognition engine analyzes the user's emotions from voice data. Specifically, it uses "IBM Watson Tone Analyzer."

[1855] Voice guidance

[1856] The voice guidance system directs users to appropriate medical facilities based on analysis results. Furthermore, it adjusts the tone and content of the voice guidance based on the user's emotional state, providing relaxation guidance as well.

[1857] Automatic telephone connection function

[1858] When a user requests to connect to a medical institution, the automated telephone connection function is activated, and the user's device makes a call to the nearest medical institution.

[1859] Follow-up

[1860] After a certain period of time, the server generates and sends follow-up messages or questionnaires to the user to check on their status.

[1861] Specific example

[1862] For example, if a user suddenly experiences severe abdominal pain at home, this system would function as follows:

[1863] User: First, the user calls 7119.

[1864] Server: The automated response system will ask, "Please tell us what problem you are experiencing."

[1865] User: "I have a severe stomach ache," is entered via voice input.

[1866] Server: Uses a speech recognition engine (Google Cloud Speech-to-Text) to convert speech data into text.

[1867] Server: The converted text data is analyzed using a natural language processing engine (Google Cloud Natural Language API) to determine the symptoms and urgency.

[1868] Server: Furthermore, the server analyzes the voice data using an emotion recognition engine (IBM Watson Tone Analyzer) to recognize the user's emotions (e.g., anxiety).

[1869] Server: Lists appropriate medical facilities and provides voice guidance such as, "Would you like to contact the nearest XX Hospital now?" It also offers relaxation guidance such as, "Relax and take a deep breath."

[1870] User: Responds with "Yes".

[1871] Server: Activates the automatic phone connection function and places a call from the user's terminal to XX Hospital.

[1872] User: Contact XX Hospital directly to receive appropriate medical care.

[1873] Server: After a certain period of time, it sends follow-up messages or surveys to the user.

[1874] User: Respond to follow-up messages and surveys, and provide feedback.

[1875] Example of a prompt

[1876] The following are examples of prompts to input into a generative AI model:

[1877] "Please explain how users who call 7119 and report sudden abdominal pain can receive medical assistance. Please explain in detail how this system works when a user experiences sudden, severe abdominal pain at home."

[1878] By using this prompt, the generating AI model can produce text that explains the specific processing flow of the system. This system allows users to receive prompt and appropriate medical assistance in emergencies, and enables emotionally sensitive responses.

[1879] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1880] Program processing steps

[1881] Step 1:

[1882] The server detects the user's call. When a user dials a specific phone number, the server detects the incoming call and activates the automated response system. The server plays a voice message to the user asking, "Please tell us what problem you are experiencing."

[1883] Input: User's phone call

[1884] Data processing: Incoming call detection, voice message playback

[1885] Output: Voice message from the system to the user.

[1886] Step 2:

[1887] The user enters their symptoms using voice or touch tones. The user can enter their symptoms by voice, such as "I have a severe stomach ache," or by using touch tones to enter the corresponding number.

[1888] Input: User voice input or push-tone input

[1889] Data processing: Acquisition of user voice, acquisition of push tone input

[1890] Output: Voice data or push-tone data

[1891] Step 3:

[1892] The server sends the voice data to the speech recognition engine to convert it into text data. The speech recognition engine (Google Cloud Speech-to-Text) is used to convert the user's voice data into text.

[1893] Input: User's voice data

[1894] Data processing: Conversion processing using a speech recognition engine.

[1895] Output: Text data

[1896] Step 4:

[1897] The server sends text data to a natural language processing engine to analyze symptoms and urgency. The natural language processing engine (Google Cloud Natural Language API) is used to analyze the text data and determine the symptoms and their urgency.

[1898] Input: Text data

[1899] Data processing: Analysis and processing using a natural language processing engine.

[1900] Output: Analysis results of symptoms and urgency

[1901] Step 5:

[1902] The server sends voice data to the emotion recognition engine, which analyzes the user's emotions. The emotion recognition engine (IBM Watson Tone Analyzer) is used to analyze the user's emotions from the voice data.

[1903] Input: Audio data

[1904] Data processing: Analysis processing using an emotion recognition engine.

[1905] Output: Emotion analysis results

[1906] Step 6:

[1907] The server lists appropriate medical institutions based on the analysis results and guides the user through voice guidance. Based on the analysis of symptoms, urgency, and emotions, the server selects the most suitable medical institutions from the database and creates a list. Next, it provides voice guidance such as, "We will guide you to nearby emergency hospitals. Shall we contact the nearest XX Hospital now?" Furthermore, taking into consideration the user's emotional state, it also provides relaxation guidance such as, "Please relax and take a deep breath."

[1908] Input: Symptom and urgency analysis results, emotion analysis results

[1909] Data processing: Database lookup, voice guidance generation.

[1910] Output: List of medical facilities, voice guidance

[1911] Step 7:

[1912] The user responds to the voice guidance and requests to connect to a medical institution. When the user responds with "yes," the server receives the response.

[1913] Input: User response data

[1914] Data processing: Analysis of response data

[1915] Output: User response result

[1916] Step 8:

[1917] The server activates the automatic telephone connection function and places a call from the user's terminal to a medical institution. Based on the response, the server activates the automatic telephone connection function again and places a call from the user's terminal to the nearest medical institution.

[1918] Input: User response result

[1919] Data processing: Activation of automatic connection function

[1920] Output: Telephone connection to a medical institution

[1921] Step 9:

[1922] The server sends follow-up messages and surveys to the user after a certain period of time. To check on the user's status and collect feedback, the server generates follow-up messages and surveys and sends them via the specified method (email or SMS).

[1923] Input: User response result

[1924] Data processing: Generating messages and surveys

[1925] Output: Follow-up message, survey

[1926] Through these steps, the system is designed to ensure that users receive prompt and appropriate medical assistance in emergencies. Furthermore, it provides relaxation guidance that takes the user's emotions into consideration, enhancing their sense of security.

[1927] (Application Example 2)

[1928] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1929] Currently, there are limited means of receiving prompt and appropriate medical assistance in the event of a sudden illness or injury while in an autonomous vehicle. Furthermore, the technology to analyze the user's symptoms and emotions and appropriately set the autonomous vehicle's route is insufficient. Therefore, there are challenges in quickly arriving at the appropriate medical facility in an emergency and enhancing the user's sense of security.

[1930] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input symptoms by voice or push tone, means for converting the input data into text data using a speech recognition engine, means for analyzing the converted text data with a natural language processing engine to determine the symptoms and urgency, means for listing appropriate medical institutions based on the determination result and guiding the user to the list with voice guidance, means for automatically contacting the guided medical institutions, means for sending follow-up messages and questionnaires to the user, and means for analyzing the user's symptoms and emotions in the autonomous vehicle with a speech recognition engine and an emotion analysis engine, and setting or resetting the route of the autonomous vehicle according to the urgency. This makes it possible for the user to receive prompt and appropriate medical assistance in an emergency, and furthermore, by appropriately setting the route of the autonomous vehicle, the user's sense of security can be enhanced.

[1931] Definitions of important words

[1932] A "user" is a person who uses the system to input symptoms using voice or push tones.

[1933] A "speech recognition engine" is a device that converts input speech data into text data.

[1934] A "natural language processing engine" is a technology that analyzes text data to determine symptoms and urgency.

[1935] "Voice guidance" is a system that provides users with a list of appropriate medical facilities via voice.

[1936] The "emotion analysis engine" analyzes the user's emotions from their voice data.

[1937] A "medical institution" is a facility that provides medical services for health problems and injuries.

[1938] An "autonomous vehicle" is a vehicle that operates automatically using programs and sensors, without requiring human intervention.

[1939] A "follow-up message" is a message sent to a user after medical care to check on their condition and request feedback.

[1940] A "survey" is a questionnaire used to collect feedback and opinions from users.

[1941] "Route setting" is the process of determining the optimal route for an autonomous vehicle to reach a specific destination.

[1942] "Urgency" is a criterion that indicates how quickly medical attention is needed for the user's symptoms.

[1943] "Push tones" are sounds generated by pressing the number buttons on a telephone, and are a means of inputting information into a system.

[1944] "Text data" refers to character information converted from audio data.

[1945] A "server" is a computer system that performs speech recognition, natural language processing, sentiment analysis, and data management.

[1946] invention specification

[1947] System Overview

[1948] This invention is a system that provides rapid and appropriate medical assistance to passengers who experience sudden illness or injury in an autonomous vehicle. The user inputs symptoms via voice or touch-tone input, which are then analyzed using a voice recognition engine and a natural language processing engine. Furthermore, an emotion analysis engine is used to analyze the user's emotions, and the system guides them to the appropriate medical facility according to the urgency of the situation. This allows the autonomous vehicle to reconfigure its route to the optimal one, enhancing the user's sense of security.

[1949] Hardware and software used

[1950] Hardware: Audio input device (microphone), audio output device (speaker), control system for autonomous vehicles, server

[1951] Software: Speech recognition engines (e.g., Google Cloud Speech-to-Text), natural language processing engines (e.g., NLTK and SpaCy), sentiment analysis engines (e.g., EmotionRecognizer), map information provision APIs (e.g., Google Maps API)

[1952] Data processing

[1953] 1. Voice input:

[1954] The user inputs their symptoms by voice. For example, they might say, "I have severe chest pain."

[1955] 2. Speech recognition and analysis:

[1956] The server sends the audio data to the speech recognition engine, where it is converted into text data.

[1957] This text data is analyzed using a natural language processing engine to determine the symptoms and their urgency.

[1958] 3. Emotion analysis:

[1959] The server sends the voice data to an emotion analysis engine, which analyzes the user's emotional state (e.g., anxiety, stress).

[1960] 4. Guidance to medical facilities and route planning:

[1961] Based on the urgency and sentiment analysis results, the server identifies the appropriate medical facility and guides the user via voice guidance.

[1962] If necessary, the autonomous vehicle's navigation system will reset the route.

[1963] 5. Automated telephone connection and follow-up:

[1964] When the user responds with "yes," the server automatically connects to the medical institution.

[1965] After a certain period of time, follow-up messages or surveys will be sent to the user.

[1966] Specific example

[1967] For example, suppose a user suddenly experiences chest pain while in an autonomous vehicle. The user inputs, "I have severe chest pain." The server recognizes this voice, converts it to text, and then analyzes it using a natural language processing engine to determine the urgency level is high. The emotion analysis engine then detects anxiety and provides a relaxation message such as, "Please relax, we will be there shortly." Next, the server guides the user to an appropriate medical facility and resets the autonomous vehicle's route.

[1968] Example of a prompt

[1969] User's voice message regarding symptoms: "I have severe chest pain."

[1970] Symptoms analyzed: "Severe chest pain"

[1971] Analyzed urgency level: "High"

[1972] Analyzed emotion: "anxiety"

[1973] The nearest hospital that was found: "〇〇 Hospital"

[1974] In this way, the present invention is a system that enables users to receive prompt and appropriate medical assistance in emergencies, and further improves user safety and peace of mind by appropriately controlling autonomous vehicles.

[1975] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1976] Program processing steps

[1977] Processing flow

[1978] Step 1:

[1979] The user inputs the symptoms using voice.

[1980] Specific action: The user experiences severe chest pain while inside the autonomous vehicle and describes the symptom by saying, "I have severe chest pain."

[1981] Input: User's voice data.

[1982] Output: Send as audio data to the server.

[1983] Step 2:

[1984] The server sends the audio data to the speech recognition engine, where it is converted into text data.

[1985] Specific operation: The server uses a speech recognition engine to convert "I have severe chest pain" into text data.

[1986] Input: Audio data.

[1987] Output: Text data.

[1988] Step 3:

[1989] The server analyzes the text data using a natural language processing engine to determine the symptoms and urgency.

[1990] Specific operation: The server inputs the text data "I have severe chest pain" into a natural language processing engine, which determines the symptom to be "chest pain" and the urgency to be "high".

[1991] Input: Text data.

[1992] Output: Symptom and urgency assessment results.

[1993] Step 4:

[1994] The server sends the voice data to an emotion analysis engine, which then analyzes the user's emotions.

[1995] Specific operation: The server analyzes the audio data using an emotion analysis engine and detects the emotional state of "anxiety."

[1996] Input: Audio data.

[1997] Output: Emotion analysis results.

[1998] Step 5:

[1999] The server identifies the appropriate medical facility based on the urgency and sentiment analysis results, and guides the user through voice guidance.

[2000] Specific operation: The server checks the medical institution database, lists the nearest medical institutions, and provides voice guidance to the user saying, "We will guide you to the nearest XX Hospital."

[2001] Input: Symptoms, urgency, and sentiment analysis results.

[2002] Output: List of medical facilities, voice guidance.

[2003] Step 6:

[2004] If necessary, the autonomous vehicle's navigation system will reset the route.

[2005] Specific operation: The server instructs the autonomous vehicle's navigation system to set an emergency route and reconfigure the optimal path.

[2006] Input: Location information of the most suitable medical facility.

[2007] Output: Reconfigured route information.

[2008] Step 7:

[2009] The system automatically connects users to medical institutions based on their responses.

[2010] Specific operation: When the user responds with "yes," the server automatically connects to the medical institution by phone.

[2011] Input: User response.

[2012] Output: Telephone connection to a medical institution.

[2013] Step 8:

[2014] After a certain period of time, follow-up messages or surveys will be sent to the user.

[2015] Specific operation: The server creates and sends follow-up messages and surveys to the user.

[2016] Input: A certain amount of time has elapsed.

[2017] Output: Follow-up messages and surveys.

[2018] In this way, each processing step allows the system to automatically perform a series of processes, from receiving voice input from the user to guiding them to a medical facility, determining the urgency of the situation, and resetting the route of the autonomous vehicle.

[2019] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2020] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2021] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2022] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2023] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2024] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2025] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2026] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2027] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2028] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2029] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2030] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2031] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2032] 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.

[2033] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2034] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2035] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[2036] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[2037] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[2038] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[2039] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[2040] The following is further disclosed regarding the embodiments described above.

[2041] (Claim 1)

[2042] A means for the user to input symptoms using voice or push tones,

[2043] A means for converting input data into text data using a speech recognition engine,

[2044] A method for analyzing converted text data using a natural language processing engine to determine symptoms and urgency,

[2045] A means of listing appropriate medical institutions based on the assessment results and guiding the user through that list using voice guidance,

[2046] A means of automatically contacting the medical institution that was referred,

[2047] A means of sending follow-up messages and surveys to users,

[2048] A system that includes this.

[2049] (Claim 2)

[2050] The system according to claim 1, which automatically connects to a medical institution by telephone based on the user's response.

[2051] (Claim 3)

[2052] The system according to claim 1, which includes means for guiding the user on how to perform first aid via voice guidance if, as a result of symptom analysis, first aid is required.

[2053] "Example 1"

[2054] (Claim 1)

[2055] A means for the user to input symptoms using voice or push tones,

[2056] A means for converting input data into text data using a speech recognition engine,

[2057] A method for analyzing converted text data using a natural language processing engine to determine symptoms and urgency,

[2058] A means of listing appropriate medical institutions based on the assessment results and guiding the user through that list using voice guidance,

[2059] A means of automatically contacting the medical institution that was referred,

[2060] A means of sending follow-up messages and surveys to users,

[2061] A means of suggesting to the user to contact the nearest medical facility via voice guidance,

[2062] Means for coordinating with a server in order to carry out the aforementioned means,

[2063] A means of collecting user responses to follow-up messages,

[2064] A system that includes this.

[2065] (Claim 2)

[2066] The system according to claim 1, which automatically connects to a medical institution by telephone based on the user's response.

[2067] (Claim 3)

[2068] The system according to claim 1, which includes means for guiding the user on how to perform first aid via voice guidance if, as a result of symptom analysis, first aid is required.

[2069] "Application Example 1"

[2070] (Claim 1)

[2071] A means for the user to input symptoms using voice or push tones,

[2072] A means for converting input data into text data using a speech recognition engine,

[2073] A method for analyzing converted text data using a natural language processing engine to determine symptoms and urgency,

[2074] A means of listing appropriate medical institutions based on the assessment results and guiding the user through that list using voice guidance,

[2075] A means of automatically contacting the medical institution that was referred,

[2076] A means of sending follow-up messages and surveys to users,

[2077] A means of automatically transporting users to appropriate medical facilities in cooperation with autonomous vehicles,

[2078] A system that includes this.

[2079] (Claim 2)

[2080] The system according to claim 1, which automatically connects to a medical institution by telephone based on the user's response.

[2081] (Claim 3)

[2082] The system according to claim 1, which includes means for guiding the user on how to perform first aid via voice guidance if, as a result of symptom analysis, first aid is required.

[2083] "Example 2 of combining an emotion engine"

[2084] (Claim 1)

[2085] A means for the user to input symptoms using voice or push tones,

[2086] A means for converting input data into text data using a speech recognition engine,

[2087] A method for analyzing converted text data using a natural language processing engine to determine symptoms and urgency,

[2088] A means for analyzing the user's emotions using an emotion recognition engine based on the judgment results and audio data,

[2089] A means of listing appropriate medical institutions based on the analysis results and guiding the user through that list using voice guidance,

[2090] A means of automatically contacting the medical institution that was referred,

[2091] A means of providing relaxation guidance by adjusting the tone and content of voice guidance based on the user's emotional state,

[2092] A means of sending follow-up messages and surveys to users,

[2093] A system that includes this.

[2094] (Claim 2)

[2095] The system according to claim 1, which automatically connects to a medical institution by telephone based on the user's response.

[2096] (Claim 3)

[2097] The system according to claim 1, which includes means for guiding the user on how to perform first aid via voice guidance if, as a result of symptom analysis, first aid is required.

[2098] "Application example 2 when combining with an emotional engine"

[2099] Claims

[2100] (Claim 1)

[2101] A means for the user to input symptoms using voice or push tones,

[2102] A means for converting input data into text data using a speech recognition engine,

[2103] A method for analyzing converted text data using a natural language processing engine to determine symptoms and urgency,

[2104] A means of listing appropriate medical institutions based on the assessment results and guiding the user through that list using voice guidance,

[2105] A means of automatically contacting the medical institution that was referred,

[2106] A means of sending follow-up messages and surveys to users,

[2107] A means for analyzing the user's symptoms and emotions within an autonomous vehicle using a speech recognition engine and an emotion analysis engine, and for setting or resetting the autonomous vehicle's route according to the degree of urgency,

[2108] A system that includes this.

[2109] (Claim 2)

[2110] The system according to claim 1, which automatically connects to a medical institution by telephone based on the user's response.

[2111] (Claim 3)

[2112] The system according to claim 1, which includes means for guiding the user on how to perform first aid via voice guidance if, as a result of symptom analysis, first aid is required. [Explanation of symbols]

[2113] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for the user to input symptoms using voice or push tones, A means for converting input data into text data using a speech recognition engine, A method for analyzing converted text data using a natural language processing engine to determine symptoms and urgency, A means of listing appropriate medical institutions based on the assessment results and guiding the user through that list using voice guidance, A means of automatically contacting the medical institution that was referred, A means of sending follow-up messages and surveys to users, A system that includes this.

2. The system according to claim 1, which automatically connects to a medical institution by telephone based on the user's response.

3. The system according to claim 1, which includes means for guiding the user on how to perform first aid via voice guidance if first aid is necessary as a result of symptom analysis.

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