system

Abstract

We provide the system. [Solution] Means for collecting audio data, A means of sending the collected audio data to the server, A means of converting audio data into text data, A means for analyzing text data and generating an appropriate response, A means of converting generated text data into audio data, A means of playing back the converted audio data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional speech recognition systems and conversational AI systems have problems such as insufficient accuracy when converting a user's voice into text data and the naturalness of the generated responses, resulting in a limited user experience. Furthermore, there are still problems in terms of the quality and response speed when re - converting the generated text data back into voice data. The present invention aims to solve these problems and provide a system that offers a more natural and rapid voice conversation experience.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system that includes means for collecting audio data, means for transmitting the collected audio data to a server, means for converting the audio data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data into audio data, and means for playing back the converted audio data. Furthermore, by using natural language processing technology for the means for converting audio data into text data and generative artificial intelligence for analyzing the text data and generating a response, highly accurate speech recognition and natural and appropriate response generation are achieved.

[0006] "Audio data" refers to data that records the content of what a user has said in digital format.

[0007] "Means of collection" refers to devices and methods for acquiring audio data, such as microphones and recording devices.

[0008] A "server" is a computer system that sends, receives, and processes data over a network.

[0009] "Means of transmission" refers to the devices or methods used to send the collected audio data to the server, such as wireless communication modules or internet connections.

[0010] A speech recognition system (ASR) is a technology that converts speech data into text data.

[0011] "Natural language processing technology" is a general term for technologies used to process human natural language using computers.

[0012] "Generative artificial intelligence" is an artificial intelligence technology that analyzes input text data and generates appropriate responses.

[0013] "Text data" refers to data in character format converted by a speech recognition system.

[0014] The "means for analyzing text data" refers to a device or method that includes technologies and algorithms for understanding text data and generating appropriate responses.

[0015] "Response" refers to the content that the system returns in response to the user's input.

[0016] The "text-to-speech system (TTS)" is a technology for converting text data into audio data.

[0017] The "means for playing" refers to a device or method for allowing the user to hear the converted audio data, such as a speaker or an audio output device.

Brief Description of the Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [[ID=2X]] [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] The present invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by a user speaking into a terminal. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, and means for playing back the converted voice data.

[0040] System details:

[0041] In an embodiment of the present invention, the user first speaks into the terminal. The terminal collects the user's voice using a microphone and temporarily stores this voice data. The collected voice data is transmitted from the terminal to the server via a wireless communication module. A secure communication protocol, such as HTTPS, is used to transmit the voice data.

[0042] Speech recognition processing:

[0043] The server receives the audio data transmitted from the terminal. Next, the Automatic Speech Recognition (ASR) system installed on the server analyzes the audio data and converts it into text data. This text data is generated based on what the user has said and uses highly accurate speech recognition technology.

[0044] Response generation:

[0045] The server then sends the converted text data to a generative artificial intelligence (for example, a model using the latest natural language processing technology, such as the GPT model), which analyzes the data. The generative AI then generates the information and appropriate responses requested by the user.

[0046] Speech synthesis:

[0047] The generated response is sent back to the server as text data. The server then sends this text data to a text-to-speech (TTS) system, which converts it into speech data.

[0048] Audio output:

[0049] Finally, the server sends the generated audio data to the terminal. The terminal plays the audio data received from the server through its speaker and provides a response to the user.

[0050] Specific example:

[0051] The following are specific examples of how to use this system.

[0052] Usage example 1:

[0053] Let's say a user speaks to their device and asks, "What's the weather like today?"

[0054] 1. User: Speaks to the device, "What's the weather like today?"

[0055] 2. Terminal: Collects audio and sends the audio data to the server.

[0056] 3. Server: Converts speech data into text using a speech recognition system.

[0057] 4. Server: Uses generative artificial intelligence to generate the response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0058] 5. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0059] 6. Terminal: Plays audio data sent from the server.

[0060] Usage example 2:

[0061] When a user speaks to the device and says, "Tell me about nearby restaurants," the system performs the following actions.

[0062] 1. User: Speaks into the device and says, "Tell me about nearby restaurants."

[0063] 2. Terminal: Collects audio and sends the audio data to the server.

[0064] 3. Server: Converts speech data into text using a speech recognition system.

[0065] 4. Server: Uses generative artificial intelligence to search for "information on nearby restaurants" and generates a response such as "There is a restaurant A nearby. Its opening hours are..."

[0066] 5. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0067] 6. Terminal: Plays audio data sent from the server.

[0068] In this way, the present invention realizes a system that can provide users with fast and natural voice responses. This allows users to interact by voice without having to read text, and to enjoy a highly intuitive and user-friendly experience.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] The user speaks to the device. For example, they might say, "What's the weather like today?"

[0072] Step 2:

[0073] The device collects the user's voice using its microphone. The collected voice data is temporarily stored in the device's memory.

[0074] Step 3:

[0075] The device sends the collected audio data to the server. A secure communication protocol, such as HTTPS, is used for transmission.

[0076] Step 4:

[0077] The server receives the audio data sent from the terminal. The received audio data is stored in the server's memory.

[0078] Step 5:

[0079] The server passes the audio data to the speech recognition system (ASR). The ASR converts the audio data into text data.

[0080] Step 6:

[0081] The server receives text data converted from the speech recognition system (ASR). For example, it might be converted to the text, "What's the weather like today?"

[0082] Step 7:

[0083] The server passes the converted text data to a generative artificial intelligence (AI). The AI ​​analyzes the text data and generates an appropriate response, for example, "It's sunny today. The maximum temperature is 25 degrees."

[0084] Step 8:

[0085] The server receives a response text generated by a generative artificial intelligence. For example, the data might say, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0086] Step 9:

[0087] The server passes the generated response text to the text-to-speech (TTS) system. The TTS converts the text data into speech data.

[0088] Step 10:

[0089] The server receives the audio data converted from the TTS system. For example, the audio data might say, "It's sunny today. The highest temperature is 25 degrees Celsius."

[0090] Step 11:

[0091] The server sends the generated audio data to the terminal. A secure communication protocol is used again for transmission.

[0092] Step 12:

[0093] The device receives audio data sent from the server. The received audio data is stored in the device's memory.

[0094] Step 13:

[0095] The device plays the received audio data through its speaker. The user hears the audio response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0096] In this way, a process is realized in which users can receive appropriate voice responses via their device in response to questions they ask.

[0097] (Example 1)

[0098] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In current speech recognition systems, the collection, analysis, response generation, and output of speech data are performed as separate processes, resulting in challenges to overall processing speed and user experience. Furthermore, if data transmission between these processes is not secure, information leaks and security problems may occur. In addition, it is necessary to convert the generated response into high-quality speech data, but if this process is inconsistent, it results in an unnatural interaction for the user.

[0100] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0101] In this invention, the server includes means for collecting audio data, means for transmitting the collected audio data using a secure communication protocol, means for converting the audio data into text data, means for analyzing the text data and generating an appropriate response, means for transmitting the generated text data to a speech synthesis system, and means for converting and playing back the audio data. This ensures that the entire process from audio data collection to final audio output is carried out securely and efficiently, providing users with high-quality audio responses.

[0102] "Audio data" refers to data in which audio is recorded and stored in digital format.

[0103] "Means of collection" refers to the hardware or software functions for acquiring and storing audio data.

[0104] A "server" is a computer system that responds to requests from clients via the internet or a local network.

[0105] "Means of transmission" refers to the hardware or software function for sending data from one point to another.

[0106] A "secure communication protocol" is a means of communication that ensures the safe transmission and reception of data, and includes protocols such as HTTPS and SSL / TLS.

[0107] "Speech recognition technology" is a technology that analyzes speech data and converts its content into text data.

[0108] "Text data" refers to the digital format in which character information is displayed or stored.

[0109] "Means of analysis" refers to the hardware or software functions used to process collected or acquired data and understand its meaning.

[0110] A "generative AI model" is a program or algorithm that uses artificial intelligence technology to generate new data or responses.

[0111] "Response" refers to the answer or reaction that a system gives to a user's inquiry or instruction.

[0112] A "speech synthesis system" refers to a technology or system that converts text data into speech data.

[0113] "Means of playback" refers to the hardware or software function that outputs audio data as sound through an output device such as a speaker.

[0114] Modes for carrying out the invention

[0115] The present invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by having a user speak to a terminal. This system includes means for collecting voice data, means for transmitting the collected voice data to a server, means for converting the voice data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data into voice data, means for playing back the converted voice data, and means for transmitting the voice data using a secure communication protocol.

[0116] Collection of audio data:

[0117] When a user speaks to the device, saying "What's the weather like today?", the device uses its built-in microphone to collect audio data. This audio data is temporarily stored in the device's local storage.

[0118] Sending audio data:

[0119] The collected audio data is sent to the server using a secure communication protocol (e.g., HTTPS). This process maintains secure communication because the data is encrypted.

[0120] Speech recognition processing:

[0121] The server sends the received audio data to an automatic speech recognition system (ASR). This system employs technology to convert audio data into text data, and converts the audio data into text data in the format "What's the weather like today?".

[0122] Response generation:

[0123] The server uses a generative AI model (e.g., generative artificial intelligence) to analyze text data and generate an appropriate response. This model generates responses based on the information requested by the user, for example, generating a text response such as "It's sunny today. The maximum temperature is 25 degrees."

[0124] Speech synthesis:

[0125] The generated text response is sent to a text-to-speech system (e.g., TTS). This system converts the text response into speech data.

[0126] Sending audio data:

[0127] The generated audio data is then sent back from the server to the terminal using a secure communication protocol.

[0128] Audio playback:

[0129] The device saves the received audio data to local storage and plays the audio using its built-in speaker. The user can hear the audio response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0130] Specific example:

[0131] The following interactions are possible as examples of use.

[0132] To obtain weather information:

[0133] 1. User: "What's the weather like today?"

[0134] 2. Terminal: Collects audio and sends it to the server via HTTPS.

[0135] 3. Server: The speech recognition system converts the voice data into the text "Please tell me today's weather."

[0136] 4. Server: The generation AI model generates the response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0137] 5. Server: Converts the generated text data into speech data using a speech synthesis system.

[0138] 6. Server: Sends audio data to the terminal.

[0139] 7. Device: Plays the received audio data.

[0140] To retrieve restaurant information:

[0141] 1. User: "Can you tell me about some nearby restaurants?"

[0142] 2. Terminal: Collects audio and sends it to the server via HTTPS.

[0143] 3. Server: The speech recognition system converts the voice data into text, "Please tell me about nearby restaurants."

[0144] 4. Server: The generation AI model generates a response such as, "There is a restaurant A nearby. Its opening hours are..."

[0145] 5. Server: Converts the generated text data into speech data using a speech synthesis system.

[0146] 6. Server: Sends audio data to the terminal.

[0147] 7. Device: Plays the received audio data.

[0148] As a result, the present invention enables intuitive voice-based interaction and provides users with a high level of convenience.

[0149] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0150] Step 1:

[0151] Collection of voice input

[0152] The user speaks to the device and says, "What's the weather like today?"

[0153] The device uses its built-in microphone to collect audio data and temporarily stores it in local storage.

[0154] Input: User's voice

[0155] Output: Collected audio data

[0156] Step 2:

[0157] Sending audio data to the server

[0158] The device sends the collected voice data to the server using a secure communication protocol (HTTPS).

[0159] Input: Collected audio data

[0160] Output: Encrypted audio data sent to the server

[0161] Step 3:

[0162] Speech recognition system processing

[0163] The server sends the received audio data to an automatic speech recognition system (ASR), which converts the audio data into text data.

[0164] Input: Encrypted audio data

[0165] Output: Converted text data (e.g., "What's the weather like today?")

[0166] Step 4:

[0167] Text data analysis

[0168] The server sends the converted text data to a generative AI model (e.g., generative artificial intelligence) for analysis. The generative AI model understands the user's intent and generates an appropriate response to the request.

[0169] Input: Converted text data

[0170] Output: Generated response text (Example: "It's sunny today. The high temperature is 25 degrees Celsius.")

[0171] Step 5:

[0172] Response text speech synthesis

[0173] The server sends the generated response text to a text-to-speech (TTS) system, which converts the text data into speech data.

[0174] Input: Generated response text

[0175] Output: Generated audio data

[0176] Step 6:

[0177] Sending audio data to a terminal

[0178] The server sends the generated audio data to the terminal via a secure communication protocol.

[0179] Input: Generated audio data

[0180] Output: Encrypted audio data sent to the terminal

[0181] Step 7:

[0182] Audio playback

[0183] The device temporarily stores the received audio data in local storage and plays it back using the built-in speaker. This allows the user to hear the audio response.

[0184] Input: Encrypted audio data sent

[0185] Output: Played audio (Example: "It's sunny today. The high temperature is 25 degrees Celsius.")

[0186] As a result, users can seamlessly receive questions and answers via voice. The specific integration of hardware and software creates a system that provides a high-quality user experience.

[0187] (Application Example 1)

[0188] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0189] Conventional food delivery services require users to manually operate them using devices such as smartphones, and the complexity of these operations and the lack of intuitive interfaces have been problematic. Furthermore, users often have to go through lengthy procedures when placing specific orders or searching for information, which has contributed to decreased customer satisfaction. This invention aims to solve these problems by providing an intuitive and efficient food delivery system using voice control.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0191] In this invention, the server includes means for converting voice data into text data, means for retrieving information based on voice commands from the user and generating an appropriate response, and means for executing a food delivery service based on the generated response. This enables the user to efficiently use a food delivery service using only their voice.

[0192] "Voice data" refers to digital data obtained by recording the user's voice.

[0193] "Means of collection" refers to the hardware and software components for capturing audio data.

[0194] A "server" is a network-connected computer system used for analyzing audio data and generating responses.

[0195] "Text data" refers to digital data consisting of strings of characters obtained by analyzing and converting audio data.

[0196] "Means of analysis" refer to software and algorithms for processing text data and generating appropriate responses based on user requests.

[0197] A "response" is the response data generated in response to a user's voice command.

[0198] "Generative artificial intelligence" refers to machine learning models that include natural language processing techniques and are used to analyze text data and generate appropriate responses.

[0199] A "prompt message" is text data input to a generative artificial intelligence system, and it contains instructions that the AI ​​uses to generate a response based on its content.

[0200] A "speech synthesis system" is a software system used to convert text data into speech data.

[0201] "Means of playback" refer to the hardware and software components that enable the user to listen to the converted audio data.

[0202] A "food delivery service" is a service that delivers food based on a user's order.

[0203] This invention relates to a system that retrieves information based on a user's voice command, generates an appropriate response, and then executes a food delivery service based on that response. This system can be used by the user speaking into a terminal.

[0204] System Configuration and Functions

[0205] This system consists of the following main components:

[0206] 1. Audio acquisition means: Includes a microphone for acquiring audio data and its control software.

[0207] 2. Means of communication with the server: Voice data is sent to the server using a secure protocol (e.g., HTTPS).

[0208] 3. Speech recognition means: A speech recognition system installed on the server (e.g., SpeechRecognition library) is used to convert the speech data into text.

[0209] 4. Response generation means: Text data is analyzed using generative artificial intelligence (e.g., GPT model) and an appropriate response is generated.

[0210] 5. Speech synthesis means: A speech synthesis system (e.g., a Text-to-Speech library) is used to convert text data into speech data.

[0211] 6. Audio playback means: The converted audio data is played back through the speaker.

[0212] System details

[0213] Voice collection and transmission

[0214] When a user speaks into the device, audio data is collected by the microphone. This audio data is temporarily stored on the device and then sent to a server using a secure protocol.

[0215] Speech recognition processing

[0216] On the server, the audio data is converted into text data using a speech recognition system (e.g., the SpeechRecognition library). At this stage, the content spoken by the user is obtained in text format.

[0217] Response generation

[0218] The converted text data is analyzed by a generative artificial intelligence model (e.g., a GPT model) to generate an appropriate response. This process uses predefined prompt statements. Examples of prompt statements are shown below:

[0219] User: I want to order a hamburger.

[0220] Assistant: Which hamburger would you like to order? Our current menu includes cheeseburgers, double cheeseburgers, and bacon burgers.

[0221] Speech synthesis and playback

[0222] The generated response is further converted into audio data using a speech synthesis system, and this audio data is sent to the terminal. The terminal then plays the received audio data through its speaker.

[0223] Specific example

[0224] For example, when a user says to the terminal, "I want to order a hamburger," the following process takes place:

[0225] 1. Audio data is collected and sent to the server.

[0226] 2. The audio data is converted to text on the server.

[0227] 3. The text data is analyzed by a generative artificial intelligence system, and a response is generated: "Which hamburger would you like to order? Our current menu includes cheeseburgers, double cheeseburgers, and bacon burgers."

[0228] 4. The generated response is converted into audio data and sent to the terminal.

[0229] 5. The audio data is played on the device.

[0230] In this way, users can efficiently utilize food delivery services using only their voice.

[0231] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0232] Step 1:

[0233] The user speaks into the device. The user's voice is collected as audio data through the microphone. Input: User's speech. Output: Audio data.

[0234] Step 2:

[0235] The terminal temporarily stores the collected audio data and sends it to the server using a secure protocol (e.g., HTTPS). Input: Audio data. Output: Audio data sent to the server.

[0236] Step 3:

[0237] The server converts the received audio data into text data using a speech recognition system (e.g., the SpeechRecognition library). Input: Audio data. Output: Text data.

[0238] Step 4:

[0239] The server sends the converted text data to a generative artificial intelligence model (e.g., a GPT model) to generate an appropriate response. Input: Text data. Output: Text data for the response.

[0240] Step 5:

[0241] The generated text data for the response is sent to a text-to-speech system (e.g., a Text-to-Speech library) and converted into speech data. Input: Text data for the response. Output: Speech data.

[0242] Step 6:

[0243] The server sends the generated audio data to the terminal. Input: Audio data. Output: Audio data sent to the terminal.

[0244] Step 7:

[0245] The device plays the received audio data through its speaker. The user can hear this audio response. Input: Audio data. Output: Audio response played from the speaker.

[0246] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0247] This invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by a user speaking into a terminal. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and generates appropriate responses based on those emotions. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, means for playing back the converted voice data, and an emotion engine that recognizes the user's emotions.

[0248] System details:

[0249] In an embodiment of the present invention, the user first speaks into the terminal. The terminal collects the user's voice using a microphone and temporarily stores this voice data. The collected voice data is transmitted from the terminal to the server via a wireless communication module. A secure communication protocol, such as HTTPS, is used to transmit the voice data.

[0250] Speech recognition processing:

[0251] The server receives the audio data transmitted from the terminal. Next, the Automatic Speech Recognition (ASR) system installed on the server analyzes the audio data and converts it into text data. This text data is generated based on what the user has said and uses highly accurate speech recognition technology.

[0252] Emotion recognition processing:

[0253] The server uses an emotion engine to analyze the user's emotions based on the converted text data. This emotion engine identifies the user's emotional state (e.g., joy, anger, sadness) from the characteristics of the voice and text.

[0254] Response generation:

[0255] The server then sends the converted text data to a generative artificial intelligence (for example, a model using the latest natural language processing techniques, such as the GPT model), taking into account the user's emotional state identified by the emotion engine, and analyzes this data. The generative AI generates the information and appropriate responses that the user is looking for, and these responses are adjusted to reflect the emotional state.

[0256] Speech synthesis:

[0257] The generated response is sent to the server as text data. The server then sends this text data to a text-to-speech (TTS) system, which converts it into speech data.

[0258] Audio output:

[0259] Finally, the server sends the generated audio data to the terminal. The terminal plays the received audio data through its speaker and provides a response to the user. This response is adjusted according to the user's emotional state, resulting in a more natural and appropriate conversation.

[0260] Specific example:

[0261] The following are specific examples of how to use this system.

[0262] Usage example 1:

[0263] Let's say the user sadly asks the device, "What's the weather like today?"

[0264] 1. User: Speaks sadly to the device, "What's the weather like today?"

[0265] 2. Terminal: Collects audio and sends the audio data to the server.

[0266] 3. Server: Converts speech data into text using a speech recognition system.

[0267] 4. Server: Uses an emotion engine to recognize the user's emotion as "sadness".

[0268] 5. Server: The converted text data and user sentiment information are sent to the generative artificial intelligence system, which then generates the response, "It's sunny today, but please take it easy and relax."

[0269] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0270] 7. Terminal: Plays audio data sent from the server.

[0271] Usage example 2:

[0272] If a user angrily asks the device, "Tell me about nearby restaurants," the system will perform the following actions.

[0273] 1. User: Speaks angrily to the device, "Tell me about nearby restaurants."

[0274] 2. Terminal: Collects audio and sends the audio data to the server.

[0275] 3. Server: Converts speech data into text using a speech recognition system.

[0276] 4. Server: Uses an emotion engine to recognize the user's emotion as "anger".

[0277] 5. Server: The converted text data and user sentiment information are sent to the generative artificial intelligence system, which then generates a response saying, "There are restaurants A, B, and C nearby. Please relax and enjoy your meal."

[0278] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0279] 7. Terminal: Plays the voice data sent from the server.

[0280] Thus, the present invention realizes a system that can provide a more appropriate and natural voice response according to the user's emotional state. As a result, the user can interact by voice without reading text, and can enjoy a very intuitive and convenient experience.

[0281] The following describes the processing flow.

[0282] Step 1:

[0283] The user speaks to the terminal. For example, the user says, "What's the weather like today?"

[0284] Step 2:

[0285] The terminal collects the user's voice with a microphone. The collected voice data is temporarily stored in the terminal's memory.

[0286] Step 3:

[0287] The terminal sends the collected voice data to the server. For transmission, a secure communication protocol, such as HTTPS, is used. [

[0288] Step 4:

[0289] The server receives the voice data sent from the terminal. The received voice data is stored in the server's memory.

[0290] Step 5:

[0291] The server passes the voice data to a voice recognition system (ASR: Automatic Speech Recognition). The ASR analyzes the voice data and converts it into text data.

[0292] Step 6:

[0293] The server receives text data converted from the speech recognition system (ASR). For example, it might be converted to the text, "What's the weather like today?"

[0294] Step 7:

[0295] The server passes the converted text data to the emotion engine. The emotion engine analyzes the user's emotional state from the characteristics of the voice and text, and recognizes it as, for example, "sadness."

[0296] Step 8:

[0297] The server receives emotion data obtained from the emotion engine. For example, emotion data such as "sadness."

[0298] Step 9:

[0299] The server sends the converted text data and sentiment data to a generative artificial intelligence (AI). The AI ​​analyzes the text data and generates an appropriate response that reflects the user's emotions. For example, it might generate a response such as, "It's sunny today, but please take it easy and relax."

[0300] Step 10:

[0301] The server receives a response text generated by a generative artificial intelligence. For example, the text might say, "It's sunny today, but please take it easy and don't overexert yourself."

[0302] Step 11:

[0303] The server passes the generated response text to the Text-to-Speech (TTS) system. The TTS system converts the text data into speech data.

[0304] Step 12:

[0305] The server receives the voice data converted from the TTS system. For example, the voice data is "Today is sunny, but please take it easy and don't overdo it."

[0306] Step 13:

[0307] The server transmits the generated voice data to the terminal. A secure communication protocol is used again for the transmission.

[0308] Step 14:

[0309] The terminal receives the voice data transmitted from the server. The received voice data is stored in the memory of the terminal.

[0310] Step 15:

[0311] The terminal plays back the received voice data through the speaker. The user hears the voice response "Today is sunny, but please take it easy and don't overdo it."

[0312] In this way, a flow is realized in which, for the question spoken by the user, an appropriate response can be received in a voice reflecting the user's emotional state via the terminal.

[0313] (Example 2)

[0314] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0315] Conventional voice recognition systems convert voice into text and generate appropriate responses, but lack the ability to adjust responses based on the user's emotions, and the dialogue may become mechanical and unnatural. The present invention attempts to realize a natural and human-like dialogue by recognizing the user's emotions and generating responses considering emotional elements based thereon.

[0316] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0317] In this invention, the server includes means for converting voice data into text data, means for analyzing the text data and recognizing the user's emotions, and means for generating an appropriate response based on the emotional state. This makes it possible to generate a natural and appropriate response that is in line with the user's emotional state.

[0318] "Voice data" refers to data recorded in digital format from what a user speaks into a device.

[0319] "Means of collection" refers to a device or method that captures audio data using a microphone built into the terminal.

[0320] "Transmission means" refers to a device or method for transmitting collected audio data to a server using a wireless communication module.

[0321] "Text data" refers to the text format of audio data converted by a speech recognition system.

[0322] "Means of conversion" refer to speech recognition systems and algorithms used to convert speech data into text data.

[0323] "Means of analysis" refers to a device or program that uses converted text data to recognize the user's emotions and interpret their meaning and content.

[0324] "Means of recognizing emotions" refer to emotion engines and algorithms that analyze the characteristics of text data to identify the user's emotional state.

[0325] "Means for generating appropriate responses" refer to generative artificial intelligence and natural language generation technologies that create responses to enable natural dialogue based on the user's emotional state and text data.

[0326] "Means for converting to audio data" refers to a speech synthesis system for converting the generated text response into audio data.

[0327] "Means of playback" refers to a device or method for playing audio data transmitted from a server through a speaker on a terminal.

[0328] The present invention is a system that collects, analyzes, generates responses to, and responds to voice data when a user speaks into a terminal. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and generates appropriate responses based on those emotions. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, and means for playing back the converted voice data.

[0329] Hardware and software to be used

[0330] 1. Terminal

[0331] Microphone: Used to collect the user's voice.

[0332] Wireless communication module: Used to transmit collected audio data to a server. Specifically, it utilizes Wi-Fi or Bluetooth.

[0333] Speaker: Used to play audio data received from the server to the user.

[0334] 2. Server

[0335] Automatic Speech Recognition (ASR): Used to convert speech data into text data. Specifically, Google® Cloud Speech-to-Text is used.

[0336] Emotion Engine: Used to recognize the user's emotions from text. Specifically, IBM Watson® Tone Analyzer is used.

[0337] Generative Artificial Intelligence (NLG: Natural Language Generation): Used to generate appropriate responses. Specifically, the OpenAI® GPT model is used.

[0338] Text-to-Speech (TTS): Used to convert generated text data into speech data. Specifically, Amazon Polly is used.

[0339] Specific example

[0340] Example 1: When a user sadly asks, "What's the weather like today?"

[0341] 1. User: Speaks sadly to the device, "What's the weather like today?"

[0342] 2. Terminal: The microphone collects audio and stores it temporarily. Then, the audio data is sent to the server via a secure protocol (HTTPS) through the wireless communication module.

[0343] 3. Server: Uses a speech recognition system to convert speech data into text data with high accuracy.

[0344] 4. Server: Uses an emotion engine to recognize the user's emotion as "sadness" from the converted text data.

[0345] 5. Server: Generative artificial intelligence generates a response that takes emotions into consideration: "It's sunny today, but please take it easy and relax."

[0346] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0347] 7. Terminal: Receives audio data from the server and plays it through the speaker.

[0348] Example 2: When a user angrily asks, "Tell me about nearby restaurants."

[0349] 1. User: Speaks angrily to the device, "Tell me about nearby restaurants."

[0350] 2. Terminal: The microphone collects audio and stores it temporarily. Then, the audio data is sent to the server via a secure protocol (HTTPS) through the wireless communication module.

[0351] 3. Server: Uses a speech recognition system to convert speech data into text data with high accuracy.

[0352] 4. Server: Uses an emotion engine to recognize the user's emotion as "anger" from the converted text data.

[0353] 5. Server: Generative artificial intelligence generates a response that takes emotions into consideration: "There are restaurants A, B, and C nearby. Please relax and enjoy your meal."

[0354] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0355] 7. Terminal: Receives audio data from the server and plays it through the speaker.

[0356] Example of a prompt

[0357] The following are examples of prompts to input into a generative artificial intelligence:

[0358] "The user is sadly asking, 'What's the weather like today?' Please generate an appropriate response that reflects this emotion."

[0359] "The user is asking, 'Tell me about nearby restaurants,' in an angry tone. Please understand this sentiment and provide an appropriate response."

[0360] In this way, the entire system works together to achieve natural dialogue that is in line with the user's emotions.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1: Collect audio data

[0363] User: Speaks into the device. For example, "What's the weather like today?"

[0364] Input: User's voice.

[0365] Terminal: Uses a built-in microphone to collect the user's voice as digital audio data and temporarily stores it. The microphone should be highly sensitive and have noise filtering capabilities.

[0366] Output: Temporarily stored audio data.

[0367] Step 2: Sending the audio data

[0368] Terminal: Prepares to transmit the collected audio data.

[0369] Input: Temporarily stored audio data.

[0370] Terminal: Uses a wireless communication module (e.g., Wi-Fi or Bluetooth) to transmit voice data to the server. A secure communication protocol (e.g., HTTPS) is used to ensure data security.

[0371] Output: Audio data sent to the server.

[0372] Step 3: Convert speech to text

[0373] Server: Receives audio data sent from the terminal.

[0374] Input: Audio data sent to the server.

[0375] Server: Uses a speech recognition system (ASR, e.g., Google Cloud Speech-to-Text) to convert speech data into text data. This system provides highly accurate conversion.

[0376] Output: Text data.

[0377] Step 4: Emotion Recognition

[0378] Server: Receives the converted text data.

[0379] Input: Text data.

[0380] Server: Uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the characteristics of text data and identify the user's emotions. For example, if a sad tone is detected in the question "What's the weather like today?", the emotion engine recognizes it as "sadness".

[0381] Output: Emotional information (e.g., "sadness").

[0382] Step 5: Response Generation

[0383] Server: Receives recognized emotion information and text data.

[0384] Input: Sentimental information and text data.

[0385] Server: Uses a generative AI model (e.g., OpenAI's GPT model) to analyze input data and generate appropriate responses that take the user's emotions into consideration. For example, if the emotion is "sadness," a response such as "It's sunny today, but please take it easy and relax" would be generated.

[0386] Output: Text data of the generated response.

[0387] Step 6: Convert the generated response text data into audio data

[0388] Server: Prepares to convert the generated response text into audio data.

[0389] Input: Text data of the generated response.

[0390] Server: Uses a text-to-speech system (TTS, e.g., Amazon Polly) to convert text data into natural-sounding speech data.

[0391] Output: Generated audio data.

[0392] Step 7: Sending and playing audio data

[0393] Server: Sends the generated audio data to the terminal.

[0394] Input: Generated audio data.

[0395] Server: Uses a secure communication protocol (e.g., HTTPS) to send the generated audio data to the terminal.

[0396] Terminal: Receives transmitted audio data and plays it back to the user through the speaker. For example, it might play the message, "It's sunny today, but please take it easy and relax."

[0397] Output: Audio data that the user can listen to.

[0398] As described above, by having each step work together, a natural dialogue that takes user emotions into consideration is achieved.

[0399] (Application Example 2)

[0400] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0401] In modern factories and production lines, smooth communication between human workers and robots is essential. However, conventional systems have insufficient interpretation of voice commands and emotional recognition, resulting in problems such as decreased work efficiency and increased worker stress. Furthermore, it is difficult for robots to understand the emotions of workers and provide appropriate feedback and guidance. Against this backdrop, there is a need for a system that can accurately recognize user voice commands, analyze emotions, and respond appropriately based on the results.

[0402] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0403] In this invention, the server includes means for collecting voice data, means for transmitting the collected voice data to the server, means for converting the voice data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data back into voice data, means for playing back the converted voice data, emotion analysis means for recognizing the user's emotions, and instruction execution means for understanding instructions and performing appropriate tasks while considering the emotional state. As a result, an efficient and less stressful work environment is possible, as workers can give instructions to the robot by voice, and those instructions are interpreted appropriately according to the emotional state.

[0404] "Means for collecting audio data" refers to a device or system that has the function of capturing user audio in real time.

[0405] "Means for transmitting collected audio data to a server" refers to a device or system that has the function of transmitting collected audio data to a remote server via a network.

[0406] "Means of converting audio data to text data" refers to software or systems that analyze audio data and convert it into corresponding text data.

[0407] "Means for analyzing text data and generating appropriate responses" refers to an algorithm or system that analyzes text data to understand the user's intent and generates a response based on the results.

[0408] "Means for converting generated text data into audio data" refers to a speech synthesis system that has the function of converting text data into audio data.

[0409] "Means for playing back converted audio data" refers to a system that plays back converted audio data through sound devices such as speakers.

[0410] "An emotion analysis method for recognizing a user's emotions" refers to a technology or system that analyzes the characteristics of a user's voice or text to identify their emotional state.

[0411] "An instruction execution means that understands instructions and performs appropriate tasks while considering the emotional state" refers to a system that has the function of appropriately interpreting the user's instructions based on the results of emotion analysis and performing predetermined tasks.

[0412] This invention is a system used by users to give voice commands to factory robots, which then appropriately understand those commands and perform the tasks. This system can collect the user's voice, analyze their emotions, and play back the generated response in voice.

[0413] System details

[0414] Hardware and software

[0415] 1. Means of collecting audio data:

[0416] The device uses its built-in microphone to collect the user's voice.

[0417] Examples include smartphones and dedicated microphone devices.

[0418] 2. Means for transmitting audio data to the server:

[0419] The terminal transmits the collected voice data to the server using a secure protocol (e.g., HTTPS) via a wireless communication module (e.g., Wi-Fi or LTE).

[0420] 3. Means of converting audio data to text data:

[0421] The server uses speech recognition software (ASR: Automatic Speech Recognition) to convert the audio data into text.

[0422] Example: Google Cloud Speech-to-Text API.

[0423] 4. Means for analyzing text data and generating appropriate responses:

[0424] The server analyzes the converted text data using natural language processing techniques (e.g., Hugging Face's transformers library) to recognize the user's emotions. Furthermore, it generates an appropriate response using generative artificial intelligence (e.g., GPT-3(registered trademark).5).

[0425] 5. Means for converting generated text data into audio data:

[0426] The text data generated on the server is converted into speech data using a text-to-speech (TTS) system.

[0427] Example: Google Text-to-Speech API.

[0428] 6. Means for playing back the converted audio data:

[0429] The terminal plays the audio data received from the server through its speaker.

[0430] 7. Emotion analysis means:

[0431] The server uses a sentiment analysis system (e.g., a sentiment-analysis model) to analyze the user's emotions.

[0432] 8. Instruction execution means:

[0433] The server sends instructions to the factory robots based on responses generated while taking emotional states into consideration, and the robots perform the appropriate tasks.

[0434] Example: Robot control system.

[0435] Specific example

[0436] Considering a scenario where a worker gives instructions to a robot using voice commands, the following specific processes would occur:

[0437] 1. Collection: The terminal collects voice messages from workers such as, "How do I fix this machine's malfunction?"

[0438] 2. Transmission: The collected audio data is sent to the server.

[0439] 3. Analysis: The audio data is converted to text data on the server, and the emotion analysis system recognizes anger from the tone of the voice.

[0440] 4. Response Generation: The generative artificial intelligence generates an appropriate response based on the prompt message, "The worker seems angry. Instruction: How do I fix this machine malfunction?" For example, it might generate a response such as, "Let's deal with the machine malfunction calmly. First, check the manual's procedures, and call support if necessary."

[0441] 5. Conversion and Output: The generated text response is converted into audio data and played back through the device's speaker.

[0442] Example of a prompt

[0443] Examples of prompt messages sent to a generative artificial intelligence are as follows:

[0444] text

[0445] The worker seems angry. Instructions: How do I fix this broken machine?

[0446] This allows the system to generate appropriate responses that take the user's emotions into account and provide them as voice messages.

[0447] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0448] Processing steps

[0449] Step 1:

[0450] The terminal collects voice from the worker using a microphone. The input is the worker's voice, and the output is the collected voice data. Specifically, the terminal's built-in microphone captures the worker's speech and temporarily stores it as digital voice data.

[0451] Step 2:

[0452] The terminal transmits the collected audio data to the server via a wireless communication module. The input is the collected audio data, and the output is the audio data transferred to the server. Specifically, the terminal uses the HTTPS protocol to send the audio data to a specified endpoint on the server.

[0453] Step 3:

[0454] The server uses speech recognition software to convert received audio data into text data. The input is audio data, and the output is the converted text data. Specifically, it uses the Google Cloud Speech-to-Text API, among others, to transcribe the audio data with high accuracy.

[0455] Step 4:

[0456] The server performs sentiment analysis from text and audio data. The input is text and audio data, and the output is the user's emotional state. Specifically, it uses the Hugging Face sentiment-analysis model to identify the emotion in the text (e.g., joy, anger, sadness).

[0457] Step 5:

[0458] The server considers the emotional state and generates an appropriate response using generative artificial intelligence. The input is text data and the emotional state, and the output is the generated text response. Specifically, a prompt sentence is input to a generative AI model such as GPT-3.5, and an appropriate response is generated as a result. For example, the prompt sentence is "The worker seems angry. Instructions: How do I fix this machine malfunction?"

[0459] Step 6:

[0460] The server converts the generated text response into speech data using a speech synthesis system. The input is the generated text response, and the output is speech data. Specifically, the Google Text-to-Speech API is used to convert text to speech.

[0461] Step 7:

[0462] The terminal plays audio data received from the server through its speaker. The input is audio data, and the output is an audio response. Specifically, the terminal's speaker system plays the audio data and provides a response to the worker.

[0463] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0464] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0465] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0466] [Second Embodiment]

[0467] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0468] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0469] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0470] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0471] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0472] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0473] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0474] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0475] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0476] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0477] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0478] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0479] The present invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by a user speaking into a terminal. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, and means for playing back the converted voice data.

[0480] System details:

[0481] In an embodiment of the present invention, the user first speaks into the terminal. The terminal collects the user's voice using a microphone and temporarily stores this voice data. The collected voice data is transmitted from the terminal to the server via a wireless communication module. A secure communication protocol, such as HTTPS, is used to transmit the voice data.

[0482] Speech recognition processing:

[0483] The server receives the audio data transmitted from the terminal. Next, the Automatic Speech Recognition (ASR) system installed on the server analyzes the audio data and converts it into text data. This text data is generated based on what the user has said and uses highly accurate speech recognition technology.

[0484] Response generation:

[0485] The server then sends the converted text data to a generative artificial intelligence (for example, a model using the latest natural language processing technology, such as the GPT model), which analyzes the data. The generative AI then generates the information and appropriate responses requested by the user.

[0486] Speech synthesis:

[0487] The generated response is sent back to the server as text data. The server then sends this text data to a text-to-speech (TTS) system, which converts it into speech data.

[0488] Audio output:

[0489] Finally, the server sends the generated audio data to the terminal. The terminal plays the audio data received from the server through its speaker and provides a response to the user.

[0490] Specific example:

[0491] The following are specific examples of how to use this system.

[0492] Usage example 1:

[0493] Let's say a user speaks to their device and asks, "What's the weather like today?"

[0494] 1. User: Speaks to the device, "What's the weather like today?"

[0495] 2. Terminal: Collects audio and sends the audio data to the server.

[0496] 3. Server: Converts speech data into text using a speech recognition system.

[0497] 4. Server: Uses generative artificial intelligence to generate the response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0498] 5. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0499] 6. Terminal: Plays audio data sent from the server.

[0500] Usage example 2:

[0501] When a user speaks to the device and says, "Tell me about nearby restaurants," the system performs the following actions.

[0502] 1. User: Speaks into the device and says, "Tell me about nearby restaurants."

[0503] 2. Terminal: Collects audio and sends the audio data to the server.

[0504] 3. Server: Converts speech data into text using a speech recognition system.

[0505] 4. Server: Uses generative artificial intelligence to search for "information on nearby restaurants" and generates a response such as "There is a restaurant A nearby. Its opening hours are..."

[0506] 5. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0507] 6. Terminal: Plays audio data sent from the server.

[0508] In this way, the present invention realizes a system that can provide users with fast and natural voice responses. This allows users to interact by voice without having to read text, and to enjoy a highly intuitive and user-friendly experience.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The user speaks to the device. For example, they might say, "What's the weather like today?"

[0512] Step 2:

[0513] The device collects the user's voice using its microphone. The collected voice data is temporarily stored in the device's memory.

[0514] Step 3:

[0515] The device sends the collected audio data to the server. A secure communication protocol, such as HTTPS, is used for transmission.

[0516] Step 4:

[0517] The server receives the audio data sent from the terminal. The received audio data is stored in the server's memory.

[0518] Step 5:

[0519] The server passes the audio data to the speech recognition system (ASR). The ASR converts the audio data into text data.

[0520] Step 6:

[0521] The server receives text data converted from the speech recognition system (ASR). For example, it might be converted to the text, "What's the weather like today?"

[0522] Step 7:

[0523] The server passes the converted text data to a generative artificial intelligence (AI). The AI ​​analyzes the text data and generates an appropriate response, for example, "It's sunny today. The maximum temperature is 25 degrees."

[0524] Step 8:

[0525] The server receives a response text generated by a generative artificial intelligence. For example, the data might say, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0526] Step 9:

[0527] The server passes the generated response text to the text-to-speech (TTS) system. The TTS converts the text data into speech data.

[0528] Step 10:

[0529] The server receives the audio data converted from the TTS system. For example, the audio data might say, "It's sunny today. The highest temperature is 25 degrees Celsius."

[0530] Step 11:

[0531] The server sends the generated audio data to the terminal. A secure communication protocol is used again for transmission.

[0532] Step 12:

[0533] The device receives audio data sent from the server. The received audio data is stored in the device's memory.

[0534] Step 13:

[0535] The device plays the received audio data through its speaker. The user hears the audio response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0536] In this way, a process is realized in which users can receive appropriate voice responses via their device in response to questions they ask.

[0537] (Example 1)

[0538] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0539] In current speech recognition systems, the collection, analysis, response generation, and output of speech data are performed as separate processes, resulting in challenges to overall processing speed and user experience. Furthermore, if data transmission between these processes is not secure, information leaks and security problems may occur. In addition, it is necessary to convert the generated response into high-quality speech data, but if this process is inconsistent, it results in an unnatural interaction for the user.

[0540] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0541] In this invention, the server includes means for collecting audio data, means for transmitting the collected audio data using a secure communication protocol, means for converting the audio data into text data, means for analyzing the text data and generating an appropriate response, means for transmitting the generated text data to a speech synthesis system, and means for converting and playing back the audio data. This ensures that the entire process from audio data collection to final audio output is carried out securely and efficiently, providing users with high-quality audio responses.

[0542] "Audio data" refers to data in which audio is recorded and stored in digital format.

[0543] "Means of collection" refers to the hardware or software functions for acquiring and storing audio data.

[0544] A "server" is a computer system that responds to requests from clients via the internet or a local network.

[0545] "Means of transmission" refers to the hardware or software function for sending data from one point to another.

[0546] A "secure communication protocol" is a means of communication that ensures the safe transmission and reception of data, and includes protocols such as HTTPS and SSL / TLS.

[0547] "Speech recognition technology" is a technology that analyzes speech data and converts its content into text data.

[0548] "Text data" refers to the digital format in which character information is displayed or stored.

[0549] "Means of analysis" refers to the hardware or software functions used to process collected or acquired data and understand its meaning.

[0550] A "generative AI model" is a program or algorithm that uses artificial intelligence technology to generate new data or responses.

[0551] "Response" refers to the answer or reaction that a system gives to a user's inquiry or instruction.

[0552] A "speech synthesis system" refers to a technology or system that converts text data into speech data.

[0553] "Means of playback" refers to the hardware or software function that outputs audio data as sound through an output device such as a speaker.

[0554] Modes for carrying out the invention

[0555] The present invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by having a user speak to a terminal. This system includes means for collecting voice data, means for transmitting the collected voice data to a server, means for converting the voice data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data into voice data, means for playing back the converted voice data, and means for transmitting the voice data using a secure communication protocol.

[0556] Collection of audio data:

[0557] When a user speaks to the device, saying "What's the weather like today?", the device uses its built-in microphone to collect audio data. This audio data is temporarily stored in the device's local storage.

[0558] Sending audio data:

[0559] The collected audio data is sent to the server using a secure communication protocol (e.g., HTTPS). This process maintains secure communication because the data is encrypted.

[0560] Speech recognition processing:

[0561] The server sends the received audio data to an automatic speech recognition system (ASR). This system employs technology to convert audio data into text data, and converts the audio data into text data in the format "What's the weather like today?".

[0562] Response generation:

[0563] The server uses a generative AI model (e.g., generative artificial intelligence) to analyze text data and generate an appropriate response. This model generates responses based on the information requested by the user, for example, generating a text response such as "It's sunny today. The maximum temperature is 25 degrees."

[0564] Speech synthesis:

[0565] The generated text response is sent to a text-to-speech system (e.g., TTS). This system converts the text response into speech data.

[0566] Sending audio data:

[0567] The generated audio data is then sent back from the server to the terminal using a secure communication protocol.

[0568] Audio playback:

[0569] The device saves the received audio data to local storage and plays the audio using its built-in speaker. The user can hear the audio response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0570] Specific example:

[0571] The following interactions are possible as examples of use.

[0572] To obtain weather information:

[0573] 1. User: "What's the weather like today?"

[0574] 2. Terminal: Collects audio and sends it to the server via HTTPS.

[0575] 3. Server: The speech recognition system converts the voice data into the text "Please tell me today's weather."

[0576] 4. Server: The generation AI model generates the response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0577] 5. Server: Converts the generated text data into speech data using a speech synthesis system.

[0578] 6. Server: Sends audio data to the terminal.

[0579] 7. Device: Plays the received audio data.

[0580] To retrieve restaurant information:

[0581] 1. User: "Can you tell me about some nearby restaurants?"

[0582] 2. Terminal: Collects audio and sends it to the server via HTTPS.

[0583] 3. Server: The speech recognition system converts the voice data into text, "Please tell me about nearby restaurants."

[0584] 4. Server: The generation AI model generates a response such as, "There is a restaurant A nearby. Its opening hours are..."

[0585] 5. Server: Converts the generated text data into speech data using a speech synthesis system.

[0586] 6. Server: Sends audio data to the terminal.

[0587] 7. Device: Plays the received audio data.

[0588] As a result, the present invention enables intuitive voice-based interaction and provides users with a high level of convenience.

[0589] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0590] Step 1:

[0591] Collection of voice input

[0592] The user speaks to the device and says, "What's the weather like today?"

[0593] The device uses its built-in microphone to collect audio data and temporarily stores it in local storage.

[0594] Input: User's voice

[0595] Output: Collected audio data

[0596] Step 2:

[0597] Sending audio data to the server

[0598] The device sends the collected voice data to the server using a secure communication protocol (HTTPS).

[0599] Input: Collected audio data

[0600] Output: Encrypted audio data sent to the server

[0601] Step 3:

[0602] Speech recognition system processing

[0603] The server sends the received audio data to an automatic speech recognition system (ASR), which converts the audio data into text data.

[0604] Input: Encrypted audio data

[0605] Output: Converted text data (e.g., "What's the weather like today?")

[0606] Step 4:

[0607] Text data analysis

[0608] The server sends the converted text data to a generative AI model (e.g., generative artificial intelligence) for analysis. The generative AI model understands the user's intent and generates an appropriate response to the request.

[0609] Input: Converted text data

[0610] Output: Generated response text (Example: "It's sunny today. The high temperature is 25 degrees Celsius.")

[0611] Step 5:

[0612] Response text speech synthesis

[0613] The server sends the generated response text to a text-to-speech (TTS) system, which converts the text data into speech data.

[0614] Input: Generated response text

[0615] Output: Generated audio data

[0616] Step 6:

[0617] Sending audio data to a terminal

[0618] The server sends the generated audio data to the terminal via a secure communication protocol.

[0619] Input: Generated audio data

[0620] Output: Encrypted audio data sent to the terminal

[0621] Step 7:

[0622] Audio playback

[0623] The device temporarily stores the received audio data in local storage and plays it back using the built-in speaker. This allows the user to hear the audio response.

[0624] Input: Encrypted audio data sent

[0625] Output: Played audio (Example: "It's sunny today. The high temperature is 25 degrees Celsius.")

[0626] As a result, users can seamlessly receive questions and answers via voice. The specific integration of hardware and software creates a system that provides a high-quality user experience.

[0627] (Application Example 1)

[0628] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0629] Conventional food delivery services require users to manually operate them using devices such as smartphones, and the complexity of these operations and the lack of intuitive interfaces have been problematic. Furthermore, users often have to go through lengthy procedures when placing specific orders or searching for information, which has contributed to decreased customer satisfaction. This invention aims to solve these problems by providing an intuitive and efficient food delivery system using voice control.

[0630] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0631] In this invention, the server includes means for converting voice data into text data, means for retrieving information based on voice commands from the user and generating an appropriate response, and means for executing a food delivery service based on the generated response. This enables the user to efficiently use a food delivery service using only their voice.

[0632] "Voice data" refers to digital data obtained by recording the user's voice.

[0633] "Means of collection" refers to the hardware and software components for capturing audio data.

[0634] A "server" is a network-connected computer system used for analyzing audio data and generating responses.

[0635] "Text data" refers to digital data consisting of strings of characters obtained by analyzing and converting audio data.

[0636] "Means of analysis" refer to software and algorithms for processing text data and generating appropriate responses based on user requests.

[0637] A "response" is the response data generated in response to a user's voice command.

[0638] "Generative artificial intelligence" refers to machine learning models that include natural language processing techniques and are used to analyze text data and generate appropriate responses.

[0639] A "prompt message" is text data input to a generative artificial intelligence system, and it contains instructions that the AI ​​uses to generate a response based on its content.

[0640] A "speech synthesis system" is a software system used to convert text data into speech data.

[0641] "Means of playback" refer to the hardware and software components that enable the user to listen to the converted audio data.

[0642] A "food delivery service" is a service that delivers food based on a user's order.

[0643] This invention relates to a system that retrieves information based on a user's voice command, generates an appropriate response, and then executes a food delivery service based on that response. This system can be used by the user speaking into a terminal.

[0644] System Configuration and Functions

[0645] This system consists of the following main components:

[0646] 1. Audio acquisition means: Includes a microphone for acquiring audio data and its control software.

[0647] 2. Means of communication with the server: Voice data is sent to the server using a secure protocol (e.g., HTTPS).

[0648] 3. Speech recognition means: A speech recognition system installed on the server (e.g., SpeechRecognition library) is used to convert the speech data into text.

[0649] 4. Response generation means: Text data is analyzed using generative artificial intelligence (e.g., GPT model) and an appropriate response is generated.

[0650] 5. Speech synthesis means: A speech synthesis system (e.g., a Text-to-Speech library) is used to convert text data into speech data.

[0651] 6. Audio playback means: The converted audio data is played back through the speaker.

[0652] System details

[0653] Voice collection and transmission

[0654] When a user speaks into the device, audio data is collected by the microphone. This audio data is temporarily stored on the device and then sent to a server using a secure protocol.

[0655] Speech recognition processing

[0656] On the server, the audio data is converted into text data using a speech recognition system (e.g., the SpeechRecognition library). At this stage, the content spoken by the user is obtained in text format.

[0657] Response generation

[0658] The converted text data is analyzed by a generative artificial intelligence model (e.g., a GPT model) to generate an appropriate response. This process uses predefined prompt statements. Examples of prompt statements are shown below:

[0659] User: I want to order a hamburger.

[0660] Assistant: Which hamburger would you like to order? Our current menu includes cheeseburgers, double cheeseburgers, and bacon burgers.

[0661] Speech synthesis and playback

[0662] The generated response is further converted into audio data using a speech synthesis system, and this audio data is sent to the terminal. The terminal then plays the received audio data through its speaker.

[0663] Specific example

[0664] For example, when a user says to the terminal, "I want to order a hamburger," the following process takes place:

[0665] 1. Audio data is collected and sent to the server.

[0666] 2. The audio data is converted to text on the server.

[0667] 3. The text data is analyzed by a generative artificial intelligence system, and a response is generated: "Which hamburger would you like to order? Our current menu includes cheeseburgers, double cheeseburgers, and bacon burgers."

[0668] 4. The generated response is converted into audio data and sent to the terminal.

[0669] 5. The audio data is played on the device.

[0670] In this way, users can efficiently utilize food delivery services using only their voice.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The user speaks into the device. The user's voice is collected as audio data through the microphone. Input: User's speech. Output: Audio data.

[0674] Step 2:

[0675] The terminal temporarily stores the collected audio data and sends it to the server using a secure protocol (e.g., HTTPS). Input: Audio data. Output: Audio data sent to the server.

[0676] Step 3:

[0677] The server converts the received audio data into text data using a speech recognition system (e.g., the SpeechRecognition library). Input: Audio data. Output: Text data.

[0678] Step 4:

[0679] The server sends the converted text data to a generative artificial intelligence model (e.g., a GPT model) to generate an appropriate response. Input: Text data. Output: Text data for the response.

[0680] Step 5:

[0681] The generated text data for the response is sent to a text-to-speech system (e.g., a Text-to-Speech library) and converted into speech data. Input: Text data for the response. Output: Speech data.

[0682] Step 6:

[0683] The server sends the generated audio data to the terminal. Input: Audio data. Output: Audio data sent to the terminal.

[0684] Step 7:

[0685] The device plays the received audio data through its speaker. The user can hear this audio response. Input: Audio data. Output: Audio response played from the speaker.

[0686] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0687] This invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by a user speaking into a terminal. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and generates appropriate responses based on those emotions. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, means for playing back the converted voice data, and an emotion engine that recognizes the user's emotions.

[0688] System details:

[0689] In an embodiment of the present invention, the user first speaks into the terminal. The terminal collects the user's voice using a microphone and temporarily stores this voice data. The collected voice data is transmitted from the terminal to the server via a wireless communication module. A secure communication protocol, such as HTTPS, is used to transmit the voice data.

[0690] Speech recognition processing:

[0691] The server receives the audio data transmitted from the terminal. Next, the Automatic Speech Recognition (ASR) system installed on the server analyzes the audio data and converts it into text data. This text data is generated based on what the user has said and uses highly accurate speech recognition technology.

[0692] Emotion recognition processing:

[0693] The server uses an emotion engine to analyze the user's emotions based on the converted text data. This emotion engine identifies the user's emotional state (e.g., joy, anger, sadness) from the characteristics of the voice and text.

[0694] Response generation:

[0695] The server then sends the converted text data to a generative artificial intelligence (for example, a model using the latest natural language processing techniques, such as the GPT model), taking into account the user's emotional state identified by the emotion engine, and analyzes this data. The generative AI generates the information and appropriate responses that the user is looking for, and these responses are adjusted to reflect the emotional state.

[0696] Speech synthesis:

[0697] The generated response is sent to the server as text data. The server then sends this text data to a text-to-speech (TTS) system, which converts it into speech data.

[0698] Audio output:

[0699] Finally, the server sends the generated audio data to the terminal. The terminal plays the received audio data through its speaker and provides a response to the user. This response is adjusted according to the user's emotional state, resulting in a more natural and appropriate conversation.

[0700] Specific example:

[0701] The following are specific examples of how to use this system.

[0702] Usage example 1:

[0703] Let's say the user sadly asks the device, "What's the weather like today?"

[0704] 1. User: Speaks sadly to the device, "What's the weather like today?"

[0705] 2. Terminal: Collects audio and sends the audio data to the server.

[0706] 3. Server: Converts speech data into text using a speech recognition system.

[0707] 4. Server: Uses an emotion engine to recognize the user's emotion as "sadness".

[0708] 5. Server: The converted text data and user sentiment information are sent to the generative artificial intelligence system, which then generates the response, "It's sunny today, but please take it easy and relax."

[0709] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0710] 7. Terminal: Plays audio data sent from the server.

[0711] Usage example 2:

[0712] If a user angrily asks the device, "Tell me about nearby restaurants," the system will perform the following actions.

[0713] 1. User: Speaks angrily to the device, "Tell me about nearby restaurants."

[0714] 2. Terminal: Collects audio and sends the audio data to the server.

[0715] 3. Server: Converts speech data into text using a speech recognition system.

[0716] 4. Server: Uses an emotion engine to recognize the user's emotion as "anger".

[0717] 5. Server: The converted text data and user sentiment information are sent to the generative artificial intelligence system, which then generates a response saying, "There are restaurants A, B, and C nearby. Please relax and enjoy your meal."

[0718] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0719] 7. Terminal: Plays audio data sent from the server.

[0720] Thus, the present invention realizes a system that can provide more appropriate and natural voice responses according to the user's emotional state. As a result, users can interact by voice without reading text, and enjoy a highly intuitive and user-friendly experience.

[0721] The following describes the processing flow.

[0722] Step 1:

[0723] The user speaks to the device. For example, they might say, "What's the weather like today?"

[0724] Step 2:

[0725] The device collects the user's voice using its microphone. The collected voice data is temporarily stored in the device's memory.

[0726] Step 3:

[0727] The device sends the collected audio data to the server. A secure communication protocol, such as HTTPS, is used for transmission.

[0728] Step 4:

[0729] The server receives the audio data sent from the terminal. The received audio data is stored in the server's memory.

[0730] Step 5:

[0731] The server passes the audio data to the speech recognition system (ASR: Automatic Speech Recognition). The ASR analyzes the audio data and converts it into text data.

[0732] Step 6:

[0733] The server receives text data converted from the speech recognition system (ASR). For example, it might be converted to the text, "What's the weather like today?"

[0734] Step 7:

[0735] The server passes the converted text data to the emotion engine. The emotion engine analyzes the user's emotional state from the characteristics of the voice and text, and recognizes it as, for example, "sadness."

[0736] Step 8:

[0737] The server receives emotion data obtained from the emotion engine. For example, emotion data such as "sadness."

[0738] Step 9:

[0739] The server sends the converted text data and sentiment data to a generative artificial intelligence (AI). The AI ​​analyzes the text data and generates an appropriate response that reflects the user's emotions. For example, it might generate a response such as, "It's sunny today, but please take it easy and relax."

[0740] Step 10:

[0741] The server receives a response text generated by a generative artificial intelligence. For example, the text might say, "It's sunny today, but please take it easy and don't overexert yourself."

[0742] Step 11:

[0743] The server passes the generated response text to the Text-to-Speech (TTS) system. The TTS system converts the text data into speech data.

[0744] Step 12:

[0745] The server receives the audio data converted from the TTS system. For example, the audio data might say, "It's sunny today, but please take it easy and don't overexert yourself."

[0746] Step 13:

[0747] The server sends the generated audio data to the terminal. A secure communication protocol is used again for transmission.

[0748] Step 14:

[0749] The device receives audio data sent from the server. The received audio data is stored in the device's memory.

[0750] Step 15:

[0751] The device plays the received audio data through its speaker. The user hears the audio response, "It's sunny today, but please take it easy and relax."

[0752] In this way, a process is realized in which, in response to a question posed by the user, an appropriate response is received via the device in the form of voice that reflects the user's emotional state.

[0753] (Example 2)

[0754] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0755] Conventional speech recognition systems convert speech into text and generate appropriate responses, but they lack the ability to adjust responses based on the user's emotions, which can result in mechanical and unnatural dialogue. This invention aims to achieve natural and human-like dialogue by recognizing the user's emotions and generating responses that take emotional elements into account.

[0756] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0757] In this invention, the server includes means for converting voice data into text data, means for analyzing the text data and recognizing the user's emotions, and means for generating an appropriate response based on the emotional state. This makes it possible to generate a natural and appropriate response that is in line with the user's emotional state.

[0758] "Voice data" refers to data recorded in digital format from what a user speaks into a device.

[0759] "Means of collection" refers to a device or method that captures audio data using a microphone built into the terminal.

[0760] "Transmission means" refers to a device or method for transmitting collected audio data to a server using a wireless communication module.

[0761] "Text data" refers to the text format of audio data converted by a speech recognition system.

[0762] "Means of conversion" refer to speech recognition systems and algorithms used to convert speech data into text data.

[0763] "Means of analysis" refers to a device or program that uses converted text data to recognize the user's emotions and interpret their meaning and content.

[0764] "Means of recognizing emotions" refer to emotion engines and algorithms that analyze the characteristics of text data to identify the user's emotional state.

[0765] "Means for generating appropriate responses" refer to generative artificial intelligence and natural language generation technologies that create responses to enable natural dialogue based on the user's emotional state and text data.

[0766] "Means for converting to audio data" refers to a speech synthesis system for converting the generated text response into audio data.

[0767] "Means of playback" refers to a device or method for playing audio data transmitted from a server through a speaker on a terminal.

[0768] The present invention is a system that collects, analyzes, generates responses to, and responds to voice data when a user speaks into a terminal. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and generates appropriate responses based on those emotions. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, and means for playing back the converted voice data.

[0769] Hardware and software to be used

[0770] 1. Terminal

[0771] Microphone: Used to collect the user's voice.

[0772] Wireless communication module: Used to transmit collected audio data to a server. Specifically, it utilizes Wi-Fi or Bluetooth.

[0773] Speaker: Used to play audio data received from the server to the user.

[0774] 2. Server

[0775] Automatic Speech Recognition (ASR): Used to convert speech data into text data. Specifically, Google Cloud Speech-to-Text is used.

[0776] Emotion Engine: Used to recognize the user's emotions from text. Specifically, IBM Watson Tone Analyzer is used.

[0777] Generative artificial intelligence (NLG: Natural Language Generation): Used to generate appropriate responses. Specifically, OpenAI's GPT model is used.

[0778] Text-to-Speech (TTS): Used to convert generated text data into speech data. Specifically, Amazon Polly is used.

[0779] Specific example

[0780] Example 1: When a user sadly asks, "What's the weather like today?"

[0781] 1. User: Speaks sadly to the device, "What's the weather like today?"

[0782] 2. Terminal: The microphone collects audio and stores it temporarily. Then, the audio data is sent to the server via a secure protocol (HTTPS) through the wireless communication module.

[0783] 3. Server: Uses a speech recognition system to convert speech data into text data with high accuracy.

[0784] 4. Server: Uses an emotion engine to recognize the user's emotion as "sadness" from the converted text data.

[0785] 5. Server: Generative artificial intelligence generates a response that takes emotions into consideration: "It's sunny today, but please take it easy and relax."

[0786] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0787] 7. Terminal: Receives audio data from the server and plays it through the speaker.

[0788] Example 2: When a user angrily asks, "Tell me about nearby restaurants."

[0789] 1. User: Speaks angrily to the device, "Tell me about nearby restaurants."

[0790] 2. Terminal: The microphone collects audio and stores it temporarily. Then, the audio data is sent to the server via a secure protocol (HTTPS) through the wireless communication module.

[0791] 3. Server: Uses a speech recognition system to convert speech data into text data with high accuracy.

[0792] 4. Server: Uses an emotion engine to recognize the user's emotion as "anger" from the converted text data.

[0793] 5. Server: Generative artificial intelligence generates a response that takes emotions into consideration: "There are restaurants A, B, and C nearby. Please relax and enjoy your meal."

[0794] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0795] 7. Terminal: Receives audio data from the server and plays it through the speaker.

[0796] Example of a prompt

[0797] The following are examples of prompts to input into a generative artificial intelligence:

[0798] "The user is sadly asking, 'What's the weather like today?' Please generate an appropriate response that reflects this emotion."

[0799] "The user is asking, 'Tell me about nearby restaurants,' in an angry tone. Please understand this sentiment and provide an appropriate response."

[0800] In this way, the entire system works together to achieve natural dialogue that is in line with the user's emotions.

[0801] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0802] Step 1: Collect audio data

[0803] User: Speaks into the device. For example, "What's the weather like today?"

[0804] Input: User's voice.

[0805] Terminal: Uses a built-in microphone to collect the user's voice as digital audio data and temporarily stores it. The microphone should be highly sensitive and have noise filtering capabilities.

[0806] Output: Temporarily stored audio data.

[0807] Step 2: Sending the audio data

[0808] Terminal: Prepares to transmit the collected audio data.

[0809] Input: Temporarily stored audio data.

[0810] Terminal: Uses a wireless communication module (e.g., Wi-Fi or Bluetooth) to transmit voice data to the server. A secure communication protocol (e.g., HTTPS) is used to ensure data security.

[0811] Output: Audio data sent to the server.

[0812] Step 3: Convert speech to text

[0813] Server: Receives audio data sent from the terminal.

[0814] Input: Audio data sent to the server.

[0815] Server: Uses a speech recognition system (ASR, e.g., Google Cloud Speech-to-Text) to convert speech data into text data. This system provides highly accurate conversion.

[0816] Output: Text data.

[0817] Step 4: Emotion Recognition

[0818] Server: Receives the converted text data.

[0819] Input: Text data.

[0820] Server: Uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the characteristics of text data and identify the user's emotions. For example, if a sad tone is detected in the question "What's the weather like today?", the emotion engine recognizes it as "sadness".

[0821] Output: Emotional information (e.g., "sadness").

[0822] Step 5: Response Generation

[0823] Server: Receives recognized emotion information and text data.

[0824] Input: Sentimental information and text data.

[0825] Server: Uses a generative AI model (e.g., OpenAI's GPT model) to analyze input data and generate appropriate responses that take the user's emotions into consideration. For example, if the emotion is "sadness," a response such as "It's sunny today, but please take it easy and relax" would be generated.

[0826] Output: Text data of the generated response.

[0827] Step 6: Convert the generated response text data into audio data

[0828] Server: Prepares to convert the generated response text into audio data.

[0829] Input: Text data of the generated response.

[0830] Server: Uses a text-to-speech system (TTS, e.g., Amazon Polly) to convert text data into natural-sounding speech data.

[0831] Output: Generated audio data.

[0832] Step 7: Sending and playing audio data

[0833] Server: Sends the generated audio data to the terminal.

[0834] Input: Generated audio data.

[0835] Server: Uses a secure communication protocol (e.g., HTTPS) to send the generated audio data to the terminal.

[0836] Terminal: Receives transmitted audio data and plays it back to the user through the speaker. For example, it might play the message, "It's sunny today, but please take it easy and relax."

[0837] Output: Audio data that the user can listen to.

[0838] As described above, by having each step work together, a natural dialogue that takes user emotions into consideration is achieved.

[0839] (Application Example 2)

[0840] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0841] In modern factories and production lines, smooth communication between human workers and robots is essential. However, conventional systems have insufficient interpretation of voice commands and emotional recognition, resulting in problems such as decreased work efficiency and increased worker stress. Furthermore, it is difficult for robots to understand the emotions of workers and provide appropriate feedback and guidance. Against this backdrop, there is a need for a system that can accurately recognize user voice commands, analyze emotions, and respond appropriately based on the results.

[0842] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0843] In this invention, the server includes means for collecting voice data, means for transmitting the collected voice data to the server, means for converting the voice data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data back into voice data, means for playing back the converted voice data, emotion analysis means for recognizing the user's emotions, and instruction execution means for understanding instructions and performing appropriate tasks while considering the emotional state. As a result, an efficient and less stressful work environment is possible, as workers can give instructions to the robot by voice, and those instructions are interpreted appropriately according to the emotional state.

[0844] "Means for collecting audio data" refers to a device or system that has the function of capturing user audio in real time.

[0845] "Means for transmitting collected audio data to a server" refers to a device or system that has the function of transmitting collected audio data to a remote server via a network.

[0846] "Means of converting audio data to text data" refers to software or systems that analyze audio data and convert it into corresponding text data.

[0847] "Means for analyzing text data and generating appropriate responses" refers to an algorithm or system that analyzes text data to understand the user's intent and generates a response based on the results.

[0848] "Means for converting generated text data into audio data" refers to a speech synthesis system that has the function of converting text data into audio data.

[0849] "Means for playing back converted audio data" refers to a system that plays back converted audio data through sound devices such as speakers.

[0850] "An emotion analysis method for recognizing a user's emotions" refers to a technology or system that analyzes the characteristics of a user's voice or text to identify their emotional state.

[0851] "An instruction execution means that understands instructions and performs appropriate tasks while considering the emotional state" refers to a system that has the function of appropriately interpreting the user's instructions based on the results of emotion analysis and performing predetermined tasks.

[0852] This invention is a system used by users to give voice commands to factory robots, which then appropriately understand those commands and perform the tasks. This system can collect the user's voice, analyze their emotions, and play back the generated response in voice.

[0853] System details

[0854] Hardware and software

[0855] 1. Means of collecting audio data:

[0856] The device uses its built-in microphone to collect the user's voice.

[0857] Examples include smartphones and dedicated microphone devices.

[0858] 2. Means for transmitting audio data to the server:

[0859] The terminal transmits the collected voice data to the server using a secure protocol (e.g., HTTPS) via a wireless communication module (e.g., Wi-Fi or LTE).

[0860] 3. Means of converting audio data to text data:

[0861] The server uses speech recognition software (ASR: Automatic Speech Recognition) to convert the audio data into text.

[0862] Example: Google Cloud Speech-to-Text API.

[0863] 4. Means for analyzing text data and generating appropriate responses:

[0864] The server analyzes the converted text data using natural language processing techniques (e.g., the Hugging Face transformers library) to recognize the user's emotions. Furthermore, it generates an appropriate response using generative artificial intelligence (e.g., GPT-3.5).

[0865] 5. Means for converting generated text data into audio data:

[0866] The text data generated on the server is converted into speech data using a text-to-speech (TTS) system.

[0867] Example: Google Text-to-Speech API.

[0868] 6. Means for playing back the converted audio data:

[0869] The terminal plays the audio data received from the server through its speaker.

[0870] 7. Emotion analysis means:

[0871] The server uses a sentiment analysis system (e.g., a sentiment-analysis model) to analyze the user's emotions.

[0872] 8. Instruction execution means:

[0873] The server sends instructions to the factory robots based on responses generated while taking emotional states into consideration, and the robots perform the appropriate tasks.

[0874] Example: Robot control system.

[0875] Specific example

[0876] Considering a scenario where a worker gives instructions to a robot using voice commands, the following specific processes would occur:

[0877] 1. Collection: The terminal collects voice messages from workers such as, "How do I fix this machine's malfunction?"

[0878] 2. Transmission: The collected audio data is sent to the server.

[0879] 3. Analysis: The audio data is converted to text data on the server, and the emotion analysis system recognizes anger from the tone of the voice.

[0880] 4. Response Generation: The generative artificial intelligence generates an appropriate response based on the prompt message, "The worker seems angry. Instruction: How do I fix this machine malfunction?" For example, it might generate a response such as, "Let's deal with the machine malfunction calmly. First, check the manual's procedures, and call support if necessary."

[0881] 5. Conversion and Output: The generated text response is converted into audio data and played back through the device's speaker.

[0882] Example of a prompt

[0883] Examples of prompt messages sent to a generative artificial intelligence are as follows:

[0884] text

[0885] The worker seems angry. Instructions: How do I fix this broken machine?

[0886] This allows the system to generate appropriate responses that take the user's emotions into account and provide them as voice messages.

[0887] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0888] Processing steps

[0889] Step 1:

[0890] The terminal collects voice from the worker using a microphone. The input is the worker's voice, and the output is the collected voice data. Specifically, the terminal's built-in microphone captures the worker's speech and temporarily stores it as digital voice data.

[0891] Step 2:

[0892] The terminal transmits the collected audio data to the server via a wireless communication module. The input is the collected audio data, and the output is the audio data transferred to the server. Specifically, the terminal uses the HTTPS protocol to send the audio data to a specified endpoint on the server.

[0893] Step 3:

[0894] The server uses speech recognition software to convert received audio data into text data. The input is audio data, and the output is the converted text data. Specifically, it uses the Google Cloud Speech-to-Text API, among others, to transcribe the audio data with high accuracy.

[0895] Step 4:

[0896] The server performs sentiment analysis from text and audio data. The input is text and audio data, and the output is the user's emotional state. Specifically, it uses the Hugging Face sentiment-analysis model to identify the emotion in the text (e.g., joy, anger, sadness).

[0897] Step 5:

[0898] The server considers the emotional state and generates an appropriate response using generative artificial intelligence. The input is text data and the emotional state, and the output is the generated text response. Specifically, a prompt sentence is input to a generative AI model such as GPT-3.5, and an appropriate response is generated as a result. For example, the prompt sentence is "The worker seems angry. Instructions: How do I fix this machine malfunction?"

[0899] Step 6:

[0900] The server converts the generated text response into speech data using a speech synthesis system. The input is the generated text response, and the output is speech data. Specifically, the Google Text-to-Speech API is used to convert text to speech.

[0901] Step 7:

[0902] The terminal plays audio data received from the server through its speaker. The input is audio data, and the output is an audio response. Specifically, the terminal's speaker system plays the audio data and provides a response to the worker.

[0903] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0904] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0905] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0906] [Third Embodiment]

[0907] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0908] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0909] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0910] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0911] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0912] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0913] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0914] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0915] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0916] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0917] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0918] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0919] The present invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by a user speaking into a terminal. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, and means for playing back the converted voice data.

[0920] System details:

[0921] In an embodiment of the present invention, the user first speaks into the terminal. The terminal collects the user's voice using a microphone and temporarily stores this voice data. The collected voice data is transmitted from the terminal to the server via a wireless communication module. A secure communication protocol, such as HTTPS, is used to transmit the voice data.

[0922] Speech recognition processing:

[0923] The server receives the audio data transmitted from the terminal. Next, the Automatic Speech Recognition (ASR) system installed on the server analyzes the audio data and converts it into text data. This text data is generated based on what the user has said and uses highly accurate speech recognition technology.

[0924] Response generation:

[0925] The server then sends the converted text data to a generative artificial intelligence (for example, a model using the latest natural language processing technology, such as the GPT model), which analyzes the data. The generative AI then generates the information and appropriate responses requested by the user.

[0926] Speech synthesis:

[0927] The generated response is sent back to the server as text data. The server then sends this text data to a text-to-speech (TTS) system, which converts it into speech data.

[0928] Audio output:

[0929] Finally, the server sends the generated audio data to the terminal. The terminal plays the audio data received from the server through its speaker and provides a response to the user.

[0930] Specific example:

[0931] The following are specific examples of how to use this system.

[0932] Usage example 1:

[0933] Let's say a user speaks to their device and asks, "What's the weather like today?"

[0934] 1. User: Speaks to the device, "What's the weather like today?"

[0935] 2. Terminal: Collects audio and sends the audio data to the server.

[0936] 3. Server: Converts speech data into text using a speech recognition system.

[0937] 4. Server: Uses generative artificial intelligence to generate the response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0938] 5. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0939] 6. Terminal: Plays audio data sent from the server.

[0940] Usage example 2:

[0941] When a user speaks to the device and says, "Tell me about nearby restaurants," the system performs the following actions.

[0942] 1. User: Speaks into the device and says, "Tell me about nearby restaurants."

[0943] 2. Terminal: Collects audio and sends the audio data to the server.

[0944] 3. Server: Converts speech data into text using a speech recognition system.

[0945] 4. Server: Uses generative artificial intelligence to search for "information on nearby restaurants" and generates a response such as "There is a restaurant A nearby. Its opening hours are..."

[0946] 5. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[0947] 6. Terminal: Plays audio data sent from the server.

[0948] In this way, the present invention realizes a system that can provide users with fast and natural voice responses. This allows users to interact by voice without having to read text, and to enjoy a highly intuitive and user-friendly experience.

[0949] The following describes the processing flow.

[0950] Step 1:

[0951] The user speaks to the device. For example, they might say, "What's the weather like today?"

[0952] Step 2:

[0953] The device collects the user's voice using its microphone. The collected voice data is temporarily stored in the device's memory.

[0954] Step 3:

[0955] The device sends the collected audio data to the server. A secure communication protocol, such as HTTPS, is used for transmission.

[0956] Step 4:

[0957] The server receives the audio data sent from the terminal. The received audio data is stored in the server's memory.

[0958] Step 5:

[0959] The server passes the audio data to the speech recognition system (ASR). The ASR converts the audio data into text data.

[0960] Step 6:

[0961] The server receives text data converted from the speech recognition system (ASR). For example, it might be converted to the text, "What's the weather like today?"

[0962] Step 7:

[0963] The server passes the converted text data to a generative artificial intelligence (AI). The AI ​​analyzes the text data and generates an appropriate response, for example, "It's sunny today. The maximum temperature is 25 degrees."

[0964] Step 8:

[0965] The server receives a response text generated by a generative artificial intelligence. For example, the data might say, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0966] Step 9:

[0967] The server passes the generated response text to the text-to-speech (TTS) system. The TTS converts the text data into speech data.

[0968] Step 10:

[0969] The server receives the audio data converted from the TTS system. For example, the audio data might say, "It's sunny today. The highest temperature is 25 degrees Celsius."

[0970] Step 11:

[0971] The server sends the generated audio data to the terminal. A secure communication protocol is used again for transmission.

[0972] Step 12:

[0973] The device receives audio data sent from the server. The received audio data is stored in the device's memory.

[0974] Step 13:

[0975] The device plays the received audio data through its speaker. The user hears the audio response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[0976] In this way, a process is realized in which users can receive appropriate voice responses via their device in response to questions they ask.

[0977] (Example 1)

[0978] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0979] In current speech recognition systems, the collection, analysis, response generation, and output of speech data are performed as separate processes, resulting in challenges to overall processing speed and user experience. Furthermore, if data transmission between these processes is not secure, information leaks and security problems may occur. In addition, it is necessary to convert the generated response into high-quality speech data, but if this process is inconsistent, it results in an unnatural interaction for the user.

[0980] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0981] In this invention, the server includes means for collecting audio data, means for transmitting the collected audio data using a secure communication protocol, means for converting the audio data into text data, means for analyzing the text data and generating an appropriate response, means for transmitting the generated text data to a speech synthesis system, and means for converting and playing back the audio data. This ensures that the entire process from audio data collection to final audio output is carried out securely and efficiently, providing users with high-quality audio responses.

[0982] "Audio data" refers to data in which audio is recorded and stored in digital format.

[0983] "Means of collection" refers to the hardware or software functions for acquiring and storing audio data.

[0984] A "server" is a computer system that responds to requests from clients via the internet or a local network.

[0985] "Means of transmission" refers to the hardware or software function for sending data from one point to another.

[0986] A "secure communication protocol" is a means of communication that ensures the safe transmission and reception of data, and includes protocols such as HTTPS and SSL / TLS.

[0987] "Speech recognition technology" is a technology that analyzes speech data and converts its content into text data.

[0988] "Text data" refers to the digital format in which character information is displayed or stored.

[0989] "Means of analysis" refers to the hardware or software functions used to process collected or acquired data and understand its meaning.

[0990] A "generative AI model" is a program or algorithm that uses artificial intelligence technology to generate new data or responses.

[0991] "Response" refers to the answer or reaction that a system gives to a user's inquiry or instruction.

[0992] A "speech synthesis system" refers to a technology or system that converts text data into speech data.

[0993] "Means of playback" refers to the hardware or software function that outputs audio data as sound through an output device such as a speaker.

[0994] Modes for carrying out the invention

[0995] The present invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by having a user speak to a terminal. This system includes means for collecting voice data, means for transmitting the collected voice data to a server, means for converting the voice data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data into voice data, means for playing back the converted voice data, and means for transmitting the voice data using a secure communication protocol.

[0996] Collection of audio data:

[0997] When a user speaks to the device, saying "What's the weather like today?", the device uses its built-in microphone to collect audio data. This audio data is temporarily stored in the device's local storage.

[0998] Sending audio data:

[0999] The collected audio data is sent to the server using a secure communication protocol (e.g., HTTPS). This process maintains secure communication because the data is encrypted.

[1000] Speech recognition processing:

[1001] The server sends the received audio data to an automatic speech recognition system (ASR). This system employs technology to convert audio data into text data, and converts the audio data into text data in the format "What's the weather like today?".

[1002] Response generation:

[1003] The server uses a generative AI model (e.g., generative artificial intelligence) to analyze text data and generate an appropriate response. This model generates responses based on the information requested by the user, for example, generating a text response such as "It's sunny today. The maximum temperature is 25 degrees."

[1004] Speech synthesis:

[1005] The generated text response is sent to a text-to-speech system (e.g., TTS). This system converts the text response into speech data.

[1006] Sending audio data:

[1007] The generated audio data is then sent back from the server to the terminal using a secure communication protocol.

[1008] Audio playback:

[1009] The device saves the received audio data to local storage and plays the audio using its built-in speaker. The user can hear the audio response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[1010] Specific example:

[1011] The following interactions are possible as examples of use.

[1012] To obtain weather information:

[1013] 1. User: "What's the weather like today?"

[1014] 2. Terminal: Collects audio and sends it to the server via HTTPS.

[1015] 3. Server: The speech recognition system converts the voice data into the text "Please tell me today's weather."

[1016] 4. Server: The generation AI model generates the response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[1017] 5. Server: Converts the generated text data into speech data using a speech synthesis system.

[1018] 6. Server: Sends audio data to the terminal.

[1019] 7. Device: Plays the received audio data.

[1020] To retrieve restaurant information:

[1021] 1. User: "Can you tell me about some nearby restaurants?"

[1022] 2. Terminal: Collects audio and sends it to the server via HTTPS.

[1023] 3. Server: The speech recognition system converts the voice data into text, "Please tell me about nearby restaurants."

[1024] 4. Server: The generation AI model generates a response such as, "There is a restaurant A nearby. Its opening hours are..."

[1025] 5. Server: Converts the generated text data into speech data using a speech synthesis system.

[1026] 6. Server: Sends audio data to the terminal.

[1027] 7. Device: Plays the received audio data.

[1028] As a result, the present invention enables intuitive voice-based interaction and provides users with a high level of convenience.

[1029] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1030] Step 1:

[1031] Collection of voice input

[1032] The user speaks to the device and says, "What's the weather like today?"

[1033] The device uses its built-in microphone to collect audio data and temporarily stores it in local storage.

[1034] Input: User's voice

[1035] Output: Collected audio data

[1036] Step 2:

[1037] Sending audio data to the server

[1038] The device sends the collected voice data to the server using a secure communication protocol (HTTPS).

[1039] Input: Collected audio data

[1040] Output: Encrypted audio data sent to the server

[1041] Step 3:

[1042] Speech recognition system processing

[1043] The server sends the received audio data to an automatic speech recognition system (ASR), which converts the audio data into text data.

[1044] Input: Encrypted audio data

[1045] Output: Converted text data (e.g., "What's the weather like today?")

[1046] Step 4:

[1047] Text data analysis

[1048] The server sends the converted text data to a generative AI model (e.g., generative artificial intelligence) for analysis. The generative AI model understands the user's intent and generates an appropriate response to the request.

[1049] Input: Converted text data

[1050] Output: Generated response text (Example: "It's sunny today. The high temperature is 25 degrees Celsius.")

[1051] Step 5:

[1052] Response text speech synthesis

[1053] The server sends the generated response text to a text-to-speech (TTS) system, which converts the text data into speech data.

[1054] Input: Generated response text

[1055] Output: Generated audio data

[1056] Step 6:

[1057] Sending audio data to a terminal

[1058] The server sends the generated audio data to the terminal via a secure communication protocol.

[1059] Input: Generated audio data

[1060] Output: Encrypted audio data sent to the terminal

[1061] Step 7:

[1062] Audio playback

[1063] The device temporarily stores the received audio data in local storage and plays it back using the built-in speaker. This allows the user to hear the audio response.

[1064] Input: Encrypted audio data sent

[1065] Output: Played audio (Example: "It's sunny today. The high temperature is 25 degrees Celsius.")

[1066] As a result, users can seamlessly receive questions and answers via voice. The specific integration of hardware and software creates a system that provides a high-quality user experience.

[1067] (Application Example 1)

[1068] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1069] Conventional food delivery services require users to manually operate them using devices such as smartphones, and the complexity of these operations and the lack of intuitive interfaces have been problematic. Furthermore, users often have to go through lengthy procedures when placing specific orders or searching for information, which has contributed to decreased customer satisfaction. This invention aims to solve these problems by providing an intuitive and efficient food delivery system using voice control.

[1070] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1071] In this invention, the server includes means for converting voice data into text data, means for retrieving information based on voice commands from the user and generating an appropriate response, and means for executing a food delivery service based on the generated response. This enables the user to efficiently use a food delivery service using only their voice.

[1072] "Voice data" refers to digital data obtained by recording the user's voice.

[1073] "Means of collection" refers to the hardware and software components for capturing audio data.

[1074] A "server" is a network-connected computer system used for analyzing audio data and generating responses.

[1075] "Text data" refers to digital data consisting of strings of characters obtained by analyzing and converting audio data.

[1076] "Means of analysis" refer to software and algorithms for processing text data and generating appropriate responses based on user requests.

[1077] A "response" is the response data generated in response to a user's voice command.

[1078] "Generative artificial intelligence" refers to machine learning models that include natural language processing techniques and are used to analyze text data and generate appropriate responses.

[1079] A "prompt message" is text data input to a generative artificial intelligence system, and it contains instructions that the AI ​​uses to generate a response based on its content.

[1080] A "speech synthesis system" is a software system used to convert text data into speech data.

[1081] "Means of playback" refer to the hardware and software components that enable the user to listen to the converted audio data.

[1082] A "food delivery service" is a service that delivers food based on a user's order.

[1083] This invention relates to a system that retrieves information based on a user's voice command, generates an appropriate response, and then executes a food delivery service based on that response. This system can be used by the user speaking into a terminal.

[1084] System Configuration and Functions

[1085] This system consists of the following main components:

[1086] 1. Audio acquisition means: Includes a microphone for acquiring audio data and its control software.

[1087] 2. Means of communication with the server: Voice data is sent to the server using a secure protocol (e.g., HTTPS).

[1088] 3. Speech recognition means: A speech recognition system installed on the server (e.g., SpeechRecognition library) is used to convert the speech data into text.

[1089] 4. Response generation means: Text data is analyzed using generative artificial intelligence (e.g., GPT model) and an appropriate response is generated.

[1090] 5. Speech synthesis means: A speech synthesis system (e.g., a Text-to-Speech library) is used to convert text data into speech data.

[1091] 6. Audio playback means: The converted audio data is played back through the speaker.

[1092] System details

[1093] Voice collection and transmission

[1094] When a user speaks into the device, audio data is collected by the microphone. This audio data is temporarily stored on the device and then sent to a server using a secure protocol.

[1095] Speech recognition processing

[1096] On the server, the audio data is converted into text data using a speech recognition system (e.g., the SpeechRecognition library). At this stage, the content spoken by the user is obtained in text format.

[1097] Response generation

[1098] The converted text data is analyzed by a generative artificial intelligence model (e.g., a GPT model) to generate an appropriate response. This process uses predefined prompt statements. Examples of prompt statements are shown below:

[1099] User: I want to order a hamburger.

[1100] Assistant: Which hamburger would you like to order? Our current menu includes cheeseburgers, double cheeseburgers, and bacon burgers.

[1101] Speech synthesis and playback

[1102] The generated response is further converted into audio data using a speech synthesis system, and this audio data is sent to the terminal. The terminal then plays the received audio data through its speaker.

[1103] Specific example

[1104] For example, when a user says to the terminal, "I want to order a hamburger," the following process takes place:

[1105] 1. Audio data is collected and sent to the server.

[1106] 2. The audio data is converted to text on the server.

[1107] 3. The text data is analyzed by a generative artificial intelligence system, and a response is generated: "Which hamburger would you like to order? Our current menu includes cheeseburgers, double cheeseburgers, and bacon burgers."

[1108] 4. The generated response is converted into audio data and sent to the terminal.

[1109] 5. The audio data is played on the device.

[1110] In this way, users can efficiently utilize food delivery services using only their voice.

[1111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1112] Step 1:

[1113] The user speaks into the device. The user's voice is collected as audio data through the microphone. Input: User's speech. Output: Audio data.

[1114] Step 2:

[1115] The terminal temporarily stores the collected audio data and sends it to the server using a secure protocol (e.g., HTTPS). Input: Audio data. Output: Audio data sent to the server.

[1116] Step 3:

[1117] The server converts the received audio data into text data using a speech recognition system (e.g., the SpeechRecognition library). Input: Audio data. Output: Text data.

[1118] Step 4:

[1119] The server sends the converted text data to a generative artificial intelligence model (e.g., a GPT model) to generate an appropriate response. Input: Text data. Output: Text data for the response.

[1120] Step 5:

[1121] The generated text data for the response is sent to a text-to-speech system (e.g., a Text-to-Speech library) and converted into speech data. Input: Text data for the response. Output: Speech data.

[1122] Step 6:

[1123] The server sends the generated audio data to the terminal. Input: Audio data. Output: Audio data sent to the terminal.

[1124] Step 7:

[1125] The device plays the received audio data through its speaker. The user can hear this audio response. Input: Audio data. Output: Audio response played from the speaker.

[1126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1127] This invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by a user speaking into a terminal. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and generates appropriate responses based on those emotions. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, means for playing back the converted voice data, and an emotion engine that recognizes the user's emotions.

[1128] System details:

[1129] In an embodiment of the present invention, the user first speaks into the terminal. The terminal collects the user's voice using a microphone and temporarily stores this voice data. The collected voice data is transmitted from the terminal to the server via a wireless communication module. A secure communication protocol, such as HTTPS, is used to transmit the voice data.

[1130] Speech recognition processing:

[1131] The server receives the audio data transmitted from the terminal. Next, the Automatic Speech Recognition (ASR) system installed on the server analyzes the audio data and converts it into text data. This text data is generated based on what the user has said and uses highly accurate speech recognition technology.

[1132] Emotion recognition processing:

[1133] The server uses an emotion engine to analyze the user's emotions based on the converted text data. This emotion engine identifies the user's emotional state (e.g., joy, anger, sadness) from the characteristics of the voice and text.

[1134] Response generation:

[1135] The server then sends the converted text data to a generative artificial intelligence (for example, a model using the latest natural language processing techniques, such as the GPT model), taking into account the user's emotional state identified by the emotion engine, and analyzes this data. The generative AI generates the information and appropriate responses that the user is looking for, and these responses are adjusted to reflect the emotional state.

[1136] Speech synthesis:

[1137] The generated response is sent to the server as text data. The server then sends this text data to a text-to-speech (TTS) system, which converts it into speech data.

[1138] Audio output:

[1139] Finally, the server sends the generated audio data to the terminal. The terminal plays the received audio data through its speaker and provides a response to the user. This response is adjusted according to the user's emotional state, resulting in a more natural and appropriate conversation.

[1140] Specific example:

[1141] The following are specific examples of how to use this system.

[1142] Usage example 1:

[1143] Let's say the user sadly asks the device, "What's the weather like today?"

[1144] 1. User: Speaks sadly to the device, "What's the weather like today?"

[1145] 2. Terminal: Collects audio and sends the audio data to the server.

[1146] 3. Server: Converts speech data into text using a speech recognition system.

[1147] 4. Server: Uses an emotion engine to recognize the user's emotion as "sadness".

[1148] 5. Server: The converted text data and user sentiment information are sent to the generative artificial intelligence system, which then generates the response, "It's sunny today, but please take it easy and relax."

[1149] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1150] 7. Terminal: Plays audio data sent from the server.

[1151] Usage example 2:

[1152] If a user angrily asks the device, "Tell me about nearby restaurants," the system will perform the following actions.

[1153] 1. User: Speaks angrily to the device, "Tell me about nearby restaurants."

[1154] 2. Terminal: Collects audio and sends the audio data to the server.

[1155] 3. Server: Converts speech data into text using a speech recognition system.

[1156] 4. Server: Uses an emotion engine to recognize the user's emotion as "anger".

[1157] 5. Server: The converted text data and user sentiment information are sent to the generative artificial intelligence system, which then generates a response saying, "There are restaurants A, B, and C nearby. Please relax and enjoy your meal."

[1158] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1159] 7. Terminal: Plays audio data sent from the server.

[1160] Thus, the present invention realizes a system that can provide more appropriate and natural voice responses according to the user's emotional state. As a result, users can interact by voice without reading text, and enjoy a highly intuitive and user-friendly experience.

[1161] The following describes the processing flow.

[1162] Step 1:

[1163] The user speaks to the device. For example, they might say, "What's the weather like today?"

[1164] Step 2:

[1165] The device collects the user's voice using its microphone. The collected voice data is temporarily stored in the device's memory.

[1166] Step 3:

[1167] The device sends the collected audio data to the server. A secure communication protocol, such as HTTPS, is used for transmission.

[1168] Step 4:

[1169] The server receives the audio data sent from the terminal. The received audio data is stored in the server's memory.

[1170] Step 5:

[1171] The server passes the audio data to the speech recognition system (ASR: Automatic Speech Recognition). The ASR analyzes the audio data and converts it into text data.

[1172] Step 6:

[1173] The server receives text data converted from the speech recognition system (ASR). For example, it might be converted to the text, "What's the weather like today?"

[1174] Step 7:

[1175] The server passes the converted text data to the emotion engine. The emotion engine analyzes the user's emotional state from the characteristics of the voice and text, and recognizes it as, for example, "sadness."

[1176] Step 8:

[1177] The server receives emotion data obtained from the emotion engine. For example, emotion data such as "sadness."

[1178] Step 9:

[1179] The server sends the converted text data and sentiment data to a generative artificial intelligence (AI). The AI ​​analyzes the text data and generates an appropriate response that reflects the user's emotions. For example, it might generate a response such as, "It's sunny today, but please take it easy and relax."

[1180] Step 10:

[1181] The server receives a response text generated by a generative artificial intelligence. For example, the text might say, "It's sunny today, but please take it easy and don't overexert yourself."

[1182] Step 11:

[1183] The server passes the generated response text to the Text-to-Speech (TTS) system. The TTS system converts the text data into speech data.

[1184] Step 12:

[1185] The server receives the audio data converted from the TTS system. For example, the audio data might say, "It's sunny today, but please take it easy and don't overexert yourself."

[1186] Step 13:

[1187] The server sends the generated audio data to the terminal. A secure communication protocol is used again for transmission.

[1188] Step 14:

[1189] The device receives audio data sent from the server. The received audio data is stored in the device's memory.

[1190] Step 15:

[1191] The device plays the received audio data through its speaker. The user hears the audio response, "It's sunny today, but please take it easy and relax."

[1192] In this way, a process is realized in which, in response to a question posed by the user, an appropriate response is received via the device in the form of voice that reflects the user's emotional state.

[1193] (Example 2)

[1194] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1195] Conventional speech recognition systems convert speech into text and generate appropriate responses, but they lack the ability to adjust responses based on the user's emotions, which can result in mechanical and unnatural dialogue. This invention aims to achieve natural and human-like dialogue by recognizing the user's emotions and generating responses that take emotional elements into account.

[1196] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1197] In this invention, the server includes means for converting voice data into text data, means for analyzing the text data and recognizing the user's emotions, and means for generating an appropriate response based on the emotional state. This makes it possible to generate a natural and appropriate response that is in line with the user's emotional state.

[1198] "Voice data" refers to data recorded in digital format from what a user speaks into a device.

[1199] "Means of collection" refers to a device or method that captures audio data using a microphone built into the terminal.

[1200] "Transmission means" refers to a device or method for transmitting collected audio data to a server using a wireless communication module.

[1201] "Text data" refers to the text format of audio data converted by a speech recognition system.

[1202] "Means of conversion" refer to speech recognition systems and algorithms used to convert speech data into text data.

[1203] "Means of analysis" refers to a device or program that uses converted text data to recognize the user's emotions and interpret their meaning and content.

[1204] "Means of recognizing emotions" refer to emotion engines and algorithms that analyze the characteristics of text data to identify the user's emotional state.

[1205] "Means for generating appropriate responses" refer to generative artificial intelligence and natural language generation technologies that create responses to enable natural dialogue based on the user's emotional state and text data.

[1206] "Means for converting to audio data" refers to a speech synthesis system for converting the generated text response into audio data.

[1207] "Means of playback" refers to a device or method for playing audio data transmitted from a server through a speaker on a terminal.

[1208] The present invention is a system that collects, analyzes, generates responses to, and responds to voice data when a user speaks into a terminal. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and generates appropriate responses based on those emotions. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, and means for playing back the converted voice data.

[1209] Hardware and software to be used

[1210] 1. Terminal

[1211] Microphone: Used to collect the user's voice.

[1212] Wireless communication module: Used to transmit collected audio data to a server. Specifically, it utilizes Wi-Fi or Bluetooth.

[1213] Speaker: Used to play audio data received from the server to the user.

[1214] 2. Server

[1215] Automatic Speech Recognition (ASR): Used to convert speech data into text data. Specifically, Google Cloud Speech-to-Text is used.

[1216] Emotion Engine: Used to recognize the user's emotions from text. Specifically, IBM Watson Tone Analyzer is used.

[1217] Generative artificial intelligence (NLG: Natural Language Generation): Used to generate appropriate responses. Specifically, OpenAI's GPT model is used.

[1218] Text-to-Speech (TTS): Used to convert generated text data into speech data. Specifically, Amazon Polly is used.

[1219] Specific example

[1220] Example 1: When a user sadly asks, "What's the weather like today?"

[1221] 1. User: Speaks sadly to the device, "What's the weather like today?"

[1222] 2. Terminal: The microphone collects audio and stores it temporarily. Then, the audio data is sent to the server via a secure protocol (HTTPS) through the wireless communication module.

[1223] 3. Server: Uses a speech recognition system to convert speech data into text data with high accuracy.

[1224] 4. Server: Uses an emotion engine to recognize the user's emotion as "sadness" from the converted text data.

[1225] 5. Server: Generative artificial intelligence generates a response that takes emotions into consideration: "It's sunny today, but please take it easy and relax."

[1226] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1227] 7. Terminal: Receives audio data from the server and plays it through the speaker.

[1228] Example 2: When a user angrily asks, "Tell me about nearby restaurants."

[1229] 1. User: Speaks angrily to the device, "Tell me about nearby restaurants."

[1230] 2. Terminal: The microphone collects audio and stores it temporarily. Then, the audio data is sent to the server via a secure protocol (HTTPS) through the wireless communication module.

[1231] 3. Server: Uses a speech recognition system to convert speech data into text data with high accuracy.

[1232] 4. Server: Uses an emotion engine to recognize the user's emotion as "anger" from the converted text data.

[1233] 5. Server: Generative artificial intelligence generates a response that takes emotions into consideration: "There are restaurants A, B, and C nearby. Please relax and enjoy your meal."

[1234] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1235] 7. Terminal: Receives audio data from the server and plays it through the speaker.

[1236] Example of a prompt

[1237] The following are examples of prompts to input into a generative artificial intelligence:

[1238] "The user is sadly asking, 'What's the weather like today?' Please generate an appropriate response that reflects this emotion."

[1239] "The user is asking, 'Tell me about nearby restaurants,' in an angry tone. Please understand this sentiment and provide an appropriate response."

[1240] In this way, the entire system works together to achieve natural dialogue that is in line with the user's emotions.

[1241] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1242] Step 1: Collect audio data

[1243] User: Speaks into the device. For example, "What's the weather like today?"

[1244] Input: User's voice.

[1245] Terminal: Uses a built-in microphone to collect the user's voice as digital audio data and temporarily stores it. The microphone should be highly sensitive and have noise filtering capabilities.

[1246] Output: Temporarily stored audio data.

[1247] Step 2: Sending the audio data

[1248] Terminal: Prepares to transmit the collected audio data.

[1249] Input: Temporarily stored audio data.

[1250] Terminal: Uses a wireless communication module (e.g., Wi-Fi or Bluetooth) to transmit voice data to the server. A secure communication protocol (e.g., HTTPS) is used to ensure data security.

[1251] Output: Audio data sent to the server.

[1252] Step 3: Convert speech to text

[1253] Server: Receives audio data sent from the terminal.

[1254] Input: Audio data sent to the server.

[1255] Server: Uses a speech recognition system (ASR, e.g., Google Cloud Speech-to-Text) to convert speech data into text data. This system provides highly accurate conversion.

[1256] Output: Text data.

[1257] Step 4: Emotion Recognition

[1258] Server: Receives the converted text data.

[1259] Input: Text data.

[1260] Server: Uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the characteristics of text data and identify the user's emotions. For example, if a sad tone is detected in the question "What's the weather like today?", the emotion engine recognizes it as "sadness".

[1261] Output: Emotional information (e.g., "sadness").

[1262] Step 5: Response Generation

[1263] Server: Receives recognized emotion information and text data.

[1264] Input: Sentimental information and text data.

[1265] Server: Uses a generative AI model (e.g., OpenAI's GPT model) to analyze input data and generate appropriate responses that take the user's emotions into consideration. For example, if the emotion is "sadness," a response such as "It's sunny today, but please take it easy and relax" would be generated.

[1266] Output: Text data of the generated response.

[1267] Step 6: Convert the generated response text data into audio data

[1268] Server: Prepares to convert the generated response text into audio data.

[1269] Input: Text data of the generated response.

[1270] Server: Uses a text-to-speech system (TTS, e.g., Amazon Polly) to convert text data into natural-sounding speech data.

[1271] Output: Generated audio data.

[1272] Step 7: Sending and playing audio data

[1273] Server: Sends the generated audio data to the terminal.

[1274] Input: Generated audio data.

[1275] Server: Uses a secure communication protocol (e.g., HTTPS) to send the generated audio data to the terminal.

[1276] Terminal: Receives transmitted audio data and plays it back to the user through the speaker. For example, it might play the message, "It's sunny today, but please take it easy and relax."

[1277] Output: Audio data that the user can listen to.

[1278] As described above, by having each step work together, a natural dialogue that takes user emotions into consideration is achieved.

[1279] (Application Example 2)

[1280] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1281] In modern factories and production lines, smooth communication between human workers and robots is essential. However, conventional systems have insufficient interpretation of voice commands and emotional recognition, resulting in problems such as decreased work efficiency and increased worker stress. Furthermore, it is difficult for robots to understand the emotions of workers and provide appropriate feedback and guidance. Against this backdrop, there is a need for a system that can accurately recognize user voice commands, analyze emotions, and respond appropriately based on the results.

[1282] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1283] In this invention, the server includes means for collecting voice data, means for transmitting the collected voice data to the server, means for converting the voice data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data back into voice data, means for playing back the converted voice data, emotion analysis means for recognizing the user's emotions, and instruction execution means for understanding instructions and performing appropriate tasks while considering the emotional state. As a result, an efficient and less stressful work environment is possible, as workers can give instructions to the robot by voice, and those instructions are interpreted appropriately according to the emotional state.

[1284] "Means for collecting audio data" refers to a device or system that has the function of capturing user audio in real time.

[1285] "Means for transmitting collected audio data to a server" refers to a device or system that has the function of transmitting collected audio data to a remote server via a network.

[1286] "Means of converting audio data to text data" refers to software or systems that analyze audio data and convert it into corresponding text data.

[1287] "Means for analyzing text data and generating appropriate responses" refers to an algorithm or system that analyzes text data to understand the user's intent and generates a response based on the results.

[1288] "Means for converting generated text data into audio data" refers to a speech synthesis system that has the function of converting text data into audio data.

[1289] "Means for playing back converted audio data" refers to a system that plays back converted audio data through sound devices such as speakers.

[1290] "An emotion analysis method for recognizing a user's emotions" refers to a technology or system that analyzes the characteristics of a user's voice or text to identify their emotional state.

[1291] "An instruction execution means that understands instructions and performs appropriate tasks while considering the emotional state" refers to a system that has the function of appropriately interpreting the user's instructions based on the results of emotion analysis and performing predetermined tasks.

[1292] This invention is a system used by users to give voice commands to factory robots, which then appropriately understand those commands and perform the tasks. This system can collect the user's voice, analyze their emotions, and play back the generated response in voice.

[1293] System details

[1294] Hardware and software

[1295] 1. Means of collecting audio data:

[1296] The device uses its built-in microphone to collect the user's voice.

[1297] Examples include smartphones and dedicated microphone devices.

[1298] 2. Means for transmitting audio data to the server:

[1299] The terminal transmits the collected voice data to the server using a secure protocol (e.g., HTTPS) via a wireless communication module (e.g., Wi-Fi or LTE).

[1300] 3. Means of converting audio data to text data:

[1301] The server uses speech recognition software (ASR: Automatic Speech Recognition) to convert the audio data into text.

[1302] Example: Google Cloud Speech-to-Text API.

[1303] 4. Means for analyzing text data and generating appropriate responses:

[1304] The server analyzes the converted text data using natural language processing techniques (e.g., the Hugging Face transformers library) to recognize the user's emotions. Furthermore, it generates an appropriate response using generative artificial intelligence (e.g., GPT-3.5).

[1305] 5. Means for converting generated text data into audio data:

[1306] The text data generated on the server is converted into speech data using a text-to-speech (TTS) system.

[1307] Example: Google Text-to-Speech API.

[1308] 6. Means for playing back the converted audio data:

[1309] The terminal plays the audio data received from the server through its speaker.

[1310] 7. Emotion analysis means:

[1311] The server uses a sentiment analysis system (e.g., a sentiment-analysis model) to analyze the user's emotions.

[1312] 8. Instruction execution means:

[1313] The server sends instructions to the factory robots based on responses generated while taking emotional states into consideration, and the robots perform the appropriate tasks.

[1314] Example: Robot control system.

[1315] Specific example

[1316] Considering a scenario where a worker gives instructions to a robot using voice commands, the following specific processes would occur:

[1317] 1. Collection: The terminal collects voice messages from workers such as, "How do I fix this machine's malfunction?"

[1318] 2. Transmission: The collected audio data is sent to the server.

[1319] 3. Analysis: The audio data is converted to text data on the server, and the emotion analysis system recognizes anger from the tone of the voice.

[1320] 4. Response Generation: The generative artificial intelligence generates an appropriate response based on the prompt message, "The worker seems angry. Instruction: How do I fix this machine malfunction?" For example, it might generate a response such as, "Let's deal with the machine malfunction calmly. First, check the manual's procedures, and call support if necessary."

[1321] 5. Conversion and Output: The generated text response is converted into audio data and played back through the device's speaker.

[1322] Example of a prompt

[1323] Examples of prompt messages sent to a generative artificial intelligence are as follows:

[1324] text

[1325] The worker seems angry. Instructions: How do I fix this broken machine?

[1326] This allows the system to generate appropriate responses that take the user's emotions into account and provide them as voice messages.

[1327] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1328] Processing steps

[1329] Step 1:

[1330] The terminal collects voice from the worker using a microphone. The input is the worker's voice, and the output is the collected voice data. Specifically, the terminal's built-in microphone captures the worker's speech and temporarily stores it as digital voice data.

[1331] Step 2:

[1332] The terminal transmits the collected audio data to the server via a wireless communication module. The input is the collected audio data, and the output is the audio data transferred to the server. Specifically, the terminal uses the HTTPS protocol to send the audio data to a specified endpoint on the server.

[1333] Step 3:

[1334] The server uses speech recognition software to convert received audio data into text data. The input is audio data, and the output is the converted text data. Specifically, it uses the Google Cloud Speech-to-Text API, among others, to transcribe the audio data with high accuracy.

[1335] Step 4:

[1336] The server performs sentiment analysis from text and audio data. The input is text and audio data, and the output is the user's emotional state. Specifically, it uses the Hugging Face sentiment-analysis model to identify the emotion in the text (e.g., joy, anger, sadness).

[1337] Step 5:

[1338] The server considers the emotional state and generates an appropriate response using generative artificial intelligence. The input is text data and the emotional state, and the output is the generated text response. Specifically, a prompt sentence is input to a generative AI model such as GPT-3.5, and an appropriate response is generated as a result. For example, the prompt sentence is "The worker seems angry. Instructions: How do I fix this machine malfunction?"

[1339] Step 6:

[1340] The server converts the generated text response into speech data using a speech synthesis system. The input is the generated text response, and the output is speech data. Specifically, the Google Text-to-Speech API is used to convert text to speech.

[1341] Step 7:

[1342] The terminal plays audio data received from the server through its speaker. The input is audio data, and the output is an audio response. Specifically, the terminal's speaker system plays the audio data and provides a response to the worker.

[1343] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1344] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1345] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1346] [Fourth Embodiment]

[1347] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1348] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1349] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1350] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1351] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1352] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1353] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1354] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1355] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1356] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1357] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1358] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1359] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1360] The present invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by a user speaking into a terminal. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, and means for playing back the converted voice data.

[1361] System details:

[1362] In an embodiment of the present invention, the user first speaks into the terminal. The terminal collects the user's voice using a microphone and temporarily stores this voice data. The collected voice data is transmitted from the terminal to the server via a wireless communication module. A secure communication protocol, such as HTTPS, is used to transmit the voice data.

[1363] Speech recognition processing:

[1364] The server receives the audio data transmitted from the terminal. Next, the Automatic Speech Recognition (ASR) system installed on the server analyzes the audio data and converts it into text data. This text data is generated based on what the user has said and uses highly accurate speech recognition technology.

[1365] Response generation:

[1366] The server then sends the converted text data to a generative artificial intelligence (for example, a model using the latest natural language processing technology, such as the GPT model), which analyzes the data. The generative AI then generates the information and appropriate responses requested by the user.

[1367] Speech synthesis:

[1368] The generated response is sent back to the server as text data. The server then sends this text data to a text-to-speech (TTS) system, which converts it into speech data.

[1369] Audio output:

[1370] Finally, the server sends the generated audio data to the terminal. The terminal plays the audio data received from the server through its speaker and provides a response to the user.

[1371] Specific example:

[1372] The following are specific examples of how to use this system.

[1373] Usage example 1:

[1374] Let's say a user speaks to their device and asks, "What's the weather like today?"

[1375] 1. User: Speaks to the device, "What's the weather like today?"

[1376] 2. Terminal: Collects audio and sends the audio data to the server.

[1377] 3. Server: Converts speech data into text using a speech recognition system.

[1378] 4. Server: Uses generative artificial intelligence to generate the response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[1379] 5. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1380] 6. Terminal: Plays audio data sent from the server.

[1381] Usage example 2:

[1382] When a user speaks to the device and says, "Tell me about nearby restaurants," the system performs the following actions.

[1383] 1. User: Speaks into the device and says, "Tell me about nearby restaurants."

[1384] 2. Terminal: Collects audio and sends the audio data to the server.

[1385] 3. Server: Converts speech data into text using a speech recognition system.

[1386] 4. Server: Uses generative artificial intelligence to search for "information on nearby restaurants" and generates a response such as "There is a restaurant A nearby. Its opening hours are..."

[1387] 5. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1388] 6. Terminal: Plays audio data sent from the server.

[1389] In this way, the present invention realizes a system that can provide users with fast and natural voice responses. This allows users to interact by voice without having to read text, and to enjoy a highly intuitive and user-friendly experience.

[1390] The following describes the processing flow.

[1391] Step 1:

[1392] The user speaks to the device. For example, they might say, "What's the weather like today?"

[1393] Step 2:

[1394] The device collects the user's voice using its microphone. The collected voice data is temporarily stored in the device's memory.

[1395] Step 3:

[1396] The device sends the collected audio data to the server. A secure communication protocol, such as HTTPS, is used for transmission.

[1397] Step 4:

[1398] The server receives the audio data sent from the terminal. The received audio data is stored in the server's memory.

[1399] Step 5:

[1400] The server passes the audio data to the speech recognition system (ASR). The ASR converts the audio data into text data.

[1401] Step 6:

[1402] The server receives text data converted from the speech recognition system (ASR). For example, it might be converted to the text, "What's the weather like today?"

[1403] Step 7:

[1404] The server passes the converted text data to a generative artificial intelligence (AI). The AI ​​analyzes the text data and generates an appropriate response, for example, "It's sunny today. The maximum temperature is 25 degrees."

[1405] Step 8:

[1406] The server receives a response text generated by a generative artificial intelligence. For example, the data might say, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[1407] Step 9:

[1408] The server passes the generated response text to the text-to-speech (TTS) system. The TTS converts the text data into speech data.

[1409] Step 10:

[1410] The server receives the audio data converted from the TTS system. For example, the audio data might say, "It's sunny today. The highest temperature is 25 degrees Celsius."

[1411] Step 11:

[1412] The server sends the generated audio data to the terminal. A secure communication protocol is used again for transmission.

[1413] Step 12:

[1414] The device receives audio data sent from the server. The received audio data is stored in the device's memory.

[1415] Step 13:

[1416] The device plays the received audio data through its speaker. The user hears the audio response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[1417] In this way, a process is realized in which users can receive appropriate voice responses via their device in response to questions they ask.

[1418] (Example 1)

[1419] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1420] In current speech recognition systems, the collection, analysis, response generation, and output of speech data are performed as separate processes, resulting in challenges to overall processing speed and user experience. Furthermore, if data transmission between these processes is not secure, information leaks and security problems may occur. In addition, it is necessary to convert the generated response into high-quality speech data, but if this process is inconsistent, it results in an unnatural interaction for the user.

[1421] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1422] In this invention, the server includes means for collecting audio data, means for transmitting the collected audio data using a secure communication protocol, means for converting the audio data into text data, means for analyzing the text data and generating an appropriate response, means for transmitting the generated text data to a speech synthesis system, and means for converting and playing back the audio data. This ensures that the entire process from audio data collection to final audio output is carried out securely and efficiently, providing users with high-quality audio responses.

[1423] "Audio data" refers to data in which audio is recorded and stored in digital format.

[1424] "Means of collection" refers to the hardware or software functions for acquiring and storing audio data.

[1425] A "server" is a computer system that responds to requests from clients via the internet or a local network.

[1426] "Means of transmission" refers to the hardware or software function for sending data from one point to another.

[1427] A "secure communication protocol" is a means of communication that ensures the safe transmission and reception of data, and includes protocols such as HTTPS and SSL / TLS.

[1428] "Speech recognition technology" is a technology that analyzes speech data and converts its content into text data.

[1429] "Text data" refers to the digital format in which character information is displayed or stored.

[1430] "Means of analysis" refers to the hardware or software functions used to process collected or acquired data and understand its meaning.

[1431] A "generative AI model" is a program or algorithm that uses artificial intelligence technology to generate new data or responses.

[1432] "Response" refers to the answer or reaction that a system gives to a user's inquiry or instruction.

[1433] A "speech synthesis system" refers to a technology or system that converts text data into speech data.

[1434] "Means of playback" refers to the hardware or software function that outputs audio data as sound through an output device such as a speaker.

[1435] Modes for carrying out the invention

[1436] The present invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by having a user speak to a terminal. This system includes means for collecting voice data, means for transmitting the collected voice data to a server, means for converting the voice data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data into voice data, means for playing back the converted voice data, and means for transmitting the voice data using a secure communication protocol.

[1437] Collection of audio data:

[1438] When a user speaks to the device, saying "What's the weather like today?", the device uses its built-in microphone to collect audio data. This audio data is temporarily stored in the device's local storage.

[1439] Sending audio data:

[1440] The collected audio data is sent to the server using a secure communication protocol (e.g., HTTPS). This process maintains secure communication because the data is encrypted.

[1441] Speech recognition processing:

[1442] The server sends the received audio data to an automatic speech recognition system (ASR). This system employs technology to convert audio data into text data, and converts the audio data into text data in the format "What's the weather like today?".

[1443] Response generation:

[1444] The server uses a generative AI model (e.g., generative artificial intelligence) to analyze text data and generate an appropriate response. This model generates responses based on the information requested by the user, for example, generating a text response such as "It's sunny today. The maximum temperature is 25 degrees."

[1445] Speech synthesis:

[1446] The generated text response is sent to a text-to-speech system (e.g., TTS). This system converts the text response into speech data.

[1447] Sending audio data:

[1448] The generated audio data is then sent back from the server to the terminal using a secure communication protocol.

[1449] Audio playback:

[1450] The device saves the received audio data to local storage and plays the audio using its built-in speaker. The user can hear the audio response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[1451] Specific example:

[1452] The following interactions are possible as examples of use.

[1453] To obtain weather information:

[1454] 1. User: "What's the weather like today?"

[1455] 2. Terminal: Collects audio and sends it to the server via HTTPS.

[1456] 3. Server: The speech recognition system converts the voice data into the text "Please tell me today's weather."

[1457] 4. Server: The generation AI model generates the response, "It's sunny today. The maximum temperature is 25 degrees Celsius."

[1458] 5. Server: Converts the generated text data into speech data using a speech synthesis system.

[1459] 6. Server: Sends audio data to the terminal.

[1460] 7. Device: Plays the received audio data.

[1461] To retrieve restaurant information:

[1462] 1. User: "Can you tell me about some nearby restaurants?"

[1463] 2. Terminal: Collects audio and sends it to the server via HTTPS.

[1464] 3. Server: The speech recognition system converts the voice data into text, "Please tell me about nearby restaurants."

[1465] 4. Server: The generation AI model generates a response such as, "There is a restaurant A nearby. Its opening hours are..."

[1466] 5. Server: Converts the generated text data into speech data using a speech synthesis system.

[1467] 6. Server: Sends audio data to the terminal.

[1468] 7. Device: Plays the received audio data.

[1469] As a result, the present invention enables intuitive voice-based interaction and provides users with a high level of convenience.

[1470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1471] Step 1:

[1472] Collection of voice input

[1473] The user speaks to the device and says, "What's the weather like today?"

[1474] The device uses its built-in microphone to collect audio data and temporarily stores it in local storage.

[1475] Input: User's voice

[1476] Output: Collected audio data

[1477] Step 2:

[1478] Sending audio data to the server

[1479] The device sends the collected voice data to the server using a secure communication protocol (HTTPS).

[1480] Input: Collected audio data

[1481] Output: Encrypted audio data sent to the server

[1482] Step 3:

[1483] Speech recognition system processing

[1484] The server sends the received audio data to an automatic speech recognition system (ASR), which converts the audio data into text data.

[1485] Input: Encrypted audio data

[1486] Output: Converted text data (e.g., "What's the weather like today?")

[1487] Step 4:

[1488] Text data analysis

[1489] The server sends the converted text data to a generative AI model (e.g., generative artificial intelligence) for analysis. The generative AI model understands the user's intent and generates an appropriate response to the request.

[1490] Input: Converted text data

[1491] Output: Generated response text (Example: "It's sunny today. The high temperature is 25 degrees Celsius.")

[1492] Step 5:

[1493] Response text speech synthesis

[1494] The server sends the generated response text to a text-to-speech (TTS) system, which converts the text data into speech data.

[1495] Input: Generated response text

[1496] Output: Generated audio data

[1497] Step 6:

[1498] Sending audio data to a terminal

[1499] The server sends the generated audio data to the terminal via a secure communication protocol.

[1500] Input: Generated audio data

[1501] Output: Encrypted audio data sent to the terminal

[1502] Step 7:

[1503] Audio playback

[1504] The device temporarily stores the received audio data in local storage and plays it back using the built-in speaker. This allows the user to hear the audio response.

[1505] Input: Encrypted audio data sent

[1506] Output: Played audio (Example: "It's sunny today. The high temperature is 25 degrees Celsius.")

[1507] As a result, users can seamlessly receive questions and answers via voice. The specific integration of hardware and software creates a system that provides a high-quality user experience.

[1508] (Application Example 1)

[1509] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1510] Conventional food delivery services require users to manually operate them using devices such as smartphones, and the complexity of these operations and the lack of intuitive interfaces have been problematic. Furthermore, users often have to go through lengthy procedures when placing specific orders or searching for information, which has contributed to decreased customer satisfaction. This invention aims to solve these problems by providing an intuitive and efficient food delivery system using voice control.

[1511] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1512] In this invention, the server includes means for converting voice data into text data, means for retrieving information based on voice commands from the user and generating an appropriate response, and means for executing a food delivery service based on the generated response. This enables the user to efficiently use a food delivery service using only their voice.

[1513] "Voice data" refers to digital data obtained by recording the user's voice.

[1514] "Means of collection" refers to the hardware and software components for capturing audio data.

[1515] A "server" is a network-connected computer system used for analyzing audio data and generating responses.

[1516] "Text data" refers to digital data consisting of strings of characters obtained by analyzing and converting audio data.

[1517] "Means of analysis" refer to software and algorithms for processing text data and generating appropriate responses based on user requests.

[1518] A "response" is the response data generated in response to a user's voice command.

[1519] "Generative artificial intelligence" refers to machine learning models that include natural language processing techniques and are used to analyze text data and generate appropriate responses.

[1520] A "prompt message" is text data input to a generative artificial intelligence system, and it contains instructions that the AI ​​uses to generate a response based on its content.

[1521] A "speech synthesis system" is a software system used to convert text data into speech data.

[1522] "Means of playback" refer to the hardware and software components that enable the user to listen to the converted audio data.

[1523] A "food delivery service" is a service that delivers food based on a user's order.

[1524] This invention relates to a system that retrieves information based on a user's voice command, generates an appropriate response, and then executes a food delivery service based on that response. This system can be used by the user speaking into a terminal.

[1525] System Configuration and Functions

[1526] This system consists of the following main components:

[1527] 1. Audio acquisition means: Includes a microphone for acquiring audio data and its control software.

[1528] 2. Means of communication with the server: Voice data is sent to the server using a secure protocol (e.g., HTTPS).

[1529] 3. Speech recognition means: A speech recognition system installed on the server (e.g., SpeechRecognition library) is used to convert the speech data into text.

[1530] 4. Response generation means: Text data is analyzed using generative artificial intelligence (e.g., GPT model) and an appropriate response is generated.

[1531] 5. Speech synthesis means: A speech synthesis system (e.g., a Text-to-Speech library) is used to convert text data into speech data.

[1532] 6. Audio playback means: The converted audio data is played back through the speaker.

[1533] System details

[1534] Voice collection and transmission

[1535] When a user speaks into the device, audio data is collected by the microphone. This audio data is temporarily stored on the device and then sent to a server using a secure protocol.

[1536] Speech recognition processing

[1537] On the server, the audio data is converted into text data using a speech recognition system (e.g., the SpeechRecognition library). At this stage, the content spoken by the user is obtained in text format.

[1538] Response generation

[1539] The converted text data is analyzed by a generative artificial intelligence model (e.g., a GPT model) to generate an appropriate response. This process uses predefined prompt statements. Examples of prompt statements are shown below:

[1540] User: I want to order a hamburger.

[1541] Assistant: Which hamburger would you like to order? Our current menu includes cheeseburgers, double cheeseburgers, and bacon burgers.

[1542] Speech synthesis and playback

[1543] The generated response is further converted into audio data using a speech synthesis system, and this audio data is sent to the terminal. The terminal then plays the received audio data through its speaker.

[1544] Specific example

[1545] For example, when a user says to the terminal, "I want to order a hamburger," the following process takes place:

[1546] 1. Audio data is collected and sent to the server.

[1547] 2. The audio data is converted to text on the server.

[1548] 3. The text data is analyzed by a generative artificial intelligence system, and a response is generated: "Which hamburger would you like to order? Our current menu includes cheeseburgers, double cheeseburgers, and bacon burgers."

[1549] 4. The generated response is converted into audio data and sent to the terminal.

[1550] 5. The audio data is played on the device.

[1551] In this way, users can efficiently utilize food delivery services using only their voice.

[1552] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1553] Step 1:

[1554] The user speaks into the device. The user's voice is collected as audio data through the microphone. Input: User's speech. Output: Audio data.

[1555] Step 2:

[1556] The terminal temporarily stores the collected audio data and sends it to the server using a secure protocol (e.g., HTTPS). Input: Audio data. Output: Audio data sent to the server.

[1557] Step 3:

[1558] The server converts the received audio data into text data using a speech recognition system (e.g., the SpeechRecognition library). Input: Audio data. Output: Text data.

[1559] Step 4:

[1560] The server sends the converted text data to a generative artificial intelligence model (e.g., a GPT model) to generate an appropriate response. Input: Text data. Output: Text data for the response.

[1561] Step 5:

[1562] The generated text data for the response is sent to a text-to-speech system (e.g., a Text-to-Speech library) and converted into speech data. Input: Text data for the response. Output: Speech data.

[1563] Step 6:

[1564] The server sends the generated audio data to the terminal. Input: Audio data. Output: Audio data sent to the terminal.

[1565] Step 7:

[1566] The device plays the received audio data through its speaker. The user can hear this audio response. Input: Audio data. Output: Audio response played from the speaker.

[1567] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1568] This invention relates to a system that enables the collection, analysis, generation of responses, and voice responses of voice data by a user speaking into a terminal. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and generates appropriate responses based on those emotions. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, means for playing back the converted voice data, and an emotion engine that recognizes the user's emotions.

[1569] System details:

[1570] In an embodiment of the present invention, the user first speaks into the terminal. The terminal collects the user's voice using a microphone and temporarily stores this voice data. The collected voice data is transmitted from the terminal to the server via a wireless communication module. A secure communication protocol, such as HTTPS, is used to transmit the voice data.

[1571] Speech recognition processing:

[1572] The server receives the audio data transmitted from the terminal. Next, the Automatic Speech Recognition (ASR) system installed on the server analyzes the audio data and converts it into text data. This text data is generated based on what the user has said and uses highly accurate speech recognition technology.

[1573] Emotion recognition processing:

[1574] The server uses an emotion engine to analyze the user's emotions based on the converted text data. This emotion engine identifies the user's emotional state (e.g., joy, anger, sadness) from the characteristics of the voice and text.

[1575] Response generation:

[1576] The server then sends the converted text data to a generative artificial intelligence (for example, a model using the latest natural language processing techniques, such as the GPT model), taking into account the user's emotional state identified by the emotion engine, and analyzes this data. The generative AI generates the information and appropriate responses that the user is looking for, and these responses are adjusted to reflect the emotional state.

[1577] Speech synthesis:

[1578] The generated response is sent to the server as text data. The server then sends this text data to a text-to-speech (TTS) system, which converts it into speech data.

[1579] Audio output:

[1580] Finally, the server sends the generated audio data to the terminal. The terminal plays the received audio data through its speaker and provides a response to the user. This response is adjusted according to the user's emotional state, resulting in a more natural and appropriate conversation.

[1581] Specific example:

[1582] The following are specific examples of how to use this system.

[1583] Usage example 1:

[1584] Let's say the user sadly asks the device, "What's the weather like today?"

[1585] 1. User: Speaks sadly to the device, "What's the weather like today?"

[1586] 2. Terminal: Collects audio and sends the audio data to the server.

[1587] 3. Server: Converts speech data into text using a speech recognition system.

[1588] 4. Server: Uses an emotion engine to recognize the user's emotion as "sadness".

[1589] 5. Server: The converted text data and user sentiment information are sent to the generative artificial intelligence system, which then generates the response, "It's sunny today, but please take it easy and relax."

[1590] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1591] 7. Terminal: Plays audio data sent from the server.

[1592] Usage example 2:

[1593] If a user angrily asks the device, "Tell me about nearby restaurants," the system will perform the following actions.

[1594] 1. User: Speaks angrily to the device, "Tell me about nearby restaurants."

[1595] 2. Terminal: Collects audio and sends the audio data to the server.

[1596] 3. Server: Converts speech data into text using a speech recognition system.

[1597] 4. Server: Uses an emotion engine to recognize the user's emotion as "anger".

[1598] 5. Server: The converted text data and user sentiment information are sent to the generative artificial intelligence system, which then generates a response saying, "There are restaurants A, B, and C nearby. Please relax and enjoy your meal."

[1599] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1600] 7. Terminal: Plays audio data sent from the server.

[1601] Thus, the present invention realizes a system that can provide more appropriate and natural voice responses according to the user's emotional state. As a result, users can interact by voice without reading text, and enjoy a highly intuitive and user-friendly experience.

[1602] The following describes the processing flow.

[1603] Step 1:

[1604] The user speaks to the device. For example, they might say, "What's the weather like today?"

[1605] Step 2:

[1606] The device collects the user's voice using its microphone. The collected voice data is temporarily stored in the device's memory.

[1607] Step 3:

[1608] The device sends the collected audio data to the server. A secure communication protocol, such as HTTPS, is used for transmission.

[1609] Step 4:

[1610] The server receives the audio data sent from the terminal. The received audio data is stored in the server's memory.

[1611] Step 5:

[1612] The server passes the audio data to the speech recognition system (ASR: Automatic Speech Recognition). The ASR analyzes the audio data and converts it into text data.

[1613] Step 6:

[1614] The server receives text data converted from the speech recognition system (ASR). For example, it might be converted to the text, "What's the weather like today?"

[1615] Step 7:

[1616] The server passes the converted text data to the emotion engine. The emotion engine analyzes the user's emotional state from the characteristics of the voice and text, and recognizes it as, for example, "sadness."

[1617] Step 8:

[1618] The server receives emotion data obtained from the emotion engine. For example, emotion data such as "sadness."

[1619] Step 9:

[1620] The server sends the converted text data and sentiment data to a generative artificial intelligence (AI). The AI ​​analyzes the text data and generates an appropriate response that reflects the user's emotions. For example, it might generate a response such as, "It's sunny today, but please take it easy and relax."

[1621] Step 10:

[1622] The server receives a response text generated by a generative artificial intelligence. For example, the text might say, "It's sunny today, but please take it easy and don't overexert yourself."

[1623] Step 11:

[1624] The server passes the generated response text to the Text-to-Speech (TTS) system. The TTS system converts the text data into speech data.

[1625] Step 12:

[1626] The server receives the audio data converted from the TTS system. For example, the audio data might say, "It's sunny today, but please take it easy and don't overexert yourself."

[1627] Step 13:

[1628] The server sends the generated audio data to the terminal. A secure communication protocol is used again for transmission.

[1629] Step 14:

[1630] The device receives audio data sent from the server. The received audio data is stored in the device's memory.

[1631] Step 15:

[1632] The device plays the received audio data through its speaker. The user hears the audio response, "It's sunny today, but please take it easy and relax."

[1633] In this way, a process is realized in which, in response to a question posed by the user, an appropriate response is received via the device in the form of voice that reflects the user's emotional state.

[1634] (Example 2)

[1635] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1636] Conventional speech recognition systems convert speech into text and generate appropriate responses, but they lack the ability to adjust responses based on the user's emotions, which can result in mechanical and unnatural dialogue. This invention aims to achieve natural and human-like dialogue by recognizing the user's emotions and generating responses that take emotional elements into account.

[1637] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1638] In this invention, the server includes means for converting voice data into text data, means for analyzing the text data and recognizing the user's emotions, and means for generating an appropriate response based on the emotional state. This makes it possible to generate a natural and appropriate response that is in line with the user's emotional state.

[1639] "Voice data" refers to data recorded in digital format from what a user speaks into a device.

[1640] "Means of collection" refers to a device or method that captures audio data using a microphone built into the terminal.

[1641] "Transmission means" refers to a device or method for transmitting collected audio data to a server using a wireless communication module.

[1642] "Text data" refers to the text format of audio data converted by a speech recognition system.

[1643] "Means of conversion" refer to speech recognition systems and algorithms used to convert speech data into text data.

[1644] "Means of analysis" refers to a device or program that uses converted text data to recognize the user's emotions and interpret their meaning and content.

[1645] "Means of recognizing emotions" refer to emotion engines and algorithms that analyze the characteristics of text data to identify the user's emotional state.

[1646] "Means for generating appropriate responses" refer to generative artificial intelligence and natural language generation technologies that create responses to enable natural dialogue based on the user's emotional state and text data.

[1647] "Means for converting to audio data" refers to a speech synthesis system for converting the generated text response into audio data.

[1648] "Means of playback" refers to a device or method for playing audio data transmitted from a server through a speaker on a terminal.

[1649] The present invention is a system that collects, analyzes, generates responses to, and responds to voice data when a user speaks into a terminal. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions and generates appropriate responses based on those emotions. The system includes means for collecting voice data, means for transmitting voice data to a server, means for converting voice data into text data, means for analyzing text data and generating appropriate responses, means for converting the generated text data into voice data, and means for playing back the converted voice data.

[1650] Hardware and software to be used

[1651] 1. Terminal

[1652] Microphone: Used to collect the user's voice.

[1653] Wireless communication module: Used to transmit collected audio data to a server. Specifically, it utilizes Wi-Fi or Bluetooth.

[1654] Speaker: Used to play audio data received from the server to the user.

[1655] 2. Server

[1656] Automatic Speech Recognition (ASR): Used to convert speech data into text data. Specifically, Google Cloud Speech-to-Text is used.

[1657] Emotion Engine: Used to recognize the user's emotions from text. Specifically, IBM Watson Tone Analyzer is used.

[1658] Generative artificial intelligence (NLG: Natural Language Generation): Used to generate appropriate responses. Specifically, OpenAI's GPT model is used.

[1659] Text-to-Speech (TTS): Used to convert generated text data into speech data. Specifically, Amazon Polly is used.

[1660] Specific example

[1661] Example 1: When a user sadly asks, "What's the weather like today?"

[1662] 1. User: Speaks sadly to the device, "What's the weather like today?"

[1663] 2. Terminal: The microphone collects audio and stores it temporarily. Then, the audio data is sent to the server via a secure protocol (HTTPS) through the wireless communication module.

[1664] 3. Server: Uses a speech recognition system to convert speech data into text data with high accuracy.

[1665] 4. Server: Uses an emotion engine to recognize the user's emotion as "sadness" from the converted text data.

[1666] 5. Server: Generative artificial intelligence generates a response that takes emotions into consideration: "It's sunny today, but please take it easy and relax."

[1667] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1668] 7. Terminal: Receives audio data from the server and plays it through the speaker.

[1669] Example 2: When a user angrily asks, "Tell me about nearby restaurants."

[1670] 1. User: Speaks angrily to the device, "Tell me about nearby restaurants."

[1671] 2. Terminal: The microphone collects audio and stores it temporarily. Then, the audio data is sent to the server via a secure protocol (HTTPS) through the wireless communication module.

[1672] 3. Server: Uses a speech recognition system to convert speech data into text data with high accuracy.

[1673] 4. Server: Uses an emotion engine to recognize the user's emotion as "anger" from the converted text data.

[1674] 5. Server: Generative artificial intelligence generates a response that takes emotions into consideration: "There are restaurants A, B, and C nearby. Please relax and enjoy your meal."

[1675] 6. Server: Sends the generated text data to the speech synthesis system and converts it into speech data.

[1676] 7. Terminal: Receives audio data from the server and plays it through the speaker.

[1677] Example of a prompt

[1678] The following are examples of prompts to input into a generative artificial intelligence:

[1679] "The user is sadly asking, 'What's the weather like today?' Please generate an appropriate response that reflects this emotion."

[1680] "The user is asking, 'Tell me about nearby restaurants,' in an angry tone. Please understand this sentiment and provide an appropriate response."

[1681] In this way, the entire system works together to achieve natural dialogue that is in line with the user's emotions.

[1682] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1683] Step 1: Collect audio data

[1684] User: Speaks into the device. For example, "What's the weather like today?"

[1685] Input: User's voice.

[1686] Terminal: Uses a built-in microphone to collect the user's voice as digital audio data and temporarily stores it. The microphone should be highly sensitive and have noise filtering capabilities.

[1687] Output: Temporarily stored audio data.

[1688] Step 2: Sending the audio data

[1689] Terminal: Prepares to transmit the collected audio data.

[1690] Input: Temporarily stored audio data.

[1691] Terminal: Uses a wireless communication module (e.g., Wi-Fi or Bluetooth) to transmit voice data to the server. A secure communication protocol (e.g., HTTPS) is used to ensure data security.

[1692] Output: Audio data sent to the server.

[1693] Step 3: Convert speech to text

[1694] Server: Receives audio data sent from the terminal.

[1695] Input: Audio data sent to the server.

[1696] Server: Uses a speech recognition system (ASR, e.g., Google Cloud Speech-to-Text) to convert speech data into text data. This system provides highly accurate conversion.

[1697] Output: Text data.

[1698] Step 4: Emotion Recognition

[1699] Server: Receives the converted text data.

[1700] Input: Text data.

[1701] Server: Uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the characteristics of text data and identify the user's emotions. For example, if a sad tone is detected in the question "What's the weather like today?", the emotion engine recognizes it as "sadness".

[1702] Output: Emotional information (e.g., "sadness").

[1703] Step 5: Response Generation

[1704] Server: Receives recognized emotion information and text data.

[1705] Input: Sentimental information and text data.

[1706] Server: Uses a generative AI model (e.g., OpenAI's GPT model) to analyze input data and generate appropriate responses that take the user's emotions into consideration. For example, if the emotion is "sadness," a response such as "It's sunny today, but please take it easy and relax" would be generated.

[1707] Output: Text data of the generated response.

[1708] Step 6: Convert the generated response text data into audio data

[1709] Server: Prepares to convert the generated response text into audio data.

[1710] Input: Text data of the generated response.

[1711] Server: Uses a text-to-speech system (TTS, e.g., Amazon Polly) to convert text data into natural-sounding speech data.

[1712] Output: Generated audio data.

[1713] Step 7: Sending and playing audio data

[1714] Server: Sends the generated audio data to the terminal.

[1715] Input: Generated audio data.

[1716] Server: Uses a secure communication protocol (e.g., HTTPS) to send the generated audio data to the terminal.

[1717] Terminal: Receives transmitted audio data and plays it back to the user through the speaker. For example, it might play the message, "It's sunny today, but please take it easy and relax."

[1718] Output: Audio data that the user can listen to.

[1719] As described above, by having each step work together, a natural dialogue that takes user emotions into consideration is achieved.

[1720] (Application Example 2)

[1721] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1722] In modern factories and production lines, smooth communication between human workers and robots is essential. However, conventional systems have insufficient interpretation of voice commands and emotional recognition, resulting in problems such as decreased work efficiency and increased worker stress. Furthermore, it is difficult for robots to understand the emotions of workers and provide appropriate feedback and guidance. Against this backdrop, there is a need for a system that can accurately recognize user voice commands, analyze emotions, and respond appropriately based on the results.

[1723] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1724] In this invention, the server includes means for collecting voice data, means for transmitting the collected voice data to the server, means for converting the voice data into text data, means for analyzing the text data and generating an appropriate response, means for converting the generated text data back into voice data, means for playing back the converted voice data, emotion analysis means for recognizing the user's emotions, and instruction execution means for understanding instructions and performing appropriate tasks while considering the emotional state. As a result, an efficient and less stressful work environment is possible, as workers can give instructions to the robot by voice, and those instructions are interpreted appropriately according to the emotional state.

[1725] "Means for collecting audio data" refers to a device or system that has the function of capturing user audio in real time.

[1726] "Means for transmitting collected audio data to a server" refers to a device or system that has the function of transmitting collected audio data to a remote server via a network.

[1727] "Means of converting audio data to text data" refers to software or systems that analyze audio data and convert it into corresponding text data.

[1728] "Means for analyzing text data and generating appropriate responses" refers to an algorithm or system that analyzes text data to understand the user's intent and generates a response based on the results.

[1729] "Means for converting generated text data into audio data" refers to a speech synthesis system that has the function of converting text data into audio data.

[1730] "Means for playing back converted audio data" refers to a system that plays back converted audio data through sound devices such as speakers.

[1731] "An emotion analysis method for recognizing a user's emotions" refers to a technology or system that analyzes the characteristics of a user's voice or text to identify their emotional state.

[1732] "An instruction execution means that understands instructions and performs appropriate tasks while considering the emotional state" refers to a system that has the function of appropriately interpreting the user's instructions based on the results of emotion analysis and performing predetermined tasks.

[1733] This invention is a system used by users to give voice commands to factory robots, which then appropriately understand those commands and perform the tasks. This system can collect the user's voice, analyze their emotions, and play back the generated response in voice.

[1734] System details

[1735] Hardware and software

[1736] 1. Means of collecting audio data:

[1737] The device uses its built-in microphone to collect the user's voice.

[1738] Examples include smartphones and dedicated microphone devices.

[1739] 2. Means for transmitting audio data to the server:

[1740] The terminal transmits the collected voice data to the server using a secure protocol (e.g., HTTPS) via a wireless communication module (e.g., Wi-Fi or LTE).

[1741] 3. Means of converting audio data to text data:

[1742] The server uses speech recognition software (ASR: Automatic Speech Recognition) to convert the audio data into text.

[1743] Example: Google Cloud Speech-to-Text API.

[1744] 4. Means for analyzing text data and generating appropriate responses:

[1745] The server analyzes the converted text data using natural language processing techniques (e.g., the Hugging Face transformers library) to recognize the user's emotions. Furthermore, it generates an appropriate response using generative artificial intelligence (e.g., GPT-3.5).

[1746] 5. Means for converting generated text data into audio data:

[1747] The text data generated on the server is converted into speech data using a text-to-speech (TTS) system.

[1748] Example: Google Text-to-Speech API.

[1749] 6. Means for playing back the converted audio data:

[1750] The terminal plays the audio data received from the server through its speaker.

[1751] 7. Emotion analysis means:

[1752] The server uses a sentiment analysis system (e.g., a sentiment-analysis model) to analyze the user's emotions.

[1753] 8. Instruction execution means:

[1754] The server sends instructions to the factory robots based on responses generated while taking emotional states into consideration, and the robots perform the appropriate tasks.

[1755] Example: Robot control system.

[1756] Specific example

[1757] Considering a scenario where a worker gives instructions to a robot using voice commands, the following specific processes would occur:

[1758] 1. Collection: The terminal collects voice messages from workers such as, "How do I fix this machine's malfunction?"

[1759] 2. Transmission: The collected audio data is sent to the server.

[1760] 3. Analysis: The audio data is converted to text data on the server, and the emotion analysis system recognizes anger from the tone of the voice.

[1761] 4. Response Generation: The generative artificial intelligence generates an appropriate response based on the prompt message, "The worker seems angry. Instruction: How do I fix this machine malfunction?" For example, it might generate a response such as, "Let's deal with the machine malfunction calmly. First, check the manual's procedures, and call support if necessary."

[1762] 5. Conversion and Output: The generated text response is converted into audio data and played back through the device's speaker.

[1763] Example of a prompt

[1764] Examples of prompt messages sent to a generative artificial intelligence are as follows:

[1765] text

[1766] The worker seems angry. Instructions: How do I fix this broken machine?

[1767] This allows the system to generate appropriate responses that take the user's emotions into account and provide them as voice messages.

[1768] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1769] Processing steps

[1770] Step 1:

[1771] The terminal collects voice from the worker using a microphone. The input is the worker's voice, and the output is the collected voice data. Specifically, the terminal's built-in microphone captures the worker's speech and temporarily stores it as digital voice data.

[1772] Step 2:

[1773] The terminal transmits the collected audio data to the server via a wireless communication module. The input is the collected audio data, and the output is the audio data transferred to the server. Specifically, the terminal uses the HTTPS protocol to send the audio data to a specified endpoint on the server.

[1774] Step 3:

[1775] The server uses speech recognition software to convert received audio data into text data. The input is audio data, and the output is the converted text data. Specifically, it uses the Google Cloud Speech-to-Text API, among others, to transcribe the audio data with high accuracy.

[1776] Step 4:

[1777] The server performs sentiment analysis from text and audio data. The input is text and audio data, and the output is the user's emotional state. Specifically, it uses the Hugging Face sentiment-analysis model to identify the emotion in the text (e.g., joy, anger, sadness).

[1778] Step 5:

[1779] The server considers the emotional state and generates an appropriate response using generative artificial intelligence. The input is text data and the emotional state, and the output is the generated text response. Specifically, a prompt sentence is input to a generative AI model such as GPT-3.5, and an appropriate response is generated as a result. For example, the prompt sentence is "The worker seems angry. Instructions: How do I fix this machine malfunction?"

[1780] Step 6:

[1781] The server converts the generated text response into speech data using a speech synthesis system. The input is the generated text response, and the output is speech data. Specifically, the Google Text-to-Speech API is used to convert text to speech.

[1782] Step 7:

[1783] The terminal plays audio data received from the server through its speaker. The input is audio data, and the output is an audio response. Specifically, the terminal's speaker system plays the audio data and provides a response to the worker.

[1784] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1785] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1786] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1787] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1788] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1789] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1790] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1791] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1792] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1793] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1794] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1795] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1796] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1797] 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.

[1798] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1799] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1800] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1801] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1802] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1803] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1804] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1805] The following is further disclosed regarding the embodiments described above.

[1806] (Claim 1)

[1807] Means of collecting audio data,

[1808] A means of sending the collected audio data to the server,

[1809] A means of converting audio data into text data,

[1810] A means for analyzing text data and generating an appropriate response,

[1811] A means of converting generated text data into audio data,

[1812] A means of playing back the converted audio data,

[1813] A system that includes this.

[1814] (Claim 2)

[1815] The system according to claim 1, wherein the means for converting audio data into text data is natural language processing technology.

[1816] (Claim 3)

[1817] The system according to claim 1, wherein the means for analyzing text data and generating an appropriate response is a generative artificial intelligence.

[1818] "Example 1"

[1819] (Claim 1)

[1820] Means of collecting audio data,

[1821] A means of sending the collected audio data to the server,

[1822] A means of converting audio data into text data,

[1823] A means for analyzing text data and generating an appropriate response,

[1824] A means of converting generated text data into audio data,

[1825] A means of playing back the converted audio data,

[1826] A means of transmitting the collected audio data using a secure communication protocol,

[1827] A means for transmitting the generated text data to a speech synthesis system,

[1828] A system that includes this.

[1829] (Claim 2)

[1830] The system according to claim 1, wherein the means for converting audio data into text data is to use automatic speech recognition technology.

[1831] (Claim 3)

[1832] The system according to claim 1, wherein the means for analyzing text data and generating an appropriate response is to use a generative AI model.

[1833] "Application Example 1"

[1834] (Claim 1)

[1835] Means of collecting audio data,

[1836] A means of sending the collected audio data to the server,

[1837] A means of converting audio data into text data,

[1838] A means for analyzing text data and generating an appropriate response,

[1839] A means of converting generated text data into audio data,

[1840] A means of playing back the converted audio data,

[1841] A means for searching for information and generating an appropriate response based on voice commands from the user,

[1842] A means of performing a food delivery service based on the generated response,

[1843] A system that includes this.

[1844] (Claim 2)

[1845] The system according to claim 1, wherein the means for converting audio data into text data is natural language processing technology.

[1846] (Claim 3)

[1847] The system according to claim 1, wherein the means for analyzing text data and generating an appropriate response uses generative artificial intelligence, and generates a response based on a prompt sentence.

[1848] "Example 2 of combining an emotion engine"

[1849] (Claim 1)

[1850] Means of collecting audio data,

[1851] A means of sending the collected audio data to the server,

[1852] A means of converting audio data into text data,

[1853] A means of analyzing text data to recognize user emotions,

[1854] A means of generating an appropriate response based on emotional state,

[1855] A means for converting the generated response into audio data,

[1856] A means of playing back the converted audio data,

[1857] A system that includes this.

[1858] (Claim 2)

[1859] The system according to claim 1, wherein the means for converting audio data into text data is natural language processing technology.

[1860] (Claim 3)

[1861] The system according to claim 1, wherein the means for generating an appropriate response based on an emotional state is to use generative artificial intelligence technology.

[1862] "Application example 2 when combining with an emotional engine"

[1863] (Claim 1)

[1864] Means of collecting audio data,

[1865] A means of sending the collected audio data to the server,

[1866] A means of converting audio data into text data,

[1867] A means for analyzing text data and generating an appropriate response,

[1868] A means of converting generated text data into audio data,

[1869] A means of playing back the converted audio data,

[1870] A means of analyzing user emotions,

[1871] An instruction execution method that takes into account emotional state to understand instructions and perform appropriate tasks,

[1872] A system that includes this.

[1873] (Claim 2)

[1874] The system according to claim 1, wherein the means for converting audio data into text data is natural language processing technology.

[1875] (Claim 3)

[1876] The system according to claim 1, wherein the means for analyzing text data and generating an appropriate response is a generative artificial intelligence. [Explanation of Symbols]

[1877] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting audio data, A means of sending the collected audio data to the server, A means of converting audio data into text data, A means for analyzing text data and generating an appropriate response, A means of converting generated text data into audio data, A means of playing back the converted audio data, A system that includes this.

2. The system according to claim 1, wherein the means for converting audio data into text data is natural language processing technology.

3. The system according to claim 1, wherein the means for analyzing text data and generating an appropriate response is a generative artificial intelligence.

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