system

Abstract

Provide a system. [Solution] means for providing a user interface for the hearing-blind; means for receiving and converting voice data into text; A means for analyzing text data and understanding user intent; means for generating an appropriate response based on the user's intent; means for converting the generated response into audio data; The system includes means for playing back the provided responses through a user interface.

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 hearing- and visually impaired people access emergency calls and customer support, conventional systems face challenges in providing a fast and accurate response. In particular, the conversion between voice and text input is not performed in real time, preventing users from quickly obtaining information. Furthermore, user interfaces can be difficult for hearing- and visually impaired people to use, creating a barrier to access. This significantly limits life-saving responses in emergencies and everyday customer support. [Means for solving the problem]

[0005] The present invention provides a system including a means for providing a user interface for the hearing-blind, a means for receiving voice data and converting it into text, and a means for analyzing the text data to understand the user's intent. Furthermore, by combining a means for generating an appropriate response based on the user's intent, a means for converting the generated response into voice data, and a means for playing the provided response through the user interface, the system enables the hearing-blind to quickly and accurately obtain the information they need. Furthermore, by adding a function for converting received voice data into text in real time and analyzing that text, and a means for acquiring the user's location information and identifying the appropriate support staff based on that information, the system further streamlines responses in emergencies and when using customer support.

[0006] "Deaf-blind" refers to people who are visually or hearing impaired.

[0007] "User interface" refers to the display screen and input means that users use to operate systems and applications.

[0008] "Voice data" refers to data that is a digital recording of voice information uttered by a user.

[0009] "Converting to text" refers to the process of converting voice data into text data.

[0010] "Text data" refers to digital written information expressed using characters and symbols.

[0011] "Analyze" refers to the process of analyzing data to understand its meaning and intent.

[0012] "User intent" refers to what the user wants from the system, their goals and needs.

[0013] "Generating a response" refers to the process by which the system prepares an answer or advice based on the user's intent.

[0014] "Converting into voice data" refers to the process of converting text data into voice.

[0015] "Real-time" refers to the state in which input data is processed and results are provided almost immediately.

[0016] "Location Information" refers to data in digital form that indicates a user's geographic location.

[0017] "Support personnel" refers to specialized staff who respond to user inquiries and requests.

[0018] An "emergency call" refers to a communication to request help in an emergency such as a fire or ambulance.

[0019] "Customer Support" refers to assistance provided to users to resolve questions or problems related to products or services. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention is a sophisticated system for the hearing- and visually impaired to access emergency calls and customer support. The system consists of a smartphone app and a cloud-based server, and by integrating speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition, it significantly improves convenience for the hearing- and visually impaired.

[0042] 1. Smartphone app functions

[0043] User Interface

[0044] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[0045] Voice to text conversion

[0046] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[0047] 2. Cloud Server Functions

[0048] Voice Recognition

[0049] The server converts the received voice data into text using a highly accurate speech recognition engine, for example, an external service such as the Google® Speech-to-Text API.

[0050] Natural Language Processing and Text Analytics

[0051] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent and derive the appropriate response for a specific task, such as filing an insurance claim or calling an emergency service.

[0052] Response generation and text-to-speech

[0053] The server generates a response to the user based on the analysis results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[0054] 3. Connecting to Support and Using Location Information

[0055] Identifying Support Personnel

[0056] The server has a database that identifies the most appropriate support person for the user's inquiry. For example, if the user calls an emergency number, the server connects the user to the nearest emergency operator.

[0057] Obtaining location information

[0058] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0059] Specific usage scenarios

[0060] Emergency call example

[0061] 1. The user opens the emergency call app and taps the call button.

[0062] 2. The device generates a call request and sends it to the server along with the location information.

[0063] 3. The server receives the call request and identifies the most suitable emergency operator.

[0064] 4. The user says, "There's a fire, please help."

[0065] 5. The device sends this audio to the server, which converts the audio data into text.

[0066] 6. The server analyzes the text data and understands the urgent intent.

[0067] 7. The server generates a response such as "Fire department is on its way. Please evacuate to a safe location" and converts it into speech.

[0068] 8. The device plays the converted audio to the user.

[0069] In this way, the present invention helps the hearing-visually impaired to receive prompt and appropriate assistance in emergencies, and also provides greater convenience to customer support services such as insurance companies and banks.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user opens the smartphone app and taps the call button.

[0073] What happens: The app detects a user tap event and generates a call request.

[0074] Step 2:

[0075] The terminal sends a call request to the server.

[0076] How it works: A call request (including metadata such as user ID and location) is sent to the server.

[0077] Step 3:

[0078] A server receives the call request and identifies available support personnel.

[0079] What it does: Queries a database on the server to find the most suitable person.

[0080] Step 4:

[0081] The device records the user's voice input and sends it to the server.

[0082] How it works: Captures voice input in real time and uploads it to a server as audio data.

[0083] Step 5:

[0084] The server converts the received voice data into text.

[0085] What it does: Calls a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[0086] Step 6:

[0087] The server sends the analyzed text to a natural language processing engine, which analyzes the user's intent.

[0088] How it works: Parsed text is sent to an NLU (Natural Language Understanding) engine to understand the user's intent.

[0089] Step 7:

[0090] The server generates relevant solutions and workarounds.

[0091] What it does: Searches the FAQ database based on intent and generates the appropriate answer.

[0092] Step 8:

[0093] The server converts the generated text into speech.

[0094] What it does: Calls a Text-to-Speech (TTS) engine to convert text to speech.

[0095] Step 9:

[0096] The terminal plays the audio data to the user.

[0097] What it does: Sends audio data to the device and plays it through the speaker.

[0098] Step 10:

[0099] The server obtains the user's location and identifies the appropriate support person.

[0100] What it does: Runs a database query to identify the best available agent based on the user's location.

[0101] Step 11:

[0102] The device periodically transmits the user's location information to the server.

[0103] How it works: Uses location services to update location data to a server in real time.

[0104] Example 1

[0105] 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."

[0106] When hearing- and visually impaired people use existing emergency call systems and customer support, they face difficulties in operating them and are unable to receive prompt responses. In particular, the accuracy of voice input recognition and intent understanding is low, which can easily lead to misunderstandings and delays. Furthermore, the lack of a means to accurately and efficiently utilize the user's location information makes it difficult to respond promptly in an emergency.

[0107] 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.

[0108] In this invention, the server includes means for receiving voice data and converting it into text using a voice recognition engine, means for analyzing the text data using a natural language understanding engine to understand the user's intent, and means for generating an appropriate response based on the user's intent. This allows hearing-visually impaired people to obtain clear voice data and send it to the server, which then uses natural language processing technology to accurately understand the intent and provide the most appropriate response quickly. Furthermore, by periodically obtaining the user's location information and identifying the most appropriate support person based on that information, emergency responses can be made even faster and more accurate.

[0109] "Hearing-blind" refers to people who have visual and / or hearing impairments and who have difficulty obtaining information or communicating through normal vision or hearing.

[0110] "User interface" means an interface that provides a means for users to interact with a system, including simple and intuitive design, voice commands, and tactile feedback tailored for users with visual and hearing impairments.

[0111] "Voice input" refers to the means by which a user provides information to the system through voice, specifically voice data collected using a high-sensitivity microphone and noise-canceling function.

[0112] "Voice Data" means data in a digital format for recording, storing, or transmitting user-uttered sounds.

[0113] A "voice recognition engine" is software that analyzes received voice data and converts it into corresponding text data; for example, a voice recognition API is used.

[0114] "Text data" refers to data in the form of a string of characters converted by a speech recognition engine and used for analysis, display, and response generation.

[0115] A "natural language understanding engine" refers to an artificial intelligence technology that analyzes text data and understands the user's intent and context.

[0116] "Text-to-speech engine" means a software technology for converting text data into synthetic speech and providing the generated response to the user in audio form.

[0117] "Location information" is data indicating the user's current location, and is obtained using GPS or other location information acquisition means.

[0118] "Support Personnel" means operators or technicians designated to respond to specific inquiries or emergency calls.

[0119] "Secure communication protocol" refers to a communication protocol for securely sending and receiving data, and includes, for example, HTTPS.

[0120] This invention is a system for the hearing- and visually impaired to access emergency calls and customer support. The system consists of a smartphone app and a cloud-based server. Specifically, it significantly improves convenience for the hearing- and visually impaired by integrating speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition.

[0121] Smartphone app functions

[0122] User Interface

[0123] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[0124] Voice to text conversion

[0125] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[0126] Cloud Server Functions

[0127] Voice Recognition

[0128] The server converts the received voice data into text using a highly accurate voice recognition engine, such as a general voice recognition API.

[0129] Natural Language Processing and Text Analytics

[0130] The server then further analyzes the converted text data using a natural language understanding (NLU) engine to accurately understand the user's intent, specifically whether they want to make an emergency call or receive customer support.

[0131] Response generation and text-to-speech

[0132] The server generates a response to the user based on the analysis results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[0133] Connecting to Support and Location

[0134] Identifying Support Personnel

[0135] The server has a database that identifies the most appropriate support person for the user's inquiry. For example, if the user calls an emergency number, the server connects the user to the nearest emergency operator.

[0136] Obtaining location information

[0137] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0138] Specific usage scenarios

[0139] Emergency call example

[0140] 1. The user opens the emergency call app and taps the call button.

[0141] 2. The device will issue a voice prompt saying, "Please state the details of your call," followed by the user saying, "There's a fire, please help."

[0142] 3. The device clearly captures the user's voice and sends it to the server.

[0143] 4. The server uses a speech recognition engine to convert the voice data into text data such as "Fire, help please."

[0144] 5. The server parses the text and understands that it is a fire emergency.

[0145] 6. The server generates a response saying, "Fire department is on its way. Please evacuate to a safe location," and converts it into audio data.

[0146] 7. The server sends the audio data to the device, which plays it back to the user.

[0147] 8. The server connects you to the most appropriate emergency operator and sends the user's location information to facilitate a quick response.

[0148] This system is designed to enable the hearing-blind to receive prompt and appropriate responses in emergencies. Users can provide clear voice input using devices equipped with high-sensitivity microphones and noise-canceling functions. This real-time speech recognition and natural language processing allows the system to accurately understand the user's intent and generate appropriate responses.

[0149] Example prompt: "There's a fire. I'd like to call emergency services."

[0150] Based on the above scenario, this prompt sentence is used:

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] A user launches a smartphone app and taps the "emergency call button." The input is a tap operation by the user, and the output is the device entering voice input mode. Specifically, the device presents a voice prompt saying, "Tell us what happened."

[0154] Step 2:

[0155] The device uses a highly sensitive microphone and noise-canceling function to capture the user's voice in real time. The user's voice is input as input, and voice data is generated as output. A specific example of the device's operation is when the user says, "There's a fire. Help me."

[0156] Step 3:

[0157] The device packetizes the acquired voice data and sends it to a cloud-based server using a secure communication protocol (e.g., HTTPS). Voice data and location information are obtained as input, and data packets are sent as output. Specifically, the voice data and location information arrive at the server.

[0158] Step 4:

[0159] The server converts the received voice data into text using a voice recognition engine (e.g., a general voice recognition API). Voice data is input, and text data is generated as output. The specific operation is to generate the text "There's a fire. Help me."

[0160] Step 5:

[0161] The server analyzes the text data using a natural language understanding engine (NLU engine) to understand the user's intent. The text data is input, and the analysis results are obtained as output. The specific behavior is that a fire emergency is understood.

[0162] Step 6:

[0163] The server generates an appropriate response message based on the analysis results and converts it into voice data using a text-to-speech (TTS) engine. The analysis results are input, and voice data is generated as output. The specific operation is to generate voice data saying, "Fire departments are on their way immediately. Please evacuate to a safe place."

[0164] Step 7:

[0165] The server sends the generated voice data to the terminal, which then plays it back. Voice data is received as input, and voice playback is provided to the user as output. Specifically, the terminal notifies the user by voice, "The fire brigade will be on its way shortly."

[0166] Step 8:

[0167] The server uses the user's location information to identify the most suitable emergency operator and connects them along with the location information. The location information and the user's intention are received as input, and the connection to the emergency operator is made as output. Specifically, the call content and location information are transmitted to the nearest fire station.

[0168] (Application example 1)

[0169] 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."

[0170] When hearing- and visually impaired people use autonomous vehicles, conventional systems face challenges in providing sufficient navigation and responding to emergencies. In particular, the lack of notifications regarding traffic control and obstacle detection hinders the creation of an environment in which hearing- and visually impaired people can travel safely. Therefore, it is necessary to provide a system that enables hearing- and visually impaired people to reach their destinations safely and efficiently and that can respond quickly in emergencies.

[0171] 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.

[0172] In this invention, the server comprises: means for providing a user interface for the hearing-visually impaired;

[0173] means for receiving and converting voice data into text;

[0174] A means for analyzing text data and understanding user intent;

[0175] means for generating an appropriate response based on the user's intent;

[0176] means for converting the generated response into audio data;

[0177] means for playing the provided responses through a user interface;

[0178] means for providing notifications regarding vehicle control and obstacle detection functions;

[0179] A means of connecting you to the nearest support center or emergency operator in case of an emergency;

[0180] a means for acquiring location information of the user and identifying an appropriate support person based on the location information;

[0181] This will enable hearing-impaired and visually impaired people to travel safely in self-driving vehicles by receiving notifications regarding traffic control and obstacle detection, and by responding quickly in emergencies.

[0182] A "user interface for the visually and hearing impaired" refers to an operating screen and input method designed to be easy to use for people with visual or hearing impairments.

[0183] "Means for receiving voice data and converting it into text" refers to a function for taking in voice-format data and converting it into character-format data.

[0184] "Means for analyzing text data and understanding user intent" refers to technology that analyzes text data and understands the user's requests and intent based on that data.

[0185] The "means for generating an appropriate response based on the user's intent" is a mechanism for generating an appropriate reply or instruction based on the analyzed user's intent.

[0186] The "means for converting the generated response into voice data" is a technique for converting the generated text-format response into voice format.

[0187] The "means for reproducing the provided response through a user interface" is a function for presenting the generated response to the user through a user interface.

[0188] "Means for providing notifications related to traffic control and obstacle detection functions" refers to technologies for controlling automated vehicles, traffic-related functions, and detecting obstacles and notifying users.

[0189] "Means for connecting to the nearest support center or emergency operator in an emergency" refers to a mechanism for quickly connecting to a nearby support center or emergency response personnel in an emergency.

[0190] "Means for acquiring the user's location information and identifying the appropriate support person based on that information" is a technology that acquires the user's current location information and selects the most appropriate support person based on that location.

[0191] This invention is a system that enables the deaf-blind to use automated vehicles safely and efficiently, improving the convenience and safety of travel for people with visual and hearing impairments. The system consists of a terminal and a cloud-based server, and realizes the coordination of speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition.

[0192] 1. Smartphone app functions

[0193] Providing a user interface

[0194] The device provides a user interface specifically tailored for the hearing-blind, with simple and intuitive icons and buttons, voice commands and haptic feedback, and special screen reader capabilities.

[0195] Voice to text conversion

[0196] The device receives the user's voice input in real time and sends it to the server. The voice input is captured using a high-sensitivity microphone and noise-canceling function to obtain clear voice data.

[0197] 2. Cloud Server Functions

[0198] Voice Recognition

[0199] The server converts the received voice data into text using a highly accurate speech recognition engine, specifically the Google Cloud Speech-to-Text API.

[0200] Natural Language Processing and Text Analytics

[0201] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent.

[0202] Response generation and text-to-speech

[0203] The server generates a response to the user based on the analysis results. The generated response is in text format, which is then converted into audio data by a text-to-speech (TTS) engine, specifically using the Google Cloud Text-to-Speech API.

[0204] 3. Vehicle control and obstacle detection

[0205] Vehicle Control

[0206] The device controls the vehicle based on the destination set by voice input, providing a safe route to the destination.

[0207] Obstacle Detection

[0208] The system detects obstacles and dangers in real time based on data obtained from the vehicle's sensors, and the device notifies the user of any detected obstacles or dangers via voice notifications.

[0209] 4. Emergency notification function

[0210] Identifying Support Personnel

[0211] The server obtains the user's location information and identifies the most suitable support person based on that information, allowing the user to be quickly connected to the nearest support center or emergency operator in the event of an emergency.

[0212] Obtaining location information

[0213] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0214] Specific usage scenarios

[0215] Scenario 1: Emergency Response

[0216] 1. The user opens the emergency call app and taps the call button.

[0217] 2. The device generates a call request and sends it to the server along with the location information.

[0218] 3. The server receives the call request and identifies the most suitable emergency operator.

[0219] 4. The user says, "There's a fire, please help."

[0220] 5. The device sends this audio to the server, which converts the audio data into text.

[0221] 6. The server analyzes the text data and understands the urgent intent.

[0222] 7. The server generates a response such as "Fire department is on its way. Please evacuate to a safe location" and converts it into speech.

[0223] 8. The device plays the converted audio to the user.

[0224] Scenario 2: Normal navigation

[0225] 1. The user speaks "Heading home."

[0226] 2. The device receives the voice data in real time and sends it to the server.

[0227] 3. The server converts the received voice data into text and analyzes it.

[0228] 4. The server calculates the appropriate route and generates a response as audio data.

[0229] 5. The device notifies the user, "Destination setting complete. Let's depart."

[0230] In these scenarios, example prompts are:

[0231] Please set your destination

[0232] "You're in danger right now. Should I call emergency services?"

[0233] "Obstacle detected. Please be careful."

[0234] In this way, the present invention can support safe and efficient travel for hearing-impaired people using automated vehicles and respond to a variety of emergency situations.

[0235] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0236] Step 1:

[0237] The device receives the user's voice input in real time and uses a high-sensitivity microphone and noise-canceling function to capture clear voice data, which is then sent directly to the server.

[0238] Input: User's voice command

[0239] Output: Clear recorded audio data

[0240] Step 2:

[0241] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, which then analyzes the voice data and returns the resulting text data to the server.

[0242] Input: Audio data sent from the device

[0243] Output: Text format data

[0244] Step 3:

[0245] The server analyzes the converted text data using a natural language understanding (NLU) engine to understand the user's intent. The analysis engine scans the text data and recognizes specific keywords and phrases to understand the user's request.

[0246] Input: Text format data

[0247] Output: Parsed user intent

[0248] Step 4:

[0249] The server generates an appropriate response based on the user's intent. The response is in text format, and this text data is used for subsequent processing. For example, if the user's intent is to call an emergency service, the server generates an emergency response message.

[0250] Input: Parsed user intent

[0251] Output: Text data for the response

[0252] Step 5:

[0253] The server converts the generated text data for the response into audio data using a text-to-speech (TTS) engine. The Google Cloud Text-to-Speech API performs this conversion and generates audio data.

[0254] Input: Text data for response

[0255] Output: Audio response data

[0256] Step 6:

[0257] The terminal plays the provided response through the user interface, and the user interface plays the audio data to notify the user.

[0258] Input: Audio response data

[0259] Output: The audio response played

[0260] Step 7:

[0261] The device controls the user's means of transportation and instructs the autonomous vehicle on the route to the set destination. During this process, it detects obstacles based on data collected by sensors and provides voice notifications as necessary.

[0262] Input: Destination information and data from sensors

[0263] Output: Vehicle control instructions and notification voice

[0264] Step 8:

[0265] The device periodically acquires the user's location information and sends it to the server, which then identifies the most appropriate support personnel and enables a prompt response in the event of an emergency.

[0266] Input: Location data

[0267] Output: Support representative identification and connection information

[0268] 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.

[0269] This invention is a system that improves the convenience of emergency calls and customer support for the hearing-blind, and in particular combines an emotion engine that recognizes the user's emotions. Comprised of a smartphone app and a cloud-based server, the system integrates emotion recognition functionality in addition to speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition, enabling even more advanced support.

[0270] 1. Smartphone app functions

[0271] User Interface

[0272] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[0273] Voice to text conversion

[0274] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[0275] 2. Cloud Server Functions

[0276] Voice Recognition

[0277] The server converts the received voice data into text using a highly accurate speech recognition engine, for example, using an external service such as the Google Speech-to-Text API.

[0278] Natural Language Processing and Text Analytics

[0279] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent and derive the appropriate response for a specific task, such as filing an insurance claim or calling an emergency service.

[0280] emotion recognition

[0281] The server is equipped with an emotion engine that recognizes the user's emotions based on voice and text data, allowing it to identify the user's emotional state, such as nervousness, anger, or sadness.

[0282] Response generation and text-to-speech

[0283] The server generates a response to the user based on the analysis results and emotion recognition results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[0284] 3. Connecting to Support and Using Location Information

[0285] Identifying Support Personnel

[0286] The server has a database that identifies the most suitable support person depending on the user's inquiry and emotional state. For example, in the case of an emergency call, the system connects the user to the nearest emergency operator.

[0287] Obtaining location information

[0288] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0289] Specific usage scenarios

[0290] Emergency call example

[0291] 1. The user opens the emergency call app and taps the call button.

[0292] 2. The device generates a call request and sends it to the server along with the location information.

[0293] 3. The server receives the call request and identifies the most suitable emergency operator.

[0294] 4. The user says, "There's a fire, please help."

[0295] 5. The device sends this audio to the server, which converts the audio data into text.

[0296] 6. The server analyzes the text data and understands the urgent intent.

[0297] 7. The server recognizes the user's emotions based on the voice and text data.

[0298] 8. The server reflects the emotion recognition results and generates a response such as "The fire department will be on its way immediately. Please evacuate to a safe place," and converts it into speech.

[0299] 9. The device plays the converted audio to the user.

[0300] In this way, the present invention helps the deaf-blind to receive prompt and appropriate assistance in emergencies. It can also provide high-quality customer support services for insurance companies and banks. By integrating emotion recognition functionality, it is possible to provide tailored responses based on the user's emotional state, further improving the user experience.

[0301] The processing flow will be explained below.

[0302] Step 1:

[0303] The user opens the smartphone app and taps the call button.

[0304] What happens: The app detects a user tap event and generates a call request.

[0305] Step 2:

[0306] The terminal sends a call request to the server.

[0307] How it works: A call request (including metadata such as user ID and location) is sent to the server.

[0308] Step 3:

[0309] A server receives the call request and identifies available support personnel.

[0310] What it does: It queries a database on the server to identify the most suitable person.

[0311] Step 4:

[0312] The device records the user's voice input and sends it to the server.

[0313] How it works: Captures voice input in real time and uploads it to a server as audio data.

[0314] Step 5:

[0315] The server converts the received voice data into text.

[0316] What it does: Calls a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[0317] Step 6:

[0318] The server analyzes the text data and understands the user's intent.

[0319] How it works: It uses a natural language understanding (NLU) engine to parse text data and determine user intent.

[0320] Step 7:

[0321] The server recognizes the user's emotions based on the voice data and text data.

[0322] How it works: The emotion recognition engine analyzes voice tone and text content to identify the user's emotional state (e.g., nervous, anger, sadness).

[0323] Step 8:

[0324] The server generates relevant solutions and workarounds.

[0325] How it works: Searches a FAQ database and generates appropriate answers based on intent and emotional state.

[0326] Step 9:

[0327] The server converts the generated text into speech.

[0328] What it does: It uses a text-to-speech (TTS) engine to convert the generated text into audio data.

[0329] Step 10:

[0330] The terminal plays the audio data to the user.

[0331] What it does: Sends audio data to the device and plays it through the speaker.

[0332] Step 11:

[0333] The server obtains the user's location information and identifies the most suitable support person.

[0334] What it does: Runs a database query to identify the best available agent based on the user's location.

[0335] Step 12:

[0336] The device periodically transmits the user's location information to the server.

[0337] How it works: Uses location services to update location data to a server in real time.

[0338] Example 2

[0339] 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."

[0340] When hearing- and visually impaired people call for emergency services or use customer support, it is difficult for them to receive prompt and appropriate responses. Furthermore, systems often fail to properly understand the user's emotional state and are unable to provide sufficient support. This creates a problem in that the safety and convenience of hearing- and visually impaired people cannot be adequately ensured in emergencies or when problems occur.

[0341] 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.

[0342] In this invention, the server includes means for recognizing the user's emotions based on the voice data and text data, means for generating an appropriate response based on the user's intention and emotions, and means for converting the generated response into voice data, thereby enabling the server to accurately grasp the user's intention and provide a detailed response according to the user's emotional state.

[0343] A "user interface for the deaf-blind" is an interface with a design and functionality that is intuitive and easy to use for users with visual or hearing impairments.

[0344] The "means for receiving voice data and converting it into text" is a technology that processes the voice input by the user as a digital signal and converts it into text format.

[0345] "Means for analyzing text data and understanding user intent" refers to a technology for analyzing converted text data to identify the user's requests and intent.

[0346] "Means for recognizing a user's emotions based on voice data and text data" is a technology for identifying a user's emotional state using acquired voice and text information.

[0347] "Means for generating appropriate responses based on the user's intentions and emotions" refers to technology that enables the system to generate optimal responses based on the user's requests and emotional state.

[0348] The "means for converting the generated response into voice data" refers to a technique for converting the text-format response generated by the system into voice format.

[0349] "Means for acquiring the user's location information and identifying the appropriate support staff based on that information" refers to technology that acquires the user's current location in real time and identifies the staff member who will provide appropriate support based on that information.

[0350] The present invention provides a system that enables the hearing-blind to receive prompt and accurate responses when calling emergency services or using customer support services. The system integrates a terminal, a cloud-based server, a user interface, speech recognition, natural language processing, emotion recognition, text-to-speech conversion, and location information acquisition functions.

[0351] First, the device provides the user with a user interface. This user interface is designed to be intuitive for the hearing-blind and includes simple icons and buttons, voice commands, haptic feedback, and special screen reader functionality. When the user speaks, the device captures the voice data using a high-sensitivity microphone and noise-canceling function and transmits it to the server in real time.

[0352] The server converts the received voice data into text using a speech recognition engine such as the Google Speech-to-Text API, generating clear, highly accurate text data. The server then analyzes this text data and uses a natural language understanding (NLU) engine to understand the user's intent. For example, the server can analyze a query such as "There's a fire. Help me." and recognize that it's an emergency.

[0353] Furthermore, the server uses the text data and the original voice data to run an emotion recognition engine to identify the user's emotional state. For example, it can detect whether the user is nervous, angry, sad, etc. Based on this, the server generates an appropriate response to the user based on the analysis results and the emotion recognition results. This response is first generated in text format and then converted into voice data using a text-to-speech (TTS) engine.

[0354] Finally, the server retransmits the generated voice data to the device, which then plays it back to the user. This allows the user to understand the instructions from the server by voice, ensuring a prompt and appropriate response in the event of an emergency. Furthermore, the server periodically obtains the user's location information and identifies the most appropriate support personnel based on that location information. This allows, for example, in the event of an emergency, the user to be quickly connected to the nearest emergency operator.

[0355] A specific example would be an emergency call scenario like this:

[0356] 1. The user opens the emergency call app and taps the call button.

[0357] 2. The device generates a call request and sends it to the server along with the location information.

[0358] 3. The server receives the call request and identifies the most suitable emergency operator.

[0359] 4. The user says, "There's a fire, please help."

[0360] 5. The device sends this audio to the server, which converts the audio data into text.

[0361] 6. The server analyzes the text data and understands the urgent intent.

[0362] 7. The server recognizes the user's emotions based on the voice and text data.

[0363] 8. The server reflects the emotion recognition results and generates a response such as "The fire department will be on its way immediately. Please evacuate to a safe place," and converts it into speech.

[0364] 9. The device plays the converted audio to the user.

[0365] Examples of prompts to input to a generative AI model include:

[0366] "Please provide a detailed explanation of the processing steps of an emergency call system for the hearing-blind. Please explain the entire process, from voice input to response generation, including specific operations."

[0367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0368] Step 1: Accept voice input

[0369] The device captures the user's voice input in real time using a high-sensitivity microphone and noise-canceling function. The input of this process is the user's voice, and the output is voice data converted into a digital signal. Specifically, when the user opens the emergency app and says "help," the device will clearly record the voice.

[0370] Step 2: Sending audio data

[0371] The device encrypts the captured voice data and sends it to a cloud-based server via the Internet. The input is the voice data converted into a digital signal, and the output is the voice data sent to the server. Specifically, the device displays the progress on the screen, saying "Sending voice data to server."

[0372] Step 3: Voice Recognition

[0373] The server converts the received voice data into text data using a voice recognition engine (e.g., Google Speech-to-Text API). The input is the voice data sent from the device, and the output is text data. Specifically, the server records the status "Converting voice data to text" in the log.

[0374] Step 4: Text Analysis

[0375] The server analyzes the converted text data using a natural language understanding (NLU) engine to understand the user's intent. The input is text data, and the output is the analysis result that represents the user's intent. Specifically, the server records the status "Emergency intent detected, preparing appropriate response" in the log.

[0376] Step 5: Emotion Recognition

[0377] The server uses an emotion recognition engine based on the text data and the original voice data to recognize the user's emotions. The input is text data and voice data, and the output is data indicating the user's emotional state. Specifically, the server executes "recognize user's emotions" and outputs the data "The user is very anxious."

[0378] Step 6: Response Generation

[0379] The server generates an appropriate response to the user based on the analysis results and emotion recognition results. The input is data indicating the user's intention and emotional state, and the output is a text response. Specifically, the server generates a text response such as "The fire department will be on its way immediately. Please evacuate to a safe place."

[0380] Step 7: Text-to-speech

[0381] The server converts the generated text response into audio data using a text-to-speech (TTS) engine. The input is the text response and the output is audio data. Specifically, the server logs the status "Converting generated response into audio format."

[0382] Step 8: Send and play a voice response

[0383] The server sends the generated voice data to the terminal, which then plays it back. The input is the voice data, and the output is the voice that is played back to the user. Specifically, the terminal tells the user, "The fire department is on its way immediately. Please evacuate to a safe place."

[0384] Step 9: Use location information

[0385] The device periodically sends its location information to the server, and the server uses this information to identify the most suitable support person. The input is the location information sent from the device, and the output is data for connecting to the appropriate support person. Specifically, the server displays a message saying "Connecting to the nearest fire station operator."

[0386] (Application example 2)

[0387] 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."

[0388] When hearing-impaired people use voice or text data to access customer support services, it is difficult for them to receive prompt and appropriate responses. Furthermore, existing systems often lack the ability to take into account the user's emotional state, resulting in insufficient support.

[0389] 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 recognizing the emotional state of the user, means for optimizing a response based on the emotional state of the user, means for transmitting voice data and location information to the server, and means for identifying the most suitable support person based on the location information. This makes it possible for hearing-impaired people to quickly receive an appropriate response according to their emotional state when they need customer support in an emergency.

[0390] A "user interface for the visually and hearing impaired" is an operation screen or input means designed to be easy to use for users with visual or hearing impairments.

[0391] "Voice data" refers to information input by voice by a user, which information is recorded in digital form.

[0392] The "means for converting to text" is a technique for converting received voice data into a meaningful written form.

[0393] "Text data" is voice data converted into character information and written in sentence format.

[0394] "Means of understanding intent" refers to technology for analyzing and understanding the purpose and request that a user is trying to communicate through voice or text.

[0395] "Means for generating a response" refers to the technology for creating appropriate answers or instructions in response to a user's intentions or inquiries.

[0396] The "means for converting into voice data" is a technique for converting a response generated as text data back into voice format.

[0397] "Means for recognizing emotional states" refers to technology that analyzes emotions from a user's voice or text and identifies emotions such as joy, anger, and sadness.

[0398] A "means for optimizing responses" is a technique for selecting and providing the most appropriate response to a user, taking into account the user's emotional state.

[0399] "Location information" is geographical data that indicates where a user is currently located.

[0400] "Means for transmitting to the server" refers to a communication technology for transmitting the necessary data from the user terminal to the cloud server.

[0401] The "means for identifying a support person" is a technology that automatically selects a person who can provide the most appropriate response based on the user's situation and location information.

[0402] This invention is a system that allows the hearing-blind to easily access emergency calls and customer support. The system is primarily composed of a smartphone application and a cloud-based server.

[0403] System configuration

[0404] Smartphone application

[0405] The smartphone application has the following main features:

[0406] 1. User Interface: Provide a clean and intuitive interface specifically designed for the hearing-blind. This interface includes simple icons and large buttons, and supports voice commands and haptic feedback.

[0407] 2. Voice input: A high-sensitivity microphone and noise-canceling function are used to clearly capture the user's voice, which is then sent to a cloud server in real time.

[0408] 3. Obtaining location information: Using a GPS sensor, the user's current location information is periodically sent to the server.

[0409] Cloud Server

[0410] The cloud server has the following functions:

[0411] 1. Speech recognition: The received voice data is converted into text data using a high-precision voice recognition engine (e.g., Google Speech-to-Text API).

[0412] 2. Natural Language Processing: Use a natural language understanding (NLU) engine to analyze text data and accurately understand user intent.

[0413] 3. Emotion Recognition: Equipped with an emotion engine that identifies the user's emotional state based on voice and text data.

[0414] 4. Response generation: Based on the analysis results and emotion recognition results, an appropriate response is generated and written in text format.

[0415] 5. Text-to-speech: Use a text-to-speech (TTS) engine (e.g., Amazon Polly) to convert the generated text response into audio data.

[0416] 6. Identifying support personnel: The system has a database for identifying appropriate support personnel or emergency operators based on the user's inquiry content and emotional state.

[0417] Processing flow

[0418] 1. Activation and Notification:

[0419] The user launches the smartphone app and taps the emergency call button. The app activates the voice input function, and the user says, "This is an emergency. Please help."

[0420] 2. Audio data processing:

[0421] The device receives the voice data and sends it to a cloud server, which uses a speech recognition engine to convert the voice data into text.

[0422] 3. Natural Language Processing and Emotion Recognition:

[0423] The server then uses a natural language understanding engine to analyze the converted text data and understand the user's intent. At the same time, an emotion recognition engine analyzes the user's emotional state.

[0424] 4. Generating an appropriate response:

[0425] The server generates a response based on the analysis results and converts it into text format.

[0426] 5. Generate a voice response:

[0427] The response text is converted into voice data by a text-to-speech engine and sent to the terminal.

[0428] 6. Response playback:

[0429] The terminal plays the converted audio data and provides instructions to the user.

[0430] Examples and prompts

[0431] For example, a scenario will be described in which a user discovers a suspicious person in his / her home at night.

[0432] 1. User action: The user opens the Safeguard app and presses the Emergency Call button.

[0433] 2. Voice input: The user says, "There is a suspicious person outside my house. Please help."

[0434] 3. Data transmission: The smartphone app sends voice data and location information to the server.

[0435] 4. Speech recognition: A speech recognition engine on the server side converts voice data into text.

[0436] 5. Analysis and emotion recognition: The NLU engine extracts keywords such as "suspicious person" and "help," and the emotion recognition engine detects the user's state of tension.

[0437] 6. Response generation: A response message is generated saying, "We will call the police. Please take refuge in a safe room in your home." and converted into speech using a TTS engine.

[0438] 7. Playback: The converted audio is played back to the user through the smartphone app.

[0439] Prompt Sentence Examples

[0440] "We have spotted a suspicious person and are calling emergency services. Please explain the situation and tell us what to do safely."

[0441] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0442] Step 1:

[0443] The user launches the smartphone app and taps the emergency call button.

[0444] Input: User taps

[0445] Output: Generate an emergency call request

[0446] Specific Actions: The user interface creates an emergency call request and enables voice input functionality.

[0447] Step 2:

[0448] The device uses a highly sensitive microphone to capture the user's voice and generates clear audio data using noise-canceling functionality.

[0449] Input: User voice input

[0450] Output: Clear audio data

[0451] Specific operation: The device captures audio, performs noise cancellation processing, generates audio data, and sends it to a cloud server.

[0452] Step 3:

[0453] The server converts the received voice data into text data using a voice recognition engine.

[0454] Input: Clear voice data

[0455] Output: Text data

[0456] Specific operation: The server runs a speech recognition engine (e.g., Google Speech-to-Text API) and converts the voice data into text data.

[0457] Step 4:

[0458] The server analyzes the text data using a natural language processing (NLU) engine to understand the user's intent.

[0459] Input: Text data

[0460] Output: User's intention information

[0461] Specific operation: The server uses a natural language processing engine to analyze the text data, extract important keywords such as "suspicious person" and "help," and understand the user's intent.

[0462] Step 5:

[0463] The server uses an emotion recognition engine to analyze the user's emotional state based on the voice and text data.

[0464] Input: Audio data, text data

[0465] Output: Emotional state information

[0466] Specific operation: The server uses an emotion recognition engine to identify emotions such as tension or fear from the user's voice tone and text content.

[0467] Step 6:

[0468] The server generates an appropriate response using a response generation engine based on the analysis results and emotional state information.

[0469] Input: User intention information, emotional state information

[0470] Output: Text response

[0471] Specific behavior: The server uses a response generation engine to create the most appropriate message for the user, generating a response such as "We will call the police. Please take shelter in a safe room in your home."

[0472] Step 7:

[0473] The server converts the generated text response into audio data using a text-to-speech (TTS) engine.

[0474] Input: Text response

[0475] Output: Response in audio data format

[0476] What happens: The server uses a text-to-speech engine (e.g., Amazon Polly) to convert the text response into audio data.

[0477] Step 8:

[0478] The terminal receives the audio data and plays it back to the user using its audio output function.

[0479] Input: Response in audio data format

[0480] Output: A voice response to the user

[0481] Specific operation: The device plays the received audio data and provides the user with specific instructions via voice, such as "We will call the police. Please evacuate to a safe room in your home."

[0482] 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.

[0483] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0484] 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.

[0485] [Second embodiment]

[0486] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0497] 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."

[0498] This invention is a sophisticated system for the hearing- and visually impaired to access emergency calls and customer support. The system consists of a smartphone app and a cloud-based server, and by integrating speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition, it significantly improves convenience for the hearing- and visually impaired.

[0499] 1. Smartphone app functions

[0500] User Interface

[0501] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[0502] Voice to text conversion

[0503] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[0504] 2. Cloud Server Functions

[0505] Voice Recognition

[0506] The server converts the received voice data into text using a highly accurate speech recognition engine, for example, using an external service such as the Google Speech-to-Text API.

[0507] Natural Language Processing and Text Analytics

[0508] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent and derive the appropriate response for a specific task, such as filing an insurance claim or calling an emergency service.

[0509] Response generation and text-to-speech

[0510] The server generates a response to the user based on the analysis results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[0511] 3. Connecting to Support and Using Location Information

[0512] Identifying Support Personnel

[0513] The server has a database that identifies the most appropriate support person for the user's inquiry. For example, if the user calls an emergency number, the server connects the user to the nearest emergency operator.

[0514] Obtaining location information

[0515] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0516] Specific usage scenarios

[0517] Emergency call example

[0518] 1. The user opens the emergency call app and taps the call button.

[0519] 2. The device generates a call request and sends it to the server along with the location information.

[0520] 3. The server receives the call request and identifies the most suitable emergency operator.

[0521] 4. The user says, "There's a fire, please help."

[0522] 5. The device sends this audio to the server, which converts the audio data into text.

[0523] 6. The server analyzes the text data and understands the urgent intent.

[0524] 7. The server generates a response such as "Fire department is on its way. Please evacuate to a safe location" and converts it into speech.

[0525] 8. The device plays the converted audio to the user.

[0526] In this way, the present invention helps the hearing-visually impaired to receive prompt and appropriate assistance in emergencies, and also provides greater convenience to customer support services such as insurance companies and banks.

[0527] The processing flow will be explained below.

[0528] Step 1:

[0529] The user opens the smartphone app and taps the call button.

[0530] What happens: The app detects a user tap event and generates a call request.

[0531] Step 2:

[0532] The terminal sends a call request to the server.

[0533] How it works: A call request (including metadata such as user ID and location) is sent to the server.

[0534] Step 3:

[0535] A server receives the call request and identifies available support personnel.

[0536] What it does: Queries a database on the server to find the most suitable person.

[0537] Step 4:

[0538] The device records the user's voice input and sends it to the server.

[0539] How it works: Captures voice input in real time and uploads it to a server as audio data.

[0540] Step 5:

[0541] The server converts the received voice data into text.

[0542] What it does: Calls a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[0543] Step 6:

[0544] The server sends the analyzed text to a natural language processing engine, which analyzes the user's intent.

[0545] How it works: Parsed text is sent to an NLU (Natural Language Understanding) engine to understand the user's intent.

[0546] Step 7:

[0547] The server generates relevant solutions and workarounds.

[0548] What it does: Searches the FAQ database based on intent and generates the appropriate answer.

[0549] Step 8:

[0550] The server converts the generated text into speech.

[0551] What it does: Calls a Text-to-Speech (TTS) engine to convert text to speech.

[0552] Step 9:

[0553] The terminal plays the audio data to the user.

[0554] What it does: Sends audio data to the device and plays it through the speaker.

[0555] Step 10:

[0556] The server obtains the user's location and identifies the appropriate support person.

[0557] What it does: Runs a database query to identify the best available agent based on the user's location.

[0558] Step 11:

[0559] The device periodically transmits the user's location information to the server.

[0560] How it works: Uses location services to update location data to a server in real time.

[0561] Example 1

[0562] 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."

[0563] When hearing- and visually impaired people use existing emergency call systems and customer support, they face difficulties in operating them and are unable to receive prompt responses. In particular, the accuracy of voice input recognition and intent understanding is low, which can easily lead to misunderstandings and delays. Furthermore, the lack of a means to accurately and efficiently utilize the user's location information makes it difficult to respond promptly in an emergency.

[0564] 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.

[0565] In this invention, the server includes means for receiving voice data and converting it into text using a voice recognition engine, means for analyzing the text data using a natural language understanding engine to understand the user's intent, and means for generating an appropriate response based on the user's intent. This allows hearing-visually impaired people to obtain clear voice data and send it to the server, which then uses natural language processing technology to accurately understand the intent and provide the most appropriate response quickly. Furthermore, by periodically obtaining the user's location information and identifying the most appropriate support person based on that information, emergency responses can be made even faster and more accurate.

[0566] "Hearing-blind" refers to people who have visual and / or hearing impairments and who have difficulty obtaining information or communicating through normal vision or hearing.

[0567] "User interface" means an interface that provides a means for users to interact with a system, including simple and intuitive design, voice commands, and tactile feedback tailored for users with visual and hearing impairments.

[0568] "Voice input" refers to the means by which a user provides information to the system through voice, specifically voice data collected using a high-sensitivity microphone and noise-canceling function.

[0569] "Voice Data" means data in a digital format for recording, storing, or transmitting user-uttered sounds.

[0570] A "voice recognition engine" is software that analyzes received voice data and converts it into corresponding text data; for example, a voice recognition API is used.

[0571] "Text data" refers to data in the form of a string of characters converted by a speech recognition engine and used for analysis, display, and response generation.

[0572] A "natural language understanding engine" refers to an artificial intelligence technology that analyzes text data and understands the user's intent and context.

[0573] "Text-to-speech engine" means a software technology for converting text data into synthetic speech and providing the generated response to the user in audio form.

[0574] "Location information" is data indicating the user's current location, and is obtained using GPS or other location information acquisition means.

[0575] "Support Personnel" means operators or technicians designated to respond to specific inquiries or emergency calls.

[0576] "Secure communication protocol" refers to a communication protocol for securely sending and receiving data, and includes, for example, HTTPS.

[0577] This invention is a system for the hearing- and visually impaired to access emergency calls and customer support. The system consists of a smartphone app and a cloud-based server. Specifically, it significantly improves convenience for the hearing- and visually impaired by integrating speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition.

[0578] Smartphone app functions

[0579] User Interface

[0580] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[0581] Voice to text conversion

[0582] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[0583] Cloud Server Functions

[0584] Voice Recognition

[0585] The server converts the received voice data into text using a highly accurate voice recognition engine, such as a general voice recognition API.

[0586] Natural Language Processing and Text Analytics

[0587] The server then further analyzes the converted text data using a natural language understanding (NLU) engine to accurately understand the user's intent, specifically whether they want to make an emergency call or receive customer support.

[0588] Response generation and text-to-speech

[0589] The server generates a response to the user based on the analysis results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[0590] Connecting to Support and Location

[0591] Identifying Support Personnel

[0592] The server has a database that identifies the most appropriate support person for the user's inquiry. For example, if the user calls an emergency number, the server connects the user to the nearest emergency operator.

[0593] Obtaining location information

[0594] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0595] Specific usage scenarios

[0596] Emergency call example

[0597] 1. The user opens the emergency call app and taps the call button.

[0598] 2. The device will issue a voice prompt saying, "Please state the details of your call," followed by the user saying, "There's a fire, please help."

[0599] 3. The device clearly captures the user's voice and sends it to the server.

[0600] 4. The server uses a speech recognition engine to convert the voice data into text data such as "Fire, help please."

[0601] 5. The server parses the text and understands that it is a fire emergency.

[0602] 6. The server generates a response saying, "Fire department is on its way. Please evacuate to a safe location," and converts it into audio data.

[0603] 7. The server sends the audio data to the device, which plays it back to the user.

[0604] 8. The server connects you to the most appropriate emergency operator and sends the user's location information to facilitate a quick response.

[0605] This system is designed to enable the hearing-blind to receive prompt and appropriate responses in emergencies. Users can provide clear voice input using devices equipped with high-sensitivity microphones and noise-canceling functions. This real-time speech recognition and natural language processing allows the system to accurately understand the user's intent and generate appropriate responses.

[0606] Example prompt: "There's a fire. I'd like to call emergency services."

[0607] Based on the above scenario, this prompt sentence is used:

[0608] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0609] Step 1:

[0610] A user launches a smartphone app and taps the "emergency call button." The input is a tap operation by the user, and the output is the device entering voice input mode. Specifically, the device presents a voice prompt saying, "Tell us what happened."

[0611] Step 2:

[0612] The device uses a highly sensitive microphone and noise-canceling function to capture the user's voice in real time. The user's voice is input as input, and voice data is generated as output. A specific example of the device's operation is when the user says, "There's a fire. Help me."

[0613] Step 3:

[0614] The device packetizes the acquired voice data and sends it to a cloud-based server using a secure communication protocol (e.g., HTTPS). Voice data and location information are obtained as input, and data packets are sent as output. Specifically, the voice data and location information arrive at the server.

[0615] Step 4:

[0616] The server converts the received voice data into text using a voice recognition engine (e.g., a general voice recognition API). Voice data is input, and text data is generated as output. The specific operation is to generate the text "There's a fire. Help me."

[0617] Step 5:

[0618] The server analyzes the text data using a natural language understanding engine (NLU engine) to understand the user's intent. The text data is input, and the analysis results are obtained as output. The specific behavior is that a fire emergency is understood.

[0619] Step 6:

[0620] The server generates an appropriate response message based on the analysis results and converts it into voice data using a text-to-speech (TTS) engine. The analysis results are input, and voice data is generated as output. The specific operation is to generate voice data saying, "Fire departments are on their way immediately. Please evacuate to a safe place."

[0621] Step 7:

[0622] The server sends the generated voice data to the terminal, which then plays it back. Voice data is received as input, and voice playback is provided to the user as output. Specifically, the terminal notifies the user by voice, "The fire brigade will be on its way shortly."

[0623] Step 8:

[0624] The server uses the user's location information to identify the most suitable emergency operator and connects them along with the location information. The location information and the user's intention are received as input, and the connection to the emergency operator is made as output. Specifically, the call content and location information are transmitted to the nearest fire station.

[0625] (Application example 1)

[0626] 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."

[0627] When hearing- and visually impaired people use autonomous vehicles, conventional systems face challenges in providing sufficient navigation and responding to emergencies. In particular, the lack of notifications regarding traffic control and obstacle detection hinders the creation of an environment in which hearing- and visually impaired people can travel safely. Therefore, it is necessary to provide a system that enables hearing- and visually impaired people to reach their destinations safely and efficiently and that can respond quickly in emergencies.

[0628] 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.

[0629] In this invention, the server comprises: means for providing a user interface for the hearing-visually impaired;

[0630] means for receiving and converting voice data into text;

[0631] A means for analyzing text data and understanding user intent;

[0632] means for generating an appropriate response based on the user's intent;

[0633] means for converting the generated response into audio data;

[0634] means for playing the provided responses through a user interface;

[0635] means for providing notifications regarding vehicle control and obstacle detection functions;

[0636] A means of connecting you to the nearest support center or emergency operator in case of an emergency;

[0637] a means for acquiring location information of the user and identifying an appropriate support person based on the location information;

[0638] This will enable hearing-impaired and visually impaired people to travel safely in self-driving vehicles by receiving notifications regarding traffic control and obstacle detection, and by responding quickly in emergencies.

[0639] A "user interface for the visually and hearing impaired" refers to an operating screen and input method designed to be easy to use for people with visual or hearing impairments.

[0640] "Means for receiving voice data and converting it into text" refers to a function for taking in voice-format data and converting it into character-format data.

[0641] "Means for analyzing text data and understanding user intent" refers to technology that analyzes text data and understands the user's requests and intent based on that data.

[0642] The "means for generating an appropriate response based on the user's intent" is a mechanism for generating an appropriate reply or instruction based on the analyzed user's intent.

[0643] The "means for converting the generated response into voice data" is a technique for converting the generated text-format response into voice format.

[0644] The "means for reproducing the provided response through a user interface" is a function for presenting the generated response to the user through a user interface.

[0645] "Means for providing notifications related to traffic control and obstacle detection functions" refers to technologies for controlling automated vehicles, traffic-related functions, and detecting obstacles and notifying users.

[0646] "Means for connecting to the nearest support center or emergency operator in an emergency" refers to a mechanism for quickly connecting to a nearby support center or emergency response personnel in an emergency.

[0647] "Means for acquiring the user's location information and identifying the appropriate support person based on that information" is a technology that acquires the user's current location information and selects the most appropriate support person based on that location.

[0648] This invention is a system that enables the deaf-blind to use automated vehicles safely and efficiently, improving the convenience and safety of travel for people with visual and hearing impairments. The system consists of a terminal and a cloud-based server, and realizes the coordination of speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition.

[0649] 1. Smartphone app functions

[0650] Providing a user interface

[0651] The device provides a user interface specifically tailored for the hearing-blind, with simple and intuitive icons and buttons, voice commands and haptic feedback, and special screen reader capabilities.

[0652] Voice to text conversion

[0653] The device receives the user's voice input in real time and sends it to the server. The voice input is captured using a high-sensitivity microphone and noise-canceling function to obtain clear voice data.

[0654] 2. Cloud Server Functions

[0655] Voice Recognition

[0656] The server converts the received voice data into text using a highly accurate speech recognition engine, specifically the Google Cloud Speech-to-Text API.

[0657] Natural Language Processing and Text Analytics

[0658] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent.

[0659] Response generation and text-to-speech

[0660] The server generates a response to the user based on the analysis results. The generated response is in text format, which is then converted into audio data by a text-to-speech (TTS) engine, specifically using the Google Cloud Text-to-Speech API.

[0661] 3. Vehicle control and obstacle detection

[0662] Vehicle Control

[0663] The device controls the vehicle based on the destination set by voice input, providing a safe route to the destination.

[0664] Obstacle Detection

[0665] The system detects obstacles and dangers in real time based on data obtained from the vehicle's sensors, and the device notifies the user of any detected obstacles or dangers via voice notifications.

[0666] 4. Emergency notification function

[0667] Identifying Support Personnel

[0668] The server obtains the user's location information and identifies the most suitable support person based on that information, allowing the user to be quickly connected to the nearest support center or emergency operator in the event of an emergency.

[0669] Obtaining location information

[0670] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0671] Specific usage scenarios

[0672] Scenario 1: Emergency Response

[0673] 1. The user opens the emergency call app and taps the call button.

[0674] 2. The device generates a call request and sends it to the server along with the location information.

[0675] 3. The server receives the call request and identifies the most suitable emergency operator.

[0676] 4. The user says, "There's a fire, please help."

[0677] 5. The device sends this audio to the server, which converts the audio data into text.

[0678] 6. The server analyzes the text data and understands the urgent intent.

[0679] 7. The server generates a response such as "Fire department is on its way. Please evacuate to a safe location" and converts it into speech.

[0680] 8. The device plays the converted audio to the user.

[0681] Scenario 2: Normal navigation

[0682] 1. The user speaks "Heading home."

[0683] 2. The device receives the voice data in real time and sends it to the server.

[0684] 3. The server converts the received voice data into text and analyzes it.

[0685] 4. The server calculates the appropriate route and generates a response as audio data.

[0686] 5. The device notifies the user, "Destination setting complete. Let's depart."

[0687] In these scenarios, example prompts are:

[0688] Please set your destination

[0689] "You're in danger right now. Should I call emergency services?"

[0690] "Obstacle detected. Please be careful."

[0691] In this way, the present invention can support safe and efficient travel for hearing-impaired people using automated vehicles and respond to a variety of emergency situations.

[0692] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0693] Step 1:

[0694] The device receives the user's voice input in real time and uses a high-sensitivity microphone and noise-canceling function to capture clear voice data, which is then sent directly to the server.

[0695] Input: User's voice command

[0696] Output: Clear recorded audio data

[0697] Step 2:

[0698] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, which then analyzes the voice data and returns the resulting text data to the server.

[0699] Input: Audio data sent from the device

[0700] Output: Text format data

[0701] Step 3:

[0702] The server analyzes the converted text data using a natural language understanding (NLU) engine to understand the user's intent. The analysis engine scans the text data and recognizes specific keywords and phrases to understand the user's request.

[0703] Input: Text format data

[0704] Output: Parsed user intent

[0705] Step 4:

[0706] The server generates an appropriate response based on the user's intent. The response is in text format, and this text data is used for subsequent processing. For example, if the user's intent is to call an emergency service, the server generates an emergency response message.

[0707] Input: Parsed user intent

[0708] Output: Text data for the response

[0709] Step 5:

[0710] The server converts the generated text data for the response into audio data using a text-to-speech (TTS) engine. The Google Cloud Text-to-Speech API performs this conversion and generates audio data.

[0711] Input: Text data for response

[0712] Output: Audio response data

[0713] Step 6:

[0714] The terminal plays the provided response through the user interface, and the user interface plays the audio data to notify the user.

[0715] Input: Audio response data

[0716] Output: The audio response played

[0717] Step 7:

[0718] The device controls the user's means of transportation and instructs the autonomous vehicle on the route to the set destination. During this process, it detects obstacles based on data collected by sensors and provides voice notifications as necessary.

[0719] Input: Destination information and data from sensors

[0720] Output: Vehicle control instructions and notification voice

[0721] Step 8:

[0722] The device periodically acquires the user's location information and sends it to the server, which then identifies the most appropriate support personnel and enables a prompt response in the event of an emergency.

[0723] Input: Location data

[0724] Output: Support representative identification and connection information

[0725] 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.

[0726] This invention is a system that improves the convenience of emergency calls and customer support for the hearing-blind, and in particular combines an emotion engine that recognizes the user's emotions. Comprised of a smartphone app and a cloud-based server, the system integrates emotion recognition functionality in addition to speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition, enabling even more advanced support.

[0727] 1. Smartphone app functions

[0728] User Interface

[0729] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[0730] Voice to text conversion

[0731] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[0732] 2. Cloud Server Functions

[0733] Voice Recognition

[0734] The server converts the received voice data into text using a highly accurate speech recognition engine, for example, using an external service such as the Google Speech-to-Text API.

[0735] Natural Language Processing and Text Analytics

[0736] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent and derive the appropriate response for a specific task, such as filing an insurance claim or calling an emergency service.

[0737] emotion recognition

[0738] The server is equipped with an emotion engine that recognizes the user's emotions based on voice and text data, allowing it to identify the user's emotional state, such as nervousness, anger, or sadness.

[0739] Response generation and text-to-speech

[0740] The server generates a response to the user based on the analysis results and emotion recognition results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[0741] 3. Connecting to Support and Using Location Information

[0742] Identifying Support Personnel

[0743] The server has a database that identifies the most suitable support person depending on the user's inquiry and emotional state. For example, in the case of an emergency call, the system connects the user to the nearest emergency operator.

[0744] Obtaining location information

[0745] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0746] Specific usage scenarios

[0747] Emergency call example

[0748] 1. The user opens the emergency call app and taps the call button.

[0749] 2. The device generates a call request and sends it to the server along with the location information.

[0750] 3. The server receives the call request and identifies the most suitable emergency operator.

[0751] 4. The user says, "There's a fire, please help."

[0752] 5. The device sends this audio to the server, which converts the audio data into text.

[0753] 6. The server analyzes the text data and understands the urgent intent.

[0754] 7. The server recognizes the user's emotions based on the voice and text data.

[0755] 8. The server reflects the emotion recognition results and generates a response such as "The fire department will be on its way immediately. Please evacuate to a safe place," and converts it into speech.

[0756] 9. The device plays the converted audio to the user.

[0757] In this way, the present invention helps the deaf-blind to receive prompt and appropriate assistance in emergencies. It can also provide high-quality customer support services for insurance companies and banks. By integrating emotion recognition functionality, it is possible to provide tailored responses based on the user's emotional state, further improving the user experience.

[0758] The processing flow will be explained below.

[0759] Step 1:

[0760] The user opens the smartphone app and taps the call button.

[0761] What happens: The app detects a user tap event and generates a call request.

[0762] Step 2:

[0763] The terminal sends a call request to the server.

[0764] How it works: A call request (including metadata such as user ID and location) is sent to the server.

[0765] Step 3:

[0766] A server receives the call request and identifies available support personnel.

[0767] What it does: It queries a database on the server to identify the most suitable person.

[0768] Step 4:

[0769] The device records the user's voice input and sends it to the server.

[0770] How it works: Captures voice input in real time and uploads it to a server as audio data.

[0771] Step 5:

[0772] The server converts the received voice data into text.

[0773] What it does: Calls a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[0774] Step 6:

[0775] The server analyzes the text data and understands the user's intent.

[0776] How it works: It uses a natural language understanding (NLU) engine to parse text data and determine user intent.

[0777] Step 7:

[0778] The server recognizes the user's emotions based on the voice data and text data.

[0779] How it works: The emotion recognition engine analyzes voice tone and text content to identify the user's emotional state (e.g., nervous, anger, sadness).

[0780] Step 8:

[0781] The server generates relevant solutions and workarounds.

[0782] How it works: Searches a FAQ database and generates appropriate answers based on intent and emotional state.

[0783] Step 9:

[0784] The server converts the generated text into speech.

[0785] What it does: It uses a text-to-speech (TTS) engine to convert the generated text into audio data.

[0786] Step 10:

[0787] The terminal plays the audio data to the user.

[0788] What it does: Sends audio data to the device and plays it through the speaker.

[0789] Step 11:

[0790] The server obtains the user's location information and identifies the most suitable support person.

[0791] What it does: Runs a database query to identify the best available agent based on the user's location.

[0792] Step 12:

[0793] The device periodically transmits the user's location information to the server.

[0794] How it works: Uses location services to update location data to a server in real time.

[0795] Example 2

[0796] 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."

[0797] When hearing- and visually impaired people call for emergency services or use customer support, it is difficult for them to receive prompt and appropriate responses. Furthermore, systems often fail to properly understand the user's emotional state and are unable to provide sufficient support. This creates a problem in that the safety and convenience of hearing- and visually impaired people cannot be adequately ensured in emergencies or when problems occur.

[0798] 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.

[0799] In this invention, the server includes means for recognizing the user's emotions based on the voice data and text data, means for generating an appropriate response based on the user's intention and emotions, and means for converting the generated response into voice data, thereby enabling the server to accurately grasp the user's intention and provide a detailed response according to the user's emotional state.

[0800] A "user interface for the deaf-blind" is an interface with a design and functionality that is intuitive and easy to use for users with visual or hearing impairments.

[0801] The "means for receiving voice data and converting it into text" is a technology that processes the voice input by the user as a digital signal and converts it into text format.

[0802] "Means for analyzing text data and understanding user intent" refers to a technology for analyzing converted text data to identify the user's requests and intent.

[0803] "Means for recognizing a user's emotions based on voice data and text data" is a technology for identifying a user's emotional state using acquired voice and text information.

[0804] "Means for generating appropriate responses based on the user's intentions and emotions" refers to technology that enables the system to generate optimal responses based on the user's requests and emotional state.

[0805] The "means for converting the generated response into voice data" refers to a technique for converting the text-format response generated by the system into voice format.

[0806] "Means for acquiring the user's location information and identifying the appropriate support staff based on that information" refers to technology that acquires the user's current location in real time and identifies the staff member who will provide appropriate support based on that information.

[0807] The present invention provides a system that enables the hearing-blind to receive prompt and accurate responses when calling emergency services or using customer support services. The system integrates a terminal, a cloud-based server, a user interface, speech recognition, natural language processing, emotion recognition, text-to-speech conversion, and location information acquisition functions.

[0808] First, the device provides the user with a user interface. This user interface is designed to be intuitive for the hearing-blind and includes simple icons and buttons, voice commands, haptic feedback, and special screen reader functionality. When the user speaks, the device captures the voice data using a high-sensitivity microphone and noise-canceling function and transmits it to the server in real time.

[0809] The server converts the received voice data into text using a speech recognition engine such as the Google Speech-to-Text API, generating clear, highly accurate text data. The server then analyzes this text data and uses a natural language understanding (NLU) engine to understand the user's intent. For example, the server can analyze a query such as "There's a fire. Help me." and recognize that it's an emergency.

[0810] Furthermore, the server uses the text data and the original voice data to run an emotion recognition engine to identify the user's emotional state. For example, it can detect whether the user is nervous, angry, sad, etc. Based on this, the server generates an appropriate response to the user based on the analysis results and the emotion recognition results. This response is first generated in text format and then converted into voice data using a text-to-speech (TTS) engine.

[0811] Finally, the server retransmits the generated voice data to the device, which then plays it back to the user. This allows the user to understand the instructions from the server by voice, ensuring a prompt and appropriate response in the event of an emergency. Furthermore, the server periodically obtains the user's location information and identifies the most appropriate support personnel based on that location information. This allows, for example, in the event of an emergency, the user to be quickly connected to the nearest emergency operator.

[0812] A specific example would be an emergency call scenario like this:

[0813] 1. The user opens the emergency call app and taps the call button.

[0814] 2. The device generates a call request and sends it to the server along with the location information.

[0815] 3. The server receives the call request and identifies the most suitable emergency operator.

[0816] 4. The user says, "There's a fire, please help."

[0817] 5. The device sends this audio to the server, which converts the audio data into text.

[0818] 6. The server analyzes the text data and understands the urgent intent.

[0819] 7. The server recognizes the user's emotions based on the voice and text data.

[0820] 8. The server reflects the emotion recognition results and generates a response such as "The fire department will be on its way immediately. Please evacuate to a safe place," and converts it into speech.

[0821] 9. The device plays the converted audio to the user.

[0822] Examples of prompts to input to a generative AI model include:

[0823] "Please provide a detailed explanation of the processing steps of an emergency call system for the hearing-blind. Please explain the entire process, from voice input to response generation, including specific operations."

[0824] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0825] Step 1: Accept voice input

[0826] The device captures the user's voice input in real time using a high-sensitivity microphone and noise-canceling function. The input of this process is the user's voice, and the output is voice data converted into a digital signal. Specifically, when the user opens the emergency app and says "help," the device will clearly record the voice.

[0827] Step 2: Sending audio data

[0828] The device encrypts the captured voice data and sends it to a cloud-based server via the Internet. The input is the voice data converted into a digital signal, and the output is the voice data sent to the server. Specifically, the device displays the progress on the screen, saying "Sending voice data to server."

[0829] Step 3: Voice Recognition

[0830] The server converts the received voice data into text data using a voice recognition engine (e.g., Google Speech-to-Text API). The input is the voice data sent from the device, and the output is text data. Specifically, the server records the status "Converting voice data to text" in the log.

[0831] Step 4: Text Analysis

[0832] The server analyzes the converted text data using a natural language understanding (NLU) engine to understand the user's intent. The input is text data, and the output is the analysis result that represents the user's intent. Specifically, the server records the status "Emergency intent detected, preparing appropriate response" in the log.

[0833] Step 5: Emotion Recognition

[0834] The server uses an emotion recognition engine based on the text data and the original voice data to recognize the user's emotions. The input is text data and voice data, and the output is data indicating the user's emotional state. Specifically, the server executes "recognize user's emotions" and outputs the data "The user is very anxious."

[0835] Step 6: Response Generation

[0836] The server generates an appropriate response to the user based on the analysis results and emotion recognition results. The input is data indicating the user's intention and emotional state, and the output is a text response. Specifically, the server generates a text response such as "The fire department will be on its way immediately. Please evacuate to a safe place."

[0837] Step 7: Text-to-speech

[0838] The server converts the generated text response into audio data using a text-to-speech (TTS) engine. The input is the text response and the output is audio data. Specifically, the server logs the status "Converting generated response into audio format."

[0839] Step 8: Send and play a voice response

[0840] The server sends the generated voice data to the terminal, which then plays it back. The input is the voice data, and the output is the voice that is played back to the user. Specifically, the terminal tells the user, "The fire department is on its way immediately. Please evacuate to a safe place."

[0841] Step 9: Use location information

[0842] The device periodically sends its location information to the server, and the server uses this information to identify the most suitable support person. The input is the location information sent from the device, and the output is data for connecting to the appropriate support person. Specifically, the server displays a message saying "Connecting to the nearest fire station operator."

[0843] (Application example 2)

[0844] 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."

[0845] When hearing-impaired people use voice or text data to access customer support services, it is difficult for them to receive prompt and appropriate responses. Furthermore, existing systems often lack the ability to take into account the user's emotional state, resulting in insufficient support.

[0846] 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 recognizing the emotional state of the user, means for optimizing a response based on the emotional state of the user, means for transmitting voice data and location information to the server, and means for identifying the most suitable support person based on the location information. This makes it possible for hearing-impaired people to quickly receive an appropriate response according to their emotional state when they need customer support in an emergency.

[0847] A "user interface for the visually and hearing impaired" is an operation screen or input means designed to be easy to use for users with visual or hearing impairments.

[0848] "Voice data" refers to information input by voice by a user, which information is recorded in digital form.

[0849] The "means for converting to text" is a technique for converting received voice data into a meaningful written form.

[0850] "Text data" is voice data converted into character information and written in sentence format.

[0851] "Means of understanding intent" refers to technology for analyzing and understanding the purpose and request that a user is trying to communicate through voice or text.

[0852] "Means for generating a response" refers to the technology for creating appropriate answers or instructions in response to a user's intentions or inquiries.

[0853] The "means for converting into voice data" is a technique for converting a response generated as text data back into voice format.

[0854] "Means for recognizing emotional states" refers to technology that analyzes emotions from a user's voice or text and identifies emotions such as joy, anger, and sadness.

[0855] A "means for optimizing responses" is a technique for selecting and providing the most appropriate response to a user, taking into account the user's emotional state.

[0856] "Location information" is geographical data that indicates where a user is currently located.

[0857] "Means for transmitting to the server" refers to a communication technology for transmitting the necessary data from the user terminal to the cloud server.

[0858] The "means for identifying a support person" is a technology that automatically selects a person who can provide the most appropriate response based on the user's situation and location information.

[0859] This invention is a system that allows the hearing-blind to easily access emergency calls and customer support. The system is primarily composed of a smartphone application and a cloud-based server.

[0860] System configuration

[0861] Smartphone application

[0862] The smartphone application has the following main features:

[0863] 1. User Interface: Provide a clean and intuitive interface specifically designed for the hearing-blind. This interface includes simple icons and large buttons, and supports voice commands and haptic feedback.

[0864] 2. Voice input: A high-sensitivity microphone and noise-canceling function are used to clearly capture the user's voice, which is then sent to a cloud server in real time.

[0865] 3. Obtaining location information: Using a GPS sensor, the user's current location information is periodically sent to the server.

[0866] Cloud Server

[0867] The cloud server has the following functions:

[0868] 1. Speech recognition: The received voice data is converted into text data using a high-precision voice recognition engine (e.g., Google Speech-to-Text API).

[0869] 2. Natural Language Processing: Use a natural language understanding (NLU) engine to analyze text data and accurately understand user intent.

[0870] 3. Emotion Recognition: Equipped with an emotion engine that identifies the user's emotional state based on voice and text data.

[0871] 4. Response generation: Based on the analysis results and emotion recognition results, an appropriate response is generated and written in text format.

[0872] 5. Text-to-speech: Use a text-to-speech (TTS) engine (e.g., Amazon Polly) to convert the generated text response into audio data.

[0873] 6. Identifying support personnel: The system has a database for identifying appropriate support personnel or emergency operators based on the user's inquiry content and emotional state.

[0874] Processing flow

[0875] 1. Activation and Notification:

[0876] The user launches the smartphone app and taps the emergency call button. The app activates the voice input function, and the user says, "This is an emergency. Please help."

[0877] 2. Audio data processing:

[0878] The device receives the voice data and sends it to a cloud server, which uses a speech recognition engine to convert the voice data into text.

[0879] 3. Natural Language Processing and Emotion Recognition:

[0880] The server then uses a natural language understanding engine to analyze the converted text data and understand the user's intent. At the same time, an emotion recognition engine analyzes the user's emotional state.

[0881] 4. Generating an appropriate response:

[0882] The server generates a response based on the analysis results and converts it into text format.

[0883] 5. Generate a voice response:

[0884] The response text is converted into voice data by a text-to-speech engine and sent to the terminal.

[0885] 6. Response playback:

[0886] The terminal plays the converted audio data and provides instructions to the user.

[0887] Examples and prompts

[0888] For example, a scenario will be described in which a user discovers a suspicious person in his / her home at night.

[0889] 1. User action: The user opens the Safeguard app and presses the Emergency Call button.

[0890] 2. Voice input: The user says, "There is a suspicious person outside my house. Please help."

[0891] 3. Data transmission: The smartphone app sends voice data and location information to the server.

[0892] 4. Speech recognition: A speech recognition engine on the server side converts voice data into text.

[0893] 5. Analysis and emotion recognition: The NLU engine extracts keywords such as "suspicious person" and "help," and the emotion recognition engine detects the user's state of tension.

[0894] 6. Response generation: A response message is generated saying, "We will call the police. Please take refuge in a safe room in your home." and converted into speech using a TTS engine.

[0895] 7. Playback: The converted audio is played back to the user through the smartphone app.

[0896] Prompt Sentence Examples

[0897] "We have spotted a suspicious person and are calling emergency services. Please explain the situation and tell us what to do safely."

[0898] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0899] Step 1:

[0900] The user launches the smartphone app and taps the emergency call button.

[0901] Input: User taps

[0902] Output: Generate an emergency call request

[0903] Specific Actions: The user interface creates an emergency call request and enables voice input functionality.

[0904] Step 2:

[0905] The device uses a highly sensitive microphone to capture the user's voice and generates clear audio data using noise-canceling functionality.

[0906] Input: User voice input

[0907] Output: Clear audio data

[0908] Specific operation: The device captures audio, performs noise cancellation processing, generates audio data, and sends it to a cloud server.

[0909] Step 3:

[0910] The server converts the received voice data into text data using a voice recognition engine.

[0911] Input: Clear voice data

[0912] Output: Text data

[0913] Specific operation: The server runs a speech recognition engine (e.g., Google Speech-to-Text API) and converts the voice data into text data.

[0914] Step 4:

[0915] The server analyzes the text data using a natural language processing (NLU) engine to understand the user's intent.

[0916] Input: Text data

[0917] Output: User's intention information

[0918] Specific operation: The server uses a natural language processing engine to analyze the text data, extract important keywords such as "suspicious person" and "help," and understand the user's intent.

[0919] Step 5:

[0920] The server uses an emotion recognition engine to analyze the user's emotional state based on the voice and text data.

[0921] Input: Audio data, text data

[0922] Output: Emotional state information

[0923] Specific operation: The server uses an emotion recognition engine to identify emotions such as tension or fear from the user's voice tone and text content.

[0924] Step 6:

[0925] The server generates an appropriate response using a response generation engine based on the analysis results and emotional state information.

[0926] Input: User intention information, emotional state information

[0927] Output: Text response

[0928] Specific behavior: The server uses a response generation engine to create the most appropriate message for the user, generating a response such as "We will call the police. Please take shelter in a safe room in your home."

[0929] Step 7:

[0930] The server converts the generated text response into audio data using a text-to-speech (TTS) engine.

[0931] Input: Text response

[0932] Output: Response in audio data format

[0933] What happens: The server uses a text-to-speech engine (e.g., Amazon Polly) to convert the text response into audio data.

[0934] Step 8:

[0935] The terminal receives the audio data and plays it back to the user using its audio output function.

[0936] Input: Response in audio data format

[0937] Output: A voice response to the user

[0938] Specific operation: The device plays the received audio data and provides the user with specific instructions via voice, such as "We will call the police. Please evacuate to a safe room in your home."

[0939] 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.

[0940] 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.

[0941] 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.

[0942] [Third embodiment]

[0943] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0944] 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.

[0945] 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).

[0946] 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.

[0947] 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.

[0948] 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).

[0949] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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."

[0955] This invention is a sophisticated system for the hearing- and visually impaired to access emergency calls and customer support. The system consists of a smartphone app and a cloud-based server, and by integrating speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition, it significantly improves convenience for the hearing- and visually impaired.

[0956] 1. Smartphone app functions

[0957] User Interface

[0958] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[0959] Voice to text conversion

[0960] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[0961] 2. Cloud Server Functions

[0962] Voice Recognition

[0963] The server converts the received voice data into text using a highly accurate speech recognition engine, for example, using an external service such as the Google Speech-to-Text API.

[0964] Natural Language Processing and Text Analytics

[0965] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent and derive the appropriate response for a specific task, such as filing an insurance claim or calling an emergency service.

[0966] Response generation and text-to-speech

[0967] The server generates a response to the user based on the analysis results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[0968] 3. Connecting to Support and Using Location Information

[0969] Identifying Support Personnel

[0970] The server has a database that identifies the most appropriate support person for the user's inquiry. For example, if the user calls an emergency number, the server connects the user to the nearest emergency operator.

[0971] Obtaining location information

[0972] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[0973] Specific usage scenarios

[0974] Emergency call example

[0975] 1. The user opens the emergency call app and taps the call button.

[0976] 2. The device generates a call request and sends it to the server along with the location information.

[0977] 3. The server receives the call request and identifies the most suitable emergency operator.

[0978] 4. The user says, "There's a fire, please help."

[0979] 5. The device sends this audio to the server, which converts the audio data into text.

[0980] 6. The server analyzes the text data and understands the urgent intent.

[0981] 7. The server generates a response such as "Fire department is on its way. Please evacuate to a safe location" and converts it into speech.

[0982] 8. The device plays the converted audio to the user.

[0983] In this way, the present invention helps the hearing-visually impaired to receive prompt and appropriate assistance in emergencies, and also provides greater convenience to customer support services such as insurance companies and banks.

[0984] The processing flow will be explained below.

[0985] Step 1:

[0986] The user opens the smartphone app and taps the call button.

[0987] What happens: The app detects a user tap event and generates a call request.

[0988] Step 2:

[0989] The terminal sends a call request to the server.

[0990] How it works: A call request (including metadata such as user ID and location) is sent to the server.

[0991] Step 3:

[0992] A server receives the call request and identifies available support personnel.

[0993] What it does: Queries a database on the server to find the most suitable person.

[0994] Step 4:

[0995] The device records the user's voice input and sends it to the server.

[0996] How it works: Captures voice input in real time and uploads it to a server as audio data.

[0997] Step 5:

[0998] The server converts the received voice data into text.

[0999] What it does: Calls a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[1000] Step 6:

[1001] The server sends the analyzed text to a natural language processing engine, which analyzes the user's intent.

[1002] How it works: Parsed text is sent to an NLU (Natural Language Understanding) engine to understand the user's intent.

[1003] Step 7:

[1004] The server generates relevant solutions and workarounds.

[1005] What it does: Searches the FAQ database based on intent and generates the appropriate answer.

[1006] Step 8:

[1007] The server converts the generated text into speech.

[1008] What it does: Calls a Text-to-Speech (TTS) engine to convert text to speech.

[1009] Step 9:

[1010] The terminal plays the audio data to the user.

[1011] What it does: Sends audio data to the device and plays it through the speaker.

[1012] Step 10:

[1013] The server obtains the user's location and identifies the appropriate support person.

[1014] What it does: Runs a database query to identify the best available agent based on the user's location.

[1015] Step 11:

[1016] The device periodically transmits the user's location information to the server.

[1017] How it works: Uses location services to update location data to a server in real time.

[1018] Example 1

[1019] 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."

[1020] When hearing- and visually impaired people use existing emergency call systems and customer support, they face difficulties in operating them and are unable to receive prompt responses. In particular, the accuracy of voice input recognition and intent understanding is low, which can easily lead to misunderstandings and delays. Furthermore, the lack of a means to accurately and efficiently utilize the user's location information makes it difficult to respond promptly in an emergency.

[1021] 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.

[1022] In this invention, the server includes means for receiving voice data and converting it into text using a voice recognition engine, means for analyzing the text data using a natural language understanding engine to understand the user's intent, and means for generating an appropriate response based on the user's intent. This allows hearing-visually impaired people to obtain clear voice data and send it to the server, which then uses natural language processing technology to accurately understand the intent and provide the most appropriate response quickly. Furthermore, by periodically obtaining the user's location information and identifying the most appropriate support person based on that information, emergency responses can be made even faster and more accurate.

[1023] "Hearing-blind" refers to people who have visual and / or hearing impairments and who have difficulty obtaining information or communicating through normal vision or hearing.

[1024] "User interface" means an interface that provides a means for users to interact with a system, including simple and intuitive design, voice commands, and tactile feedback tailored for users with visual and hearing impairments.

[1025] "Voice input" refers to the means by which a user provides information to the system through voice, specifically voice data collected using a high-sensitivity microphone and noise-canceling function.

[1026] "Voice Data" means data in a digital format for recording, storing, or transmitting user-uttered sounds.

[1027] A "voice recognition engine" is software that analyzes received voice data and converts it into corresponding text data; for example, a voice recognition API is used.

[1028] "Text data" refers to data in the form of a string of characters converted by a speech recognition engine and used for analysis, display, and response generation.

[1029] A "natural language understanding engine" refers to an artificial intelligence technology that analyzes text data and understands the user's intent and context.

[1030] "Text-to-speech engine" means a software technology for converting text data into synthetic speech and providing the generated response to the user in audio form.

[1031] "Location information" is data indicating the user's current location, and is obtained using GPS or other location information acquisition means.

[1032] "Support Personnel" means operators or technicians designated to respond to specific inquiries or emergency calls.

[1033] "Secure communication protocol" refers to a communication protocol for securely sending and receiving data, and includes, for example, HTTPS.

[1034] This invention is a system for the hearing- and visually impaired to access emergency calls and customer support. The system consists of a smartphone app and a cloud-based server. Specifically, it significantly improves convenience for the hearing- and visually impaired by integrating speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition.

[1035] Smartphone app functions

[1036] User Interface

[1037] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[1038] Voice to text conversion

[1039] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[1040] Cloud Server Functions

[1041] Voice Recognition

[1042] The server converts the received voice data into text using a highly accurate voice recognition engine, such as a general voice recognition API.

[1043] Natural Language Processing and Text Analytics

[1044] The server then further analyzes the converted text data using a natural language understanding (NLU) engine to accurately understand the user's intent, specifically whether they want to make an emergency call or receive customer support.

[1045] Response generation and text-to-speech

[1046] The server generates a response to the user based on the analysis results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[1047] Connecting to Support and Location

[1048] Identifying Support Personnel

[1049] The server has a database that identifies the most appropriate support person for the user's inquiry. For example, if the user calls an emergency number, the server connects the user to the nearest emergency operator.

[1050] Obtaining location information

[1051] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[1052] Specific usage scenarios

[1053] Emergency call example

[1054] 1. The user opens the emergency call app and taps the call button.

[1055] 2. The device will issue a voice prompt saying, "Please state the details of your call," followed by the user saying, "There's a fire, please help."

[1056] 3. The device clearly captures the user's voice and sends it to the server.

[1057] 4. The server uses a speech recognition engine to convert the voice data into text data such as "Fire, help please."

[1058] 5. The server parses the text and understands that it is a fire emergency.

[1059] 6. The server generates a response saying, "Fire department is on its way. Please evacuate to a safe location," and converts it into audio data.

[1060] 7. The server sends the audio data to the device, which plays it back to the user.

[1061] 8. The server connects you to the most appropriate emergency operator and sends the user's location information to facilitate a quick response.

[1062] This system is designed to enable the hearing-blind to receive prompt and appropriate responses in emergencies. Users can provide clear voice input using devices equipped with high-sensitivity microphones and noise-canceling functions. This real-time speech recognition and natural language processing allows the system to accurately understand the user's intent and generate appropriate responses.

[1063] Example prompt: "There's a fire. I'd like to call emergency services."

[1064] Based on the above scenario, this prompt sentence is used:

[1065] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1066] Step 1:

[1067] A user launches a smartphone app and taps the "emergency call button." The input is a tap operation by the user, and the output is the device entering voice input mode. Specifically, the device presents a voice prompt saying, "Tell us what happened."

[1068] Step 2:

[1069] The device uses a highly sensitive microphone and noise-canceling function to capture the user's voice in real time. The user's voice is input as input, and voice data is generated as output. A specific example of the device's operation is when the user says, "There's a fire. Help me."

[1070] Step 3:

[1071] The device packetizes the acquired voice data and sends it to a cloud-based server using a secure communication protocol (e.g., HTTPS). Voice data and location information are obtained as input, and data packets are sent as output. Specifically, the voice data and location information arrive at the server.

[1072] Step 4:

[1073] The server converts the received voice data into text using a voice recognition engine (e.g., a general voice recognition API). Voice data is input, and text data is generated as output. The specific operation is to generate the text "There's a fire. Help me."

[1074] Step 5:

[1075] The server analyzes the text data using a natural language understanding engine (NLU engine) to understand the user's intent. The text data is input, and the analysis results are obtained as output. The specific behavior is that a fire emergency is understood.

[1076] Step 6:

[1077] The server generates an appropriate response message based on the analysis results and converts it into voice data using a text-to-speech (TTS) engine. The analysis results are input, and voice data is generated as output. The specific operation is to generate voice data saying, "Fire departments are on their way immediately. Please evacuate to a safe place."

[1078] Step 7:

[1079] The server sends the generated voice data to the terminal, which then plays it back. Voice data is received as input, and voice playback is provided to the user as output. Specifically, the terminal notifies the user by voice, "The fire brigade will be on its way shortly."

[1080] Step 8:

[1081] The server uses the user's location information to identify the most suitable emergency operator and connects them along with the location information. The location information and the user's intention are received as input, and the connection to the emergency operator is made as output. Specifically, the call content and location information are transmitted to the nearest fire station.

[1082] (Application example 1)

[1083] 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."

[1084] When hearing- and visually impaired people use autonomous vehicles, conventional systems face challenges in providing sufficient navigation and responding to emergencies. In particular, the lack of notifications regarding traffic control and obstacle detection hinders the creation of an environment in which hearing- and visually impaired people can travel safely. Therefore, it is necessary to provide a system that enables hearing- and visually impaired people to reach their destinations safely and efficiently and that can respond quickly in emergencies.

[1085] 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.

[1086] In this invention, the server comprises: means for providing a user interface for the hearing-visually impaired;

[1087] means for receiving and converting voice data into text;

[1088] A means for analyzing text data and understanding user intent;

[1089] means for generating an appropriate response based on the user's intent;

[1090] means for converting the generated response into audio data;

[1091] means for playing the provided responses through a user interface;

[1092] means for providing notifications regarding vehicle control and obstacle detection functions;

[1093] A means of connecting you to the nearest support center or emergency operator in case of an emergency;

[1094] a means for acquiring location information of the user and identifying an appropriate support person based on the location information;

[1095] This will enable hearing-impaired and visually impaired people to travel safely in self-driving vehicles by receiving notifications regarding traffic control and obstacle detection, and by responding quickly in emergencies.

[1096] A "user interface for the visually and hearing impaired" refers to an operating screen and input method designed to be easy to use for people with visual or hearing impairments.

[1097] "Means for receiving voice data and converting it into text" refers to a function for taking in voice-format data and converting it into character-format data.

[1098] "Means for analyzing text data and understanding user intent" refers to technology that analyzes text data and understands the user's requests and intent based on that data.

[1099] The "means for generating an appropriate response based on the user's intent" is a mechanism for generating an appropriate reply or instruction based on the analyzed user's intent.

[1100] The "means for converting the generated response into voice data" is a technique for converting the generated text-format response into voice format.

[1101] The "means for reproducing the provided response through a user interface" is a function for presenting the generated response to the user through a user interface.

[1102] "Means for providing notifications related to traffic control and obstacle detection functions" refers to technologies for controlling automated vehicles, traffic-related functions, and detecting obstacles and notifying users.

[1103] "Means for connecting to the nearest support center or emergency operator in an emergency" refers to a mechanism for quickly connecting to a nearby support center or emergency response personnel in an emergency.

[1104] "Means for acquiring the user's location information and identifying the appropriate support person based on that information" is a technology that acquires the user's current location information and selects the most appropriate support person based on that location.

[1105] This invention is a system that enables the deaf-blind to use automated vehicles safely and efficiently, improving the convenience and safety of travel for people with visual and hearing impairments. The system consists of a terminal and a cloud-based server, and realizes the coordination of speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition.

[1106] 1. Smartphone app functions

[1107] Providing a user interface

[1108] The device provides a user interface specifically tailored for the hearing-blind, with simple and intuitive icons and buttons, voice commands and haptic feedback, and special screen reader capabilities.

[1109] Voice to text conversion

[1110] The device receives the user's voice input in real time and sends it to the server. The voice input is captured using a high-sensitivity microphone and noise-canceling function to obtain clear voice data.

[1111] 2. Cloud Server Functions

[1112] Voice Recognition

[1113] The server converts the received voice data into text using a highly accurate speech recognition engine, specifically the Google Cloud Speech-to-Text API.

[1114] Natural Language Processing and Text Analytics

[1115] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent.

[1116] Response generation and text-to-speech

[1117] The server generates a response to the user based on the analysis results. The generated response is in text format, which is then converted into audio data by a text-to-speech (TTS) engine, specifically using the Google Cloud Text-to-Speech API.

[1118] 3. Vehicle control and obstacle detection

[1119] Vehicle Control

[1120] The device controls the vehicle based on the destination set by voice input, providing a safe route to the destination.

[1121] Obstacle Detection

[1122] The system detects obstacles and dangers in real time based on data obtained from the vehicle's sensors, and the device notifies the user of any detected obstacles or dangers via voice notifications.

[1123] 4. Emergency notification function

[1124] Identifying Support Personnel

[1125] The server obtains the user's location information and identifies the most suitable support person based on that information, allowing the user to be quickly connected to the nearest support center or emergency operator in the event of an emergency.

[1126] Obtaining location information

[1127] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[1128] Specific usage scenarios

[1129] Scenario 1: Emergency Response

[1130] 1. The user opens the emergency call app and taps the call button.

[1131] 2. The device generates a call request and sends it to the server along with the location information.

[1132] 3. The server receives the call request and identifies the most suitable emergency operator.

[1133] 4. The user says, "There's a fire, please help."

[1134] 5. The device sends this audio to the server, which converts the audio data into text.

[1135] 6. The server analyzes the text data and understands the urgent intent.

[1136] 7. The server generates a response such as "Fire department is on its way. Please evacuate to a safe location" and converts it into speech.

[1137] 8. The device plays the converted audio to the user.

[1138] Scenario 2: Normal navigation

[1139] 1. The user speaks "Heading home."

[1140] 2. The device receives the voice data in real time and sends it to the server.

[1141] 3. The server converts the received voice data into text and analyzes it.

[1142] 4. The server calculates the appropriate route and generates a response as audio data.

[1143] 5. The device notifies the user, "Destination setting complete. Let's depart."

[1144] In these scenarios, example prompts are:

[1145] Please set your destination

[1146] "You're in danger right now. Should I call emergency services?"

[1147] "Obstacle detected. Please be careful."

[1148] In this way, the present invention can support safe and efficient travel for hearing-impaired people using automated vehicles and respond to a variety of emergency situations.

[1149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1150] Step 1:

[1151] The device receives the user's voice input in real time and uses a high-sensitivity microphone and noise-canceling function to capture clear voice data, which is then sent directly to the server.

[1152] Input: User's voice command

[1153] Output: Clear recorded audio data

[1154] Step 2:

[1155] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, which then analyzes the voice data and returns the resulting text data to the server.

[1156] Input: Audio data sent from the device

[1157] Output: Text format data

[1158] Step 3:

[1159] The server analyzes the converted text data using a natural language understanding (NLU) engine to understand the user's intent. The analysis engine scans the text data and recognizes specific keywords and phrases to understand the user's request.

[1160] Input: Text format data

[1161] Output: Parsed user intent

[1162] Step 4:

[1163] The server generates an appropriate response based on the user's intent. The response is in text format, and this text data is used for subsequent processing. For example, if the user's intent is to call an emergency service, the server generates an emergency response message.

[1164] Input: Parsed user intent

[1165] Output: Text data for the response

[1166] Step 5:

[1167] The server converts the generated text data for the response into audio data using a text-to-speech (TTS) engine. The Google Cloud Text-to-Speech API performs this conversion and generates audio data.

[1168] Input: Text data for response

[1169] Output: Audio response data

[1170] Step 6:

[1171] The terminal plays the provided response through the user interface, and the user interface plays the audio data to notify the user.

[1172] Input: Audio response data

[1173] Output: The audio response played

[1174] Step 7:

[1175] The device controls the user's means of transportation and instructs the autonomous vehicle on the route to the set destination. During this process, it detects obstacles based on data collected by sensors and provides voice notifications as necessary.

[1176] Input: Destination information and data from sensors

[1177] Output: Vehicle control instructions and notification voice

[1178] Step 8:

[1179] The device periodically acquires the user's location information and sends it to the server, which then identifies the most appropriate support personnel and enables a prompt response in the event of an emergency.

[1180] Input: Location data

[1181] Output: Support representative identification and connection information

[1182] 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.

[1183] This invention is a system that improves the convenience of emergency calls and customer support for the hearing-blind, and in particular combines an emotion engine that recognizes the user's emotions. Comprised of a smartphone app and a cloud-based server, the system integrates emotion recognition functionality in addition to speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition, enabling even more advanced support.

[1184] 1. Smartphone app functions

[1185] User Interface

[1186] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[1187] Voice to text conversion

[1188] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[1189] 2. Cloud Server Functions

[1190] Voice Recognition

[1191] The server converts the received voice data into text using a highly accurate speech recognition engine, for example, using an external service such as the Google Speech-to-Text API.

[1192] Natural Language Processing and Text Analytics

[1193] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent and derive the appropriate response for a specific task, such as filing an insurance claim or calling an emergency service.

[1194] emotion recognition

[1195] The server is equipped with an emotion engine that recognizes the user's emotions based on voice and text data, allowing it to identify the user's emotional state, such as nervousness, anger, or sadness.

[1196] Response generation and text-to-speech

[1197] The server generates a response to the user based on the analysis results and emotion recognition results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[1198] 3. Connecting to Support and Using Location Information

[1199] Identifying Support Personnel

[1200] The server has a database that identifies the most suitable support person depending on the user's inquiry and emotional state. For example, in the case of an emergency call, the system connects the user to the nearest emergency operator.

[1201] Obtaining location information

[1202] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[1203] Specific usage scenarios

[1204] Emergency call example

[1205] 1. The user opens the emergency call app and taps the call button.

[1206] 2. The device generates a call request and sends it to the server along with the location information.

[1207] 3. The server receives the call request and identifies the most suitable emergency operator.

[1208] 4. The user says, "There's a fire, please help."

[1209] 5. The device sends this audio to the server, which converts the audio data into text.

[1210] 6. The server analyzes the text data and understands the urgent intent.

[1211] 7. The server recognizes the user's emotions based on the voice and text data.

[1212] 8. The server reflects the emotion recognition results and generates a response such as "The fire department will be on its way immediately. Please evacuate to a safe place," and converts it into speech.

[1213] 9. The device plays the converted audio to the user.

[1214] In this way, the present invention helps the deaf-blind to receive prompt and appropriate assistance in emergencies. It can also provide high-quality customer support services for insurance companies and banks. By integrating emotion recognition functionality, it is possible to provide tailored responses based on the user's emotional state, further improving the user experience.

[1215] The processing flow will be explained below.

[1216] Step 1:

[1217] The user opens the smartphone app and taps the call button.

[1218] What happens: The app detects a user tap event and generates a call request.

[1219] Step 2:

[1220] The terminal sends a call request to the server.

[1221] How it works: A call request (including metadata such as user ID and location) is sent to the server.

[1222] Step 3:

[1223] A server receives the call request and identifies available support personnel.

[1224] What it does: It queries a database on the server to identify the most suitable person.

[1225] Step 4:

[1226] The device records the user's voice input and sends it to the server.

[1227] How it works: Captures voice input in real time and uploads it to a server as audio data.

[1228] Step 5:

[1229] The server converts the received voice data into text.

[1230] What it does: Calls a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[1231] Step 6:

[1232] The server analyzes the text data and understands the user's intent.

[1233] How it works: It uses a natural language understanding (NLU) engine to parse text data and determine user intent.

[1234] Step 7:

[1235] The server recognizes the user's emotions based on the voice data and text data.

[1236] How it works: The emotion recognition engine analyzes voice tone and text content to identify the user's emotional state (e.g., nervous, anger, sadness).

[1237] Step 8:

[1238] The server generates relevant solutions and workarounds.

[1239] How it works: Searches a FAQ database and generates appropriate answers based on intent and emotional state.

[1240] Step 9:

[1241] The server converts the generated text into speech.

[1242] What it does: It uses a text-to-speech (TTS) engine to convert the generated text into audio data.

[1243] Step 10:

[1244] The terminal plays the audio data to the user.

[1245] What it does: Sends audio data to the device and plays it through the speaker.

[1246] Step 11:

[1247] The server obtains the user's location information and identifies the most suitable support person.

[1248] What it does: Runs a database query to identify the best available agent based on the user's location.

[1249] Step 12:

[1250] The device periodically transmits the user's location information to the server.

[1251] How it works: Uses location services to update location data to a server in real time.

[1252] Example 2

[1253] 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."

[1254] When hearing- and visually impaired people call for emergency services or use customer support, it is difficult for them to receive prompt and appropriate responses. Furthermore, systems often fail to properly understand the user's emotional state and are unable to provide sufficient support. This creates a problem in that the safety and convenience of hearing- and visually impaired people cannot be adequately ensured in emergencies or when problems occur.

[1255] 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.

[1256] In this invention, the server includes means for recognizing the user's emotions based on the voice data and text data, means for generating an appropriate response based on the user's intention and emotions, and means for converting the generated response into voice data, thereby enabling the server to accurately grasp the user's intention and provide a detailed response according to the user's emotional state.

[1257] A "user interface for the deaf-blind" is an interface with a design and functionality that is intuitive and easy to use for users with visual or hearing impairments.

[1258] The "means for receiving voice data and converting it into text" is a technology that processes the voice input by the user as a digital signal and converts it into text format.

[1259] "Means for analyzing text data and understanding user intent" refers to a technology for analyzing converted text data to identify the user's requests and intent.

[1260] "Means for recognizing a user's emotions based on voice data and text data" is a technology for identifying a user's emotional state using acquired voice and text information.

[1261] "Means for generating appropriate responses based on the user's intentions and emotions" refers to technology that enables the system to generate optimal responses based on the user's requests and emotional state.

[1262] The "means for converting the generated response into voice data" refers to a technique for converting the text-format response generated by the system into voice format.

[1263] "Means for acquiring the user's location information and identifying the appropriate support staff based on that information" refers to technology that acquires the user's current location in real time and identifies the staff member who will provide appropriate support based on that information.

[1264] The present invention provides a system that enables the hearing-blind to receive prompt and accurate responses when calling emergency services or using customer support services. The system integrates a terminal, a cloud-based server, a user interface, speech recognition, natural language processing, emotion recognition, text-to-speech conversion, and location information acquisition functions.

[1265] First, the device provides the user with a user interface. This user interface is designed to be intuitive for the hearing-blind and includes simple icons and buttons, voice commands, haptic feedback, and special screen reader functionality. When the user speaks, the device captures the voice data using a high-sensitivity microphone and noise-canceling function and transmits it to the server in real time.

[1266] The server converts the received voice data into text using a speech recognition engine such as the Google Speech-to-Text API, generating clear, highly accurate text data. The server then analyzes this text data and uses a natural language understanding (NLU) engine to understand the user's intent. For example, the server can analyze a query such as "There's a fire. Help me." and recognize that it's an emergency.

[1267] Furthermore, the server uses the text data and the original voice data to run an emotion recognition engine to identify the user's emotional state. For example, it can detect whether the user is nervous, angry, sad, etc. Based on this, the server generates an appropriate response to the user based on the analysis results and the emotion recognition results. This response is first generated in text format and then converted into voice data using a text-to-speech (TTS) engine.

[1268] Finally, the server retransmits the generated voice data to the device, which then plays it back to the user. This allows the user to understand the instructions from the server by voice, ensuring a prompt and appropriate response in the event of an emergency. Furthermore, the server periodically obtains the user's location information and identifies the most appropriate support personnel based on that location information. This allows, for example, in the event of an emergency, the user to be quickly connected to the nearest emergency operator.

[1269] A specific example would be an emergency call scenario like this:

[1270] 1. The user opens the emergency call app and taps the call button.

[1271] 2. The device generates a call request and sends it to the server along with the location information.

[1272] 3. The server receives the call request and identifies the most suitable emergency operator.

[1273] 4. The user says, "There's a fire, please help."

[1274] 5. The device sends this audio to the server, which converts the audio data into text.

[1275] 6. The server analyzes the text data and understands the urgent intent.

[1276] 7. The server recognizes the user's emotions based on the voice and text data.

[1277] 8. The server reflects the emotion recognition results and generates a response such as "The fire department will be on its way immediately. Please evacuate to a safe place," and converts it into speech.

[1278] 9. The device plays the converted audio to the user.

[1279] Examples of prompts to input to a generative AI model include:

[1280] "Please provide a detailed explanation of the processing steps of an emergency call system for the hearing-blind. Please explain the entire process, from voice input to response generation, including specific operations."

[1281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1282] Step 1: Accept voice input

[1283] The device captures the user's voice input in real time using a high-sensitivity microphone and noise-canceling function. The input of this process is the user's voice, and the output is voice data converted into a digital signal. Specifically, when the user opens the emergency app and says "help," the device will clearly record the voice.

[1284] Step 2: Sending audio data

[1285] The device encrypts the captured voice data and sends it to a cloud-based server via the Internet. The input is the voice data converted into a digital signal, and the output is the voice data sent to the server. Specifically, the device displays the progress on the screen, saying "Sending voice data to server."

[1286] Step 3: Voice Recognition

[1287] The server converts the received voice data into text data using a voice recognition engine (e.g., Google Speech-to-Text API). The input is the voice data sent from the device, and the output is text data. Specifically, the server records the status "Converting voice data to text" in the log.

[1288] Step 4: Text Analysis

[1289] The server analyzes the converted text data using a natural language understanding (NLU) engine to understand the user's intent. The input is text data, and the output is the analysis result that represents the user's intent. Specifically, the server records the status "Emergency intent detected, preparing appropriate response" in the log.

[1290] Step 5: Emotion Recognition

[1291] The server uses an emotion recognition engine based on the text data and the original voice data to recognize the user's emotions. The input is text data and voice data, and the output is data indicating the user's emotional state. Specifically, the server executes "recognize user's emotions" and outputs the data "The user is very anxious."

[1292] Step 6: Response Generation

[1293] The server generates an appropriate response to the user based on the analysis results and emotion recognition results. The input is data indicating the user's intention and emotional state, and the output is a text response. Specifically, the server generates a text response such as "The fire department will be on its way immediately. Please evacuate to a safe place."

[1294] Step 7: Text-to-speech

[1295] The server converts the generated text response into audio data using a text-to-speech (TTS) engine. The input is the text response and the output is audio data. Specifically, the server logs the status "Converting generated response into audio format."

[1296] Step 8: Send and play a voice response

[1297] The server sends the generated voice data to the terminal, which then plays it back. The input is the voice data, and the output is the voice that is played back to the user. Specifically, the terminal tells the user, "The fire department is on its way immediately. Please evacuate to a safe place."

[1298] Step 9: Use location information

[1299] The device periodically sends its location information to the server, and the server uses this information to identify the most suitable support person. The input is the location information sent from the device, and the output is data for connecting to the appropriate support person. Specifically, the server displays a message saying "Connecting to the nearest fire station operator."

[1300] (Application example 2)

[1301] 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."

[1302] When hearing-impaired people use voice or text data to access customer support services, it is difficult for them to receive prompt and appropriate responses. Furthermore, existing systems often lack the ability to take into account the user's emotional state, resulting in insufficient support.

[1303] 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 recognizing the emotional state of the user, means for optimizing a response based on the emotional state of the user, means for transmitting voice data and location information to the server, and means for identifying the most suitable support person based on the location information. This makes it possible for hearing-impaired people to quickly receive an appropriate response according to their emotional state when they need customer support in an emergency.

[1304] A "user interface for the visually and hearing impaired" is an operation screen or input means designed to be easy to use for users with visual or hearing impairments.

[1305] "Voice data" refers to information input by voice by a user, which information is recorded in digital form.

[1306] The "means for converting to text" is a technique for converting received voice data into a meaningful written form.

[1307] "Text data" is voice data converted into character information and written in sentence format.

[1308] "Means of understanding intent" refers to technology for analyzing and understanding the purpose and request that a user is trying to communicate through voice or text.

[1309] "Means for generating a response" refers to the technology for creating appropriate answers or instructions in response to a user's intentions or inquiries.

[1310] The "means for converting into voice data" is a technique for converting a response generated as text data back into voice format.

[1311] "Means for recognizing emotional states" refers to technology that analyzes emotions from a user's voice or text and identifies emotions such as joy, anger, and sadness.

[1312] A "means for optimizing responses" is a technique for selecting and providing the most appropriate response to a user, taking into account the user's emotional state.

[1313] "Location information" is geographical data that indicates where a user is currently located.

[1314] "Means for transmitting to the server" refers to a communication technology for transmitting the necessary data from the user terminal to the cloud server.

[1315] The "means for identifying a support person" is a technology that automatically selects a person who can provide the most appropriate response based on the user's situation and location information.

[1316] This invention is a system that allows the hearing-blind to easily access emergency calls and customer support. The system is primarily composed of a smartphone application and a cloud-based server.

[1317] System configuration

[1318] Smartphone application

[1319] The smartphone application has the following main features:

[1320] 1. User Interface: Provide a clean and intuitive interface specifically designed for the hearing-blind. This interface includes simple icons and large buttons, and supports voice commands and haptic feedback.

[1321] 2. Voice input: A high-sensitivity microphone and noise-canceling function are used to clearly capture the user's voice, which is then sent to a cloud server in real time.

[1322] 3. Obtaining location information: Using a GPS sensor, the user's current location information is periodically sent to the server.

[1323] Cloud Server

[1324] The cloud server has the following functions:

[1325] 1. Speech recognition: The received voice data is converted into text data using a high-precision voice recognition engine (e.g., Google Speech-to-Text API).

[1326] 2. Natural Language Processing: Use a natural language understanding (NLU) engine to analyze text data and accurately understand user intent.

[1327] 3. Emotion Recognition: Equipped with an emotion engine that identifies the user's emotional state based on voice and text data.

[1328] 4. Response generation: Based on the analysis results and emotion recognition results, an appropriate response is generated and written in text format.

[1329] 5. Text-to-speech: Use a text-to-speech (TTS) engine (e.g., Amazon Polly) to convert the generated text response into audio data.

[1330] 6. Identifying support personnel: The system has a database for identifying appropriate support personnel or emergency operators based on the user's inquiry content and emotional state.

[1331] Processing flow

[1332] 1. Activation and Notification:

[1333] The user launches the smartphone app and taps the emergency call button. The app activates the voice input function, and the user says, "This is an emergency. Please help."

[1334] 2. Audio data processing:

[1335] The device receives the voice data and sends it to a cloud server, which uses a speech recognition engine to convert the voice data into text.

[1336] 3. Natural Language Processing and Emotion Recognition:

[1337] The server then uses a natural language understanding engine to analyze the converted text data and understand the user's intent. At the same time, an emotion recognition engine analyzes the user's emotional state.

[1338] 4. Generating an appropriate response:

[1339] The server generates a response based on the analysis results and converts it into text format.

[1340] 5. Generate a voice response:

[1341] The response text is converted into voice data by a text-to-speech engine and sent to the terminal.

[1342] 6. Response playback:

[1343] The terminal plays the converted audio data and provides instructions to the user.

[1344] Examples and prompts

[1345] For example, a scenario will be described in which a user discovers a suspicious person in his / her home at night.

[1346] 1. User action: The user opens the Safeguard app and presses the Emergency Call button.

[1347] 2. Voice input: The user says, "There is a suspicious person outside my house. Please help."

[1348] 3. Data transmission: The smartphone app sends voice data and location information to the server.

[1349] 4. Speech recognition: A speech recognition engine on the server side converts voice data into text.

[1350] 5. Analysis and emotion recognition: The NLU engine extracts keywords such as "suspicious person" and "help," and the emotion recognition engine detects the user's state of tension.

[1351] 6. Response generation: A response message is generated saying, "We will call the police. Please take refuge in a safe room in your home." and converted into speech using a TTS engine.

[1352] 7. Playback: The converted audio is played back to the user through the smartphone app.

[1353] Prompt Sentence Examples

[1354] "We have spotted a suspicious person and are calling emergency services. Please explain the situation and tell us what to do safely."

[1355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1356] Step 1:

[1357] The user launches the smartphone app and taps the emergency call button.

[1358] Input: User taps

[1359] Output: Generate an emergency call request

[1360] Specific Actions: The user interface creates an emergency call request and enables voice input functionality.

[1361] Step 2:

[1362] The device uses a highly sensitive microphone to capture the user's voice and generates clear audio data using noise-canceling functionality.

[1363] Input: User voice input

[1364] Output: Clear audio data

[1365] Specific operation: The device captures audio, performs noise cancellation processing, generates audio data, and sends it to a cloud server.

[1366] Step 3:

[1367] The server converts the received voice data into text data using a voice recognition engine.

[1368] Input: Clear voice data

[1369] Output: Text data

[1370] Specific operation: The server runs a speech recognition engine (e.g., Google Speech-to-Text API) and converts the voice data into text data.

[1371] Step 4:

[1372] The server analyzes the text data using a natural language processing (NLU) engine to understand the user's intent.

[1373] Input: Text data

[1374] Output: User's intention information

[1375] Specific operation: The server uses a natural language processing engine to analyze the text data, extract important keywords such as "suspicious person" and "help," and understand the user's intent.

[1376] Step 5:

[1377] The server uses an emotion recognition engine to analyze the user's emotional state based on the voice and text data.

[1378] Input: Audio data, text data

[1379] Output: Emotional state information

[1380] Specific operation: The server uses an emotion recognition engine to identify emotions such as tension or fear from the user's voice tone and text content.

[1381] Step 6:

[1382] The server generates an appropriate response using a response generation engine based on the analysis results and emotional state information.

[1383] Input: User intention information, emotional state information

[1384] Output: Text response

[1385] Specific behavior: The server uses a response generation engine to create the most appropriate message for the user, generating a response such as "We will call the police. Please take shelter in a safe room in your home."

[1386] Step 7:

[1387] The server converts the generated text response into audio data using a text-to-speech (TTS) engine.

[1388] Input: Text response

[1389] Output: Response in audio data format

[1390] What happens: The server uses a text-to-speech engine (e.g., Amazon Polly) to convert the text response into audio data.

[1391] Step 8:

[1392] The terminal receives the audio data and plays it back to the user using its audio output function.

[1393] Input: Response in audio data format

[1394] Output: A voice response to the user

[1395] Specific operation: The device plays the received audio data and provides the user with specific instructions via voice, such as "We will call the police. Please evacuate to a safe room in your home."

[1396] 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.

[1397] 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.

[1398] 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.

[1399] [Fourth embodiment]

[1400] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1401] 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.

[1402] 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).

[1403] 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.

[1404] 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.

[1405] 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).

[1406] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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."

[1413] This invention is a sophisticated system for the hearing- and visually impaired to access emergency calls and customer support. The system consists of a smartphone app and a cloud-based server, and by integrating speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition, it significantly improves convenience for the hearing- and visually impaired.

[1414] 1. Smartphone app functions

[1415] User Interface

[1416] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[1417] Voice to text conversion

[1418] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[1419] 2. Cloud Server Functions

[1420] Voice Recognition

[1421] The server converts the received voice data into text using a highly accurate speech recognition engine, for example, using an external service such as the Google Speech-to-Text API.

[1422] Natural Language Processing and Text Analytics

[1423] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent and derive the appropriate response for a specific task, such as filing an insurance claim or calling an emergency service.

[1424] Response generation and text-to-speech

[1425] The server generates a response to the user based on the analysis results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[1426] 3. Connecting to Support and Using Location Information

[1427] Identifying Support Personnel

[1428] The server has a database that identifies the most appropriate support person for the user's inquiry. For example, if the user calls an emergency number, the server connects the user to the nearest emergency operator.

[1429] Obtaining location information

[1430] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[1431] Specific usage scenarios

[1432] Emergency call example

[1433] 1. The user opens the emergency call app and taps the call button.

[1434] 2. The device generates a call request and sends it to the server along with the location information.

[1435] 3. The server receives the call request and identifies the most suitable emergency operator.

[1436] 4. The user says, "There's a fire, please help."

[1437] 5. The device sends this audio to the server, which converts the audio data into text.

[1438] 6. The server analyzes the text data and understands the urgent intent.

[1439] 7. The server generates a response such as "Fire department is on its way. Please evacuate to a safe location" and converts it into speech.

[1440] 8. The device plays the converted audio to the user.

[1441] In this way, the present invention helps the hearing-visually impaired to receive prompt and appropriate assistance in emergencies, and also provides greater convenience to customer support services such as insurance companies and banks.

[1442] The processing flow will be explained below.

[1443] Step 1:

[1444] The user opens the smartphone app and taps the call button.

[1445] What happens: The app detects a user tap event and generates a call request.

[1446] Step 2:

[1447] The terminal sends a call request to the server.

[1448] How it works: A call request (including metadata such as user ID and location) is sent to the server.

[1449] Step 3:

[1450] A server receives the call request and identifies available support personnel.

[1451] What it does: Queries a database on the server to find the most suitable person.

[1452] Step 4:

[1453] The device records the user's voice input and sends it to the server.

[1454] How it works: Captures voice input in real time and uploads it to a server as audio data.

[1455] Step 5:

[1456] The server converts the received voice data into text.

[1457] What it does: Calls a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[1458] Step 6:

[1459] The server sends the analyzed text to a natural language processing engine, which analyzes the user's intent.

[1460] How it works: Parsed text is sent to an NLU (Natural Language Understanding) engine to understand the user's intent.

[1461] Step 7:

[1462] The server generates relevant solutions and workarounds.

[1463] What it does: Searches the FAQ database based on intent and generates the appropriate answer.

[1464] Step 8:

[1465] The server converts the generated text into speech.

[1466] What it does: Calls a Text-to-Speech (TTS) engine to convert text to speech.

[1467] Step 9:

[1468] The terminal plays the audio data to the user.

[1469] What it does: Sends audio data to the device and plays it through the speaker.

[1470] Step 10:

[1471] The server obtains the user's location and identifies the appropriate support person.

[1472] What it does: Runs a database query to identify the best available agent based on the user's location.

[1473] Step 11:

[1474] The device periodically transmits the user's location information to the server.

[1475] How it works: Uses location services to update location data to a server in real time.

[1476] Example 1

[1477] 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."

[1478] When hearing- and visually impaired people use existing emergency call systems and customer support, they face difficulties in operating them and are unable to receive prompt responses. In particular, the accuracy of voice input recognition and intent understanding is low, which can easily lead to misunderstandings and delays. Furthermore, the lack of a means to accurately and efficiently utilize the user's location information makes it difficult to respond promptly in an emergency.

[1479] 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.

[1480] In this invention, the server includes means for receiving voice data and converting it into text using a voice recognition engine, means for analyzing the text data using a natural language understanding engine to understand the user's intent, and means for generating an appropriate response based on the user's intent. This allows hearing-visually impaired people to obtain clear voice data and send it to the server, which then uses natural language processing technology to accurately understand the intent and provide the most appropriate response quickly. Furthermore, by periodically obtaining the user's location information and identifying the most appropriate support person based on that information, emergency responses can be made even faster and more accurate.

[1481] "Hearing-blind" refers to people who have visual and / or hearing impairments and who have difficulty obtaining information or communicating through normal vision or hearing.

[1482] "User interface" means an interface that provides a means for users to interact with a system, including simple and intuitive design, voice commands, and tactile feedback tailored for users with visual and hearing impairments.

[1483] "Voice input" refers to the means by which a user provides information to the system through voice, specifically voice data collected using a high-sensitivity microphone and noise-canceling function.

[1484] "Voice Data" means data in a digital format for recording, storing, or transmitting user-uttered sounds.

[1485] A "voice recognition engine" is software that analyzes received voice data and converts it into corresponding text data; for example, a voice recognition API is used.

[1486] "Text data" refers to data in the form of a string of characters converted by a speech recognition engine and used for analysis, display, and response generation.

[1487] A "natural language understanding engine" refers to an artificial intelligence technology that analyzes text data and understands the user's intent and context.

[1488] "Text-to-speech engine" means a software technology for converting text data into synthetic speech and providing the generated response to the user in audio form.

[1489] "Location information" is data indicating the user's current location, and is obtained using GPS or other location information acquisition means.

[1490] "Support Personnel" means operators or technicians designated to respond to specific inquiries or emergency calls.

[1491] "Secure communication protocol" refers to a communication protocol for securely sending and receiving data, and includes, for example, HTTPS.

[1492] This invention is a system for the hearing- and visually impaired to access emergency calls and customer support. The system consists of a smartphone app and a cloud-based server. Specifically, it significantly improves convenience for the hearing- and visually impaired by integrating speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition.

[1493] Smartphone app functions

[1494] User Interface

[1495] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[1496] Voice to text conversion

[1497] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[1498] Cloud Server Functions

[1499] Voice Recognition

[1500] The server converts the received voice data into text using a highly accurate voice recognition engine, such as a general voice recognition API.

[1501] Natural Language Processing and Text Analytics

[1502] The server then further analyzes the converted text data using a natural language understanding (NLU) engine to accurately understand the user's intent, specifically whether they want to make an emergency call or receive customer support.

[1503] Response generation and text-to-speech

[1504] The server generates a response to the user based on the analysis results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[1505] Connecting to Support and Location

[1506] Identifying Support Personnel

[1507] The server has a database that identifies the most appropriate support person for the user's inquiry. For example, if the user calls an emergency number, the server connects the user to the nearest emergency operator.

[1508] Obtaining location information

[1509] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[1510] Specific usage scenarios

[1511] Emergency call example

[1512] 1. The user opens the emergency call app and taps the call button.

[1513] 2. The device will issue a voice prompt saying, "Please state the details of your call," followed by the user saying, "There's a fire, please help."

[1514] 3. The device clearly captures the user's voice and sends it to the server.

[1515] 4. The server uses a speech recognition engine to convert the voice data into text data such as "Fire, help please."

[1516] 5. The server parses the text and understands that it is a fire emergency.

[1517] 6. The server generates a response saying, "Fire department is on its way. Please evacuate to a safe location," and converts it into audio data.

[1518] 7. The server sends the audio data to the device, which plays it back to the user.

[1519] 8. The server connects you to the most appropriate emergency operator and sends the user's location information to facilitate a quick response.

[1520] This system is designed to enable the hearing-blind to receive prompt and appropriate responses in emergencies. Users can provide clear voice input using devices equipped with high-sensitivity microphones and noise-canceling functions. This real-time speech recognition and natural language processing allows the system to accurately understand the user's intent and generate appropriate responses.

[1521] Example prompt: "There's a fire. I'd like to call emergency services."

[1522] Based on the above scenario, this prompt sentence is used:

[1523] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1524] Step 1:

[1525] A user launches a smartphone app and taps the "emergency call button." The input is a tap operation by the user, and the output is the device entering voice input mode. Specifically, the device presents a voice prompt saying, "Tell us what happened."

[1526] Step 2:

[1527] The device uses a highly sensitive microphone and noise-canceling function to capture the user's voice in real time. The user's voice is input as input, and voice data is generated as output. A specific example of the device's operation is when the user says, "There's a fire. Help me."

[1528] Step 3:

[1529] The device packetizes the acquired voice data and sends it to a cloud-based server using a secure communication protocol (e.g., HTTPS). Voice data and location information are obtained as input, and data packets are sent as output. Specifically, the voice data and location information arrive at the server.

[1530] Step 4:

[1531] The server converts the received voice data into text using a voice recognition engine (e.g., a general voice recognition API). Voice data is input, and text data is generated as output. The specific operation is to generate the text "There's a fire. Help me."

[1532] Step 5:

[1533] The server analyzes the text data using a natural language understanding engine (NLU engine) to understand the user's intent. The text data is input, and the analysis results are obtained as output. The specific behavior is that a fire emergency is understood.

[1534] Step 6:

[1535] The server generates an appropriate response message based on the analysis results and converts it into voice data using a text-to-speech (TTS) engine. The analysis results are input, and voice data is generated as output. The specific operation is to generate voice data saying, "Fire departments are on their way immediately. Please evacuate to a safe place."

[1536] Step 7:

[1537] The server sends the generated voice data to the terminal, which then plays it back. Voice data is received as input, and voice playback is provided to the user as output. Specifically, the terminal notifies the user by voice, "The fire brigade will be on its way shortly."

[1538] Step 8:

[1539] The server uses the user's location information to identify the most suitable emergency operator and connects them along with the location information. The location information and the user's intention are received as input, and the connection to the emergency operator is made as output. Specifically, the call content and location information are transmitted to the nearest fire station.

[1540] (Application example 1)

[1541] 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."

[1542] When hearing- and visually impaired people use autonomous vehicles, conventional systems face challenges in providing sufficient navigation and responding to emergencies. In particular, the lack of notifications regarding traffic control and obstacle detection hinders the creation of an environment in which hearing- and visually impaired people can travel safely. Therefore, it is necessary to provide a system that enables hearing- and visually impaired people to reach their destinations safely and efficiently and that can respond quickly in emergencies.

[1543] 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.

[1544] In this invention, the server comprises: means for providing a user interface for the hearing-visually impaired;

[1545] means for receiving and converting voice data into text;

[1546] A means for analyzing text data and understanding user intent;

[1547] means for generating an appropriate response based on the user's intent;

[1548] means for converting the generated response into audio data;

[1549] means for playing the provided responses through a user interface;

[1550] means for providing notifications regarding vehicle control and obstacle detection functions;

[1551] A means of connecting you to the nearest support center or emergency operator in case of an emergency;

[1552] a means for acquiring location information of the user and identifying an appropriate support person based on the location information;

[1553] This will enable hearing-impaired and visually impaired people to travel safely in self-driving vehicles by receiving notifications regarding traffic control and obstacle detection, and by responding quickly in emergencies.

[1554] A "user interface for the visually and hearing impaired" refers to an operating screen and input method designed to be easy to use for people with visual or hearing impairments.

[1555] "Means for receiving voice data and converting it into text" refers to a function for taking in voice-format data and converting it into character-format data.

[1556] "Means for analyzing text data and understanding user intent" refers to technology that analyzes text data and understands the user's requests and intent based on that data.

[1557] The "means for generating an appropriate response based on the user's intent" is a mechanism for generating an appropriate reply or instruction based on the analyzed user's intent.

[1558] The "means for converting the generated response into voice data" is a technique for converting the generated text-format response into voice format.

[1559] The "means for reproducing the provided response through a user interface" is a function for presenting the generated response to the user through a user interface.

[1560] "Means for providing notifications related to traffic control and obstacle detection functions" refers to technologies for controlling automated vehicles, traffic-related functions, and detecting obstacles and notifying users.

[1561] "Means for connecting to the nearest support center or emergency operator in an emergency" refers to a mechanism for quickly connecting to a nearby support center or emergency response personnel in an emergency.

[1562] "Means for acquiring the user's location information and identifying the appropriate support person based on that information" is a technology that acquires the user's current location information and selects the most appropriate support person based on that location.

[1563] This invention is a system that enables the deaf-blind to use automated vehicles safely and efficiently, improving the convenience and safety of travel for people with visual and hearing impairments. The system consists of a terminal and a cloud-based server, and realizes the coordination of speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition.

[1564] 1. Smartphone app functions

[1565] Providing a user interface

[1566] The device provides a user interface specifically tailored for the hearing-blind, with simple and intuitive icons and buttons, voice commands and haptic feedback, and special screen reader capabilities.

[1567] Voice to text conversion

[1568] The device receives the user's voice input in real time and sends it to the server. The voice input is captured using a high-sensitivity microphone and noise-canceling function to obtain clear voice data.

[1569] 2. Cloud Server Functions

[1570] Voice Recognition

[1571] The server converts the received voice data into text using a highly accurate speech recognition engine, specifically the Google Cloud Speech-to-Text API.

[1572] Natural Language Processing and Text Analytics

[1573] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent.

[1574] Response generation and text-to-speech

[1575] The server generates a response to the user based on the analysis results. The generated response is in text format, which is then converted into audio data by a text-to-speech (TTS) engine, specifically using the Google Cloud Text-to-Speech API.

[1576] 3. Vehicle control and obstacle detection

[1577] Vehicle Control

[1578] The device controls the vehicle based on the destination set by voice input, providing a safe route to the destination.

[1579] Obstacle Detection

[1580] The system detects obstacles and dangers in real time based on data obtained from the vehicle's sensors, and the device notifies the user of any detected obstacles or dangers via voice notifications.

[1581] 4. Emergency notification function

[1582] Identifying Support Personnel

[1583] The server obtains the user's location information and identifies the most suitable support person based on that information, allowing the user to be quickly connected to the nearest support center or emergency operator in the event of an emergency.

[1584] Obtaining location information

[1585] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[1586] Specific usage scenarios

[1587] Scenario 1: Emergency Response

[1588] 1. The user opens the emergency call app and taps the call button.

[1589] 2. The device generates a call request and sends it to the server along with the location information.

[1590] 3. The server receives the call request and identifies the most suitable emergency operator.

[1591] 4. The user says, "There's a fire, please help."

[1592] 5. The device sends this audio to the server, which converts the audio data into text.

[1593] 6. The server analyzes the text data and understands the urgent intent.

[1594] 7. The server generates a response such as "Fire department is on its way. Please evacuate to a safe location" and converts it into speech.

[1595] 8. The device plays the converted audio to the user.

[1596] Scenario 2: Normal navigation

[1597] 1. The user speaks "Heading home."

[1598] 2. The device receives the voice data in real time and sends it to the server.

[1599] 3. The server converts the received voice data into text and analyzes it.

[1600] 4. The server calculates the appropriate route and generates a response as audio data.

[1601] 5. The device notifies the user, "Destination setting complete. Let's depart."

[1602] In these scenarios, example prompts are:

[1603] Please set your destination

[1604] "You're in danger right now. Should I call emergency services?"

[1605] "Obstacle detected. Please be careful."

[1606] In this way, the present invention can support safe and efficient travel for hearing-impaired people using automated vehicles and respond to a variety of emergency situations.

[1607] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1608] Step 1:

[1609] The device receives the user's voice input in real time and uses a high-sensitivity microphone and noise-canceling function to capture clear voice data, which is then sent directly to the server.

[1610] Input: User's voice command

[1611] Output: Clear recorded audio data

[1612] Step 2:

[1613] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API, which then analyzes the voice data and returns the resulting text data to the server.

[1614] Input: Audio data sent from the device

[1615] Output: Text format data

[1616] Step 3:

[1617] The server analyzes the converted text data using a natural language understanding (NLU) engine to understand the user's intent. The analysis engine scans the text data and recognizes specific keywords and phrases to understand the user's request.

[1618] Input: Text format data

[1619] Output: Parsed user intent

[1620] Step 4:

[1621] The server generates an appropriate response based on the user's intent. The response is in text format, and this text data is used for subsequent processing. For example, if the user's intent is to call an emergency service, the server generates an emergency response message.

[1622] Input: Parsed user intent

[1623] Output: Text data for the response

[1624] Step 5:

[1625] The server converts the generated text data for the response into audio data using a text-to-speech (TTS) engine. The Google Cloud Text-to-Speech API performs this conversion and generates audio data.

[1626] Input: Text data for response

[1627] Output: Audio response data

[1628] Step 6:

[1629] The terminal plays the provided response through the user interface, and the user interface plays the audio data to notify the user.

[1630] Input: Audio response data

[1631] Output: The audio response played

[1632] Step 7:

[1633] The device controls the user's means of transportation and instructs the autonomous vehicle on the route to the set destination. During this process, it detects obstacles based on data collected by sensors and provides voice notifications as necessary.

[1634] Input: Destination information and data from sensors

[1635] Output: Vehicle control instructions and notification voice

[1636] Step 8:

[1637] The device periodically acquires the user's location information and sends it to the server, which then identifies the most appropriate support personnel and enables a prompt response in the event of an emergency.

[1638] Input: Location data

[1639] Output: Support representative identification and connection information

[1640] 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.

[1641] This invention is a system that improves the convenience of emergency calls and customer support for the hearing-blind, and in particular combines an emotion engine that recognizes the user's emotions. Comprised of a smartphone app and a cloud-based server, the system integrates emotion recognition functionality in addition to speech recognition, text analysis, natural language processing, text-to-speech conversion, and location information acquisition, enabling even more advanced support.

[1642] 1. Smartphone app functions

[1643] User Interface

[1644] The device provides a user interface specifically tailored for the hearing-blind, using simple and intuitive icons and buttons, with voice commands, haptic feedback, and special screen reader capabilities.

[1645] Voice to text conversion

[1646] The device receives the user's voice input in real time and sends it to the server. A high-sensitivity microphone and noise-canceling function are used for voice input, ensuring clear voice data.

[1647] 2. Cloud Server Functions

[1648] Voice Recognition

[1649] The server converts the received voice data into text using a highly accurate speech recognition engine, for example, using an external service such as the Google Speech-to-Text API.

[1650] Natural Language Processing and Text Analytics

[1651] The server then further analyzes the converted text using a natural language understanding (NLU) engine to accurately understand the user's intent and derive the appropriate response for a specific task, such as filing an insurance claim or calling an emergency service.

[1652] emotion recognition

[1653] The server is equipped with an emotion engine that recognizes the user's emotions based on voice and text data, allowing it to identify the user's emotional state, such as nervousness, anger, or sadness.

[1654] Response generation and text-to-speech

[1655] The server generates a response to the user based on the analysis results and emotion recognition results. The generated response is in text format, and a text-to-speech (TTS) engine is used to convert it into audio data.

[1656] 3. Connecting to Support and Using Location Information

[1657] Identifying Support Personnel

[1658] The server has a database that identifies the most suitable support person depending on the user's inquiry and emotional state. For example, in the case of an emergency call, the system connects the user to the nearest emergency operator.

[1659] Obtaining location information

[1660] The device periodically acquires the user's location information and sends it to the server, enabling a rapid response in emergencies.

[1661] Specific usage scenarios

[1662] Emergency call example

[1663] 1. The user opens the emergency call app and taps the call button.

[1664] 2. The device generates a call request and sends it to the server along with the location information.

[1665] 3. The server receives the call request and identifies the most suitable emergency operator.

[1666] 4. The user says, "There's a fire, please help."

[1667] 5. The device sends this audio to the server, which converts the audio data into text.

[1668] 6. The server analyzes the text data and understands the urgent intent.

[1669] 7. The server recognizes the user's emotions based on the voice and text data.

[1670] 8. The server reflects the emotion recognition results and generates a response such as "The fire department will be on its way immediately. Please evacuate to a safe place," and converts it into speech.

[1671] 9. The device plays the converted audio to the user.

[1672] In this way, the present invention helps the deaf-blind to receive prompt and appropriate assistance in emergencies. It can also provide high-quality customer support services for insurance companies and banks. By integrating emotion recognition functionality, it is possible to provide tailored responses based on the user's emotional state, further improving the user experience.

[1673] The processing flow will be explained below.

[1674] Step 1:

[1675] The user opens the smartphone app and taps the call button.

[1676] What happens: The app detects a user tap event and generates a call request.

[1677] Step 2:

[1678] The terminal sends a call request to the server.

[1679] How it works: A call request (including metadata such as user ID and location) is sent to the server.

[1680] Step 3:

[1681] A server receives the call request and identifies available support personnel.

[1682] What it does: It queries a database on the server to identify the most suitable person.

[1683] Step 4:

[1684] The device records the user's voice input and sends it to the server.

[1685] How it works: Captures voice input in real time and uploads it to a server as audio data.

[1686] Step 5:

[1687] The server converts the received voice data into text.

[1688] What it does: Calls a speech recognition engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[1689] Step 6:

[1690] The server analyzes the text data and understands the user's intent.

[1691] How it works: It uses a natural language understanding (NLU) engine to parse text data and determine user intent.

[1692] Step 7:

[1693] The server recognizes the user's emotions based on the voice data and text data.

[1694] How it works: The emotion recognition engine analyzes voice tone and text content to identify the user's emotional state (e.g., nervous, anger, sadness).

[1695] Step 8:

[1696] The server generates relevant solutions and workarounds.

[1697] How it works: Searches a FAQ database and generates appropriate answers based on intent and emotional state.

[1698] Step 9:

[1699] The server converts the generated text into speech.

[1700] What it does: It uses a text-to-speech (TTS) engine to convert the generated text into audio data.

[1701] Step 10:

[1702] The terminal plays the audio data to the user.

[1703] What it does: Sends audio data to the device and plays it through the speaker.

[1704] Step 11:

[1705] The server obtains the user's location information and identifies the most suitable support person.

[1706] What it does: Runs a database query to identify the best available agent based on the user's location.

[1707] Step 12:

[1708] The device periodically transmits the user's location information to the server.

[1709] How it works: Uses location services to update location data to a server in real time.

[1710] Example 2

[1711] 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."

[1712] When hearing- and visually impaired people call for emergency services or use customer support, it is difficult for them to receive prompt and appropriate responses. Furthermore, systems often fail to properly understand the user's emotional state and are unable to provide sufficient support. This creates a problem in that the safety and convenience of hearing- and visually impaired people cannot be adequately ensured in emergencies or when problems occur.

[1713] 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.

[1714] In this invention, the server includes means for recognizing the user's emotions based on the voice data and text data, means for generating an appropriate response based on the user's intention and emotions, and means for converting the generated response into voice data, thereby enabling the server to accurately grasp the user's intention and provide a detailed response according to the user's emotional state.

[1715] A "user interface for the deaf-blind" is an interface with a design and functionality that is intuitive and easy to use for users with visual or hearing impairments.

[1716] The "means for receiving voice data and converting it into text" is a technology that processes the voice input by the user as a digital signal and converts it into text format.

[1717] "Means for analyzing text data and understanding user intent" refers to a technology for analyzing converted text data to identify the user's requests and intent.

[1718] "Means for recognizing a user's emotions based on voice data and text data" is a technology for identifying a user's emotional state using acquired voice and text information.

[1719] "Means for generating appropriate responses based on the user's intentions and emotions" refers to technology that enables the system to generate optimal responses based on the user's requests and emotional state.

[1720] The "means for converting the generated response into voice data" refers to a technique for converting the text-format response generated by the system into voice format.

[1721] "Means for acquiring the user's location information and identifying the appropriate support staff based on that information" refers to technology that acquires the user's current location in real time and identifies the staff member who will provide appropriate support based on that information.

[1722] The present invention provides a system that enables the hearing-blind to receive prompt and accurate responses when calling emergency services or using customer support services. The system integrates a terminal, a cloud-based server, a user interface, speech recognition, natural language processing, emotion recognition, text-to-speech conversion, and location information acquisition functions.

[1723] First, the device provides the user with a user interface. This user interface is designed to be intuitive for the hearing-blind and includes simple icons and buttons, voice commands, haptic feedback, and special screen reader functionality. When the user speaks, the device captures the voice data using a high-sensitivity microphone and noise-canceling function and transmits it to the server in real time.

[1724] The server converts the received voice data into text using a speech recognition engine such as the Google Speech-to-Text API, generating clear, highly accurate text data. The server then analyzes this text data and uses a natural language understanding (NLU) engine to understand the user's intent. For example, the server can analyze a query such as "There's a fire. Help me." and recognize that it's an emergency.

[1725] Furthermore, the server uses the text data and the original voice data to run an emotion recognition engine to identify the user's emotional state. For example, it can detect whether the user is nervous, angry, sad, etc. Based on this, the server generates an appropriate response to the user based on the analysis results and the emotion recognition results. This response is first generated in text format and then converted into voice data using a text-to-speech (TTS) engine.

[1726] Finally, the server retransmits the generated voice data to the device, which then plays it back to the user. This allows the user to understand the instructions from the server by voice, ensuring a prompt and appropriate response in the event of an emergency. Furthermore, the server periodically obtains the user's location information and identifies the most appropriate support personnel based on that location information. This allows, for example, in the event of an emergency, the user to be quickly connected to the nearest emergency operator.

[1727] A specific example would be an emergency call scenario like this:

[1728] 1. The user opens the emergency call app and taps the call button.

[1729] 2. The device generates a call request and sends it to the server along with the location information.

[1730] 3. The server receives the call request and identifies the most suitable emergency operator.

[1731] 4. The user says, "There's a fire, please help."

[1732] 5. The device sends this audio to the server, which converts the audio data into text.

[1733] 6. The server analyzes the text data and understands the urgent intent.

[1734] 7. The server recognizes the user's emotions based on the voice and text data.

[1735] 8. The server reflects the emotion recognition results and generates a response such as "The fire department will be on its way immediately. Please evacuate to a safe place," and converts it into speech.

[1736] 9. The device plays the converted audio to the user.

[1737] Examples of prompts to input to a generative AI model include:

[1738] "Please provide a detailed explanation of the processing steps of an emergency call system for the hearing-blind. Please explain the entire process, from voice input to response generation, including specific operations."

[1739] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1740] Step 1: Accept voice input

[1741] The device captures the user's voice input in real time using a high-sensitivity microphone and noise-canceling function. The input of this process is the user's voice, and the output is voice data converted into a digital signal. Specifically, when the user opens the emergency app and says "help," the device will clearly record the voice.

[1742] Step 2: Sending audio data

[1743] The device encrypts the captured voice data and sends it to a cloud-based server via the Internet. The input is the voice data converted into a digital signal, and the output is the voice data sent to the server. Specifically, the device displays the progress on the screen, saying "Sending voice data to server."

[1744] Step 3: Voice Recognition

[1745] The server converts the received voice data into text data using a voice recognition engine (e.g., Google Speech-to-Text API). The input is the voice data sent from the device, and the output is text data. Specifically, the server records the status "Converting voice data to text" in the log.

[1746] Step 4: Text Analysis

[1747] The server analyzes the converted text data using a natural language understanding (NLU) engine to understand the user's intent. The input is text data, and the output is the analysis result that represents the user's intent. Specifically, the server records the status "Emergency intent detected, preparing appropriate response" in the log.

[1748] Step 5: Emotion Recognition

[1749] The server uses an emotion recognition engine based on the text data and the original voice data to recognize the user's emotions. The input is text data and voice data, and the output is data indicating the user's emotional state. Specifically, the server executes "recognize user's emotions" and outputs the data "The user is very anxious."

[1750] Step 6: Response Generation

[1751] The server generates an appropriate response to the user based on the analysis results and emotion recognition results. The input is data indicating the user's intention and emotional state, and the output is a text response. Specifically, the server generates a text response such as "The fire department will be on its way immediately. Please evacuate to a safe place."

[1752] Step 7: Text-to-speech

[1753] The server converts the generated text response into audio data using a text-to-speech (TTS) engine. The input is the text response and the output is audio data. Specifically, the server logs the status "Converting generated response into audio format."

[1754] Step 8: Send and play a voice response

[1755] The server sends the generated voice data to the terminal, which then plays it back. The input is the voice data, and the output is the voice that is played back to the user. Specifically, the terminal tells the user, "The fire department is on its way immediately. Please evacuate to a safe place."

[1756] Step 9: Use location information

[1757] The device periodically sends its location information to the server, and the server uses this information to identify the most suitable support person. The input is the location information sent from the device, and the output is data for connecting to the appropriate support person. Specifically, the server displays a message saying "Connecting to the nearest fire station operator."

[1758] (Application example 2)

[1759] 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."

[1760] When hearing-impaired people use voice or text data to access customer support services, it is difficult for them to receive prompt and appropriate responses. Furthermore, existing systems often lack the ability to take into account the user's emotional state, resulting in insufficient support.

[1761] 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 recognizing the emotional state of the user, means for optimizing a response based on the emotional state of the user, means for transmitting voice data and location information to the server, and means for identifying the most suitable support person based on the location information. This makes it possible for hearing-impaired people to quickly receive an appropriate response according to their emotional state when they need customer support in an emergency.

[1762] A "user interface for the visually and hearing impaired" is an operation screen or input means designed to be easy to use for users with visual or hearing impairments.

[1763] "Voice data" refers to information input by voice by a user, which information is recorded in digital form.

[1764] The "means for converting to text" is a technique for converting received voice data into a meaningful written form.

[1765] "Text data" is voice data converted into character information and written in sentence format.

[1766] "Means of understanding intent" refers to technology for analyzing and understanding the purpose and request that a user is trying to communicate through voice or text.

[1767] "Means for generating a response" refers to the technology for creating appropriate answers or instructions in response to a user's intentions or inquiries.

[1768] The "means for converting into voice data" is a technique for converting a response generated as text data back into voice format.

[1769] "Means for recognizing emotional states" refers to technology that analyzes emotions from a user's voice or text and identifies emotions such as joy, anger, and sadness.

[1770] A "means for optimizing responses" is a technique for selecting and providing the most appropriate response to a user, taking into account the user's emotional state.

[1771] "Location information" is geographical data that indicates where a user is currently located.

[1772] "Means for transmitting to the server" refers to a communication technology for transmitting the necessary data from the user terminal to the cloud server.

[1773] The "means for identifying a support person" is a technology that automatically selects a person who can provide the most appropriate response based on the user's situation and location information.

[1774] This invention is a system that allows the hearing-blind to easily access emergency calls and customer support. The system is primarily composed of a smartphone application and a cloud-based server.

[1775] System configuration

[1776] Smartphone application

[1777] The smartphone application has the following main features:

[1778] 1. User Interface: Provide a clean and intuitive interface specifically designed for the hearing-blind. This interface includes simple icons and large buttons, and supports voice commands and haptic feedback.

[1779] 2. Voice input: A high-sensitivity microphone and noise-canceling function are used to clearly capture the user's voice, which is then sent to a cloud server in real time.

[1780] 3. Obtaining location information: Using a GPS sensor, the user's current location information is periodically sent to the server.

[1781] Cloud Server

[1782] The cloud server has the following functions:

[1783] 1. Speech recognition: The received voice data is converted into text data using a high-precision voice recognition engine (e.g., Google Speech-to-Text API).

[1784] 2. Natural Language Processing: Use a natural language understanding (NLU) engine to analyze text data and accurately understand user intent.

[1785] 3. Emotion Recognition: Equipped with an emotion engine that identifies the user's emotional state based on voice and text data.

[1786] 4. Response generation: Based on the analysis results and emotion recognition results, an appropriate response is generated and written in text format.

[1787] 5. Text-to-speech: Use a text-to-speech (TTS) engine (e.g., Amazon Polly) to convert the generated text response into audio data.

[1788] 6. Identifying support personnel: The system has a database for identifying appropriate support personnel or emergency operators based on the user's inquiry content and emotional state.

[1789] Processing flow

[1790] 1. Activation and Notification:

[1791] The user launches the smartphone app and taps the emergency call button. The app activates the voice input function, and the user says, "This is an emergency. Please help."

[1792] 2. Audio data processing:

[1793] The device receives the voice data and sends it to a cloud server, which uses a speech recognition engine to convert the voice data into text.

[1794] 3. Natural Language Processing and Emotion Recognition:

[1795] The server then uses a natural language understanding engine to analyze the converted text data and understand the user's intent. At the same time, an emotion recognition engine analyzes the user's emotional state.

[1796] 4. Generating an appropriate response:

[1797] The server generates a response based on the analysis results and converts it into text format.

[1798] 5. Generate a voice response:

[1799] The response text is converted into voice data by a text-to-speech engine and sent to the terminal.

[1800] 6. Response playback:

[1801] The terminal plays the converted audio data and provides instructions to the user.

[1802] Examples and prompts

[1803] For example, a scenario will be described in which a user discovers a suspicious person in his / her home at night.

[1804] 1. User action: The user opens the Safeguard app and presses the Emergency Call button.

[1805] 2. Voice input: The user says, "There is a suspicious person outside my house. Please help."

[1806] 3. Data transmission: The smartphone app sends voice data and location information to the server.

[1807] 4. Speech recognition: A speech recognition engine on the server side converts voice data into text.

[1808] 5. Analysis and emotion recognition: The NLU engine extracts keywords such as "suspicious person" and "help," and the emotion recognition engine detects the user's state of tension.

[1809] 6. Response generation: A response message is generated saying, "We will call the police. Please take refuge in a safe room in your home." and converted into speech using a TTS engine.

[1810] 7. Playback: The converted audio is played back to the user through the smartphone app.

[1811] Prompt Sentence Examples

[1812] "We have spotted a suspicious person and are calling emergency services. Please explain the situation and tell us what to do safely."

[1813] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1814] Step 1:

[1815] The user launches the smartphone app and taps the emergency call button.

[1816] Input: User taps

[1817] Output: Generate an emergency call request

[1818] Specific Actions: The user interface creates an emergency call request and enables voice input functionality.

[1819] Step 2:

[1820] The device uses a highly sensitive microphone to capture the user's voice and generates clear audio data using noise-canceling functionality.

[1821] Input: User voice input

[1822] Output: Clear audio data

[1823] Specific operation: The device captures audio, performs noise cancellation processing, generates audio data, and sends it to a cloud server.

[1824] Step 3:

[1825] The server converts the received voice data into text data using a voice recognition engine.

[1826] Input: Clear voice data

[1827] Output: Text data

[1828] Specific operation: The server runs a speech recognition engine (e.g., Google Speech-to-Text API) and converts the voice data into text data.

[1829] Step 4:

[1830] The server analyzes the text data using a natural language processing (NLU) engine to understand the user's intent.

[1831] Input: Text data

[1832] Output: User's intention information

[1833] Specific operation: The server uses a natural language processing engine to analyze the text data, extract important keywords such as "suspicious person" and "help," and understand the user's intent.

[1834] Step 5:

[1835] The server uses an emotion recognition engine to analyze the user's emotional state based on the voice and text data.

[1836] Input: Audio data, text data

[1837] Output: Emotional state information

[1838] Specific operation: The server uses an emotion recognition engine to identify emotions such as tension or fear from the user's voice tone and text content.

[1839] Step 6:

[1840] The server generates an appropriate response using a response generation engine based on the analysis results and emotional state information.

[1841] Input: User intention information, emotional state information

[1842] Output: Text response

[1843] Specific behavior: The server uses a response generation engine to create the most appropriate message for the user, generating a response such as "We will call the police. Please take shelter in a safe room in your home."

[1844] Step 7:

[1845] The server converts the generated text response into audio data using a text-to-speech (TTS) engine.

[1846] Input: Text response

[1847] Output: Response in audio data format

[1848] What happens: The server uses a text-to-speech engine (e.g., Amazon Polly) to convert the text response into audio data.

[1849] Step 8:

[1850] The terminal receives the audio data and plays it back to the user using its audio output function.

[1851] Input: Response in audio data format

[1852] Output: A voice response to the user

[1853] Specific operation: The device plays the received audio data and provides the user with specific instructions via voice, such as "We will call the police. Please evacuate to a safe room in your home."

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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).

[1861] 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.

[1862] 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."

[1863] 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.

[1864] 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).

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] 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.

[1873] 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.

[1874] 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.

[1875] The following is further disclosed regarding the above embodiment.

[1876] (Claim 1)

[1877] means for providing a user interface for the hearing-blind;

[1878] means for receiving and converting voice data into text;

[1879] A means for analyzing text data and understanding user intent;

[1880] means for generating an appropriate response based on the user's intent;

[1881] means for converting the generated response into audio data;

[1882] The system includes means for playing back the provided responses through a user interface.

[1883] (Claim 2)

[1884] 10. The system of claim 1, wherein the system converts received voice data into text in real time and analyzes the text.

[1885] (Claim 3)

[1886] 10. The system of claim 1, further comprising means for obtaining location information of the user and identifying an appropriate support person based on the location information.

[1887]

[1888] "Example 1"

[1889] (Claim 1)

[1890] means for providing a user interface for the hearing-blind;

[1891] A means for capturing and transmitting user voice input using a high-sensitivity microphone and noise-canceling function;

[1892] means for receiving voice data and converting it to text using a speech recognition engine;

[1893] A means of analyzing text data using a natural language understanding engine to understand the user's intent,

[1894] means for generating an appropriate response based on the user's intent;

[1895] means for converting the generated response into audio data using a text-to-speech engine;

[1896] means for playing the provided responses through a user interface;

[1897] means for periodically acquiring and transmitting user location information;

[1898] A system including a means for identifying a support person based on location information.

[1899] (Claim 2)

[1900] 10. The system of claim 1, wherein the system converts received voice data into text in real time and analyzes the text.

[1901] (Claim 3)

[1902] 10. The system of claim 1, further comprising means for obtaining location information of the user and identifying an appropriate support person based on the location information.

[1903] "Application Example 1"

[1904] (Claim 1)

[1905] means for providing a user interface for the hearing-blind;

[1906] means for receiving and converting voice data into text;

[1907] A means for analyzing text data and understanding user intent;

[1908] means for generating an appropriate response based on the user's intent;

[1909] means for converting the generated response into audio data;

[1910] means for playing the provided responses through a user interface;

[1911] means for providing notifications regarding vehicle control and obstacle detection functions;

[1912] A system that includes a means to connect to the nearest support center or emergency operator in case of an emergency.

[1913] (Claim 2)

[1914] 10. The system of claim 1, wherein the system converts received voice data into text in real time and analyzes the text.

[1915] (Claim 3)

[1916] 10. The system of claim 1, further comprising means for obtaining location information of the user and identifying an appropriate support person based on the location information.

[1917] "Example 2: Combining Emotion Engines"

[1918] (Claim 1)

[1919] means for providing a user interface for the hearing-blind;

[1920] means for receiving and converting voice data into text;

[1921] A means for analyzing text data and understanding user intent;

[1922] A means for recognizing a user's emotion based on voice data and text data;

[1923] means for generating an appropriate response based on the user's intent and emotions;

[1924] means for converting the generated response into audio data;

[1925] The system includes means for playing back the provided responses through a user interface.

[1926] (Claim 2)

[1927] 10. The system of claim 1, wherein the system converts received voice data into text in real time, analyzes the text, and performs emotion recognition.

[1928] (Claim 3)

[1929] 10. The system of claim 1, further comprising means for obtaining location information of the user and identifying an appropriate support person based on the location information.

[1930] "Application example 2 when combining emotion engines"

[1931] (Claim 1)

[1932] means for providing a user interface for the hearing-blind;

[1933] means for receiving and converting voice data into text;

[1934] A means for analyzing text data and understanding user intent;

[1935] means for generating an appropriate response based on the user's intent;

[1936] means for converting the generated response into audio data;

[1937] means for playing the provided responses through a user interface;

[1938] means for recognizing the emotional state of a user;

[1939] means for optimizing responses based on the emotional state of the user;

[1940] means for transmitting voice data and location information to a server;

[1941] A system including a means for identifying the best support personnel based on location information.

[1942] (Claim 2)

[1943] 10. The system of claim 1, wherein the system converts received voice data into text in real time and analyzes the text.

[1944] (Claim 3)

[1945] 10. The system of claim 1, further comprising means for obtaining location information of the user and identifying an appropriate support person based on the location information. [Explanation of symbols]

[1946] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for providing a user interface for the hearing-blind; means for receiving and converting voice data into text; A means for analyzing text data and understanding user intent; means for generating an appropriate response based on the user's intent; means for converting the generated response into audio data; The system includes means for playing back the provided responses through a user interface.

2. 10. The system of claim 1, wherein the system converts received voice data to text in real time and analyzes the text.

3. The system of claim 1 further comprising means for acquiring location information of the user and identifying an appropriate support person based on the location information.

Images