system

Abstract

Provide a system. [Solution] means for acquiring a user's voice; A means for converting the acquired voice data into text data; means for transmitting the converted character data to a server; A means for generating a response using generative artificial intelligence based on the character data transmitted to the server; means for transmitting the generated response to the terminal; means for converting the transmitted response into speech and playing it back to the user; A system including:

Description

[Technical Field]

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

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

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

[0004] Loneliness and social isolation among the elderly are major problems in modern society. It is known that in many cases, a lack of daily conversation and interaction leads to psychological stress and illness. In contrast, conventional pet robots for comforting purposes could only respond with simple responses, making it difficult to provide deep or knowledgeable conversation. Therefore, a new system is needed that can realize the advanced communication desired by the elderly and provide daily comfort and a sense of security. [Means for solving the problem]

[0005] The present invention provides a system that acquires a user's voice, converts the voice data into text data, uses generative artificial intelligence to generate an appropriate response, and outputs the response as a voice to engage in conversation. Specifically, the system includes a means for acquiring the user's voice, a means for converting the voice data into text data, and a means for transmitting the converted text data to a server. The system also includes a means on the server side for generating an appropriate response from the received text data using generative artificial intelligence, and a means for transmitting the generated response to a terminal. The system further includes a means for converting the transmitted response into voice and playing it back to the user. This system makes it possible to provide the deep dialogue and knowledgeable conversations that elderly people need, bringing them daily healing and a sense of security.

[0006] "User" refers to the person who operates the system and provides voice input.

[0007] "Means for obtaining 'voice'" refers to a device or technology that records the user's speaking voice.

[0008] "Means for converting to text data" refers to software or algorithms for converting captured audio data into text format.

[0009] "Server" refers to a computer system that receives and processes data from client terminals.

[0010] "Generative artificial intelligence" refers to machine learning models and algorithms that generate appropriate responses based on user input data.

[0011] "Means for generating a response" refers to technology that uses generative artificial intelligence to create a response to a user's input.

[0012] "Terminal" refers to a hardware device for direct interaction with a user.

[0013] "Means for converting into speech and playing it back to the user" refers to a device or technology that converts the generated text response into speech using speech synthesis technology and actually plays it back to the user.

[0014] "Creative storytelling" refers to the function in which generative artificial intelligence creates fictional stories and tells them to users.

[0015] "Historical commentary" refers to the function of generative artificial intelligence to explain historical facts and events.

[0016] "Everyday conversation" refers to the normal dialogue that a generative artificial intelligence has with a user.

[0017] "Speech Recognition Software" means software for converting speech into text form.

[0018] "Text-to-speech engine" refers to technology that converts text data into speech.

[0019] "Learning means" refers to machine learning algorithms that improve the quality of the system's responses through user interaction. [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] The system of the present invention uses generative artificial intelligence to generate and output appropriate responses based on information input by voice from the user. This system is composed of a terminal (pet robot), a server, and user interaction.

[0042] System Overview

[0043] 1. User voice input

[0044] The terminal captures the user's voice via a microphone.

[0045] Voice input is the starting point for an interactive conversation and is converted into text using voice recognition software.

[0046] 2. Converting voice data to text

[0047] The terminal converts the acquired voice data into text data using voice recognition software.

[0048] This converted text data becomes information for analyzing the user's intention.

[0049] 3. Sending text data to the server

[0050] The terminal transmits the converted text data to the server using the HTTP protocol.

[0051] The server receives the text data and prepares to generate a response using generative artificial intelligence.

[0052] 4. Response Generation Using Generative AI

[0053] The server generates an appropriate response based on the received text data using generative artificial intelligence (e.g., using a natural language processing model).

[0054] Responses are dynamically generated based on the user's request, which may include fictional stories, historical commentary, or everyday conversation.

[0055] 5. Terminal transmission of generated response

[0056] The server sends the response created by the generative artificial intelligence to the terminal in JSON format or similar.

[0057] This allows the terminal to receive the generated response.

[0058] 6. Response transcription and output

[0059] The terminal converts the text response received from the server into speech using a text-to-speech engine.

[0060] This voice response is played back to the user through the device's built-in speaker.

[0061] This allows users to have real-time interactions.

[0062] Specific examples

[0063] A specific example of this system is shown below.

[0064] User says: "Hi, what have you been up to today?"

[0065] The device captures the speech, converts it into text, and sends it to the server: "Hello, what have you been up to today?"

[0066] The server uses generative artificial intelligence to generate a response from the text data it receives: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0067] The device receives the generated response, converts it into speech, and speaks to the user: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0068] In this way, the system can realize natural conversation with the user and provide daily comfort and a sense of security to the elderly.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The user speaks to the device (pet robot), for example, by voice input such as "Hello, what did you do today?"

[0072] Step 2:

[0073] The device captures the user's voice via a built-in microphone, and the voice data is temporarily stored in memory.

[0074] Step 3:

[0075] The device converts the captured voice data into text data using voice recognition software (e.g., Google® Speech-to-Text API). This process converts the voice data into text format.

[0076] Step 4:

[0077] The terminal sends the converted character data to the server by including it in an HTTP request. Specifically, the data is sent using an HTTP POST request.

[0078] Step 5:

[0079] The server analyzes the received text data and passes it as input to a generative AI (e.g., GPT-3 (registered trademark)), which generates an appropriate response to the user's input.

[0080] Step 6:

[0081] The generative AI generates a response based on the user's input and returns the response in text format to the server, such as "I did a lot today, including taking a walk and reading."

[0082] Step 7:

[0083] The server then sends the generated text response to the device, typically as JSON formatted data.

[0084] Step 8:

[0085] The device converts the received text response into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[0086] Step 9:

[0087] The device then plays the converted response to the user through its built-in speaker, for example, "You've done a lot today, including taking a walk and reading."

[0088] Step 10:

[0089] The terminal then waits for the user's new voice input and repeats the above process to maintain a continuous conversation. This continuous interaction allows the user to enjoy a natural conversation with the pet robot.

[0090] Example 1

[0091] 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."

[0092] Conventional voice dialogue systems have limitations in the accuracy of speech recognition and the naturalness of response generation, often preventing smooth dialogue with users. Furthermore, when generating responses only for specific content, they lack flexibility and are unable to fully respond to diverse user needs. In particular, when considering use as a daily conversation partner for elderly people and others, the system's responses often feel mechanical, making it difficult to provide comfort and a sense of security to the user.

[0093] 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.

[0094] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into text data, and means for generating a response based on the converted text data using generative artificial intelligence, thereby enabling natural and flexible responses to user voice input to be provided in real time.

[0095] "User" refers to a person who uses the system to provide voice input.

[0096] The "means for acquiring voice" refers to a device that collects voice uttered by a user as digital data using a voice input device such as a microphone.

[0097] "Means for converting voice data into text data" refers to the process of converting captured voice data into text format data using voice recognition software.

[0098] "Means for transmitting character data to a server" refers to a communication protocol (generally HTTP, etc.) and its implementation for transmitting text data to a server over a network.

[0099] "Means for generating responses using generative artificial intelligence" refers to a process that uses a natural language processing model to generate natural-sounding responses based on received text data.

[0100] "Means for transmitting to the terminal" refers to a communication protocol and its implementation for transmitting the generated response data from the server to the terminal.

[0101] "Means for converting the response into speech and playing it to the user" refers to the process of converting the generated response into speech using a text-to-speech engine and playing it to the user through a speaker.

[0102] The system of the present invention uses generative artificial intelligence to generate and output appropriate responses based on information input by voice from a user. This system is composed of a terminal (e.g., a pet robot equipped with a voice input device), a server, and user interaction.

[0103] System Overview

[0104] 1. User voice input

[0105] A user speaks into a device equipped with a voice input device (microphone). For example, they say, "Hello, what did you do today?"

[0106] The device captures the user's voice through a microphone and temporarily stores it as digital voice data.

[0107] 2. Converting voice data to text

[0108] The device sends the captured voice data to speech recognition software (e.g., Google Cloud Speech-to-Text API), which analyzes the voice waveform and converts it into text such as "Hello, what have you been up to today?"

[0109] The converted text data is stored in the terminal's memory.

[0110] 3. Sending text data to the server

[0111] The terminal transmits the converted text data to the server using the HTTP protocol, and this transmission process uses network communication.

[0112] The server receives the text data sent as an HTTP request.

[0113] 4. Response Generation Using Generative AI

[0114] The server uses generative artificial intelligence (e.g., GPT-3) to generate an appropriate response based on the received text data.

[0115] Responses are natural responses to user input, such as "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0116] 5. Terminal transmission of generated response

[0117] The server encodes the generated response into JSON format and sends it to the terminal as an HTTP response.

[0118] The terminal receives the HTTP response from the server and retains the generated text response.

[0119] 6. Response transcription and output

[0120] The device inputs the text response received from the server into a text-to-speech engine (e.g., Amazon Polly) and converts it into voice data.

[0121] The device plays the generated voice data over its built-in speaker and responds to the user by saying, "Hello! I did a lot of things today, like taking a walk and reading a book. How about you?"

[0122] Specific examples

[0123] A specific example of this system is shown below.

[0124] User says: "Hi, what have you been up to today?"

[0125] The device captures the speech, converts it into text, and sends it to the server: "Hello, what have you been up to today?"

[0126] The server uses generative artificial intelligence to generate a response from the text data it receives: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0127] The device receives the generated response, converts it into speech, and speaks to the user: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0128] In this way, the system can realize natural conversation with the user, providing everyday comfort and a sense of security, especially to the elderly.

[0129] Prompt Sentence Examples

[0130] The following is an example of a prompt sentence that can be used to generate an appropriate response from a generative artificial intelligence.

[0131] Generate a response when the user says hello:

[0132] User: "Hi, what have you been up to today?"

[0133] Response: "Hi! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0134] Using this prompt, the generative artificial intelligence will generate an appropriate response to the input from the user.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] A user speaks into a terminal equipped with a voice input device (microphone), for example, saying, "Hello, what did you do today?"

[0138] Input: User's voice

[0139] Output: Digital audio data

[0140] Specific operation: The device's microphone captures the user's voice and temporarily stores it in memory as digital audio data.

[0141] Step 2:

[0142] The device sends the captured voice data to speech recognition software, which analyzes the voice waveform and converts it into text.

[0143] Input: Digital audio data

[0144] Output: Text data

[0145] Specific operation: The device calls the Google Cloud Speech-to-Text API, converts the audio waveform into text data such as "Hello, what did you do today?", and stores it in the device's memory.

[0146] Step 3:

[0147] The terminal transmits the converted text data to the server using the HTTP protocol.

[0148] Input: Text data

[0149] Output: HTTP request (payload contains text data)

[0150] Specific operation: The terminal constructs an HTTP POST request and sends it to the server endpoint over the network, with the converted text data included in the request payload.

[0151] Step 4:

[0152] The server receives the HTTP request, extracts the text data from the payload, and then uses generative artificial intelligence to generate an appropriate response.

[0153] Input: Text data

[0154] Output: The generated response text

[0155] Specific operation: The server inputs the received text data into a generative artificial intelligence (e.g., GPT-3) and generates a response text such as, "Hello! I did a lot of things today. For example, I took a walk and read a book. How about you?"

[0156] Step 5:

[0157] The server encodes the generated response text into JSON format and sends it to the terminal as an HTTP response.

[0158] Input: Generated response text

[0159] Output: HTTP response (payload contains response text)

[0160] Specific operation: The server encodes the generated response text as a JSON object, constructs an HTTP response, and sends it to the terminal.

[0161] Step 6:

[0162] The device analyzes the HTTP response received from the server and inputs the response text into the text-to-speech engine, which converts it into audio data and plays it back through the speaker.

[0163] Input: HTTP response (including response text)

[0164] Output: Audio data

[0165] Specific operation: The device calls the Amazon Polly API, converts the response text into speech data, and plays the generated speech data through the device's built-in speaker, responding to the user, "Hello! I did a lot of things today, like taking a walk and reading a book. How about you?"

[0166] The above process makes it possible to realize natural and smooth dialogue in response to voice input from the user.

[0167] (Application example 1)

[0168] 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."

[0169] The challenge is to improve the convenience and safety of autonomous vehicles by making them respond smoothly to user voice commands, such as setting destinations, adjusting the environment, and responding to emergencies. There is also a need to process user voice interactions in the vehicle more naturally and efficiently.

[0170] 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.

[0171] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into text data, means for transmitting the converted text data to the server, means for generating a response using generative artificial intelligence based on the text data transmitted to the server, means for transmitting the generated response to the terminal, means for converting the transmitted response into voice and playing it back to the user, means for processing instructions regarding environmental adjustment and destination setting in the autonomous vehicle, and automatic contact means for responding to emergencies, thereby enabling a rapid response to user voice instructions in the vehicle and improving safety.

[0172] The "means for acquiring the user's voice" refers to a device or method that detects the voice signal emitted by the user and captures it as digital data.

[0173] The "means for converting acquired voice data into character data" refers to a device or software that uses voice recognition technology to represent a voice signal as a corresponding character string.

[0174] The "means for transmitting converted character data to a server" refers to a communication technique or device that sends text-formatted data to a remote server via a network.

[0175] "Means for generating a response using generative artificial intelligence based on character data sent to the server" refers to a function that processes received text data and creates an appropriate response using generative artificial intelligence such as a natural language generation model.

[0176] The "means for transmitting the generated response to the terminal" refers to a communication technique or device that transmits the generated text response back to the terminal via the network.

[0177] The "means for converting the transmitted response into voice and playing it back to the user" refers to a technology or device that converts text data into a voice signal and plays it back to the user through a voice output device.

[0178] "Means for processing instructions regarding environmental adjustments and destination settings within an autonomous vehicle" refers to a function that executes actions such as changing air conditioning settings or navigation destinations on the internal systems of an autonomous vehicle based on voice instructions from the user.

[0179] "Automatic emergency response contact" refers to technology or devices that automatically contact designated emergency contacts or rescue services when an emergency, such as an accident or health issue, is detected.

[0180] An embodiment of the present invention will be described in detail below. The present invention is a system that responds to voice commands from a user in an autonomous vehicle, adjusts the in-vehicle environment, sets a destination, and responds to emergencies. The present invention is realized by combining a smartphone, a speaker, a microphone, and a server.

[0181] 1. System Configuration

[0182] The user issues voice instructions using a microphone installed in the autonomous vehicle.

[0183] The device (smartphone) receives the voice and converts it into text data using voice recognition technology, using the Google Cloud Speech-to-Text API.

[0184] The terminal sends the converted text data to the server using the HTTP protocol.

[0185] The server generates an appropriate response based on the received text data using generative artificial intelligence (e.g., GPT-4 (registered trademark) from OpenAI (registered trademark)).

[0186] The server generates a response and sends it to the device in JSON format.

[0187] The device converts the received response into audio using the Google Cloud Text-to-Speech API and plays it back to the user through the car's speakers.

[0188] 2. Operational Description

[0189] When a user gets into a vehicle and issues a voice command, the voice is picked up by a microphone. The smartphone converts this voice data into text data and sends it to a server. The server uses generative artificial intelligence to create a response based on the voice command and sends it back to the device. The device then converts the returned response back into voice and plays it through the vehicle's speakers.

[0190] 3. Usage example

[0191] 3.1 Destination setting

[0192] User: "Set my next destination"

[0193] The response generated by the generative AI model: "What address?"

[0194] User: "1-1 Marunouchi, Chiyoda-ku, Tokyo"

[0195] The final response generated by the generative AI model: "Destination set to 1-1 Marunouchi, Chiyoda-ku, Tokyo."

[0196] 3.2 Adjusting the in-car environment

[0197] User: "Turn the air conditioner on a little cooler."

[0198] Response generated by the generative AI model: "I'll turn down the air conditioner."

[0199] 3.3 Emergency response

[0200] User: "Emergency, there's been an accident."

[0201] The response generated by the generative AI model: "Call your emergency contact."

[0202] Examples of prompts:

[0203] "Set next destination"

[0204] "Turn the air conditioner on a little cooler."

[0205] "It's an emergency, there's been an accident."

[0206] In this way, this system utilizes speech recognition and generative AI models to efficiently conduct real-time voice dialogue within autonomous vehicles, providing a system that is highly convenient for users and can respond quickly in emergencies.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] The user issues a voice command. Voice data is acquired by the user speaking into a microphone in the autonomous vehicle. The voice command is acquired as input and sent to the terminal as voice data via the microphone.

[0210] Step 2:

[0211] The device converts the voice data into text data. The device converts the acquired voice data into text data using the Google Cloud Speech-to-Text API. The input voice data is converted into output text data.

[0212] Step 3:

[0213] The terminal transmits the converted text data to the server. The terminal transmits the converted text data to the server using the HTTP protocol. The input text data is transmitted to the server as an HTTP request.

[0214] Step 4:

[0215] The server receives the text data and generates a response using generative AI. The server inputs the received text data into OpenAI's GPT-4 model to generate an appropriate response. Based on the input text data, the generative AI model outputs the response text.

[0216] Step 5:

[0217] The server sends the generated response to the terminal. The server sends the generated response in JSON format to the terminal. The response text as input is sent to the terminal as a JSON format HTTP response.

[0218] Step 6:

[0219] The device converts the response text into speech and plays it to the user. The device converts the received response text into audio data using the Google Cloud Text-to-Speech API and plays it through the car speaker. The input response text is converted into audio data and output to the user using the speaker.

[0220] 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.

[0221] The system of the present invention uses generative artificial intelligence to generate appropriate responses based on information input by voice from the user, and then outputs the responses as voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide responses that correspond to the user's emotions. This system is composed of a terminal (pet robot), a server, and user interaction.

[0222] System Overview

[0223] 1. User voice input

[0224] The terminal captures the user's voice via a microphone.

[0225] Voice input is the starting point for an interactive conversation and is converted into text using voice recognition software.

[0226] 2. Converting voice data to text

[0227] The device converts the acquired voice data into text data using voice recognition software (e.g., Google Speech-to-Text API).

[0228] This converted text data becomes information for analyzing the user's intention.

[0229] 3. User Emotion Recognition

[0230] The terminal uses an emotion engine to analyze the user's emotions from the converted text data and voice data.

[0231] The user's emotions can be recognized as emotional states such as joy, sadness, anger, and surprise.

[0232] 4. Sending text data and emotion data to the server

[0233] The device sends the converted text data and emotion data in an HTTP request to the server, which then receives information including the user's speech and current emotional state.

[0234] 5. Response Generation Using Generative AI

[0235] The server generates an appropriate response using generative artificial intelligence (e.g., GPT-3) based on the received text data and emotion data.

[0236] Responses are dynamically adjusted based on the user's input and emotional state: for example, if the user is perceived as sad, responses may include encouragement and comfort.

[0237] 6. Terminal transmission of generated response

[0238] The server sends the response created by the generative AI to the terminal in JSON format, etc., which allows the terminal to receive the generated response.

[0239] 7. Response transcription and output

[0240] The device converts the text response received from the server into speech using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[0241] This voice response is played back to the user through the device's built-in speaker.

[0242] 8. Audio Response to the User

[0243] The user can listen to the voice response from the terminal and then make a new voice input, maintaining a continuous conversation between the user and the terminal.

[0244] Specific examples

[0245] A specific example of this system is shown below.

[0246] User says: "Hi, what have you been up to today?"

[0247] The device receives the voice, converts it into text, and then analyzes the emotion using an emotion engine. Analysis result: "Neutral"

[0248] The device sends the converted text data and emotion data to the server: "Hello, what did you do today?" and "Neutral."

[0249] The server uses generative artificial intelligence to generate a response from the text and emotion data it receives: "I did a lot of things today, for example, I took a walk and read a book."

[0250] The device receives the generated response, converts it into speech, and speaks it back to the user: "I did a lot of things today. For example, I took a walk and read a book."

[0251] In this way, the system can realize natural conversation with the user and provide responses that correspond to the user's emotions, thereby providing everyday comfort and a sense of security to the elderly.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] The user speaks to the device (pet robot), for example, by voice input such as "Hello, what did you do today?"

[0255] Step 2:

[0256] The device picks up the user's voice via a built-in microphone, and the voice data is recorded and temporarily stored in memory.

[0257] Step 3:

[0258] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). This process converts the voice data into text format.

[0259] Step 4:

[0260] The device inputs the converted text data into an emotion engine to analyze the user's emotions. Emotional states such as joy, sadness, anger, and surprise are recognized as emotion data.

[0261] Step 5:

[0262] The device sends the converted text data and analyzed emotion data to the server in an HTTP request. Specifically, the data is sent using an HTTP POST request.

[0263] Step 6:

[0264] The server analyzes the received text and emotion data and passes it as input to a generative AI (e.g., GPT-3), which generates an appropriate response based on the user's input and emotional state.

[0265] Step 7:

[0266] The generative AI generates a response based on the user's input and emotions, and returns the response in text format to the server. For example, it generates a response such as, "I did a lot today. I took a walk, read, etc."

[0267] Step 8:

[0268] The server sends the generated text response to the device in a data format such as JSON, allowing the device to receive the generated response.

[0269] Step 9:

[0270] The device converts the received text response into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[0271] Step 10:

[0272] The device then plays the converted response to the user through its built-in speaker, for example, "You've done a lot today, including taking a walk and reading."

[0273] Step 11:

[0274] The terminal again waits for new voice input from the user and repeats the above process, maintaining continuous conversation.

[0275] Specific examples

[0276] User says: "Today has been a really bad day."

[0277] The device receives the voice, converts it into text, and then analyzes the emotion using an emotion engine. Analysis result: "Sadness"

[0278] The device sends the converted text data and emotion data to the server: "Today was really bad" and "Sadness."

[0279] The server uses generative artificial intelligence to generate a response from the text and emotion data it receives: "That was tough. But there are always good days. Tell me your story."

[0280] The device receives the generated response, converts it into speech, and speaks to the user: "That was tough, but there are always good days. Tell me your story."

[0281] In this way, the system can realize natural conversation with the user and provide responses that correspond to the user's emotions, thereby providing everyday comfort and a sense of security to the elderly.

[0282] Example 2

[0283] 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."

[0284] Conventional speech recognition systems have the problem that responses to user input are not based on emotion, making it difficult to have natural conversations with users or communicate with them in a way that takes their emotions into consideration. Furthermore, simply converting speech data into text data does not allow for accurate understanding of the user's emotions and intentions, making it difficult to provide appropriate responses. Furthermore, systems lack a mechanism for maintaining continuous interaction with the user.

[0285] 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.

[0286] In this invention, the server includes means for analyzing the user's emotions from the user's voice data and character data, means for transmitting the converted character data and analyzed emotion data to the server, and means for generating a response using generative artificial intelligence based on the character data and emotion data transmitted to the server. This makes it possible to generate appropriate responses while taking the user's emotions into consideration, and to maintain a continuous and natural conversation.

[0287] "User" refers to the entity that provides speech input to interact with the system.

[0288] "Terminal" refers to a device for capturing voice, converting voice data to text data, analyzing emotions, and playing back voice responses.

[0289] "Server" refers to a computing device that uses generative artificial intelligence to generate an appropriate response based on data sent by a user and transmits the response to a terminal.

[0290] "Voice data" refers to data in which the user's voice is recorded in digital format and acquired via a microphone or the like.

[0291] "Text data" refers to data that has been converted from voice data into text using voice recognition software.

[0292] "Emotion data" refers to data that indicates the user's emotional state analyzed using an emotion engine.

[0293] "Generative AI" refers to machine learning algorithms that generate appropriate text responses based on a user's input and emotional state.

[0294] "Text-to-speech engine" refers to software for converting text data into speech data.

[0295] "Response" refers to a text or voice response that a generative artificial intelligence generates in response to input from a user.

[0296] The system of the present invention uses generative artificial intelligence to generate appropriate responses based on information input by voice from the user, and outputs the responses as voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide responses that correspond to the user's emotions. This system is composed of a terminal, a server, and user interaction.

[0297] System configuration

[0298] 1. User: The entity that provides voice input to the system.

[0299] 2. Terminal: A device that captures the user's voice using a built-in microphone, converts it into text data using voice recognition software (e.g., voice recognition API), and performs emotion analysis using an emotion engine. The captured data is sent to a server.

[0300] 3. Server: A device that receives text data and emotion data and uses generative artificial intelligence (e.g., a generative AI model) to generate an appropriate response. The generated response is sent to the device.

[0301] 4. Text-to-speech engine: Software that converts the generated response into speech and plays it through the device's speaker.

[0302] Hardware and software used

[0303] Devices: Robot pets and smart devices

[0304] Speech recognition software: Speech recognition API (e.g. Google Speech-to-Text API)

[0305] Emotion engine: Emotion analysis software (e.g., emotion recognition API)

[0306] Server: High-performance computer (e.g., cloud server)

[0307] Generative AI: Generative AI models (e.g., GPT-3)

[0308] Text-to-speech engine: TTS engine (e.g. Google Text-to-Speech)

[0309] Specific examples

[0310] A specific example of this system is shown below.

[0311] Example of operation

[0312] 1. User says: "Hi, what have you been up to today?"

[0313] 2. Your device captures your voice and converts it to text using speech recognition software. The resulting text is: "Hello, what have you been up to today?"

[0314] 3. The device analyzes the emotion using the emotion engine. Analysis result: "Neutral"

[0315] 4. The device sends the converted text data and emotion data to the server. Data sent: "Hello, what did you do today?" and "Neutral"

[0316] 5. The server uses generative artificial intelligence to generate a response from the received data: "I did a lot of things today, like take a walk and read a book."

[0317] 6. The device receives the generated response and converts it into speech using a text-to-speech engine.

[0318] 7. The device plays the generated voice data from the speaker. Playback voice: "I did a lot of things today. For example, I took a walk and read a book."

[0319] Prompt Sentence Examples

[0320] Example prompts to input to the generative AI model:

[0321] "Generate a response for a situation where the user is feeling sad. User input: "Something's not going well today" and emotion: "sad""

[0322] Expected output:

[0323] "I see things didn't go well today and you had a hard time. You have days like that. Let me know if there's anything I can do for you."

[0324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0325] Step 1:

[0326] The user provides input by voice.

[0327] Specific action: The user speaks to the system, for example, "Hello, what have you been up to today?"

[0328] Input: User's voice

[0329] Output: None (obtained as audio)

[0330] Step 2:

[0331] The device receives the audio.

[0332] Specific operation: The device's built-in microphone captures the user's voice.

[0333] Input: User's voice

[0334] Output: Digitized audio data

[0335] Step 3:

[0336] The terminal converts the voice data into text data.

[0337] Specific operation: Uses speech recognition software (e.g., Google Speech-to-Text API) to convert voice data into text data.

[0338] Input: Digital audio data

[0339] Output: Text data "Hello, what did you do today?"

[0340] Step 4:

[0341] The device analyzes the user's emotions from the voice and text data it acquires.

[0342] What it does: It uses emotion analysis software to process the converted text data and the original audio data to identify the user's emotions.

[0343] Input: Converted text and audio data

[0344] Output: Emotion data "Neutral"

[0345] Step 5:

[0346] The terminal transmits the converted character data and the analyzed emotion data to the server.

[0347] Specific operation: Create an HTTP request and send text data and emotion data to the server.

[0348] Input: Text data and emotion data

[0349] Output: "Hello, what have you been up to today?" and "Neutral" data sent to the server

[0350] Step 6:

[0351] The server generates the response using generative artificial intelligence.

[0352] Specific operation: Based on the received text data and emotion data, a response is generated using a generative AI model (e.g., GPT-3).

[0353] Input: Text data and emotion data

[0354] Output: Response text "I did a lot of things today, like taking a walk and reading a book."

[0355] Step 7:

[0356] The server generates a response and sends it to the terminal.

[0357] Specific operation: The generated text response is sent to the terminal in JSON format or similar.

[0358] Input: Generated response text: "I did a lot of things today. For example, I took a walk and read a book."

[0359] Output: Response text sent to the terminal

[0360] Step 8:

[0361] The device converts the transmitted response into speech.

[0362] What it does: Uses a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech) to convert the generated text response into audio data.

[0363] Input: Generated response text

[0364] Output: "I did a lot of things today. For example, I took a walk and read a book."

[0365] Step 9:

[0366] The terminal outputs a voice response to the user.

[0367] Specific operation: Play the generated audio data from the device's built-in speaker.

[0368] Input: Audio data

[0369] Output: The user hears a spoken response saying, "I did a lot of things today. For example, I took a walk and read a book."

[0370] (Application example 2)

[0371] 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."

[0372] In factory environments, the lack of an interactive system that can efficiently recognize workers' voices and analyze their emotions is a problem. Conventional systems simply receive voice instructions and are unable to respond to workers' stress and emotional state, limiting their effectiveness in improving efficiency and the work environment. In particular, the lack of emotional support can lead to increased worker stress and work errors, which is a major issue.

[0373] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0374] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into character data, means for transmitting the converted character data and emotional data, means for generating a response based on the emotional data using generative artificial intelligence, means for converting the generated response into voice and playing it back to the user, and means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and providing work support and stress management responses. This enables efficient work support based on the voice instructions of workers in a factory environment and detailed support according to their emotional state.

[0375] A "means for acquiring user voice" is a device or method for capturing voice signals emitted by a user.

[0376] "Means for converting acquired voice data into text data" refers to technology or software for converting voice signals into text-format data.

[0377] The "means for transmitting converted character data and emotional data to the server" refers to a communication means for transmitting the character data converted from the voice and the emotional state to the server.

[0378] "Means for generating a response using generative artificial intelligence based on character data and emotional data sent to a server" refers to a method for utilizing generative artificial intelligence to create an appropriate response based on data received by a server.

[0379] "Means for sending the generated response to the terminal" refers to a technology for sending the response created by the generative artificial intelligence back to the client device.

[0380] The "means for converting the transmitted response into voice and playing it back to the user" refers to a means for converting the response received as text data into voice format and letting the user hear it.

[0381] "Means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and responding with work support and stress management" refers to technology that receives voice instructions from workers in a factory, analyzes them to support their work, and provides responses and support according to the worker's emotional state.

[0382] An "emotion engine" is software or technology for analyzing a user's emotional state and recognizing specific emotions.

[0383] "Generative AI" is a type of AI technology that generates appropriate text or responses based on given data or input.

[0384] MODE FOR CARRYING OUT THE INVENTION

[0385] The system according to the present invention is designed to receive voice instructions from workers in a factory environment, analyze their emotions, and provide appropriate work support and stress management responses. Specific embodiments of the system are described below.

[0386] 1. System Configuration

[0387] Hardware:

[0388] Audio collection device (microphone): Used to capture the voice of the worker.

[0389] Client device (terminal): Required to process data acquired from the audio collection device and communicate with the server.

[0390] software:

[0391] Speech recognition software: Used to convert voice data into text data (e.g., Google Speech-to-Text API).

[0392] Sentiment engine: Used to analyze the emotional state of the user (e.g., the sentiment analysis pipeline in the transformers package).

[0393] Generative AI: Used to generate appropriate responses based on the data it receives (e.g., OpenAI GPT-3).

[0394] Text-to-speech engine (TTS engine): Used to convert text data into speech (e.g., Google Text-to-Speech).

[0395] 2. Program processing overview

[0396] Get user's voice input:

[0397] The user (worker) speaks into the client device. The terminal captures the voice via the voice collection device (microphone). This voice data is collected in the first step.

[0398] Audio to text conversion:

[0399] The device uses voice recognition software to convert the captured voice data into text data, which is then sent to the server as user instructions.

[0400] User Emotion Recognition:

[0401] The terminal uses an emotion engine to analyze the user's emotions from the converted text and voice data, thereby recognizing the worker's emotional state (e.g., stress, anxiety, joy, etc.).

[0402] Sending text data and emotion data to the server:

[0403] The terminal transmits the converted character data and emotion data to the server, which receives and processes the data to generate an appropriate response.

[0404] Response generation using generative artificial intelligence:

[0405] The server uses generative artificial intelligence to generate a response based on the received text data and emotion data. For example, if a worker is feeling stressed, it generates a response that will alleviate that stress.

[0406] Terminal sending of generated response:

[0407] The response generated by the server is sent to the client device, which receives the response data for further processing.

[0408] Response transcription and output:

[0409] The terminal converts the response data received from the server into voice using a text-to-speech engine, and the voice response is played back to the worker through the terminal's built-in speaker.

[0410] 3. Specific examples

[0411] Here's a specific example of how it could be used in a factory: If a worker says to the terminal, "The line isn't working properly today, please check it," the system will respond as follows:

[0412] Worker statement:

[0413] "The line is not working well today, so I'd like you to check it."

[0414] Robot response:

[0415] "I'll check the line. You seem a little stressed, let's take a breather if you don't mind?"

[0416] 4. Prompt Sentence

[0417] It is also important to provide examples of prompts to use when inputting text to the generative AI. Examples are shown below.

[0418] "A user says, 'Line is not working well today, please check it out.' The emotion is 'Anxiety.' Generate an appropriate response."

[0419] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0420] Step 1:

[0421] Voice input acquisition

[0422] The terminal uses a voice collection device (microphone) to capture the voice of the user (worker). This voice data becomes the initial input to the system. When the user says, "The line is not working well today, please check it," the voice data is captured by the terminal.

[0423] Step 2:

[0424] Converting audio data to text

[0425] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). The input is voice data, and the output is text data. This conversion process extracts voice features and maps them to text.

[0426] Step 3:

[0427] emotion recognition

[0428] The device analyzes the user's emotions from the converted text and audio data. It uses an emotion engine (e.g., the emotion analysis pipeline in the transformers package) to output an emotion label from the input text data. For example, if the user's words are judged to be "anxiety," the emotion label "anxiety" is output.

[0429] Step 4:

[0430] Sending data to the server

[0431] The device sends the converted text data and emotion data to the server. The input is text data and emotion data, and the output is an HTTP request to the server. At this stage, the text data and emotion data are packaged in JSON format and sent.

[0432] Step 5:

[0433] Response Generation

[0434] The server generates an appropriate response using generative artificial intelligence (e.g., OpenAI GPT-3) based on the received text data and emotion data. The input to the server is the text data and emotion label, and the output is the response text. In this process, the prompt sentence is input into the generative AI model, and the generated response text is obtained.

[0435] For example: "A user says, 'Line is not working well today, please check on me.' The emotion is 'anxiety.' Generate an appropriate response."

[0436] Step 6:

[0437] Response data sent to terminal

[0438] The server sends the generated response data to the terminal. The server's input is the generated response text, and the output is an HTTP response to the terminal. Again, the response data is packaged in JSON format and sent.

[0439] Step 7:

[0440] Audio conversion and output

[0441] The device converts the response data received from the server into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech). The input is the response text, and the output is audio data. This audio data is played back to the user using the device's built-in speaker.

[0442] Step 8:

[0443] Responding to the user

[0444] Finally, the user hears the voice response from the terminal and can continue to make new voice input. This series of processes maintains a continuous conversation between the user and the terminal. The output to the user is the voice response played from the terminal.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Second embodiment]

[0449] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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."

[0461] The system of the present invention uses generative artificial intelligence to generate and output appropriate responses based on information input by voice from the user. This system is composed of a terminal (pet robot), a server, and user interaction.

[0462] System Overview

[0463] 1. User voice input

[0464] The terminal captures the user's voice via a microphone.

[0465] Voice input is the starting point for an interactive conversation and is converted into text using voice recognition software.

[0466] 2. Converting voice data to text

[0467] The terminal converts the acquired voice data into text data using voice recognition software.

[0468] This converted text data becomes information for analyzing the user's intention.

[0469] 3. Sending text data to the server

[0470] The terminal transmits the converted text data to the server using the HTTP protocol.

[0471] The server receives the text data and prepares to generate a response using generative artificial intelligence.

[0472] 4. Response Generation Using Generative AI

[0473] The server generates an appropriate response based on the received text data using generative artificial intelligence (e.g., using a natural language processing model).

[0474] Responses are dynamically generated based on the user's request, which may include fictional stories, historical commentary, or everyday conversation.

[0475] 5. Terminal transmission of generated response

[0476] The server sends the response created by the generative artificial intelligence to the terminal in JSON format or similar.

[0477] This allows the terminal to receive the generated response.

[0478] 6. Response transcription and output

[0479] The terminal converts the text response received from the server into speech using a text-to-speech engine.

[0480] This voice response is played back to the user through the device's built-in speaker.

[0481] This allows users to have real-time interactions.

[0482] Specific examples

[0483] A specific example of this system is shown below.

[0484] User says: "Hi, what have you been up to today?"

[0485] The device captures the speech, converts it into text, and sends it to the server: "Hello, what have you been up to today?"

[0486] The server uses generative artificial intelligence to generate a response from the text data it receives: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0487] The device receives the generated response, converts it into speech, and speaks to the user: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0488] In this way, the system can realize natural conversation with the user and provide daily comfort and a sense of security to the elderly.

[0489] The processing flow will be explained below.

[0490] Step 1:

[0491] The user speaks to the device (pet robot), for example, by voice input such as "Hello, what did you do today?"

[0492] Step 2:

[0493] The device captures the user's voice via a built-in microphone, and the voice data is temporarily stored in memory.

[0494] Step 3:

[0495] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). This process converts the voice data into text format.

[0496] Step 4:

[0497] The terminal sends the converted character data to the server by including it in an HTTP request. Specifically, the data is sent using an HTTP POST request.

[0498] Step 5:

[0499] The server analyzes the received text data and passes it as input to a generative AI (e.g., GPT-3), which generates an appropriate response to the user's input.

[0500] Step 6:

[0501] The generative AI generates a response based on the user's input and returns the response in text format to the server, such as "I did a lot today, including taking a walk and reading."

[0502] Step 7:

[0503] The server then sends the generated text response to the device, typically as JSON formatted data.

[0504] Step 8:

[0505] The device converts the received text response into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[0506] Step 9:

[0507] The device then plays the converted response to the user through its built-in speaker, for example, "You've done a lot today, including taking a walk and reading."

[0508] Step 10:

[0509] The terminal then waits for the user's new voice input and repeats the above process to maintain a continuous conversation. This continuous interaction allows the user to enjoy a natural conversation with the pet robot.

[0510] Example 1

[0511] 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."

[0512] Conventional voice dialogue systems have limitations in the accuracy of speech recognition and the naturalness of response generation, often preventing smooth dialogue with users. Furthermore, when generating responses only for specific content, they lack flexibility and are unable to fully respond to diverse user needs. In particular, when considering use as a daily conversation partner for elderly people and others, the system's responses often feel mechanical, making it difficult to provide comfort and a sense of security to the user.

[0513] 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.

[0514] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into text data, and means for generating a response based on the converted text data using generative artificial intelligence, thereby enabling natural and flexible responses to user voice input to be provided in real time.

[0515] "User" refers to a person who uses the system to provide voice input.

[0516] The "means for acquiring voice" refers to a device that collects voice uttered by a user as digital data using a voice input device such as a microphone.

[0517] "Means for converting voice data into text data" refers to the process of converting captured voice data into text format data using voice recognition software.

[0518] "Means for transmitting character data to a server" refers to a communication protocol (generally HTTP, etc.) and its implementation for transmitting text data to a server over a network.

[0519] "Means for generating responses using generative artificial intelligence" refers to a process that uses a natural language processing model to generate natural-sounding responses based on received text data.

[0520] "Means for transmitting to the terminal" refers to a communication protocol and its implementation for transmitting the generated response data from the server to the terminal.

[0521] "Means for converting the response into speech and playing it to the user" refers to the process of converting the generated response into speech using a text-to-speech engine and playing it to the user through a speaker.

[0522] The system of the present invention uses generative artificial intelligence to generate and output appropriate responses based on information input by voice from a user. This system is composed of a terminal (e.g., a pet robot equipped with a voice input device), a server, and user interaction.

[0523] System Overview

[0524] 1. User voice input

[0525] A user speaks into a device equipped with a voice input device (microphone). For example, they say, "Hello, what did you do today?"

[0526] The device captures the user's voice through a microphone and temporarily stores it as digital voice data.

[0527] 2. Converting voice data to text

[0528] The device sends the captured voice data to speech recognition software (e.g., Google Cloud Speech-to-Text API), which analyzes the voice waveform and converts it into text such as "Hello, what have you been up to today?"

[0529] The converted text data is stored in the terminal's memory.

[0530] 3. Sending text data to the server

[0531] The terminal transmits the converted text data to the server using the HTTP protocol, and this transmission process uses network communication.

[0532] The server receives the text data sent as an HTTP request.

[0533] 4. Response Generation Using Generative AI

[0534] The server uses generative artificial intelligence (e.g., GPT-3) to generate an appropriate response based on the received text data.

[0535] Responses are natural responses to user input, such as "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0536] 5. Terminal transmission of generated response

[0537] The server encodes the generated response into JSON format and sends it to the terminal as an HTTP response.

[0538] The terminal receives the HTTP response from the server and retains the generated text response.

[0539] 6. Response transcription and output

[0540] The device inputs the text response received from the server into a text-to-speech engine (e.g., Amazon Polly) and converts it into voice data.

[0541] The device plays the generated voice data over its built-in speaker and responds to the user by saying, "Hello! I did a lot of things today, like taking a walk and reading a book. How about you?"

[0542] Specific examples

[0543] A specific example of this system is shown below.

[0544] User says: "Hi, what have you been up to today?"

[0545] The device captures the speech, converts it into text, and sends it to the server: "Hello, what have you been up to today?"

[0546] The server uses generative artificial intelligence to generate a response from the text data it receives: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0547] The device receives the generated response, converts it into speech, and speaks to the user: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0548] In this way, the system can realize natural conversation with the user, providing everyday comfort and a sense of security, especially to the elderly.

[0549] Prompt Sentence Examples

[0550] The following is an example of a prompt sentence that can be used to generate an appropriate response from a generative artificial intelligence.

[0551] Generate a response when the user says hello:

[0552] User: "Hi, what have you been up to today?"

[0553] Response: "Hi! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0554] Using this prompt, the generative artificial intelligence will generate an appropriate response to the input from the user.

[0555] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0556] Step 1:

[0557] A user speaks into a terminal equipped with a voice input device (microphone), for example, saying, "Hello, what did you do today?"

[0558] Input: User's voice

[0559] Output: Digital audio data

[0560] Specific operation: The device's microphone captures the user's voice and temporarily stores it in memory as digital audio data.

[0561] Step 2:

[0562] The device sends the captured voice data to speech recognition software, which analyzes the voice waveform and converts it into text.

[0563] Input: Digital audio data

[0564] Output: Text data

[0565] Specific operation: The device calls the Google Cloud Speech-to-Text API, converts the audio waveform into text data such as "Hello, what did you do today?", and stores it in the device's memory.

[0566] Step 3:

[0567] The terminal transmits the converted text data to the server using the HTTP protocol.

[0568] Input: Text data

[0569] Output: HTTP request (payload contains text data)

[0570] Specific operation: The terminal constructs an HTTP POST request and sends it to the server endpoint over the network, with the converted text data included in the request payload.

[0571] Step 4:

[0572] The server receives the HTTP request, extracts the text data from the payload, and then uses generative artificial intelligence to generate an appropriate response.

[0573] Input: Text data

[0574] Output: The generated response text

[0575] Specific operation: The server inputs the received text data into a generative artificial intelligence (e.g., GPT-3) and generates a response text such as, "Hello! I did a lot of things today. For example, I took a walk and read a book. How about you?"

[0576] Step 5:

[0577] The server encodes the generated response text into JSON format and sends it to the terminal as an HTTP response.

[0578] Input: Generated response text

[0579] Output: HTTP response (payload contains response text)

[0580] Specific operation: The server encodes the generated response text as a JSON object, constructs an HTTP response, and sends it to the terminal.

[0581] Step 6:

[0582] The device analyzes the HTTP response received from the server and inputs the response text into the text-to-speech engine, which converts it into audio data and plays it back through the speaker.

[0583] Input: HTTP response (including response text)

[0584] Output: Audio data

[0585] Specific operation: The device calls the Amazon Polly API, converts the response text into speech data, and plays the generated speech data through the device's built-in speaker, responding to the user, "Hello! I did a lot of things today, like taking a walk and reading a book. How about you?"

[0586] The above process makes it possible to realize natural and smooth dialogue in response to voice input from the user.

[0587] (Application example 1)

[0588] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0589] The challenge is to improve the convenience and safety of autonomous vehicles by making them respond smoothly to user voice commands, such as setting destinations, adjusting the environment, and responding to emergencies. There is also a need to process user voice interactions in the vehicle more naturally and efficiently.

[0590] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0591] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into text data, means for transmitting the converted text data to the server, means for generating a response using generative artificial intelligence based on the text data transmitted to the server, means for transmitting the generated response to the terminal, means for converting the transmitted response into voice and playing it back to the user, means for processing instructions regarding environmental adjustment and destination setting in the autonomous vehicle, and automatic contact means for responding to emergencies, thereby enabling a rapid response to user voice instructions in the vehicle and improving safety.

[0592] The "means for acquiring the user's voice" refers to a device or method that detects the voice signal emitted by the user and captures it as digital data.

[0593] The "means for converting acquired voice data into character data" refers to a device or software that uses voice recognition technology to represent a voice signal as a corresponding character string.

[0594] The "means for transmitting converted character data to a server" refers to a communication technique or device that sends text-formatted data to a remote server via a network.

[0595] "Means for generating a response using generative artificial intelligence based on character data sent to the server" refers to a function that processes received text data and creates an appropriate response using generative artificial intelligence such as a natural language generation model.

[0596] The "means for transmitting the generated response to the terminal" refers to a communication technique or device that transmits the generated text response back to the terminal via the network.

[0597] The "means for converting the transmitted response into voice and playing it back to the user" refers to a technology or device that converts text data into a voice signal and plays it back to the user through a voice output device.

[0598] "Means for processing instructions regarding environmental adjustments and destination settings within an autonomous vehicle" refers to a function that executes actions such as changing air conditioning settings or navigation destinations on the internal systems of an autonomous vehicle based on voice instructions from the user.

[0599] "Automatic emergency response contact" refers to technology or devices that automatically contact designated emergency contacts or rescue services when an emergency, such as an accident or health issue, is detected.

[0600] An embodiment of the present invention will be described in detail below. The present invention is a system that responds to voice commands from a user in an autonomous vehicle, adjusts the in-vehicle environment, sets a destination, and responds to emergencies. The present invention is realized by combining a smartphone, a speaker, a microphone, and a server.

[0601] 1. System Configuration

[0602] The user issues voice instructions using a microphone installed in the autonomous vehicle.

[0603] The device (smartphone) receives the voice and converts it into text data using voice recognition technology, using the Google Cloud Speech-to-Text API.

[0604] The terminal sends the converted text data to the server using the HTTP protocol.

[0605] The server generates an appropriate response based on the received text data using generative artificial intelligence (e.g., OpenAI's GPT-4).

[0606] The server generates a response and sends it to the device in JSON format.

[0607] The device converts the received response into audio using the Google Cloud Text-to-Speech API and plays it back to the user through the car's speakers.

[0608] 2. Operational Description

[0609] When a user gets into a vehicle and issues a voice command, the voice is picked up by a microphone. The smartphone converts this voice data into text data and sends it to a server. The server uses generative artificial intelligence to create a response based on the voice command and sends it back to the device. The device then converts the returned response back into voice and plays it through the vehicle's speakers.

[0610] 3. Usage example

[0611] 3.1 Destination setting

[0612] User: "Set my next destination"

[0613] The response generated by the generative AI model: "What address?"

[0614] User: "1-1 Marunouchi, Chiyoda-ku, Tokyo"

[0615] The final response generated by the generative AI model: "Destination set to 1-1 Marunouchi, Chiyoda-ku, Tokyo."

[0616] 3.2 Adjusting the in-car environment

[0617] User: "Turn the air conditioner on a little cooler."

[0618] Response generated by the generative AI model: "I'll turn down the air conditioner."

[0619] 3.3 Emergency response

[0620] User: "Emergency, there's been an accident."

[0621] The response generated by the generative AI model: "Call your emergency contact."

[0622] Examples of prompts:

[0623] "Set next destination"

[0624] "Turn the air conditioner on a little cooler."

[0625] "It's an emergency, there's been an accident."

[0626] In this way, this system utilizes speech recognition and generative AI models to efficiently conduct real-time voice dialogue within autonomous vehicles, providing a system that is highly convenient for users and can respond quickly in emergencies.

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

[0628] Step 1:

[0629] The user issues a voice command. Voice data is acquired by the user speaking into a microphone in the autonomous vehicle. The voice command is acquired as input and sent to the terminal as voice data via the microphone.

[0630] Step 2:

[0631] The device converts the voice data into text data. The device converts the acquired voice data into text data using the Google Cloud Speech-to-Text API. The input voice data is converted into output text data.

[0632] Step 3:

[0633] The terminal transmits the converted text data to the server. The terminal transmits the converted text data to the server using the HTTP protocol. The input text data is transmitted to the server as an HTTP request.

[0634] Step 4:

[0635] The server receives the text data and generates a response using generative AI. The server inputs the received text data into OpenAI's GPT-4 model to generate an appropriate response. Based on the input text data, the generative AI model outputs the response text.

[0636] Step 5:

[0637] The server sends the generated response to the terminal. The server sends the generated response in JSON format to the terminal. The response text as input is sent to the terminal as a JSON format HTTP response.

[0638] Step 6:

[0639] The device converts the response text into speech and plays it to the user. The device converts the received response text into audio data using the Google Cloud Text-to-Speech API and plays it through the car speaker. The input response text is converted into audio data and output to the user using the speaker.

[0640] 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.

[0641] The system of the present invention uses generative artificial intelligence to generate appropriate responses based on information input by voice from the user, and then outputs the responses as voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide responses that correspond to the user's emotions. This system is composed of a terminal (pet robot), a server, and user interaction.

[0642] System Overview

[0643] 1. User voice input

[0644] The terminal captures the user's voice via a microphone.

[0645] Voice input is the starting point for an interactive conversation and is converted into text using voice recognition software.

[0646] 2. Converting voice data to text

[0647] The device converts the acquired voice data into text data using voice recognition software (e.g., Google Speech-to-Text API).

[0648] This converted text data becomes information for analyzing the user's intention.

[0649] 3. User Emotion Recognition

[0650] The terminal uses an emotion engine to analyze the user's emotions from the converted text data and voice data.

[0651] The user's emotions can be recognized as emotional states such as joy, sadness, anger, and surprise.

[0652] 4. Sending text data and emotion data to the server

[0653] The device sends the converted text data and emotion data in an HTTP request to the server, which then receives information including the user's speech and current emotional state.

[0654] 5. Response Generation Using Generative AI

[0655] The server generates an appropriate response using generative artificial intelligence (e.g., GPT-3) based on the received text data and emotion data.

[0656] Responses are dynamically adjusted based on the user's input and emotional state: for example, if the user is perceived as sad, responses may include encouragement and comfort.

[0657] 6. Terminal transmission of generated response

[0658] The server sends the response created by the generative AI to the terminal in JSON format, etc., which allows the terminal to receive the generated response.

[0659] 7. Response transcription and output

[0660] The device converts the text response received from the server into speech using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[0661] This voice response is played back to the user through the device's built-in speaker.

[0662] 8. Audio Response to the User

[0663] The user can listen to the voice response from the terminal and then make a new voice input, maintaining a continuous conversation between the user and the terminal.

[0664] Specific examples

[0665] A specific example of this system is shown below.

[0666] User says: "Hi, what have you been up to today?"

[0667] The device receives the voice, converts it into text, and then analyzes the emotion using an emotion engine. Analysis result: "Neutral"

[0668] The device sends the converted text data and emotion data to the server: "Hello, what did you do today?" and "Neutral."

[0669] The server uses generative artificial intelligence to generate a response from the text and emotion data it receives: "I did a lot of things today, for example, I took a walk and read a book."

[0670] The device receives the generated response, converts it into speech, and speaks it back to the user: "I did a lot of things today. For example, I took a walk and read a book."

[0671] In this way, the system can realize natural conversation with the user and provide responses that correspond to the user's emotions, thereby providing everyday comfort and a sense of security to the elderly.

[0672] The processing flow will be explained below.

[0673] Step 1:

[0674] The user speaks to the device (pet robot), for example, by voice input such as "Hello, what did you do today?"

[0675] Step 2:

[0676] The device picks up the user's voice via a built-in microphone, and the voice data is recorded and temporarily stored in memory.

[0677] Step 3:

[0678] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). This process converts the voice data into text format.

[0679] Step 4:

[0680] The device inputs the converted text data into an emotion engine to analyze the user's emotions. Emotional states such as joy, sadness, anger, and surprise are recognized as emotion data.

[0681] Step 5:

[0682] The device sends the converted text data and analyzed emotion data to the server in an HTTP request. Specifically, the data is sent using an HTTP POST request.

[0683] Step 6:

[0684] The server analyzes the received text and emotion data and passes it as input to a generative AI (e.g., GPT-3), which generates an appropriate response based on the user's input and emotional state.

[0685] Step 7:

[0686] The generative AI generates a response based on the user's input and emotions, and returns the response in text format to the server. For example, it generates a response such as, "I did a lot today. I took a walk, read, etc."

[0687] Step 8:

[0688] The server sends the generated text response to the device in a data format such as JSON, allowing the device to receive the generated response.

[0689] Step 9:

[0690] The device converts the received text response into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[0691] Step 10:

[0692] The device then plays the converted response to the user through its built-in speaker, for example, "You've done a lot today, including taking a walk and reading."

[0693] Step 11:

[0694] The terminal again waits for new voice input from the user and repeats the above process, maintaining continuous conversation.

[0695] Specific examples

[0696] User says: "Today has been a really bad day."

[0697] The device receives the voice, converts it into text, and then analyzes the emotion using an emotion engine. Analysis result: "Sadness"

[0698] The device sends the converted text data and emotion data to the server: "Today was really bad" and "Sadness."

[0699] The server uses generative artificial intelligence to generate a response from the text and emotion data it receives: "That was tough. But there are always good days. Tell me your story."

[0700] The device receives the generated response, converts it into speech, and speaks to the user: "That was tough, but there are always good days. Tell me your story."

[0701] In this way, the system can realize natural conversation with the user and provide responses that correspond to the user's emotions, thereby providing everyday comfort and a sense of security to the elderly.

[0702] Example 2

[0703] 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."

[0704] Conventional speech recognition systems have the problem that responses to user input are not based on emotion, making it difficult to have natural conversations with users or communicate with them in a way that takes their emotions into consideration. Furthermore, simply converting speech data into text data does not allow for accurate understanding of the user's emotions and intentions, making it difficult to provide appropriate responses. Furthermore, systems lack a mechanism for maintaining continuous interaction with the user.

[0705] 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.

[0706] In this invention, the server includes means for analyzing the user's emotions from the user's voice data and character data, means for transmitting the converted character data and analyzed emotion data to the server, and means for generating a response using generative artificial intelligence based on the character data and emotion data transmitted to the server. This makes it possible to generate appropriate responses while taking the user's emotions into consideration, and to maintain a continuous and natural conversation.

[0707] "User" refers to the entity that provides speech input to interact with the system.

[0708] "Terminal" refers to a device for capturing voice, converting voice data to text data, analyzing emotions, and playing back voice responses.

[0709] "Server" refers to a computing device that uses generative artificial intelligence to generate an appropriate response based on data sent by a user and transmits the response to a terminal.

[0710] "Voice data" refers to data in which the user's voice is recorded in digital format and acquired via a microphone or the like.

[0711] "Text data" refers to data that has been converted from voice data into text using voice recognition software.

[0712] "Emotion data" refers to data that indicates the user's emotional state analyzed using an emotion engine.

[0713] "Generative AI" refers to machine learning algorithms that generate appropriate text responses based on a user's input and emotional state.

[0714] "Text-to-speech engine" refers to software for converting text data into speech data.

[0715] "Response" refers to a text or voice response that a generative artificial intelligence generates in response to input from a user.

[0716] The system of the present invention uses generative artificial intelligence to generate appropriate responses based on information input by voice from the user, and outputs the responses as voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide responses that correspond to the user's emotions. This system is composed of a terminal, a server, and user interaction.

[0717] System configuration

[0718] 1. User: The entity that provides voice input to the system.

[0719] 2. Terminal: A device that captures the user's voice using a built-in microphone, converts it into text data using voice recognition software (e.g., voice recognition API), and performs emotion analysis using an emotion engine. The captured data is sent to a server.

[0720] 3. Server: A device that receives text data and emotion data and uses generative artificial intelligence (e.g., a generative AI model) to generate an appropriate response. The generated response is sent to the device.

[0721] 4. Text-to-speech engine: Software that converts the generated response into speech and plays it through the device's speaker.

[0722] Hardware and software used

[0723] Devices: Robot pets and smart devices

[0724] Speech recognition software: Speech recognition API (e.g. Google Speech-to-Text API)

[0725] Emotion engine: Emotion analysis software (e.g., emotion recognition API)

[0726] Server: High-performance computer (e.g., cloud server)

[0727] Generative AI: Generative AI models (e.g., GPT-3)

[0728] Text-to-speech engine: TTS engine (e.g. Google Text-to-Speech)

[0729] Specific examples

[0730] A specific example of this system is shown below.

[0731] Example of operation

[0732] 1. User says: "Hi, what have you been up to today?"

[0733] 2. Your device captures your voice and converts it to text using speech recognition software. The resulting text is: "Hello, what have you been up to today?"

[0734] 3. The device analyzes the emotion using the emotion engine. Analysis result: "Neutral"

[0735] 4. The device sends the converted text data and emotion data to the server. Data sent: "Hello, what did you do today?" and "Neutral"

[0736] 5. The server uses generative artificial intelligence to generate a response from the received data: "I did a lot of things today, like take a walk and read a book."

[0737] 6. The device receives the generated response and converts it into speech using a text-to-speech engine.

[0738] 7. The device plays the generated voice data from the speaker. Playback voice: "I did a lot of things today. For example, I took a walk and read a book."

[0739] Prompt Sentence Examples

[0740] Example prompts to input to the generative AI model:

[0741] "Generate a response for a situation where the user is feeling sad. User input: "Something's not going well today" and emotion: "sad""

[0742] Expected output:

[0743] "I see things didn't go well today and you had a hard time. You have days like that. Let me know if there's anything I can do for you."

[0744] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0745] Step 1:

[0746] The user provides input by voice.

[0747] Specific action: The user speaks to the system, for example, "Hello, what have you been up to today?"

[0748] Input: User's voice

[0749] Output: None (obtained as audio)

[0750] Step 2:

[0751] The device receives the audio.

[0752] Specific operation: The device's built-in microphone captures the user's voice.

[0753] Input: User's voice

[0754] Output: Digitized audio data

[0755] Step 3:

[0756] The terminal converts the voice data into text data.

[0757] Specific operation: Uses speech recognition software (e.g., Google Speech-to-Text API) to convert voice data into text data.

[0758] Input: Digital audio data

[0759] Output: Text data "Hello, what did you do today?"

[0760] Step 4:

[0761] The device analyzes the user's emotions from the voice and text data it acquires.

[0762] What it does: It uses emotion analysis software to process the converted text data and the original audio data to identify the user's emotions.

[0763] Input: Converted text and audio data

[0764] Output: Emotion data "Neutral"

[0765] Step 5:

[0766] The terminal transmits the converted character data and the analyzed emotion data to the server.

[0767] Specific operation: Create an HTTP request and send text data and emotion data to the server.

[0768] Input: Text data and emotion data

[0769] Output: "Hello, what have you been up to today?" and "Neutral" data sent to the server

[0770] Step 6:

[0771] The server generates the response using generative artificial intelligence.

[0772] Specific operation: Based on the received text data and emotion data, a response is generated using a generative AI model (e.g., GPT-3).

[0773] Input: Text data and emotion data

[0774] Output: Response text "I did a lot of things today, like taking a walk and reading a book."

[0775] Step 7:

[0776] The server generates a response and sends it to the terminal.

[0777] Specific operation: The generated text response is sent to the terminal in JSON format or similar.

[0778] Input: Generated response text: "I did a lot of things today. For example, I took a walk and read a book."

[0779] Output: Response text sent to the terminal

[0780] Step 8:

[0781] The device converts the transmitted response into speech.

[0782] What it does: Uses a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech) to convert the generated text response into audio data.

[0783] Input: Generated response text

[0784] Output: "I did a lot of things today. For example, I took a walk and read a book."

[0785] Step 9:

[0786] The terminal outputs a voice response to the user.

[0787] Specific operation: Play the generated audio data from the device's built-in speaker.

[0788] Input: Audio data

[0789] Output: The user hears a spoken response saying, "I did a lot of things today. For example, I took a walk and read a book."

[0790] (Application example 2)

[0791] 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."

[0792] In factory environments, the lack of an interactive system that can efficiently recognize workers' voices and analyze their emotions is a problem. Conventional systems simply receive voice instructions and are unable to respond to workers' stress and emotional state, limiting their effectiveness in improving efficiency and the work environment. In particular, the lack of emotional support can lead to increased worker stress and work errors, which is a major issue.

[0793] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0794] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into character data, means for transmitting the converted character data and emotional data, means for generating a response based on the emotional data using generative artificial intelligence, means for converting the generated response into voice and playing it back to the user, and means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and providing work support and stress management responses. This enables efficient work support based on the voice instructions of workers in a factory environment and detailed support according to their emotional state.

[0795] A "means for acquiring user voice" is a device or method for capturing voice signals emitted by a user.

[0796] "Means for converting acquired voice data into text data" refers to technology or software for converting voice signals into text-format data.

[0797] The "means for transmitting converted character data and emotional data to the server" refers to a communication means for transmitting the character data converted from the voice and the emotional state to the server.

[0798] "Means for generating a response using generative artificial intelligence based on character data and emotional data sent to a server" refers to a method for utilizing generative artificial intelligence to create an appropriate response based on data received by a server.

[0799] "Means for sending the generated response to the terminal" refers to a technology for sending the response created by the generative artificial intelligence back to the client device.

[0800] The "means for converting the transmitted response into voice and playing it back to the user" refers to a means for converting the response received as text data into voice format and letting the user hear it.

[0801] "Means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and responding with work support and stress management" refers to technology that receives voice instructions from workers in a factory, analyzes them to support their work, and provides responses and support according to the worker's emotional state.

[0802] An "emotion engine" is software or technology for analyzing a user's emotional state and recognizing specific emotions.

[0803] "Generative AI" is a type of AI technology that generates appropriate text or responses based on given data or input.

[0804] MODE FOR CARRYING OUT THE INVENTION

[0805] The system according to the present invention is designed to receive voice instructions from workers in a factory environment, analyze their emotions, and provide appropriate work support and stress management responses. Specific embodiments of the system are described below.

[0806] 1. System Configuration

[0807] Hardware:

[0808] Audio collection device (microphone): Used to capture the voice of the worker.

[0809] Client device (terminal): Required to process data acquired from the audio collection device and communicate with the server.

[0810] software:

[0811] Speech recognition software: Used to convert voice data into text data (e.g., Google Speech-to-Text API).

[0812] Sentiment engine: Used to analyze the emotional state of the user (e.g., the sentiment analysis pipeline in the transformers package).

[0813] Generative AI: Used to generate appropriate responses based on the data it receives (e.g., OpenAI GPT-3).

[0814] Text-to-speech engine (TTS engine): Used to convert text data into speech (e.g., Google Text-to-Speech).

[0815] 2. Program processing overview

[0816] Get user's voice input:

[0817] The user (worker) speaks into the client device. The terminal captures the voice via the voice collection device (microphone). This voice data is collected in the first step.

[0818] Audio to text conversion:

[0819] The device uses voice recognition software to convert the captured voice data into text data, which is then sent to the server as user instructions.

[0820] User Emotion Recognition:

[0821] The terminal uses an emotion engine to analyze the user's emotions from the converted text and voice data, thereby recognizing the worker's emotional state (e.g., stress, anxiety, joy, etc.).

[0822] Sending text data and emotion data to the server:

[0823] The terminal transmits the converted character data and emotion data to the server, which receives and processes the data to generate an appropriate response.

[0824] Response generation using generative artificial intelligence:

[0825] The server uses generative artificial intelligence to generate a response based on the received text data and emotion data. For example, if a worker is feeling stressed, it generates a response that will alleviate that stress.

[0826] Terminal sending of generated response:

[0827] The response generated by the server is sent to the client device, which receives the response data for further processing.

[0828] Response transcription and output:

[0829] The terminal converts the response data received from the server into voice using a text-to-speech engine, and the voice response is played back to the worker through the terminal's built-in speaker.

[0830] 3. Specific examples

[0831] Here's a specific example of how it could be used in a factory: If a worker says to the terminal, "The line isn't working properly today, please check it," the system will respond as follows:

[0832] Worker statement:

[0833] "The line is not working well today, so I'd like you to check it."

[0834] Robot response:

[0835] "I'll check the line. You seem a little stressed, let's take a breather if you don't mind?"

[0836] 4. Prompt Sentence

[0837] It is also important to provide examples of prompts to use when inputting text to the generative AI. Examples are shown below.

[0838] "A user says, 'Line is not working well today, please check it out.' The emotion is 'Anxiety.' Generate an appropriate response."

[0839] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0840] Step 1:

[0841] Voice input acquisition

[0842] The terminal uses a voice collection device (microphone) to capture the voice of the user (worker). This voice data becomes the initial input to the system. When the user says, "The line is not working well today, please check it," the voice data is captured by the terminal.

[0843] Step 2:

[0844] Converting audio data to text

[0845] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). The input is voice data, and the output is text data. This conversion process extracts voice features and maps them to text.

[0846] Step 3:

[0847] emotion recognition

[0848] The device analyzes the user's emotions from the converted text and audio data. It uses an emotion engine (e.g., the emotion analysis pipeline in the transformers package) to output an emotion label from the input text data. For example, if the user's words are judged to be "anxiety," the emotion label "anxiety" is output.

[0849] Step 4:

[0850] Sending data to the server

[0851] The device sends the converted text data and emotion data to the server. The input is text data and emotion data, and the output is an HTTP request to the server. At this stage, the text data and emotion data are packaged in JSON format and sent.

[0852] Step 5:

[0853] Response Generation

[0854] The server generates an appropriate response using generative artificial intelligence (e.g., OpenAI GPT-3) based on the received text data and emotion data. The input to the server is the text data and emotion label, and the output is the response text. In this process, the prompt sentence is input into the generative AI model, and the generated response text is obtained.

[0855] For example: "A user says, 'Line is not working well today, please check on me.' The emotion is 'anxiety.' Generate an appropriate response."

[0856] Step 6:

[0857] Response data sent to terminal

[0858] The server sends the generated response data to the terminal. The server's input is the generated response text, and the output is an HTTP response to the terminal. Again, the response data is packaged in JSON format and sent.

[0859] Step 7:

[0860] Audio conversion and output

[0861] The device converts the response data received from the server into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech). The input is the response text, and the output is audio data. This audio data is played back to the user using the device's built-in speaker.

[0862] Step 8:

[0863] Responding to the user

[0864] Finally, the user hears the voice response from the terminal and can continue to make new voice input. This series of processes maintains a continuous conversation between the user and the terminal. The output to the user is the voice response played from the terminal.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] [Third embodiment]

[0869] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0870] 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.

[0871] 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).

[0872] 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.

[0873] 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.

[0874] 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).

[0875] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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."

[0881] The system of the present invention uses generative artificial intelligence to generate and output appropriate responses based on information input by voice from the user. This system is composed of a terminal (pet robot), a server, and user interaction.

[0882] System Overview

[0883] 1. User voice input

[0884] The terminal captures the user's voice via a microphone.

[0885] Voice input is the starting point for an interactive conversation and is converted into text using voice recognition software.

[0886] 2. Converting voice data to text

[0887] The terminal converts the acquired voice data into text data using voice recognition software.

[0888] This converted text data becomes information for analyzing the user's intention.

[0889] 3. Sending text data to the server

[0890] The terminal transmits the converted text data to the server using the HTTP protocol.

[0891] The server receives the text data and prepares to generate a response using generative artificial intelligence.

[0892] 4. Response Generation Using Generative AI

[0893] The server generates an appropriate response based on the received text data using generative artificial intelligence (e.g., using a natural language processing model).

[0894] Responses are dynamically generated based on the user's request, which may include fictional stories, historical commentary, or everyday conversation.

[0895] 5. Terminal transmission of generated response

[0896] The server sends the response created by the generative artificial intelligence to the terminal in JSON format or similar.

[0897] This allows the terminal to receive the generated response.

[0898] 6. Response transcription and output

[0899] The terminal converts the text response received from the server into speech using a text-to-speech engine.

[0900] This voice response is played back to the user through the device's built-in speaker.

[0901] This allows users to have real-time interactions.

[0902] Specific examples

[0903] A specific example of this system is shown below.

[0904] User says: "Hi, what have you been up to today?"

[0905] The device captures the speech, converts it into text, and sends it to the server: "Hello, what have you been up to today?"

[0906] The server uses generative artificial intelligence to generate a response from the text data it receives: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0907] The device receives the generated response, converts it into speech, and speaks to the user: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0908] In this way, the system can realize natural conversation with the user and provide daily comfort and a sense of security to the elderly.

[0909] The processing flow will be explained below.

[0910] Step 1:

[0911] The user speaks to the device (pet robot), for example, by voice input such as "Hello, what did you do today?"

[0912] Step 2:

[0913] The device captures the user's voice via a built-in microphone, and the voice data is temporarily stored in memory.

[0914] Step 3:

[0915] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). This process converts the voice data into text format.

[0916] Step 4:

[0917] The terminal sends the converted character data to the server by including it in an HTTP request. Specifically, the data is sent using an HTTP POST request.

[0918] Step 5:

[0919] The server analyzes the received text data and passes it as input to a generative AI (e.g., GPT-3), which generates an appropriate response to the user's input.

[0920] Step 6:

[0921] The generative AI generates a response based on the user's input and returns the response in text format to the server, such as "I did a lot today, including taking a walk and reading."

[0922] Step 7:

[0923] The server then sends the generated text response to the device, typically as JSON formatted data.

[0924] Step 8:

[0925] The device converts the received text response into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[0926] Step 9:

[0927] The device then plays the converted response to the user through its built-in speaker, for example, "You've done a lot today, including taking a walk and reading."

[0928] Step 10:

[0929] The terminal then waits for the user's new voice input and repeats the above process to maintain a continuous conversation. This continuous interaction allows the user to enjoy a natural conversation with the pet robot.

[0930] Example 1

[0931] 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."

[0932] Conventional voice dialogue systems have limitations in the accuracy of speech recognition and the naturalness of response generation, often preventing smooth dialogue with users. Furthermore, when generating responses only for specific content, they lack flexibility and are unable to fully respond to diverse user needs. In particular, when considering use as a daily conversation partner for elderly people and others, the system's responses often feel mechanical, making it difficult to provide comfort and a sense of security to the user.

[0933] 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.

[0934] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into text data, and means for generating a response based on the converted text data using generative artificial intelligence, thereby enabling natural and flexible responses to user voice input to be provided in real time.

[0935] "User" refers to a person who uses the system to provide voice input.

[0936] The "means for acquiring voice" refers to a device that collects voice uttered by a user as digital data using a voice input device such as a microphone.

[0937] "Means for converting voice data into text data" refers to the process of converting captured voice data into text format data using voice recognition software.

[0938] "Means for transmitting character data to a server" refers to a communication protocol (generally HTTP, etc.) and its implementation for transmitting text data to a server over a network.

[0939] "Means for generating responses using generative artificial intelligence" refers to a process that uses a natural language processing model to generate natural-sounding responses based on received text data.

[0940] "Means for transmitting to the terminal" refers to a communication protocol and its implementation for transmitting the generated response data from the server to the terminal.

[0941] "Means for converting the response into speech and playing it to the user" refers to the process of converting the generated response into speech using a text-to-speech engine and playing it to the user through a speaker.

[0942] The system of the present invention uses generative artificial intelligence to generate and output appropriate responses based on information input by voice from a user. This system is composed of a terminal (e.g., a pet robot equipped with a voice input device), a server, and user interaction.

[0943] System Overview

[0944] 1. User voice input

[0945] A user speaks into a device equipped with a voice input device (microphone). For example, they say, "Hello, what did you do today?"

[0946] The device captures the user's voice through a microphone and temporarily stores it as digital voice data.

[0947] 2. Converting voice data to text

[0948] The device sends the captured voice data to speech recognition software (e.g., Google Cloud Speech-to-Text API), which analyzes the voice waveform and converts it into text such as "Hello, what have you been up to today?"

[0949] The converted text data is stored in the terminal's memory.

[0950] 3. Sending text data to the server

[0951] The terminal transmits the converted text data to the server using the HTTP protocol, and this transmission process uses network communication.

[0952] The server receives the text data sent as an HTTP request.

[0953] 4. Response Generation Using Generative AI

[0954] The server uses generative artificial intelligence (e.g., GPT-3) to generate an appropriate response based on the received text data.

[0955] Responses are natural responses to user input, such as "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0956] 5. Terminal transmission of generated response

[0957] The server encodes the generated response into JSON format and sends it to the terminal as an HTTP response.

[0958] The terminal receives the HTTP response from the server and retains the generated text response.

[0959] 6. Response transcription and output

[0960] The device inputs the text response received from the server into a text-to-speech engine (e.g., Amazon Polly) and converts it into voice data.

[0961] The device plays the generated voice data over its built-in speaker and responds to the user by saying, "Hello! I did a lot of things today, like taking a walk and reading a book. How about you?"

[0962] Specific examples

[0963] A specific example of this system is shown below.

[0964] User says: "Hi, what have you been up to today?"

[0965] The device captures the speech, converts it into text, and sends it to the server: "Hello, what have you been up to today?"

[0966] The server uses generative artificial intelligence to generate a response from the text data it receives: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0967] The device receives the generated response, converts it into speech, and speaks to the user: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0968] In this way, the system can realize natural conversation with the user, providing everyday comfort and a sense of security, especially to the elderly.

[0969] Prompt Sentence Examples

[0970] The following is an example of a prompt sentence that can be used to generate an appropriate response from a generative artificial intelligence.

[0971] Generate a response when the user says hello:

[0972] User: "Hi, what have you been up to today?"

[0973] Response: "Hi! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[0974] Using this prompt, the generative artificial intelligence will generate an appropriate response to the input from the user.

[0975] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0976] Step 1:

[0977] A user speaks into a terminal equipped with a voice input device (microphone), for example, saying, "Hello, what did you do today?"

[0978] Input: User's voice

[0979] Output: Digital audio data

[0980] Specific operation: The device's microphone captures the user's voice and temporarily stores it in memory as digital audio data.

[0981] Step 2:

[0982] The device sends the captured voice data to speech recognition software, which analyzes the voice waveform and converts it into text.

[0983] Input: Digital audio data

[0984] Output: Text data

[0985] Specific operation: The device calls the Google Cloud Speech-to-Text API, converts the audio waveform into text data such as "Hello, what did you do today?", and stores it in the device's memory.

[0986] Step 3:

[0987] The terminal transmits the converted text data to the server using the HTTP protocol.

[0988] Input: Text data

[0989] Output: HTTP request (payload contains text data)

[0990] Specific operation: The terminal constructs an HTTP POST request and sends it to the server endpoint over the network, with the converted text data included in the request payload.

[0991] Step 4:

[0992] The server receives the HTTP request, extracts the text data from the payload, and then uses generative artificial intelligence to generate an appropriate response.

[0993] Input: Text data

[0994] Output: The generated response text

[0995] Specific operation: The server inputs the received text data into a generative artificial intelligence (e.g., GPT-3) and generates a response text such as, "Hello! I did a lot of things today. For example, I took a walk and read a book. How about you?"

[0996] Step 5:

[0997] The server encodes the generated response text into JSON format and sends it to the terminal as an HTTP response.

[0998] Input: Generated response text

[0999] Output: HTTP response (payload contains response text)

[1000] Specific operation: The server encodes the generated response text as a JSON object, constructs an HTTP response, and sends it to the terminal.

[1001] Step 6:

[1002] The device analyzes the HTTP response received from the server and inputs the response text into the text-to-speech engine, which converts it into audio data and plays it back through the speaker.

[1003] Input: HTTP response (including response text)

[1004] Output: Audio data

[1005] Specific operation: The device calls the Amazon Polly API, converts the response text into speech data, and plays the generated speech data through the device's built-in speaker, responding to the user, "Hello! I did a lot of things today, like taking a walk and reading a book. How about you?"

[1006] The above process makes it possible to realize natural and smooth dialogue in response to voice input from the user.

[1007] (Application example 1)

[1008] 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."

[1009] The challenge is to improve the convenience and safety of autonomous vehicles by making them respond smoothly to user voice commands, such as setting destinations, adjusting the environment, and responding to emergencies. There is also a need to process user voice interactions in the vehicle more naturally and efficiently.

[1010] 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.

[1011] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into text data, means for transmitting the converted text data to the server, means for generating a response using generative artificial intelligence based on the text data transmitted to the server, means for transmitting the generated response to the terminal, means for converting the transmitted response into voice and playing it back to the user, means for processing instructions regarding environmental adjustment and destination setting in the autonomous vehicle, and automatic contact means for responding to emergencies, thereby enabling a rapid response to user voice instructions in the vehicle and improving safety.

[1012] The "means for acquiring the user's voice" refers to a device or method that detects the voice signal emitted by the user and captures it as digital data.

[1013] The "means for converting acquired voice data into character data" refers to a device or software that uses voice recognition technology to represent a voice signal as a corresponding character string.

[1014] The "means for transmitting converted character data to a server" refers to a communication technique or device that sends text-formatted data to a remote server via a network.

[1015] "Means for generating a response using generative artificial intelligence based on character data sent to the server" refers to a function that processes received text data and creates an appropriate response using generative artificial intelligence such as a natural language generation model.

[1016] The "means for transmitting the generated response to the terminal" refers to a communication technique or device that transmits the generated text response back to the terminal via the network.

[1017] The "means for converting the transmitted response into voice and playing it back to the user" refers to a technology or device that converts text data into a voice signal and plays it back to the user through a voice output device.

[1018] "Means for processing instructions regarding environmental adjustments and destination settings within an autonomous vehicle" refers to a function that executes actions such as changing air conditioning settings or navigation destinations on the internal systems of an autonomous vehicle based on voice instructions from the user.

[1019] "Automatic emergency response contact" refers to technology or devices that automatically contact designated emergency contacts or rescue services when an emergency, such as an accident or health issue, is detected.

[1020] An embodiment of the present invention will be described in detail below. The present invention is a system that responds to voice commands from a user in an autonomous vehicle, adjusts the in-vehicle environment, sets a destination, and responds to emergencies. The present invention is realized by combining a smartphone, a speaker, a microphone, and a server.

[1021] 1. System Configuration

[1022] The user issues voice instructions using a microphone installed in the autonomous vehicle.

[1023] The device (smartphone) receives the voice and converts it into text data using voice recognition technology, using the Google Cloud Speech-to-Text API.

[1024] The terminal sends the converted text data to the server using the HTTP protocol.

[1025] The server generates an appropriate response based on the received text data using generative artificial intelligence (e.g., OpenAI's GPT-4).

[1026] The server generates a response and sends it to the device in JSON format.

[1027] The device converts the received response into audio using the Google Cloud Text-to-Speech API and plays it back to the user through the car's speakers.

[1028] 2. Operational Description

[1029] When a user gets into a vehicle and issues a voice command, the voice is picked up by a microphone. The smartphone converts this voice data into text data and sends it to a server. The server uses generative artificial intelligence to create a response based on the voice command and sends it back to the device. The device then converts the returned response back into voice and plays it through the vehicle's speakers.

[1030] 3. Usage example

[1031] 3.1 Destination setting

[1032] User: "Set my next destination"

[1033] The response generated by the generative AI model: "What address?"

[1034] User: "1-1 Marunouchi, Chiyoda-ku, Tokyo"

[1035] The final response generated by the generative AI model: "Destination set to 1-1 Marunouchi, Chiyoda-ku, Tokyo."

[1036] 3.2 Adjusting the in-car environment

[1037] User: "Turn the air conditioner on a little cooler."

[1038] Response generated by the generative AI model: "I'll turn down the air conditioner."

[1039] 3.3 Emergency response

[1040] User: "Emergency, there's been an accident."

[1041] The response generated by the generative AI model: "Call your emergency contact."

[1042] Examples of prompts:

[1043] "Set next destination"

[1044] "Turn the air conditioner on a little cooler."

[1045] "It's an emergency, there's been an accident."

[1046] In this way, this system utilizes speech recognition and generative AI models to efficiently conduct real-time voice dialogue within autonomous vehicles, providing a system that is highly convenient for users and can respond quickly in emergencies.

[1047] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1048] Step 1:

[1049] The user issues a voice command. Voice data is acquired by the user speaking into a microphone in the autonomous vehicle. The voice command is acquired as input and sent to the terminal as voice data via the microphone.

[1050] Step 2:

[1051] The device converts the voice data into text data. The device converts the acquired voice data into text data using the Google Cloud Speech-to-Text API. The input voice data is converted into output text data.

[1052] Step 3:

[1053] The terminal transmits the converted text data to the server. The terminal transmits the converted text data to the server using the HTTP protocol. The input text data is transmitted to the server as an HTTP request.

[1054] Step 4:

[1055] The server receives the text data and generates a response using generative AI. The server inputs the received text data into OpenAI's GPT-4 model to generate an appropriate response. Based on the input text data, the generative AI model outputs the response text.

[1056] Step 5:

[1057] The server sends the generated response to the terminal. The server sends the generated response in JSON format to the terminal. The response text as input is sent to the terminal as a JSON format HTTP response.

[1058] Step 6:

[1059] The device converts the response text into speech and plays it to the user. The device converts the received response text into audio data using the Google Cloud Text-to-Speech API and plays it through the car speaker. The input response text is converted into audio data and output to the user using the speaker.

[1060] 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.

[1061] The system of the present invention uses generative artificial intelligence to generate appropriate responses based on information input by voice from the user, and then outputs the responses as voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide responses that correspond to the user's emotions. This system is composed of a terminal (pet robot), a server, and user interaction.

[1062] System Overview

[1063] 1. User voice input

[1064] The terminal captures the user's voice via a microphone.

[1065] Voice input is the starting point for an interactive conversation and is converted into text using voice recognition software.

[1066] 2. Converting voice data to text

[1067] The device converts the acquired voice data into text data using voice recognition software (e.g., Google Speech-to-Text API).

[1068] This converted text data becomes information for analyzing the user's intention.

[1069] 3. User Emotion Recognition

[1070] The terminal uses an emotion engine to analyze the user's emotions from the converted text data and voice data.

[1071] The user's emotions can be recognized as emotional states such as joy, sadness, anger, and surprise.

[1072] 4. Sending text data and emotion data to the server

[1073] The device sends the converted text data and emotion data in an HTTP request to the server, which then receives information including the user's speech and current emotional state.

[1074] 5. Response Generation Using Generative AI

[1075] The server generates an appropriate response using generative artificial intelligence (e.g., GPT-3) based on the received text data and emotion data.

[1076] Responses are dynamically adjusted based on the user's input and emotional state: for example, if the user is perceived as sad, responses may include encouragement and comfort.

[1077] 6. Terminal transmission of generated response

[1078] The server sends the response created by the generative AI to the terminal in JSON format, etc., which allows the terminal to receive the generated response.

[1079] 7. Response transcription and output

[1080] The device converts the text response received from the server into speech using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[1081] This voice response is played back to the user through the device's built-in speaker.

[1082] 8. Audio Response to the User

[1083] The user can listen to the voice response from the terminal and then make a new voice input, maintaining a continuous conversation between the user and the terminal.

[1084] Specific examples

[1085] A specific example of this system is shown below.

[1086] User says: "Hi, what have you been up to today?"

[1087] The device receives the voice, converts it into text, and then analyzes the emotion using an emotion engine. Analysis result: "Neutral"

[1088] The device sends the converted text data and emotion data to the server: "Hello, what did you do today?" and "Neutral."

[1089] The server uses generative artificial intelligence to generate a response from the text and emotion data it receives: "I did a lot of things today, for example, I took a walk and read a book."

[1090] The device receives the generated response, converts it into speech, and speaks it back to the user: "I did a lot of things today. For example, I took a walk and read a book."

[1091] In this way, the system can realize natural conversation with the user and provide responses that correspond to the user's emotions, thereby providing everyday comfort and a sense of security to the elderly.

[1092] The processing flow will be explained below.

[1093] Step 1:

[1094] The user speaks to the device (pet robot), for example, by voice input such as "Hello, what did you do today?"

[1095] Step 2:

[1096] The device picks up the user's voice via a built-in microphone, and the voice data is recorded and temporarily stored in memory.

[1097] Step 3:

[1098] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). This process converts the voice data into text format.

[1099] Step 4:

[1100] The device inputs the converted text data into an emotion engine to analyze the user's emotions. Emotional states such as joy, sadness, anger, and surprise are recognized as emotion data.

[1101] Step 5:

[1102] The device sends the converted text data and analyzed emotion data to the server in an HTTP request. Specifically, the data is sent using an HTTP POST request.

[1103] Step 6:

[1104] The server analyzes the received text and emotion data and passes it as input to a generative AI (e.g., GPT-3), which generates an appropriate response based on the user's input and emotional state.

[1105] Step 7:

[1106] The generative AI generates a response based on the user's input and emotions, and returns the response in text format to the server. For example, it generates a response such as, "I did a lot today. I took a walk, read, etc."

[1107] Step 8:

[1108] The server sends the generated text response to the device in a data format such as JSON, allowing the device to receive the generated response.

[1109] Step 9:

[1110] The device converts the received text response into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[1111] Step 10:

[1112] The device then plays the converted response to the user through its built-in speaker, for example, "You've done a lot today, including taking a walk and reading."

[1113] Step 11:

[1114] The terminal again waits for new voice input from the user and repeats the above process, maintaining continuous conversation.

[1115] Specific examples

[1116] User says: "Today has been a really bad day."

[1117] The device receives the voice, converts it into text, and then analyzes the emotion using an emotion engine. Analysis result: "Sadness"

[1118] The device sends the converted text data and emotion data to the server: "Today was really bad" and "Sadness."

[1119] The server uses generative artificial intelligence to generate a response from the text and emotion data it receives: "That was tough. But there are always good days. Tell me your story."

[1120] The device receives the generated response, converts it into speech, and speaks to the user: "That was tough, but there are always good days. Tell me your story."

[1121] In this way, the system can realize natural conversation with the user and provide responses that correspond to the user's emotions, thereby providing everyday comfort and a sense of security to the elderly.

[1122] Example 2

[1123] 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."

[1124] Conventional speech recognition systems have the problem that responses to user input are not based on emotion, making it difficult to have natural conversations with users or communicate with them in a way that takes their emotions into consideration. Furthermore, simply converting speech data into text data does not allow for accurate understanding of the user's emotions and intentions, making it difficult to provide appropriate responses. Furthermore, systems lack a mechanism for maintaining continuous interaction with the user.

[1125] 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.

[1126] In this invention, the server includes means for analyzing the user's emotions from the user's voice data and character data, means for transmitting the converted character data and analyzed emotion data to the server, and means for generating a response using generative artificial intelligence based on the character data and emotion data transmitted to the server. This makes it possible to generate appropriate responses while taking the user's emotions into consideration, and to maintain a continuous and natural conversation.

[1127] "User" refers to the entity that provides speech input to interact with the system.

[1128] "Terminal" refers to a device for capturing voice, converting voice data to text data, analyzing emotions, and playing back voice responses.

[1129] "Server" refers to a computing device that uses generative artificial intelligence to generate an appropriate response based on data sent by a user and transmits the response to a terminal.

[1130] "Voice data" refers to data in which the user's voice is recorded in digital format and acquired via a microphone or the like.

[1131] "Text data" refers to data that has been converted from voice data into text using voice recognition software.

[1132] "Emotion data" refers to data that indicates the user's emotional state analyzed using an emotion engine.

[1133] "Generative AI" refers to machine learning algorithms that generate appropriate text responses based on a user's input and emotional state.

[1134] "Text-to-speech engine" refers to software for converting text data into speech data.

[1135] "Response" refers to a text or voice response that a generative artificial intelligence generates in response to input from a user.

[1136] The system of the present invention uses generative artificial intelligence to generate appropriate responses based on information input by voice from the user, and outputs the responses as voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide responses that correspond to the user's emotions. This system is composed of a terminal, a server, and user interaction.

[1137] System configuration

[1138] 1. User: The entity that provides voice input to the system.

[1139] 2. Terminal: A device that captures the user's voice using a built-in microphone, converts it into text data using voice recognition software (e.g., voice recognition API), and performs emotion analysis using an emotion engine. The captured data is sent to a server.

[1140] 3. Server: A device that receives text data and emotion data and uses generative artificial intelligence (e.g., a generative AI model) to generate an appropriate response. The generated response is sent to the device.

[1141] 4. Text-to-speech engine: Software that converts the generated response into speech and plays it through the device's speaker.

[1142] Hardware and software used

[1143] Devices: Robot pets and smart devices

[1144] Speech recognition software: Speech recognition API (e.g. Google Speech-to-Text API)

[1145] Emotion engine: Emotion analysis software (e.g., emotion recognition API)

[1146] Server: High-performance computer (e.g., cloud server)

[1147] Generative AI: Generative AI models (e.g., GPT-3)

[1148] Text-to-speech engine: TTS engine (e.g. Google Text-to-Speech)

[1149] Specific examples

[1150] A specific example of this system is shown below.

[1151] Example of operation

[1152] 1. User says: "Hi, what have you been up to today?"

[1153] 2. Your device captures your voice and converts it to text using speech recognition software. The resulting text is: "Hello, what have you been up to today?"

[1154] 3. The device analyzes the emotion using the emotion engine. Analysis result: "Neutral"

[1155] 4. The device sends the converted text data and emotion data to the server. Data sent: "Hello, what did you do today?" and "Neutral"

[1156] 5. The server uses generative artificial intelligence to generate a response from the received data: "I did a lot of things today, like take a walk and read a book."

[1157] 6. The device receives the generated response and converts it into speech using a text-to-speech engine.

[1158] 7. The device plays the generated voice data from the speaker. Playback voice: "I did a lot of things today. For example, I took a walk and read a book."

[1159] Prompt Sentence Examples

[1160] Example prompts to input to the generative AI model:

[1161] "Generate a response for a situation where the user is feeling sad. User input: "Something's not going well today" and emotion: "sad""

[1162] Expected output:

[1163] "I see things didn't go well today and you had a hard time. You have days like that. Let me know if there's anything I can do for you."

[1164] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1165] Step 1:

[1166] The user provides input by voice.

[1167] Specific action: The user speaks to the system, for example, "Hello, what have you been up to today?"

[1168] Input: User's voice

[1169] Output: None (obtained as audio)

[1170] Step 2:

[1171] The device receives the audio.

[1172] Specific operation: The device's built-in microphone captures the user's voice.

[1173] Input: User's voice

[1174] Output: Digitized audio data

[1175] Step 3:

[1176] The terminal converts the voice data into text data.

[1177] Specific operation: Uses speech recognition software (e.g., Google Speech-to-Text API) to convert voice data into text data.

[1178] Input: Digital audio data

[1179] Output: Text data "Hello, what did you do today?"

[1180] Step 4:

[1181] The device analyzes the user's emotions from the voice and text data it acquires.

[1182] What it does: It uses emotion analysis software to process the converted text data and the original audio data to identify the user's emotions.

[1183] Input: Converted text and audio data

[1184] Output: Emotion data "Neutral"

[1185] Step 5:

[1186] The terminal transmits the converted character data and the analyzed emotion data to the server.

[1187] Specific operation: Create an HTTP request and send text data and emotion data to the server.

[1188] Input: Text data and emotion data

[1189] Output: "Hello, what have you been up to today?" and "Neutral" data sent to the server

[1190] Step 6:

[1191] The server generates the response using generative artificial intelligence.

[1192] Specific operation: Based on the received text data and emotion data, a response is generated using a generative AI model (e.g., GPT-3).

[1193] Input: Text data and emotion data

[1194] Output: Response text "I did a lot of things today, like taking a walk and reading a book."

[1195] Step 7:

[1196] The server generates a response and sends it to the terminal.

[1197] Specific operation: The generated text response is sent to the terminal in JSON format or similar.

[1198] Input: Generated response text: "I did a lot of things today. For example, I took a walk and read a book."

[1199] Output: Response text sent to the terminal

[1200] Step 8:

[1201] The device converts the transmitted response into speech.

[1202] What it does: Uses a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech) to convert the generated text response into audio data.

[1203] Input: Generated response text

[1204] Output: "I did a lot of things today. For example, I took a walk and read a book."

[1205] Step 9:

[1206] The terminal outputs a voice response to the user.

[1207] Specific operation: Play the generated audio data from the device's built-in speaker.

[1208] Input: Audio data

[1209] Output: The user hears a spoken response saying, "I did a lot of things today. For example, I took a walk and read a book."

[1210] (Application example 2)

[1211] 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."

[1212] In factory environments, the lack of an interactive system that can efficiently recognize workers' voices and analyze their emotions is a problem. Conventional systems simply receive voice instructions and are unable to respond to workers' stress and emotional state, limiting their effectiveness in improving efficiency and the work environment. In particular, the lack of emotional support can lead to increased worker stress and work errors, which is a major issue.

[1213] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1214] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into character data, means for transmitting the converted character data and emotional data, means for generating a response based on the emotional data using generative artificial intelligence, means for converting the generated response into voice and playing it back to the user, and means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and providing work support and stress management responses. This enables efficient work support based on the voice instructions of workers in a factory environment and detailed support according to their emotional state.

[1215] A "means for acquiring user voice" is a device or method for capturing voice signals emitted by a user.

[1216] "Means for converting acquired voice data into text data" refers to technology or software for converting voice signals into text-format data.

[1217] The "means for transmitting converted character data and emotional data to the server" refers to a communication means for transmitting the character data converted from the voice and the emotional state to the server.

[1218] "Means for generating a response using generative artificial intelligence based on character data and emotional data sent to a server" refers to a method for utilizing generative artificial intelligence to create an appropriate response based on data received by a server.

[1219] "Means for sending the generated response to the terminal" refers to a technology for sending the response created by the generative artificial intelligence back to the client device.

[1220] The "means for converting the transmitted response into voice and playing it back to the user" refers to a means for converting the response received as text data into voice format and letting the user hear it.

[1221] "Means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and responding with work support and stress management" refers to technology that receives voice instructions from workers in a factory, analyzes them to support their work, and provides responses and support according to the worker's emotional state.

[1222] An "emotion engine" is software or technology for analyzing a user's emotional state and recognizing specific emotions.

[1223] "Generative AI" is a type of AI technology that generates appropriate text or responses based on given data or input.

[1224] MODE FOR CARRYING OUT THE INVENTION

[1225] The system according to the present invention is designed to receive voice instructions from workers in a factory environment, analyze their emotions, and provide appropriate work support and stress management responses. Specific embodiments of the system are described below.

[1226] 1. System Configuration

[1227] Hardware:

[1228] Audio collection device (microphone): Used to capture the voice of the worker.

[1229] Client device (terminal): Required to process data acquired from the audio collection device and communicate with the server.

[1230] software:

[1231] Speech recognition software: Used to convert voice data into text data (e.g., Google Speech-to-Text API).

[1232] Sentiment engine: Used to analyze the emotional state of the user (e.g., the sentiment analysis pipeline in the transformers package).

[1233] Generative AI: Used to generate appropriate responses based on the data it receives (e.g., OpenAI GPT-3).

[1234] Text-to-speech engine (TTS engine): Used to convert text data into speech (e.g., Google Text-to-Speech).

[1235] 2. Program processing overview

[1236] Get user's voice input:

[1237] The user (worker) speaks into the client device. The terminal captures the voice via the voice collection device (microphone). This voice data is collected in the first step.

[1238] Audio to text conversion:

[1239] The device uses voice recognition software to convert the captured voice data into text data, which is then sent to the server as user instructions.

[1240] User Emotion Recognition:

[1241] The terminal uses an emotion engine to analyze the user's emotions from the converted text and voice data, thereby recognizing the worker's emotional state (e.g., stress, anxiety, joy, etc.).

[1242] Sending text data and emotion data to the server:

[1243] The terminal transmits the converted character data and emotion data to the server, which receives and processes the data to generate an appropriate response.

[1244] Response generation using generative artificial intelligence:

[1245] The server uses generative artificial intelligence to generate a response based on the received text data and emotion data. For example, if a worker is feeling stressed, it generates a response that will alleviate that stress.

[1246] Terminal sending of generated response:

[1247] The response generated by the server is sent to the client device, which receives the response data for further processing.

[1248] Response transcription and output:

[1249] The terminal converts the response data received from the server into voice using a text-to-speech engine, and the voice response is played back to the worker through the terminal's built-in speaker.

[1250] 3. Specific examples

[1251] Here's a specific example of how it could be used in a factory: If a worker says to the terminal, "The line isn't working properly today, please check it," the system will respond as follows:

[1252] Worker statement:

[1253] "The line is not working well today, so I'd like you to check it."

[1254] Robot response:

[1255] "I'll check the line. You seem a little stressed, let's take a breather if you don't mind?"

[1256] 4. Prompt Sentence

[1257] It is also important to provide examples of prompts to use when inputting text to the generative AI. Examples are shown below.

[1258] "A user says, 'Line is not working well today, please check it out.' The emotion is 'Anxiety.' Generate an appropriate response."

[1259] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1260] Step 1:

[1261] Voice input acquisition

[1262] The terminal uses a voice collection device (microphone) to capture the voice of the user (worker). This voice data becomes the initial input to the system. When the user says, "The line is not working well today, please check it," the voice data is captured by the terminal.

[1263] Step 2:

[1264] Converting audio data to text

[1265] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). The input is voice data, and the output is text data. This conversion process extracts voice features and maps them to text.

[1266] Step 3:

[1267] emotion recognition

[1268] The device analyzes the user's emotions from the converted text and audio data. It uses an emotion engine (e.g., the emotion analysis pipeline in the transformers package) to output an emotion label from the input text data. For example, if the user's words are judged to be "anxiety," the emotion label "anxiety" is output.

[1269] Step 4:

[1270] Sending data to the server

[1271] The device sends the converted text data and emotion data to the server. The input is text data and emotion data, and the output is an HTTP request to the server. At this stage, the text data and emotion data are packaged in JSON format and sent.

[1272] Step 5:

[1273] Response Generation

[1274] The server generates an appropriate response using generative artificial intelligence (e.g., OpenAI GPT-3) based on the received text data and emotion data. The input to the server is the text data and emotion label, and the output is the response text. In this process, the prompt sentence is input into the generative AI model, and the generated response text is obtained.

[1275] For example: "A user says, 'Line is not working well today, please check on me.' The emotion is 'anxiety.' Generate an appropriate response."

[1276] Step 6:

[1277] Response data sent to terminal

[1278] The server sends the generated response data to the terminal. The server's input is the generated response text, and the output is an HTTP response to the terminal. Again, the response data is packaged in JSON format and sent.

[1279] Step 7:

[1280] Audio conversion and output

[1281] The device converts the response data received from the server into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech). The input is the response text, and the output is audio data. This audio data is played back to the user using the device's built-in speaker.

[1282] Step 8:

[1283] Responding to the user

[1284] Finally, the user hears the voice response from the terminal and can continue to make new voice input. This series of processes maintains a continuous conversation between the user and the terminal. The output to the user is the voice response played from the terminal.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] [Fourth embodiment]

[1289] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1290] 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.

[1291] 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).

[1292] 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.

[1293] 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.

[1294] 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).

[1295] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1296] 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.

[1297] 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.

[1298] 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.

[1299] 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.

[1300] 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.

[1301] 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."

[1302] The system of the present invention uses generative artificial intelligence to generate and output appropriate responses based on information input by voice from the user. This system is composed of a terminal (pet robot), a server, and user interaction.

[1303] System Overview

[1304] 1. User voice input

[1305] The terminal captures the user's voice via a microphone.

[1306] Voice input is the starting point for an interactive conversation and is converted into text using voice recognition software.

[1307] 2. Converting voice data to text

[1308] The terminal converts the acquired voice data into text data using voice recognition software.

[1309] This converted text data becomes information for analyzing the user's intention.

[1310] 3. Sending text data to the server

[1311] The terminal transmits the converted text data to the server using the HTTP protocol.

[1312] The server receives the text data and prepares to generate a response using generative artificial intelligence.

[1313] 4. Response Generation Using Generative AI

[1314] The server generates an appropriate response based on the received text data using generative artificial intelligence (e.g., using a natural language processing model).

[1315] Responses are dynamically generated based on the user's request, which may include fictional stories, historical commentary, or everyday conversation.

[1316] 5. Terminal transmission of generated response

[1317] The server sends the response created by the generative artificial intelligence to the terminal in JSON format or similar.

[1318] This allows the terminal to receive the generated response.

[1319] 6. Response transcription and output

[1320] The terminal converts the text response received from the server into speech using a text-to-speech engine.

[1321] This voice response is played back to the user through the device's built-in speaker.

[1322] This allows users to have real-time interactions.

[1323] Specific examples

[1324] A specific example of this system is shown below.

[1325] User says: "Hi, what have you been up to today?"

[1326] The device captures the speech, converts it into text, and sends it to the server: "Hello, what have you been up to today?"

[1327] The server uses generative artificial intelligence to generate a response from the text data it receives: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[1328] The device receives the generated response, converts it into speech, and speaks to the user: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[1329] In this way, the system can realize natural conversation with the user and provide daily comfort and a sense of security to the elderly.

[1330] The processing flow will be explained below.

[1331] Step 1:

[1332] The user speaks to the device (pet robot), for example, by voice input such as "Hello, what did you do today?"

[1333] Step 2:

[1334] The device captures the user's voice via a built-in microphone, and the voice data is temporarily stored in memory.

[1335] Step 3:

[1336] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). This process converts the voice data into text format.

[1337] Step 4:

[1338] The terminal sends the converted character data to the server by including it in an HTTP request. Specifically, the data is sent using an HTTP POST request.

[1339] Step 5:

[1340] The server analyzes the received text data and passes it as input to a generative AI (e.g., GPT-3), which generates an appropriate response to the user's input.

[1341] Step 6:

[1342] The generative AI generates a response based on the user's input and returns the response in text format to the server, such as "I did a lot today, including taking a walk and reading."

[1343] Step 7:

[1344] The server then sends the generated text response to the device, typically as JSON formatted data.

[1345] Step 8:

[1346] The device converts the received text response into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[1347] Step 9:

[1348] The device then plays the converted response to the user through its built-in speaker, for example, "You've done a lot today, including taking a walk and reading."

[1349] Step 10:

[1350] The terminal then waits for the user's new voice input and repeats the above process to maintain a continuous conversation. This continuous interaction allows the user to enjoy a natural conversation with the pet robot.

[1351] Example 1

[1352] 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."

[1353] Conventional voice dialogue systems have limitations in the accuracy of speech recognition and the naturalness of response generation, often preventing smooth dialogue with users. Furthermore, when generating responses only for specific content, they lack flexibility and are unable to fully respond to diverse user needs. In particular, when considering use as a daily conversation partner for elderly people and others, the system's responses often feel mechanical, making it difficult to provide comfort and a sense of security to the user.

[1354] 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.

[1355] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into text data, and means for generating a response based on the converted text data using generative artificial intelligence, thereby enabling natural and flexible responses to user voice input to be provided in real time.

[1356] "User" refers to a person who uses the system to provide voice input.

[1357] The "means for acquiring voice" refers to a device that collects voice uttered by a user as digital data using a voice input device such as a microphone.

[1358] "Means for converting voice data into text data" refers to the process of converting captured voice data into text format data using voice recognition software.

[1359] "Means for transmitting character data to a server" refers to a communication protocol (generally HTTP, etc.) and its implementation for transmitting text data to a server over a network.

[1360] "Means for generating responses using generative artificial intelligence" refers to a process that uses a natural language processing model to generate natural-sounding responses based on received text data.

[1361] "Means for transmitting to the terminal" refers to a communication protocol and its implementation for transmitting the generated response data from the server to the terminal.

[1362] "Means for converting the response into speech and playing it to the user" refers to the process of converting the generated response into speech using a text-to-speech engine and playing it to the user through a speaker.

[1363] The system of the present invention uses generative artificial intelligence to generate and output appropriate responses based on information input by voice from a user. This system is composed of a terminal (e.g., a pet robot equipped with a voice input device), a server, and user interaction.

[1364] System Overview

[1365] 1. User voice input

[1366] A user speaks into a device equipped with a voice input device (microphone). For example, they say, "Hello, what did you do today?"

[1367] The device captures the user's voice through a microphone and temporarily stores it as digital voice data.

[1368] 2. Converting voice data to text

[1369] The device sends the captured voice data to speech recognition software (e.g., Google Cloud Speech-to-Text API), which analyzes the voice waveform and converts it into text such as "Hello, what have you been up to today?"

[1370] The converted text data is stored in the terminal's memory.

[1371] 3. Sending text data to the server

[1372] The terminal transmits the converted text data to the server using the HTTP protocol, and this transmission process uses network communication.

[1373] The server receives the text data sent as an HTTP request.

[1374] 4. Response Generation Using Generative AI

[1375] The server uses generative artificial intelligence (e.g., GPT-3) to generate an appropriate response based on the received text data.

[1376] Responses are natural responses to user input, such as "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[1377] 5. Terminal transmission of generated response

[1378] The server encodes the generated response into JSON format and sends it to the terminal as an HTTP response.

[1379] The terminal receives the HTTP response from the server and retains the generated text response.

[1380] 6. Response transcription and output

[1381] The device inputs the text response received from the server into a text-to-speech engine (e.g., Amazon Polly) and converts it into voice data.

[1382] The device plays the generated voice data over its built-in speaker and responds to the user by saying, "Hello! I did a lot of things today, like taking a walk and reading a book. How about you?"

[1383] Specific examples

[1384] A specific example of this system is shown below.

[1385] User says: "Hi, what have you been up to today?"

[1386] The device captures the speech, converts it into text, and sends it to the server: "Hello, what have you been up to today?"

[1387] The server uses generative artificial intelligence to generate a response from the text data it receives: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[1388] The device receives the generated response, converts it into speech, and speaks to the user: "Hello! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[1389] In this way, the system can realize natural conversation with the user, providing everyday comfort and a sense of security, especially to the elderly.

[1390] Prompt Sentence Examples

[1391] The following is an example of a prompt sentence that can be used to generate an appropriate response from a generative artificial intelligence.

[1392] Generate a response when the user says hello:

[1393] User: "Hi, what have you been up to today?"

[1394] Response: "Hi! I've been doing a lot today, like taking a walk and reading a book. How about you?"

[1395] Using this prompt, the generative artificial intelligence will generate an appropriate response to the input from the user.

[1396] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1397] Step 1:

[1398] A user speaks into a terminal equipped with a voice input device (microphone), for example, saying, "Hello, what did you do today?"

[1399] Input: User's voice

[1400] Output: Digital audio data

[1401] Specific operation: The device's microphone captures the user's voice and temporarily stores it in memory as digital audio data.

[1402] Step 2:

[1403] The device sends the captured voice data to speech recognition software, which analyzes the voice waveform and converts it into text.

[1404] Input: Digital audio data

[1405] Output: Text data

[1406] Specific operation: The device calls the Google Cloud Speech-to-Text API, converts the audio waveform into text data such as "Hello, what did you do today?", and stores it in the device's memory.

[1407] Step 3:

[1408] The terminal transmits the converted text data to the server using the HTTP protocol.

[1409] Input: Text data

[1410] Output: HTTP request (payload contains text data)

[1411] Specific operation: The terminal constructs an HTTP POST request and sends it to the server endpoint over the network, with the converted text data included in the request payload.

[1412] Step 4:

[1413] The server receives the HTTP request, extracts the text data from the payload, and then uses generative artificial intelligence to generate an appropriate response.

[1414] Input: Text data

[1415] Output: The generated response text

[1416] Specific operation: The server inputs the received text data into a generative artificial intelligence (e.g., GPT-3) and generates a response text such as, "Hello! I did a lot of things today. For example, I took a walk and read a book. How about you?"

[1417] Step 5:

[1418] The server encodes the generated response text into JSON format and sends it to the terminal as an HTTP response.

[1419] Input: Generated response text

[1420] Output: HTTP response (payload contains response text)

[1421] Specific operation: The server encodes the generated response text as a JSON object, constructs an HTTP response, and sends it to the terminal.

[1422] Step 6:

[1423] The device analyzes the HTTP response received from the server and inputs the response text into the text-to-speech engine, which converts it into audio data and plays it back through the speaker.

[1424] Input: HTTP response (including response text)

[1425] Output: Audio data

[1426] Specific operation: The device calls the Amazon Polly API, converts the response text into speech data, and plays the generated speech data through the device's built-in speaker, responding to the user, "Hello! I did a lot of things today, like taking a walk and reading a book. How about you?"

[1427] The above process makes it possible to realize natural and smooth dialogue in response to voice input from the user.

[1428] (Application example 1)

[1429] 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."

[1430] The challenge is to improve the convenience and safety of autonomous vehicles by making them respond smoothly to user voice commands, such as setting destinations, adjusting the environment, and responding to emergencies. There is also a need to process user voice interactions in the vehicle more naturally and efficiently.

[1431] 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.

[1432] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into text data, means for transmitting the converted text data to the server, means for generating a response using generative artificial intelligence based on the text data transmitted to the server, means for transmitting the generated response to the terminal, means for converting the transmitted response into voice and playing it back to the user, means for processing instructions regarding environmental adjustment and destination setting in the autonomous vehicle, and automatic contact means for responding to emergencies, thereby enabling a rapid response to user voice instructions in the vehicle and improving safety.

[1433] The "means for acquiring the user's voice" refers to a device or method that detects the voice signal emitted by the user and captures it as digital data.

[1434] The "means for converting acquired voice data into character data" refers to a device or software that uses voice recognition technology to represent a voice signal as a corresponding character string.

[1435] The "means for transmitting converted character data to a server" refers to a communication technique or device that sends text-formatted data to a remote server via a network.

[1436] "Means for generating a response using generative artificial intelligence based on character data sent to the server" refers to a function that processes received text data and creates an appropriate response using generative artificial intelligence such as a natural language generation model.

[1437] The "means for transmitting the generated response to the terminal" refers to a communication technique or device that transmits the generated text response back to the terminal via the network.

[1438] The "means for converting the transmitted response into voice and playing it back to the user" refers to a technology or device that converts text data into a voice signal and plays it back to the user through a voice output device.

[1439] "Means for processing instructions regarding environmental adjustments and destination settings within an autonomous vehicle" refers to a function that executes actions such as changing air conditioning settings or navigation destinations on the internal systems of an autonomous vehicle based on voice instructions from the user.

[1440] "Automatic emergency response contact" refers to technology or devices that automatically contact designated emergency contacts or rescue services when an emergency, such as an accident or health issue, is detected.

[1441] An embodiment of the present invention will be described in detail below. The present invention is a system that responds to voice commands from a user in an autonomous vehicle, adjusts the in-vehicle environment, sets a destination, and responds to emergencies. The present invention is realized by combining a smartphone, a speaker, a microphone, and a server.

[1442] 1. System Configuration

[1443] The user issues voice instructions using a microphone installed in the autonomous vehicle.

[1444] The device (smartphone) receives the voice and converts it into text data using voice recognition technology, using the Google Cloud Speech-to-Text API.

[1445] The terminal sends the converted text data to the server using the HTTP protocol.

[1446] The server generates an appropriate response based on the received text data using generative artificial intelligence (e.g., OpenAI's GPT-4).

[1447] The server generates a response and sends it to the device in JSON format.

[1448] The device converts the received response into audio using the Google Cloud Text-to-Speech API and plays it back to the user through the car's speakers.

[1449] 2. Operational Description

[1450] When a user gets into a vehicle and issues a voice command, the voice is picked up by a microphone. The smartphone converts this voice data into text data and sends it to a server. The server uses generative artificial intelligence to create a response based on the voice command and sends it back to the device. The device then converts the returned response back into voice and plays it through the vehicle's speakers.

[1451] 3. Usage example

[1452] 3.1 Destination setting

[1453] User: "Set my next destination"

[1454] The response generated by the generative AI model: "What address?"

[1455] User: "1-1 Marunouchi, Chiyoda-ku, Tokyo"

[1456] The final response generated by the generative AI model: "Destination set to 1-1 Marunouchi, Chiyoda-ku, Tokyo."

[1457] 3.2 Adjusting the in-car environment

[1458] User: "Turn the air conditioner on a little cooler."

[1459] Response generated by the generative AI model: "I'll turn down the air conditioner."

[1460] 3.3 Emergency response

[1461] User: "Emergency, there's been an accident."

[1462] The response generated by the generative AI model: "Call your emergency contact."

[1463] Examples of prompts:

[1464] "Set next destination"

[1465] "Turn the air conditioner on a little cooler."

[1466] "It's an emergency, there's been an accident."

[1467] In this way, this system utilizes speech recognition and generative AI models to efficiently conduct real-time voice dialogue within autonomous vehicles, providing a system that is highly convenient for users and can respond quickly in emergencies.

[1468] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1469] Step 1:

[1470] The user issues a voice command. Voice data is acquired by the user speaking into a microphone in the autonomous vehicle. The voice command is acquired as input and sent to the terminal as voice data via the microphone.

[1471] Step 2:

[1472] The device converts the voice data into text data. The device converts the acquired voice data into text data using the Google Cloud Speech-to-Text API. The input voice data is converted into output text data.

[1473] Step 3:

[1474] The terminal transmits the converted text data to the server. The terminal transmits the converted text data to the server using the HTTP protocol. The input text data is transmitted to the server as an HTTP request.

[1475] Step 4:

[1476] The server receives the text data and generates a response using generative AI. The server inputs the received text data into OpenAI's GPT-4 model to generate an appropriate response. Based on the input text data, the generative AI model outputs the response text.

[1477] Step 5:

[1478] The server sends the generated response to the terminal. The server sends the generated response in JSON format to the terminal. The response text as input is sent to the terminal as a JSON formatted HTTP response.

[1479] Step 6:

[1480] The device converts the response text into speech and plays it to the user. The device converts the received response text into audio data using the Google Cloud Text-to-Speech API and plays it through the car speaker. The input response text is converted into audio data and output to the user using the speaker.

[1481] 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.

[1482] The system of the present invention uses generative artificial intelligence to generate appropriate responses based on information input by voice from the user, and then outputs the responses as voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide responses that correspond to the user's emotions. This system is composed of a terminal (pet robot), a server, and user interaction.

[1483] System Overview

[1484] 1. User voice input

[1485] The terminal captures the user's voice via a microphone.

[1486] Voice input is the starting point for an interactive conversation and is converted into text using voice recognition software.

[1487] 2. Converting voice data to text

[1488] The device converts the acquired voice data into text data using voice recognition software (e.g., Google Speech-to-Text API).

[1489] This converted text data becomes information for analyzing the user's intention.

[1490] 3. User Emotion Recognition

[1491] The terminal uses an emotion engine to analyze the user's emotions from the converted text data and voice data.

[1492] The user's emotions can be recognized as emotional states such as joy, sadness, anger, and surprise.

[1493] 4. Sending text data and emotion data to the server

[1494] The device sends the converted text data and emotion data in an HTTP request to the server, which then receives information including the user's speech and current emotional state.

[1495] 5. Response Generation Using Generative AI

[1496] The server generates an appropriate response using generative artificial intelligence (e.g., GPT-3) based on the received text data and emotion data.

[1497] Responses are dynamically adjusted based on the user's input and emotional state: for example, if the user is perceived as sad, responses may include encouragement and comfort.

[1498] 6. Terminal transmission of generated response

[1499] The server sends the response created by the generative AI to the terminal in JSON format, etc., which allows the terminal to receive the generated response.

[1500] 7. Response transcription and output

[1501] The device converts the text response received from the server into speech using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[1502] This voice response is played back to the user through the device's built-in speaker.

[1503] 8. Audio Response to the User

[1504] The user can listen to the voice response from the terminal and then make a new voice input, maintaining a continuous conversation between the user and the terminal.

[1505] Specific examples

[1506] A specific example of this system is shown below.

[1507] User says: "Hi, what have you been up to today?"

[1508] The device receives the voice, converts it into text, and then analyzes the emotion using an emotion engine. Analysis result: "Neutral"

[1509] The device sends the converted text data and emotion data to the server: "Hello, what did you do today?" and "Neutral."

[1510] The server uses generative artificial intelligence to generate a response from the text and emotion data it receives: "I did a lot of things today, for example, I took a walk and read a book."

[1511] The device receives the generated response, converts it into speech, and speaks it back to the user: "I did a lot of things today. For example, I took a walk and read a book."

[1512] In this way, the system can realize natural conversation with the user and provide responses that correspond to the user's emotions, thereby providing everyday comfort and a sense of security to the elderly.

[1513] The processing flow will be explained below.

[1514] Step 1:

[1515] The user speaks to the device (pet robot), for example, by voice input such as "Hello, what did you do today?"

[1516] Step 2:

[1517] The device picks up the user's voice via a built-in microphone, and the voice data is recorded and temporarily stored in memory.

[1518] Step 3:

[1519] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). This process converts the voice data into text format.

[1520] Step 4:

[1521] The device inputs the converted text data into an emotion engine to analyze the user's emotions. Emotional states such as joy, sadness, anger, and surprise are recognized as emotion data.

[1522] Step 5:

[1523] The device sends the converted text data and analyzed emotion data to the server in an HTTP request. Specifically, the data is sent using an HTTP POST request.

[1524] Step 6:

[1525] The server analyzes the received text and emotion data and passes it as input to a generative AI (e.g., GPT-3), which generates an appropriate response based on the user's input and emotional state.

[1526] Step 7:

[1527] The generative AI generates a response based on the user's input and emotions, and returns the response in text format to the server. For example, it generates a response such as, "I did a lot today. I took a walk, read, etc."

[1528] Step 8:

[1529] The server sends the generated text response to the device in a data format such as JSON, allowing the device to receive the generated response.

[1530] Step 9:

[1531] The device converts the received text response into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech).

[1532] Step 10:

[1533] The device then plays the converted response to the user through its built-in speaker, for example, "You've done a lot today, including taking a walk and reading."

[1534] Step 11:

[1535] The terminal again waits for new voice input from the user and repeats the above process, maintaining continuous conversation.

[1536] Specific examples

[1537] User says: "Today has been a really bad day."

[1538] The device receives the voice, converts it into text, and then analyzes the emotion using an emotion engine. Analysis result: "Sadness"

[1539] The device sends the converted text data and emotion data to the server: "Today was really bad" and "Sadness."

[1540] The server uses generative artificial intelligence to generate a response from the text and emotion data it receives: "That was tough. But there are always good days. Tell me your story."

[1541] The device receives the generated response, converts it into speech, and speaks to the user: "That was tough, but there are always good days. Tell me your story."

[1542] In this way, the system can realize natural conversation with the user and provide responses that correspond to the user's emotions, thereby providing everyday comfort and a sense of security to the elderly.

[1543] Example 2

[1544] 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."

[1545] Conventional speech recognition systems have the problem that responses to user input are not based on emotion, making it difficult to have natural conversations with users or communicate with them in a way that takes their emotions into consideration. Furthermore, simply converting speech data into text data does not allow for accurate understanding of the user's emotions and intentions, making it difficult to provide appropriate responses. Furthermore, systems lack a mechanism for maintaining continuous interaction with the user.

[1546] 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.

[1547] In this invention, the server includes means for analyzing the user's emotions from the user's voice data and character data, means for transmitting the converted character data and analyzed emotion data to the server, and means for generating a response using generative artificial intelligence based on the character data and emotion data transmitted to the server. This makes it possible to generate appropriate responses while taking the user's emotions into consideration, and to maintain a continuous and natural conversation.

[1548] "User" refers to the entity that provides speech input to interact with the system.

[1549] "Terminal" refers to a device for capturing voice, converting voice data to text data, analyzing emotions, and playing back voice responses.

[1550] "Server" refers to a computing device that uses generative artificial intelligence to generate an appropriate response based on data sent by a user and transmits the response to a terminal.

[1551] "Voice data" refers to data in which the user's voice is recorded in digital format and acquired via a microphone or the like.

[1552] "Text data" refers to data that has been converted from voice data into text using voice recognition software.

[1553] "Emotion data" refers to data that indicates the user's emotional state analyzed using an emotion engine.

[1554] "Generative AI" refers to machine learning algorithms that generate appropriate text responses based on a user's input and emotional state.

[1555] "Text-to-speech engine" refers to software for converting text data into speech data.

[1556] "Response" refers to a text or voice response that a generative artificial intelligence generates in response to input from a user.

[1557] The system of the present invention uses generative artificial intelligence to generate appropriate responses based on information input by voice from the user, and outputs the responses as voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide responses that correspond to the user's emotions. This system is composed of a terminal, a server, and user interaction.

[1558] System configuration

[1559] 1. User: The entity that provides voice input to the system.

[1560] 2. Terminal: A device that captures the user's voice using a built-in microphone, converts it into text data using voice recognition software (e.g., voice recognition API), and performs emotion analysis using an emotion engine. The captured data is sent to a server.

[1561] 3. Server: A device that receives text data and emotion data and uses generative artificial intelligence (e.g., a generative AI model) to generate an appropriate response. The generated response is sent to the device.

[1562] 4. Text-to-speech engine: Software that converts the generated response into speech and plays it through the device's speaker.

[1563] Hardware and software used

[1564] Devices: Robot pets and smart devices

[1565] Speech recognition software: Speech recognition API (e.g. Google Speech-to-Text API)

[1566] Emotion engine: Emotion analysis software (e.g., emotion recognition API)

[1567] Server: High-performance computer (e.g., cloud server)

[1568] Generative AI: Generative AI models (e.g., GPT-3)

[1569] Text-to-speech engine: TTS engine (e.g. Google Text-to-Speech)

[1570] Specific examples

[1571] A specific example of this system is shown below.

[1572] Example of operation

[1573] 1. User says: "Hi, what have you been up to today?"

[1574] 2. Your device captures your voice and converts it to text using speech recognition software. The resulting text is: "Hello, what have you been up to today?"

[1575] 3. The device analyzes the emotion using the emotion engine. Analysis result: "Neutral"

[1576] 4. The device sends the converted text data and emotion data to the server. Data sent: "Hello, what did you do today?" and "Neutral"

[1577] 5. The server uses generative artificial intelligence to generate a response from the received data: "I did a lot of things today, like take a walk and read a book."

[1578] 6. The device receives the generated response and converts it into speech using a text-to-speech engine.

[1579] 7. The device plays the generated voice data from the speaker. Playback voice: "I did a lot of things today. For example, I took a walk and read a book."

[1580] Prompt Sentence Examples

[1581] Example prompts to input to the generative AI model:

[1582] "Generate a response for a situation where the user is feeling sad. User input: "Something's not going well today" and emotion: "sad""

[1583] Expected output:

[1584] "I see things didn't go well today and you had a hard time. You have days like that. Let me know if there's anything I can do for you."

[1585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1586] Step 1:

[1587] The user provides input by voice.

[1588] Specific action: The user speaks to the system, for example, "Hello, what have you been up to today?"

[1589] Input: User's voice

[1590] Output: None (obtained as audio)

[1591] Step 2:

[1592] The device receives the audio.

[1593] Specific operation: The device's built-in microphone captures the user's voice.

[1594] Input: User's voice

[1595] Output: Digitized audio data

[1596] Step 3:

[1597] The terminal converts the voice data into text data.

[1598] Specific operation: Uses speech recognition software (e.g., Google Speech-to-Text API) to convert voice data into text data.

[1599] Input: Digital audio data

[1600] Output: Text data "Hello, what did you do today?"

[1601] Step 4:

[1602] The device analyzes the user's emotions from the voice and text data it acquires.

[1603] What it does: It uses emotion analysis software to process the converted text data and the original audio data to identify the user's emotions.

[1604] Input: Converted text and audio data

[1605] Output: Emotion data "Neutral"

[1606] Step 5:

[1607] The terminal transmits the converted character data and the analyzed emotion data to the server.

[1608] Specific operation: Create an HTTP request and send text data and emotion data to the server.

[1609] Input: Text data and emotion data

[1610] Output: "Hello, what have you been up to today?" and "Neutral" data sent to the server

[1611] Step 6:

[1612] The server generates the response using generative artificial intelligence.

[1613] Specific operation: Based on the received text data and emotion data, a response is generated using a generative AI model (e.g., GPT-3).

[1614] Input: Text data and emotion data

[1615] Output: Response text "I did a lot of things today, like taking a walk and reading a book."

[1616] Step 7:

[1617] The server generates a response and sends it to the terminal.

[1618] Specific operation: The generated text response is sent to the terminal in JSON format or similar.

[1619] Input: Generated response text: "I did a lot of things today. For example, I took a walk and read a book."

[1620] Output: Response text sent to the terminal

[1621] Step 8:

[1622] The device converts the transmitted response into speech.

[1623] What it does: Uses a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech) to convert the generated text response into audio data.

[1624] Input: Generated response text

[1625] Output: "I did a lot of things today. For example, I took a walk and read a book."

[1626] Step 9:

[1627] The terminal outputs a voice response to the user.

[1628] Specific operation: Play the generated audio data from the device's built-in speaker.

[1629] Input: Audio data

[1630] Output: The user hears a spoken response saying, "I did a lot of things today. For example, I took a walk and read a book."

[1631] (Application example 2)

[1632] 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."

[1633] In factory environments, the lack of an interactive system that can efficiently recognize workers' voices and analyze their emotions is a problem. Conventional systems simply receive voice instructions and are unable to respond to workers' stress and emotional state, limiting their effectiveness in improving efficiency and the work environment. In particular, the lack of emotional support can lead to increased worker stress and work errors, which is a major issue.

[1634] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1635] In this invention, the server includes means for acquiring user voice, means for converting the acquired voice data into character data, means for transmitting the converted character data and emotional data, means for generating a response based on the emotional data using generative artificial intelligence, means for converting the generated response into voice and playing it back to the user, and means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and providing work support and stress management responses. This enables efficient work support based on the voice instructions of workers in a factory environment and detailed support according to their emotional state.

[1636] A "means for acquiring user voice" is a device or method for capturing voice signals emitted by a user.

[1637] "Means for converting acquired voice data into text data" refers to technology or software for converting voice signals into text-format data.

[1638] The "means for transmitting converted character data and emotional data to the server" refers to a communication means for transmitting the character data converted from the voice and the emotional state to the server.

[1639] "Means for generating a response using generative artificial intelligence based on character data and emotional data sent to a server" refers to a method for utilizing generative artificial intelligence to create an appropriate response based on data received by a server.

[1640] "Means for sending the generated response to the terminal" refers to a technology for sending the response created by the generative artificial intelligence back to the client device.

[1641] The "means for converting the transmitted response into voice and playing it back to the user" refers to a means for converting the response received as text data into voice format and letting the user hear it.

[1642] "Means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and responding with work support and stress management" refers to technology that receives voice instructions from workers in a factory, analyzes them to support their work, and provides responses and support according to the worker's emotional state.

[1643] An "emotion engine" is software or technology for analyzing a user's emotional state and recognizing specific emotions.

[1644] "Generative AI" is a type of AI technology that generates appropriate text or responses based on given data or input.

[1645] MODE FOR CARRYING OUT THE INVENTION

[1646] The system according to the present invention is designed to receive voice instructions from workers in a factory environment, analyze their emotions, and provide appropriate work support and stress management responses. Specific embodiments of the system are described below.

[1647] 1. System Configuration

[1648] Hardware:

[1649] Audio collection device (microphone): Used to capture the voice of the worker.

[1650] Client device (terminal): Required to process data acquired from the audio collection device and communicate with the server.

[1651] software:

[1652] Speech recognition software: Used to convert voice data into text data (e.g., Google Speech-to-Text API).

[1653] Sentiment engine: Used to analyze the emotional state of the user (e.g., the sentiment analysis pipeline in the transformers package).

[1654] Generative AI: Used to generate appropriate responses based on the data it receives (e.g., OpenAI GPT-3).

[1655] Text-to-speech engine (TTS engine): Used to convert text data into speech (e.g., Google Text-to-Speech).

[1656] 2. Program processing overview

[1657] Get user's voice input:

[1658] The user (worker) speaks into the client device. The terminal captures the voice via the voice collection device (microphone). This voice data is collected in the first step.

[1659] Audio to text conversion:

[1660] The device uses voice recognition software to convert the captured voice data into text data, which is then sent to the server as user instructions.

[1661] User Emotion Recognition:

[1662] The terminal uses an emotion engine to analyze the user's emotions from the converted text and voice data, thereby recognizing the worker's emotional state (e.g., stress, anxiety, joy, etc.).

[1663] Sending text data and emotion data to the server:

[1664] The terminal transmits the converted character data and emotion data to the server, which receives and processes the data to generate an appropriate response.

[1665] Response generation using generative artificial intelligence:

[1666] The server uses generative artificial intelligence to generate a response based on the received text data and emotion data. For example, if a worker is feeling stressed, it generates a response that will alleviate that stress.

[1667] Terminal sending of generated response:

[1668] The response generated by the server is sent to the client device, which receives the response data for further processing.

[1669] Response transcription and output:

[1670] The terminal converts the response data received from the server into voice using a text-to-speech engine, and the voice response is played back to the worker through the terminal's built-in speaker.

[1671] 3. Specific examples

[1672] Here's a specific example of how it could be used in a factory: If a worker says to the terminal, "The line isn't working properly today, please check it," the system will respond as follows:

[1673] Worker statement:

[1674] "The line is not working well today, so I'd like you to check it."

[1675] Robot response:

[1676] "I'll check the line. You seem a little stressed, let's take a breather if you don't mind?"

[1677] 4. Prompt Sentence

[1678] It is also important to provide examples of prompts to use when inputting text to the generative AI. Examples are shown below.

[1679] "A user says, 'Line is not working well today, please check it out.' The emotion is 'Anxiety.' Generate an appropriate response."

[1680] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1681] Step 1:

[1682] Voice input acquisition

[1683] The terminal uses a voice collection device (microphone) to capture the voice of the user (worker). This voice data becomes the initial input to the system. When the user says, "The line is not working well today, please check it," the voice data is captured by the terminal.

[1684] Step 2:

[1685] Converting audio data to text

[1686] The device converts the captured voice data into text data using voice recognition software (e.g., Google Speech-to-Text API). The input is voice data, and the output is text data. This conversion process extracts voice features and maps them to text.

[1687] Step 3:

[1688] emotion recognition

[1689] The device analyzes the user's emotions from the converted text and audio data. It uses an emotion engine (e.g., the emotion analysis pipeline in the transformers package) to output an emotion label from the input text data. For example, if the user's words are judged to be "anxiety," the emotion label "anxiety" is output.

[1690] Step 4:

[1691] Sending data to the server

[1692] The device sends the converted text data and emotion data to the server. The input is text data and emotion data, and the output is an HTTP request to the server. At this stage, the text data and emotion data are packaged in JSON format and sent.

[1693] Step 5:

[1694] Response Generation

[1695] The server generates an appropriate response using generative artificial intelligence (e.g., OpenAI GPT-3) based on the received text data and emotion data. The input to the server is the text data and emotion label, and the output is the response text. In this process, the prompt sentence is input into the generative AI model, and the generated response text is obtained.

[1696] For example: "A user says, 'Line is not working well today, please check on me.' The emotion is 'anxiety.' Generate an appropriate response."

[1697] Step 6:

[1698] Response data sent to terminal

[1699] The server sends the generated response data to the terminal. The server's input is the generated response text, and the output is an HTTP response to the terminal. Again, the response data is packaged in JSON format and sent.

[1700] Step 7:

[1701] Audio conversion and output

[1702] The device converts the response data received from the server into audio using a text-to-speech engine (TTS engine, e.g., Google Text-to-Speech). The input is the response text, and the output is audio data. This audio data is played back to the user using the device's built-in speaker.

[1703] Step 8:

[1704] Responding to the user

[1705] Finally, the user hears the voice response from the terminal and can continue to make new voice input. This series of processes maintains a continuous conversation between the user and the terminal. The output to the user is the voice response played from the terminal.

[1706] 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.

[1707] 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.

[1708] 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 robot 414.

[1709] 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.

[1710] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1711] 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.

[1712] 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).

[1713] 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.

[1714] 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."

[1715] 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.

[1716] 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).

[1717] 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.

[1718] 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.

[1719] 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.

[1720] 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.

[1721] 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.

[1722] 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.

[1723] 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.

[1724] 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.

[1725] 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.

[1726] 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.

[1727] The following is further disclosed regarding the above embodiment.

[1728] (Claim 1)

[1729] means for acquiring a user's voice;

[1730] A means for converting the acquired voice data into text data;

[1731] means for transmitting the converted character data to a server;

[1732] A means for generating a response using generative artificial intelligence based on the character data transmitted to the server;

[1733] means for transmitting the generated response to the terminal;

[1734] means for converting the transmitted response into speech and playing it back to the user;

[1735] A system including:

[1736] (Claim 2)

[1737] The system of claim 1, further comprising means for the generative artificial intelligence to provide one of an original story, a historical commentary, or everyday conversation.

[1738] (Claim 3)

[1739] 10. The system of claim 1, wherein the means for obtaining user voice data includes a microphone.

[1740] (Claim 4)

[1741] 2. The system of claim 1, wherein the means for converting the acquired voice data into text data includes voice recognition software.

[1742] (Claim 5)

[1743] 10. The system of claim 1, wherein the means for converting the generated response into speech and playing it to the user comprises a text-to-speech engine.

[1744] (Claim 6)

[1745] 10. The system of claim 1, further comprising a learning means for continually improving responses through interaction with a user.

[1746] "Example 1"

[1747] (Claim 1)

[1748] means for acquiring a user's voice;

[1749] A means for converting the acquired voice data into text data;

[1750] means for transmitting the converted character data to a server;

[1751] A means for generating a response using generative artificial intelligence based on the character data transmitted to the server;

[1752] means for transmitting the generated response to the terminal;

[1753] means for converting the transmitted response into speech and playing it back to the user;

[1754] A system including:

[1755] (Claim 2)

[1756] The system of claim 1, further comprising means for the generative artificial intelligence to provide one of an original story, a historical commentary, or everyday conversation.

[1757] (Claim 3)

[1758] 10. The system of claim 1, wherein the means for obtaining user voice data includes a voice input device.

[1759] "Application Example 1"

[1760] (Claim 1)

[1761] means for acquiring a user's voice;

[1762] A means for converting the acquired voice data into text data;

[1763] means for transmitting the converted character data to a server;

[1764] A means for generating a response using generative artificial intelligence based on the character data transmitted to the server;

[1765] means for transmitting the generated response to the terminal;

[1766] means for converting the transmitted response into speech and playing it back to the user;

[1767] means for processing instructions regarding environmental adjustment and destination setting within the autonomous vehicle;

[1768] Automated contact methods for emergency response;

[1769] A system including:

[1770] (Claim 2)

[1771] The system of claim 1, further comprising means for the generative artificial intelligence to provide one of a fictional story, a historical commentary, an everyday conversation, and a description of the vehicle's situation.

[1772] (Claim 3)

[1773] 10. The system of claim 1, wherein the means for acquiring user voice data includes a microphone, and the means for converting the generated response into voice and playing it back to the user utilizes a speaker on the smartphone.

[1774] "Example 2: Combining Emotion Engines"

[1775] (Claim 1)

[1776] means for acquiring a user's voice;

[1777] A means for converting the acquired voice data into text data;

[1778] A means for analyzing user emotions from the acquired voice data and text data;

[1779] means for transmitting the converted character data and the analyzed emotion data to a server;

[1780] A means for generating a response using generative artificial intelligence based on the character data and emotion data transmitted to the server;

[1781] means for transmitting the generated response to the terminal;

[1782] means for converting the transmitted response into speech and playing it back to the user;

[1783] A system including:

[1784] (Claim 2)

[1785] The system of claim 1, further comprising means for the generative artificial intelligence to provide one of an original story, a historical commentary, or everyday conversation.

[1786] (Claim 3)

[1787] 10. The system of claim 1, wherein the means for obtaining user voice data includes a microphone.

[1788] "Application example 2 when combining emotion engines"

[1789] (Claim 1)

[1790] means for acquiring a user's voice;

[1791] A means for converting the acquired voice data into text data;

[1792] means for transmitting the converted character data and emotion data to a server;

[1793] A means for generating a response using generative artificial intelligence based on the character data and emotion data transmitted to the server;

[1794] means for transmitting the generated response to the terminal;

[1795] means for converting the transmitted response into speech and playing it back to the user;

[1796] A means for receiving voice instructions from workers in a factory environment, analyzing their emotions, and providing work assistance and stress management responses;

[1797] A system including:

[1798] (Claim 2)

[1799] The system of claim 1, wherein the generative artificial intelligence has a means for providing creative stories, historical commentary, everyday conversation, as well as work assistance and stress management responses.

[1800] (Claim 3)

[1801] 10. The system of claim 1, wherein the means for obtaining user voice data includes a voice collection device. [Explanation of symbols]

[1802] 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 acquiring a user's voice; A means for converting the acquired voice data into text data; means for transmitting the converted character data to a server; A means for generating a response using generative artificial intelligence based on the character data transmitted to the server; means for transmitting the generated response to the terminal; means for converting the transmitted response into speech and playing it back to the user; A system including:

2. The system according to claim 1, further comprising means for the generative artificial intelligence to provide any one of an original story, a historical commentary, and everyday conversation.

3. 2. The system of claim 1, wherein the means for obtaining user voice data includes a microphone.

4. 2. The system of claim 1, wherein the means for converting the captured voice data into text data includes voice recognition software.

5. 2. The system of claim 1, wherein the means for converting the generated response into speech and playing it to the user comprises a text-to-speech engine.

6. 10. The system of claim 1, further comprising a learning means for continually improving responses through interaction with a user.

Images