system

Abstract

Provide a system. [Solution] means for obtaining voice data from a user; means for converting the voice data into text data; means for analyzing the text data; means for generating a diagnosis result based on the analysis result; means for notifying a user of the diagnosis result; means for generating a referral letter or prescription data based on the diagnosis result; means for transmitting the referral letter or prescription data to a designated destination; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern medical services, limited medical resources in developing countries overseas, remote islands within Japan, and depopulated areas pose a challenge, making it difficult to provide appropriate diagnoses and treatment promptly. Furthermore, when an explosive infectious disease like the coronavirus occurs, face-to-face diagnosis becomes risky and more difficult. Under these circumstances, there is a need for a means to obtain a quick and accurate diagnosis. Furthermore, even in large urban hospitals, the high number of patients reduces medical efficiency, placing a burden on both patients and medical professionals. A system is needed to resolve these issues of access to medical care. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring voice data from a user and converting it into text data, a means for analyzing the converted text data, and a means for generating a diagnosis based on the analysis results. The system also includes a means for notifying the user of the generated diagnosis results, generating a referral letter or prescription data as needed, and sending it to a designated destination. This system enables rapid and accurate diagnosis even in remote areas or areas with limited medical resources. Furthermore, even in large hospitals, sending medical interview data to doctors in advance improves the efficiency of medical treatment and enhances the quality of medical services.

[0006] "User" refers to an individual or end user who utilizes the System to diagnose their own condition.

[0007] "Voice data" refers to data captured as a digital signal from information spoken by a user through a microphone or terminal.

[0008] "Text data" refers to voice data converted into character information, and includes the user's symptoms and medical interview details.

[0009] "Analysis" refers to the process of identifying symptoms and making a diagnosis using algorithms and databases based on text data.

[0010] "Diagnosis results" refers to data that indicates medical judgments and guidelines for the user's symptoms, generated based on analyzed information.

[0011] "Notification" refers to the communication means or message used to communicate the generated diagnostic results to the user.

[0012] "Referral" refers to an official document that a user sends to a medical institution to receive further specialized medical care for a specific condition.

[0013] "Prescription Data" refers to a digital prescription containing medication information required by a user.

[0014] "Destination" refers to the medical institution or pharmacy to which the generated referral letter and prescription data will be sent.

[0015] "System" refers to the general term for hardware and software used to perform a series of processes, such as collecting, converting, analyzing, notifying, and transmitting voice data. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] MODE FOR CARRYING OUT THE INVENTION

[0038] The present invention is a system for efficient diagnosis even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals. This system converts voice data from the user into text data, analyzes it, generates appropriate diagnostic results, and includes a series of processes for generating and transmitting referral letters and prescription data as needed. The program processing of this system is described in detail below.

[0039] User voice input

[0040] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0041] Speech recognition and text data conversion

[0042] The device uses a speech recognition engine to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then sent over the Internet to a server.

[0043] Data analysis and generation of diagnostic results

[0044] The server analyzes the received text data. First, it uses natural language processing technology to extract the meaning of keywords and sentences within the text data. Next, it compares this with a case database and generates an appropriate diagnosis. For example, based on the information "frequent headaches" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0045] Notification of diagnosis results and measures

[0046] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message. For example, the device may advise the user, "Your symptoms are likely a mild migraine. Please take over-the-counter painkillers." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0047] Creation and transmission of referral letters and prescription data

[0048] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0049] Transmission of pre-examination data in large hospitals

[0050] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0051] Specific examples

[0052] Suppose a user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the speech, converts it into text data, and sends it to the server. The server analyzes this, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." It also generates a referral letter saying, "If your symptoms do not improve after one week, please see a local clinic for an examination," and sends it to the designated clinic.

[0053] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources.In addition, even in large hospitals, the efficiency of medical treatment can be improved by providing pre-examination data in advance.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user starts up a device such as a smartphone or tablet and opens the dedicated application. The application displays a prompt asking, "Do you want to start the medical interview?", and the user responds, "Yes."

[0057] Step 2:

[0058] The device records the user's voice using a microphone. The user says, "I've had a headache and a fever lately." The device then activates its speech recognition engine and converts the user's voice data into text data.

[0059] Step 3:

[0060] The device encrypts the converted text data and sends it to the server using a secure communication protocol. The server decrypts the received encrypted data and passes it to the analysis module.

[0061] Step 4:

[0062] The server analyzes the text data, extracts the meaning of keywords and sentences within the data using natural language processing technology, and obtains symptom information such as "headache" or "fever." This information is then compared with a case database.

[0063] Step 5:

[0064] The server then applies a diagnostic algorithm based on the match, for example determining whether the combination of "headache" and "fever" is consistent with a mild cold, and generates an appropriate diagnosis and, if necessary, formulates countermeasures.

[0065] Step 6:

[0066] The server generates diagnostic results and sends countermeasure information in a data format such as JSON to the terminal. The terminal analyzes the received data and displays a message to the user saying, "Take over-the-counter cold medicine."

[0067] Step 7:

[0068] The server generates referral and prescription data based on the diagnosis. For example, if the patient's symptoms do not improve, a referral will be generated that includes a request to "see a doctor at a designated clinic."

[0069] Step 8:

[0070] The server electronically transmits the generated referral letter and prescription data to the designated medical institution or pharmacy. For example, the referral letter is transmitted via email or a dedicated medical data communication protocol.

[0071] Step 9:

[0072] The terminal receives a notification of completion of transmission from the server and displays a message to the user saying, "The referral letter has been sent to the clinic." This lets the user know the next step (actual medical treatment).

[0073] Step 10:

[0074] When a user visits a clinic, the medical interview data they have entered in advance is sent to the doctor's terminal, allowing the doctor to conduct a quick and appropriate examination based on that data.

[0075] Example 1

[0076] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0077] This will solve the problem of difficulty in making prompt and appropriate diagnoses in remote areas and areas with limited access to medical care. There is also a need to improve the efficiency of medical treatment in large hospitals. This will aim to improve medical care in various regions.

[0078] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0079] In this invention, the server includes means for acquiring voice data from a user, means for using a voice recognition engine to convert the voice data into text data, and means for analyzing the text data using natural language processing technology, thereby enabling more efficient and appropriate medical care in remote islands, depopulated areas, and large hospitals.

[0080] "User" refers to an individual or patient who uses the system to provide voice input.

[0081] "Voice data" refers to voice information input by a user using a terminal.

[0082] "Text data" refers to the text information resulting from converting voice data using a voice recognition engine.

[0083] "Speech recognition engine" refers to software or algorithms for converting voice data into text data.

[0084] "Natural language processing technology" refers to technology for analyzing the meaning of keywords and sentences within text data.

[0085] A "case database" refers to a collection of information that stores data on medical conditions and cases.

[0086] "Diagnosis result" refers to an evaluation of the disease condition obtained by analyzing text data and comparing it with a case database.

[0087] "Notifying" refers to the act of communicating diagnostic results and related information to the user.

[0088] A "letter of referral" refers to a document created to refer a user to another medical institution.

[0089] "Prescription data" refers to digital information that specifies medications to be prescribed to a user.

[0090] "Information terminal" refers to a hardware device (e.g., smartphone, tablet) that acquires voice data and transmits it to a server.

[0091] "Server" refers to a computer system for analyzing text data, generating diagnostic results, and notifying users.

[0092] This invention is a system for efficient diagnosis in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals. This system converts voice data from the user into text data, analyzes it, and generates appropriate diagnostic results. It also includes a series of processes for generating and sending referral letters and prescription data as needed.

[0093] User voice input

[0094] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0095] Speech recognition and text data conversion

[0096] The device uses a speech recognition engine, such as Google® Speech-to-Text, to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then transmitted over the Internet to a server.

[0097] Data analysis and generation of diagnostic results

[0098] The server analyzes the received text data. First, it uses natural language processing technology (e.g., OpenAI® GPT-4®) to extract the meaning of keywords and sentences in the text data. Next, it compares this with a case database (e.g., an ICD-10 database) and generates an appropriate diagnosis. For example, based on the information "frequent headaches" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0099] Notification of diagnosis results and measures

[0100] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message. For example, the device may advise the user, "Your symptoms are likely a mild migraine. Please take over-the-counter painkillers." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0101] Creation and transmission of referral letters and prescription data

[0102] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0103] Transmission of pre-examination data in large hospitals

[0104] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0105] Specific examples

[0106] For example, suppose a user living on a remote island uses this system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the speech, converts it into text data, and sends it to the server. The server analyzes this text data, diagnoses it as a mild cold, and notifies the user to "take over-the-counter cold medicine." It also generates a referral letter stating, "If your symptoms do not improve after one week, please see a local clinic for an examination," and sends it to the designated clinic.

[0107] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources.In addition, even in large hospitals, the efficiency of medical treatment can be improved by providing pre-examination data in advance.

[0108] Specific examples of prompts to input to generative AI models

[0109] "Please explain a situation where a user is using a smartphone app to complain about a headache and fever. Then, please explain in detail the process of using a speech recognition engine to convert the data into text and send it to the server."

[0110] Please explain in detail how the server uses natural language processing to generate a diagnosis result based on the text data converted by the speech recognition engine and notify the user.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1: User Speech Input

[0113] 1. The user launches a dedicated application using an information terminal such as a smartphone or tablet.

[0114] 2. The user taps the microphone button in the app and speaks their symptoms. For example, they can say, "I've had a headache and a fever lately."

[0115] 3. The device's microphone collects the audio and captures the audio data, which is then input into the device's application.

[0116] Input: User's voice

[0117] Output: Collected audio data

[0118] Step 2: Speech recognition and text conversion

[0119] 1. The device calls a speech recognition engine (e.g., Google Speech-to-Text) and converts the voice data into text data.

[0120] 2. The speech recognition engine analyzes the voice data and generates text data such as, "I've had a headache and a fever lately."

[0121] 3. The generated text data is stored in the device's memory and then sent to a server via the Internet.

[0122] Input: Collected audio data

[0123] Output: Converted text data

[0124] Step 3: Data analysis and generation of diagnostic results

[0125] 1. The server analyzes the received text data. First, it uses natural language processing technology (e.g., OpenAI GPT-4) to extract the meaning of keywords and sentences in the text.

[0126] 2. The server compares the extracted keywords with a case database (e.g., ICD-10 database).

[0127] 3. The server generates an appropriate diagnosis based on the matching results. For example, the information "frequent headaches" and "fever" can be used to diagnose a "mild cold."

[0128] Input: Text data

[0129] Output: Diagnostic results

[0130] Step 4: Notification of diagnosis results and measures

[0131] 1. The server generates a message containing the analysis and diagnostic results.

[0132] 2. The server sends this message to the device over the Internet.

[0133] 3. The device notifies the user of the received message. The notification may be a voice message or a text message. For example, the device may display a message saying, "Your symptoms are likely a mild migraine. Please take an over-the-counter pain reliever."

[0134] Input: Diagnostic results

[0135] Output: A notification message to the user

[0136] Step 5: Generate and send referral and prescription data

[0137] 1. The server generates referral or prescription data based on the diagnosis.

[0138] 2. The referral letter is sent electronically to the designated hospital, and the prescription data is sent to the designated pharmacy.

[0139] 3. The user receives a notification and, if necessary, visits the hospital or pharmacy referred to them. For example, the user may be notified that "a specific medication for migraines has been prescribed. Please pick it up at the nearest pharmacy."

[0140] Input: Diagnostic results

[0141] Output: Referral letter or prescription data

[0142] Step 6: Submission of pre-examination data in large hospitals

[0143] 1. If a user is scheduled to receive medical treatment at a large hospital, they can input and submit medical interview data in advance.

[0144] 2. The server analyzes the received pre-examination data and sends it to the appropriate doctor.

[0145] 3. Doctors can understand the patient's symptoms and background information in advance, improving the efficiency of consultations. For example, the interview data may include information such as "the patient has a history of migraines in the past and is still suffering from them."

[0146] Input: Pre-examination data

[0147] Output: Information notification to doctor

[0148] (Application example 1)

[0149] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0150] In today's world, the number of users of online shopping sites is increasing, creating a demand for efficient and fast methods for searching for products and completing purchases. Users who have difficulty using traditional methods, such as the elderly and visually impaired, also need a more user-friendly interface. Furthermore, it is difficult to perform all steps of the process, from product search to purchase and delivery status confirmation, all in one system, especially in remote locations or during busy periods.

[0151] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0152] In this invention, the server includes means for acquiring voice data from a user, means for converting the voice data into text data, means for analyzing the text data, means for generating a diagnosis based on the analysis result, means for notifying the user of the diagnosis result, means for generating a referral letter or prescription data based on the diagnosis result, means for transmitting the referral letter or prescription data to a specified destination, means for searching for products based on voice input, means for notifying the user of information about the products, means for purchasing the products, and means for notifying the user of the delivery status of the products. This enables a user to search for and purchase products by voice and check the delivery status.

[0153] "Voice data" refers to data indicating voice information spoken by a user to a terminal.

[0154] "Text data" refers to character information converted from voice data using voice recognition technology.

[0155] "Analysis" is the process of extracting and understanding the meaning and intent of information based on text data.

[0156] A "diagnosis result" is an output that indicates the user's health condition and necessary countermeasures, generated based on the information obtained through analysis.

[0157] A "letter of referral" is an electronic document that refers a user to a specific medical institution based on the diagnosis results.

[0158] "Prescription data" is electronic data generated to prescribe specific medications based on diagnostic results.

[0159] "Product search" is the process of searching a database for the product a user is looking for using voice input.

[0160] "Product information" is detailed information about products found by a search, including data such as price, availability, and description.

[0161] The "purchase procedure" is a process in which the user carries out various procedures necessary to purchase the product selected by the user.

[0162] "Delivery status" is information indicating the current delivery status of the purchased item.

[0163] "Notification" refers to the general process of providing information to a user through audio or text.

[0164] This invention provides a system that allows users to search for products, complete purchase procedures, and check delivery status by voice. This system automates the process from voice acquisition to analysis and notification, providing users with a smooth online shopping experience.

[0165] System configuration

[0166] The system uses the following hardware and software:

[0167] Hardware used

[0168] 1. Smartphones and tablets: These are devices that allow users to input information using their voice.

[0169] 2. Server: A computing resource that analyzes voice data and generates and transmits the necessary information.

[0170] Software used

[0171] 1. Google Cloud Speech-to-Text API: A speech recognition technology for converting voice data into text data.

[0172] 2. Google Cloud Natural Language API: A natural language processing technology that analyzes text data to extract and understand product information.

[0173] 3. Flask: It is a web application framework, used to process requests from users and return the required responses.

[0174] Examples of data processing and data calculation

[0175] 1. Acquisition of voice data: The user launches a dedicated app on a device such as a smartphone and describes the product they want by voice. For example, they might say, "I've been looking for a smartphone recently."

[0176] 2. Speech recognition: The captured voice data is converted into text data using the Google Cloud Speech-to-Text API.

[0177] 3. Text data analysis: The text data is analyzed using the Google Cloud Natural Language API to extract keywords related to the product the user is looking for.

[0178] 4. Product information generation: Based on the extracted keywords, the system generates appropriate product information and notifies the user.

[0179] 5. Purchase procedure and delivery status notification: If the user decides to purchase, the system will process the purchase and notify the delivery status after the purchase is completed.

[0180] Specific examples

[0181] 1. Usage scenarios:

[0182] The user launches the app and says, "I've been looking for my smartphone lately."

[0183] The server converts the voice data into text data and analyzes it.

[0184] Based on the results of analyzing the text data, the system notifies the user of smartphone candidates.

[0185] The user selects from the candidates and completes the purchase procedure.

[0186] After purchase, users will be notified of delivery status in real time.

[0187] 2. Example prompt:

[0188] User: "I've been looking for a smartphone lately."

[0189] App: "We found some recommended products, including the iPhone 13 (¥100,000) and the Samsung Galaxy (¥80,000)."

[0190] As described above, this system allows users to seamlessly search for products and complete purchase procedures through voice input, providing a highly convenient mail-order system that is particularly applicable to the elderly and visually impaired.

[0191] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0192] Step 1:

[0193] The user launches a dedicated mail-order assistant app on their smartphone. They then speak into the app, saying, "I've been looking for my smartphone lately." This input is captured through the device's microphone. The input data is then converted into voice data, which is then sent to the next stage of processing.

[0194] Step 2:

[0195] The device sends the acquired voice data to the Google Cloud Speech-to-Text API. The server receives this voice data and converts it into text data using speech recognition technology. At this stage, the input is voice data, and the output is the converted text data. For example, the generated text data is "I've been looking for my smartphone recently."

[0196] Step 3:

[0197] The server uses the Google Cloud Natural Language API to analyze the text data. The server performs data analysis to extract important keywords from the text data. The input is the converted text data, and the output is a list of extracted keywords. For example, the keyword "smartphone" is extracted.

[0198] Step 4:

[0199] The server searches its internal database based on the extracted keywords to collect relevant product information. This search process searches all product information in the database for information that matches the keywords. The input is the extracted keywords, and the output is a list of relevant products. For example, a list containing information such as "iPhone 13" and "Samsung Galaxy" is output.

[0200] Step 5:

[0201] The server notifies the user of the collected product information. The device displays or voices the product information received from the server. The input is a list of product information, and the output is a notification to the user (for example, a screen display or voice announcement). For example, the user might be presented with a message saying, "We've found some recommended products: iPhone 13 (100,000 yen), Samsung Galaxy (80,000 yen)."

[0202] Step 6:

[0203] The user selects the desired product by voice or operation and begins the purchase process. The terminal receives the user's selection and sends the information for the purchase process to the server. The input is the user's selection data, and the output is a signal indicating the start of the purchase process.

[0204] Step 7:

[0205] The server processes the purchase, queries the user for any necessary information, verifies payment and shipping information, and guides the user accordingly. The input is the user's purchase information and data from the server's checkout process, and the output is a notification that the purchase is complete. Once the user provides the appropriate payment information, the purchase is complete.

[0206] Step 8:

[0207] After the purchase is completed, the server tracks the delivery status in real time and notifies the user. The terminal displays or voices the delivery status information it periodically receives from the server. The input is delivery status data, and the output is a notification to the user (e.g., the current delivery status).

[0208] This series of processing steps allows users to smoothly search for products, complete purchase procedures, and check delivery status through voice input.

[0209] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0210] MODE FOR CARRYING OUT THE INVENTION

[0211] The present invention is a system for efficient diagnosis, even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take the user's emotions into consideration. This system converts voice data from the user into text data, analyzes it, and generates appropriate diagnostic results. It also includes a series of processes for generating and transmitting referral letters and prescription data as needed. Furthermore, it is possible to recognize the user's emotions using an emotion engine and adjust the diagnostic results based on these. The program processing of this system is described in detail below.

[0212] User voice input

[0213] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0214] Speech recognition and text data conversion

[0215] The device uses a speech recognition engine to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then sent over the Internet to a server.

[0216] Emotion recognition by emotion engine

[0217] The server analyzes the voice data along with the text data and uses an emotion engine to recognize the user's emotions. For example, it identifies emotions such as "anxiety" or "tension" from the tone, speed, and intonation of the user's voice. This emotion data is also used for analysis.

[0218] Data analysis and generation of diagnostic results

[0219] The server analyzes the received text data and emotion data. First, it uses natural language processing technology to extract the meaning of keywords and sentences in the text data. Next, it compares this with a case database and generates an appropriate diagnosis. For example, based on the information on "headache" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0220] Notification of diagnosis results and measures

[0221] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message, with the tone adjusted based on the emotional data. For example, the device may provide the user with advice such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0222] Creation and transmission of referral letters and prescription data

[0223] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0224] Transmission of pre-examination data in large hospitals

[0225] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0226] Specific examples

[0227] Suppose a user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text data, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." Furthermore, the emotion engine recognizes the user's anxiety, and as a result, adjusts the notification message to be more reassuring. An additional message is sent, such as, "If you have any concerns, please contact us immediately."

[0228] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[0229] The processing flow will be explained below.

[0230] MODE FOR CARRYING OUT THE INVENTION

[0231] (Processing steps of a system that combines an emotion engine)

[0232] Step 1:

[0233] The user starts up a device such as a smartphone or tablet and opens the dedicated application. The application displays a prompt asking, "Do you want to start the medical interview?", and the user responds, "Yes."

[0234] Step 2:

[0235] The device records the user's voice using a microphone. The user says, "I've had a headache and a fever lately." The device then activates its speech recognition engine and converts the user's voice data into text data.

[0236] Step 3:

[0237] The device encrypts the converted text and voice data and sends it to the server using a secure communication protocol. The server then decrypts the received encrypted data and passes it to the analysis module and emotion engine.

[0238] Step 4:

[0239] The server analyzes the text data and uses natural language processing technology to extract the meaning of keywords and sentences in the text data, obtaining symptom information such as "headache" or "fever." This information is then compared with a case database.

[0240] Step 5:

[0241] The server's emotion engine analyzes the voice data and recognizes the user's emotions, identifying emotions such as "anxiety" or "tension" based on the tone, speed, and intonation of the user's voice.

[0242] Step 6:

[0243] The server then applies a diagnostic algorithm based on the matching results, for example, determining whether the combination of "headache" and "fever" is consistent with a mild cold. Emotional data is also factored in to improve the reliability of the diagnosis.

[0244] Step 7:

[0245] The server generates diagnostic results and sends countermeasure information to the device in a data format such as JSON. The device analyzes the received data, adjusts the tone of the message based on the emotion recognition results, and notifies the user. For example, a countermeasure message such as "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller" may be displayed.

[0246] Step 8:

[0247] The server generates referral and prescription data based on the diagnosis. For example, if the patient's symptoms do not improve, a referral will be generated that includes a request to "see a doctor at a designated clinic."

[0248] Step 9:

[0249] The server electronically transmits the generated referral letter and prescription data to the designated medical institution or pharmacy. For example, the referral letter is transmitted via email or a dedicated medical data communication protocol.

[0250] Step 10:

[0251] The terminal receives a notification of completion of transmission from the server and displays a message to the user saying, "The referral letter has been sent to the clinic." This lets the user know the next step (actual medical treatment).

[0252] Specific examples

[0253] A user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text and voice data, diagnoses it as a mild cold, and sends a notification to the user stating, "Please take over-the-counter cold medicine," adjusted based on the emotion data. The emotion engine then recognizes the user's anxiety and sends an additional message such as, "If you have any concerns, please contact us immediately."

[0254] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[0255] Example 2

[0256] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0257] There is a demand for efficient diagnosis even in remote areas or areas with limited access to medical care. There is also a need to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take user emotions into account. However, conventional systems are limited to simple analysis of voice data, making it difficult to provide diagnostic results that take user emotions into account or improve medical treatment efficiency by sending pre-examination data in advance. The objective of this project is to solve these problems.

[0258] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0259] In this invention, the server includes means for acquiring voice data from a user, means for converting the voice data into text data, means for recognizing emotions from the voice data and text data, means for analyzing the text data, means for generating a diagnosis result based on the analysis result and the emotion recognition result, means for notifying the diagnosis result in a tone corresponding to the user's emotion, means for generating a referral letter or prescription data based on the diagnosis result, and means for transmitting the referral letter or prescription data to a specified destination. This enables prompt and appropriate medical care even in remote islands and depopulated areas, reduces the psychological burden on users, and improves the efficiency of medical treatment in large hospitals.

[0260] A "user" is an individual who utilizes the system to provide voice input and receive diagnostic results.

[0261] "Voice data" refers to voice information input by a user through a terminal.

[0262] A "terminal" is a device that allows voice input, such as a smartphone, tablet, or PC.

[0263] The "server" is a central computer system that analyzes data sent from the terminals and generates and transmits diagnostic results.

[0264] "Text data" refers to text information converted from voice data using voice recognition technology.

[0265] "Voice recognition technology" refers to the technology that analyzes voice data and converts it into text data.

[0266] "Emotion recognition" refers to technology that identifies a user's emotions from voice data and text data.

[0267] "Data analysis algorithm" is a technology that generates diagnostic results based on text data and emotion recognition results.

[0268] "Diagnosis results" refers to medical judgments and instructions provided to the user based on the results of the analysis by the server.

[0269] "Notification by tone" refers to a method of notifying the diagnosis result using a tone or expression that matches the user's emotions.

[0270] A "letter of referral" is a document intended to introduce a patient to a medical institution designated by the user.

[0271] "Prescription data" refers to the digital information required to receive medication at a pharmacy designated by the user.

[0272] MODE FOR CARRYING OUT THE INVENTION

[0273] This invention is a system for efficient diagnosis, even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take the user's emotions into consideration. This system involves a series of processes: the user inputs voice data, converts the voice data into text data, analyzes it, generates appropriate diagnostic results, and generates and transmits referral letters and prescription data as needed. Furthermore, it is possible to recognize the user's emotions using emotion recognition technology and adjust diagnostic results based on this.

[0274] Hardware and Software

[0275] 1. Device:

[0276] Use a device such as a smartphone, tablet, or computer. These devices must have a microphone that can accept voice input.

[0277] 2. Speech Recognition Engine:

[0278] To convert the "voice data" into "text data," "voice recognition technology" is used. As a specific example, a "cloud-based voice recognition service" is used.

[0279] 3. Emotion Recognition Engine:

[0280] To read emotions from the user's voice and text data, we use "emotion recognition technology." As a specific example, we use "cloud-based emotion analysis service."

[0281] 4. Data analysis algorithm:

[0282] "Data analysis algorithms" are used to analyze "text data" and "emotion data" and generate appropriate diagnostic results. Specific examples of this include "natural language processing technology."

[0283] 5. Notification Engine:

[0284] To notify the user of the diagnosis results, a "notification engine" is used. As a specific example, text-to-speech technology is used.

[0285] 6. Electronic Document Generation Systems:

[0286] An "electronic document generation system" is used to generate referral letters and prescription data and send them to designated destinations.

[0287] Specific operation example

[0288] The user operates their smartphone, launches a dedicated application, and voice-records their symptoms. For example, they might say, "I've had a headache recently, and I also have a fever." The device then converts this voice into text data using a cloud-based voice recognition service and sends it to a server.

[0289] The server uses a cloud-based emotion analysis service to recognize the user's emotions based on the received text and voice data. For example, emotions such as "anxiety" or "tension" can be identified from the tone, speed, and intonation of the user's voice.

[0290] The server then uses natural language processing technology to analyze the text data and compares it with a case database based on the meaning of the keywords and sentences obtained. This generates an appropriate diagnosis. For example, based on the information on "headache" and "fever," it determines whether the condition is urgent and, if necessary, directs emergency treatment to a large hospital.

[0291] The diagnosis results are notified to the user using voice synthesis technology. The tone of the notification is adjusted based on the emotion recognition results. For example, the system provides a solution such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller." In addition, if the symptoms are severe, an emergency notification will be sent stating, "Go to the nearest hospital immediately."

[0292] Finally, the server uses the "electronic document generation system" to generate a referral letter and prescription data based on the diagnosis results, and sends them to the designated hospital or pharmacy. For example, if the patient suffers from persistent migraines, a notification will be sent to the user stating, "Migraine medication has been prescribed. Please pick it up at the nearest pharmacy."

[0293] Prompt Sentence Examples

[0294] Let's say a user living on a remote island uses this system because they are experiencing symptoms of a headache and fever. The user launches the application and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text data, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." The emotion engine also recognizes the user's anxiety, and as a result, adjusts the notification message to be more reassuring. An additional message is sent, such as, "If you have any concerns, please contact us immediately."

[0295] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[0296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0297] Step 1: User Speech Input

[0298] The user launches a dedicated application on a device such as a smartphone or tablet and inputs their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." The device then picks up the voice data via its microphone.

[0299] Input: Verbal symptom report from user

[0300] Output: Captured audio data

[0301] Step 2: Speech recognition and text conversion

[0302] The device sends the captured voice data to cloud-based speech recognition technology (e.g., Google Cloud Speech-to-Text) and converts it into text data, which includes the user's spoken symptoms and other relevant information. The device then sends the text data to a server.

[0303] Input: Captured audio data

[0304] Output: Converted text data

[0305] Step 3: Emotion recognition by the emotion engine

[0306] The server analyzes the voice data as well as the text data and uses emotion recognition technology (e.g., IBM Watson (registered trademark) Tone Analyzer) to recognize the user's emotions. For example, it identifies emotions such as "anxiety" or "tension" from the tone, speed, and intonation of the user's voice.

[0307] Input: Audio and text data

[0308] Output: Recognized emotion data

[0309] Step 4: Data analysis and generation of diagnostic results

[0310] The server analyzes the received text data and emotion data using natural language processing technology (e.g., Amazon Comprehend). First, it extracts the meaning of keywords and sentences in the text data and compares them with a case database. As a result of the analysis, it generates an appropriate diagnosis.

[0311] Input: Text data and emotion data

[0312] Output: Diagnostic result data

[0313] Step 5: Notification of diagnosis results and measures

[0314] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message using text-to-speech technology, with the tone adjusted based on the emotional data. For example, the device provides the user with advice such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller."

[0315] Input: Diagnostic result data

[0316] Output: Notification message sent to the user

[0317] Step 6: Generate and send referral and prescription data

[0318] The server generates a referral letter or prescription data using an electronic document generation system (e.g., DocuSign API) based on the diagnosis results. The referral letter is electronically sent to the designated hospital, and the prescription data is sent to the designated pharmacy.

[0319] Input: Diagnostic result data

[0320] Output: Referral data or prescription data

[0321] Step 7: Sending pre-examination data in large hospitals

[0322] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0323] Input: User interview data

[0324] Output: Pre-examination data and its analysis results

[0325] (Application example 2)

[0326] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0327] The present invention relates to a system for conducting prompt and appropriate health examinations even in areas with limited or remote medical access, and for implementing emergency response using autonomous vehicles as needed. The system also aims to reduce the psychological burden of users by providing diagnostic results that take their emotions into consideration. In particular, there is a need for improved safety and efficiency by including a function to instruct an autonomous vehicle to quickly travel to an appropriate medical institution when emergency medical care is required.

[0328] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0329] In this invention, the server includes means for recognizing emotions from voice data and text data from a user and adjusting a diagnosis result according to the emotion, means for issuing instructions to a control system of an autonomous vehicle based on the diagnosis result, and means for converting the voice data into text data, thereby providing an appropriate diagnosis result according to the emotion, and further enabling rapid transport to a medical institution using the autonomous vehicle in an emergency.

[0330] "Voice data" refers to the content of a user's speech, and is information acquired as a voice signal.

[0331] "Text data" refers to character information that has been converted by analyzing voice data, and is data that is the subject of natural language processing.

[0332] "Analysis Results" refers to diagnostic information and recommended actions generated based on the text data and other related data.

[0333] "Diagnosis results" refer to conclusions and recommended measures regarding the user's health condition based on the analysis results.

[0334] A "letter of referral" is a medical referral document issued to another medical institution based on diagnostic results.

[0335] "Prescription data" is prescription information for medicines generated based on diagnostic results, and is digital data sent to a pharmacy.

[0336] "Emotion recognition" is a technology that analyzes voice data and text data to identify a user's psychological state.

[0337] An "autonomous vehicle" is a vehicle that drives autonomously using a drive control system and has the ability to head to a destination based on instructions from passengers.

[0338] A "control system" is a general term for programs and hardware that manage the operation of machines and devices and operate them according to specific instructions.

[0339] A "server" is a computer system used to analyze audio and text data and generate and provide diagnostic results.

[0340] "Speech recognition technology" is a technology that converts voice data into text data, and obtains the content of a user's speech as text information.

[0341] "Data analysis algorithms" refer to mathematical methods and processes that use machine learning and statistical analysis to derive diagnostic results based on collected data.

[0342] The system of the present invention is designed to diagnose the health status of passengers in an autonomous vehicle in real time and provide necessary measures. Specific embodiments for implementing this system will be described below.

[0343] System Overview

[0344] The system mainly consists of the following components:

[0345] 1. A device that acquires the user's voice data

[0346] 2. Server that analyzes voice data

[0347] 3. Control Systems for Autonomous Vehicles

[0348] Acquiring and converting audio data

[0349] When a user talks about their health condition in an autonomous vehicle, the vehicle's built-in microphone (hardware) captures the voice data. This data is converted into text data via the device's built-in voice recognition engine (software). For example, if a user says, "I have chest pain," the voice data is converted into text data using the Google Speech-to-Text API.

[0350] emotion recognition

[0351] The converted text and voice data is sent to a server and analyzed by an emotion engine (software). The analysis uses IBM Watson Tone Analyzer to identify emotions from the user's speech. For example, emotions such as "urgency" or "anxiety" can be recognized from the tone and intonation of the user's voice.

[0352] Data analysis and generation of diagnostic results

[0353] The server uses natural language processing technology to generate appropriate diagnostic results based on the analyzed text data and emotion data. This process uses data analysis algorithms such as Scikit-Learn and Tensorflow®. For example, if the symptom "chest pain" is recognized, it will be diagnosed as a possible heart disease and determine that immediate medical attention is required.

[0354] Notification of diagnostic results and control of autonomous vehicles

[0355] The generated diagnostic results are communicated to the user via a display and speaker in the vehicle. Furthermore, in the event of an emergency, instructions are sent to the autonomous vehicle's control system to direct the vehicle to the nearest medical facility. This control is performed using an autonomous driving platform such as NVIDIA DRIVE.

[0356] emergency notification

[0357] If necessary, the server will use the Twilio API to send emergency notifications to designated medical institutions, including the user's symptoms and location information, to prompt medical institutions to take action.

[0358] Specific examples

[0359] For example, if a user experiences chest tightness while in a self-driving vehicle, the above process would be executed as follows:

[0360] 1. User: Says, "I have chest pains."

[0361] 2. Device: Convert the audio into text data using the Google Speech-to-Text API.

[0362] 3. Server: IBM Watson Tone Analyzer recognizes the emotion as "urgent."

[0363] 4. Server: Using TensorFlow, diagnoses the possibility of heart disease and determines that emergency treatment is necessary.

[0364] 5. Vehicle: Route to the nearest hospital.

[0365] 6. Server: Uses Twilio API to send emergency notifications to designated hospitals.

[0366] Prompt Sentence Examples

[0367] Here are some example prompts to input to a generative AI model:

[0368] A user says "I'm having chest pain" in their car. Convert the voice data to text, perform sentiment analysis, generate a diagnosis based on the symptoms, notify medical authorities if emergency response is required, and the autonomous vehicle will head to the nearest hospital. Please provide detailed instructions for the following tasks:

[0369] 1. Speech Recognition and Text Conversion

[0370] 2. Emotion recognition

[0371] 3. Data analysis and diagnostic results generation

[0372] 4. Notification of diagnostic results

[0373] 5. How to send emergency notifications

[0374] 6. Autonomous Vehicle Operation Interface

[0375] The above is an embodiment of the present invention. This system enables prompt and appropriate medical examinations and emergency response even in remote areas or places with limited medical access.

[0376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0377] Step 1: Acquire audio data

[0378] The user speaks about their health condition in the autonomous vehicle. This speech is picked up by a microphone built into the vehicle. The input is raw audio data, and the output is a digital audio file.

[0379] Step 2: Converting audio data to text data

[0380] The device converts the captured audio data into text data using the Google Speech-to-Text API. Specifically, it sends the audio file to the API and receives the text data returned by the API. The input is audio data and the output is text data.

[0381] Step 3: Analyze the text and audio data

[0382] The server sends the received text data and original voice data to the IBM Watson Tone Analyzer for emotion analysis. The input is text data and voice data, and the output is emotion data. Specifically, the content of the text and the intonation and tone of the voice are analyzed to identify emotions such as "urgency" and "anxiety."

[0383] Step 4: Generate diagnostic results

[0384] The server uses TensorFlow to generate appropriate diagnosis results based on the text data and emotion data. The input is text data and emotion data, and the output is the diagnosis result. Specifically, it uses natural language processing technology to analyze the meaning of the text and compares it with existing cases in a database to make a diagnosis.

[0385] Step 5: Notification of diagnosis results

[0386] The server sends the generated diagnostic results to a display device or speaker in the vehicle and notifies the user. The input is the diagnostic results, and the output is a display message or a voice message. Specifically, a message based on the diagnostic results is displayed on the screen or read aloud.

[0387] Step 6: Controlling the autonomous vehicle

[0388] If necessary, the server issues instructions to the autonomous vehicle's control system based on the diagnosis results, which in turn sets up a route to the nearest medical facility. The input is the diagnosis results, and the output is route instructions for the autonomous vehicle. Specifically, the server uses the NVIDIA DRIVE platform to control the vehicle's navigation system.

[0389] Step 7: Send emergency notifications

[0390] The server uses the Twilio API to send emergency notifications to designated medical institutions. The input is the user's symptoms and location information, and the output is a notification message to the medical institution. Specifically, the message generated by the server is sent in real time to the medical institution, urging them to take emergency action.

[0391] These steps will allow for a prompt and appropriate response if a user experiences a health issue while in a self-driving vehicle.

[0392] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0393] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0394] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0395] [Second embodiment]

[0396] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0397] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0398] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0399] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0400] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0401] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0402] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0403] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0404] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0405] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0406] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0407] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0408] MODE FOR CARRYING OUT THE INVENTION

[0409] The present invention is a system for efficient diagnosis even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals. This system converts voice data from the user into text data, analyzes it, generates appropriate diagnostic results, and includes a series of processes for generating and transmitting referral letters and prescription data as needed. The program processing of this system is described in detail below.

[0410] User voice input

[0411] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0412] Speech recognition and text data conversion

[0413] The device uses a speech recognition engine to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then sent over the Internet to a server.

[0414] Data analysis and generation of diagnostic results

[0415] The server analyzes the received text data. First, it uses natural language processing technology to extract the meaning of keywords and sentences within the text data. Next, it compares this with a case database and generates an appropriate diagnosis. For example, based on the information "frequent headaches" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0416] Notification of diagnosis results and measures

[0417] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message. For example, the device may advise the user, "Your symptoms are likely a mild migraine. Please take over-the-counter painkillers." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0418] Creation and transmission of referral letters and prescription data

[0419] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0420] Transmission of pre-examination data in large hospitals

[0421] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0422] Specific examples

[0423] Suppose a user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the speech, converts it into text data, and sends it to the server. The server analyzes this, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." It also generates a referral letter saying, "If your symptoms do not improve after one week, please see a local clinic for an examination," and sends it to the designated clinic.

[0424] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources.In addition, even in large hospitals, the efficiency of medical treatment can be improved by providing pre-examination data in advance.

[0425] The processing flow will be explained below.

[0426] Step 1:

[0427] The user starts up a device such as a smartphone or tablet and opens the dedicated application. The application displays a prompt asking, "Do you want to start the medical interview?", and the user responds, "Yes."

[0428] Step 2:

[0429] The device records the user's voice using a microphone. The user says, "I've had a headache and a fever lately." The device then activates its speech recognition engine and converts the user's voice data into text data.

[0430] Step 3:

[0431] The device encrypts the converted text data and sends it to the server using a secure communication protocol. The server decrypts the received encrypted data and passes it to the analysis module.

[0432] Step 4:

[0433] The server analyzes the text data, extracts the meaning of keywords and sentences within the data using natural language processing technology, and obtains symptom information such as "headache" or "fever." This information is then compared with a case database.

[0434] Step 5:

[0435] The server then applies a diagnostic algorithm based on the match, for example determining whether the combination of "headache" and "fever" is consistent with a mild cold, and generates an appropriate diagnosis and, if necessary, formulates countermeasures.

[0436] Step 6:

[0437] The server generates diagnostic results and sends countermeasure information in a data format such as JSON to the terminal. The terminal analyzes the received data and displays a message to the user saying, "Take over-the-counter cold medicine."

[0438] Step 7:

[0439] The server generates referral and prescription data based on the diagnosis. For example, if the patient's symptoms do not improve, a referral will be generated that includes a request to "see a doctor at a designated clinic."

[0440] Step 8:

[0441] The server electronically transmits the generated referral letter and prescription data to the designated medical institution or pharmacy. For example, the referral letter is transmitted via email or a dedicated medical data communication protocol.

[0442] Step 9:

[0443] The terminal receives a notification of completion of transmission from the server and displays a message to the user saying, "The referral letter has been sent to the clinic." This lets the user know the next step (actual medical treatment).

[0444] Step 10:

[0445] When a user visits a clinic, the medical interview data they have entered in advance is sent to the doctor's terminal, allowing the doctor to conduct a quick and appropriate examination based on that data.

[0446] Example 1

[0447] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0448] This will solve the problem of difficulty in making prompt and appropriate diagnoses in remote areas and areas with limited access to medical care. There is also a need to improve the efficiency of medical treatment in large hospitals. This will aim to improve medical care in various regions.

[0449] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0450] In this invention, the server includes means for acquiring voice data from a user, means for using a voice recognition engine to convert the voice data into text data, and means for analyzing the text data using natural language processing technology, thereby enabling more efficient and appropriate medical care in remote islands, depopulated areas, and large hospitals.

[0451] "User" refers to an individual or patient who uses the system to provide voice input.

[0452] "Voice data" refers to voice information input by a user using a terminal.

[0453] "Text data" refers to the text information resulting from converting voice data using a voice recognition engine.

[0454] "Speech recognition engine" refers to software or algorithms for converting voice data into text data.

[0455] "Natural language processing technology" refers to technology for analyzing the meaning of keywords and sentences within text data.

[0456] A "case database" refers to a collection of information that stores data on medical conditions and cases.

[0457] "Diagnosis result" refers to an evaluation of the disease condition obtained by analyzing text data and comparing it with a case database.

[0458] "Notifying" refers to the act of communicating diagnostic results and related information to the user.

[0459] A "letter of referral" refers to a document created to refer a user to another medical institution.

[0460] "Prescription data" refers to digital information that specifies medications to be prescribed to a user.

[0461] "Information terminal" refers to a hardware device (e.g., smartphone, tablet) that acquires voice data and transmits it to a server.

[0462] "Server" refers to a computer system for analyzing text data, generating diagnostic results, and notifying users.

[0463] This invention is a system for efficient diagnosis in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals. This system converts voice data from the user into text data, analyzes it, and generates appropriate diagnostic results. It also includes a series of processes for generating and sending referral letters and prescription data as needed.

[0464] User voice input

[0465] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0466] Speech recognition and text data conversion

[0467] The device uses a speech recognition engine, such as Google Speech-to-Text, to convert the user's speech into text data, including the user's symptoms and other relevant information, which is then sent over the internet to a server.

[0468] Data analysis and generation of diagnostic results

[0469] The server analyzes the received text data. First, it uses natural language processing technology (e.g., OpenAI GPT-4) to extract the meaning of keywords and sentences in the text data. Next, it compares this with a case database (e.g., the ICD-10 database) and generates an appropriate diagnosis. For example, based on the information "frequent headaches" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0470] Notification of diagnosis results and measures

[0471] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message. For example, the device may advise the user, "Your symptoms are likely a mild migraine. Please take over-the-counter painkillers." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0472] Creation and transmission of referral letters and prescription data

[0473] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0474] Transmission of pre-examination data in large hospitals

[0475] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0476] Specific examples

[0477] For example, suppose a user living on a remote island uses this system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the speech, converts it into text data, and sends it to the server. The server analyzes this text data, diagnoses it as a mild cold, and notifies the user to "take over-the-counter cold medicine." It also generates a referral letter stating, "If your symptoms do not improve after one week, please see a local clinic for an examination," and sends it to the designated clinic.

[0478] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources.In addition, even in large hospitals, the efficiency of medical treatment can be improved by providing pre-examination data in advance.

[0479] Specific examples of prompts to input to generative AI models

[0480] "Please explain a situation where a user is using a smartphone app to complain about a headache and fever. Then, please explain in detail the process of using a speech recognition engine to convert the data into text and send it to the server."

[0481] Please explain in detail how the server uses natural language processing to generate a diagnosis result based on the text data converted by the speech recognition engine and notify the user.

[0482] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0483] Step 1: User Speech Input

[0484] 1. The user launches a dedicated application using an information terminal such as a smartphone or tablet.

[0485] 2. The user taps the microphone button in the app and speaks their symptoms. For example, they can say, "I've had a headache and a fever lately."

[0486] 3. The device's microphone collects the audio and captures the audio data, which is then input into the device's application.

[0487] Input: User's voice

[0488] Output: Collected audio data

[0489] Step 2: Speech recognition and text conversion

[0490] 1. The device calls a speech recognition engine (e.g., Google Speech-to-Text) and converts the voice data into text data.

[0491] 2. The speech recognition engine analyzes the voice data and generates text data such as, "I've had a headache and a fever lately."

[0492] 3. The generated text data is stored in the device's memory and then sent to a server via the Internet.

[0493] Input: Collected audio data

[0494] Output: Converted text data

[0495] Step 3: Data analysis and generation of diagnostic results

[0496] 1. The server analyzes the received text data. First, it uses natural language processing technology (e.g., OpenAI GPT-4) to extract the meaning of keywords and sentences in the text.

[0497] 2. The server compares the extracted keywords with a case database (e.g., ICD-10 database).

[0498] 3. The server generates an appropriate diagnosis based on the matching results. For example, the information "frequent headaches" and "fever" can be used to diagnose a "mild cold."

[0499] Input: Text data

[0500] Output: Diagnostic results

[0501] Step 4: Notification of diagnosis results and measures

[0502] 1. The server generates a message containing the analysis and diagnostic results.

[0503] 2. The server sends this message to the device over the Internet.

[0504] 3. The device notifies the user of the received message. The notification may be a voice message or a text message. For example, the device may display a message saying, "Your symptoms are likely a mild migraine. Please take an over-the-counter pain reliever."

[0505] Input: Diagnostic results

[0506] Output: A notification message to the user

[0507] Step 5: Generate and send referral and prescription data

[0508] 1. The server generates referral or prescription data based on the diagnosis.

[0509] 2. The referral letter is sent electronically to the designated hospital, and the prescription data is sent to the designated pharmacy.

[0510] 3. The user receives a notification and, if necessary, visits the hospital or pharmacy referred to them. For example, the user may be notified that "a specific medication for migraines has been prescribed. Please pick it up at the nearest pharmacy."

[0511] Input: Diagnostic results

[0512] Output: Referral letter or prescription data

[0513] Step 6: Submission of pre-examination data in large hospitals

[0514] 1. If a user is scheduled to receive medical treatment at a large hospital, they can input and submit medical interview data in advance.

[0515] 2. The server analyzes the received pre-examination data and sends it to the appropriate doctor.

[0516] 3. Doctors can understand the patient's symptoms and background information in advance, improving the efficiency of consultations. For example, the interview data may include information such as "the patient has a history of migraines in the past and is still suffering from them."

[0517] Input: Pre-examination data

[0518] Output: Information notification to doctor

[0519] (Application example 1)

[0520] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0521] In today's world, the number of users of online shopping sites is increasing, creating a demand for efficient and fast methods for searching for products and completing purchases. Users who have difficulty using traditional methods, such as the elderly and visually impaired, also need a more user-friendly interface. Furthermore, it is difficult to perform all steps of the process, from product search to purchase and delivery status confirmation, all in one system, especially in remote locations or during busy periods.

[0522] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0523] In this invention, the server includes means for acquiring voice data from a user, means for converting the voice data into text data, means for analyzing the text data, means for generating a diagnosis based on the analysis result, means for notifying the user of the diagnosis result, means for generating a referral letter or prescription data based on the diagnosis result, means for transmitting the referral letter or prescription data to a specified destination, means for searching for products based on voice input, means for notifying the user of information about the products, means for purchasing the products, and means for notifying the user of the delivery status of the products. This enables a user to search for and purchase products by voice and check the delivery status.

[0524] "Voice data" refers to data indicating voice information spoken by a user to a terminal.

[0525] "Text data" refers to character information converted from voice data using voice recognition technology.

[0526] "Analysis" is the process of extracting and understanding the meaning and intent of information based on text data.

[0527] A "diagnosis result" is an output that indicates the user's health condition and necessary countermeasures, generated based on the information obtained through analysis.

[0528] A "letter of referral" is an electronic document that refers a user to a specific medical institution based on the diagnosis results.

[0529] "Prescription data" is electronic data generated to prescribe specific medications based on diagnostic results.

[0530] "Product search" is the process of searching a database for the product a user is looking for using voice input.

[0531] "Product information" is detailed information about products found by a search, including data such as price, availability, and description.

[0532] The "purchase procedure" is a process in which the user carries out various procedures necessary to purchase the product selected by the user.

[0533] "Delivery status" is information indicating the current delivery status of the purchased item.

[0534] "Notification" refers to the general process of providing information to a user through audio or text.

[0535] This invention provides a system that allows users to search for products, complete purchase procedures, and check delivery status by voice. This system automates the process from voice acquisition to analysis and notification, providing users with a smooth online shopping experience.

[0536] System configuration

[0537] The system uses the following hardware and software:

[0538] Hardware used

[0539] 1. Smartphones and tablets: These are devices that allow users to input information using their voice.

[0540] 2. Server: A computing resource that analyzes voice data and generates and transmits the necessary information.

[0541] Software used

[0542] 1. Google Cloud Speech-to-Text API: A speech recognition technology for converting voice data into text data.

[0543] 2. Google Cloud Natural Language API: A natural language processing technology that analyzes text data to extract and understand product information.

[0544] 3. Flask: It is a web application framework, used to process requests from users and return the required responses.

[0545] Examples of data processing and data calculation

[0546] 1. Acquisition of voice data: The user launches a dedicated app on a device such as a smartphone and describes the product they want by voice. For example, they might say, "I've been looking for a smartphone recently."

[0547] 2. Speech recognition: The captured voice data is converted into text data using the Google Cloud Speech-to-Text API.

[0548] 3. Text data analysis: The text data is analyzed using the Google Cloud Natural Language API to extract keywords related to the product the user is looking for.

[0549] 4. Product information generation: Based on the extracted keywords, the system generates appropriate product information and notifies the user.

[0550] 5. Purchase procedure and delivery status notification: If the user decides to purchase, the system will process the purchase and notify the delivery status after the purchase is completed.

[0551] Specific examples

[0552] 1. Usage scenarios:

[0553] The user launches the app and says, "I've been looking for my smartphone lately."

[0554] The server converts the voice data into text data and analyzes it.

[0555] Based on the results of analyzing the text data, the system notifies the user of smartphone candidates.

[0556] The user selects from the candidates and completes the purchase procedure.

[0557] After purchase, users will be notified of delivery status in real time.

[0558] 2. Example prompt:

[0559] User: "I've been looking for a smartphone lately."

[0560] App: "We found some great deals, including the iPhone 13 (¥100,000) and the Samsung Galaxy (¥80,000)."

[0561] As described above, this system allows users to seamlessly search for products and complete purchase procedures through voice input, providing a highly convenient mail-order system that is particularly applicable to the elderly and visually impaired.

[0562] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0563] Step 1:

[0564] The user launches a dedicated mail-order assistant app on their smartphone. They then speak into the app, saying, "I've been looking for my smartphone lately." This input is captured through the device's microphone. The input data is then converted into voice data, which is then sent to the next stage of processing.

[0565] Step 2:

[0566] The device sends the acquired voice data to the Google Cloud Speech-to-Text API. The server receives this voice data and converts it into text data using speech recognition technology. At this stage, the input is voice data, and the output is the converted text data. For example, the generated text data is "I've been looking for my smartphone recently."

[0567] Step 3:

[0568] The server uses the Google Cloud Natural Language API to analyze the text data. The server performs data analysis to extract important keywords from the text data. The input is the converted text data, and the output is a list of extracted keywords. For example, the keyword "smartphone" is extracted.

[0569] Step 4:

[0570] The server searches its internal database based on the extracted keywords to collect relevant product information. This search process searches all product information in the database for information that matches the keywords. The input is the extracted keywords, and the output is a list of relevant products. For example, a list containing information such as "iPhone 13" and "Samsung Galaxy" is output.

[0571] Step 5:

[0572] The server notifies the user of the collected product information. The device displays or voices the product information received from the server. The input is a list of product information, and the output is a notification to the user (for example, a screen display or voice announcement). For example, the user might be presented with a message saying, "We've found some recommended products: iPhone 13 (100,000 yen), Samsung Galaxy (80,000 yen)."

[0573] Step 6:

[0574] The user selects the desired product by voice or operation and begins the purchase process. The terminal receives the user's selection and sends the information for the purchase process to the server. The input is the user's selection data, and the output is a signal indicating the start of the purchase process.

[0575] Step 7:

[0576] The server processes the purchase, queries the user for any necessary information, verifies payment and shipping information, and guides the user accordingly. The input is the user's purchase information and data from the server's checkout process, and the output is a notification that the purchase is complete. Once the user provides the appropriate payment information, the purchase is complete.

[0577] Step 8:

[0578] After the purchase is completed, the server tracks the delivery status in real time and notifies the user. The terminal displays or voices the delivery status information it periodically receives from the server. The input is delivery status data, and the output is a notification to the user (e.g., the current delivery status).

[0579] This series of processing steps allows users to smoothly search for products, complete purchase procedures, and check delivery status through voice input.

[0580] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0581] MODE FOR CARRYING OUT THE INVENTION

[0582] The present invention is a system for efficient diagnosis, even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take the user's emotions into consideration. This system converts voice data from the user into text data, analyzes it, and generates appropriate diagnostic results. It also includes a series of processes for generating and transmitting referral letters and prescription data as needed. Furthermore, it is possible to recognize the user's emotions using an emotion engine and adjust the diagnostic results based on these. The program processing of this system is described in detail below.

[0583] User voice input

[0584] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0585] Speech recognition and text data conversion

[0586] The device uses a speech recognition engine to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then sent over the Internet to a server.

[0587] Emotion recognition by emotion engine

[0588] The server analyzes the voice data along with the text data and uses an emotion engine to recognize the user's emotions. For example, it identifies emotions such as "anxiety" or "tension" from the tone, speed, and intonation of the user's voice. This emotion data is also used for analysis.

[0589] Data analysis and generation of diagnostic results

[0590] The server analyzes the received text data and emotion data. First, it uses natural language processing technology to extract the meaning of keywords and sentences in the text data. Next, it compares this with a case database and generates an appropriate diagnosis. For example, based on the information on "headache" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0591] Notification of diagnosis results and measures

[0592] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message, with the tone adjusted based on the emotional data. For example, the device may provide the user with advice such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0593] Creation and transmission of referral letters and prescription data

[0594] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0595] Transmission of pre-examination data in large hospitals

[0596] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0597] Specific examples

[0598] Suppose a user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text data, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." Furthermore, the emotion engine recognizes the user's anxiety, and as a result, adjusts the notification message to be more reassuring. An additional message is sent, such as, "If you have any concerns, please contact us immediately."

[0599] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[0600] The processing flow will be explained below.

[0601] MODE FOR CARRYING OUT THE INVENTION

[0602] (Processing steps of a system that combines an emotion engine)

[0603] Step 1:

[0604] The user starts up a device such as a smartphone or tablet and opens the dedicated application. The application displays a prompt asking, "Do you want to start the medical interview?", and the user responds, "Yes."

[0605] Step 2:

[0606] The device records the user's voice using a microphone. The user says, "I've had a headache and a fever lately." The device then activates its speech recognition engine and converts the user's voice data into text data.

[0607] Step 3:

[0608] The device encrypts the converted text and voice data and sends it to the server using a secure communication protocol. The server then decrypts the received encrypted data and passes it to the analysis module and emotion engine.

[0609] Step 4:

[0610] The server analyzes the text data and uses natural language processing technology to extract the meaning of keywords and sentences in the text data, obtaining symptom information such as "headache" or "fever." This information is then compared with a case database.

[0611] Step 5:

[0612] The server's emotion engine analyzes the voice data and recognizes the user's emotions, identifying emotions such as "anxiety" or "tension" based on the tone, speed, and intonation of the user's voice.

[0613] Step 6:

[0614] The server then applies a diagnostic algorithm based on the matching results, for example, determining whether the combination of "headache" and "fever" is consistent with a mild cold. Emotional data is also factored in to improve the reliability of the diagnosis.

[0615] Step 7:

[0616] The server generates diagnostic results and sends countermeasure information to the device in a data format such as JSON. The device analyzes the received data, adjusts the tone of the message based on the emotion recognition results, and notifies the user. For example, a countermeasure message such as "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller" may be displayed.

[0617] Step 8:

[0618] The server generates referral and prescription data based on the diagnosis. For example, if the patient's symptoms do not improve, a referral will be generated that includes a request to "see a doctor at a designated clinic."

[0619] Step 9:

[0620] The server electronically transmits the generated referral letter and prescription data to the designated medical institution or pharmacy. For example, the referral letter is transmitted via email or a dedicated medical data communication protocol.

[0621] Step 10:

[0622] The terminal receives a notification of completion of transmission from the server and displays a message to the user saying, "The referral letter has been sent to the clinic." This lets the user know the next step (actual medical treatment).

[0623] Specific examples

[0624] A user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text and voice data, diagnoses it as a mild cold, and sends a notification to the user stating, "Please take over-the-counter cold medicine," adjusted based on the emotion data. The emotion engine then recognizes the user's anxiety and sends an additional message such as, "If you have any concerns, please contact us immediately."

[0625] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[0626] Example 2

[0627] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0628] There is a demand for efficient diagnosis even in remote areas or areas with limited access to medical care. There is also a need to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take user emotions into account. However, conventional systems are limited to simple analysis of voice data, making it difficult to provide diagnostic results that take user emotions into account or improve medical treatment efficiency by sending pre-examination data in advance. The objective of this project is to solve these problems.

[0629] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0630] In this invention, the server includes means for acquiring voice data from a user, means for converting the voice data into text data, means for recognizing emotions from the voice data and text data, means for analyzing the text data, means for generating a diagnosis result based on the analysis result and the emotion recognition result, means for notifying the diagnosis result in a tone corresponding to the user's emotion, means for generating a referral letter or prescription data based on the diagnosis result, and means for transmitting the referral letter or prescription data to a specified destination. This enables prompt and appropriate medical care even in remote islands and depopulated areas, reduces the psychological burden on users, and improves the efficiency of medical treatment in large hospitals.

[0631] A "user" is an individual who utilizes the system to provide voice input and receive diagnostic results.

[0632] "Voice data" refers to voice information input by a user through a terminal.

[0633] A "terminal" is a device that allows voice input, such as a smartphone, tablet, or PC.

[0634] The "server" is a central computer system that analyzes data sent from the terminals and generates and transmits diagnostic results.

[0635] "Text data" refers to text information converted from voice data using voice recognition technology.

[0636] "Voice recognition technology" refers to the technology that analyzes voice data and converts it into text data.

[0637] "Emotion recognition" refers to technology that identifies a user's emotions from voice data and text data.

[0638] "Data analysis algorithm" is a technology that generates diagnostic results based on text data and emotion recognition results.

[0639] "Diagnosis results" refers to medical judgments and instructions provided to the user based on the results of the analysis by the server.

[0640] "Notification by tone" refers to a method of notifying the diagnosis result using a tone or expression that matches the user's emotions.

[0641] A "letter of referral" is a document intended to introduce a patient to a medical institution designated by the user.

[0642] "Prescription data" refers to the digital information required to receive medication at a pharmacy designated by the user.

[0643] MODE FOR CARRYING OUT THE INVENTION

[0644] This invention is a system for efficient diagnosis, even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take the user's emotions into consideration. This system involves a series of processes: the user inputs voice data, converts the voice data into text data, analyzes it, generates appropriate diagnostic results, and generates and transmits referral letters and prescription data as needed. Furthermore, it is possible to recognize the user's emotions using emotion recognition technology and adjust diagnostic results based on this.

[0645] Hardware and Software

[0646] 1. Device:

[0647] Use a device such as a smartphone, tablet, or computer. These devices must have a microphone that can accept voice input.

[0648] 2. Speech Recognition Engine:

[0649] To convert the "voice data" into "text data," "voice recognition technology" is used. As a specific example, a "cloud-based voice recognition service" is used.

[0650] 3. Emotion Recognition Engine:

[0651] To read emotions from the user's voice and text data, we use "emotion recognition technology." As a specific example, we use "cloud-based emotion analysis service."

[0652] 4. Data analysis algorithm:

[0653] "Data analysis algorithms" are used to analyze "text data" and "emotion data" and generate appropriate diagnostic results. Specific examples of this include "natural language processing technology."

[0654] 5. Notification Engine:

[0655] To notify the user of the diagnosis results, a "notification engine" is used. As a specific example, text-to-speech technology is used.

[0656] 6. Electronic Document Generation Systems:

[0657] An "electronic document generation system" is used to generate referral letters and prescription data and send them to designated destinations.

[0658] Specific operation example

[0659] The user operates their smartphone, launches a dedicated application, and voice-records their symptoms. For example, they might say, "I've had a headache recently, and I also have a fever." The device then converts this voice into text data using a cloud-based voice recognition service and sends it to a server.

[0660] The server uses a cloud-based emotion analysis service to recognize the user's emotions based on the received text and voice data. For example, emotions such as "anxiety" or "tension" can be identified from the tone, speed, and intonation of the user's voice.

[0661] The server then uses natural language processing technology to analyze the text data and compares it with a case database based on the meaning of the keywords and sentences obtained. This generates an appropriate diagnosis. For example, based on the information on "headache" and "fever," it determines whether the condition is urgent and, if necessary, directs emergency treatment to a large hospital.

[0662] The diagnosis results are notified to the user using voice synthesis technology. The tone of the notification is adjusted based on the emotion recognition results. For example, the system provides a solution such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller." In addition, if the symptoms are severe, an emergency notification will be sent stating, "Go to the nearest hospital immediately."

[0663] Finally, the server uses the "electronic document generation system" to generate a referral letter and prescription data based on the diagnosis results, and sends them to the designated hospital or pharmacy. For example, if the patient suffers from persistent migraines, a notification will be sent to the user stating, "Migraine medication has been prescribed. Please pick it up at the nearest pharmacy."

[0664] Prompt Sentence Examples

[0665] Let's say a user living on a remote island uses this system because they are experiencing symptoms of a headache and fever. The user launches the application and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text data, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." The emotion engine also recognizes the user's anxiety, and as a result, adjusts the notification message to be more reassuring. An additional message is sent, such as, "If you have any concerns, please contact us immediately."

[0666] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[0667] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0668] Step 1: User Speech Input

[0669] The user launches a dedicated application on a device such as a smartphone or tablet and inputs their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." The device then picks up the voice data via its microphone.

[0670] Input: Verbal symptom report from user

[0671] Output: Captured audio data

[0672] Step 2: Speech recognition and text conversion

[0673] The device sends the captured voice data to cloud-based speech recognition technology (e.g., Google Cloud Speech-to-Text) and converts it into text data, which includes the user's spoken symptoms and other relevant information. The device then sends the text data to a server.

[0674] Input: Captured audio data

[0675] Output: Converted text data

[0676] Step 3: Emotion recognition by the emotion engine

[0677] The server analyzes the voice data as well as the text data and uses emotion recognition technology (e.g., IBM Watson Tone Analyzer) to recognize the user's emotions. For example, it identifies emotions such as "anxiety" or "tension" from the tone, speed, and intonation of the user's voice.

[0678] Input: Audio and text data

[0679] Output: Recognized emotion data

[0680] Step 4: Data analysis and generation of diagnostic results

[0681] The server analyzes the received text data and emotion data using natural language processing technology (e.g., Amazon Comprehend). First, it extracts the meaning of keywords and sentences in the text data and compares them with a case database. As a result of the analysis, it generates an appropriate diagnosis.

[0682] Input: Text data and emotion data

[0683] Output: Diagnostic result data

[0684] Step 5: Notification of diagnosis results and measures

[0685] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message using text-to-speech technology, with the tone adjusted based on the emotional data. For example, the device provides the user with advice such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller."

[0686] Input: Diagnostic result data

[0687] Output: Notification message sent to the user

[0688] Step 6: Generate and send referral and prescription data

[0689] The server generates a referral letter or prescription data using an electronic document generation system (e.g., DocuSign API) based on the diagnosis results. The referral letter is electronically sent to the designated hospital, and the prescription data is sent to the designated pharmacy.

[0690] Input: Diagnostic result data

[0691] Output: Referral data or prescription data

[0692] Step 7: Sending pre-examination data in large hospitals

[0693] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0694] Input: User interview data

[0695] Output: Pre-examination data and its analysis results

[0696] (Application example 2)

[0697] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0698] The present invention relates to a system for conducting prompt and appropriate health examinations even in areas with limited or remote medical access, and for implementing emergency response using autonomous vehicles as needed. The system also aims to reduce the psychological burden of users by providing diagnostic results that take their emotions into consideration. In particular, there is a need for improved safety and efficiency by including a function to instruct an autonomous vehicle to quickly travel to an appropriate medical institution when emergency medical care is required.

[0699] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0700] In this invention, the server includes means for recognizing emotions from voice data and text data from a user and adjusting a diagnosis result according to the emotion, means for issuing instructions to a control system of an autonomous vehicle based on the diagnosis result, and means for converting the voice data into text data, thereby providing an appropriate diagnosis result according to the emotion, and further enabling rapid transport to a medical institution using the autonomous vehicle in an emergency.

[0701] "Voice data" refers to the content of a user's speech, and is information acquired as a voice signal.

[0702] "Text data" refers to character information that has been converted by analyzing voice data, and is data that is the subject of natural language processing.

[0703] "Analysis Results" refers to diagnostic information and recommended actions generated based on the text data and other related data.

[0704] "Diagnosis results" refer to conclusions and recommended measures regarding the user's health condition based on the analysis results.

[0705] A "letter of referral" is a medical referral document issued to another medical institution based on diagnostic results.

[0706] "Prescription data" is prescription information for medicines generated based on diagnostic results, and is digital data sent to a pharmacy.

[0707] "Emotion recognition" is a technology that analyzes voice data and text data to identify a user's psychological state.

[0708] An "autonomous vehicle" is a vehicle that drives autonomously using a drive control system and has the ability to head to a destination based on instructions from passengers.

[0709] A "control system" is a general term for programs and hardware that manage the operation of machines and devices and operate them according to specific instructions.

[0710] A "server" is a computer system used to analyze audio and text data and generate and provide diagnostic results.

[0711] "Speech recognition technology" is a technology that converts voice data into text data, and obtains the content of a user's speech as text information.

[0712] "Data analysis algorithms" refer to mathematical methods and processes that use machine learning and statistical analysis to derive diagnostic results based on collected data.

[0713] The system of the present invention is designed to diagnose the health status of passengers in an autonomous vehicle in real time and provide necessary measures. Specific embodiments for implementing this system will be described below.

[0714] System Overview

[0715] The system mainly consists of the following components:

[0716] 1. A device that acquires the user's voice data

[0717] 2. Server that analyzes voice data

[0718] 3. Control Systems for Autonomous Vehicles

[0719] Acquiring and converting audio data

[0720] When a user talks about their health condition in an autonomous vehicle, the vehicle's built-in microphone (hardware) captures the voice data. This data is converted into text data via the device's built-in voice recognition engine (software). For example, if a user says, "I have chest pain," the voice data is converted into text data using the Google Speech-to-Text API.

[0721] emotion recognition

[0722] The converted text and voice data is sent to a server and analyzed by an emotion engine (software). The analysis uses IBM Watson Tone Analyzer to identify emotions from the user's speech. For example, emotions such as "urgency" or "anxiety" can be recognized from the tone and intonation of the user's voice.

[0723] Data analysis and generation of diagnostic results

[0724] The server uses natural language processing technology to generate appropriate diagnostic results based on the analyzed text data and emotion data. This process uses data analysis algorithms such as Scikit-Learn and TensorFlow. For example, if the symptom "chest pain" is recognized, it will be diagnosed as a possible heart disease and determine that immediate medical attention is required.

[0725] Notification of diagnostic results and control of autonomous vehicles

[0726] The generated diagnostic results are communicated to the user via a display and speaker in the vehicle. Furthermore, in the event of an emergency, instructions are sent to the autonomous vehicle's control system to direct the vehicle to the nearest medical facility. This control is performed using an autonomous driving platform such as NVIDIA DRIVE.

[0727] emergency notification

[0728] If necessary, the server will use the Twilio API to send emergency notifications to designated medical institutions, including the user's symptoms and location information, to prompt medical institutions to take action.

[0729] Specific examples

[0730] For example, if a user experiences chest tightness while in a self-driving vehicle, the above process would be executed as follows:

[0731] 1. User: Says, "I have chest pains."

[0732] 2. Device: Convert the audio into text data using the Google Speech-to-Text API.

[0733] 3. Server: IBM Watson Tone Analyzer recognizes the emotion as "urgent."

[0734] 4. Server: Using TensorFlow, diagnoses the possibility of heart disease and determines that emergency treatment is necessary.

[0735] 5. Vehicle: Route to the nearest hospital.

[0736] 6. Server: Uses Twilio API to send emergency notifications to designated hospitals.

[0737] Prompt Sentence Examples

[0738] Here are some example prompts to input to a generative AI model:

[0739] A user says "I'm having chest pain" in their car. Convert the voice data to text, perform sentiment analysis, generate a diagnosis based on the symptoms, notify medical authorities if emergency response is required, and the autonomous vehicle will head to the nearest hospital. Please provide detailed instructions for the following tasks:

[0740] 1. Speech Recognition and Text Conversion

[0741] 2. Emotion recognition

[0742] 3. Data analysis and diagnostic results generation

[0743] 4. Notification of diagnostic results

[0744] 5. How to send emergency notifications

[0745] 6. Autonomous Vehicle Operation Interface

[0746] The above is an embodiment of the present invention. This system enables prompt and appropriate medical examinations and emergency response even in remote areas or places with limited medical access.

[0747] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0748] Step 1: Acquire audio data

[0749] The user speaks about their health condition in the autonomous vehicle. This speech is picked up by a microphone built into the vehicle. The input is raw audio data, and the output is a digital audio file.

[0750] Step 2: Converting audio data to text data

[0751] The device converts the captured audio data into text data using the Google Speech-to-Text API. Specifically, it sends the audio file to the API and receives the text data returned by the API. The input is audio data and the output is text data.

[0752] Step 3: Analyze the text and audio data

[0753] The server sends the received text data and original voice data to the IBM Watson Tone Analyzer for emotion analysis. The input is text data and voice data, and the output is emotion data. Specifically, the content of the text and the intonation and tone of the voice are analyzed to identify emotions such as "urgency" and "anxiety."

[0754] Step 4: Generate diagnostic results

[0755] The server uses TensorFlow to generate appropriate diagnosis results based on the text data and emotion data. The input is text data and emotion data, and the output is the diagnosis result. Specifically, it uses natural language processing technology to analyze the meaning of the text and compares it with existing cases in a database to make a diagnosis.

[0756] Step 5: Notification of diagnosis results

[0757] The server sends the generated diagnostic results to a display device or speaker in the vehicle and notifies the user. The input is the diagnostic results, and the output is a display message or a voice message. Specifically, a message based on the diagnostic results is displayed on the screen or read aloud.

[0758] Step 6: Controlling the autonomous vehicle

[0759] If necessary, the server issues instructions to the autonomous vehicle's control system based on the diagnosis results, which in turn sets up a route to the nearest medical facility. The input is the diagnosis results, and the output is route instructions for the autonomous vehicle. Specifically, the server uses the NVIDIA DRIVE platform to control the vehicle's navigation system.

[0760] Step 7: Send emergency notifications

[0761] The server uses the Twilio API to send emergency notifications to designated medical institutions. The input is the user's symptoms and location information, and the output is a notification message to the medical institution. Specifically, the message generated by the server is sent in real time to the medical institution, urging them to take emergency action.

[0762] These steps will allow for a prompt and appropriate response if a user experiences a health issue while in a self-driving vehicle.

[0763] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0764] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0765] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0766] [Third embodiment]

[0767] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0768] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0769] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0770] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0771] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0772] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0773] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0774] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0775] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0776] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0777] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0778] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0779] MODE FOR CARRYING OUT THE INVENTION

[0780] The present invention is a system for efficient diagnosis even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals. This system converts voice data from the user into text data, analyzes it, generates appropriate diagnostic results, and includes a series of processes for generating and transmitting referral letters and prescription data as needed. The program processing of this system is described in detail below.

[0781] User voice input

[0782] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0783] Speech recognition and text data conversion

[0784] The device uses a speech recognition engine to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then sent over the Internet to a server.

[0785] Data analysis and generation of diagnostic results

[0786] The server analyzes the received text data. First, it uses natural language processing technology to extract the meaning of keywords and sentences within the text data. Next, it compares this with a case database and generates an appropriate diagnosis. For example, based on the information "frequent headaches" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0787] Notification of diagnosis results and measures

[0788] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message. For example, the device may advise the user, "Your symptoms are likely a mild migraine. Please take over-the-counter painkillers." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0789] Creation and transmission of referral letters and prescription data

[0790] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0791] Transmission of pre-examination data in large hospitals

[0792] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0793] Specific examples

[0794] Suppose a user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the speech, converts it into text data, and sends it to the server. The server analyzes this, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." It also generates a referral letter saying, "If your symptoms do not improve after one week, please see a local clinic for an examination," and sends it to the designated clinic.

[0795] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources.In addition, even in large hospitals, the efficiency of medical treatment can be improved by providing pre-examination data in advance.

[0796] The processing flow will be explained below.

[0797] Step 1:

[0798] The user starts up a device such as a smartphone or tablet and opens the dedicated application. The application displays a prompt asking, "Do you want to start the medical interview?", and the user responds, "Yes."

[0799] Step 2:

[0800] The device records the user's voice using a microphone. The user says, "I've had a headache and a fever lately." The device then activates its speech recognition engine and converts the user's voice data into text data.

[0801] Step 3:

[0802] The device encrypts the converted text data and sends it to the server using a secure communication protocol. The server decrypts the received encrypted data and passes it to the analysis module.

[0803] Step 4:

[0804] The server analyzes the text data, extracts the meaning of keywords and sentences within the data using natural language processing technology, and obtains symptom information such as "headache" or "fever." This information is then compared with a case database.

[0805] Step 5:

[0806] The server then applies a diagnostic algorithm based on the match, for example determining whether the combination of "headache" and "fever" is consistent with a mild cold, and generates an appropriate diagnosis and, if necessary, formulates countermeasures.

[0807] Step 6:

[0808] The server generates diagnostic results and sends countermeasure information in a data format such as JSON to the terminal. The terminal analyzes the received data and displays a message to the user saying, "Take over-the-counter cold medicine."

[0809] Step 7:

[0810] The server generates referral and prescription data based on the diagnosis. For example, if the patient's symptoms do not improve, a referral will be generated that includes a request to "see a doctor at a designated clinic."

[0811] Step 8:

[0812] The server electronically transmits the generated referral letter and prescription data to the designated medical institution or pharmacy. For example, the referral letter is transmitted via email or a dedicated medical data communication protocol.

[0813] Step 9:

[0814] The terminal receives a notification of completion of transmission from the server and displays a message to the user saying, "The referral letter has been sent to the clinic." This lets the user know the next step (actual medical treatment).

[0815] Step 10:

[0816] When a user visits a clinic, the medical interview data they have entered in advance is sent to the doctor's terminal, allowing the doctor to conduct a quick and appropriate examination based on that data.

[0817] Example 1

[0818] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0819] This will solve the problem of difficulty in making prompt and appropriate diagnoses in remote areas and areas with limited access to medical care. There is also a need to improve the efficiency of medical treatment in large hospitals. This will aim to improve medical care in various regions.

[0820] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0821] In this invention, the server includes means for acquiring voice data from a user, means for using a voice recognition engine to convert the voice data into text data, and means for analyzing the text data using natural language processing technology, thereby enabling more efficient and appropriate medical care in remote islands, depopulated areas, and large hospitals.

[0822] "User" refers to an individual or patient who uses the system to provide voice input.

[0823] "Voice data" refers to voice information input by a user using a terminal.

[0824] "Text data" refers to the text information resulting from converting voice data using a voice recognition engine.

[0825] "Speech recognition engine" refers to software or algorithms for converting voice data into text data.

[0826] "Natural language processing technology" refers to technology for analyzing the meaning of keywords and sentences within text data.

[0827] A "case database" refers to a collection of information that stores data on medical conditions and cases.

[0828] "Diagnosis result" refers to an evaluation of the disease condition obtained by analyzing text data and comparing it with a case database.

[0829] "Notifying" refers to the act of communicating diagnostic results and related information to the user.

[0830] A "letter of referral" refers to a document created to refer a user to another medical institution.

[0831] "Prescription data" refers to digital information that specifies medications to be prescribed to a user.

[0832] "Information terminal" refers to a hardware device (e.g., smartphone, tablet) that acquires voice data and transmits it to a server.

[0833] "Server" refers to a computer system for analyzing text data, generating diagnostic results, and notifying users.

[0834] This invention is a system for efficient diagnosis in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals. This system converts voice data from the user into text data, analyzes it, and generates appropriate diagnostic results. It also includes a series of processes for generating and sending referral letters and prescription data as needed.

[0835] User voice input

[0836] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0837] Speech recognition and text data conversion

[0838] The device uses a speech recognition engine, such as Google Speech-to-Text, to convert the user's speech into text data, including the user's symptoms and other relevant information, which is then sent over the internet to a server.

[0839] Data analysis and generation of diagnostic results

[0840] The server analyzes the received text data. First, it uses natural language processing technology (e.g., OpenAI GPT-4) to extract the meaning of keywords and sentences in the text data. Next, it compares this with a case database (e.g., the ICD-10 database) and generates an appropriate diagnosis. For example, based on the information "frequent headaches" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0841] Notification of diagnosis results and measures

[0842] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message. For example, the device may advise the user, "Your symptoms are likely a mild migraine. Please take over-the-counter painkillers." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0843] Creation and transmission of referral letters and prescription data

[0844] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0845] Transmission of pre-examination data in large hospitals

[0846] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0847] Specific examples

[0848] For example, suppose a user living on a remote island uses this system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the speech, converts it into text data, and sends it to the server. The server analyzes this text data, diagnoses it as a mild cold, and notifies the user to "take over-the-counter cold medicine." It also generates a referral letter stating, "If your symptoms do not improve after one week, please see a local clinic for an examination," and sends it to the designated clinic.

[0849] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources.In addition, even in large hospitals, the efficiency of medical treatment can be improved by providing pre-examination data in advance.

[0850] Specific examples of prompts to input to generative AI models

[0851] "Please explain a situation where a user is using a smartphone app to complain about a headache and fever. Then, please explain in detail the process of using a speech recognition engine to convert the data into text and send it to the server."

[0852] Please explain in detail how the server uses natural language processing to generate a diagnosis result based on the text data converted by the speech recognition engine and notify the user.

[0853] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0854] Step 1: User Speech Input

[0855] 1. The user launches a dedicated application using an information terminal such as a smartphone or tablet.

[0856] 2. The user taps the microphone button in the app and speaks their symptoms. For example, they can say, "I've had a headache and a fever lately."

[0857] 3. The device's microphone collects the audio and captures the audio data, which is then input into the device's application.

[0858] Input: User's voice

[0859] Output: Collected audio data

[0860] Step 2: Speech recognition and text conversion

[0861] 1. The device calls a speech recognition engine (e.g., Google Speech-to-Text) and converts the voice data into text data.

[0862] 2. The speech recognition engine analyzes the voice data and generates text data such as, "I've had a headache and a fever lately."

[0863] 3. The generated text data is stored in the device's memory and then sent to a server via the Internet.

[0864] Input: Collected audio data

[0865] Output: Converted text data

[0866] Step 3: Data analysis and generation of diagnostic results

[0867] 1. The server analyzes the received text data. First, it uses natural language processing technology (e.g., OpenAI GPT-4) to extract the meaning of keywords and sentences in the text.

[0868] 2. The server compares the extracted keywords with a case database (e.g., ICD-10 database).

[0869] 3. The server generates an appropriate diagnosis based on the matching results. For example, the information "frequent headaches" and "fever" can be used to diagnose a "mild cold."

[0870] Input: Text data

[0871] Output: Diagnostic results

[0872] Step 4: Notification of diagnosis results and measures

[0873] 1. The server generates a message containing the analysis and diagnostic results.

[0874] 2. The server sends this message to the device over the Internet.

[0875] 3. The device notifies the user of the received message. The notification may be a voice message or a text message. For example, the device may display a message saying, "Your symptoms are likely a mild migraine. Please take an over-the-counter pain reliever."

[0876] Input: Diagnostic results

[0877] Output: A notification message to the user

[0878] Step 5: Generate and send referral and prescription data

[0879] 1. The server generates referral or prescription data based on the diagnosis.

[0880] 2. The referral letter is sent electronically to the designated hospital, and the prescription data is sent to the designated pharmacy.

[0881] 3. The user receives a notification and, if necessary, visits the hospital or pharmacy referred to them. For example, the user may be notified that "a specific medication for migraines has been prescribed. Please pick it up at the nearest pharmacy."

[0882] Input: Diagnostic results

[0883] Output: Referral letter or prescription data

[0884] Step 6: Submission of pre-examination data in large hospitals

[0885] 1. If a user is scheduled to receive medical treatment at a large hospital, they can input and submit medical interview data in advance.

[0886] 2. The server analyzes the received pre-examination data and sends it to the appropriate doctor.

[0887] 3. Doctors can understand the patient's symptoms and background information in advance, improving the efficiency of consultations. For example, the interview data may include information such as "the patient has a history of migraines in the past and is still suffering from them."

[0888] Input: Pre-examination data

[0889] Output: Information notification to doctor

[0890] (Application example 1)

[0891] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0892] In today's world, the number of users of online shopping sites is increasing, creating a demand for efficient and fast methods for searching for products and completing purchases. Users who have difficulty using traditional methods, such as the elderly and visually impaired, also need a more user-friendly interface. Furthermore, it is difficult to perform all steps of the process, from product search to purchase and delivery status confirmation, all in one system, especially in remote locations or during busy periods.

[0893] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0894] In this invention, the server includes means for acquiring voice data from a user, means for converting the voice data into text data, means for analyzing the text data, means for generating a diagnosis based on the analysis result, means for notifying the user of the diagnosis result, means for generating a referral letter or prescription data based on the diagnosis result, means for transmitting the referral letter or prescription data to a specified destination, means for searching for products based on voice input, means for notifying the user of information about the products, means for purchasing the products, and means for notifying the user of the delivery status of the products. This enables a user to search for and purchase products by voice and check the delivery status.

[0895] "Voice data" refers to data indicating voice information spoken by a user to a terminal.

[0896] "Text data" refers to character information converted from voice data using voice recognition technology.

[0897] "Analysis" is the process of extracting and understanding the meaning and intent of information based on text data.

[0898] A "diagnosis result" is an output that indicates the user's health condition and necessary countermeasures, generated based on the information obtained through analysis.

[0899] A "letter of referral" is an electronic document that refers a user to a specific medical institution based on the diagnosis results.

[0900] "Prescription data" is electronic data generated to prescribe specific medications based on diagnostic results.

[0901] "Product search" is the process of searching a database for the product a user is looking for using voice input.

[0902] "Product information" is detailed information about products found by a search, including data such as price, availability, and description.

[0903] The "purchase procedure" is a process in which the user carries out various procedures necessary to purchase the product selected by the user.

[0904] "Delivery status" is information indicating the current delivery status of the purchased item.

[0905] "Notification" refers to the general process of providing information to a user through audio or text.

[0906] This invention provides a system that allows users to search for products, complete purchase procedures, and check delivery status by voice. This system automates the process from voice acquisition to analysis and notification, providing users with a smooth online shopping experience.

[0907] System configuration

[0908] The system uses the following hardware and software:

[0909] Hardware used

[0910] 1. Smartphones and tablets: These are devices that allow users to input information using their voice.

[0911] 2. Server: A computing resource that analyzes voice data and generates and transmits the necessary information.

[0912] Software used

[0913] 1. Google Cloud Speech-to-Text API: A speech recognition technology for converting voice data into text data.

[0914] 2. Google Cloud Natural Language API: A natural language processing technology that analyzes text data to extract and understand product information.

[0915] 3. Flask: It is a web application framework, used to process requests from users and return the required responses.

[0916] Examples of data processing and data calculation

[0917] 1. Acquisition of voice data: The user launches a dedicated app on a device such as a smartphone and describes the product they want by voice. For example, they might say, "I've been looking for a smartphone recently."

[0918] 2. Speech recognition: The captured voice data is converted into text data using the Google Cloud Speech-to-Text API.

[0919] 3. Text data analysis: The text data is analyzed using the Google Cloud Natural Language API to extract keywords related to the product the user is looking for.

[0920] 4. Product information generation: Based on the extracted keywords, the system generates appropriate product information and notifies the user.

[0921] 5. Purchase procedure and delivery status notification: If the user decides to purchase, the system will process the purchase and notify the delivery status after the purchase is completed.

[0922] Specific examples

[0923] 1. Usage scenarios:

[0924] The user launches the app and says, "I've been looking for my smartphone lately."

[0925] The server converts the voice data into text data and analyzes it.

[0926] Based on the results of analyzing the text data, the system notifies the user of smartphone candidates.

[0927] The user selects from the candidates and completes the purchase procedure.

[0928] After purchase, users will be notified of delivery status in real time.

[0929] 2. Example prompt:

[0930] User: "I've been looking for a smartphone lately."

[0931] App: "We found some great deals, including the iPhone 13 (¥100,000) and the Samsung Galaxy (¥80,000)."

[0932] As described above, this system allows users to seamlessly search for products and complete purchase procedures through voice input, providing a highly convenient mail-order system that is particularly applicable to the elderly and visually impaired.

[0933] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0934] Step 1:

[0935] The user launches a dedicated mail-order assistant app on their smartphone. They then speak into the app, saying, "I've been looking for my smartphone lately." This input is captured through the device's microphone. The input data is then converted into voice data, which is then sent to the next stage of processing.

[0936] Step 2:

[0937] The device sends the acquired voice data to the Google Cloud Speech-to-Text API. The server receives this voice data and converts it into text data using speech recognition technology. At this stage, the input is voice data, and the output is the converted text data. For example, the generated text data is "I've been looking for my smartphone recently."

[0938] Step 3:

[0939] The server uses the Google Cloud Natural Language API to analyze the text data. The server performs data analysis to extract important keywords from the text data. The input is the converted text data, and the output is a list of extracted keywords. For example, the keyword "smartphone" is extracted.

[0940] Step 4:

[0941] The server searches its internal database based on the extracted keywords to collect relevant product information. This search process searches all product information in the database for information that matches the keywords. The input is the extracted keywords, and the output is a list of relevant products. For example, a list containing information such as "iPhone 13" and "Samsung Galaxy" is output.

[0942] Step 5:

[0943] The server notifies the user of the collected product information. The device displays or voices the product information received from the server. The input is a list of product information, and the output is a notification to the user (for example, a screen display or voice announcement). For example, the user might be presented with a message saying, "We've found some recommended products: iPhone 13 (100,000 yen), Samsung Galaxy (80,000 yen)."

[0944] Step 6:

[0945] The user selects the desired product by voice or operation and begins the purchase process. The terminal receives the user's selection and sends the information for the purchase process to the server. The input is the user's selection data, and the output is a signal indicating the start of the purchase process.

[0946] Step 7:

[0947] The server processes the purchase, queries the user for any necessary information, verifies payment and shipping information, and guides the user accordingly. The input is the user's purchase information and data from the server's checkout process, and the output is a notification that the purchase is complete. Once the user provides the appropriate payment information, the purchase is complete.

[0948] Step 8:

[0949] After the purchase is completed, the server tracks the delivery status in real time and notifies the user. The terminal displays or voices the delivery status information it periodically receives from the server. The input is delivery status data, and the output is a notification to the user (e.g., the current delivery status).

[0950] This series of processing steps allows users to smoothly search for products, complete purchase procedures, and check delivery status through voice input.

[0951] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0952] MODE FOR CARRYING OUT THE INVENTION

[0953] The present invention is a system for efficient diagnosis, even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take the user's emotions into consideration. This system converts voice data from the user into text data, analyzes it, and generates appropriate diagnostic results. It also includes a series of processes for generating and transmitting referral letters and prescription data as needed. Furthermore, it is possible to recognize the user's emotions using an emotion engine and adjust the diagnostic results based on these. The program processing of this system is described in detail below.

[0954] User voice input

[0955] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[0956] Speech recognition and text data conversion

[0957] The device uses a speech recognition engine to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then sent over the Internet to a server.

[0958] Emotion recognition by emotion engine

[0959] The server analyzes the voice data along with the text data and uses an emotion engine to recognize the user's emotions. For example, it identifies emotions such as "anxiety" or "tension" from the tone, speed, and intonation of the user's voice. This emotion data is also used for analysis.

[0960] Data analysis and generation of diagnostic results

[0961] The server analyzes the received text data and emotion data. First, it uses natural language processing technology to extract the meaning of keywords and sentences in the text data. Next, it compares this with a case database and generates an appropriate diagnosis. For example, based on the information on "headache" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[0962] Notification of diagnosis results and measures

[0963] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message, with the tone adjusted based on the emotional data. For example, the device may provide the user with advice such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[0964] Creation and transmission of referral letters and prescription data

[0965] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[0966] Transmission of pre-examination data in large hospitals

[0967] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[0968] Specific examples

[0969] Suppose a user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text data, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." Furthermore, the emotion engine recognizes the user's anxiety, and as a result, adjusts the notification message to be more reassuring. An additional message is sent, such as, "If you have any concerns, please contact us immediately."

[0970] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[0971] The processing flow will be explained below.

[0972] MODE FOR CARRYING OUT THE INVENTION

[0973] (Processing steps of a system that combines an emotion engine)

[0974] Step 1:

[0975] The user starts up a device such as a smartphone or tablet and opens the dedicated application. The application displays a prompt asking, "Do you want to start the medical interview?", and the user responds, "Yes."

[0976] Step 2:

[0977] The device records the user's voice using a microphone. The user says, "I've had a headache and a fever lately." The device then activates its speech recognition engine and converts the user's voice data into text data.

[0978] Step 3:

[0979] The device encrypts the converted text and voice data and sends it to the server using a secure communication protocol. The server then decrypts the received encrypted data and passes it to the analysis module and emotion engine.

[0980] Step 4:

[0981] The server analyzes the text data and uses natural language processing technology to extract the meaning of keywords and sentences in the text data, obtaining symptom information such as "headache" or "fever." This information is then compared with a case database.

[0982] Step 5:

[0983] The server's emotion engine analyzes the voice data and recognizes the user's emotions, identifying emotions such as "anxiety" or "tension" based on the tone, speed, and intonation of the user's voice.

[0984] Step 6:

[0985] The server then applies a diagnostic algorithm based on the matching results, for example, determining whether the combination of "headache" and "fever" is consistent with a mild cold. Emotional data is also factored in to improve the reliability of the diagnosis.

[0986] Step 7:

[0987] The server generates diagnostic results and sends countermeasure information to the device in a data format such as JSON. The device analyzes the received data, adjusts the tone of the message based on the emotion recognition results, and notifies the user. For example, a countermeasure message such as "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller" may be displayed.

[0988] Step 8:

[0989] The server generates referral and prescription data based on the diagnosis. For example, if the patient's symptoms do not improve, a referral will be generated that includes a request to "see a doctor at a designated clinic."

[0990] Step 9:

[0991] The server electronically transmits the generated referral letter and prescription data to the designated medical institution or pharmacy. For example, the referral letter is transmitted via email or a dedicated medical data communication protocol.

[0992] Step 10:

[0993] The terminal receives a notification of completion of transmission from the server and displays a message to the user saying, "The referral letter has been sent to the clinic." This lets the user know the next step (actual medical treatment).

[0994] Specific examples

[0995] A user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text and voice data, diagnoses it as a mild cold, and sends a notification to the user stating, "Please take over-the-counter cold medicine," adjusted based on the emotion data. The emotion engine then recognizes the user's anxiety and sends an additional message such as, "If you have any concerns, please contact us immediately."

[0996] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[0997] Example 2

[0998] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0999] There is a demand for efficient diagnosis even in remote areas or areas with limited access to medical care. There is also a need to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take user emotions into account. However, conventional systems are limited to simple analysis of voice data, making it difficult to provide diagnostic results that take user emotions into account or improve medical treatment efficiency by sending pre-examination data in advance. The objective of this project is to solve these problems.

[1000] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1001] In this invention, the server includes means for acquiring voice data from a user, means for converting the voice data into text data, means for recognizing emotions from the voice data and text data, means for analyzing the text data, means for generating a diagnosis result based on the analysis result and the emotion recognition result, means for notifying the diagnosis result in a tone corresponding to the user's emotion, means for generating a referral letter or prescription data based on the diagnosis result, and means for transmitting the referral letter or prescription data to a specified destination. This enables prompt and appropriate medical care even in remote islands and depopulated areas, reduces the psychological burden on users, and improves the efficiency of medical treatment in large hospitals.

[1002] A "user" is an individual who utilizes the system to provide voice input and receive diagnostic results.

[1003] "Voice data" refers to voice information input by a user through a terminal.

[1004] A "terminal" is a device that allows voice input, such as a smartphone, tablet, or PC.

[1005] The "server" is a central computer system that analyzes data sent from the terminals and generates and transmits diagnostic results.

[1006] "Text data" refers to text information converted from voice data using voice recognition technology.

[1007] "Voice recognition technology" refers to the technology that analyzes voice data and converts it into text data.

[1008] "Emotion recognition" refers to technology that identifies a user's emotions from voice data and text data.

[1009] "Data analysis algorithm" is a technology that generates diagnostic results based on text data and emotion recognition results.

[1010] "Diagnosis results" refers to medical judgments and instructions provided to the user based on the results of the analysis by the server.

[1011] "Notification by tone" refers to a method of notifying the diagnosis result using a tone or expression that matches the user's emotions.

[1012] A "letter of referral" is a document intended to introduce a patient to a medical institution designated by the user.

[1013] "Prescription data" refers to the digital information required to receive medication at a pharmacy designated by the user.

[1014] MODE FOR CARRYING OUT THE INVENTION

[1015] This invention is a system for efficient diagnosis, even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take the user's emotions into consideration. This system involves a series of processes: the user inputs voice data, converts the voice data into text data, analyzes it, generates appropriate diagnostic results, and generates and transmits referral letters and prescription data as needed. Furthermore, it is possible to recognize the user's emotions using emotion recognition technology and adjust diagnostic results based on this.

[1016] Hardware and Software

[1017] 1. Device:

[1018] Use a device such as a smartphone, tablet, or computer. These devices must have a microphone that can accept voice input.

[1019] 2. Speech Recognition Engine:

[1020] To convert the "voice data" into "text data," "voice recognition technology" is used. As a specific example, a "cloud-based voice recognition service" is used.

[1021] 3. Emotion Recognition Engine:

[1022] To read emotions from the user's voice and text data, we use "emotion recognition technology." As a specific example, we use "cloud-based emotion analysis service."

[1023] 4. Data analysis algorithm:

[1024] "Data analysis algorithms" are used to analyze "text data" and "emotion data" and generate appropriate diagnostic results. Specific examples of this include "natural language processing technology."

[1025] 5. Notification Engine:

[1026] To notify the user of the diagnosis results, a "notification engine" is used. As a specific example, text-to-speech technology is used.

[1027] 6. Electronic Document Generation Systems:

[1028] An "electronic document generation system" is used to generate referral letters and prescription data and send them to designated destinations.

[1029] Specific operation example

[1030] The user operates their smartphone, launches a dedicated application, and voice-records their symptoms. For example, they might say, "I've had a headache recently, and I also have a fever." The device then converts this voice into text data using a cloud-based voice recognition service and sends it to a server.

[1031] The server uses a cloud-based emotion analysis service to recognize the user's emotions based on the received text and voice data. For example, emotions such as "anxiety" or "tension" can be identified from the tone, speed, and intonation of the user's voice.

[1032] The server then uses natural language processing technology to analyze the text data and compares it with a case database based on the meaning of the keywords and sentences obtained. This generates an appropriate diagnosis. For example, based on the information on "headache" and "fever," it determines whether the condition is urgent and, if necessary, directs emergency treatment to a large hospital.

[1033] The diagnosis results are notified to the user using voice synthesis technology. The tone of the notification is adjusted based on the emotion recognition results. For example, the system provides a solution such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller." In addition, if the symptoms are severe, an emergency notification will be sent stating, "Go to the nearest hospital immediately."

[1034] Finally, the server uses the "electronic document generation system" to generate a referral letter and prescription data based on the diagnosis results, and sends them to the designated hospital or pharmacy. For example, if the patient suffers from persistent migraines, a notification will be sent to the user stating, "Migraine medication has been prescribed. Please pick it up at the nearest pharmacy."

[1035] Prompt Sentence Examples

[1036] Let's say a user living on a remote island uses this system because they are experiencing symptoms of a headache and fever. The user launches the application and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text data, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." The emotion engine also recognizes the user's anxiety, and as a result, adjusts the notification message to be more reassuring. An additional message is sent, such as, "If you have any concerns, please contact us immediately."

[1037] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[1038] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1039] Step 1: User Speech Input

[1040] The user launches a dedicated application on a device such as a smartphone or tablet and inputs their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." The device then picks up the voice data via its microphone.

[1041] Input: Verbal symptom report from user

[1042] Output: Captured audio data

[1043] Step 2: Speech recognition and text conversion

[1044] The device sends the captured voice data to cloud-based speech recognition technology (e.g., Google Cloud Speech-to-Text) and converts it into text data, which includes the user's spoken symptoms and other relevant information. The device then sends the text data to a server.

[1045] Input: Captured audio data

[1046] Output: Converted text data

[1047] Step 3: Emotion recognition by the emotion engine

[1048] The server analyzes the voice data as well as the text data and uses emotion recognition technology (e.g., IBM Watson Tone Analyzer) to recognize the user's emotions. For example, it identifies emotions such as "anxiety" or "tension" from the tone, speed, and intonation of the user's voice.

[1049] Input: Audio and text data

[1050] Output: Recognized emotion data

[1051] Step 4: Data analysis and generation of diagnostic results

[1052] The server analyzes the received text data and emotion data using natural language processing technology (e.g., Amazon Comprehend). First, it extracts the meaning of keywords and sentences in the text data and compares them with a case database. As a result of the analysis, it generates an appropriate diagnosis.

[1053] Input: Text data and emotion data

[1054] Output: Diagnostic result data

[1055] Step 5: Notification of diagnosis results and measures

[1056] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message using text-to-speech technology, with the tone adjusted based on the emotional data. For example, the device provides the user with advice such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller."

[1057] Input: Diagnostic result data

[1058] Output: Notification message sent to the user

[1059] Step 6: Generate and send referral and prescription data

[1060] The server generates a referral letter or prescription data using an electronic document generation system (e.g., DocuSign API) based on the diagnosis results. The referral letter is electronically sent to the designated hospital, and the prescription data is sent to the designated pharmacy.

[1061] Input: Diagnostic result data

[1062] Output: Referral data or prescription data

[1063] Step 7: Sending pre-examination data in large hospitals

[1064] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[1065] Input: User interview data

[1066] Output: Pre-examination data and its analysis results

[1067] (Application example 2)

[1068] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1069] The present invention relates to a system for conducting prompt and appropriate health examinations even in areas with limited or remote medical access, and for implementing emergency response using autonomous vehicles as needed. The system also aims to reduce the psychological burden of users by providing diagnostic results that take their emotions into consideration. In particular, there is a need for improved safety and efficiency by including a function to instruct an autonomous vehicle to quickly travel to an appropriate medical institution when emergency medical care is required.

[1070] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1071] In this invention, the server includes means for recognizing emotions from voice data and text data from a user and adjusting a diagnosis result according to the emotion, means for issuing instructions to a control system of an autonomous vehicle based on the diagnosis result, and means for converting the voice data into text data, thereby providing an appropriate diagnosis result according to the emotion, and further enabling rapid transport to a medical institution using the autonomous vehicle in an emergency.

[1072] "Voice data" refers to the content of a user's speech, and is information acquired as a voice signal.

[1073] "Text data" refers to character information that has been converted by analyzing voice data, and is data that is the subject of natural language processing.

[1074] "Analysis Results" refers to diagnostic information and recommended actions generated based on the text data and other related data.

[1075] "Diagnosis results" refer to conclusions and recommended measures regarding the user's health condition based on the analysis results.

[1076] A "letter of referral" is a medical referral document issued to another medical institution based on diagnostic results.

[1077] "Prescription data" is prescription information for medicines generated based on diagnostic results, and is digital data sent to a pharmacy.

[1078] "Emotion recognition" is a technology that analyzes voice data and text data to identify a user's psychological state.

[1079] An "autonomous vehicle" is a vehicle that drives autonomously using a drive control system and has the ability to head to a destination based on instructions from passengers.

[1080] A "control system" is a general term for programs and hardware that manage the operation of machines and devices and operate them according to specific instructions.

[1081] A "server" is a computer system used to analyze audio and text data and generate and provide diagnostic results.

[1082] "Speech recognition technology" is a technology that converts voice data into text data, and obtains the content of a user's speech as text information.

[1083] "Data analysis algorithms" refer to mathematical methods and processes that use machine learning and statistical analysis to derive diagnostic results based on collected data.

[1084] The system of the present invention is designed to diagnose the health status of passengers in an autonomous vehicle in real time and provide necessary measures. Specific embodiments for implementing this system will be described below.

[1085] System Overview

[1086] The system mainly consists of the following components:

[1087] 1. A device that acquires the user's voice data

[1088] 2. Server that analyzes voice data

[1089] 3. Control Systems for Autonomous Vehicles

[1090] Acquiring and converting audio data

[1091] When a user talks about their health condition in an autonomous vehicle, the vehicle's built-in microphone (hardware) captures the voice data. This data is converted into text data via the device's built-in voice recognition engine (software). For example, if a user says, "I have chest pain," the voice data is converted into text data using the Google Speech-to-Text API.

[1092] emotion recognition

[1093] The converted text and voice data is sent to a server and analyzed by an emotion engine (software). The analysis uses IBM Watson Tone Analyzer to identify emotions from the user's speech. For example, emotions such as "urgency" or "anxiety" can be recognized from the tone and intonation of the user's voice.

[1094] Data analysis and generation of diagnostic results

[1095] The server uses natural language processing technology to generate appropriate diagnostic results based on the analyzed text data and emotion data. This process uses data analysis algorithms such as Scikit-Learn and TensorFlow. For example, if the symptom "chest pain" is recognized, it will be diagnosed as a possible heart disease and determine that immediate medical attention is required.

[1096] Notification of diagnostic results and control of autonomous vehicles

[1097] The generated diagnostic results are communicated to the user via a display and speaker in the vehicle. Furthermore, in the event of an emergency, instructions are sent to the autonomous vehicle's control system to direct the vehicle to the nearest medical facility. This control is performed using an autonomous driving platform such as NVIDIA DRIVE.

[1098] emergency notification

[1099] If necessary, the server will use the Twilio API to send emergency notifications to designated medical institutions, including the user's symptoms and location information, to prompt medical institutions to take action.

[1100] Specific examples

[1101] For example, if a user experiences chest tightness while in a self-driving vehicle, the above process would be executed as follows:

[1102] 1. User: Says, "I have chest pains."

[1103] 2. Device: Convert the audio into text data using the Google Speech-to-Text API.

[1104] 3. Server: IBM Watson Tone Analyzer recognizes the emotion as "urgent."

[1105] 4. Server: Using TensorFlow, diagnoses the possibility of heart disease and determines that emergency treatment is necessary.

[1106] 5. Vehicle: Route to the nearest hospital.

[1107] 6. Server: Uses Twilio API to send emergency notifications to designated hospitals.

[1108] Prompt Sentence Examples

[1109] Here are some example prompts to input to a generative AI model:

[1110] A user says "I'm having chest pain" in their car. Convert the voice data to text, perform sentiment analysis, generate a diagnosis based on the symptoms, notify medical authorities if emergency response is required, and the autonomous vehicle will head to the nearest hospital. Please provide detailed instructions for the following tasks:

[1111] 1. Speech Recognition and Text Conversion

[1112] 2. Emotion recognition

[1113] 3. Data analysis and diagnostic results generation

[1114] 4. Notification of diagnostic results

[1115] 5. How to send emergency notifications

[1116] 6. Autonomous Vehicle Operation Interface

[1117] The above is an embodiment of the present invention. This system enables prompt and appropriate medical examinations and emergency response even in remote areas or places with limited medical access.

[1118] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1119] Step 1: Acquire audio data

[1120] The user speaks about their health condition in the autonomous vehicle. This speech is picked up by a microphone built into the vehicle. The input is raw audio data, and the output is a digital audio file.

[1121] Step 2: Converting audio data to text data

[1122] The device converts the captured audio data into text data using the Google Speech-to-Text API. Specifically, it sends the audio file to the API and receives the text data returned by the API. The input is audio data and the output is text data.

[1123] Step 3: Analyze the text and audio data

[1124] The server sends the received text data and original voice data to the IBM Watson Tone Analyzer for emotion analysis. The input is text data and voice data, and the output is emotion data. Specifically, the content of the text and the intonation and tone of the voice are analyzed to identify emotions such as "urgency" and "anxiety."

[1125] Step 4: Generate diagnostic results

[1126] The server uses TensorFlow to generate appropriate diagnosis results based on the text data and emotion data. The input is text data and emotion data, and the output is the diagnosis result. Specifically, it uses natural language processing technology to analyze the meaning of the text and compares it with existing cases in a database to make a diagnosis.

[1127] Step 5: Notification of diagnosis results

[1128] The server sends the generated diagnostic results to a display device or speaker in the vehicle and notifies the user. The input is the diagnostic results, and the output is a display message or a voice message. Specifically, a message based on the diagnostic results is displayed on the screen or read aloud.

[1129] Step 6: Controlling the autonomous vehicle

[1130] If necessary, the server issues instructions to the autonomous vehicle's control system based on the diagnosis results, which in turn sets up a route to the nearest medical facility. The input is the diagnosis results, and the output is route instructions for the autonomous vehicle. Specifically, the server uses the NVIDIA DRIVE platform to control the vehicle's navigation system.

[1131] Step 7: Send emergency notifications

[1132] The server uses the Twilio API to send emergency notifications to designated medical institutions. The input is the user's symptoms and location information, and the output is a notification message to the medical institution. Specifically, the message generated by the server is sent in real time to the medical institution, urging them to take emergency action.

[1133] These steps will allow for a prompt and appropriate response if a user experiences a health issue while in a self-driving vehicle.

[1134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1136] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1137] [Fourth embodiment]

[1138] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1142] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1145] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1147] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1149] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1150] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1151] MODE FOR CARRYING OUT THE INVENTION

[1152] The present invention is a system for efficient diagnosis even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals. This system converts voice data from the user into text data, analyzes it, generates appropriate diagnostic results, and includes a series of processes for generating and transmitting referral letters and prescription data as needed. The program processing of this system is described in detail below.

[1153] User voice input

[1154] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[1155] Speech recognition and text data conversion

[1156] The device uses a speech recognition engine to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then sent over the Internet to a server.

[1157] Data analysis and generation of diagnostic results

[1158] The server analyzes the received text data. First, it uses natural language processing technology to extract the meaning of keywords and sentences within the text data. Next, it compares this with a case database and generates an appropriate diagnosis. For example, based on the information "frequent headaches" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[1159] Notification of diagnosis results and measures

[1160] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message. For example, the device may advise the user, "Your symptoms are likely a mild migraine. Please take over-the-counter painkillers." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[1161] Creation and transmission of referral letters and prescription data

[1162] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[1163] Transmission of pre-examination data in large hospitals

[1164] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[1165] Specific examples

[1166] Suppose a user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the speech, converts it into text data, and sends it to the server. The server analyzes this, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." It also generates a referral letter saying, "If your symptoms do not improve after one week, please see a local clinic for an examination," and sends it to the designated clinic.

[1167] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources.In addition, even in large hospitals, the efficiency of medical treatment can be improved by providing pre-examination data in advance.

[1168] The processing flow will be explained below.

[1169] Step 1:

[1170] The user starts up a device such as a smartphone or tablet and opens the dedicated application. The application displays a prompt asking, "Do you want to start the medical interview?", and the user responds, "Yes."

[1171] Step 2:

[1172] The device records the user's voice using a microphone. The user says, "I've had a headache and a fever lately." The device then activates its speech recognition engine and converts the user's voice data into text data.

[1173] Step 3:

[1174] The device encrypts the converted text data and sends it to the server using a secure communication protocol. The server decrypts the received encrypted data and passes it to the analysis module.

[1175] Step 4:

[1176] The server analyzes the text data, extracts the meaning of keywords and sentences within the data using natural language processing technology, and obtains symptom information such as "headache" or "fever." This information is then compared with a case database.

[1177] Step 5:

[1178] The server then applies a diagnostic algorithm based on the match, for example determining whether the combination of "headache" and "fever" is consistent with a mild cold, and generates an appropriate diagnosis and, if necessary, formulates countermeasures.

[1179] Step 6:

[1180] The server generates diagnostic results and sends countermeasure information in a data format such as JSON to the terminal. The terminal analyzes the received data and displays a message to the user saying, "Take over-the-counter cold medicine."

[1181] Step 7:

[1182] The server generates referral and prescription data based on the diagnosis. For example, if the patient's symptoms do not improve, a referral will be generated that includes a request to "see a doctor at a designated clinic."

[1183] Step 8:

[1184] The server electronically transmits the generated referral letter and prescription data to the designated medical institution or pharmacy. For example, the referral letter is transmitted via email or a dedicated medical data communication protocol.

[1185] Step 9:

[1186] The terminal receives a notification of completion of transmission from the server and displays a message to the user saying, "The referral letter has been sent to the clinic." This lets the user know the next step (actual medical treatment).

[1187] Step 10:

[1188] When a user visits a clinic, the medical interview data they have entered in advance is sent to the doctor's terminal, allowing the doctor to conduct a quick and appropriate examination based on that data.

[1189] Example 1

[1190] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1191] This will solve the problem of difficulty in making prompt and appropriate diagnoses in remote areas and areas with limited access to medical care. There is also a need to improve the efficiency of medical treatment in large hospitals. This will aim to improve medical care in various regions.

[1192] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1193] In this invention, the server includes means for acquiring voice data from a user, means for using a voice recognition engine to convert the voice data into text data, and means for analyzing the text data using natural language processing technology, thereby enabling more efficient and appropriate medical care in remote islands, depopulated areas, and large hospitals.

[1194] "User" refers to an individual or patient who uses the system to provide voice input.

[1195] "Voice data" refers to voice information input by a user using a terminal.

[1196] "Text data" refers to the text information resulting from converting voice data using a voice recognition engine.

[1197] "Speech recognition engine" refers to software or algorithms for converting voice data into text data.

[1198] "Natural language processing technology" refers to technology for analyzing the meaning of keywords and sentences within text data.

[1199] A "case database" refers to a collection of information that stores data on medical conditions and cases.

[1200] "Diagnosis result" refers to an evaluation of the disease condition obtained by analyzing text data and comparing it with a case database.

[1201] "Notifying" refers to the act of communicating diagnostic results and related information to the user.

[1202] A "letter of referral" refers to a document created to refer a user to another medical institution.

[1203] "Prescription data" refers to digital information that specifies medications to be prescribed to a user.

[1204] "Information terminal" refers to a hardware device (e.g., smartphone, tablet) that acquires voice data and transmits it to a server.

[1205] "Server" refers to a computer system for analyzing text data, generating diagnostic results, and notifying users.

[1206] This invention is a system for efficient diagnosis in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals. This system converts voice data from the user into text data, analyzes it, and generates appropriate diagnostic results. It also includes a series of processes for generating and sending referral letters and prescription data as needed.

[1207] User voice input

[1208] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[1209] Speech recognition and text data conversion

[1210] The device uses a speech recognition engine, such as Google Speech-to-Text, to convert the user's speech into text data, including the user's symptoms and other relevant information, which is then sent over the internet to a server.

[1211] Data analysis and generation of diagnostic results

[1212] The server analyzes the received text data. First, it uses natural language processing technology (e.g., OpenAI GPT-4) to extract the meaning of keywords and sentences in the text data. Next, it compares this with a case database (e.g., the ICD-10 database) and generates an appropriate diagnosis. For example, based on the information "frequent headaches" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[1213] Notification of diagnosis results and measures

[1214] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message. For example, the device may advise the user, "Your symptoms are likely a mild migraine. Please take over-the-counter painkillers." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[1215] Creation and transmission of referral letters and prescription data

[1216] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[1217] Transmission of pre-examination data in large hospitals

[1218] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[1219] Specific examples

[1220] For example, suppose a user living on a remote island uses this system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the speech, converts it into text data, and sends it to the server. The server analyzes this text data, diagnoses it as a mild cold, and notifies the user to "take over-the-counter cold medicine." It also generates a referral letter stating, "If your symptoms do not improve after one week, please see a local clinic for an examination," and sends it to the designated clinic.

[1221] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources.In addition, even in large hospitals, the efficiency of medical treatment can be improved by providing pre-examination data in advance.

[1222] Specific examples of prompts to input to generative AI models

[1223] "Please explain a situation where a user is using a smartphone app to complain about a headache and fever. Then, please explain in detail the process of using a speech recognition engine to convert the data into text and send it to the server."

[1224] Please explain in detail how the server uses natural language processing to generate a diagnosis result based on the text data converted by the speech recognition engine and notify the user.

[1225] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1226] Step 1: User Speech Input

[1227] 1. The user launches a dedicated application using an information terminal such as a smartphone or tablet.

[1228] 2. The user taps the microphone button in the app and speaks their symptoms. For example, they can say, "I've had a headache and a fever lately."

[1229] 3. The device's microphone collects the audio and captures the audio data, which is then input into the device's application.

[1230] Input: User's voice

[1231] Output: Collected audio data

[1232] Step 2: Speech recognition and text conversion

[1233] 1. The device calls a speech recognition engine (e.g., Google Speech-to-Text) and converts the voice data into text data.

[1234] 2. The speech recognition engine analyzes the voice data and generates text data such as, "I've had a headache and a fever lately."

[1235] 3. The generated text data is stored in the device's memory and then sent to a server via the Internet.

[1236] Input: Collected audio data

[1237] Output: Converted text data

[1238] Step 3: Data analysis and generation of diagnostic results

[1239] 1. The server analyzes the received text data. First, it uses natural language processing technology (e.g., OpenAI GPT-4) to extract the meaning of keywords and sentences in the text.

[1240] 2. The server compares the extracted keywords with a case database (e.g., ICD-10 database).

[1241] 3. The server generates an appropriate diagnosis based on the matching results. For example, the information "frequent headaches" and "fever" can be used to diagnose a "mild cold."

[1242] Input: Text data

[1243] Output: Diagnostic results

[1244] Step 4: Notification of diagnosis results and measures

[1245] 1. The server generates a message containing the analysis and diagnostic results.

[1246] 2. The server sends this message to the device over the Internet.

[1247] 3. The device notifies the user of the received message. The notification may be a voice message or a text message. For example, the device may display a message saying, "Your symptoms are likely a mild migraine. Please take an over-the-counter pain reliever."

[1248] Input: Diagnostic results

[1249] Output: A notification message to the user

[1250] Step 5: Generate and send referral and prescription data

[1251] 1. The server generates referral or prescription data based on the diagnosis.

[1252] 2. The referral letter is sent electronically to the designated hospital, and the prescription data is sent to the designated pharmacy.

[1253] 3. The user receives a notification and, if necessary, visits the hospital or pharmacy referred to them. For example, the user may be notified that "a specific medication for migraines has been prescribed. Please pick it up at the nearest pharmacy."

[1254] Input: Diagnostic results

[1255] Output: Referral letter or prescription data

[1256] Step 6: Submission of pre-examination data in large hospitals

[1257] 1. If a user is scheduled to receive medical treatment at a large hospital, they can input and submit medical interview data in advance.

[1258] 2. The server analyzes the received pre-examination data and sends it to the appropriate doctor.

[1259] 3. Doctors can understand the patient's symptoms and background information in advance, improving the efficiency of consultations. For example, the interview data may include information such as "the patient has a history of migraines in the past and is still suffering from them."

[1260] Input: Pre-examination data

[1261] Output: Information notification to doctor

[1262] (Application example 1)

[1263] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1264] In today's world, the number of users of online shopping sites is increasing, creating a demand for efficient and fast methods for searching for products and completing purchases. Users who have difficulty using traditional methods, such as the elderly and visually impaired, also need a more user-friendly interface. Furthermore, it is difficult to perform all steps of the process, from product search to purchase and delivery status confirmation, all in one system, especially in remote locations or during busy periods.

[1265] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1266] In this invention, the server includes means for acquiring voice data from a user, means for converting the voice data into text data, means for analyzing the text data, means for generating a diagnosis based on the analysis result, means for notifying the user of the diagnosis result, means for generating a referral letter or prescription data based on the diagnosis result, means for transmitting the referral letter or prescription data to a specified destination, means for searching for products based on voice input, means for notifying the user of information about the products, means for purchasing the products, and means for notifying the user of the delivery status of the products. This enables a user to search for and purchase products by voice and check the delivery status.

[1267] "Voice data" refers to data indicating voice information spoken by a user to a terminal.

[1268] "Text data" refers to character information converted from voice data using voice recognition technology.

[1269] "Analysis" is the process of extracting and understanding the meaning and intent of information based on text data.

[1270] A "diagnosis result" is an output that indicates the user's health condition and necessary countermeasures, generated based on the information obtained through analysis.

[1271] A "letter of referral" is an electronic document that refers a user to a specific medical institution based on the diagnosis results.

[1272] "Prescription data" is electronic data generated to prescribe specific medications based on diagnostic results.

[1273] "Product search" is the process of searching a database for the product a user is looking for using voice input.

[1274] "Product information" is detailed information about products found by a search, including data such as price, availability, and description.

[1275] The "purchase procedure" is a process in which the user carries out various procedures necessary to purchase the product selected by the user.

[1276] "Delivery status" is information indicating the current delivery status of the purchased item.

[1277] "Notification" refers to the general process of providing information to a user through audio or text.

[1278] This invention provides a system that allows users to search for products, complete purchase procedures, and check delivery status by voice. This system automates the process from voice acquisition to analysis and notification, providing users with a smooth online shopping experience.

[1279] System configuration

[1280] The system uses the following hardware and software:

[1281] Hardware used

[1282] 1. Smartphones and tablets: These are devices that allow users to input information using their voice.

[1283] 2. Server: A computing resource that analyzes voice data and generates and transmits the necessary information.

[1284] Software used

[1285] 1. Google Cloud Speech-to-Text API: A speech recognition technology for converting voice data into text data.

[1286] 2. Google Cloud Natural Language API: A natural language processing technology that analyzes text data to extract and understand product information.

[1287] 3. Flask: It is a web application framework, used to process requests from users and return the required responses.

[1288] Examples of data processing and data calculation

[1289] 1. Acquisition of voice data: The user launches a dedicated app on a device such as a smartphone and describes the product they want by voice. For example, they might say, "I've been looking for a smartphone recently."

[1290] 2. Speech recognition: The captured voice data is converted into text data using the Google Cloud Speech-to-Text API.

[1291] 3. Text data analysis: The text data is analyzed using the Google Cloud Natural Language API to extract keywords related to the product the user is looking for.

[1292] 4. Product information generation: Based on the extracted keywords, the system generates appropriate product information and notifies the user.

[1293] 5. Purchase procedure and delivery status notification: If the user decides to purchase, the system will process the purchase and notify the delivery status after the purchase is completed.

[1294] Specific examples

[1295] 1. Usage scenarios:

[1296] The user launches the app and says, "I've been looking for my smartphone lately."

[1297] The server converts the voice data into text data and analyzes it.

[1298] Based on the results of analyzing the text data, the system notifies the user of smartphone candidates.

[1299] The user selects from the candidates and completes the purchase procedure.

[1300] After purchase, users will be notified of delivery status in real time.

[1301] 2. Example prompt:

[1302] User: "I've been looking for a smartphone lately."

[1303] App: "We found some great deals, including the iPhone 13 (¥100,000) and the Samsung Galaxy (¥80,000)."

[1304] As described above, this system allows users to seamlessly search for products and complete purchase procedures through voice input, providing a highly convenient mail-order system that is particularly applicable to the elderly and visually impaired.

[1305] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1306] Step 1:

[1307] The user launches a dedicated mail-order assistant app on their smartphone. They then speak into the app, saying, "I've been looking for my smartphone lately." This input is captured through the device's microphone. The input data is then converted into voice data, which is then sent to the next stage of processing.

[1308] Step 2:

[1309] The device sends the acquired voice data to the Google Cloud Speech-to-Text API. The server receives this voice data and converts it into text data using speech recognition technology. At this stage, the input is voice data, and the output is the converted text data. For example, the generated text data is "I've been looking for my smartphone recently."

[1310] Step 3:

[1311] The server uses the Google Cloud Natural Language API to analyze the text data. The server performs data analysis to extract important keywords from the text data. The input is the converted text data, and the output is a list of extracted keywords. For example, the keyword "smartphone" is extracted.

[1312] Step 4:

[1313] The server searches its internal database based on the extracted keywords to collect relevant product information. This search process searches all product information in the database for information that matches the keywords. The input is the extracted keywords, and the output is a list of relevant products. For example, a list containing information such as "iPhone 13" and "Samsung Galaxy" is output.

[1314] Step 5:

[1315] The server notifies the user of the collected product information. The device displays or voices the product information received from the server. The input is a list of product information, and the output is a notification to the user (for example, a screen display or voice announcement). For example, the user might be presented with a message saying, "We've found some recommended products: iPhone 13 (100,000 yen), Samsung Galaxy (80,000 yen)."

[1316] Step 6:

[1317] The user selects the desired product by voice or operation and begins the purchase process. The terminal receives the user's selection and sends the information for the purchase process to the server. The input is the user's selection data, and the output is a signal indicating the start of the purchase process.

[1318] Step 7:

[1319] The server processes the purchase, queries the user for any necessary information, verifies payment and shipping information, and guides the user accordingly. The input is the user's purchase information and data from the server's checkout process, and the output is a notification that the purchase is complete. Once the user provides the appropriate payment information, the purchase is complete.

[1320] Step 8:

[1321] After the purchase is completed, the server tracks the delivery status in real time and notifies the user. The terminal displays or voices the delivery status information it periodically receives from the server. The input is delivery status data, and the output is a notification to the user (e.g., the current delivery status).

[1322] This series of processing steps allows users to smoothly search for products, complete purchase procedures, and check delivery status through voice input.

[1323] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1324] MODE FOR CARRYING OUT THE INVENTION

[1325] The present invention is a system for efficient diagnosis, even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take the user's emotions into consideration. This system converts voice data from the user into text data, analyzes it, and generates appropriate diagnostic results. It also includes a series of processes for generating and transmitting referral letters and prescription data as needed. Furthermore, it is possible to recognize the user's emotions using an emotion engine and adjust the diagnostic results based on these. The program processing of this system is described in detail below.

[1326] User voice input

[1327] Users launch a dedicated application on a device such as a smartphone or tablet and input their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." This voice is then recognized by the device's built-in microphone.

[1328] Speech recognition and text data conversion

[1329] The device uses a speech recognition engine to convert the user's speech into text data, including the user's spoken symptoms and other relevant information, which is then sent over the Internet to a server.

[1330] Emotion recognition by emotion engine

[1331] The server analyzes the voice data along with the text data and uses an emotion engine to recognize the user's emotions. For example, it identifies emotions such as "anxiety" or "tension" from the tone, speed, and intonation of the user's voice. This emotion data is also used for analysis.

[1332] Data analysis and generation of diagnostic results

[1333] The server analyzes the received text data and emotion data. First, it uses natural language processing technology to extract the meaning of keywords and sentences in the text data. Next, it compares this with a case database and generates an appropriate diagnosis. For example, based on the information on "headache" and "fever," it determines whether the condition is urgent and, in some cases, whether emergency treatment at a large hospital is required.

[1334] Notification of diagnosis results and measures

[1335] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message, with the tone adjusted based on the emotional data. For example, the device may provide the user with advice such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller." In addition, if the symptoms are severe, an emergency notification may be sent stating, "Go to the nearest hospital immediately."

[1336] Creation and transmission of referral letters and prescription data

[1337] Based on the diagnosis, the server can generate a referral or prescription data. The referral is electronically sent to a designated hospital, and the prescription data is sent to a designated pharmacy. For example, if the migraine symptoms persist, the user will be notified that "a specific medication for migraines has been prescribed. Please pick it up at your nearest pharmacy."

[1338] Transmission of pre-examination data in large hospitals

[1339] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[1340] Specific examples

[1341] Suppose a user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text data, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." Furthermore, the emotion engine recognizes the user's anxiety, and as a result, adjusts the notification message to be more reassuring. An additional message is sent, such as, "If you have any concerns, please contact us immediately."

[1342] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[1343] The processing flow will be explained below.

[1344] MODE FOR CARRYING OUT THE INVENTION

[1345] (Processing steps of a system that combines an emotion engine)

[1346] Step 1:

[1347] The user starts up a device such as a smartphone or tablet and opens the dedicated application. The application displays a prompt asking, "Do you want to start the medical interview?", and the user responds, "Yes."

[1348] Step 2:

[1349] The device records the user's voice using a microphone. The user says, "I've had a headache and a fever lately." The device then activates its speech recognition engine and converts the user's voice data into text data.

[1350] Step 3:

[1351] The device encrypts the converted text and voice data and sends it to the server using a secure communication protocol. The server then decrypts the received encrypted data and passes it to the analysis module and emotion engine.

[1352] Step 4:

[1353] The server analyzes the text data and uses natural language processing technology to extract the meaning of keywords and sentences in the text data, obtaining symptom information such as "headache" or "fever." This information is then compared with a case database.

[1354] Step 5:

[1355] The server's emotion engine analyzes the voice data and recognizes the user's emotions, identifying emotions such as "anxiety" or "tension" based on the tone, speed, and intonation of the user's voice.

[1356] Step 6:

[1357] The server then applies a diagnostic algorithm based on the matching results, for example, determining whether the combination of "headache" and "fever" is consistent with a mild cold. Emotional data is also factored in to improve the reliability of the diagnosis.

[1358] Step 7:

[1359] The server generates diagnostic results and sends countermeasure information to the device in a data format such as JSON. The device analyzes the received data, adjusts the tone of the message based on the emotion recognition results, and notifies the user. For example, a countermeasure message such as "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller" may be displayed.

[1360] Step 8:

[1361] The server generates referral and prescription data based on the diagnosis. For example, if the patient's symptoms do not improve, a referral will be generated that includes a request to "see a doctor at a designated clinic."

[1362] Step 9:

[1363] The server electronically transmits the generated referral letter and prescription data to the designated medical institution or pharmacy. For example, the referral letter is transmitted via email or a dedicated medical data communication protocol.

[1364] Step 10:

[1365] The terminal receives a notification of completion of transmission from the server and displays a message to the user saying, "The referral letter has been sent to the clinic." This lets the user know the next step (actual medical treatment).

[1366] Specific examples

[1367] A user living on a remote island uses the system because they are experiencing symptoms of a headache and fever. The user launches the app and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text and voice data, diagnoses it as a mild cold, and sends a notification to the user stating, "Please take over-the-counter cold medicine," adjusted based on the emotion data. The emotion engine then recognizes the user's anxiety and sends an additional message such as, "If you have any concerns, please contact us immediately."

[1368] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[1369] Example 2

[1370] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1371] There is a demand for efficient diagnosis even in remote areas or areas with limited access to medical care. There is also a need to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take user emotions into account. However, conventional systems are limited to simple analysis of voice data, making it difficult to provide diagnostic results that take user emotions into account or improve medical treatment efficiency by sending pre-examination data in advance. The objective of this project is to solve these problems.

[1372] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1373] In this invention, the server includes means for acquiring voice data from a user, means for converting the voice data into text data, means for recognizing emotions from the voice data and text data, means for analyzing the text data, means for generating a diagnosis result based on the analysis result and the emotion recognition result, means for notifying the diagnosis result in a tone corresponding to the user's emotion, means for generating a referral letter or prescription data based on the diagnosis result, and means for transmitting the referral letter or prescription data to a specified destination. This enables prompt and appropriate medical care even in remote islands and depopulated areas, reduces the psychological burden on users, and improves the efficiency of medical treatment in large hospitals.

[1374] A "user" is an individual who utilizes the system to provide voice input and receive diagnostic results.

[1375] "Voice data" refers to voice information input by a user through a terminal.

[1376] A "terminal" is a device that allows voice input, such as a smartphone, tablet, or PC.

[1377] The "server" is a central computer system that analyzes data sent from the terminals and generates and transmits diagnostic results.

[1378] "Text data" refers to text information converted from voice data using voice recognition technology.

[1379] "Voice recognition technology" refers to the technology that analyzes voice data and converts it into text data.

[1380] "Emotion recognition" refers to technology that identifies a user's emotions from voice data and text data.

[1381] "Data analysis algorithm" is a technology that generates diagnostic results based on text data and emotion recognition results.

[1382] "Diagnosis results" refers to medical judgments and instructions provided to the user based on the results of the analysis by the server.

[1383] "Notification by tone" refers to a method of notifying the diagnosis result using a tone or expression that matches the user's emotions.

[1384] A "letter of referral" is a document intended to introduce a patient to a medical institution designated by the user.

[1385] "Prescription data" refers to the digital information required to receive medication at a pharmacy designated by the user.

[1386] MODE FOR CARRYING OUT THE INVENTION

[1387] This invention is a system for efficient diagnosis, even in remote areas or areas with limited access to medical care. It also aims to improve the efficiency of medical treatment in large hospitals and provide diagnostic results that take the user's emotions into consideration. This system involves a series of processes: the user inputs voice data, converts the voice data into text data, analyzes it, generates appropriate diagnostic results, and generates and transmits referral letters and prescription data as needed. Furthermore, it is possible to recognize the user's emotions using emotion recognition technology and adjust diagnostic results based on this.

[1388] Hardware and Software

[1389] 1. Device:

[1390] Use a device such as a smartphone, tablet, or computer. These devices must have a microphone that can accept voice input.

[1391] 2. Speech Recognition Engine:

[1392] To convert the "voice data" into "text data," "voice recognition technology" is used. As a specific example, a "cloud-based voice recognition service" is used.

[1393] 3. Emotion Recognition Engine:

[1394] To read emotions from the user's voice and text data, we use "emotion recognition technology." As a specific example, we use "cloud-based emotion analysis service."

[1395] 4. Data analysis algorithm:

[1396] "Data analysis algorithms" are used to analyze "text data" and "emotion data" and generate appropriate diagnostic results. Specific examples of this include "natural language processing technology."

[1397] 5. Notification Engine:

[1398] To notify the user of the diagnosis results, a "notification engine" is used. As a specific example, text-to-speech technology is used.

[1399] 6. Electronic Document Generation Systems:

[1400] An "electronic document generation system" is used to generate referral letters and prescription data and send them to designated destinations.

[1401] Specific operation example

[1402] The user operates their smartphone, launches a dedicated application, and voice-records their symptoms. For example, they might say, "I've had a headache recently, and I also have a fever." The device then converts this voice into text data using a cloud-based voice recognition service and sends it to a server.

[1403] The server uses a cloud-based emotion analysis service to recognize the user's emotions based on the received text and voice data. For example, emotions such as "anxiety" or "tension" can be identified from the tone, speed, and intonation of the user's voice.

[1404] The server then uses natural language processing technology to analyze the text data and compares it with a case database based on the meaning of the keywords and sentences obtained. This generates an appropriate diagnosis. For example, based on the information on "headache" and "fever," it determines whether the condition is urgent and, if necessary, directs emergency treatment to a large hospital.

[1405] The diagnosis results are notified to the user using voice synthesis technology. The tone of the notification is adjusted based on the emotion recognition results. For example, the system provides a solution such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller." In addition, if the symptoms are severe, an emergency notification will be sent stating, "Go to the nearest hospital immediately."

[1406] Finally, the server uses the "electronic document generation system" to generate a referral letter and prescription data based on the diagnosis results, and sends them to the designated hospital or pharmacy. For example, if the patient suffers from persistent migraines, a notification will be sent to the user stating, "Migraine medication has been prescribed. Please pick it up at the nearest pharmacy."

[1407] Prompt Sentence Examples

[1408] Let's say a user living on a remote island uses this system because they are experiencing symptoms of a headache and fever. The user launches the application and says, "I've had a headache and a fever recently." The device recognizes the voice, converts it into text data, and sends it to the server. The server analyzes the text data, diagnoses it as a mild cold, and sends the user a notification saying, "Please take over-the-counter cold medicine." The emotion engine also recognizes the user's anxiety, and as a result, adjusts the notification message to be more reassuring. An additional message is sent, such as, "If you have any concerns, please contact us immediately."

[1409] This will enable prompt and appropriate medical care even in remote islands, depopulated areas, and areas with limited medical resources, while also reducing the psychological burden on users. Even in large hospitals, the efficiency of medical care can be improved by providing pre-examination data in advance.

[1410] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1411] Step 1: User Speech Input

[1412] The user launches a dedicated application on a device such as a smartphone or tablet and inputs their symptoms by voice. For example, they might say, "I've had a headache recently and a fever." The device then picks up the voice data via its microphone.

[1413] Input: Verbal symptom report from user

[1414] Output: Captured audio data

[1415] Step 2: Speech recognition and text conversion

[1416] The device sends the captured voice data to cloud-based speech recognition technology (e.g., Google Cloud Speech-to-Text) and converts it into text data, which includes the user's spoken symptoms and other relevant information. The device then sends the text data to a server.

[1417] Input: Captured audio data

[1418] Output: Converted text data

[1419] Step 3: Emotion recognition by the emotion engine

[1420] The server analyzes the voice data as well as the text data and uses emotion recognition technology (e.g., IBM Watson Tone Analyzer) to recognize the user's emotions. For example, it identifies emotions such as "anxiety" or "tension" from the tone, speed, and intonation of the user's voice.

[1421] Input: Audio and text data

[1422] Output: Recognized emotion data

[1423] Step 4: Data analysis and generation of diagnostic results

[1424] The server analyzes the received text data and emotion data using natural language processing technology (e.g., Amazon Comprehend). First, it extracts the meaning of keywords and sentences in the text data and compares them with a case database. As a result of the analysis, it generates an appropriate diagnosis.

[1425] Input: Text data and emotion data

[1426] Output: Diagnostic result data

[1427] Step 5: Notification of diagnosis results and measures

[1428] The server sends the analysis and diagnosis results back to the device, which then notifies the user. The notification is displayed as a voice or text message using text-to-speech technology, with the tone adjusted based on the emotional data. For example, the device provides the user with advice such as, "Your symptoms are likely a mild migraine. Please take an over-the-counter painkiller."

[1429] Input: Diagnostic result data

[1430] Output: Notification message sent to the user

[1431] Step 6: Generate and send referral and prescription data

[1432] The server generates a referral letter or prescription data using an electronic document generation system (e.g., DocuSign API) based on the diagnosis results. The referral letter is electronically sent to the designated hospital, and the prescription data is sent to the designated pharmacy.

[1433] Input: Diagnostic result data

[1434] Output: Referral data or prescription data

[1435] Step 7: Sending pre-examination data in large hospitals

[1436] When a user plans to visit a large hospital, they can input and submit medical interview data in advance. The server analyzes this data and sends it to the appropriate doctor. This allows doctors to understand the patient's symptoms and background information in advance, improving the efficiency of medical examinations.

[1437] Input: User interview data

[1438] Output: Pre-examination data and its analysis results

[1439] (Application example 2)

[1440] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1441] The present invention relates to a system for conducting prompt and appropriate health examinations even in areas with limited or remote medical access, and for implementing emergency response using autonomous vehicles as needed. The system also aims to reduce the psychological burden of users by providing diagnostic results that take their emotions into consideration. In particular, there is a need for improved safety and efficiency by including a function to instruct an autonomous vehicle to quickly travel to an appropriate medical institution when emergency medical care is required.

[1442] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1443] In this invention, the server includes means for recognizing emotions from voice data and text data from a user and adjusting a diagnosis result according to the emotion, means for issuing instructions to a control system of an autonomous vehicle based on the diagnosis result, and means for converting the voice data into text data, thereby providing an appropriate diagnosis result according to the emotion, and further enabling rapid transport to a medical institution using the autonomous vehicle in an emergency.

[1444] "Voice data" refers to the content of a user's speech, and is information acquired as a voice signal.

[1445] "Text data" refers to character information that has been converted by analyzing voice data, and is data that is the subject of natural language processing.

[1446] "Analysis Results" refers to diagnostic information and recommended actions generated based on the text data and other related data.

[1447] "Diagnosis results" refer to conclusions and recommended measures regarding the user's health condition based on the analysis results.

[1448] A "letter of referral" is a medical referral document issued to another medical institution based on diagnostic results.

[1449] "Prescription data" is prescription information for medicines generated based on diagnostic results, and is digital data sent to a pharmacy.

[1450] "Emotion recognition" is a technology that analyzes voice data and text data to identify a user's psychological state.

[1451] An "autonomous vehicle" is a vehicle that drives autonomously using a drive control system and has the ability to head to a destination based on instructions from passengers.

[1452] A "control system" is a general term for programs and hardware that manage the operation of machines and devices and operate them according to specific instructions.

[1453] A "server" is a computer system used to analyze audio and text data and generate and provide diagnostic results.

[1454] "Speech recognition technology" is a technology that converts voice data into text data, and obtains the content of a user's speech as text information.

[1455] "Data analysis algorithms" refer to mathematical methods and processes that use machine learning and statistical analysis to derive diagnostic results based on collected data.

[1456] The system of the present invention is designed to diagnose the health status of passengers in an autonomous vehicle in real time and provide necessary measures. Specific embodiments for implementing this system will be described below.

[1457] System Overview

[1458] The system mainly consists of the following components:

[1459] 1. A device that acquires the user's voice data

[1460] 2. Server that analyzes voice data

[1461] 3. Control Systems for Autonomous Vehicles

[1462] Acquiring and converting audio data

[1463] When a user talks about their health condition in an autonomous vehicle, the vehicle's built-in microphone (hardware) captures the voice data. This data is converted into text data via the device's built-in voice recognition engine (software). For example, if a user says, "I have chest pain," the voice data is converted into text data using the Google Speech-to-Text API.

[1464] emotion recognition

[1465] The converted text and voice data is sent to a server and analyzed by an emotion engine (software). The analysis uses IBM Watson Tone Analyzer to identify emotions from the user's speech. For example, emotions such as "urgency" or "anxiety" can be recognized from the tone and intonation of the user's voice.

[1466] Data analysis and generation of diagnostic results

[1467] The server uses natural language processing technology to generate appropriate diagnostic results based on the analyzed text data and emotion data. This process uses data analysis algorithms such as Scikit-Learn and TensorFlow. For example, if the symptom "chest pain" is recognized, it will be diagnosed as a possible heart disease and determine that immediate medical attention is required.

[1468] Notification of diagnostic results and control of autonomous vehicles

[1469] The generated diagnostic results are communicated to the user via a display and speaker in the vehicle. Furthermore, in the event of an emergency, instructions are sent to the autonomous vehicle's control system to direct the vehicle to the nearest medical facility. This control is performed using an autonomous driving platform such as NVIDIA DRIVE.

[1470] emergency notification

[1471] If necessary, the server will use the Twilio API to send emergency notifications to designated medical institutions, including the user's symptoms and location information, to prompt medical institutions to take action.

[1472] Specific examples

[1473] For example, if a user experiences chest tightness while in a self-driving vehicle, the above process would be executed as follows:

[1474] 1. User: Says, "I have chest pains."

[1475] 2. Device: Convert the audio into text data using the Google Speech-to-Text API.

[1476] 3. Server: IBM Watson Tone Analyzer recognizes the emotion as "urgent."

[1477] 4. Server: Using TensorFlow, diagnoses the possibility of heart disease and determines that emergency treatment is necessary.

[1478] 5. Vehicle: Route to the nearest hospital.

[1479] 6. Server: Uses Twilio API to send emergency notifications to designated hospitals.

[1480] Prompt Sentence Examples

[1481] Here are some example prompts to input to a generative AI model:

[1482] A user says "I'm having chest pain" in their car. Convert the voice data to text, perform sentiment analysis, generate a diagnosis based on the symptoms, notify medical authorities if emergency response is required, and the autonomous vehicle will head to the nearest hospital. Please provide detailed instructions for the following tasks:

[1483] 1. Speech Recognition and Text Conversion

[1484] 2. Emotion recognition

[1485] 3. Data analysis and diagnostic results generation

[1486] 4. Notification of diagnostic results

[1487] 5. How to send emergency notifications

[1488] 6. Autonomous Vehicle Operation Interface

[1489] The above is an embodiment of the present invention. This system enables prompt and appropriate medical examinations and emergency response even in remote areas or places with limited medical access.

[1490] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1491] Step 1: Acquire audio data

[1492] The user speaks about their health condition in the autonomous vehicle. This speech is picked up by a microphone built into the vehicle. The input is raw audio data, and the output is a digital audio file.

[1493] Step 2: Converting audio data to text data

[1494] The device converts the captured audio data into text data using the Google Speech-to-Text API. Specifically, it sends the audio file to the API and receives the text data returned by the API. The input is audio data and the output is text data.

[1495] Step 3: Analyze the text and audio data

[1496] The server sends the received text data and original voice data to the IBM Watson Tone Analyzer for emotion analysis. The input is text data and voice data, and the output is emotion data. Specifically, the content of the text and the intonation and tone of the voice are analyzed to identify emotions such as "urgency" and "anxiety."

[1497] Step 4: Generate diagnostic results

[1498] The server uses TensorFlow to generate appropriate diagnosis results based on the text data and emotion data. The input is text data and emotion data, and the output is the diagnosis result. Specifically, it uses natural language processing technology to analyze the meaning of the text and compares it with existing cases in a database to make a diagnosis.

[1499] Step 5: Notification of diagnosis results

[1500] The server sends the generated diagnostic results to a display device or speaker in the vehicle and notifies the user. The input is the diagnostic results, and the output is a display message or a voice message. Specifically, a message based on the diagnostic results is displayed on the screen or read aloud.

[1501] Step 6: Controlling the autonomous vehicle

[1502] If necessary, the server issues instructions to the autonomous vehicle's control system based on the diagnosis results, which in turn sets up a route to the nearest medical facility. The input is the diagnosis results, and the output is route instructions for the autonomous vehicle. Specifically, the server uses the NVIDIA DRIVE platform to control the vehicle's navigation system.

[1503] Step 7: Send emergency notifications

[1504] The server uses the Twilio API to send emergency notifications to designated medical institutions. The input is the user's symptoms and location information, and the output is a notification message to the medical institution. Specifically, the message generated by the server is sent in real time to the medical institution, urging them to take emergency action.

[1505] These steps will allow for a prompt and appropriate response if a user experiences a health issue while in a self-driving vehicle.

[1506] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1507] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1508] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1509] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1510] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1511] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1512] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1513] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1514] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1515] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1516] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1517] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1518] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1519] 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.

[1520] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1521] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1522] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1523] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1524] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1525] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1526] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1527] The following is further disclosed regarding the above embodiment.

[1528] (Claim 1)

[1529] means for obtaining voice data from a user;

[1530] means for converting the voice data into text data;

[1531] means for analyzing the text data;

[1532] means for generating a diagnosis result based on the analysis result;

[1533] means for notifying a user of the diagnosis result;

[1534] means for generating a referral letter or prescription data based on the diagnosis result;

[1535] means for transmitting the referral letter or prescription data to a designated destination;

[1536] A system including:

[1537] (Claim 2)

[1538] a terminal for acquiring voice data;

[1539] a server that analyzes the voice data;

[1540] 10. The system of claim 1, comprising:

[1541] (Claim 3)

[1542] A voice recognition technology that converts user voice data acquired by the terminal into text data;

[1543] a data analysis algorithm that generates an appropriate diagnosis result based on the text data analyzed by the server;

[1544] 10. The system of claim 1, comprising:

[1545] "Example 1"

[1546] (Claim 1)

[1547] means for obtaining voice data from a user;

[1548] means for using a speech recognition engine to convert the speech data into text data;

[1549] means for analyzing the text data using natural language processing technology;

[1550] a means for generating a diagnosis based on the analysis result by comparing it with a case database;

[1551] means for notifying a user of the diagnosis result;

[1552] means for generating a referral letter or prescription data based on the diagnosis result;

[1553] means for transmitting the referral letter or prescription data to a designated destination;

[1554] A system including:

[1555] (Claim 2)

[1556] an information terminal that acquires voice data;

[1557] a server that analyzes the voice data;

[1558] 10. The system of claim 1, comprising:

[1559] (Claim 3)

[1560] A voice recognition technology that converts user voice data acquired by the terminal into text data;

[1561] a data analysis algorithm that generates an appropriate diagnosis result based on the text data analyzed by the server;

[1562] 10. The system of claim 1, comprising:

[1563] "Application Example 1"

[1564] (Claim 1)

[1565] means for obtaining voice data from a user;

[1566] means for converting the voice data into text data;

[1567] means for analyzing the text data;

[1568] means for generating a diagnosis result based on the analysis result;

[1569] means for notifying a user of the diagnosis result;

[1570] means for generating a referral letter or prescription data based on the diagnosis result;

[1571] means for transmitting the referral letter or prescription data to a designated destination;

[1572] a means for searching for products based on voice input;

[1573] a means for notifying the user of information about the product;

[1574] A means for carrying out a purchase procedure for the product;

[1575] means for notifying the user of the delivery status of the product;

[1576] A system including:

[1577] (Claim 2)

[1578] a terminal for acquiring voice data;

[1579] a server that analyzes the voice data;

[1580] 10. The system of claim 1, comprising:

[1581] (Claim 3)

[1582] A voice recognition technology that converts user voice data acquired by the terminal into text data;

[1583] a data analysis algorithm that generates appropriate diagnostic results and product information based on the text data analyzed by the server;

[1584] 10. The system of claim 1, comprising:

[1585] "Example 2: Combining Emotion Engines"

[1586] (Claim 1)

[1587] means for obtaining voice data from a user;

[1588] means for converting the voice data into text data;

[1589] means for recognizing emotions from the voice data and text data;

[1590] means for analyzing the text data;

[1591] means for generating a diagnosis result based on the analysis result and the emotion recognition result;

[1592] means for notifying the user of the diagnosis result in a tone corresponding to the user's emotion;

[1593] means for generating a referral letter or prescription data based on the diagnosis result;

[1594] means for transmitting the referral letter or prescription data to a designated destination;

[1595] A system including:

[1596] (Claim 2)

[1597] a terminal for acquiring voice data;

[1598] a server that analyzes the voice data and the text data;

[1599] 10. The system of claim 1, comprising:

[1600] (Claim 3)

[1601] A voice recognition technology that converts user voice data acquired by the terminal into text data;

[1602] a data analysis algorithm that generates an appropriate diagnosis result based on the text data and emotion recognition results analyzed by the server;

[1603] 10. The system of claim 1, comprising:

[1604] "Application example 2 when combining emotion engines"

[1605] (Claim 1)

[1606] means for obtaining voice data from a user;

[1607] means for converting the voice data into text data;

[1608] means for analyzing the text data;

[1609] means for generating a diagnosis result based on the analysis result;

[1610] means for notifying a user of the diagnosis result;

[1611] means for generating a referral letter or prescription data based on the diagnosis result;

[1612] means for transmitting the referral letter or prescription data to a designated destination;

[1613] means for recognizing emotions from the voice data and text data and adjusting a diagnosis result according to the emotions;

[1614] means for issuing instructions to a control system of the autonomous vehicle based on the diagnosis results;

[1615] A system including:

[1616] (Claim 2)

[1617] a terminal for acquiring voice data;

[1618] a server that analyzes the voice data;

[1619] A control system for an autonomous vehicle;

[1620] 10. The system of claim 1, comprising:

[1621] (Claim 3)

[1622] A voice recognition technology that converts user voice data acquired by the terminal into text data;

[1623] an algorithm for recognizing emotions from the text data and voice data analyzed by the server;

[1624] a data analysis algorithm that generates an appropriate diagnosis result by taking the emotion data into consideration;

[1625] an algorithm that issues instructions to a control system of the autonomous vehicle based on the diagnosis results;

[1626] 10. The system of claim 1, comprising: [Explanation of symbols]

[1627] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for obtaining voice data from a user; means for converting the voice data into text data; means for analyzing the text data; means for generating a diagnosis result based on the analysis result; means for notifying a user of the diagnosis result; means for generating a referral letter or prescription data based on the diagnosis result; means for transmitting the referral letter or prescription data to a designated destination; A system including:

2. a terminal for acquiring voice data; a server that analyzes the voice data; The system of claim 1 , comprising:

3. A voice recognition technology that converts user voice data acquired by the terminal into text data; a data analysis algorithm that generates an appropriate diagnosis result based on the text data analyzed by the server; The system of claim 1 , comprising:

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