System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting speech data by a user; means for converting the speech data into text data by a speech recognition engine; means for translating the text data into a standard language by a natural language processing engine; and means for providing the translated text data in the standard language to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When medical students and healthcare professionals provide medical care in rural or remote areas, local dialects and foreign languages ​​can become communication barriers. This language barrier not only hinders accurate diagnosis and treatment, but also significantly reduces the learning efficiency of medical students. The time and effort spent learning the local language can distract students from their primary medical studies and practice. This issue must be addressed as soon as possible, especially in medical settings where human lives are at stake. [Means for solving the problem]

[0005] The present invention provides a system that uses speech recognition technology and natural language processing technology to input speech data from a user, convert the input speech data into text data using a speech recognition engine, and translate the text data into standard Japanese using a natural language processing engine. This system includes the following means.

[0006] The system includes a means for a user to input voice data, a means for converting the voice data into text data using a voice recognition engine, a means for translating the text data into standard Japanese using a natural language processing engine, and a means for providing the translated text data in standard Japanese to the user.

[0007] It further includes means for transmitting voice data to a server via a network, means for converting the voice data received by the server into text data using a voice recognition engine, means for translating the converted text data into standard Japanese using a natural language processing engine, and means for transmitting the translated text data in standard Japanese back to the user terminal.

[0008] In addition, the system includes a means for the user terminal to output the translated text data in standard Japanese aloud using a speech synthesis engine, and a means for providing the user with the output standard Japanese speech, thereby realizing prompt and accurate communication without the need for local dialects or foreign languages ​​to be a barrier for medical students and medical professionals.

[0009] A "user" is someone who inputs voice data, such as a medical student or medical professional who uses the system.

[0010] A "terminal" is a digital device used by a user to input speech and receive translation results, and includes devices such as a smartphone, tablet, or computer.

[0011] "Voice data" refers to words or sentences spoken by a user into a terminal and captured in digital format via a microphone.

[0012] A "server" is a computer system that receives voice data from a terminal via a network and processes the data.

[0013] A "voice recognition engine" is a software or hardware system that analyzes voice data and converts it into text data.

[0014] "Text data" is character string data converted from voice data by a voice recognition engine.

[0015] A "natural language processing engine" is a software or hardware system that analyzes input text data and translates it into another language or text in a different format.

[0016] "Standard language" is the commonly understood form of language, as opposed to a dialect or foreign language used in a particular region or group.

[0017] A "network" is a communication system for transmitting and receiving data between a terminal and a server, and includes the Internet, a local area network (LAN), and the like.

[0018] A "speech synthesis engine" is a software or hardware system that converts text data into speech and plays it back. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] As an embodiment of the present invention, we provide a system in which a user inputs voice data, converts it into text data using a voice recognition engine, and then translates the text data into standard Japanese using a natural language processing engine. Below, we will explain the details of the system and the program processing in natural language, and also show an embodiment with specific examples.

[0041] System Configuration

[0042] The system mainly consists of the following components:

[0043] 1. User terminal: Inputs voice data and displays / outputs the results.

[0044] 2. Server: Responsible for processing voice data.

[0045] 3. Speech recognition engine: Converts voice data into text data.

[0046] 4. Natural language processing engine: Translates text data into standard Japanese.

[0047] 5. Network: Sends and receives data between user terminals and servers.

[0048] Program processing

[0049] 1. Acquiring voice input

[0050] During medical activities in the field, the user speaks a patient's conversation into the terminal. For example, the patient says in the local dialect, "Hey, are you okay?"

[0051] The device uses a built-in microphone to capture audio and stores it in a buffer as audio data.

[0052] 2. Preprocessing and transmission of audio data

[0053] The terminal converts the audio data into a specific format, compresses and encrypts the data, and then transmits it to a server via a network.

[0054] 3. Voice Recognition

[0055] The server passes the received voice data to a voice recognition engine, which converts the voice data into text data. For example, "Onshan, you're OK" is converted into text data like "Onshan, you're OK."

[0056] 4. Language Processing and Translation

[0057] The server passes the text data to a natural language processing engine, which translates the dialect into standard Japanese. For example, "Onshan, you're OK" is translated into "Are you OK?"

[0058] 5. Sending and displaying translation results

[0059] The server then transmits the translation results to the terminal again via the network.

[0060] The device stores the translated text in a buffer and displays it in the user interface, for example, displaying "Are you OK?" on the screen.

[0061] If necessary, the device will use a speech synthesis engine to play "Are you OK?"

[0062] Specific examples

[0063] As a concrete example, consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you OK?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you OK?", using a natural language processing engine. The translation result is then sent back to the device and displayed to the user or played aloud. This process allows the user to understand the local dialect and respond quickly and accurately.

[0064] The above is an embodiment of the present invention. This system enables medical students and medical professionals to carry out medical activities efficiently without having to worry about the local language barrier.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user starts to input voice into the terminal. Specifically, in a medical setting, a patient speaks in a local dialect, saying, "Hey, are you okay?" If the user cannot understand what is being said, they can input voice into the microphone of the terminal.

[0068] Step 2:

[0069] The device uses a microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[0070] Step 3:

[0071] The device converts the captured audio data into a format (e.g., WAV or MP3), which includes encoding to preserve audio quality.

[0072] Step 4:

[0073] The device then sends the converted voice data to the server via the network, compressing the data for efficient data transfer and encrypting it to ensure security.

[0074] Step 5:

[0075] The server receives the voice data sent over the network, decodes it again, and passes it to the voice recognition engine.

[0076] Step 6:

[0077] The server's speech recognition engine analyzes the voice data and converts it into the corresponding text data, "You're OK, Onshan." This conversion process uses advanced speech analysis algorithms.

[0078] Step 7:

[0079] The server passes the generated text data to a natural language processing engine, which analyzes the text data and translates it into the corresponding standard Japanese text, "Are you OK?"

[0080] Step 8:

[0081] The server then sends the translated standard Japanese text data back to the terminal via the network, and this transmission is also securely encrypted.

[0082] Step 9:

[0083] The terminal stores the received standard Japanese text data, "Are you OK?", in a buffer. The stored text is immediately displayed to the user.

[0084] Step 10:

[0085] The device passes the text data to a speech synthesis engine as needed and outputs the translation results as voice. Specifically, the speech synthesis engine reads out to the user, "Are you okay?"

[0086] This flow allows users to understand local dialects in real time and provide prompt and accurate medical care.

[0087] Example 1

[0088] 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."

[0089] In situations where local dialects and specific languages ​​are barriers, there is a lack of practical and efficient means of communication. Especially in medical settings, where quick and accurate communication with patients is required, a system is needed that can translate dialects into standard Japanese and provide appropriate responses. This system also needs to ensure data security and minimize processing time lags.

[0090] 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.

[0091] In this invention, the server includes means for converting voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, and means for re-encrypting the translated standard Japanese text data and transmitting it to the user terminal. This allows the user to respond quickly and accurately by converting local dialects or specific languages ​​into standard Japanese.

[0092] A "user" is a person who uses the system to input voice data and check the translation results output.

[0093] "Speech data" refers to speech information input by a user that is subject to analysis and translation.

[0094] A "speech recognition engine" is a software or hardware system that converts voice data into text data.

[0095] "Text data" is character information of voice data converted by a voice recognition engine.

[0096] A "natural language processing engine" is a software system that analyzes text data and translates it from a specific language into standard Japanese.

[0097] "Standard language" is a general form of language that is easily understood by many people, rather than a specific region or dialect.

[0098] A "user terminal" is a device that allows a user to input speech and display or play back the translation results.

[0099] A "server" is a remote computer network resource that processes and manages audio data.

[0100] A "network" is a communications infrastructure that enables data communication between user terminals and servers.

[0101] A "speech synthesis engine" is a software or hardware system that converts text data into speech.

[0102] "Encryption" is a method of transforming data based on a certain algorithm in order to protect the data.

[0103] "Decryption" is a method of restoring encrypted data to its original state.

[0104] "Compression" is the process of reducing the size of data.

[0105] As an embodiment of the present invention, a system is provided in which a user inputs voice data and the data is translated into standard Japanese using a voice recognition engine and a natural language processing engine. The detailed configuration and operation of this system are described below.

[0106] System Configuration

[0107] This system consists of the following main components:

[0108] 1. User terminal: An input device such as a smartphone, tablet, or laptop. The user inputs voice data and the results are displayed and output.

[0109] 2. Server: Responsible for processing voice data. Uses a cloud server (e.g., AWS EC2, Google Cloud).

[0110] 3. Speech recognition engine: Converts voice data into text data, for example, using Google Speech-to-Text or Amazon Transcribe.

[0111] 4. Natural language processing engine: Translates text data into standard Japanese. Uses OpenAI GPT-3, Google BERT, etc.

[0112] 5. Network: Wi-Fi or 4G / 5G data connection to send and receive data between user devices and the server.

[0113] 6. Speech synthesis engine: Converts the translation results into speech and plays it back. Examples include Amazon Polly and Google Text-to-Speech.

[0114] Overview of program processing

[0115] When a user inputs voice data, the device's built-in microphone captures the voice and stores it in a buffer.The device then converts the voice data into a certain format (e.g., SIL format), compresses (gzip) and encrypts (AES), and transmits it to the server over the network.

[0116] The server decrypts the received encrypted voice data and passes it to a speech recognition engine (e.g., Google Speech-to-Text) to convert it into text data. The converted text data is temporarily stored in a database. The server then passes the text data to a natural language processing engine (e.g., OpenAI GPT-3) to translate the dialect into standard Japanese.

[0117] The translated text data in standard Japanese is then re-encrypted and sent over the network to the device. The device stores the translated text data in a buffer and displays it on the user interface. If necessary, a speech synthesis engine (e.g., Google Text-to-Speech) is used to play back the translation results aloud.

[0118] Specific examples

[0119] As a concrete example, consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you OK?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you OK?", using a natural language processing engine. The translation result is then sent back to the device and displayed to the user or played aloud. This process allows the user to understand the local dialect and respond quickly and accurately.

[0120] Prompt Sentence Examples

[0121] Write a prompt for a system that supports patient interaction in a hospital. Explain the program process for translating spoken dialect into standard Japanese and displaying the translation.

[0122] Specific names of hardware and software to be used

[0123] User devices: smartphones, tablets, laptops

[0124] Server: Cloud server (e.g. AWS EC2, Google Cloud)

[0125] Speech recognition engine: Google Speech-to-Text, Amazon Transcribe

[0126] Natural language processing engine: OpenAI GPT-3, Google BERT

[0127] Speech synthesis engine: Amazon Polly, Google Text-to-Speech

[0128] Network: Wi-Fi, 4G / 5G data connection

[0129] The above is an embodiment of the present invention, which allows users to efficiently respond and receive appropriate services without having to deal with local dialects or specific language barriers.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Divide the processing flow of the system program into processing steps

[0132] Step 1: Getting voice input

[0133] Step 2: Preprocess and transmit audio data

[0134] Step 3: Voice Recognition

[0135] Step 4: Language Processing and Translation

[0136] Step 5: Send and view the translation results

[0137] Specific explanation of each processing step

[0138] Step 1: Getting voice input

[0139] The user speaks into the device. The input voice data is captured by the device's built-in microphone and saved in a buffer. For example, the user says "Onshan, you're OK" in a dialect.

[0140] Input: User's voice

[0141] Data processing: Converting audio into digital data

[0142] Output: Buffered audio data

[0143] Specific behavior:

[0144] The user taps the voice input button.

[0145] The device's microphone is activated and records what the user says.

[0146] When recording is complete, the audio data is saved in a buffer in WAV format.

[0147] Step 2: Preprocess and transmit audio data

[0148] The device converts the audio data in the buffer into SIL format, compresses it with gzip, encrypts it with AES, and sends it over the network to the server.

[0149] Input: Buffered audio data

[0150] Data processing: format conversion, compression, encryption

[0151] Output: Encrypted audio data is sent to the server

[0152] Specific behavior:

[0153] Run a Python script to convert the audio data into SIL format.

[0154] Compress the data using the gzip library.

[0155] Encrypt the data using an AES encryption library (e.g. PyCryptodome).

[0156] Send the encrypted data to the server in an HTTP POST request.

[0157] Step 3: Voice Recognition

[0158] The server decrypts the received encrypted data using AES, passes the voice data to a speech recognition engine (e.g., Google Speech-to-Text API), and converts it into text data.

[0159] Input: Encrypted audio data

[0160] Data processing: decoding, voice recognition

[0161] Output: Text data

[0162] Specific behavior:

[0163] The server decrypts the received data using AES.

[0164] Sends SIL formatted audio data to the Speech-to-Text API.

[0165] Receives text data and stores it in a MongoDB database.

[0166] Step 4: Language Processing and Translation

[0167] The server passes the stored text data to a natural language processing engine (e.g., OpenAI GPT-3) to translate the dialect into standard Japanese.

[0168] Input: Text data

[0169] Data processing: Translation processing

[0170] Output: Standard Japanese text data

[0171] Specific behavior:

[0172] The server retrieves the text data from the database.

[0173] Send the text data to the GPT-3 API as a prompt.

[0174] The translated text data is obtained and stored in a database.

[0175] Step 5: Send and view the translation results

[0176] The server sends the translation result text data to the device. The device receives this data and displays it on the user interface. If necessary, it plays it aloud using a speech synthesis engine (e.g., Google Text-to-Speech API).

[0177] Input: Translated standard Japanese text data

[0178] Data processing: display or voice synthesis

[0179] Output: Text data displayed in a user interface or audio played

[0180] Specific behavior:

[0181] The server sends the translation results to the device via an HTTP POST request.

[0182] The terminal stores the received data in a buffer and displays it on the user interface.

[0183] If necessary, a TTS (Text-to-Speech) API is called and the translation result is played back as synthesized speech.

[0184] (Application example 1)

[0185] 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."

[0186] In autonomous vehicles, when passengers give instructions in a dialect or a foreign language, it is difficult for the vehicle's navigation system to accurately understand the instructions and respond appropriately. It is necessary to overcome this language barrier and provide a system that can translate passenger instructions into standard Japanese or a specified language in real time, allowing autonomous vehicles to respond accurately and quickly.

[0187] 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.

[0188] In this invention, the server includes means for a user to input voice data, means for converting the voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, means for transmitting the translated text data in standard Japanese to an in-vehicle system, and means for transmitting instructions to a navigation system of the in-vehicle system, thereby enabling an autonomous vehicle to accurately understand instructions given in a passenger's dialect or foreign language and respond appropriately in real time.

[0189] "Means for a user to input voice data" refers to a device or interface that allows a user to input voice data using their voice.

[0190] "Means for converting into text data using a voice recognition engine" refers to software or hardware for analyzing voice data and converting it into character text data.

[0191] "Means for translating into standard Japanese using a natural language processing engine" refers to software or hardware for translating text data into standard Japanese.

[0192] "Means for transmitting the translated text data in standard Japanese to the in-vehicle system" refers to a communication function for transmitting the translated text data to the in-vehicle system.

[0193] "Means for transmitting instructions to the navigation system of the in-vehicle system" refers to the interface and communication means for transmitting instructions to the navigation system.

[0194] "Means for transmitting to a server via a network" refers to a communication system for transmitting audio data to a server via the Internet or a local network.

[0195] "Means for transmitting text data to the in-vehicle system" refers to a communication function for transmitting translated text data to the in-vehicle system.

[0196] "Means for outputting voice using a voice synthesis engine" refers to software or hardware for analyzing text data and outputting it as voice.

[0197] "Means for providing a user with voice output in standard Japanese" refers to a device for providing a user with voice generated by a speech synthesis engine through speakers, headphones, etc.

[0198] A "server" refers to a computer or cloud service that processes voice and text data over a network.

[0199] MODE FOR CARRYING OUT THE INVENTION

[0200] As an embodiment of the present invention, a system will be described in which a user boards an autonomous vehicle and inputs voice data. When a passenger gives instructions in a dialect or foreign language, the system translates the voice into standard Japanese and transmits it to the navigation system of the autonomous vehicle.

[0201] System Configuration

[0202] The system mainly consists of the following components:

[0203] 1. User terminal: Inputs voice data and displays / outputs the results.

[0204] 2. Server: Responsible for processing voice data.

[0205] 3. Speech recognition engine: Converts voice data into text data.

[0206] 4. Natural language processing engine: Translates text data into standard Japanese.

[0207] 5. In-car navigation system: Navigate based on translated instructions.

[0208] 6. Network: Sends and receives data between the user terminal and the server, and between the server and the in-vehicle system.

[0209] System Operation

[0210] 1. Acquiring voice input: Users input voice data via their smartphones or tablets. When a passenger says in their local dialect, "I want to go to the nearest convenience store around here," the voice is captured by the device.

[0211] 2. Audio data preprocessing and transmission: The captured audio data is converted into a certain format, compressed, encrypted, and then transmitted to the server over the network.

[0212] 3. Speech recognition: The server analyzes the received voice data using a voice recognition engine and converts it into text data.

[0213] 4. Language processing and translation: The server passes the text data to a natural language processing engine to translate the dialect or foreign language into standard Japanese.

[0214] 5. Sending the translation results: The translated text data in standard Japanese is sent to the user terminal and the in-vehicle system via the network.

[0215] Hardware and software used

[0216] Speech recognition engine: Uses the speech_recognition library.

[0217] Natural language processing engine: uses the googletrans library.

[0218] Speech synthesis engine: Uses the pyttsx3 library.

[0219] Network communication: Internet or local network.

[0220] Specific examples

[0221] For example, if a passenger gets into an autonomous taxi and says in a local dialect, "I'd like to go to the nearest convenience store around here," the system will recognize the speech, translate it into standard Japanese, and communicate it to the navigation system as, "Do you want to go to the nearest convenience store around here?" The navigation system will follow this instruction, calculate the optimal route, and head to the specified destination.

[0222] Prompt Sentence Examples

[0223] The voice recognition engine converts the input voice data into text data, and the natural language processing engine translates that text data into standard Japanese.

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1: Getting voice input

[0226] Users input voice data via their smartphones or tablets. When a passenger says something in their local dialect like, "I want to go to the nearest convenience store around here," the voice is captured by the device. The input voice data is stored in a buffer.

[0227] Step 2: Preprocess and transmit audio data

[0228] The device converts the captured audio data into a certain format, specifically preprocessing it by reducing noise and normalizing the volume. The preprocessed audio data is then compressed and encrypted and sent over the network to the server. The input is the captured audio data, and the output is the preprocessed audio data.

[0229] Step 3: Voice Recognition

[0230] The server passes the received audio data to a speech recognition engine. The speech recognition engine (for example, the speech_recognition library) analyzes the audio data and converts it into text data. The input is preprocessed audio data, and the output is the text data converted from the audio.

[0231] Step 4: Language Processing and Translation

[0232] The server passes the converted text data to a natural language processing engine. The natural language processing engine (for example, the GoogleTrans library) translates the dialect or foreign language text data into standard Japanese. The input is the text data generated by the speech recognition engine, and the output is the text data translated into standard Japanese.

[0233] Step 5: Send the translation

[0234] The server then compresses and encrypts the translated text data again and sends it to the user's device and the in-vehicle system via the network. The input is the text data translated into standard Japanese, and the output is the compressed and encrypted text data.

[0235] Step 6: Speech synthesis and navigation instructions

[0236] The user device converts the translated text data into standard Japanese using a speech synthesis engine and provides it to the passenger through the speaker. The in-vehicle navigation system receives the translated instructions, calculates the optimal route, and heads to the specified destination. The input is the translated text data in standard Japanese, and the output is the voice data and instructions from the navigation system.

[0237] The above steps realize a system that enables an autonomous vehicle to accurately understand instructions given by a user in a dialect or foreign language and quickly and accurately navigate to the destination.

[0238] 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.

[0239] The present invention is a system in which a user inputs voice data, converts it into text data using a voice recognition engine, and then translates the text data into standard Japanese using a natural language processing engine. This system is also combined with an emotion engine that recognizes the user's emotions. Details of the system and program processing are explained in natural language below, and an embodiment is shown with specific examples.

[0240] System Configuration

[0241] The system mainly consists of the following components:

[0242] 1. User terminal: Inputs voice data and displays / outputs the results.

[0243] 2. Server: Responsible for processing voice data.

[0244] 3. Speech recognition engine: Converts voice data into text data.

[0245] 4. Natural language processing engine: Translates text data into standard Japanese.

[0246] 5. Emotion engine: Analyzes text data and recognizes user emotions.

[0247] 6. Network: Sends and receives data between user terminals and servers.

[0248] Program processing

[0249] 1. Acquiring voice input

[0250] During medical activities in the field, the user speaks to the terminal about the patient's conversation. For example, the patient says in a local dialect, "Hey, are you okay?" The user cannot understand the words, so they input the voice into the terminal.

[0251] 2. Preprocessing and transmission of audio data

[0252] The device uses the built-in microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[0253] The audio data is converted into a certain format, and the data is compressed and encrypted before being sent to a server via a network.

[0254] 3. Voice Recognition

[0255] The server passes the received voice data to a voice recognition engine, which converts the voice data into corresponding text data. For example, "Onshan, you're OK" is converted into text data like "Onshan, you're OK."

[0256] 4. Language Processing and Translation

[0257] The server passes the text data to a natural language processing engine, which translates the dialect into standard Japanese. For example, "Onshan, you're OK" is translated into "Are you OK?"

[0258] 5. Emotion analysis

[0259] The server sends the translated text data in standard Japanese to the emotion engine, which analyzes the user's emotions. As a result, data with emotional information added is generated. For example, if a patient is worried, the text data generated is, "Are you okay? (Worried)."

[0260] 6. Sending and displaying translation results and emotional information

[0261] The server then sends the translation results and emotion information back to the terminal via the network.

[0262] The device stores the received standard Japanese text data and emotion information in a buffer and displays it on the user interface. For example, the screen displays "Are you okay? (Worried)."

[0263] If necessary, the device will use a speech synthesis engine to play the voice "Are you OK? (I'm worried)."

[0264] Specific examples

[0265] As a concrete example, consider a scenario in which a user working in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you okay?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you okay?", using a natural language processing engine. The emotion engine then analyzes the user's emotion, and if it recognizes it as "worried," text data is created with that information added. This text data is then sent back to the device and displayed to the user or output as voice. This process allows the user to not only understand the local dialect, but also to understand the patient's emotion, enabling more appropriate treatment.

[0266] The above is an embodiment of the present invention. Through this system, medical students and medical professionals can efficiently carry out medical activities without having to deal with the language or emotional barriers of the local area.

[0267] The processing flow will be explained below.

[0268] Step 1:

[0269] The user starts to input voice into the terminal. Specifically, in a medical setting, a patient says in a local dialect, "Hey, are you okay?" If the user cannot understand what is being said, they can input voice into the microphone of the terminal.

[0270] Step 2:

[0271] The device uses a microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[0272] Step 3:

[0273] The device converts the captured audio data into a format (e.g., WAV or MP3), which includes encoding to preserve audio quality.

[0274] Step 4:

[0275] The device then sends the converted voice data to the server via the network, compressing the data for efficient data transfer and encrypting it to ensure security.

[0276] Step 5:

[0277] The server receives the voice data sent over the network, decodes it again, and passes it to the voice recognition engine.

[0278] Step 6:

[0279] The server's speech recognition engine analyzes the voice data and converts it into the corresponding text data, "You're OK, Onshan." This conversion process uses advanced speech analysis algorithms.

[0280] Step 7:

[0281] The server passes the generated text data to a natural language processing engine, which analyzes the text data and translates it into the corresponding standard Japanese text, "Are you OK?"

[0282] Step 8:

[0283] The server sends the translated text data in standard Japanese to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the context and phrasing of the text data to extract specific emotions (e.g., "worry").

[0284] Step 9:

[0285] The server adds the results of the sentiment analysis to the translated standard Japanese text data. For example, the text data generated is "Are you okay? (Worried)."

[0286] Step 10:

[0287] The server then sends the translation results and emotion information to the device via the network, and this transmission is also encrypted and secure.

[0288] Step 11:

[0289] The terminal stores the received standard Japanese text data "Are you OK? (I'm worried)" in a buffer. The stored text is immediately displayed on the user interface.

[0290] Step 12:

[0291] The device passes the text data to a speech synthesis engine as needed and outputs the translation results as voice. Specifically, the speech synthesis engine reads out to the user, "Are you okay? (I'm worried)."

[0292] This flow allows users to understand local dialects in real time and also grasp the patient's emotions, enabling more appropriate medical care.

[0293] Example 2

[0294] 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."

[0295] In local medical activities, users often have difficulty understanding patients who speak in local dialects. It is also difficult to accurately grasp the patient's emotions. In such situations, there is a high possibility that appropriate medical care will be delayed, so a method to overcome the language and emotional barriers is needed.

[0296] 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.

[0297] In this invention, the server includes means for preprocessing voice data, means for converting the preprocessed voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, means for analyzing emotions using an emotion analysis engine, and means for transmitting the text data with added emotion information to a user terminal. This makes it possible to translate the voice data into standard Japanese, analyze the patient's emotions, and provide the results to the user.

[0298] "Audio data" refers to data obtained by converting an audio signal into digital information.

[0299] "Preprocessing" refers to processes such as noise removal and volume normalization that are performed to make audio data easier to analyze.

[0300] A "server" is a centralized computing device that processes audio data over a network.

[0301] A "voice recognition engine" is software or hardware for converting voice data into text data.

[0302] "Text data" is character information converted by a voice recognition engine.

[0303] A "natural language processing engine" is software or hardware for analyzing text data and performing specific language processing (e.g., translation).

[0304] "Standard language" is a common, widely understood form of language, not a regional dialect.

[0305] An "emotion analysis engine" is software or hardware that identifies emotions from text data and adds that emotional information.

[0306] "Emotion information" is data relating to the type and intensity of emotions added to text data.

[0307] A "user terminal" is a device that allows a user to input voice data and display or output the results as voice.

[0308] A "network" is a communications infrastructure for sending and receiving data.

[0309] A "voice synthesis engine" is software or hardware that converts text data into voice data and outputs the voice.

[0310] The present invention is a system in which a user inputs voice data, converts it into text data using a voice recognition engine, translates the text data into standard Japanese using a natural language processing engine, and recognizes the user's emotions using an emotion analysis engine. This system is mainly composed of a user terminal, a server, a voice recognition engine, a natural language processing engine, an emotion analysis engine, and a network.

[0311] System Configuration

[0312] 1. User Device

[0313] This device inputs and preprocesses voice data. It has a built-in microphone for capturing the user's voice input. It also has the function of preprocessing the voice data and sending it to a server via a network. It also has the function of displaying the results and outputting the voice.

[0314] 2. Server

[0315] It is a centralized computing device that processes voice data. It has the function of passing received voice data to a voice recognition engine and converting it into text data. It also has the function of translating text data into standard Japanese using a natural language processing engine and analyzing emotions using an emotion analysis engine.

[0316] 3. Speech Recognition Engine

[0317] It is software or hardware that converts voice data into text data. For example, a common voice recognition engine is the Google Speech-to-Text API.

[0318] 4. Natural Language Processing Engine

[0319] It is software or hardware used to analyze text data and perform specific language processing (e.g., translation). For example, Microsoft Azure Cognitive Services is often used.

[0320] 5. Sentiment Analysis Engine

[0321] This is software or hardware that identifies emotions from text data and adds that emotional information. IBM Watson Tone Analyzer is commonly used.

[0322] 6. Network

[0323] It is a communications infrastructure for sending and receiving data. For example, a user terminal and a server are connected via the Internet.

[0324] System Operation

[0325] Acquiring voice input

[0326] The user speaks voice data into the terminal. For example, if the patient says "Hey, are you OK?" in a local dialect, the user inputs the voice data into the terminal.

[0327] Audio data preprocessing

[0328] The device uses the built-in microphone to capture audio data, temporarily stores it in the device's buffer, and then performs preprocessing such as noise reduction and volume normalization before converting it to a certain format (e.g., PCM).

[0329] Sending audio data

[0330] The terminal compresses and encrypts the pre-processed voice data and transmits it to the server via the network.

[0331] Voice Recognition

[0332] The server passes the received voice data to a speech recognition engine (for example, Google Speech-to-Text API) and converts the voice data into text data. For example, "Onshan, are you OK?" becomes "Onshan, are you OK?"

[0333] Language Processing and Translation

[0334] The server passes the text data to a natural language processing engine (e.g., Microsoft Azure Cognitive Services) and translates the dialect into standard Japanese. For example, "You okay?" is translated into "Are you okay?"

[0335] Emotion analysis

[0336] The server then sends the translated text data in standard Japanese to a sentiment analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. As a result, text data with emotional information such as "worry" is generated.

[0337] Sending the results

[0338] The server then sends the translation results and emotion information back to the terminal via the network.

[0339] Display and Audio Output

[0340] The device stores the received standard Japanese text data and emotion information in the device's buffer and displays it on the user interface. For example, the device displays "Are you okay? (Worried)" on the screen. If necessary, the device also outputs this text data as voice using a speech synthesis engine (e.g., Amazon Polly). For example, the device plays back the voice "Are you okay? (Worried)."

[0341] Specific examples

[0342] Consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you okay?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server uses a speech recognition engine to convert the speech data into text, which is then translated into standard Japanese, "Are you okay?", through a natural language processing engine. The emotion analysis engine then analyzes the user's emotions, and if it recognizes the emotion as "worried," text data is created with that information added. This text data is then sent back to the device and displayed to the user or output as voice. This process allows the user to not only understand the local dialect, but also to understand the patient's emotions, enabling more appropriate treatment.

[0343] Example prompts to input to the generative AI model

[0344] A patient from the Kyushu region said in a local dialect, "Onshan, you're OK." Please translate this speech data into standard Japanese, "Are you OK?" Also, please analyze the emotions perceived from the statement and display the results.

[0345] The above is an embodiment of the present invention. Through this system, medical professionals can overcome the language and emotional barriers of the local area and carry out medical activities efficiently.

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Program processing flow

[0348] Step 1: Getting voice input

[0349] The user inputs voice into the device. For example, the user speaks the patient's voice directly into the device, saying, "Hey, are you okay?" The device has a built-in microphone for capturing voice. The input voice data is temporarily stored in the device's buffer.

[0350] Step 2: Preprocessing the audio data

[0351] The device uses the built-in microphone to capture audio data and performs preprocessing such as noise reduction and volume normalization. Specifically, the audio data is filtered to remove noise and the volume level is made uniform. The audio data is then converted into a certain format (e.g., PCM format). The preprocessed audio data is output.

[0352] Step 3: Sending audio data

[0353] The device compresses the pre-processed audio data, encrypts it using SSL / TLS, and then transmits it to the server via the network. Through this transmission process, the encrypted audio data arrives at the server.

[0354] Step 4: Voice Recognition

[0355] The server passes the received voice data to a voice recognition engine. For example, the H voice recognition engine, which is commonly used as a voice recognition engine, is applied. The voice recognition engine analyzes the input voice data and converts it into the corresponding text data. For example, the voice saying "Onshan, are you OK?" is converted into the text data "Onshan, are you OK?" This converted text data is output.

[0356] Step 5: Sending text data

[0357] The server sends the converted text data to the natural language processing engine. Through this sending process, the text data is passed to the natural language processing engine.

[0358] Step 6: Natural Language Processing and Translation

[0359] The server uses a natural language processing engine (e.g., a widely used natural language processing engine) to translate the text data from the local language (dialect) to standard Japanese. For example, the text "Are you OK?" is translated to "Are you OK?" This translated text data in standard Japanese is the output.

[0360] Step 7: Sentiment Analysis

[0361] The server sends the translated text data in standard Japanese to a sentiment analysis engine. The sentiment analysis engine (for example, a commonly used sentiment analysis engine) analyzes emotions from the text and adds the emotional information. For example, the text "Are you okay?" is analyzed with the emotional information "worried." The text data with this emotional information added is the output.

[0362] Step 8: Sending the results

[0363] The server then transmits the text data with the added emotion information back to the terminal via the network. Through this transmission process, the text data with the added emotion information arrives at the terminal.

[0364] Step 9: Display and audio output of results

[0365] The device stores the received text data with the added emotion information in the device's buffer. It then displays it on the user interface. For example, the device displays "Are you okay? (Worried)" on the screen. If necessary, the device uses a speech synthesis engine to output the text data as voice. For example, the device plays back the voice "Are you okay? (Worried)."

[0366] The above is the specific processing flow of the program of this system.

[0367] (Application example 2)

[0368] 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."

[0369] In situations where it is necessary not only to understand the local language or dialect, but also to instantly grasp the speaker's emotions, conventional technologies face the challenge of being unable to respond appropriately. Security services, in particular, require accurate understanding of the speech of visitors and suspicious individuals and analyzing their emotions to detect potential risks early and respond safely and quickly. However, current speech recognition and translation systems lack the ability to analyze emotions, making it difficult to meet these requirements.

[0370] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for transmitting voice data to the server via a network, means for converting the voice data received by the server into text data using a voice recognition engine, means for translating the converted text data into standard Japanese using a natural language processing engine, means for adding emotional information to the translated text data in standard Japanese, and means for transmitting the text data including the emotional information back to the user terminal. This enables security services to translate the statements of visitors and suspicious individuals in real time and analyze their emotions, thereby enabling early detection of potential risks and safe and prompt response.

[0371] A "user terminal" is a device that inputs voice data and displays and outputs the results.

[0372] "Speech data" refers to data obtained from a user's speech that is converted into text data by a speech recognition engine.

[0373] A "voice recognition engine" is a software engine for converting voice data into text data.

[0374] "Text data" is data expressed as characters that has been converted by a voice recognition engine.

[0375] A "natural language processing engine" is a software engine for translating text data into standard Japanese.

[0376] "Standard language" is the common language form used when text data is translated.

[0377] An "emotion analysis engine" is a software engine for analyzing emotional information from text data.

[0378] "Emotion information" is data that indicates the speaker's emotions and is added by an emotion analysis engine.

[0379] A "user interface" is a means for displaying translation results and emotional information on a user terminal.

[0380] A "server" is a remote computer system responsible for processing audio data.

[0381] A "network" is a communications infrastructure that transmits and receives data between user terminals and servers.

[0382] A "speech synthesis engine" is a software engine for converting text data into speech.

[0383] This system inputs voice data from a user, converts it into text data using a voice recognition engine, translates it into standard Japanese using a natural language processing engine, and then adds emotional information using an emotion analysis engine.Finally, the translation results and emotional information are displayed on a user interface or output as voice using a voice synthesis engine.

[0384] System Configuration

[0385] The system mainly consists of the following components:

[0386] 1. User terminal: Inputs voice data and displays / outputs the results.

[0387] 2. Server: Responsible for processing voice data.

[0388] 3. Speech recognition engine: Converts voice data into text data.

[0389] 4. Natural language processing engine: Translates text data into standard Japanese.

[0390] 5. Sentiment analysis engine: Analyzes text data and adds emotional information.

[0391] 6. Network: Sends and receives data between user terminals and servers.

[0392] 7. Speech synthesis engine: Converts translated text data into speech.

[0393] Program processing

[0394] The server receives the voice data via the network and converts it into text data using a speech recognition engine. A specific example of a speech recognition engine is the Google Cloud Speech-to-Text API. The converted text data is then translated into standard Japanese using Amazon Translate. Emotional information is added to the translated standard Japanese text data using IBM Watson's emotion analysis API. This text data, including the emotional information, is then sent back to the user's device and displayed on the user interface or output as audio using a speech synthesis engine (e.g., Google Text-to-Speech).

[0395] Hardware, software, and data processing / calculation used

[0396] Hardware: Smart glasses

[0397] Audio data is acquired using the built-in microphone.

[0398] The display shows translation results and emotional information.

[0399] software:

[0400] Speech recognition engine: Google Cloud Speech-to-Text API

[0401] Natural language processing engine: Amazon Translate

[0402] Sentiment analysis engine: IBM Watson Sentiment Analysis API

[0403] Speech synthesis engine: Google Text-to-Speech

[0404] Specific examples

[0405] For example, consider a scenario in which security staff working at an airport use smart glasses to communicate with visitors. If a visitor speaks in a dialect or with a different accent, saying, "There's no problem here, right?", the staff member will capture the voice data using the smart glasses' built-in microphone and send it to a server in real time. The server will convert the voice data into text data using a speech recognition engine, and then translate it into standard Japanese, "There's no danger here, right?" using a natural language processing engine. The emotion analysis engine will then recognize the voice data as "worried," and text data with that information added will be generated. This text data will then be sent back to the smart glasses, and the message "There's no danger here, right? (worried)" will appear on the display.

[0406] Example prompts to input to the generative AI model

[0407] Input the following prompts into the AI ​​model to get translation and sentiment analysis results:

[0408] "Please translate the following dialect speech data into standard Japanese and analyze the sentiment: 'There's nothing wrong with this, right?'"

[0409] In this way, security staff can understand local language and sentiment and respond appropriately.The system can be used in many other applications as well.

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1:

[0412] The user terminal acquires the voice data. The user speaks into the built-in microphone of the smart glasses and inputs the voice data, such as "There's no problem here, right?". This voice data is temporarily stored in a buffer.

[0413] Step 2:

[0414] The user terminal sends voice data to the server. The voice data is converted to WAV format, encrypted, and sent over the network. The input is the voice data, and the output is the voice data sent to the server.

[0415] Step 3:

[0416] The server uses a speech recognition engine to convert the voice data into text data. Using the Google Cloud Speech-to-Text API, the input is the transmitted voice data, and the output is the text data "There's nothing wrong with this, right?"

[0417] Step 4:

[0418] The server uses a natural language processing engine to translate the text data into standard Japanese. Using Amazon Translate, the input is the converted text data "There's no problem here, right?", and the output is standard Japanese "There's no danger here, right?"

[0419] Step 5:

[0420] The server uses an emotion analysis engine to add emotional information to the translated text data. Using IBM Watson's emotion analysis API, it recognizes "worried," and the input is text data in standard Japanese, and the output is text data that reads, "This place isn't dangerous, is it? (worried)."

[0421] Step 6:

[0422] The server sends the text data containing emotion information to the user terminal again. It is encrypted again and sent over the network. The input is the text data containing emotion information, and the output is the text data sent to the user terminal.

[0423] Step 7:

[0424] The user terminal displays the received text data on the user interface. At this time, the display shows "This place is not dangerous, right? (Worried)." The input is the received text data containing emotional information, and the output is the display.

[0425] Step 8:

[0426] If necessary, the user device uses a speech synthesis engine to convert text data into speech and output it. Using Google Text-to-Speech, the input is text data and the output is speech.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Second embodiment]

[0431] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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."

[0443] As an embodiment of the present invention, we provide a system in which a user inputs voice data, converts it into text data using a voice recognition engine, and then translates the text data into standard Japanese using a natural language processing engine. Below, we will explain the details of the system and the program processing in natural language, and also show an embodiment with specific examples.

[0444] System Configuration

[0445] The system mainly consists of the following components:

[0446] 1. User terminal: Inputs voice data and displays / outputs the results.

[0447] 2. Server: Responsible for processing voice data.

[0448] 3. Speech recognition engine: Converts voice data into text data.

[0449] 4. Natural language processing engine: Translates text data into standard Japanese.

[0450] 5. Network: Sends and receives data between user terminals and servers.

[0451] Program processing

[0452] 1. Acquiring voice input

[0453] During medical activities in the field, the user speaks a patient's conversation into the terminal. For example, the patient says in the local dialect, "Hey, are you okay?"

[0454] The device uses a built-in microphone to capture audio and stores it in a buffer as audio data.

[0455] 2. Preprocessing and transmission of audio data

[0456] The terminal converts the audio data into a specific format, compresses and encrypts the data, and then transmits it to a server via a network.

[0457] 3. Voice Recognition

[0458] The server passes the received voice data to a voice recognition engine, which converts the voice data into text data. For example, "Onshan, you're OK" is converted into text data like "Onshan, you're OK."

[0459] 4. Language Processing and Translation

[0460] The server passes the text data to a natural language processing engine, which translates the dialect into standard Japanese. For example, "Onshan, you're OK" is translated into "Are you OK?"

[0461] 5. Sending and displaying translation results

[0462] The server then transmits the translation results to the terminal again via the network.

[0463] The device stores the translated text in a buffer and displays it in the user interface, for example, displaying "Are you OK?" on the screen.

[0464] If necessary, the device will use a speech synthesis engine to play "Are you OK?"

[0465] Specific examples

[0466] As a concrete example, consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you OK?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you OK?", using a natural language processing engine. The translation result is then sent back to the device and displayed to the user or played aloud. This process allows the user to understand the local dialect and respond quickly and accurately.

[0467] The above is an embodiment of the present invention. This system enables medical students and medical professionals to carry out medical activities efficiently without having to worry about the local language barrier.

[0468] The processing flow will be explained below.

[0469] Step 1:

[0470] The user starts to input voice into the terminal. Specifically, in a medical setting, a patient speaks in a local dialect, saying, "Hey, are you okay?" If the user cannot understand what is being said, they can input voice into the microphone of the terminal.

[0471] Step 2:

[0472] The device uses a microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[0473] Step 3:

[0474] The device converts the captured audio data into a format (e.g., WAV or MP3), which includes encoding to preserve audio quality.

[0475] Step 4:

[0476] The device then sends the converted voice data to the server via the network, compressing the data for efficient data transfer and encrypting it to ensure security.

[0477] Step 5:

[0478] The server receives the voice data sent over the network, decodes it again, and passes it to the voice recognition engine.

[0479] Step 6:

[0480] The server's speech recognition engine analyzes the voice data and converts it into the corresponding text data, "You're OK, Onshan." This conversion process uses advanced speech analysis algorithms.

[0481] Step 7:

[0482] The server passes the generated text data to a natural language processing engine, which analyzes the text data and translates it into the corresponding standard Japanese text, "Are you OK?"

[0483] Step 8:

[0484] The server then sends the translated standard Japanese text data back to the terminal via the network, and this transmission is also securely encrypted.

[0485] Step 9:

[0486] The terminal stores the received standard Japanese text data, "Are you OK?", in a buffer. The stored text is immediately displayed to the user.

[0487] Step 10:

[0488] The device passes the text data to a speech synthesis engine as needed and outputs the translation results as voice. Specifically, the speech synthesis engine reads out to the user, "Are you okay?"

[0489] This flow allows users to understand local dialects in real time and provide prompt and accurate medical care.

[0490] Example 1

[0491] 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."

[0492] In situations where local dialects and specific languages ​​are barriers, there is a lack of practical and efficient means of communication. Especially in medical settings, where quick and accurate communication with patients is required, a system is needed that can translate dialects into standard Japanese and provide appropriate responses. This system also needs to ensure data security and minimize processing time lags.

[0493] 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.

[0494] In this invention, the server includes means for converting voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, and means for re-encrypting the translated standard Japanese text data and transmitting it to the user terminal. This allows the user to respond quickly and accurately by converting local dialects or specific languages ​​into standard Japanese.

[0495] A "user" is a person who uses the system to input voice data and check the translation results output.

[0496] "Speech data" refers to speech information input by a user that is subject to analysis and translation.

[0497] A "speech recognition engine" is a software or hardware system that converts voice data into text data.

[0498] "Text data" is character information of voice data converted by a voice recognition engine.

[0499] A "natural language processing engine" is a software system that analyzes text data and translates it from a specific language into standard Japanese.

[0500] "Standard language" is a general form of language that is easily understood by many people, rather than a specific region or dialect.

[0501] A "user terminal" is a device that allows a user to input speech and display or play back the translation results.

[0502] A "server" is a remote computer network resource that processes and manages audio data.

[0503] A "network" is a communications infrastructure that enables data communication between user terminals and servers.

[0504] A "speech synthesis engine" is a software or hardware system that converts text data into speech.

[0505] "Encryption" is a method of transforming data based on a certain algorithm in order to protect the data.

[0506] "Decryption" is a method of restoring encrypted data to its original state.

[0507] "Compression" is the process of reducing the size of data.

[0508] As an embodiment of the present invention, a system is provided in which a user inputs voice data and the data is translated into standard Japanese using a voice recognition engine and a natural language processing engine. The detailed configuration and operation of this system are described below.

[0509] System Configuration

[0510] This system consists of the following main components:

[0511] 1. User terminal: An input device such as a smartphone, tablet, or laptop. The user inputs voice data and the results are displayed and output.

[0512] 2. Server: Responsible for processing voice data. Uses a cloud server (e.g., AWS EC2, Google Cloud).

[0513] 3. Speech recognition engine: Converts voice data into text data, for example, using Google Speech-to-Text or Amazon Transcribe.

[0514] 4. Natural language processing engine: Translates text data into standard Japanese. Uses OpenAI GPT-3, Google BERT, etc.

[0515] 5. Network: Wi-Fi or 4G / 5G data connection to send and receive data between user devices and the server.

[0516] 6. Speech synthesis engine: Converts the translation results into speech and plays it back. Examples include Amazon Polly and Google Text-to-Speech.

[0517] Overview of program processing

[0518] When a user inputs voice data, the device's built-in microphone captures the voice and stores it in a buffer.The device then converts the voice data into a certain format (e.g., SIL format), compresses (gzip) and encrypts (AES), and transmits it to the server over the network.

[0519] The server decrypts the received encrypted voice data and passes it to a speech recognition engine (e.g., Google Speech-to-Text) to convert it into text data. The converted text data is temporarily stored in a database. The server then passes the text data to a natural language processing engine (e.g., OpenAI GPT-3) to translate the dialect into standard Japanese.

[0520] The translated text data in standard Japanese is then re-encrypted and sent over the network to the device. The device stores the translated text data in a buffer and displays it on the user interface. If necessary, a speech synthesis engine (e.g., Google Text-to-Speech) is used to play back the translation results aloud.

[0521] Specific examples

[0522] As a concrete example, consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you OK?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you OK?", using a natural language processing engine. The translation result is then sent back to the device and displayed to the user or played aloud. This process allows the user to understand the local dialect and respond quickly and accurately.

[0523] Prompt Sentence Examples

[0524] Write a prompt for a system that supports patient interaction in a hospital. Explain the program process for translating spoken dialect into standard Japanese and displaying the translation.

[0525] Specific names of hardware and software to be used

[0526] User devices: smartphones, tablets, laptops

[0527] Server: Cloud server (e.g. AWS EC2, Google Cloud)

[0528] Speech recognition engine: Google Speech-to-Text, Amazon Transcribe

[0529] Natural language processing engine: OpenAI GPT-3, Google BERT

[0530] Speech synthesis engine: Amazon Polly, Google Text-to-Speech

[0531] Network: Wi-Fi, 4G / 5G data connection

[0532] The above is an embodiment of the present invention, which allows users to efficiently respond and receive appropriate services without having to deal with local dialects or specific language barriers.

[0533] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0534] Divide the processing flow of the system program into processing steps

[0535] Step 1: Getting voice input

[0536] Step 2: Preprocess and transmit audio data

[0537] Step 3: Voice Recognition

[0538] Step 4: Language Processing and Translation

[0539] Step 5: Send and view the translation results

[0540] Specific explanation of each processing step

[0541] Step 1: Getting voice input

[0542] The user speaks into the device. The input voice data is captured by the device's built-in microphone and saved in a buffer. For example, the user says "Onshan, you're OK" in a dialect.

[0543] Input: User's voice

[0544] Data processing: Converting audio into digital data

[0545] Output: Buffered audio data

[0546] Specific behavior:

[0547] The user taps the voice input button.

[0548] The device's microphone is activated and records what the user says.

[0549] When recording is complete, the audio data is saved in a buffer in WAV format.

[0550] Step 2: Preprocess and transmit audio data

[0551] The device converts the audio data in the buffer into SIL format, compresses it with gzip, encrypts it with AES, and sends it over the network to the server.

[0552] Input: Buffered audio data

[0553] Data processing: format conversion, compression, encryption

[0554] Output: Encrypted audio data is sent to the server

[0555] Specific behavior:

[0556] Run a Python script to convert the audio data into SIL format.

[0557] Compress the data using the gzip library.

[0558] Encrypt the data using an AES encryption library (e.g. PyCryptodome).

[0559] Send the encrypted data to the server in an HTTP POST request.

[0560] Step 3: Voice Recognition

[0561] The server decrypts the received encrypted data using AES, passes the voice data to a speech recognition engine (e.g., Google Speech-to-Text API), and converts it into text data.

[0562] Input: Encrypted audio data

[0563] Data processing: decoding, voice recognition

[0564] Output: Text data

[0565] Specific behavior:

[0566] The server decrypts the received data using AES.

[0567] Sends SIL formatted audio data to the Speech-to-Text API.

[0568] Receives text data and stores it in a MongoDB database.

[0569] Step 4: Language Processing and Translation

[0570] The server passes the stored text data to a natural language processing engine (e.g., OpenAI GPT-3) to translate the dialect into standard Japanese.

[0571] Input: Text data

[0572] Data processing: Translation processing

[0573] Output: Standard Japanese text data

[0574] Specific behavior:

[0575] The server retrieves the text data from the database.

[0576] Send the text data to the GPT-3 API as a prompt.

[0577] The translated text data is obtained and stored in a database.

[0578] Step 5: Send and view the translation results

[0579] The server sends the translation result text data to the device. The device receives this data and displays it on the user interface. If necessary, it plays it aloud using a speech synthesis engine (e.g., Google Text-to-Speech API).

[0580] Input: Translated standard Japanese text data

[0581] Data processing: display or voice synthesis

[0582] Output: Text data displayed in a user interface or audio played

[0583] Specific behavior:

[0584] The server sends the translation results to the device via an HTTP POST request.

[0585] The terminal stores the received data in a buffer and displays it on the user interface.

[0586] If necessary, a TTS (Text-to-Speech) API is called and the translation result is played back as synthesized speech.

[0587] (Application example 1)

[0588] 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."

[0589] In autonomous vehicles, when passengers give instructions in a dialect or a foreign language, it is difficult for the vehicle's navigation system to accurately understand the instructions and respond appropriately. It is necessary to overcome this language barrier and provide a system that can translate passenger instructions into standard Japanese or a specified language in real time, allowing autonomous vehicles to respond accurately and quickly.

[0590] 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.

[0591] In this invention, the server includes means for a user to input voice data, means for converting the voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, means for transmitting the translated text data in standard Japanese to an in-vehicle system, and means for transmitting instructions to a navigation system of the in-vehicle system, thereby enabling an autonomous vehicle to accurately understand instructions given in a passenger's dialect or foreign language and respond appropriately in real time.

[0592] "Means for a user to input voice data" refers to a device or interface that allows a user to input voice data using their voice.

[0593] "Means for converting into text data using a voice recognition engine" refers to software or hardware for analyzing voice data and converting it into character text data.

[0594] "Means for translating into standard Japanese using a natural language processing engine" refers to software or hardware for translating text data into standard Japanese.

[0595] "Means for transmitting the translated text data in standard Japanese to the in-vehicle system" refers to a communication function for transmitting the translated text data to the in-vehicle system.

[0596] "Means for transmitting instructions to the navigation system of the in-vehicle system" refers to the interface and communication means for transmitting instructions to the navigation system.

[0597] "Means for transmitting to a server via a network" refers to a communication system for transmitting audio data to a server via the Internet or a local network.

[0598] "Means for transmitting text data to the in-vehicle system" refers to a communication function for transmitting translated text data to the in-vehicle system.

[0599] "Means for outputting voice using a voice synthesis engine" refers to software or hardware for analyzing text data and outputting it as voice.

[0600] "Means for providing a user with voice output in standard Japanese" refers to a device for providing a user with voice generated by a speech synthesis engine through speakers, headphones, etc.

[0601] A "server" refers to a computer or cloud service that processes voice and text data over a network.

[0602] MODE FOR CARRYING OUT THE INVENTION

[0603] As an embodiment of the present invention, a system will be described in which a user boards an autonomous vehicle and inputs voice data. When a passenger gives instructions in a dialect or foreign language, the system translates the voice into standard Japanese and transmits it to the navigation system of the autonomous vehicle.

[0604] System Configuration

[0605] The system mainly consists of the following components:

[0606] 1. User terminal: Inputs voice data and displays / outputs the results.

[0607] 2. Server: Responsible for processing voice data.

[0608] 3. Speech recognition engine: Converts voice data into text data.

[0609] 4. Natural language processing engine: Translates text data into standard Japanese.

[0610] 5. In-car navigation system: Navigate based on translated instructions.

[0611] 6. Network: Sends and receives data between the user terminal and the server, and between the server and the in-vehicle system.

[0612] System Operation

[0613] 1. Acquiring voice input: Users input voice data via their smartphones or tablets. When a passenger says in their local dialect, "I want to go to the nearest convenience store around here," the voice is captured by the device.

[0614] 2. Audio data preprocessing and transmission: The captured audio data is converted into a certain format, compressed, encrypted, and then transmitted to the server over the network.

[0615] 3. Speech recognition: The server analyzes the received voice data using a voice recognition engine and converts it into text data.

[0616] 4. Language processing and translation: The server passes the text data to a natural language processing engine to translate the dialect or foreign language into standard Japanese.

[0617] 5. Sending the translation results: The translated text data in standard Japanese is sent to the user terminal and the in-vehicle system via the network.

[0618] Hardware and software used

[0619] Speech recognition engine: Uses the speech_recognition library.

[0620] Natural language processing engine: uses the googletrans library.

[0621] Speech synthesis engine: Uses the pyttsx3 library.

[0622] Network communication: Internet or local network.

[0623] Specific examples

[0624] For example, if a passenger gets into an autonomous taxi and says in a local dialect, "I'd like to go to the nearest convenience store around here," the system will recognize the speech, translate it into standard Japanese, and communicate it to the navigation system as, "Do you want to go to the nearest convenience store around here?" The navigation system will follow this instruction, calculate the optimal route, and head to the specified destination.

[0625] Prompt Sentence Examples

[0626] The voice recognition engine converts the input voice data into text data, and the natural language processing engine translates that text data into standard Japanese.

[0627] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0628] Step 1: Getting voice input

[0629] Users input voice data via their smartphones or tablets. When a passenger says something in their local dialect like, "I want to go to the nearest convenience store around here," the voice is captured by the device. The input voice data is stored in a buffer.

[0630] Step 2: Preprocess and transmit audio data

[0631] The device converts the captured audio data into a certain format, specifically preprocessing it by reducing noise and normalizing the volume. The preprocessed audio data is then compressed and encrypted and sent over the network to the server. The input is the captured audio data, and the output is the preprocessed audio data.

[0632] Step 3: Voice Recognition

[0633] The server passes the received audio data to a speech recognition engine. The speech recognition engine (for example, the speech_recognition library) analyzes the audio data and converts it into text data. The input is preprocessed audio data, and the output is the text data converted from the audio.

[0634] Step 4: Language Processing and Translation

[0635] The server passes the converted text data to a natural language processing engine. The natural language processing engine (for example, the GoogleTrans library) translates the dialect or foreign language text data into standard Japanese. The input is the text data generated by the speech recognition engine, and the output is the text data translated into standard Japanese.

[0636] Step 5: Send the translation

[0637] The server then compresses and encrypts the translated text data again and sends it to the user's device and the in-vehicle system via the network. The input is the text data translated into standard Japanese, and the output is the compressed and encrypted text data.

[0638] Step 6: Speech synthesis and navigation instructions

[0639] The user device converts the translated text data into standard Japanese using a speech synthesis engine and provides it to the passenger through the speaker. The in-vehicle navigation system receives the translated instructions, calculates the optimal route, and heads to the specified destination. The input is the translated text data in standard Japanese, and the output is the voice data and instructions from the navigation system.

[0640] The above steps realize a system that enables an autonomous vehicle to accurately understand instructions given by a user in a dialect or foreign language and quickly and accurately navigate to the destination.

[0641] 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.

[0642] The present invention is a system in which a user inputs voice data, converts it into text data using a voice recognition engine, and then translates the text data into standard Japanese using a natural language processing engine. This system is also combined with an emotion engine that recognizes the user's emotions. Details of the system and program processing are explained in natural language below, and an embodiment is shown with specific examples.

[0643] System Configuration

[0644] The system mainly consists of the following components:

[0645] 1. User terminal: Inputs voice data and displays / outputs the results.

[0646] 2. Server: Responsible for processing voice data.

[0647] 3. Speech recognition engine: Converts voice data into text data.

[0648] 4. Natural language processing engine: Translates text data into standard Japanese.

[0649] 5. Emotion engine: Analyzes text data and recognizes user emotions.

[0650] 6. Network: Sends and receives data between user terminals and servers.

[0651] Program processing

[0652] 1. Acquiring voice input

[0653] During medical activities in the field, the user speaks to the terminal about the patient's conversation. For example, the patient says in a local dialect, "Hey, are you okay?" The user cannot understand the words, so they input the voice into the terminal.

[0654] 2. Preprocessing and transmission of audio data

[0655] The device uses the built-in microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[0656] The audio data is converted into a certain format, and the data is compressed and encrypted before being sent to a server via a network.

[0657] 3. Voice Recognition

[0658] The server passes the received voice data to a voice recognition engine, which converts the voice data into corresponding text data. For example, "Onshan, you're OK" is converted into text data like "Onshan, you're OK."

[0659] 4. Language Processing and Translation

[0660] The server passes the text data to a natural language processing engine, which translates the dialect into standard Japanese. For example, "Onshan, you're OK" is translated into "Are you OK?"

[0661] 5. Emotion analysis

[0662] The server sends the translated text data in standard Japanese to the emotion engine, which analyzes the user's emotions. As a result, data with emotional information added is generated. For example, if a patient is worried, the text data generated is, "Are you okay? (Worried)."

[0663] 6. Sending and displaying translation results and emotional information

[0664] The server then sends the translation results and emotion information back to the terminal via the network.

[0665] The device stores the received standard Japanese text data and emotion information in a buffer and displays it on the user interface. For example, the screen displays "Are you okay? (Worried)."

[0666] If necessary, the device will use a speech synthesis engine to play the voice "Are you OK? (I'm worried)."

[0667] Specific examples

[0668] As a concrete example, consider a scenario in which a user working in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you okay?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you okay?", using a natural language processing engine. The emotion engine then analyzes the user's emotion, and if it recognizes it as "worried," text data is created with that information added. This text data is then sent back to the device and displayed to the user or output as voice. This process allows the user to not only understand the local dialect, but also to understand the patient's emotion, enabling more appropriate treatment.

[0669] The above is an embodiment of the present invention. Through this system, medical students and medical professionals can efficiently carry out medical activities without having to deal with the language or emotional barriers of the local area.

[0670] The processing flow will be explained below.

[0671] Step 1:

[0672] The user starts to input voice into the terminal. Specifically, in a medical setting, a patient says in a local dialect, "Hey, are you okay?" If the user cannot understand what is being said, they can input voice into the microphone of the terminal.

[0673] Step 2:

[0674] The device uses a microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[0675] Step 3:

[0676] The device converts the captured audio data into a format (e.g., WAV or MP3), which includes encoding to preserve audio quality.

[0677] Step 4:

[0678] The device then sends the converted voice data to the server via the network, compressing the data for efficient data transfer and encrypting it to ensure security.

[0679] Step 5:

[0680] The server receives the voice data sent over the network, decodes it again, and passes it to the voice recognition engine.

[0681] Step 6:

[0682] The server's speech recognition engine analyzes the voice data and converts it into the corresponding text data, "You're OK, Onshan." This conversion process uses advanced speech analysis algorithms.

[0683] Step 7:

[0684] The server passes the generated text data to a natural language processing engine, which analyzes the text data and translates it into the corresponding standard Japanese text, "Are you OK?"

[0685] Step 8:

[0686] The server sends the translated text data in standard Japanese to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the context and phrasing of the text data to extract specific emotions (e.g., "worry").

[0687] Step 9:

[0688] The server adds the results of the sentiment analysis to the translated standard Japanese text data. For example, the text data generated is "Are you okay? (Worried)."

[0689] Step 10:

[0690] The server then sends the translation results and emotion information to the device via the network, and this transmission is also encrypted and secure.

[0691] Step 11:

[0692] The terminal stores the received standard Japanese text data "Are you OK? (I'm worried)" in a buffer. The stored text is immediately displayed on the user interface.

[0693] Step 12:

[0694] The device passes the text data to a speech synthesis engine as needed and outputs the translation results as voice. Specifically, the speech synthesis engine reads out to the user, "Are you okay? (I'm worried)."

[0695] This flow allows users to understand local dialects in real time and also grasp the patient's emotions, enabling more appropriate medical care.

[0696] Example 2

[0697] 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."

[0698] In local medical activities, users often have difficulty understanding patients who speak in local dialects. It is also difficult to accurately grasp the patient's emotions. In such situations, there is a high possibility that appropriate medical care will be delayed, so a method to overcome the language and emotional barriers is needed.

[0699] 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.

[0700] In this invention, the server includes means for preprocessing voice data, means for converting the preprocessed voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, means for analyzing emotions using an emotion analysis engine, and means for transmitting the text data with added emotion information to a user terminal. This makes it possible to translate the voice data into standard Japanese, analyze the patient's emotions, and provide the results to the user.

[0701] "Audio data" refers to data obtained by converting an audio signal into digital information.

[0702] "Preprocessing" refers to processes such as noise removal and volume normalization that are performed to make audio data easier to analyze.

[0703] A "server" is a centralized computing device that processes audio data over a network.

[0704] A "voice recognition engine" is software or hardware for converting voice data into text data.

[0705] "Text data" is character information converted by a voice recognition engine.

[0706] A "natural language processing engine" is software or hardware for analyzing text data and performing specific language processing (e.g., translation).

[0707] "Standard language" is a common, widely understood form of language, not a regional dialect.

[0708] An "emotion analysis engine" is software or hardware that identifies emotions from text data and adds that emotional information.

[0709] "Emotion information" is data relating to the type and intensity of emotions added to text data.

[0710] A "user terminal" is a device that allows a user to input voice data and display or output the results as voice.

[0711] A "network" is a communications infrastructure for sending and receiving data.

[0712] A "voice synthesis engine" is software or hardware that converts text data into voice data and outputs the voice.

[0713] The present invention is a system in which a user inputs voice data, converts it into text data using a voice recognition engine, translates the text data into standard Japanese using a natural language processing engine, and recognizes the user's emotions using an emotion analysis engine. This system is mainly composed of a user terminal, a server, a voice recognition engine, a natural language processing engine, an emotion analysis engine, and a network.

[0714] System Configuration

[0715] 1. User Device

[0716] This device inputs and preprocesses voice data. It has a built-in microphone for capturing the user's voice input. It also has the function of preprocessing the voice data and sending it to a server via a network. It also has the function of displaying the results and outputting the voice.

[0717] 2. Server

[0718] It is a centralized computing device that processes voice data. It has the function of passing received voice data to a voice recognition engine and converting it into text data. It also has the function of translating text data into standard Japanese using a natural language processing engine and analyzing emotions using an emotion analysis engine.

[0719] 3. Speech Recognition Engine

[0720] It is software or hardware that converts voice data into text data. For example, a common voice recognition engine is the Google Speech-to-Text API.

[0721] 4. Natural Language Processing Engine

[0722] It is software or hardware used to analyze text data and perform specific language processing (e.g., translation). For example, Microsoft Azure Cognitive Services is often used.

[0723] 5. Sentiment Analysis Engine

[0724] This is software or hardware that identifies emotions from text data and adds that emotional information. IBM Watson Tone Analyzer is commonly used.

[0725] 6. Network

[0726] It is a communications infrastructure for sending and receiving data. For example, a user terminal and a server are connected via the Internet.

[0727] System Operation

[0728] Acquiring voice input

[0729] The user speaks voice data into the terminal. For example, if the patient says "Hey, are you OK?" in a local dialect, the user inputs the voice data into the terminal.

[0730] Audio data preprocessing

[0731] The device uses the built-in microphone to capture audio data, temporarily stores it in the device's buffer, and then performs preprocessing such as noise reduction and volume normalization before converting it to a certain format (e.g., PCM).

[0732] Sending audio data

[0733] The terminal compresses and encrypts the pre-processed voice data and transmits it to the server via the network.

[0734] Voice Recognition

[0735] The server passes the received voice data to a speech recognition engine (for example, Google Speech-to-Text API) and converts the voice data into text data. For example, "Onshan, are you OK?" becomes "Onshan, are you OK?"

[0736] Language Processing and Translation

[0737] The server passes the text data to a natural language processing engine (e.g., Microsoft Azure Cognitive Services) and translates the dialect into standard Japanese. For example, "You okay?" is translated into "Are you okay?"

[0738] Emotion analysis

[0739] The server then sends the translated text data in standard Japanese to a sentiment analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. As a result, text data with emotional information such as "worry" is generated.

[0740] Sending the results

[0741] The server then sends the translation results and emotion information back to the terminal via the network.

[0742] Display and Audio Output

[0743] The device stores the received standard Japanese text data and emotion information in the device's buffer and displays it on the user interface. For example, the device displays "Are you okay? (Worried)" on the screen. If necessary, the device also outputs this text data as voice using a speech synthesis engine (e.g., Amazon Polly). For example, the device plays back the voice "Are you okay? (Worried)."

[0744] Specific examples

[0745] Consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you okay?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server uses a speech recognition engine to convert the speech data into text, which is then translated into standard Japanese, "Are you okay?", through a natural language processing engine. The emotion analysis engine then analyzes the user's emotions, and if it recognizes the emotion as "worried," text data is created with that information added. This text data is then sent back to the device and displayed to the user or output as voice. This process allows the user to not only understand the local dialect, but also to understand the patient's emotions, enabling more appropriate treatment.

[0746] Example prompts to input to the generative AI model

[0747] A patient from the Kyushu region said in a local dialect, "Onshan, you're OK." Please translate this speech data into standard Japanese, "Are you OK?" Also, please analyze the emotions perceived from the statement and display the results.

[0748] The above is an embodiment of the present invention. Through this system, medical professionals can overcome the language and emotional barriers of the local area and carry out medical activities efficiently.

[0749] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0750] Program processing flow

[0751] Step 1: Getting voice input

[0752] The user inputs voice into the device. For example, the user speaks the patient's voice directly into the device, saying, "Hey, are you okay?" The device has a built-in microphone for capturing voice. The input voice data is temporarily stored in the device's buffer.

[0753] Step 2: Preprocessing the audio data

[0754] The device uses the built-in microphone to capture audio data and performs preprocessing such as noise reduction and volume normalization. Specifically, the audio data is filtered to remove noise and the volume level is made uniform. The audio data is then converted into a certain format (e.g., PCM format). The preprocessed audio data is output.

[0755] Step 3: Sending audio data

[0756] The device compresses the pre-processed audio data, encrypts it using SSL / TLS, and then transmits it to the server via the network. Through this transmission process, the encrypted audio data arrives at the server.

[0757] Step 4: Voice Recognition

[0758] The server passes the received voice data to a voice recognition engine. For example, the H voice recognition engine, which is commonly used as a voice recognition engine, is applied. The voice recognition engine analyzes the input voice data and converts it into the corresponding text data. For example, the voice saying "Onshan, are you OK?" is converted into the text data "Onshan, are you OK?" This converted text data is output.

[0759] Step 5: Sending text data

[0760] The server sends the converted text data to the natural language processing engine. Through this sending process, the text data is passed to the natural language processing engine.

[0761] Step 6: Natural Language Processing and Translation

[0762] The server uses a natural language processing engine (e.g., a widely used natural language processing engine) to translate the text data from the local language (dialect) to standard Japanese. For example, the text "Are you OK?" is translated to "Are you OK?" This translated text data in standard Japanese is the output.

[0763] Step 7: Sentiment Analysis

[0764] The server sends the translated text data in standard Japanese to a sentiment analysis engine. The sentiment analysis engine (for example, a commonly used sentiment analysis engine) analyzes emotions from the text and adds the emotional information. For example, the text "Are you okay?" is analyzed with the emotional information "worried." The text data with this emotional information added is the output.

[0765] Step 8: Sending the results

[0766] The server then transmits the text data with the added emotion information back to the terminal via the network. Through this transmission process, the text data with the added emotion information arrives at the terminal.

[0767] Step 9: Display and audio output of results

[0768] The device stores the received text data with the added emotion information in the device's buffer. It then displays it on the user interface. For example, the device displays "Are you okay? (Worried)" on the screen. If necessary, the device uses a speech synthesis engine to output the text data as voice. For example, the device plays back the voice "Are you okay? (Worried)."

[0769] The above is the specific processing flow of the program of this system.

[0770] (Application example 2)

[0771] 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."

[0772] In situations where it is necessary not only to understand the local language or dialect, but also to instantly grasp the speaker's emotions, conventional technologies face the challenge of being unable to respond appropriately. Security services, in particular, require accurate understanding of the speech of visitors and suspicious individuals and analyzing their emotions to detect potential risks early and respond safely and quickly. However, current speech recognition and translation systems lack the ability to analyze emotions, making it difficult to meet these requirements.

[0773] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for transmitting voice data to the server via a network, means for converting the voice data received by the server into text data using a voice recognition engine, means for translating the converted text data into standard Japanese using a natural language processing engine, means for adding emotional information to the translated text data in standard Japanese, and means for transmitting the text data including the emotional information back to the user terminal. This enables security services to translate the statements of visitors and suspicious individuals in real time and analyze their emotions, thereby enabling early detection of potential risks and safe and prompt response.

[0774] A "user terminal" is a device that inputs voice data and displays and outputs the results.

[0775] "Speech data" refers to data obtained from a user's speech that is converted into text data by a speech recognition engine.

[0776] A "voice recognition engine" is a software engine for converting voice data into text data.

[0777] "Text data" is data expressed as characters that has been converted by a voice recognition engine.

[0778] A "natural language processing engine" is a software engine for translating text data into standard Japanese.

[0779] "Standard language" is the common language form used when text data is translated.

[0780] An "emotion analysis engine" is a software engine for analyzing emotional information from text data.

[0781] "Emotion information" is data that indicates the speaker's emotions and is added by an emotion analysis engine.

[0782] A "user interface" is a means for displaying translation results and emotional information on a user terminal.

[0783] A "server" is a remote computer system responsible for processing audio data.

[0784] A "network" is a communications infrastructure that transmits and receives data between user terminals and servers.

[0785] A "speech synthesis engine" is a software engine for converting text data into speech.

[0786] This system inputs voice data from a user, converts it into text data using a voice recognition engine, translates it into standard Japanese using a natural language processing engine, and then adds emotional information using an emotion analysis engine.Finally, the translation results and emotional information are displayed on a user interface or output as voice using a voice synthesis engine.

[0787] System Configuration

[0788] The system mainly consists of the following components:

[0789] 1. User terminal: Inputs voice data and displays / outputs the results.

[0790] 2. Server: Responsible for processing voice data.

[0791] 3. Speech recognition engine: Converts voice data into text data.

[0792] 4. Natural language processing engine: Translates text data into standard Japanese.

[0793] 5. Sentiment analysis engine: Analyzes text data and adds emotional information.

[0794] 6. Network: Sends and receives data between user terminals and servers.

[0795] 7. Speech synthesis engine: Converts translated text data into speech.

[0796] Program processing

[0797] The server receives the voice data via the network and converts it into text data using a speech recognition engine. A specific example of a speech recognition engine is the Google Cloud Speech-to-Text API. The converted text data is then translated into standard Japanese using Amazon Translate. Emotional information is added to the translated standard Japanese text data using IBM Watson's emotion analysis API. This text data, including the emotional information, is then sent back to the user's device and displayed on the user interface or output as audio using a speech synthesis engine (e.g., Google Text-to-Speech).

[0798] Hardware, software, and data processing / calculation used

[0799] Hardware: Smart glasses

[0800] Audio data is acquired using the built-in microphone.

[0801] The display shows translation results and emotional information.

[0802] software:

[0803] Speech recognition engine: Google Cloud Speech-to-Text API

[0804] Natural language processing engine: Amazon Translate

[0805] Sentiment analysis engine: IBM Watson Sentiment Analysis API

[0806] Speech synthesis engine: Google Text-to-Speech

[0807] Specific examples

[0808] For example, consider a scenario in which security staff working at an airport use smart glasses to communicate with visitors. If a visitor speaks in a dialect or with a different accent, saying, "There's no problem here, right?", the staff member will capture the voice data using the smart glasses' built-in microphone and send it to a server in real time. The server will convert the voice data into text data using a speech recognition engine, and then translate it into standard Japanese, "There's no danger here, right?" using a natural language processing engine. The emotion analysis engine will then recognize the voice data as "worried," and text data with that information added will be generated. This text data will then be sent back to the smart glasses, and the message "There's no danger here, right? (worried)" will appear on the display.

[0809] Example prompts to input to the generative AI model

[0810] Input the following prompts into the AI ​​model to get translation and sentiment analysis results:

[0811] "Please translate the following dialect speech data into standard Japanese and analyze the sentiment: 'There's nothing wrong with this, right?'"

[0812] In this way, security staff can understand local language and sentiment and respond appropriately.The system can be used in many other applications as well.

[0813] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0814] Step 1:

[0815] The user terminal acquires the voice data. The user speaks into the built-in microphone of the smart glasses and inputs the voice data, such as "There's no problem here, right?". This voice data is temporarily stored in a buffer.

[0816] Step 2:

[0817] The user terminal sends voice data to the server. The voice data is converted to WAV format, encrypted, and sent over the network. The input is the voice data, and the output is the voice data sent to the server.

[0818] Step 3:

[0819] The server uses a speech recognition engine to convert the voice data into text data. Using the Google Cloud Speech-to-Text API, the input is the transmitted voice data, and the output is the text data "There's nothing wrong with this, right?"

[0820] Step 4:

[0821] The server uses a natural language processing engine to translate the text data into standard Japanese. Using Amazon Translate, the input is the converted text data "There's no problem here, right?", and the output is standard Japanese "There's no danger here, right?"

[0822] Step 5:

[0823] The server uses an emotion analysis engine to add emotional information to the translated text data. Using IBM Watson's emotion analysis API, it recognizes "worried," and the input is text data in standard Japanese, and the output is text data that reads, "This place isn't dangerous, is it? (worried)."

[0824] Step 6:

[0825] The server sends the text data containing emotion information to the user terminal again. It is encrypted again and sent over the network. The input is the text data containing emotion information, and the output is the text data sent to the user terminal.

[0826] Step 7:

[0827] The user terminal displays the received text data on the user interface. At this time, the display shows "This place is not dangerous, right? (Worried)." The input is the received text data containing emotional information, and the output is the display.

[0828] Step 8:

[0829] If necessary, the user device uses a speech synthesis engine to convert text data into speech and output it. Using Google Text-to-Speech, the input is text data and the output is speech.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] [Third embodiment]

[0834] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0835] 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.

[0836] 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).

[0837] 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.

[0838] 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.

[0839] 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).

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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."

[0846] As an embodiment of the present invention, we provide a system in which a user inputs voice data, converts it into text data using a voice recognition engine, and then translates the text data into standard Japanese using a natural language processing engine. Below, we will explain the details of the system and the program processing in natural language, and also show an embodiment with specific examples.

[0847] System Configuration

[0848] The system mainly consists of the following components:

[0849] 1. User terminal: Inputs voice data and displays / outputs the results.

[0850] 2. Server: Responsible for processing voice data.

[0851] 3. Speech recognition engine: Converts voice data into text data.

[0852] 4. Natural language processing engine: Translates text data into standard Japanese.

[0853] 5. Network: Sends and receives data between user terminals and servers.

[0854] Program processing

[0855] 1. Acquiring voice input

[0856] During medical activities in the field, the user speaks a patient's conversation into the terminal. For example, the patient says in the local dialect, "Hey, are you okay?"

[0857] The device uses a built-in microphone to capture audio and stores it in a buffer as audio data.

[0858] 2. Preprocessing and transmission of audio data

[0859] The terminal converts the audio data into a specific format, compresses and encrypts the data, and then transmits it to a server via a network.

[0860] 3. Voice Recognition

[0861] The server passes the received voice data to a voice recognition engine, which converts the voice data into text data. For example, "Onshan, you're OK" is converted into text data like "Onshan, you're OK."

[0862] 4. Language Processing and Translation

[0863] The server passes the text data to a natural language processing engine, which translates the dialect into standard Japanese. For example, "Onshan, you're OK" is translated into "Are you OK?"

[0864] 5. Sending and displaying translation results

[0865] The server then transmits the translation results to the terminal again via the network.

[0866] The device stores the translated text in a buffer and displays it in the user interface, for example, displaying "Are you OK?" on the screen.

[0867] If necessary, the device will use a speech synthesis engine to play "Are you OK?"

[0868] Specific examples

[0869] As a concrete example, consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you OK?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you OK?", using a natural language processing engine. The translation result is then sent back to the device and displayed to the user or played aloud. This process allows the user to understand the local dialect and respond quickly and accurately.

[0870] The above is an embodiment of the present invention. This system enables medical students and medical professionals to carry out medical activities efficiently without having to worry about the local language barrier.

[0871] The processing flow will be explained below.

[0872] Step 1:

[0873] The user starts to input voice into the terminal. Specifically, in a medical setting, a patient speaks in a local dialect, saying, "Hey, are you okay?" If the user cannot understand what is being said, they can input voice into the microphone of the terminal.

[0874] Step 2:

[0875] The device uses a microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[0876] Step 3:

[0877] The device converts the captured audio data into a format (e.g., WAV or MP3), which includes encoding to preserve audio quality.

[0878] Step 4:

[0879] The device then sends the converted voice data to the server via the network, compressing the data for efficient data transfer and encrypting it to ensure security.

[0880] Step 5:

[0881] The server receives the voice data sent over the network, decodes it again, and passes it to the voice recognition engine.

[0882] Step 6:

[0883] The server's speech recognition engine analyzes the voice data and converts it into the corresponding text data, "You're OK, Onshan." This conversion process uses advanced speech analysis algorithms.

[0884] Step 7:

[0885] The server passes the generated text data to a natural language processing engine, which analyzes the text data and translates it into the corresponding standard Japanese text, "Are you OK?"

[0886] Step 8:

[0887] The server then sends the translated standard Japanese text data back to the terminal via the network, and this transmission is also securely encrypted.

[0888] Step 9:

[0889] The terminal stores the received standard Japanese text data, "Are you OK?", in a buffer. The stored text is immediately displayed to the user.

[0890] Step 10:

[0891] The device passes the text data to a speech synthesis engine as needed and outputs the translation results as voice. Specifically, the speech synthesis engine reads out to the user, "Are you okay?"

[0892] This flow allows users to understand local dialects in real time and provide prompt and accurate medical care.

[0893] Example 1

[0894] 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."

[0895] In situations where local dialects and specific languages ​​are barriers, there is a lack of practical and efficient means of communication. Especially in medical settings, where quick and accurate communication with patients is required, a system is needed that can translate dialects into standard Japanese and provide appropriate responses. This system also needs to ensure data security and minimize processing time lags.

[0896] 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.

[0897] In this invention, the server includes means for converting voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, and means for re-encrypting the translated standard Japanese text data and transmitting it to the user terminal. This allows the user to respond quickly and accurately by converting local dialects or specific languages ​​into standard Japanese.

[0898] A "user" is a person who uses the system to input voice data and check the translation results output.

[0899] "Speech data" refers to speech information input by a user that is subject to analysis and translation.

[0900] A "speech recognition engine" is a software or hardware system that converts voice data into text data.

[0901] "Text data" is character information of voice data converted by a voice recognition engine.

[0902] A "natural language processing engine" is a software system that analyzes text data and translates it from a specific language into standard Japanese.

[0903] "Standard language" is a general form of language that is easily understood by many people, rather than a specific region or dialect.

[0904] A "user terminal" is a device that allows a user to input speech and display or play back the translation results.

[0905] A "server" is a remote computer network resource that processes and manages audio data.

[0906] A "network" is a communications infrastructure that enables data communication between user terminals and servers.

[0907] A "speech synthesis engine" is a software or hardware system that converts text data into speech.

[0908] "Encryption" is a method of transforming data based on a certain algorithm in order to protect the data.

[0909] "Decryption" is a method of restoring encrypted data to its original state.

[0910] "Compression" is the process of reducing the size of data.

[0911] As an embodiment of the present invention, a system is provided in which a user inputs voice data and the data is translated into standard Japanese using a voice recognition engine and a natural language processing engine. The detailed configuration and operation of this system are described below.

[0912] System Configuration

[0913] This system consists of the following main components:

[0914] 1. User terminal: An input device such as a smartphone, tablet, or laptop. The user inputs voice data and the results are displayed and output.

[0915] 2. Server: Responsible for processing voice data. Uses a cloud server (e.g., AWS EC2, Google Cloud).

[0916] 3. Speech recognition engine: Converts voice data into text data, for example, using Google Speech-to-Text or Amazon Transcribe.

[0917] 4. Natural language processing engine: Translates text data into standard Japanese. Uses OpenAI GPT-3, Google BERT, etc.

[0918] 5. Network: Wi-Fi or 4G / 5G data connection to send and receive data between user devices and the server.

[0919] 6. Speech synthesis engine: Converts the translation results into speech and plays it back. Examples include Amazon Polly and Google Text-to-Speech.

[0920] Overview of program processing

[0921] When a user inputs voice data, the device's built-in microphone captures the voice and stores it in a buffer.The device then converts the voice data into a certain format (e.g., SIL format), compresses (gzip) and encrypts (AES), and transmits it to the server over the network.

[0922] The server decrypts the received encrypted voice data and passes it to a speech recognition engine (e.g., Google Speech-to-Text) to convert it into text data. The converted text data is temporarily stored in a database. The server then passes the text data to a natural language processing engine (e.g., OpenAI GPT-3) to translate the dialect into standard Japanese.

[0923] The translated text data in standard Japanese is then re-encrypted and sent over the network to the device. The device stores the translated text data in a buffer and displays it on the user interface. If necessary, a speech synthesis engine (e.g., Google Text-to-Speech) is used to play back the translation results aloud.

[0924] Specific examples

[0925] As a concrete example, consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you OK?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you OK?", using a natural language processing engine. The translation result is then sent back to the device and displayed to the user or played aloud. This process allows the user to understand the local dialect and respond quickly and accurately.

[0926] Prompt Sentence Examples

[0927] Write a prompt for a system that supports patient interaction in a hospital. Explain the program process for translating spoken dialect into standard Japanese and displaying the translation.

[0928] Specific names of hardware and software to be used

[0929] User devices: smartphones, tablets, laptops

[0930] Server: Cloud server (e.g. AWS EC2, Google Cloud)

[0931] Speech recognition engine: Google Speech-to-Text, Amazon Transcribe

[0932] Natural language processing engine: OpenAI GPT-3, Google BERT

[0933] Speech synthesis engine: Amazon Polly, Google Text-to-Speech

[0934] Network: Wi-Fi, 4G / 5G data connection

[0935] The above is an embodiment of the present invention, which allows users to efficiently respond and receive appropriate services without having to deal with local dialects or specific language barriers.

[0936] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0937] Divide the processing flow of the system program into processing steps

[0938] Step 1: Getting voice input

[0939] Step 2: Preprocess and transmit audio data

[0940] Step 3: Voice Recognition

[0941] Step 4: Language Processing and Translation

[0942] Step 5: Send and view the translation results

[0943] Specific explanation of each processing step

[0944] Step 1: Getting voice input

[0945] The user speaks into the device. The input voice data is captured by the device's built-in microphone and saved in a buffer. For example, the user says "Onshan, you're OK" in a dialect.

[0946] Input: User's voice

[0947] Data processing: Converting audio into digital data

[0948] Output: Buffered audio data

[0949] Specific behavior:

[0950] The user taps the voice input button.

[0951] The device's microphone is activated and records what the user says.

[0952] When recording is complete, the audio data is saved in a buffer in WAV format.

[0953] Step 2: Preprocess and transmit audio data

[0954] The device converts the audio data in the buffer into SIL format, compresses it with gzip, encrypts it with AES, and sends it over the network to the server.

[0955] Input: Buffered audio data

[0956] Data processing: format conversion, compression, encryption

[0957] Output: Encrypted audio data is sent to the server

[0958] Specific behavior:

[0959] Run a Python script to convert the audio data into SIL format.

[0960] Compress the data using the gzip library.

[0961] Encrypt the data using an AES encryption library (e.g. PyCryptodome).

[0962] Send the encrypted data to the server in an HTTP POST request.

[0963] Step 3: Voice Recognition

[0964] The server decrypts the received encrypted data using AES, passes the voice data to a speech recognition engine (e.g., Google Speech-to-Text API), and converts it into text data.

[0965] Input: Encrypted audio data

[0966] Data processing: decoding, voice recognition

[0967] Output: Text data

[0968] Specific behavior:

[0969] The server decrypts the received data using AES.

[0970] Sends SIL formatted audio data to the Speech-to-Text API.

[0971] Receives text data and stores it in a MongoDB database.

[0972] Step 4: Language Processing and Translation

[0973] The server passes the stored text data to a natural language processing engine (e.g., OpenAI GPT-3) to translate the dialect into standard Japanese.

[0974] Input: Text data

[0975] Data processing: Translation processing

[0976] Output: Standard Japanese text data

[0977] Specific behavior:

[0978] The server retrieves the text data from the database.

[0979] Send the text data to the GPT-3 API as a prompt.

[0980] The translated text data is obtained and stored in a database.

[0981] Step 5: Send and view the translation results

[0982] The server sends the translation result text data to the device. The device receives this data and displays it on the user interface. If necessary, it plays it aloud using a speech synthesis engine (e.g., Google Text-to-Speech API).

[0983] Input: Translated standard Japanese text data

[0984] Data processing: display or voice synthesis

[0985] Output: Text data displayed in a user interface or audio played

[0986] Specific behavior:

[0987] The server sends the translation results to the device via an HTTP POST request.

[0988] The terminal stores the received data in a buffer and displays it on the user interface.

[0989] If necessary, a TTS (Text-to-Speech) API is called and the translation result is played back as synthesized speech.

[0990] (Application example 1)

[0991] 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."

[0992] In autonomous vehicles, when passengers give instructions in a dialect or a foreign language, it is difficult for the vehicle's navigation system to accurately understand the instructions and respond appropriately. It is necessary to overcome this language barrier and provide a system that can translate passenger instructions into standard Japanese or a specified language in real time, allowing autonomous vehicles to respond accurately and quickly.

[0993] 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.

[0994] In this invention, the server includes means for a user to input voice data, means for converting the voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, means for transmitting the translated text data in standard Japanese to an in-vehicle system, and means for transmitting instructions to a navigation system of the in-vehicle system, thereby enabling an autonomous vehicle to accurately understand instructions given in a passenger's dialect or foreign language and respond appropriately in real time.

[0995] "Means for a user to input voice data" refers to a device or interface that allows a user to input voice data using their voice.

[0996] "Means for converting into text data using a voice recognition engine" refers to software or hardware for analyzing voice data and converting it into character text data.

[0997] "Means for translating into standard Japanese using a natural language processing engine" refers to software or hardware for translating text data into standard Japanese.

[0998] "Means for transmitting the translated text data in standard Japanese to the in-vehicle system" refers to a communication function for transmitting the translated text data to the in-vehicle system.

[0999] "Means for transmitting instructions to the navigation system of the in-vehicle system" refers to the interface and communication means for transmitting instructions to the navigation system.

[1000] "Means for transmitting to a server via a network" refers to a communication system for transmitting audio data to a server via the Internet or a local network.

[1001] "Means for transmitting text data to the in-vehicle system" refers to a communication function for transmitting translated text data to the in-vehicle system.

[1002] "Means for outputting voice using a voice synthesis engine" refers to software or hardware for analyzing text data and outputting it as voice.

[1003] "Means for providing a user with voice output in standard Japanese" refers to a device for providing a user with voice generated by a speech synthesis engine through speakers, headphones, etc.

[1004] A "server" refers to a computer or cloud service that processes voice and text data over a network.

[1005] MODE FOR CARRYING OUT THE INVENTION

[1006] As an embodiment of the present invention, a system will be described in which a user boards an autonomous vehicle and inputs voice data. When a passenger gives instructions in a dialect or foreign language, the system translates the voice into standard Japanese and transmits it to the navigation system of the autonomous vehicle.

[1007] System Configuration

[1008] The system mainly consists of the following components:

[1009] 1. User terminal: Inputs voice data and displays / outputs the results.

[1010] 2. Server: Responsible for processing voice data.

[1011] 3. Speech recognition engine: Converts voice data into text data.

[1012] 4. Natural language processing engine: Translates text data into standard Japanese.

[1013] 5. In-car navigation system: Navigate based on translated instructions.

[1014] 6. Network: Sends and receives data between the user terminal and the server, and between the server and the in-vehicle system.

[1015] System Operation

[1016] 1. Acquiring voice input: Users input voice data via their smartphones or tablets. When a passenger says in their local dialect, "I want to go to the nearest convenience store around here," the voice is captured by the device.

[1017] 2. Audio data preprocessing and transmission: The captured audio data is converted into a certain format, compressed, encrypted, and then transmitted to the server over the network.

[1018] 3. Speech recognition: The server analyzes the received voice data using a voice recognition engine and converts it into text data.

[1019] 4. Language processing and translation: The server passes the text data to a natural language processing engine to translate the dialect or foreign language into standard Japanese.

[1020] 5. Sending the translation results: The translated text data in standard Japanese is sent to the user terminal and the in-vehicle system via the network.

[1021] Hardware and software used

[1022] Speech recognition engine: Uses the speech_recognition library.

[1023] Natural language processing engine: uses the googletrans library.

[1024] Speech synthesis engine: Uses the pyttsx3 library.

[1025] Network communication: Internet or local network.

[1026] Specific examples

[1027] For example, if a passenger gets into an autonomous taxi and says in a local dialect, "I'd like to go to the nearest convenience store around here," the system will recognize the speech, translate it into standard Japanese, and communicate it to the navigation system as, "Do you want to go to the nearest convenience store around here?" The navigation system will follow this instruction, calculate the optimal route, and head to the specified destination.

[1028] Prompt Sentence Examples

[1029] The voice recognition engine converts the input voice data into text data, and the natural language processing engine translates that text data into standard Japanese.

[1030] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1031] Step 1: Getting voice input

[1032] Users input voice data via their smartphones or tablets. When a passenger says something in their local dialect like, "I want to go to the nearest convenience store around here," the voice is captured by the device. The input voice data is stored in a buffer.

[1033] Step 2: Preprocess and transmit audio data

[1034] The device converts the captured audio data into a certain format, specifically preprocessing it by reducing noise and normalizing the volume. The preprocessed audio data is then compressed and encrypted and sent over the network to the server. The input is the captured audio data, and the output is the preprocessed audio data.

[1035] Step 3: Voice Recognition

[1036] The server passes the received audio data to a speech recognition engine. The speech recognition engine (for example, the speech_recognition library) analyzes the audio data and converts it into text data. The input is preprocessed audio data, and the output is the text data converted from the audio.

[1037] Step 4: Language Processing and Translation

[1038] The server passes the converted text data to a natural language processing engine. The natural language processing engine (for example, the GoogleTrans library) translates the dialect or foreign language text data into standard Japanese. The input is the text data generated by the speech recognition engine, and the output is the text data translated into standard Japanese.

[1039] Step 5: Send the translation

[1040] The server then compresses and encrypts the translated text data again and sends it to the user's device and the in-vehicle system via the network. The input is the text data translated into standard Japanese, and the output is the compressed and encrypted text data.

[1041] Step 6: Speech synthesis and navigation instructions

[1042] The user device converts the translated text data into standard Japanese using a speech synthesis engine and provides it to the passenger through the speaker. The in-vehicle navigation system receives the translated instructions, calculates the optimal route, and heads to the specified destination. The input is the translated text data in standard Japanese, and the output is the voice data and instructions from the navigation system.

[1043] The above steps realize a system that enables an autonomous vehicle to accurately understand instructions given by a user in a dialect or foreign language and quickly and accurately navigate to the destination.

[1044] 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.

[1045] The present invention is a system in which a user inputs voice data, converts it into text data using a voice recognition engine, and then translates the text data into standard Japanese using a natural language processing engine. This system is also combined with an emotion engine that recognizes the user's emotions. Details of the system and program processing are explained in natural language below, and an embodiment is shown with specific examples.

[1046] System Configuration

[1047] The system mainly consists of the following components:

[1048] 1. User terminal: Inputs voice data and displays / outputs the results.

[1049] 2. Server: Responsible for processing voice data.

[1050] 3. Speech recognition engine: Converts voice data into text data.

[1051] 4. Natural language processing engine: Translates text data into standard Japanese.

[1052] 5. Emotion engine: Analyzes text data and recognizes user emotions.

[1053] 6. Network: Sends and receives data between user terminals and servers.

[1054] Program processing

[1055] 1. Acquiring voice input

[1056] During medical activities in the field, the user speaks to the terminal about the patient's conversation. For example, the patient says in a local dialect, "Hey, are you okay?" The user cannot understand the words, so they input the voice into the terminal.

[1057] 2. Preprocessing and transmission of audio data

[1058] The device uses the built-in microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[1059] The audio data is converted into a certain format, and the data is compressed and encrypted before being sent to a server via a network.

[1060] 3. Voice Recognition

[1061] The server passes the received voice data to a voice recognition engine, which converts the voice data into corresponding text data. For example, "Onshan, you're OK" is converted into text data like "Onshan, you're OK."

[1062] 4. Language Processing and Translation

[1063] The server passes the text data to a natural language processing engine, which translates the dialect into standard Japanese. For example, "Onshan, you're OK" is translated into "Are you OK?"

[1064] 5. Emotion analysis

[1065] The server sends the translated text data in standard Japanese to the emotion engine, which analyzes the user's emotions. As a result, data with emotional information added is generated. For example, if a patient is worried, the text data generated is, "Are you okay? (Worried)."

[1066] 6. Sending and displaying translation results and emotional information

[1067] The server then sends the translation results and emotion information back to the terminal via the network.

[1068] The device stores the received standard Japanese text data and emotion information in a buffer and displays it on the user interface. For example, the screen displays "Are you okay? (Worried)."

[1069] If necessary, the device will use a speech synthesis engine to play the voice "Are you OK? (I'm worried)."

[1070] Specific examples

[1071] As a concrete example, consider a scenario in which a user working in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you okay?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you okay?", using a natural language processing engine. The emotion engine then analyzes the user's emotion, and if it recognizes it as "worried," text data is created with that information added. This text data is then sent back to the device and displayed to the user or output as voice. This process allows the user to not only understand the local dialect, but also to understand the patient's emotion, enabling more appropriate treatment.

[1072] The above is an embodiment of the present invention. Through this system, medical students and medical professionals can efficiently carry out medical activities without having to deal with the language or emotional barriers of the local area.

[1073] The processing flow will be explained below.

[1074] Step 1:

[1075] The user starts to input voice into the terminal. Specifically, in a medical setting, a patient says in a local dialect, "Hey, are you okay?" If the user cannot understand what is being said, they can input voice into the microphone of the terminal.

[1076] Step 2:

[1077] The device uses a microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[1078] Step 3:

[1079] The device converts the captured audio data into a format (e.g., WAV or MP3), which includes encoding to preserve audio quality.

[1080] Step 4:

[1081] The device then sends the converted voice data to the server via the network, compressing the data for efficient data transfer and encrypting it to ensure security.

[1082] Step 5:

[1083] The server receives the voice data sent over the network, decodes it again, and passes it to the voice recognition engine.

[1084] Step 6:

[1085] The server's speech recognition engine analyzes the voice data and converts it into the corresponding text data, "You're OK, Onshan." This conversion process uses advanced speech analysis algorithms.

[1086] Step 7:

[1087] The server passes the generated text data to a natural language processing engine, which analyzes the text data and translates it into the corresponding standard Japanese text, "Are you OK?"

[1088] Step 8:

[1089] The server sends the translated text data in standard Japanese to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the context and phrasing of the text data to extract specific emotions (e.g., "worry").

[1090] Step 9:

[1091] The server adds the results of the sentiment analysis to the translated standard Japanese text data. For example, the resulting text data is "Are you okay? (Worried)."

[1092] Step 10:

[1093] The server then sends the translation results and emotion information to the device via the network, and this transmission is also encrypted and secure.

[1094] Step 11:

[1095] The terminal stores the received standard Japanese text data "Are you OK? (I'm worried)" in a buffer. The stored text is immediately displayed on the user interface.

[1096] Step 12:

[1097] The device passes the text data to a speech synthesis engine as needed and outputs the translation results as voice. Specifically, the speech synthesis engine reads out to the user, "Are you okay? (I'm worried)."

[1098] This flow allows users to understand local dialects in real time and also grasp the patient's emotions, enabling more appropriate medical care.

[1099] Example 2

[1100] 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."

[1101] In local medical activities, users often have difficulty understanding patients who speak in local dialects. It is also difficult to accurately grasp the patient's emotions. In such situations, there is a high possibility that appropriate medical care will be delayed, so a method to overcome the language and emotional barriers is needed.

[1102] 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.

[1103] In this invention, the server includes means for preprocessing voice data, means for converting the preprocessed voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, means for analyzing emotions using an emotion analysis engine, and means for transmitting the text data with added emotion information to a user terminal. This makes it possible to translate the voice data into standard Japanese, analyze the patient's emotions, and provide the results to the user.

[1104] "Audio data" refers to data obtained by converting an audio signal into digital information.

[1105] "Preprocessing" refers to processes such as noise removal and volume normalization that are performed to make audio data easier to analyze.

[1106] A "server" is a centralized computing device that processes audio data over a network.

[1107] A "voice recognition engine" is software or hardware for converting voice data into text data.

[1108] "Text data" is character information converted by a voice recognition engine.

[1109] A "natural language processing engine" is software or hardware for analyzing text data and performing specific language processing (e.g., translation).

[1110] "Standard language" is a common, widely understood form of language, not a regional dialect.

[1111] An "emotion analysis engine" is software or hardware that identifies emotions from text data and adds that emotional information.

[1112] "Emotion information" is data relating to the type and intensity of emotions added to text data.

[1113] A "user terminal" is a device that allows a user to input voice data and display or output the results as voice.

[1114] A "network" is a communications infrastructure for sending and receiving data.

[1115] A "voice synthesis engine" is software or hardware that converts text data into voice data and outputs the voice.

[1116] The present invention is a system in which a user inputs voice data, converts it into text data using a voice recognition engine, translates the text data into standard Japanese using a natural language processing engine, and recognizes the user's emotions using an emotion analysis engine. This system is mainly composed of a user terminal, a server, a voice recognition engine, a natural language processing engine, an emotion analysis engine, and a network.

[1117] System Configuration

[1118] 1. User Device

[1119] This device inputs and preprocesses voice data. It has a built-in microphone for capturing the user's voice input. It also has the function of preprocessing the voice data and sending it to a server via a network. It also has the function of displaying the results and outputting the voice.

[1120] 2. Server

[1121] It is a centralized computing device that processes voice data. It has the function of passing received voice data to a voice recognition engine and converting it into text data. It also has the function of translating text data into standard Japanese using a natural language processing engine and analyzing emotions using an emotion analysis engine.

[1122] 3. Speech Recognition Engine

[1123] It is software or hardware that converts voice data into text data. For example, a common voice recognition engine is the Google Speech-to-Text API.

[1124] 4. Natural Language Processing Engine

[1125] It is software or hardware used to analyze text data and perform specific language processing (e.g., translation). For example, Microsoft Azure Cognitive Services is often used.

[1126] 5. Sentiment Analysis Engine

[1127] This is software or hardware that identifies emotions from text data and adds that emotional information. IBM Watson Tone Analyzer is commonly used.

[1128] 6. Network

[1129] It is a communications infrastructure for sending and receiving data. For example, a user terminal and a server are connected via the Internet.

[1130] System Operation

[1131] Acquiring voice input

[1132] The user speaks voice data into the terminal. For example, if the patient says "Hey, are you OK?" in a local dialect, the user inputs the voice data into the terminal.

[1133] Audio data preprocessing

[1134] The device uses the built-in microphone to capture audio data, temporarily stores it in the device's buffer, and then performs preprocessing such as noise reduction and volume normalization before converting it to a certain format (e.g., PCM).

[1135] Sending audio data

[1136] The terminal compresses and encrypts the pre-processed voice data and transmits it to the server via the network.

[1137] Voice Recognition

[1138] The server passes the received voice data to a speech recognition engine (for example, Google Speech-to-Text API) and converts the voice data into text data. For example, "Onshan, are you OK?" becomes "Onshan, are you OK?"

[1139] Language Processing and Translation

[1140] The server passes the text data to a natural language processing engine (e.g., Microsoft Azure Cognitive Services) and translates the dialect into standard Japanese. For example, "You okay?" is translated into "Are you okay?"

[1141] Emotion analysis

[1142] The server then sends the translated text data in standard Japanese to a sentiment analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. As a result, text data with emotional information such as "worry" is generated.

[1143] Sending the results

[1144] The server then sends the translation results and emotion information back to the terminal via the network.

[1145] Display and Audio Output

[1146] The device stores the received standard Japanese text data and emotion information in the device's buffer and displays it on the user interface. For example, the screen displays "Are you okay? (Worried)." If necessary, the device also outputs this text data aloud using a speech synthesis engine (e.g., Amazon Polly). For example, the device plays back the voice "Are you okay? (Worried)."

[1147] Specific examples

[1148] Consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you okay?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server uses a speech recognition engine to convert the speech data into text, which is then translated into standard Japanese, "Are you okay?", through a natural language processing engine. The emotion analysis engine then analyzes the user's emotions, and if it recognizes the emotion as "worried," text data is created with that information added. This text data is then sent back to the device and displayed to the user or output as voice. This process allows the user to not only understand the local dialect, but also to understand the patient's emotions, enabling more appropriate treatment.

[1149] Example prompts to input to the generative AI model

[1150] A patient from the Kyushu region said in a local dialect, "Onshan, you're OK." Please translate this speech data into standard Japanese, "Are you OK?" Also, please analyze the emotions perceived from the statement and display the results.

[1151] The above is an embodiment of the present invention. Through this system, medical professionals can overcome the language and emotional barriers of the local area and carry out medical activities efficiently.

[1152] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1153] Program processing flow

[1154] Step 1: Getting voice input

[1155] The user inputs voice into the device. For example, the user speaks the patient's voice directly into the device, saying, "Hey, are you okay?" The device has a built-in microphone for capturing voice. The input voice data is temporarily stored in the device's buffer.

[1156] Step 2: Preprocessing the audio data

[1157] The device uses the built-in microphone to capture audio data and performs preprocessing such as noise reduction and volume normalization. Specifically, the audio data is filtered to remove noise and the volume level is made uniform. The audio data is then converted into a certain format (e.g., PCM format). The preprocessed audio data is output.

[1158] Step 3: Sending audio data

[1159] The device compresses the pre-processed audio data, encrypts it using SSL / TLS, and then transmits it to the server via the network. Through this transmission process, the encrypted audio data arrives at the server.

[1160] Step 4: Voice Recognition

[1161] The server passes the received voice data to a voice recognition engine. For example, the H voice recognition engine, which is commonly used as a voice recognition engine, is applied. The voice recognition engine analyzes the input voice data and converts it into the corresponding text data. For example, the voice saying "Onshan, are you OK?" is converted into the text data "Onshan, are you OK?" This converted text data is output.

[1162] Step 5: Sending text data

[1163] The server sends the converted text data to the natural language processing engine. Through this sending process, the text data is passed to the natural language processing engine.

[1164] Step 6: Natural Language Processing and Translation

[1165] The server uses a natural language processing engine (e.g., a widely used natural language processing engine) to translate the text data from the local language (dialect) to standard Japanese. For example, the text "Are you OK?" is translated to "Are you OK?" This translated text data in standard Japanese is the output.

[1166] Step 7: Sentiment Analysis

[1167] The server sends the translated text data in standard Japanese to a sentiment analysis engine. The sentiment analysis engine (for example, a commonly used sentiment analysis engine) analyzes emotions from the text and adds the emotional information. For example, the text "Are you okay?" is analyzed with the emotional information "worried." The text data with this emotional information added is the output.

[1168] Step 8: Sending the results

[1169] The server then transmits the text data with the added emotion information back to the terminal via the network. Through this transmission process, the text data with the added emotion information arrives at the terminal.

[1170] Step 9: Display and audio output of results

[1171] The device stores the received text data with the added emotion information in the device's buffer. It then displays it on the user interface. For example, the device displays "Are you okay? (Worried)" on the screen. If necessary, the device uses a speech synthesis engine to output the text data as voice. For example, the device plays back the voice "Are you okay? (Worried)."

[1172] The above is the specific processing flow of the program of this system.

[1173] (Application example 2)

[1174] 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."

[1175] In situations where it is necessary not only to understand the local language or dialect, but also to instantly grasp the speaker's emotions, conventional technologies face the challenge of being unable to respond appropriately. Security services, in particular, require accurate understanding of the speech of visitors and suspicious individuals and analyzing their emotions to detect potential risks early and respond safely and quickly. However, current speech recognition and translation systems lack the ability to analyze emotions, making it difficult to meet these requirements.

[1176] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for transmitting voice data to the server via a network, means for converting the voice data received by the server into text data using a voice recognition engine, means for translating the converted text data into standard Japanese using a natural language processing engine, means for adding emotional information to the translated text data in standard Japanese, and means for transmitting the text data including the emotional information back to the user terminal. This enables security services to translate the statements of visitors and suspicious individuals in real time and analyze their emotions, thereby enabling early detection of potential risks and safe and prompt response.

[1177] A "user terminal" is a device that inputs voice data and displays and outputs the results.

[1178] "Speech data" refers to data obtained from a user's speech that is converted into text data by a speech recognition engine.

[1179] A "voice recognition engine" is a software engine for converting voice data into text data.

[1180] "Text data" is data expressed as characters that has been converted by a voice recognition engine.

[1181] A "natural language processing engine" is a software engine for translating text data into standard Japanese.

[1182] "Standard language" is the common language form used when text data is translated.

[1183] An "emotion analysis engine" is a software engine for analyzing emotional information from text data.

[1184] "Emotion information" is data that indicates the speaker's emotions and is added by an emotion analysis engine.

[1185] A "user interface" is a means for displaying translation results and emotional information on a user terminal.

[1186] A "server" is a remote computer system responsible for processing audio data.

[1187] A "network" is a communications infrastructure that transmits and receives data between user terminals and servers.

[1188] A "speech synthesis engine" is a software engine for converting text data into speech.

[1189] This system inputs voice data from a user, converts it into text data using a voice recognition engine, translates it into standard Japanese using a natural language processing engine, and then adds emotional information using an emotion analysis engine.Finally, the translation results and emotional information are displayed on a user interface or output as voice using a voice synthesis engine.

[1190] System Configuration

[1191] The system mainly consists of the following components:

[1192] 1. User terminal: Inputs voice data and displays / outputs the results.

[1193] 2. Server: Responsible for processing voice data.

[1194] 3. Speech recognition engine: Converts voice data into text data.

[1195] 4. Natural language processing engine: Translates text data into standard Japanese.

[1196] 5. Sentiment analysis engine: Analyzes text data and adds emotional information.

[1197] 6. Network: Sends and receives data between user terminals and servers.

[1198] 7. Speech synthesis engine: Converts translated text data into speech.

[1199] Program processing

[1200] The server receives the voice data via the network and converts it into text data using a speech recognition engine. A specific example of a speech recognition engine is the Google Cloud Speech-to-Text API. The converted text data is then translated into standard Japanese using Amazon Translate. Emotional information is added to the translated standard Japanese text data using IBM Watson's emotion analysis API. This text data, including the emotional information, is then sent back to the user's device and displayed on the user interface or output as audio using a speech synthesis engine (e.g., Google Text-to-Speech).

[1201] Hardware, software, and data processing / calculation used

[1202] Hardware: Smart glasses

[1203] Audio data is acquired using the built-in microphone.

[1204] The display shows translation results and emotional information.

[1205] software:

[1206] Speech recognition engine: Google Cloud Speech-to-Text API

[1207] Natural language processing engine: Amazon Translate

[1208] Sentiment analysis engine: IBM Watson Sentiment Analysis API

[1209] Speech synthesis engine: Google Text-to-Speech

[1210] Specific examples

[1211] For example, consider a scenario in which security staff working at an airport use smart glasses to communicate with visitors. If a visitor speaks in a dialect or with a different accent, saying, "There's no problem here, right?", the staff member will capture the voice data using the smart glasses' built-in microphone and send it to a server in real time. The server will convert the voice data into text data using a speech recognition engine, and then translate it into standard Japanese, "There's no danger here, right?" using a natural language processing engine. The emotion analysis engine will then recognize the voice data as "worried," and text data with that information added will be generated. This text data will then be sent back to the smart glasses, and the message "There's no danger here, right? (worried)" will appear on the display.

[1212] Example prompts to input to the generative AI model

[1213] Input the following prompts into the AI ​​model to get translation and sentiment analysis results:

[1214] "Please translate the following dialect speech data into standard Japanese and analyze the sentiment: 'There's nothing wrong with this, right?'"

[1215] In this way, security staff can understand local language and sentiment and respond appropriately.The system can be used in many other applications as well.

[1216] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1217] Step 1:

[1218] The user terminal acquires the voice data. The user speaks into the built-in microphone of the smart glasses and inputs the voice data, such as "There's no problem here, right?". This voice data is temporarily stored in a buffer.

[1219] Step 2:

[1220] The user terminal sends voice data to the server. The voice data is converted to WAV format, encrypted, and sent over the network. The input is the voice data, and the output is the voice data sent to the server.

[1221] Step 3:

[1222] The server uses a speech recognition engine to convert the voice data into text data. Using the Google Cloud Speech-to-Text API, the input is the transmitted voice data, and the output is the text data "There's nothing wrong with this, right?"

[1223] Step 4:

[1224] The server uses a natural language processing engine to translate the text data into standard Japanese. Using Amazon Translate, the input is the converted text data "There's no problem here, right?", and the output is standard Japanese "There's no danger here, right?"

[1225] Step 5:

[1226] The server uses an emotion analysis engine to add emotional information to the translated text data. Using IBM Watson's emotion analysis API, it recognizes "worried," and the input is text data in standard Japanese, and the output is text data that reads, "This place isn't dangerous, is it? (worried)."

[1227] Step 6:

[1228] The server sends the text data containing emotion information to the user terminal again. It is encrypted again and sent over the network. The input is the text data containing emotion information, and the output is the text data sent to the user terminal.

[1229] Step 7:

[1230] The user terminal displays the received text data on the user interface. At this time, the display shows "This place is not dangerous, right? (Worried)." The input is the received text data containing emotional information, and the output is the display.

[1231] Step 8:

[1232] If necessary, the user device uses a speech synthesis engine to convert text data into speech and output it. Using Google Text-to-Speech, the input is text data and the output is speech.

[1233] 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.

[1234] 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.

[1235] 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.

[1236] [Fourth embodiment]

[1237] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1238] 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.

[1239] 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).

[1240] 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.

[1241] 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.

[1242] 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).

[1243] 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.

[1244] 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.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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."

[1250] As an embodiment of the present invention, we provide a system in which a user inputs voice data, converts it into text data using a voice recognition engine, and then translates the text data into standard Japanese using a natural language processing engine. Below, we will explain the details of the system and the program processing in natural language, and also show an embodiment with specific examples.

[1251] System Configuration

[1252] The system mainly consists of the following components:

[1253] 1. User terminal: Inputs voice data and displays / outputs the results.

[1254] 2. Server: Responsible for processing voice data.

[1255] 3. Speech recognition engine: Converts voice data into text data.

[1256] 4. Natural language processing engine: Translates text data into standard Japanese.

[1257] 5. Network: Sends and receives data between user terminals and servers.

[1258] Program processing

[1259] 1. Acquiring voice input

[1260] During medical activities in the field, the user speaks a patient's conversation into the terminal. For example, the patient says in the local dialect, "Hey, are you okay?"

[1261] The device uses a built-in microphone to capture audio and stores it in a buffer as audio data.

[1262] 2. Preprocessing and transmission of audio data

[1263] The terminal converts the audio data into a specific format, compresses and encrypts the data, and then transmits it to a server via a network.

[1264] 3. Voice Recognition

[1265] The server passes the received voice data to a voice recognition engine, which converts the voice data into text data. For example, "Onshan, you're OK" is converted into text data like "Onshan, you're OK."

[1266] 4. Language Processing and Translation

[1267] The server passes the text data to a natural language processing engine, which translates the dialect into standard Japanese. For example, "Onshan, you're OK" is translated into "Are you OK?"

[1268] 5. Sending and displaying translation results

[1269] The server then transmits the translation results to the terminal again via the network.

[1270] The device stores the translated text in a buffer and displays it in the user interface, for example, displaying "Are you OK?" on the screen.

[1271] If necessary, the device will use a speech synthesis engine to play "Are you OK?"

[1272] Specific examples

[1273] As a concrete example, consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you OK?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you OK?", using a natural language processing engine. The translation result is then sent back to the device and displayed to the user or played aloud. This process allows the user to understand the local dialect and respond quickly and accurately.

[1274] The above is an embodiment of the present invention. This system enables medical students and medical professionals to carry out medical activities efficiently without having to worry about the local language barrier.

[1275] The processing flow will be explained below.

[1276] Step 1:

[1277] The user starts to input voice into the terminal. Specifically, in a medical setting, a patient speaks in a local dialect, saying, "Hey, are you okay?" If the user cannot understand what is being said, they can input voice into the microphone of the terminal.

[1278] Step 2:

[1279] The device uses a microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[1280] Step 3:

[1281] The device converts the captured audio data into a format (e.g., WAV or MP3), which includes encoding to preserve audio quality.

[1282] Step 4:

[1283] The device then sends the converted voice data to the server via the network, compressing the data for efficient data transfer and encrypting it to ensure security.

[1284] Step 5:

[1285] The server receives the voice data sent over the network, decodes it again, and passes it to the voice recognition engine.

[1286] Step 6:

[1287] The server's speech recognition engine analyzes the voice data and converts it into the corresponding text data, "You're OK, Onshan." This conversion process uses advanced speech analysis algorithms.

[1288] Step 7:

[1289] The server passes the generated text data to a natural language processing engine, which analyzes the text data and translates it into the corresponding standard Japanese text, "Are you OK?"

[1290] Step 8:

[1291] The server then sends the translated standard Japanese text data back to the terminal via the network, and this transmission is also securely encrypted.

[1292] Step 9:

[1293] The terminal stores the received standard Japanese text data, "Are you OK?", in a buffer. The stored text is immediately displayed to the user.

[1294] Step 10:

[1295] The device passes the text data to a speech synthesis engine as needed and outputs the translation results as voice. Specifically, the speech synthesis engine reads out to the user, "Are you okay?"

[1296] This flow allows users to understand local dialects in real time and provide prompt and accurate medical care.

[1297] Example 1

[1298] 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."

[1299] In situations where local dialects and specific languages ​​are barriers, there is a lack of practical and efficient means of communication. Especially in medical settings, where quick and accurate communication with patients is required, a system is needed that can translate dialects into standard Japanese and provide appropriate responses. This system also needs to ensure data security and minimize processing time lags.

[1300] 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.

[1301] In this invention, the server includes means for converting voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, and means for re-encrypting the translated standard Japanese text data and transmitting it to the user terminal. This allows the user to respond quickly and accurately by converting local dialects or specific languages ​​into standard Japanese.

[1302] A "user" is a person who uses the system to input voice data and check the translation results output.

[1303] "Speech data" refers to speech information input by a user that is subject to analysis and translation.

[1304] A "speech recognition engine" is a software or hardware system that converts voice data into text data.

[1305] "Text data" is character information of voice data converted by a voice recognition engine.

[1306] A "natural language processing engine" is a software system that analyzes text data and translates it from a specific language into standard Japanese.

[1307] "Standard language" is a general form of language that is easily understood by many people, rather than a specific region or dialect.

[1308] A "user terminal" is a device that allows a user to input speech and display or play back the translation results.

[1309] A "server" is a remote computer network resource that processes and manages audio data.

[1310] A "network" is a communications infrastructure that enables data communication between user terminals and servers.

[1311] A "speech synthesis engine" is a software or hardware system that converts text data into speech.

[1312] "Encryption" is a method of transforming data based on a certain algorithm in order to protect the data.

[1313] "Decryption" is a method of restoring encrypted data to its original state.

[1314] "Compression" is the process of reducing the size of data.

[1315] As an embodiment of the present invention, a system is provided in which a user inputs voice data and the data is translated into standard Japanese using a voice recognition engine and a natural language processing engine. The detailed configuration and operation of this system are described below.

[1316] System Configuration

[1317] This system consists of the following main components:

[1318] 1. User terminal: An input device such as a smartphone, tablet, or laptop. The user inputs voice data and the results are displayed and output.

[1319] 2. Server: Responsible for processing voice data. Uses a cloud server (e.g., AWS EC2, Google Cloud).

[1320] 3. Speech recognition engine: Converts voice data into text data, for example, using Google Speech-to-Text or Amazon Transcribe.

[1321] 4. Natural language processing engine: Translates text data into standard Japanese. Uses OpenAI GPT-3, Google BERT, etc.

[1322] 5. Network: Wi-Fi or 4G / 5G data connection to send and receive data between user devices and the server.

[1323] 6. Speech synthesis engine: Converts the translation results into speech and plays it back. Examples include Amazon Polly and Google Text-to-Speech.

[1324] Overview of program processing

[1325] When a user inputs voice data, the device's built-in microphone captures the voice and stores it in a buffer.The device then converts the voice data into a certain format (e.g., SIL format), compresses (gzip) and encrypts (AES), and transmits it to the server over the network.

[1326] The server decrypts the received encrypted voice data and passes it to a speech recognition engine (e.g., Google Speech-to-Text) to convert it into text data. The converted text data is temporarily stored in a database. The server then passes the text data to a natural language processing engine (e.g., OpenAI GPT-3) to translate the dialect into standard Japanese.

[1327] The translated text data in standard Japanese is then re-encrypted and sent over the network to the device. The device stores the translated text data in a buffer and displays it on the user interface. If necessary, a speech synthesis engine (e.g., Google Text-to-Speech) is used to play back the translation results aloud.

[1328] Specific examples

[1329] As a concrete example, consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you OK?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you OK?", using a natural language processing engine. The translation result is then sent back to the device and displayed to the user or played aloud. This process allows the user to understand the local dialect and respond quickly and accurately.

[1330] Prompt Sentence Examples

[1331] Write a prompt for a system that supports patient interaction in a hospital. Explain the program process for translating spoken dialect into standard Japanese and displaying the translation.

[1332] Specific names of hardware and software to be used

[1333] User devices: smartphones, tablets, laptops

[1334] Server: Cloud server (e.g. AWS EC2, Google Cloud)

[1335] Speech recognition engine: Google Speech-to-Text, Amazon Transcribe

[1336] Natural language processing engine: OpenAI GPT-3, Google BERT

[1337] Speech synthesis engine: Amazon Polly, Google Text-to-Speech

[1338] Network: Wi-Fi, 4G / 5G data connection

[1339] The above is an embodiment of the present invention, which allows users to efficiently respond and receive appropriate services without having to deal with local dialects or specific language barriers.

[1340] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1341] Divide the processing flow of the system program into processing steps

[1342] Step 1: Getting voice input

[1343] Step 2: Preprocess and transmit audio data

[1344] Step 3: Voice Recognition

[1345] Step 4: Language Processing and Translation

[1346] Step 5: Send and view the translation results

[1347] Specific explanation of each processing step

[1348] Step 1: Getting voice input

[1349] The user speaks into the device. The input voice data is captured by the device's built-in microphone and saved in a buffer. For example, the user says "Onshan, you're OK" in a dialect.

[1350] Input: User's voice

[1351] Data processing: Converting audio into digital data

[1352] Output: Buffered audio data

[1353] Specific behavior:

[1354] The user taps the voice input button.

[1355] The device's microphone is activated and records what the user says.

[1356] When recording is complete, the audio data is saved in a buffer in WAV format.

[1357] Step 2: Preprocess and transmit audio data

[1358] The device converts the audio data in the buffer into SIL format, compresses it with gzip, encrypts it with AES, and sends it over the network to the server.

[1359] Input: Buffered audio data

[1360] Data processing: format conversion, compression, encryption

[1361] Output: Encrypted audio data is sent to the server

[1362] Specific behavior:

[1363] Run a Python script to convert the audio data into SIL format.

[1364] Compress the data using the gzip library.

[1365] Encrypt the data using an AES encryption library (e.g. PyCryptodome).

[1366] Send the encrypted data to the server in an HTTP POST request.

[1367] Step 3: Voice Recognition

[1368] The server decrypts the received encrypted data using AES, passes the voice data to a speech recognition engine (e.g., Google Speech-to-Text API), and converts it into text data.

[1369] Input: Encrypted audio data

[1370] Data processing: decoding, voice recognition

[1371] Output: Text data

[1372] Specific behavior:

[1373] The server decrypts the received data using AES.

[1374] Sends SIL formatted audio data to the Speech-to-Text API.

[1375] Receives text data and stores it in a MongoDB database.

[1376] Step 4: Language Processing and Translation

[1377] The server passes the stored text data to a natural language processing engine (e.g., OpenAI GPT-3) to translate the dialect into standard Japanese.

[1378] Input: Text data

[1379] Data processing: Translation processing

[1380] Output: Standard Japanese text data

[1381] Specific behavior:

[1382] The server retrieves the text data from the database.

[1383] Send the text data to the GPT-3 API as a prompt.

[1384] The translated text data is obtained and stored in a database.

[1385] Step 5: Send and view the translation results

[1386] The server sends the translation result text data to the device. The device receives this data and displays it on the user interface. If necessary, it plays it aloud using a speech synthesis engine (e.g., Google Text-to-Speech API).

[1387] Input: Translated standard Japanese text data

[1388] Data processing: display or voice synthesis

[1389] Output: Text data displayed in a user interface or audio played

[1390] Specific behavior:

[1391] The server sends the translation results to the device via an HTTP POST request.

[1392] The terminal stores the received data in a buffer and displays it on the user interface.

[1393] If necessary, a TTS (Text-to-Speech) API is called and the translation result is played back as synthesized speech.

[1394] (Application example 1)

[1395] 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."

[1396] In autonomous vehicles, when passengers give instructions in a dialect or a foreign language, it is difficult for the vehicle's navigation system to accurately understand the instructions and respond appropriately. It is necessary to overcome this language barrier and provide a system that can translate passenger instructions into standard Japanese or a specified language in real time, allowing autonomous vehicles to respond accurately and quickly.

[1397] 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.

[1398] In this invention, the server includes means for a user to input voice data, means for converting the voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, means for transmitting the translated text data in standard Japanese to an in-vehicle system, and means for transmitting instructions to a navigation system of the in-vehicle system, thereby enabling an autonomous vehicle to accurately understand instructions given in a passenger's dialect or foreign language and respond appropriately in real time.

[1399] "Means for a user to input voice data" refers to a device or interface that allows a user to input voice data using their voice.

[1400] "Means for converting into text data using a voice recognition engine" refers to software or hardware for analyzing voice data and converting it into character text data.

[1401] "Means for translating into standard Japanese using a natural language processing engine" refers to software or hardware for translating text data into standard Japanese.

[1402] "Means for transmitting the translated text data in standard Japanese to the in-vehicle system" refers to a communication function for transmitting the translated text data to the in-vehicle system.

[1403] "Means for transmitting instructions to the navigation system of the in-vehicle system" refers to the interface and communication means for transmitting instructions to the navigation system.

[1404] "Means for transmitting to a server via a network" refers to a communication system for transmitting audio data to a server via the Internet or a local network.

[1405] "Means for transmitting text data to the in-vehicle system" refers to a communication function for transmitting translated text data to the in-vehicle system.

[1406] "Means for outputting voice using a voice synthesis engine" refers to software or hardware for analyzing text data and outputting it as voice.

[1407] "Means for providing a user with voice output in standard Japanese" refers to a device for providing a user with voice generated by a speech synthesis engine through speakers, headphones, etc.

[1408] A "server" refers to a computer or cloud service that processes voice and text data over a network.

[1409] MODE FOR CARRYING OUT THE INVENTION

[1410] As an embodiment of the present invention, a system will be described in which a user boards an autonomous vehicle and inputs voice data. When a passenger gives instructions in a dialect or foreign language, the system translates the voice into standard Japanese and transmits it to the navigation system of the autonomous vehicle.

[1411] System Configuration

[1412] The system mainly consists of the following components:

[1413] 1. User terminal: Inputs voice data and displays / outputs the results.

[1414] 2. Server: Responsible for processing voice data.

[1415] 3. Speech recognition engine: Converts voice data into text data.

[1416] 4. Natural language processing engine: Translates text data into standard Japanese.

[1417] 5. In-car navigation system: Navigate based on translated instructions.

[1418] 6. Network: Sends and receives data between the user terminal and the server, and between the server and the in-vehicle system.

[1419] System Operation

[1420] 1. Acquiring voice input: Users input voice data via their smartphones or tablets. When a passenger says in their local dialect, "I want to go to the nearest convenience store around here," the voice is captured by the device.

[1421] 2. Audio data preprocessing and transmission: The captured audio data is converted into a certain format, compressed, encrypted, and then transmitted to the server over the network.

[1422] 3. Speech recognition: The server analyzes the received voice data using a voice recognition engine and converts it into text data.

[1423] 4. Language processing and translation: The server passes the text data to a natural language processing engine to translate the dialect or foreign language into standard Japanese.

[1424] 5. Sending the translation results: The translated text data in standard Japanese is sent to the user terminal and the in-vehicle system via the network.

[1425] Hardware and software used

[1426] Speech recognition engine: Uses the speech_recognition library.

[1427] Natural language processing engine: uses the googletrans library.

[1428] Speech synthesis engine: Uses the pyttsx3 library.

[1429] Network communication: Internet or local network.

[1430] Specific examples

[1431] For example, if a passenger gets into an autonomous taxi and says in a local dialect, "I'd like to go to the nearest convenience store around here," the system will recognize the speech, translate it into standard Japanese, and communicate it to the navigation system as, "Do you want to go to the nearest convenience store around here?" The navigation system will follow this instruction, calculate the optimal route, and head to the specified destination.

[1432] Prompt Sentence Examples

[1433] The voice recognition engine converts the input voice data into text data, and the natural language processing engine translates that text data into standard Japanese.

[1434] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1435] Step 1: Getting voice input

[1436] Users input voice data via their smartphones or tablets. When a passenger says something in their local dialect like, "I want to go to the nearest convenience store around here," the voice is captured by the device. The input voice data is stored in a buffer.

[1437] Step 2: Preprocess and transmit audio data

[1438] The device converts the captured audio data into a certain format, specifically preprocessing it by reducing noise and normalizing the volume. The preprocessed audio data is then compressed and encrypted and sent over the network to the server. The input is the captured audio data, and the output is the preprocessed audio data.

[1439] Step 3: Voice Recognition

[1440] The server passes the received audio data to a speech recognition engine. The speech recognition engine (for example, the speech_recognition library) analyzes the audio data and converts it into text data. The input is preprocessed audio data, and the output is the text data converted from the audio.

[1441] Step 4: Language Processing and Translation

[1442] The server passes the converted text data to a natural language processing engine. The natural language processing engine (for example, the GoogleTrans library) translates the dialect or foreign language text data into standard Japanese. The input is the text data generated by the speech recognition engine, and the output is the text data translated into standard Japanese.

[1443] Step 5: Send the translation

[1444] The server then compresses and encrypts the translated text data again and sends it to the user's device and the in-vehicle system via the network. The input is the text data translated into standard Japanese, and the output is the compressed and encrypted text data.

[1445] Step 6: Speech synthesis and navigation instructions

[1446] The user device converts the translated text data into standard Japanese using a speech synthesis engine and provides it to the passenger through the speaker. The in-vehicle navigation system receives the translated instructions, calculates the optimal route, and heads to the specified destination. The input is the translated text data in standard Japanese, and the output is the voice data and instructions from the navigation system.

[1447] The above steps realize a system that enables an autonomous vehicle to accurately understand instructions given by a user in a dialect or foreign language and quickly and accurately navigate to the destination.

[1448] 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.

[1449] The present invention is a system in which a user inputs voice data, converts it into text data using a voice recognition engine, and then translates the text data into standard Japanese using a natural language processing engine. This system is also combined with an emotion engine that recognizes the user's emotions. Details of the system and program processing are explained in natural language below, and an embodiment is shown with specific examples.

[1450] System Configuration

[1451] The system mainly consists of the following components:

[1452] 1. User terminal: Inputs voice data and displays / outputs the results.

[1453] 2. Server: Responsible for processing voice data.

[1454] 3. Speech recognition engine: Converts voice data into text data.

[1455] 4. Natural language processing engine: Translates text data into standard Japanese.

[1456] 5. Emotion engine: Analyzes text data and recognizes user emotions.

[1457] 6. Network: Sends and receives data between user terminals and servers.

[1458] Program processing

[1459] 1. Acquiring voice input

[1460] During medical activities in the field, the user speaks to the terminal about the patient's conversation. For example, the patient says in a local dialect, "Hey, are you okay?" The user cannot understand the words, so they input the voice into the terminal.

[1461] 2. Preprocessing and transmission of audio data

[1462] The device uses the built-in microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[1463] The audio data is converted into a certain format, and the data is compressed and encrypted before being sent to a server via a network.

[1464] 3. Voice Recognition

[1465] The server passes the received voice data to a voice recognition engine, which converts the voice data into corresponding text data. For example, "Onshan, you're OK" is converted into text data like "Onshan, you're OK."

[1466] 4. Language Processing and Translation

[1467] The server passes the text data to a natural language processing engine, which translates the dialect into standard Japanese. For example, "Onshan, you're OK" is translated into "Are you OK?"

[1468] 5. Emotion analysis

[1469] The server sends the translated text data in standard Japanese to the emotion engine, which analyzes the user's emotions. As a result, data with emotional information added is generated. For example, if a patient is worried, the text data generated is, "Are you okay? (Worried)."

[1470] 6. Sending and displaying translation results and emotional information

[1471] The server then sends the translation results and emotion information back to the terminal via the network.

[1472] The device stores the received standard Japanese text data and emotion information in a buffer and displays it on the user interface. For example, the screen displays "Are you okay? (Worried)."

[1473] If necessary, the device will use a speech synthesis engine to play the voice "Are you OK? (I'm worried)."

[1474] Specific examples

[1475] As a concrete example, consider a scenario in which a user working in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you okay?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server converts the speech data into text using a speech recognition engine, and then translates it into standard Japanese, "Are you okay?", using a natural language processing engine. The emotion engine then analyzes the user's emotion, and if it recognizes it as "worried," text data is created with that information added. This text data is then sent back to the device and displayed to the user or output as voice. This process allows the user to not only understand the local dialect, but also to understand the patient's emotion, enabling more appropriate treatment.

[1476] The above is an embodiment of the present invention. Through this system, medical students and medical professionals can efficiently carry out medical activities without having to deal with the language or emotional barriers of the local area.

[1477] The processing flow will be explained below.

[1478] Step 1:

[1479] The user starts to input voice into the terminal. Specifically, in a medical setting, a patient says in a local dialect, "Hey, are you okay?" If the user cannot understand what is being said, they can input voice into the microphone of the terminal.

[1480] Step 2:

[1481] The device uses a microphone to capture the user's voice data, which is first temporarily stored in the device's buffer.

[1482] Step 3:

[1483] The device converts the captured audio data into a format (e.g., WAV or MP3), which includes encoding to preserve audio quality.

[1484] Step 4:

[1485] The device then sends the converted voice data to the server via the network, compressing the data for efficient data transfer and encrypting it to ensure security.

[1486] Step 5:

[1487] The server receives the voice data sent over the network, decodes it again, and passes it to the voice recognition engine.

[1488] Step 6:

[1489] The server's speech recognition engine analyzes the voice data and converts it into the corresponding text data, "You're OK, Onshan." This conversion process uses advanced speech analysis algorithms.

[1490] Step 7:

[1491] The server passes the generated text data to a natural language processing engine, which analyzes the text data and translates it into the corresponding standard Japanese text, "Are you OK?"

[1492] Step 8:

[1493] The server sends the translated text data in standard Japanese to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the context and phrasing of the text data to extract specific emotions (e.g., "worry").

[1494] Step 9:

[1495] The server adds the results of the sentiment analysis to the translated standard Japanese text data. For example, the text data generated is "Are you okay? (Worried)."

[1496] Step 10:

[1497] The server then sends the translation results and emotion information to the device via the network, and this transmission is also encrypted and secure.

[1498] Step 11:

[1499] The terminal stores the received standard Japanese text data "Are you OK? (I'm worried)" in a buffer. The stored text is immediately displayed on the user interface.

[1500] Step 12:

[1501] The device passes the text data to a speech synthesis engine as needed and outputs the translation results as voice. Specifically, the speech synthesis engine reads out to the user, "Are you okay? (I'm worried)."

[1502] This flow allows users to understand local dialects in real time and also grasp the patient's emotions, enabling more appropriate medical care.

[1503] Example 2

[1504] 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."

[1505] In local medical activities, users often have difficulty understanding patients who speak in local dialects. It is also difficult to accurately grasp the patient's emotions. In such situations, there is a high possibility that appropriate medical care will be delayed, so a method to overcome the language and emotional barriers is needed.

[1506] 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.

[1507] In this invention, the server includes means for preprocessing voice data, means for converting the preprocessed voice data into text data using a voice recognition engine, means for translating the text data into standard Japanese using a natural language processing engine, means for analyzing emotions using an emotion analysis engine, and means for transmitting the text data with added emotion information to a user terminal. This makes it possible to translate the voice data into standard Japanese, analyze the patient's emotions, and provide the results to the user.

[1508] "Audio data" refers to data obtained by converting an audio signal into digital information.

[1509] "Preprocessing" refers to processes such as noise removal and volume normalization that are performed to make audio data easier to analyze.

[1510] A "server" is a centralized computing device that processes audio data over a network.

[1511] A "voice recognition engine" is software or hardware for converting voice data into text data.

[1512] "Text data" is character information converted by a voice recognition engine.

[1513] A "natural language processing engine" is software or hardware for analyzing text data and performing specific language processing (e.g., translation).

[1514] "Standard language" is a common, widely understood form of language, not a regional dialect.

[1515] An "emotion analysis engine" is software or hardware that identifies emotions from text data and adds that emotional information.

[1516] "Emotion information" is data relating to the type and intensity of emotions added to text data.

[1517] A "user terminal" is a device that allows a user to input voice data and display or output the results as voice.

[1518] A "network" is a communications infrastructure for sending and receiving data.

[1519] A "voice synthesis engine" is software or hardware that converts text data into voice data and outputs the voice.

[1520] The present invention is a system in which a user inputs voice data, converts it into text data using a voice recognition engine, translates the text data into standard Japanese using a natural language processing engine, and recognizes the user's emotions using an emotion analysis engine. This system is mainly composed of a user terminal, a server, a voice recognition engine, a natural language processing engine, an emotion analysis engine, and a network.

[1521] System Configuration

[1522] 1. User Device

[1523] This device inputs and preprocesses voice data. It has a built-in microphone for capturing the user's voice input. It also has the function of preprocessing the voice data and sending it to a server via a network. It also has the function of displaying the results and outputting the voice.

[1524] 2. Server

[1525] It is a centralized computing device that processes voice data. It has the function of passing received voice data to a voice recognition engine and converting it into text data. It also has the function of translating text data into standard Japanese using a natural language processing engine and analyzing emotions using an emotion analysis engine.

[1526] 3. Speech Recognition Engine

[1527] It is software or hardware that converts voice data into text data. For example, a common voice recognition engine is the Google Speech-to-Text API.

[1528] 4. Natural Language Processing Engine

[1529] It is software or hardware used to analyze text data and perform specific language processing (e.g., translation). For example, Microsoft Azure Cognitive Services is often used.

[1530] 5. Sentiment Analysis Engine

[1531] This is software or hardware that identifies emotions from text data and adds that emotional information. IBM Watson Tone Analyzer is commonly used.

[1532] 6. Network

[1533] It is a communications infrastructure for sending and receiving data. For example, a user terminal and a server are connected via the Internet.

[1534] System Operation

[1535] Acquiring voice input

[1536] The user speaks voice data into the terminal. For example, if the patient says "Hey, are you OK?" in a local dialect, the user inputs the voice data into the terminal.

[1537] Audio data preprocessing

[1538] The device uses the built-in microphone to capture audio data, temporarily stores it in the device's buffer, and then performs preprocessing such as noise reduction and volume normalization before converting it to a certain format (e.g., PCM format).

[1539] Sending audio data

[1540] The terminal compresses and encrypts the pre-processed voice data and transmits it to the server via the network.

[1541] Voice Recognition

[1542] The server passes the received voice data to a speech recognition engine (for example, Google Speech-to-Text API) and converts the voice data into text data. For example, "Onshan, are you OK?" becomes "Onshan, are you OK?"

[1543] Language Processing and Translation

[1544] The server passes the text data to a natural language processing engine (e.g., Microsoft Azure Cognitive Services) and translates the dialect into standard Japanese. For example, "You okay?" is translated into "Are you okay?"

[1545] Emotion analysis

[1546] The server then sends the translated text data in standard Japanese to a sentiment analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. As a result, text data with emotional information such as "worry" is generated.

[1547] Sending the results

[1548] The server then sends the translation results and emotion information back to the terminal via the network.

[1549] Display and Audio Output

[1550] The device stores the received standard Japanese text data and emotion information in the device's buffer and displays it on the user interface. For example, the device displays "Are you okay? (Worried)" on the screen. If necessary, the device also outputs this text data as voice using a speech synthesis engine (e.g., Amazon Polly). For example, the device plays back the voice "Are you okay? (Worried)."

[1551] Specific examples

[1552] Consider a scenario in which a user working in the medical field in a remote area of ​​Kyushu is conversing with a patient. The patient says in a local dialect, "Hey, are you okay?", and the user inputs the speech into the device. The device captures the speech data and sends it to the server. The server uses a speech recognition engine to convert the speech data into text, which is then translated into standard Japanese, "Are you okay?", through a natural language processing engine. The emotion analysis engine then analyzes the user's emotions, and if it recognizes the emotion as "worried," text data is created with that information added. This text data is then sent back to the device and displayed to the user or output as voice. This process allows the user to not only understand the local dialect, but also to understand the patient's emotions, enabling more appropriate treatment.

[1553] Example prompts to input to the generative AI model

[1554] A patient from the Kyushu region said in a local dialect, "Onshan, you're OK." Please translate this speech data into standard Japanese, "Are you OK?" Also, please analyze the emotions perceived from the statement and display the results.

[1555] The above is an embodiment of the present invention. Through this system, medical professionals can overcome the language and emotional barriers of the local area and carry out medical activities efficiently.

[1556] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1557] Program processing flow

[1558] Step 1: Getting voice input

[1559] The user inputs voice into the device. For example, the user speaks the patient's voice directly into the device, saying, "Hey, are you okay?" The device has a built-in microphone for capturing voice. The input voice data is temporarily stored in the device's buffer.

[1560] Step 2: Preprocessing the audio data

[1561] The device uses the built-in microphone to capture audio data and performs preprocessing such as noise reduction and volume normalization. Specifically, the audio data is filtered to remove noise and the volume level is made uniform. The audio data is then converted into a certain format (e.g., PCM format). The preprocessed audio data is output.

[1562] Step 3: Sending audio data

[1563] The device compresses the pre-processed audio data, encrypts it using SSL / TLS, and then transmits it to the server via the network. Through this transmission process, the encrypted audio data arrives at the server.

[1564] Step 4: Voice Recognition

[1565] The server passes the received voice data to a voice recognition engine. For example, the H voice recognition engine, which is commonly used as a voice recognition engine, is applied. The voice recognition engine analyzes the input voice data and converts it into the corresponding text data. For example, the voice saying "Onshan, are you OK?" is converted into the text data "Onshan, are you OK?" This converted text data is output.

[1566] Step 5: Sending text data

[1567] The server sends the converted text data to the natural language processing engine. Through this sending process, the text data is passed to the natural language processing engine.

[1568] Step 6: Natural Language Processing and Translation

[1569] The server uses a natural language processing engine (e.g., a widely used natural language processing engine) to translate the text data from the local language (dialect) to standard Japanese. For example, the text "Are you OK?" is translated to "Are you OK?" This translated text data in standard Japanese is the output.

[1570] Step 7: Sentiment Analysis

[1571] The server sends the translated text data in standard Japanese to a sentiment analysis engine. The sentiment analysis engine (for example, a commonly used sentiment analysis engine) analyzes emotions from the text and adds the emotional information. For example, the text "Are you okay?" is analyzed with the emotional information "worried." The text data with this emotional information added is the output.

[1572] Step 8: Sending the results

[1573] The server then transmits the text data with the added emotion information back to the terminal via the network. Through this transmission process, the text data with the added emotion information arrives at the terminal.

[1574] Step 9: Display and audio output of results

[1575] The device stores the received text data with the added emotion information in the device's buffer. It then displays it on the user interface. For example, the device displays "Are you okay? (Worried)" on the screen. If necessary, the device uses a speech synthesis engine to output the text data as voice. For example, the device plays back the voice "Are you okay? (Worried)."

[1576] The above is the specific processing flow of the program of this system.

[1577] (Application example 2)

[1578] 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."

[1579] In situations where it is necessary not only to understand the local language or dialect, but also to instantly grasp the speaker's emotions, conventional technologies face the challenge of being unable to respond appropriately. Security services, in particular, require accurate understanding of the speech of visitors and suspicious individuals and analyzing their emotions to detect potential risks early and respond safely and quickly. However, current speech recognition and translation systems lack the ability to analyze emotions, making it difficult to meet these requirements.

[1580] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for transmitting voice data to the server via a network, means for converting the voice data received by the server into text data using a voice recognition engine, means for translating the converted text data into standard Japanese using a natural language processing engine, means for adding emotional information to the translated text data in standard Japanese, and means for transmitting the text data including the emotional information back to the user terminal. This enables security services to translate the statements of visitors and suspicious individuals in real time and analyze their emotions, thereby enabling early detection of potential risks and safe and prompt response.

[1581] A "user terminal" is a device that inputs voice data and displays and outputs the results.

[1582] "Speech data" refers to data obtained from a user's speech that is converted into text data by a speech recognition engine.

[1583] A "voice recognition engine" is a software engine for converting voice data into text data.

[1584] "Text data" is data expressed as characters that has been converted by a voice recognition engine.

[1585] A "natural language processing engine" is a software engine for translating text data into standard Japanese.

[1586] "Standard language" is the common language form used when text data is translated.

[1587] An "emotion analysis engine" is a software engine for analyzing emotional information from text data.

[1588] "Emotion information" is data that indicates the speaker's emotions and is added by an emotion analysis engine.

[1589] A "user interface" is a means for displaying translation results and emotional information on a user terminal.

[1590] A "server" is a remote computer system responsible for processing audio data.

[1591] A "network" is a communications infrastructure that transmits and receives data between user terminals and servers.

[1592] A "speech synthesis engine" is a software engine for converting text data into speech.

[1593] This system inputs voice data from a user, converts it into text data using a voice recognition engine, translates it into standard Japanese using a natural language processing engine, and then adds emotional information using an emotion analysis engine.Finally, the translation results and emotional information are displayed on a user interface or output as voice using a voice synthesis engine.

[1594] System Configuration

[1595] The system mainly consists of the following components:

[1596] 1. User terminal: Inputs voice data and displays / outputs the results.

[1597] 2. Server: Responsible for processing voice data.

[1598] 3. Speech recognition engine: Converts voice data into text data.

[1599] 4. Natural language processing engine: Translates text data into standard Japanese.

[1600] 5. Sentiment analysis engine: Analyzes text data and adds emotional information.

[1601] 6. Network: Sends and receives data between user terminals and servers.

[1602] 7. Speech synthesis engine: Converts translated text data into speech.

[1603] Program processing

[1604] The server receives the voice data via the network and converts it into text data using a speech recognition engine. A specific example of a speech recognition engine is the Google Cloud Speech-to-Text API. The converted text data is then translated into standard Japanese using Amazon Translate. Emotional information is added to the translated standard Japanese text data using IBM Watson's emotion analysis API. This text data, including the emotional information, is then sent back to the user's device and displayed on the user interface or output as audio using a speech synthesis engine (e.g., Google Text-to-Speech).

[1605] Hardware, software, and data processing / calculation used

[1606] Hardware: Smart glasses

[1607] Audio data is acquired using the built-in microphone.

[1608] The display shows translation results and emotional information.

[1609] software:

[1610] Speech recognition engine: Google Cloud Speech-to-Text API

[1611] Natural language processing engine: Amazon Translate

[1612] Sentiment analysis engine: IBM Watson Sentiment Analysis API

[1613] Speech synthesis engine: Google Text-to-Speech

[1614] Specific examples

[1615] For example, consider a scenario in which security staff working at an airport use smart glasses to communicate with visitors. If a visitor speaks in a dialect or with a different accent, saying, "There's no problem here, right?", the staff member will capture the voice data using the smart glasses' built-in microphone and send it to a server in real time. The server will convert the voice data into text data using a speech recognition engine, and then translate it into standard Japanese, "There's no danger here, right?" using a natural language processing engine. The emotion analysis engine will then recognize the voice data as "worried," and text data with that information added will be generated. This text data will then be sent back to the smart glasses, and the message "There's no danger here, right? (worried)" will appear on the display.

[1616] Example prompts to input to the generative AI model

[1617] Input the following prompts into the AI ​​model to get translation and sentiment analysis results:

[1618] "Please translate the following dialect speech data into standard Japanese and analyze the sentiment: 'There's nothing wrong with this, right?'"

[1619] In this way, security staff can understand local language and sentiment and respond appropriately.The system can be used in many other applications as well.

[1620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1621] Step 1:

[1622] The user terminal acquires the voice data. The user speaks into the built-in microphone of the smart glasses and inputs the voice data, such as "There's no problem here, right?". This voice data is temporarily stored in a buffer.

[1623] Step 2:

[1624] The user terminal sends voice data to the server. The voice data is converted to WAV format, encrypted, and sent over the network. The input is the voice data, and the output is the voice data sent to the server.

[1625] Step 3:

[1626] The server uses a speech recognition engine to convert the voice data into text data. Using the Google Cloud Speech-to-Text API, the input is the transmitted voice data, and the output is the text data "There's nothing wrong with this, right?"

[1627] Step 4:

[1628] The server uses a natural language processing engine to translate the text data into standard Japanese. Using Amazon Translate, the input is the converted text data "There's no problem here, right?", and the output is standard Japanese "There's no danger here, right?"

[1629] Step 5:

[1630] The server uses an emotion analysis engine to add emotional information to the translated text data. Using IBM Watson's emotion analysis API, it recognizes "worried," and the input is text data in standard Japanese, and the output is text data that reads, "This place isn't dangerous, is it? (worried)."

[1631] Step 6:

[1632] The server sends the text data containing emotion information to the user terminal again. It is encrypted again and sent over the network. The input is the text data containing emotion information, and the output is the text data sent to the user terminal.

[1633] Step 7:

[1634] The user terminal displays the received text data on the user interface. At this time, the display shows "This place is not dangerous, right? (Worried)." The input is the received text data containing emotional information, and the output is the display.

[1635] Step 8:

[1636] If necessary, the user device uses a speech synthesis engine to convert text data into speech and output it. Using Google Text-to-Speech, the input is text data and the output is speech.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1642] 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.

[1643] 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).

[1644] 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.

[1645] 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."

[1646] 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.

[1647] 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).

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] The following is further disclosed regarding the above embodiment.

[1659] (Claim 1)

[1660] a means for a user to input voice data;

[1661] means for converting the voice data into text data using a voice recognition engine;

[1662] means for translating the text data into standard Japanese using a natural language processing engine;

[1663] means for providing the translated standard Japanese text data to a user;

[1664] A system including:

[1665] (Claim 2)

[1666] means for transmitting the voice data to a server via a network;

[1667] means for converting the voice data received by the server into text data using a voice recognition engine;

[1668] means for translating the converted text data into standard Japanese using a natural language processing engine;

[1669] means for transmitting the translated standard Japanese text data to a user terminal again;

[1670] 10. The system of claim 1, comprising:

[1671] (Claim 3)

[1672] a means for outputting the translated standard Japanese text data by voice using a voice synthesis engine in the user terminal;

[1673] means for providing the user with the speech output of standard Japanese;

[1674] 10. The system of claim 1, comprising:

[1675] "Example 1"

[1676] (Claim 1)

[1677] a means for a user to input voice data;

[1678] means for converting the voice data into text data using a voice recognition engine;

[1679] means for translating the text data into standard Japanese using a natural language processing engine;

[1680] means for providing the translated standard Japanese text data to a user;

[1681] means for converting the audio data into a certain format, compressing and encrypting the data, and then transmitting the data to a server;

[1682] means for decoding the voice data received by the server and passing it to a voice recognition engine to convert it into text data;

[1683] means for re-encrypting the translated standard text data and transmitting it to a user terminal;

[1684] A system including:

[1685] (Claim 2)

[1686] a means for outputting the translated standard Japanese text data by voice using a voice synthesis engine in the user terminal;

[1687] means for providing the user with the speech output of standard Japanese;

[1688] 10. The system of claim 1, comprising:

[1689] (Claim 3)

[1690] The voice recognition engine converts the voice data into text data via a network,

[1691] The natural language processing engine translates the text data into standard Japanese.

[1692] 10. The system of claim 1.

[1693] "Application Example 1"

[1694] (Claim 1)

[1695] a means for a user to input voice data;

[1696] means for converting the voice data into text data using a voice recognition engine;

[1697] means for translating the text data into standard Japanese using a natural language processing engine;

[1698] means for transmitting the translated standard Japanese text data to an in-vehicle system;

[1699] means for transmitting instructions to a navigation system of said in-vehicle system;

[1700] A system including:

[1701] (Claim 2)

[1702] means for transmitting the voice data to a server via a network;

[1703] means for converting the voice data received by the server into text data using a voice recognition engine;

[1704] means for translating the converted text data into standard Japanese using a natural language processing engine;

[1705] means for transmitting the translated standard Japanese text data to a user terminal again;

[1706] a means for transmitting the text data to an in-vehicle system by the user terminal;

[1707] 10. The system of claim 1, comprising:

[1708] (Claim 3)

[1709] a means for outputting the translated standard Japanese text data by voice using a voice synthesis engine in the user terminal;

[1710] means for providing the user with the speech output of standard Japanese;

[1711] a means for the in-vehicle system to perform navigation based on the translated text data;

[1712] 10. The system of claim 1, comprising:

[1713] "Example 2: Combining Emotion Engines"

[1714] (Claim 1)

[1715] a means for a user to input voice data;

[1716] means for preprocessing the audio data;

[1717] means for transmitting the preprocessed audio data to a server;

[1718] A means for converting the voice data received by the server into text data using a voice recognition engine;

[1719] means for translating the converted text data into standard Japanese using a natural language processing engine;

[1720] means for analyzing emotions in the translated standard Japanese text data using an emotion analysis engine;

[1721] means for transmitting the text data to which the emotion information is added to a user terminal;

[1722] means for providing a user with text data to which the emotion information has been added;

[1723] A system including:

[1724] (Claim 2)

[1725] means for transmitting the text data to which the emotion information has been added again to the user terminal via a network;

[1726] means for displaying the text data received by the user terminal;

[1727] means for visually or aurally providing the text data including the emotion information to a user;

[1728] 10. The system of claim 1, comprising:

[1729] (Claim 3)

[1730] a means for outputting the translated standard Japanese text data by voice using a voice synthesis engine in the user terminal;

[1731] means for providing the user with the speech output of standard Japanese;

[1732] 10. The system of claim 1, comprising:

[1733] "Application example 2 when combining emotion engines"

[1734] (Claim 1)

[1735] a means for a user to input voice data;

[1736] means for converting the voice data into text data using a voice recognition engine;

[1737] means for translating the text data into standard Japanese using a natural language processing engine;

[1738] means for providing the translated standard Japanese text data to a user;

[1739] means for adding emotional information to the text data using an emotional analysis engine;

[1740] means for displaying text data including the emotion information on a user interface;

[1741] A system including:

[1742] (Claim 2)

[1743] means for transmitting the voice data to a server via a network;

[1744] means for converting the voice data received by the server into text data using a voice recognition engine;

[1745] means for translating the converted text data into standard Japanese using a natural language processing engine;

[1746] means for adding emotional information to the translated standard Japanese text data;

[1747] means for transmitting the text data including the emotion information to the user terminal again;

[1748] 10. The system of claim 1, comprising:

[1749] (Claim 3)

[1750] a means for outputting the translated standard Japanese text data by voice using a voice synthesis engine in the user terminal;

[1751] means for providing the user with the speech output of standard Japanese;

[1752] 10. The system of claim 1, comprising: [Explanation of symbols]

[1753] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input voice data; means for converting the voice data into text data using a voice recognition engine; means for translating the text data into standard Japanese using a natural language processing engine; means for providing the translated standard Japanese text data to a user; A system including:

2. means for transmitting the voice data to a server via a network; means for converting the voice data received by the server into text data using a voice recognition engine; means for translating the converted text data into standard Japanese using a natural language processing engine; means for transmitting the translated standard Japanese text data to a user terminal again; The system of claim 1 , comprising:

3. a means for outputting the translated standard Japanese text data by voice using a voice synthesis engine in the user terminal; means for providing the user with the speech output of standard Japanese; The system of claim 1 , comprising:

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