system

Abstract

We provide the system. [Solution] A means for capturing the user's voice, A means of converting captured audio data into text data, A means for sending the converted text data to a server over a network, A means for generating a response based on text data received by the server, A means for sending the generated response text data to the terminal, A means for converting received response 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 in 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] Social isolation and lack of conversation in the life of the elderly living alone are factors that increase the risk of dementia. In contrast, means for supporting daily conversations and answering questions are required. However, the technologies so far have not sufficiently provided a conversation system in a form that can be easily used by the elderly, and as a result, prevention of dementia and improvement of the quality of life have not been realized. Therefore, there is a demand for providing a system that provides continuous conversations and answers questions in a form that can be easily used by the elderly in their daily lives.

Means for Solving the Problems

[0005] The present invention provides a system including means for capturing user voice, means for converting captured voice data into text data, means for transmitting the converted text data to a server via a network, means for generating a response based on the received text data at the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into voice data, and means for playing back the converted voice data. In particular, the generated response text data is created using a generative AI model to achieve advanced conversational capabilities and flexible responses. Furthermore, by including a microphone for capturing user voice data, a speech recognition engine for converting captured voice data into text data, and communication means for transmitting the converted text data to a server, the system can be used intuitively by the elderly and facilitates everyday conversations. This can help maintain and improve the cognitive function of the elderly and improve their quality of life.

[0006] "Means for capturing user voice" refers to a device and method for electronically capturing the voice spoken by a user.

[0007] "Means for converting audio data to text data" refers to software and algorithms for converting captured audio data into text string data.

[0008] "Means for sending text data to a server over a network" refers to communication interfaces and protocols for transferring text data to a remote server via the Internet or other communication networks.

[0009] "Means for generating a response" refers to software and algorithms for generating an appropriate response message based on text data received on the server side.

[0010] "Means for sending generated response text data to the terminal" refers to communication means for transferring a response message in text data format generated on the server to the terminal via the network.

[0011] "Means for converting received response text data into audio data" refers to speech synthesis software and hardware for converting response messages in text format into audio data.

[0012] "Means for playing back converted audio data" refers to speakers or other audio output devices that play back the generated audio data so that the user can hear it.

[0013] A "generative AI model" is an artificial intelligence model that uses natural language processing techniques to analyze and generate text data.

[0014] A "speech recognition engine" is software and algorithms that receive speech data as input and output corresponding text data.

[0015] "Communication means" refers to a combination of hardware and software for transmitting and receiving data from one party to another. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 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 Example 2 when an 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 an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

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

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

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0021] 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to a system that captures user voice data, converts it into text data, and sends it to a server via a network. The server generates and sends back a response, and the terminal converts that response back into voice data and plays it back. The aim of this system is to provide a conversational support system that elderly people can use on a daily basis, thereby reducing the risk of isolation and dementia.

[0038] Program processing details

[0039] 1. Audio Capture

[0040] The device captures the user's voice.

[0041] The microphone built into the device activates and receives voice input from the user. For example, if the user says, "What's the weather like today?", the device captures this voice as digital data. This digital data is then sent to the subsequent speech recognition process.

[0042] 2. Speech Recognition

[0043] The device converts the audio data into text data.

[0044] The captured audio data is sent to a speech recognition engine and converted into text data. For example, the captured audio "What's the weather like today?" is converted into the text "What's the weather like today?" using speech recognition technology.

[0045] 3. Sending text data

[0046] The device sends text data to the server.

[0047] The converted text data is sent to the server over the network. For example, text data can be sent to the server using an HTTP request.

[0048] 4. Response generation

[0049] The server generates responses using an AI model.

[0050] The server generates a response based on the received text data. Specifically, it uses a generative AI model to generate an appropriate response, such as "It's sunny today," in response to the text "What's the weather like today?". This response text data is then sent from the server to the terminal.

[0051] 5. Send response data

[0052] The server sends the generated response data to the terminal.

[0053] The response data generated by the server is then sent back to the terminal via the network. For example, it may be returned to the terminal as an HTTP response.

[0054] 6. Speech synthesis

[0055] The device converts received text data into audio data.

[0056] The terminal invokes a speech synthesis engine to convert the received response text data into speech. This converts the text "It's sunny today." into corresponding speech data.

[0057] 7. Audio Playback

[0058] The device plays audio data to respond to the user.

[0059] The device's speaker plays audio data and responds to the user. For example, the speaker might play the message, "It's sunny today."

[0060] Specific Scenario Examples

[0061] 1. The user asks the device, "What day is it today?"

[0062] 2. The device captures the user's voice and converts it into text, "What day is it today?", using a speech recognition engine.

[0063] 3. The converted text is sent to the server.

[0064] 4. The server uses a generative AI model to generate the response "Today is October 5th."

[0065] 5. The server sends a response back to the terminal.

[0066] 6. The terminal receives the response and converts it into speech data using a speech synthesis engine.

[0067] 7. The user hears the audio message "Today is October 5th" through the device's speaker.

[0068] Thus, the present invention provides a system that supports the resolution of questions and dialogues in the daily lives of elderly people, and contributes to maintaining cognitive function and improving quality of life.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] The user speaks to the device.

[0072] The user speaks and asks a question to the device. For example, the user might say, "What's the weather like today?"

[0073] Step 2:

[0074] The device captures the user's voice.

[0075] The device uses its built-in microphone to capture the user's speech as audio data. The captured audio data is temporarily stored in memory.

[0076] Step 3:

[0077] The device converts the audio data into text data.

[0078] The device activates its speech recognition engine (e.g., speech recognition software) and converts the captured audio data into text data. For example, the audio "What's the weather like today?" is converted into the text data "What's the weather like today?".

[0079] Step 4:

[0080] The device sends text data to the server.

[0081] The terminal uses its network connection to send the converted text data to the server. The text data "What's the weather like today?" is sent to the server as an HTTP request.

[0082] Step 5:

[0083] The server receives text data.

[0084] The server parses the received HTTP request and retrieves the text data it contains. The server receives the text data "What's the weather like today?".

[0085] Step 6:

[0086] The server generates responses using an AI model.

[0087] The server inputs the received text data into a generative AI model (e.g., a natural language processing model). The AI ​​model analyzes the text "What's the weather like today?" and generates the response "It's sunny today."

[0088] Step 7:

[0089] The server sends the response text data to the terminal.

[0090] The server creates an HTTP response to send the generated response text data back to the terminal. The text data "It's sunny today." is sent to the terminal.

[0091] Step 8:

[0092] The device converts the received response text data into audio data.

[0093] The terminal activates a speech synthesis engine to convert the received text data into speech. The speech synthesis engine converts the text "It's sunny today." into speech data.

[0094] Step 9:

[0095] The device plays audio data.

[0096] The device plays the generated audio data through its speaker. The user can hear the message "It's sunny today." coming from the speaker.

[0097] In this way, this system contributes to improving the quality of life for the elderly by providing intuitive and rapid responses to users' questions.

[0098] (Example 1)

[0099] 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."

[0100] In modern society, it is important to provide means for elderly people to engage in daily conversations without becoming isolated. Elderly people often have difficulty with youthful conversations and access to information, which increases the risk of dementia. This invention aims to solve these problems and provide a conversation support system that is easy for elderly people to use.

[0101] 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.

[0102] In this invention, the server includes means for capturing audio, means for converting the captured audio data into text data, means for transmitting the converted text data to the server via a network, means for generating a response based on the text data received by the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into audio data, and means for playing back the converted audio data. This enables elderly people to interact with the system using their own voice and obtain information and responses in real time.

[0103] "Means for capturing speech" refers to a device or technology that converts a user's speech into digital speech data.

[0104] "Means for converting audio data into text data" refers to a technology or device that analyzes captured audio data and generates a corresponding string of characters.

[0105] "Means for sending text data to a server over a network" refers to a device or technology that sends generated text data to a remote server using a communication protocol.

[0106] "Means for generating a response based on text data received on the server" refers to a device or technology that generates an appropriate response based on text data received on the server side.

[0107] "Means for sending generated response text data to a terminal" refers to a device or technology that sends the response generated from the server back to the terminal via the network.

[0108] "Means for converting received response text data into audio data" refers to a device or technology that converts received text data back into audio data.

[0109] "Means for playing back converted audio data" refers to a device or technology that plays back converted audio data as physical sound.

[0110] A "generative AI model" is an artificial intelligence model that generates responses in natural language based on input data.

[0111] A "prompt statement" is an input statement given to a generative AI model, and it is an instruction statement that the model uses to generate a response.

[0112] This invention relates to a system that captures user voice data, converts it into text data, and sends it to a server via a network. The server generates and sends back a response, and the terminal converts that response back into voice data and plays it back. This system aims to provide a conversational support system that elderly people can use on a daily basis, thereby reducing the risk of isolation and dementia.

[0113] The system configuration includes terminals and servers. Specific hardware and software examples include terminals equipped with a microphone, speaker, speech recognition engine (e.g., Google® Cloud Speech-to-Text API), and speech synthesis engine (e.g., Amazon Polly API). The server is equipped with a generative AI model (e.g., OpenAI® GPT-3®) that generates responses based on text data.

[0114] Audio Capture

[0115] The device captures the user's voice. Specifically, the microphone built into the device operates and captures the user's voice input as digital data. For example, if the user says, "What's the weather like today?", the device's microphone picks up the voice and converts it into a digital signal.

[0116] Speech recognition

[0117] The device converts the captured audio data into text data. This process uses a speech recognition engine (e.g., Google Cloud Speech-to-Text API). For example, the captured audio "What's the weather like today?" is converted into the text "What's the weather like today?" by speech recognition technology.

[0118] Sending text data

[0119] The terminal sends the converted text data to the server. Specifically, it sends the text data to the server using an HTTP request.

[0120] Response generation

[0121] The server generates a response based on the text data it receives. A generative AI model (e.g., OpenAI GPT-3) is used. For example, in response to the text "What's the weather like today?", the generative AI model generates the response "It's sunny today."

[0122] Send response data

[0123] The server sends the generated response data to the terminal. Specifically, the response data is returned to the terminal as an HTTP response.

[0124] Speech synthesis

[0125] The device calls a speech synthesis engine (e.g., Amazon Polly API) to convert the received response text data into speech data. For example, the text "It's sunny today." is converted into the corresponding speech data.

[0126] Audio playback

[0127] The device's speaker plays audio data and responds to the user. For example, the speaker might play the message, "It's sunny today."

[0128] Specific Scenario Examples

[0129] 1. The user speaks to the device saying, "What day is it today?" The spoken words are captured by the device's microphone.

[0130] 2. The device captures the user's voice as digital data and converts it into text, "What day is it today?", using a speech recognition engine.

[0131] 3. The terminal sends the converted text data to the server as an HTTP POST request.

[0132] 4. The server uses a generative AI model (e.g., OpenAI GPT-3) to generate the response "Today is October 5th."

[0133] 5. The server sends the generated response data to the terminal as an HTTP response.

[0134] 6. The terminal converts the received response into audio data, "Today is October 5th," using a speech synthesis engine (e.g., Amazon Polly API).

[0135] 7. The device's speaker plays a voice message saying, "Today is October 5th," informing the user.

[0136] Example of a prompt:

[0137] "What's the weather like today?"

[0138] "What day is it today?"

[0139] "Please tell me today's news."

[0140] Thus, the present invention provides a system that makes it easier for elderly people to resolve questions in their daily lives, contributing to the maintenance of cognitive function and improvement of quality of life.

[0141] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0142] Step 1:

[0143] Audio Capture

[0144] The device captures the user's voice. Specifically, the microphone built into the device operates and captures the user's speech as digital data. For example, if the user says, "What's the weather like today?", the device's microphone picks up the voice and converts it into a digital signal.

[0145] Input: User's voice

[0146] Output: Digital audio data (audio waveform data)

[0147] Step 2:

[0148] Speech recognition

[0149] The device converts the audio data into text data. The captured audio data is sent to a speech recognition engine (e.g., Google Cloud Speech-to-Text API) for analysis. For example, the speech recognition engine analyzes the digital audio and converts it into text such as "What's the weather like today?". This process involves noise filtering and speech feature extraction.

[0150] Input: Digital audio data

[0151] Output: Recognized text data ("What's the weather like today?")

[0152] Step 3:

[0153] Sending text data

[0154] The device sends text data to the server. The device's application constructs an HTTP POST request and includes the text data as a payload. Specifically, the converted text data is sent to the server as an HTTP request.

[0155] Input: Recognized text data

[0156] Output: HTTP POST request (including text data)

[0157] Step 4:

[0158] Response generation

[0159] The server generates a response based on the text data it receives. Specifically, the server inputs text data into a generative AI model (e.g., OpenAI GPT-3) to generate an appropriate response. For example, in response to the text "What's the weather like today?", the generative AI model generates the response "It's sunny today." This process involves natural language processing and contextual analysis.

[0160] Input: HTTP POST request (including text data)

[0161] Output: Generated response text data ("It's sunny today.")

[0162] Step 5:

[0163] Send response data

[0164] The server sends the generated response data to the terminal. The server constructs an HTTP response, including the generated response text data as the payload. This response reaches the terminal via the internet.

[0165] Input: Generated response text data

[0166] Output: HTTP response (including response text data)

[0167] Step 6:

[0168] Speech synthesis

[0169] The device calls a speech synthesis engine (e.g., Amazon Polly API) to convert the received response text data into speech data. The device's application sends the text data to the speech synthesis API and receives the speech data returned by the API. This speech data is saved on the device as a corresponding audio file.

[0170] Input: Received response text data

[0171] Output: Synthesized audio data (audio file)

[0172] Step 7:

[0173] Audio playback

[0174] The device's speaker plays audio data and responds to the user. The device's audio playback function reads the audio file and plays the message "It's sunny today." through the speaker. During this process, volume adjustment and playback timing control are also performed.

[0175] Input: Synthesized audio data (audio file)

[0176] Output: Audio playback from the speaker ("It's sunny today.")

[0177] (Application Example 1)

[0178] 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."

[0179] In traditional factory operations, a major challenge was the time and effort required for workers to perform complex procedures and check situations. Furthermore, it was difficult for workers to give instructions using voice commands without using their hands and to receive real-time responses. This resulted in decreased work efficiency and an increased risk of errors and accidents. A simple and effective support system was particularly needed for elderly workers.

[0180] 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.

[0181] In this invention, the server includes means for capturing the user's voice, means for converting the captured voice data into text data, means for transmitting the converted text data to the server via a network, means for generating a response based on the text data received by the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into voice data, means for playing back the converted voice data, means for capturing the worker's voice using smart glasses and converting work instructions into text data using a voice recognition engine, means for converting the generated response text data into voice data using a speech synthesis engine, and means for playing back the voice data from the smart glasses' speaker. This makes it possible for workers to easily give instructions using only their voice without using their hands and to obtain appropriate responses in real time.

[0182] A "user" refers to a person who utilizes the system of the present invention, and who wears smart glasses at the work site and gives instructions to the robot or system using voice.

[0183] "Means of capturing audio" refers to devices such as microphones used to capture the voice spoken by a user as digital data.

[0184] "Means of converting audio data into text data" refers to a speech recognition engine that analyzes captured audio data and converts the words and phrases within it into text format.

[0185] "Means of sending text data to a server over a network" refers to communication devices or software used to send converted text data to a remote server using the internet or other networks.

[0186] "Means for generating a response based on text data received by the server" refers to generative AI models or response generation algorithms that analyze the text data received by the server and generate an appropriate response.

[0187] "Means for sending generated response text data to the terminal" refers to a communication function for sending text data generated on the server back to the user's terminal via the network.

[0188] "Means for converting received response text data into speech data" refers to the speech synthesis engine used by the terminal to convert received text data into speech.

[0189] "Means for playing back converted audio data" refers to speakers or other sound devices used by the terminal to play back the converted audio data in a format that can be heard by the user.

[0190] "Smart glasses" refer to wearable devices equipped with the ability to capture, play back, and perform other data processing.

[0191] "Means of converting work instructions into text data" refers to speech recognition technology that recognizes work instructions spoken by a user into smart glasses and converts them into text format.

[0192] A "speech synthesis engine" refers to software or algorithms used to convert text data into natural-sounding speech.

[0193] A "speaker" refers to an audio device that converts audio signals into sound waves and reproduces them in a format that can be heard by the user.

[0194] This invention relates to a voice interaction system using smart glasses for use in factories. This system captures the voice of workers and enables real-time instruction, aiming to improve work efficiency and safety. Specific embodiments for carrying out this invention are described below.

[0195] 1. System Configuration

[0196] The system of this invention consists of smart glasses worn by the user (worker), a speech recognition engine, a server, a generative AI model, a speech synthesis engine, and communication means.

[0197] 2. Smart Glasses

[0198] Smart glasses have a built-in microphone that captures the user's voice. This voice data is initially processed within the smart glasses and then sent to the voice recognition engine.

[0199] 3. Speech Recognition Engine

[0200] The speech recognition engine converts captured audio data into text data. Specifically, it uses technologies such as Google Cloud Speech-to-Text and Amazon Transcribe. This engine analyzes the received audio data and generates the corresponding string.

[0201] 4. Servers and Generative AI Models

[0202] The server receives text data sent from the speech recognition engine and generates an appropriate response using a generative AI model (e.g., GPT-4®). An example of a prompt is shown below.

[0203] Example of a prompt:

[0204] Please generate the appropriate response to the following sentence: 'Check the placement of the parts'

[0205] 5. Means of communication

[0206] Text data and response data are sent and received between smart glasses and servers via the internet or other networks. Therefore, communication capabilities using HTTP requests and responses are required.

[0207] 6. Speech synthesis engine

[0208] The received response text data is converted into speech data by a speech synthesis engine (e.g., Amazon Polly or Google Text-to-Speech).

[0209] 7. Speakers

[0210] The converted audio data is played back through the speaker built into the smart glasses, and the user receives an audio response. For example, a message such as "The component placement is normal" might be played.

[0211] Specific example

[0212] When a user speaks to the smart glasses, "How is the engine doing?", the smart glasses capture the voice and convert it into text data, "How is the engine doing?", using a speech recognition engine. The converted text is sent to a server, where a generative AI model (GPT-4) generates the response, "The engine is doing well." The response is sent back to the smart glasses, converted into audio data by a speech synthesis engine, and played back as "The engine is doing well." from the smart glasses' speaker.

[0213] The above describes a specific embodiment for carrying out the present invention. With this system, workers can easily give instructions by voice without using their hands and receive appropriate responses in real time.

[0214] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0215] Step 1:

[0216] The user speaks into the smart glasses (e.g., "How is the engine?"). The smart glasses' built-in microphone captures the user's voice and records it as digital audio data.

[0217] Input: User's voice

[0218] Output: Digital audio data

[0219] Step 2:

[0220] The captured digital audio data is sent to the speech recognition engine in the smart glasses. The speech recognition engine (e.g., Google Cloud Speech-to-Text) analyzes the digital audio and converts it into corresponding text data (e.g., "How is the engine doing?").

[0221] Input: Digital audio data

[0222] Output: Text data

[0223] Step 3:

[0224] Text data is sent to the server via the network. Communication takes place through HTTP requests, and the text data reaches the server.

[0225] Input: Text data

[0226] Output: HTTP request to the server

[0227] Step 4:

[0228] The server processes the received text data and passes it to a generative AI model (e.g., GPT-4). The generative AI model generates an appropriate response (e.g., "The engine is functioning normally.") based on the text data.

[0229] Input: Text data

[0230] Output: Response text data

[0231] Step 5:

[0232] The server sends the generated response text data back to the smart glasses over the network. The response text data is sent as an HTTP response.

[0233] Input: Response text data

[0234] Output: HTTP response to the terminal (smart glasses)

[0235] Step 6:

[0236] The smart glasses pass the received response text data to a speech synthesis engine (e.g., Amazon Polly) and convert it into speech data.

[0237] Input: Response text data

[0238] Output: Audio data

[0239] Step 7:

[0240] The speaker in the smart glasses plays audio data and responds to the user verbally (e.g., "The engine is functioning normally.").

[0241] Input: Audio data

[0242] Output: Voice response to the user

[0243] 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.

[0244] This invention relates to a system that captures a user's voice, converts it into text data, sends it to a server, and the server generates a response which is then sent back to the terminal. This system further aims to provide more appropriate and personalized responses by incorporating an emotion engine that recognizes the user's emotions. This system provides conversational support in a form that elderly people can use on a daily basis, aiming to reduce the risk of isolation and dementia.

[0245] Program processing details

[0246] 1. Audio Capture

[0247] The device captures the user's voice.

[0248] The microphone built into the device activates and receives voice input from the user. For example, if the user says, "What's the weather like today?", the device captures this voice as digital data. This digital data is stored in memory and sent to subsequent voice recognition and emotion recognition processes.

[0249] 2. Speech Recognition and Emotion Recognition

[0250] The device converts voice data into text data and recognizes emotions.

[0251] The captured audio data is sent to a speech recognition engine and converted into text data. For example, the voice "What's the weather like today?" is converted into the text "What's the weather like today?". At the same time, an emotion engine analyzes the audio data to identify the user's emotions (e.g., joy, anger, sadness, etc.).

[0252] 3. Sending text data and sentiment data

[0253] The device sends text data and sentiment data to the server.

[0254] The converted text data and recognized sentiment information are sent to the server over the network. For example, the text data "What's the weather like today?" and sentiment data (e.g., neutral) are sent to the server as an HTTP request.

[0255] 4. Response generation

[0256] The server uses an AI model to generate responses and adjusts them based on emotions.

[0257] The server generates a response based on the received text data and sentiment data. Specifically, it uses a generative AI model to generate an appropriate response to "What's the weather like today?" such as "It's sunny today." Furthermore, it adjusts the tone and content of the response based on the sentiment data. For example, if the user seems sad, it will generate a response such as "It's sunny today. I hope that makes you feel a little better."

[0258] 5. Send response data

[0259] The server sends the generated response data to the terminal.

[0260] The response data generated by the server is then sent back to the terminal via the network. For example, the adjusted response text data, "It's sunny today. I hope this makes you feel a little better," is sent to the terminal as an HTTP response.

[0261] 6. Speech synthesis

[0262] The device converts received text data into audio data.

[0263] The terminal activates a speech synthesis engine to convert the received response text data into speech. The speech synthesis engine converts the received text "It's sunny today. I hope you feel a little better." into speech data.

[0264] 7. Audio Playback

[0265] The device plays audio data.

[0266] The device's speaker plays audio data and responds to the user. For example, it might play a message like, "It's sunny today. I hope it makes you feel a little better."

[0267] Specific Scenario Examples

[0268] 1. The user asks the device, "What day is it today?"

[0269] 2. The device captures the user's voice and converts it into text, "What day is it today?", using a speech recognition engine. Simultaneously, an emotion engine analyzes the user's tone and speed of voice and recognizes that the user is slightly tired.

[0270] 3. The converted text and sentiment data are sent to the server.

[0271] 4. The server uses a generative AI model to generate the response, "Today is October 5th," but adjusts the response to the user's fatigue level, changing it to, "Today is October 5th. How about taking a short break?"

[0272] 5. The server sends the adjusted response to the terminal.

[0273] 6. The terminal receives the response and converts it into speech data using a speech synthesis engine.

[0274] 7. A voice message plays from the device's speaker saying, "Today is October 5th. How about taking a short break?"

[0275] In this way, the present invention provides responses according to the user's emotional state, thereby improving the user experience and contributing to reducing the sense of isolation and stress of the elderly.

[0276] The following describes the processing flow.

[0277] Step 1:

[0278] The user speaks to the terminal

[0279] The user speaks and asks a question towards the terminal. For example, when the user says "What's the weather like today?", the speech content is captured as audio data.

[0280] Step 2:

[0281] The terminal captures the user's voice

[0282] The microphone installed in the terminal operates to capture the user's speech as an audio signal. The captured audio signal is converted into digital audio data and recorded in the memory.

[0283] Step 3:

[0284] The terminal converts the audio data into text data

[0285] The voice recognition engine of the terminal analyzes the captured audio data and converts it into corresponding text data. For example, the audio data "What's the weather like today?" is converted into the text data "What's the weather like today?".

[0286] Step 4:

[0287] The terminal converts the audio data into emotion data

[0288] Simultaneously, the device's emotion engine analyzes the voice data to recognize the user's emotions. For example, it identifies that the user is feeling neutral based on factors such as voice tone, word choice, and volume.

[0289] Step 5:

[0290] The device sends text data and sentiment data to the server.

[0291] The device sends the converted text data and recognized sentiment data to the server via the network connection. For example, an HTTP request sends the text data "What's the weather like today?" and sentiment data (neutral) to the server.

[0292] Step 6:

[0293] The server receives text data and sentiment data.

[0294] The server parses the received HTTP request and retrieves text data and sentiment data. The server receives the text data "What's the weather like today?" and sentiment data (neutral).

[0295] Step 7:

[0296] The server generates responses using an AI model.

[0297] The server uses a generative AI model to generate a response based on the received text data. For example, in response to "What's the weather like today?", it generates the response "It's sunny today."

[0298] Step 8:

[0299] The server adjusts its response based on emotion.

[0300] Based on the recognized emotion data, the server further adjusts the content and tone of the response. For example, if the user is recognized as having a neutral emotion, the server may respond with "It's sunny today." or adjust it to be a bit more detailed like "It's sunny today. It's a nice day to go out."

[0301] Step 9:

[0302] The server sends the response text data to the terminal

[0303] The server creates an HTTP response containing the adjusted response and sends it to the terminal. For example, the text data "It's sunny today. It's a nice day to go out." is sent to the terminal

[0304] Step 10:

[0305] The terminal converts the received response text data into voice data

[0306] The terminal sends the received text data to a text-to-speech engine to convert it into voice data. The text-to-speech engine converts the text "It's sunny today. It's a nice day to go out." into the corresponding voice data

[0307] Step 11:

[0308] The terminal plays the voice data

[0309] The speaker of the terminal plays the generated voice data and responds to the user. For example, the voice "It's sunny today. It's a nice day to go out." comes out from the speaker

[0310] With this system, an appropriate response according to the user's emotion is provided, aiming to improve the user experience. The present invention is expected to particularly reduce the sense of isolation among the elderly and promote communication

[0311] (Example 2)

[0312] 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 device 14 as the "terminal".

[0313] In recent years, the increasing sense of isolation and dementia risk among the elderly has attracted attention as a social problem. In response, there is a growing need for dialogue support systems that the elderly can use on a daily basis. However, existing dialogue systems provide uniform responses without considering the user's emotions, making it difficult to provide appropriate support tailored to individual situations. Therefore, there is a need to develop dialogue support systems that provide personalized responses that respond to the user's emotions.

[0314] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for generating a response based on received text data and emotion data and adjusting it according to emotion, means for transmitting the generated response text data to a terminal, and means for converting the received response text data into voice data. This enables personalized responses that correspond to the user's emotional state.

[0315] A "user" refers to a person who uses a system.

[0316] "Means of capturing audio" refers to devices or processes for converting user speech into digital data.

[0317] "Means of converting audio data into text data" refers to the process or technology of analyzing audio signals and converting them into textual information.

[0318] "Emotional data" refers to emotional information extracted from the user's voice and behavior.

[0319] "Means of transmission over a network" refers to the processes and technologies used to transmit data over the internet or other communication networks.

[0320] A "server" refers to a computer system that performs data processing and provides services over a network.

[0321] "Means for generating a response" refers to the process or technology for generating an appropriate response based on input data.

[0322] A "generative AI model" refers to a model or algorithm that uses artificial intelligence technology to generate text or decisions.

[0323] A "terminal" refers to a computer device or equipment that a user directly operates.

[0324] "Means of playing audio data" refers to the process or technology of playing audio signals through output devices such as speakers.

[0325] "Acoustic acquisition device" refers to hardware or a device used to capture sound.

[0326] A "speech recognition device" refers to a technology or device that analyzes speech data and converts it into text data.

[0327] An "emotion recognition device" refers to technology or equipment that analyzes and identifies emotions from a user's voice and behavior.

[0328] "Communication equipment" refers to devices and technologies used for sending and receiving data.

[0329] This invention provides a system that allows users to obtain information and receive appropriate responses through voice interaction. The system aims to provide a more personalized experience by offering responses that take into account the user's emotional state.

[0330] The system consists of three main components: the user, the terminal, and the server. The user speaks to the terminal using their voice. A microphone built into the terminal captures the user's voice and converts it into digital data. This uses a microphone as an acoustic data acquisition device. For example, if the user says, "What's the weather like today?", the terminal's microphone captures this voice and converts it into digital data.

[0331] The device converts the acquired audio data into text data using a speech recognition engine (e.g., Google Cloud Speech-to-Text). This speech recognition process converts the audio "What's the weather like today?" into the text data "What's the weather like today?". Simultaneously, an emotion recognition device (e.g., Affectiva's SDK) is used to extract emotion data from the user's voice. For example, based on the tone and speed of the user's voice, it is identified that the user is in a neutral emotional state.

[0332] The converted text data and sentiment data are sent from the terminal to the server via a communication device. Specifically, they are sent as an HTTP POST request. The request payload includes the text data "What's the weather like today?" and the sentiment data "Neutral".

[0333] The server generates a response based on the received text data and sentiment data. This uses a generative AI model (e.g., OpenAI's GPT-4). This model takes a prompt as input and generates an appropriate response. For example, if the server receives the prompt "The user asked 'What's the weather like today?' The sentiment is neutral," it will generate the response "It's sunny today." Then, taking the sentiment data into consideration, the response is adjusted to produce the final text "It's sunny today. I hope that makes you feel a little better."

[0334] The generated response text data is sent from the server to the terminal. This is also sent as an HTTP response. The terminal converts the received response text data into audio data using a speech synthesis engine (for example, Amazon Polly). Finally, the terminal's speaker plays the audio data and responds to the user. Specifically, the audio plays, "It's sunny today. I hope this makes you feel a little better."

[0335] Examples of prompt messages include the following:

[0336] "The user asked, 'What's the weather like today?' The emotion is neutral."

[0337] This system provides personalized responses tailored to the user's emotional state, resulting in a more natural and comfortable conversational experience. This is expected to contribute to reducing feelings of isolation among the elderly and lowering the risk of dementia.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The user inputs voice.

[0341] The user speaks into the device. At this time, the user's voice is captured by the microphone. For example, the user says, "What's the weather like today?" The input is the user's voice, and the output is the digital audio data captured by the microphone.

[0342] Step 2:

[0343] The device converts the audio data into text data.

[0344] The device sends the captured audio data to a speech recognition engine (e.g., Google Cloud Speech-to-Text). This engine analyzes the audio data and converts it into text data. For example, the audio data "What's the weather like today?" is converted to the text "What's the weather like today?". The input is digital audio data, and the output is text data.

[0345] Step 3:

[0346] The device extracts emotional data from the voice data.

[0347] The device sends the captured audio data to an emotion recognition engine (e.g., Affectiva's SDK). This engine analyzes the voice tone, speed, and other parameters to identify emotion data. For example, a neutral tone would be recognized as "neutral." The input is digital audio data, and the output is emotion data.

[0348] Step 4:

[0349] The device sends text data and sentiment data to the server.

[0350] The converted text data and sentiment data are sent to the server via the terminal's communication device. Specifically, they are sent as an HTTP POST request. For example, the text "What's the weather like today?" and the sentiment "Neutral" are sent to the server. The input is text data and sentiment data, and the output is an HTTP request.

[0351] Step 5:

[0352] The server generates and adjusts the response.

[0353] Based on the text data and sentiment data received by the server, a prompt is input to a generative AI model (e.g., GPT-4) to generate a response. For example, if the prompt "The user asked 'What's the weather like today?' The sentiment is neutral." is input, the response "It's sunny today." is generated. Then, the response is adjusted considering the sentiment data, and the text "It's sunny today. I hope you feel a little better." is generated. The input is text data and sentiment data, and the output is the adjusted response text.

[0354] Step 6:

[0355] The server sends response data to the terminal.

[0356] The server sends the generated response text data to the terminal. This is also sent as an HTTP response. For example, the response text "It's sunny today. I hope this makes you feel a little better." is sent to the terminal. The input is the response text data, and the output is the HTTP response.

[0357] Step 7:

[0358] The device converts the response text data into speech data.

[0359] The device sends the received response text data to a speech synthesis engine (e.g., Amazon Polly) and converts it into speech data. For example, the text data "It's sunny today. I hope that makes you feel a little better." is converted into speech data. The input is response text data, and the output is speech data.

[0360] Step 8:

[0361] The device plays audio data.

[0362] The device's speaker plays audio data. For example, the user might hear the voice say, "It's sunny today. I hope it makes you feel a little better." The input is audio data, and the output is the played audio.

[0363] By clearly indicating the specific actions, inputs, and outputs performed at each step, the overall flow of the system can be understood more concretely. The entire process, from the user inputting voice commands to the terminal playing back the response in audio, becomes clear.

[0364] (Application Example 2)

[0365] 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".

[0366] When elderly people use food delivery services, they often encounter problems such as complicated operation or a lack of emotionally sensitive responses, which reduces convenience and satisfaction. Furthermore, because elderly people are more likely to be isolated and at risk of dementia, they require support in their daily lives.

[0367] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for capturing the user's voice, means for converting the captured voice data into text data, means for recognizing the user's emotions based on the converted text data, means for transmitting the converted text data and recognized emotion information to the server via a network, means for generating a response based on the received text data and emotion information at the server and adjusting the generated response text data based on the emotion information, means for transmitting the adjusted response text data to a terminal, means for converting the received response text data into voice data, and means for playing back the converted voice data. As a result, elderly people can easily use the food delivery service through voice interaction and obtain appropriate responses according to their emotions, thereby improving the convenience and satisfaction of use and contributing to a reduction in feelings of isolation.

[0368] "Means for capturing user voice" refers to a device that acquires voice input from a user as digital data.

[0369] "Means for converting captured audio data into text data" refers to a device or software that analyzes audio data and converts it into corresponding textual information.

[0370] "Means for recognizing user emotions based on converted text data" refers to a device or software that identifies user emotions from text data and extracts them as emotional information.

[0371] "Means for transmitting converted text data and recognized sentiment information to a server via a network" refers to a device or software that transmits text data and sentiment information to a server via the internet or other communication networks.

[0372] "Means for generating a response based on received text data and sentiment information, and for adjusting the generated response text data based on sentiment information" refers to a device or software that generates an appropriate response based on data received on a server and modifies the content and expression of the response according to the user's sentiment.

[0373] "Means for sending adjusted response text data to a terminal" refers to a device or software that sends a response generated and adjusted on a server to a user's terminal via a network.

[0374] "Means for converting received response text data into audio data" refers to a device or software that outputs a response received as text data as audio data.

[0375] "Means for playing back converted audio data" refers to a device that plays back audio data through a speaker or similar device, allowing the user to listen to it.

[0376] A "generative AI model" is an algorithm or system that uses machine learning to generate responses from data.

[0377] A "prompt sentence" is the text input to a generative AI model, and it forms the basis for response generation.

[0378] A "sensing device" is a hardware device or sensor used to capture audio or other input data.

[0379] A "speech recognition engine" is software or hardware that converts speech into text in real time or from recorded data.

[0380] "Communication means" refers to the entire set of hardware and software used to send and receive data.

[0381] To implement this invention, a system will be constructed using the following hardware and software: a system that processes a series of operations from the user inputting voice into a terminal until an appropriate response is finally returned in voice.

[0382] Hardware to use

[0383] Terminal: This refers to devices that serve as the user interface, such as smartphones, smart glasses, and head-mounted displays.

[0384] Microphone: A sensing device used to capture the user's voice.

[0385] Server: A central processing unit that handles voice data conversion, emotion recognition, and response generation.

[0386] Speaker: An output device for playing back generated audio data.

[0387] Software to use

[0388] Speech recognition engine: Used to convert speech data into text data. For example, the Google Speech-to-Text API.

[0389] Emotion recognition engine: Used to identify user emotions from text data. For example, IBM Watson® Tone Analyzer.

[0390] Generative AI models: Used to customize generated responses according to the user's emotions. For example, OpenAI GPT-3.

[0391] Speech synthesis engine: Used to generate speech data from text data. For example, Amazon Polly.

[0392] Processing flow

[0393] The device's microphone captures the user's voice, acquiring the digital data. Then, a speech recognition engine converts this digital data into text. The converted text data is sent to an emotion recognition engine, which identifies the user's emotions.

[0394] Text data and sentiment information are transmitted to a server via the network. The server uses a generative AI model based on the received data to generate an appropriate response. Next, the generated response is refined based on the sentiment information. The refined response text data is sent to the terminal and converted into speech data using a speech synthesis engine. Finally, the response is played back to the user through the terminal's speaker.

[0395] Specific example

[0396] When a user asks the device, "What do you recommend for lunch?", the audio is captured and converted into text data. The emotion recognition engine detects that the user's voice sounds a little tired. Based on this text data ("What do you recommend for lunch?") and the emotion data (a little tired), the server generates and sends the response, "Curry rice is recommended. It's easy to prepare." The device then converts this response back into speech and plays it through its speaker.

[0397] Examples of prompt statements that are input to the generative AI model are as follows:

[0398] User said: "What would you recommend for lunch?" (Emotion: A little tired). Generate a response.

[0399] By using such a system, elderly people can easily use food delivery services through voice interaction and receive appropriate responses that reflect their emotions.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The user speaks into the device. The user says, "What do you recommend for lunch?" The device's microphone captures the user's voice and obtains it as digital audio data.

[0403] Step 2:

[0404] The device converts the captured audio data into text data. At this stage, a speech recognition engine (for example, Google Speech-to-Text API) converts the digital audio data into text data such as "What dishes would you recommend for lunch?". This process outputs the audio data as text information.

[0405] Step 3:

[0406] The terminal sends the converted text data to the emotion recognition engine. The emotion recognition engine (e.g., IBM Watson Tone Analyzer) analyzes the text data and identifies the user's emotion (in this case, slightly tired). The output of this step includes emotion data along with the text data.

[0407] Step 4:

[0408] The device sends the converted text data and recognized sentiment data to the server over the network. The transmitted data arrives at the server as an HTTP request. In this step, the input text data "What do you recommend for lunch?" and sentiment data (slightly tired) are sent.

[0409] Step 5:

[0410] The server generates a response based on the text and sentiment data it receives. The server uses a generative AI model (e.g., OpenAI GPT-3) to create a prompt: "User said: "What do you recommend for lunch?" (Emotion: A little tired). Generate a response." Based on this, it generates the response "Curry rice is recommended. It's easy to cook." In this step, the prompt is taken as input, an appropriate response is generated, and it is output as text data.

[0411] Step 6:

[0412] The server adjusts the generated response data based on the user's mood. The generated response text data is appropriately adjusted in tone, content, and other aspects based on the user's mood (e.g., slightly tired). For example, if the user is tired, the response tone will be set to a gentler one. This results in the final adjusted response text data being output.

[0413] Step 7:

[0414] The server sends the pre-arranged response text data to the terminal over the network. This also arrives at the terminal as an HTTP response. The output of this step is the pre-arranged response text data "Curry rice is recommended. It's easy to cook."

[0415] Step 8:

[0416] The terminal converts the received response text data into speech data. A speech synthesis engine (e.g., Amazon Polly) analyzes the text data and generates the corresponding speech data. In this step, text data is input and speech data is output.

[0417] Step 9:

[0418] The device's speaker plays the converted audio data. Finally, the device's speaker outputs the message, "Curry rice is recommended. It's easy to cook."

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Second Embodiment]

[0423] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention relates to a system that captures user voice data, converts it into text data, and sends it to a server via a network. The server generates and sends back a response, and the terminal converts that response back into voice data and plays it back. The aim of this system is to provide a conversational support system that elderly people can use on a daily basis, thereby reducing the risk of isolation and dementia.

[0436] Program processing details

[0437] 1. Audio Capture

[0438] The device captures the user's voice.

[0439] The microphone built into the device activates and receives voice input from the user. For example, if the user says, "What's the weather like today?", the device captures this voice as digital data. This digital data is then sent to the subsequent speech recognition process.

[0440] 2. Speech Recognition

[0441] The device converts the audio data into text data.

[0442] The captured audio data is sent to a speech recognition engine and converted into text data. For example, the captured audio "What's the weather like today?" is converted into the text "What's the weather like today?" using speech recognition technology.

[0443] 3. Sending text data

[0444] The device sends text data to the server.

[0445] The converted text data is sent to the server over the network. For example, text data can be sent to the server using an HTTP request.

[0446] 4. Response generation

[0447] The server generates responses using an AI model.

[0448] The server generates a response based on the received text data. Specifically, it uses a generative AI model to generate an appropriate response, such as "It's sunny today," in response to the text "What's the weather like today?". This response text data is then sent from the server to the terminal.

[0449] 5. Send response data

[0450] The server sends the generated response data to the terminal.

[0451] The response data generated by the server is then sent back to the terminal via the network. For example, it may be returned to the terminal as an HTTP response.

[0452] 6. Speech synthesis

[0453] The device converts received text data into audio data.

[0454] The terminal invokes a speech synthesis engine to convert the received response text data into speech. This converts the text "It's sunny today." into corresponding speech data.

[0455] 7. Audio Playback

[0456] The device plays audio data to respond to the user.

[0457] The device's speaker plays audio data and responds to the user. For example, the speaker might play the message, "It's sunny today."

[0458] Specific Scenario Examples

[0459] 1. The user asks the device, "What day is it today?"

[0460] 2. The device captures the user's voice and converts it into text, "What day is it today?", using a speech recognition engine.

[0461] 3. The converted text is sent to the server.

[0462] 4. The server uses a generative AI model to generate the response "Today is October 5th."

[0463] 5. The server sends a response back to the terminal.

[0464] 6. The terminal receives the response and converts it into speech data using a speech synthesis engine.

[0465] 7. The user hears the audio message "Today is October 5th" through the device's speaker.

[0466] Thus, the present invention provides a system that supports the resolution of questions and dialogues in the daily lives of elderly people, and contributes to maintaining cognitive function and improving quality of life.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The user speaks to the device.

[0470] The user speaks and asks a question to the device. For example, the user might say, "What's the weather like today?"

[0471] Step 2:

[0472] The device captures the user's voice.

[0473] The device uses its built-in microphone to capture the user's speech as audio data. The captured audio data is temporarily stored in memory.

[0474] Step 3:

[0475] The device converts the audio data into text data.

[0476] The device activates its speech recognition engine (e.g., speech recognition software) and converts the captured audio data into text data. For example, the audio "What's the weather like today?" is converted into the text data "What's the weather like today?".

[0477] Step 4:

[0478] The device sends text data to the server.

[0479] The terminal uses its network connection to send the converted text data to the server. The text data "What's the weather like today?" is sent to the server as an HTTP request.

[0480] Step 5:

[0481] The server receives text data.

[0482] The server parses the received HTTP request and retrieves the text data it contains. The server receives the text data "What's the weather like today?".

[0483] Step 6:

[0484] The server generates responses using an AI model.

[0485] The server inputs the received text data into a generative AI model (e.g., a natural language processing model). The AI ​​model analyzes the text "What's the weather like today?" and generates the response "It's sunny today."

[0486] Step 7:

[0487] The server sends the response text data to the terminal.

[0488] The server creates an HTTP response to send the generated response text data back to the terminal. The text data "It's sunny today." is sent to the terminal.

[0489] Step 8:

[0490] The device converts the received response text data into audio data.

[0491] The terminal activates a speech synthesis engine to convert the received text data into speech. The speech synthesis engine converts the text "It's sunny today." into speech data.

[0492] Step 9:

[0493] The device plays audio data.

[0494] The device plays the generated audio data through its speaker. The user can hear the message "It's sunny today." coming from the speaker.

[0495] In this way, this system contributes to improving the quality of life for the elderly by providing intuitive and rapid responses to users' questions.

[0496] (Example 1)

[0497] 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."

[0498] In modern society, it is important to provide means for elderly people to engage in daily conversations without becoming isolated. Elderly people often have difficulty with youthful conversations and access to information, which increases the risk of dementia. This invention aims to solve these problems and provide a conversation support system that is easy for elderly people to use.

[0499] 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.

[0500] In this invention, the server includes means for capturing audio, means for converting the captured audio data into text data, means for transmitting the converted text data to the server via a network, means for generating a response based on the text data received by the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into audio data, and means for playing back the converted audio data. This enables elderly people to interact with the system using their own voice and obtain information and responses in real time.

[0501] "Means for capturing speech" refers to a device or technology that converts a user's speech into digital speech data.

[0502] "Means for converting audio data into text data" refers to a technology or device that analyzes captured audio data and generates a corresponding string of characters.

[0503] "Means for sending text data to a server over a network" refers to a device or technology that sends generated text data to a remote server using a communication protocol.

[0504] "Means for generating a response based on text data received on the server" refers to a device or technology that generates an appropriate response based on text data received on the server side.

[0505] "Means for sending generated response text data to a terminal" refers to a device or technology that sends the response generated from the server back to the terminal via the network.

[0506] "Means for converting received response text data into audio data" refers to a device or technology that converts received text data back into audio data.

[0507] "Means for playing back converted audio data" refers to a device or technology that plays back converted audio data as physical sound.

[0508] A "generative AI model" is an artificial intelligence model that generates responses in natural language based on input data.

[0509] A "prompt statement" is an input statement given to a generative AI model, and it is an instruction statement that the model uses to generate a response.

[0510] This invention relates to a system that captures user voice data, converts it into text data, and sends it to a server via a network. The server generates and sends back a response, and the terminal converts that response back into voice data and plays it back. This system aims to provide a conversational support system that elderly people can use on a daily basis, thereby reducing the risk of isolation and dementia.

[0511] The system configuration includes terminals and servers. Specific hardware and software examples include terminals equipped with a microphone, speaker, speech recognition engine (e.g., Google Cloud Speech-to-Text API), and speech synthesis engine (e.g., Amazon Polly API). The server is equipped with a generative AI model (e.g., OpenAI GPT-3) that generates responses based on text data.

[0512] Audio Capture

[0513] The device captures the user's voice. Specifically, the microphone built into the device operates and captures the user's voice input as digital data. For example, if the user says, "What's the weather like today?", the device's microphone picks up the voice and converts it into a digital signal.

[0514] Speech recognition

[0515] The device converts the captured audio data into text data. This process uses a speech recognition engine (e.g., Google Cloud Speech-to-Text API). For example, the captured audio "What's the weather like today?" is converted into the text "What's the weather like today?" by speech recognition technology.

[0516] Sending text data

[0517] The terminal sends the converted text data to the server. Specifically, it sends the text data to the server using an HTTP request.

[0518] Response generation

[0519] The server generates a response based on the text data it receives. A generative AI model (e.g., OpenAI GPT-3) is used. For example, in response to the text "What's the weather like today?", the generative AI model generates the response "It's sunny today."

[0520] Send response data

[0521] The server sends the generated response data to the terminal. Specifically, the response data is returned to the terminal as an HTTP response.

[0522] Speech synthesis

[0523] The device calls a speech synthesis engine (e.g., Amazon Polly API) to convert the received response text data into speech data. For example, the text "It's sunny today." is converted into the corresponding speech data.

[0524] Audio playback

[0525] The device's speaker plays audio data and responds to the user. For example, the speaker might play the message, "It's sunny today."

[0526] Specific Scenario Examples

[0527] 1. The user speaks to the device saying, "What day is it today?" The spoken words are captured by the device's microphone.

[0528] 2. The device captures the user's voice as digital data and converts it into text, "What day is it today?", using a speech recognition engine.

[0529] 3. The terminal sends the converted text data to the server as an HTTP POST request.

[0530] 4. The server uses a generative AI model (e.g., OpenAI GPT-3) to generate the response "Today is October 5th."

[0531] 5. The server sends the generated response data to the terminal as an HTTP response.

[0532] 6. The terminal converts the received response into audio data, "Today is October 5th," using a speech synthesis engine (e.g., Amazon Polly API).

[0533] 7. The device's speaker plays a voice message saying, "Today is October 5th," informing the user.

[0534] Example of a prompt:

[0535] "What's the weather like today?"

[0536] "What day is it today?"

[0537] "Please tell me today's news."

[0538] Thus, the present invention provides a system that makes it easier for elderly people to resolve questions in their daily lives, contributing to the maintenance of cognitive function and improvement of quality of life.

[0539] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0540] Step 1:

[0541] Audio Capture

[0542] The device captures the user's voice. Specifically, the microphone built into the device operates and captures the user's speech as digital data. For example, if the user says, "What's the weather like today?", the device's microphone picks up the voice and converts it into a digital signal.

[0543] Input: User's voice

[0544] Output: Digital audio data (audio waveform data)

[0545] Step 2:

[0546] Speech recognition

[0547] The device converts the audio data into text data. The captured audio data is sent to a speech recognition engine (e.g., Google Cloud Speech-to-Text API) for analysis. For example, the speech recognition engine analyzes the digital audio and converts it into text such as "What's the weather like today?". This process involves noise filtering and speech feature extraction.

[0548] Input: Digital audio data

[0549] Output: Recognized text data ("What's the weather like today?")

[0550] Step 3:

[0551] Sending text data

[0552] The device sends text data to the server. The device's application constructs an HTTP POST request and includes the text data as a payload. Specifically, the converted text data is sent to the server as an HTTP request.

[0553] Input: Recognized text data

[0554] Output: HTTP POST request (including text data)

[0555] Step 4:

[0556] Response generation

[0557] The server generates a response based on the text data it receives. Specifically, the server inputs text data into a generative AI model (e.g., OpenAI GPT-3) to generate an appropriate response. For example, in response to the text "What's the weather like today?", the generative AI model generates the response "It's sunny today." This process involves natural language processing and contextual analysis.

[0558] Input: HTTP POST request (including text data)

[0559] Output: Generated response text data ("It's sunny today.")

[0560] Step 5:

[0561] Send response data

[0562] The server sends the generated response data to the terminal. The server constructs an HTTP response, including the generated response text data as the payload. This response reaches the terminal via the internet.

[0563] Input: Generated response text data

[0564] Output: HTTP response (including response text data)

[0565] Step 6:

[0566] Speech synthesis

[0567] The device calls a speech synthesis engine (e.g., Amazon Polly API) to convert the received response text data into speech data. The device's application sends the text data to the speech synthesis API and receives the speech data returned by the API. This speech data is saved on the device as a corresponding audio file.

[0568] Input: Received response text data

[0569] Output: Synthesized audio data (audio file)

[0570] Step 7:

[0571] Audio playback

[0572] The device's speaker plays audio data and responds to the user. The device's audio playback function reads the audio file and plays the message "It's sunny today." through the speaker. During this process, volume adjustment and playback timing control are also performed.

[0573] Input: Synthesized audio data (audio file)

[0574] Output: Audio playback from the speaker ("It's sunny today.")

[0575] (Application Example 1)

[0576] 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."

[0577] In traditional factory operations, a major challenge was the time and effort required for workers to perform complex procedures and check situations. Furthermore, it was difficult for workers to give instructions using voice commands without using their hands and to receive real-time responses. This resulted in decreased work efficiency and an increased risk of errors and accidents. A simple and effective support system was particularly needed for elderly workers.

[0578] 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.

[0579] In this invention, the server includes means for capturing the user's voice, means for converting the captured voice data into text data, means for transmitting the converted text data to the server via a network, means for generating a response based on the text data received by the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into voice data, means for playing back the converted voice data, means for capturing the worker's voice using smart glasses and converting work instructions into text data using a voice recognition engine, means for converting the generated response text data into voice data using a speech synthesis engine, and means for playing back the voice data from the smart glasses' speaker. This makes it possible for workers to easily give instructions using only their voice without using their hands and to obtain appropriate responses in real time.

[0580] A "user" refers to a person who utilizes the system of the present invention, and who wears smart glasses at the work site and gives instructions to the robot or system using voice.

[0581] "Means of capturing audio" refers to devices such as microphones used to capture the voice spoken by a user as digital data.

[0582] "Means of converting audio data into text data" refers to a speech recognition engine that analyzes captured audio data and converts the words and phrases within it into text format.

[0583] "Means of sending text data to a server over a network" refers to communication devices or software used to send converted text data to a remote server using the internet or other networks.

[0584] "Means for generating a response based on text data received by the server" refers to generative AI models or response generation algorithms that analyze the text data received by the server and generate an appropriate response.

[0585] "Means for sending generated response text data to the terminal" refers to a communication function for sending text data generated on the server back to the user's terminal via the network.

[0586] "Means for converting received response text data into speech data" refers to the speech synthesis engine used by the terminal to convert received text data into speech.

[0587] "Means for playing back converted audio data" refers to speakers or other sound devices used by the terminal to play back the converted audio data in a format that can be heard by the user.

[0588] "Smart glasses" refer to wearable devices equipped with the ability to capture, play back, and perform other data processing.

[0589] "Means of converting work instructions into text data" refers to speech recognition technology that recognizes work instructions spoken by a user into smart glasses and converts them into text format.

[0590] A "speech synthesis engine" refers to software or algorithms used to convert text data into natural-sounding speech.

[0591] A "speaker" refers to an audio device that converts audio signals into sound waves and reproduces them in a format that can be heard by the user.

[0592] This invention relates to a voice interaction system using smart glasses for use in factories. This system captures the voice of workers and enables real-time instruction, aiming to improve work efficiency and safety. Specific embodiments for carrying out this invention are described below.

[0593] 1. System Configuration

[0594] The system of this invention consists of smart glasses worn by the user (worker), a speech recognition engine, a server, a generative AI model, a speech synthesis engine, and communication means.

[0595] 2. Smart Glasses

[0596] Smart glasses have a built-in microphone that captures the user's voice. This voice data is initially processed within the smart glasses and then sent to the voice recognition engine.

[0597] 3. Speech Recognition Engine

[0598] The speech recognition engine converts captured audio data into text data. Specifically, it uses technologies such as Google Cloud Speech-to-Text and Amazon Transcribe. This engine analyzes the received audio data and generates the corresponding string.

[0599] 4. Servers and Generative AI Models

[0600] The server receives text data sent from the speech recognition engine and generates an appropriate response using a generative AI model (e.g., GPT-4). An example of a prompt is shown below.

[0601] Example of a prompt:

[0602] Please generate the appropriate response to the following sentence: 'Check the placement of the parts'

[0603] 5. Means of communication

[0604] Text data and response data are sent and received between smart glasses and servers via the internet or other networks. Therefore, communication capabilities using HTTP requests and responses are required.

[0605] 6. Speech synthesis engine

[0606] The received response text data is converted into speech data by a speech synthesis engine (e.g., Amazon Polly or Google Text-to-Speech).

[0607] 7. Speakers

[0608] The converted audio data is played back through the speaker built into the smart glasses, and the user receives an audio response. For example, a message such as "The component placement is normal" might be played.

[0609] Specific example

[0610] When a user speaks to the smart glasses, "How is the engine doing?", the smart glasses capture the voice and convert it into text data, "How is the engine doing?", using a speech recognition engine. The converted text is sent to a server, where a generative AI model (GPT-4) generates the response, "The engine is doing well." The response is sent back to the smart glasses, converted into audio data by a speech synthesis engine, and played back as "The engine is doing well." from the smart glasses' speaker.

[0611] The above describes a specific embodiment for carrying out the present invention. With this system, workers can easily give instructions by voice without using their hands and receive appropriate responses in real time.

[0612] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0613] Step 1:

[0614] The user speaks into the smart glasses (e.g., "How is the engine?"). The smart glasses' built-in microphone captures the user's voice and records it as digital audio data.

[0615] Input: User's voice

[0616] Output: Digital audio data

[0617] Step 2:

[0618] The captured digital audio data is sent to the speech recognition engine in the smart glasses. The speech recognition engine (e.g., Google Cloud Speech-to-Text) analyzes the digital audio and converts it into corresponding text data (e.g., "How is the engine doing?").

[0619] Input: Digital audio data

[0620] Output: Text data

[0621] Step 3:

[0622] Text data is sent to the server via the network. Communication takes place through HTTP requests, and the text data reaches the server.

[0623] Input: Text data

[0624] Output: HTTP request to the server

[0625] Step 4:

[0626] The server processes the received text data and passes it to a generative AI model (e.g., GPT-4). The generative AI model generates an appropriate response (e.g., "The engine is functioning normally.") based on the text data.

[0627] Input: Text data

[0628] Output: Response text data

[0629] Step 5:

[0630] The server sends the generated response text data back to the smart glasses over the network. The response text data is sent as an HTTP response.

[0631] Input: Response text data

[0632] Output: HTTP response to the terminal (smart glasses)

[0633] Step 6:

[0634] The smart glasses pass the received response text data to a speech synthesis engine (e.g., Amazon Polly) and convert it into speech data.

[0635] Input: Response text data

[0636] Output: Audio data

[0637] Step 7:

[0638] The speaker in the smart glasses plays audio data and responds to the user verbally (e.g., "The engine is functioning normally.").

[0639] Input: Audio data

[0640] Output: Voice response to the user

[0641] 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.

[0642] This invention relates to a system that captures a user's voice, converts it into text data, sends it to a server, and the server generates a response which is then sent back to the terminal. This system further aims to provide more appropriate and personalized responses by incorporating an emotion engine that recognizes the user's emotions. This system provides conversational support in a form that elderly people can use on a daily basis, aiming to reduce the risk of isolation and dementia.

[0643] Program processing details

[0644] 1. Audio Capture

[0645] The device captures the user's voice.

[0646] The microphone built into the device activates and receives voice input from the user. For example, if the user says, "What's the weather like today?", the device captures this voice as digital data. This digital data is stored in memory and sent to subsequent voice recognition and emotion recognition processes.

[0647] 2. Speech Recognition and Emotion Recognition

[0648] The device converts voice data into text data and recognizes emotions.

[0649] The captured audio data is sent to a speech recognition engine and converted into text data. For example, the voice "What's the weather like today?" is converted into the text "What's the weather like today?". At the same time, an emotion engine analyzes the audio data to identify the user's emotions (e.g., joy, anger, sadness, etc.).

[0650] 3. Sending text data and sentiment data

[0651] The device sends text data and sentiment data to the server.

[0652] The converted text data and recognized sentiment information are sent to the server over the network. For example, the text data "What's the weather like today?" and sentiment data (e.g., neutral) are sent to the server as an HTTP request.

[0653] 4. Response generation

[0654] The server uses an AI model to generate responses and adjusts them based on emotions.

[0655] The server generates a response based on the received text data and sentiment data. Specifically, it uses a generative AI model to generate an appropriate response to "What's the weather like today?" such as "It's sunny today." Furthermore, it adjusts the tone and content of the response based on the sentiment data. For example, if the user seems sad, it will generate a response such as "It's sunny today. I hope that makes you feel a little better."

[0656] 5. Send response data

[0657] The server sends the generated response data to the terminal.

[0658] The response data generated by the server is then sent back to the terminal via the network. For example, the adjusted response text data, "It's sunny today. I hope this makes you feel a little better," is sent to the terminal as an HTTP response.

[0659] 6. Speech synthesis

[0660] The device converts received text data into audio data.

[0661] The terminal activates a speech synthesis engine to convert the received response text data into speech. The speech synthesis engine converts the received text "It's sunny today. I hope you feel a little better." into speech data.

[0662] 7. Audio Playback

[0663] The device plays audio data.

[0664] The device's speaker plays audio data and responds to the user. For example, it might play a message like, "It's sunny today. I hope it makes you feel a little better."

[0665] Specific Scenario Examples

[0666] 1. The user asks the device, "What day is it today?"

[0667] 2. The device captures the user's voice and converts it into text, "What day is it today?", using a speech recognition engine. Simultaneously, an emotion engine analyzes the user's tone and speed of voice and recognizes that the user is slightly tired.

[0668] 3. The converted text and sentiment data are sent to the server.

[0669] 4. The server uses a generative AI model to generate the response, "Today is October 5th," but adjusts the response to the user's fatigue level, changing it to, "Today is October 5th. How about taking a short break?"

[0670] 5. The server sends the adjusted response to the terminal.

[0671] 6. The terminal receives the response and converts it into speech data using a speech synthesis engine.

[0672] 7. A voice message plays from the device's speaker saying, "Today is October 5th. How about taking a short break?"

[0673] Thus, by providing responses that correspond to the user's emotional state, the present invention improves the user experience and contributes to reducing feelings of isolation and stress among the elderly.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The user speaks to the device.

[0677] The user speaks and asks a question to the device. For example, if the user says, "What's the weather like today?", the spoken content is captured as audio data.

[0678] Step 2:

[0679] The device captures the user's voice.

[0680] The microphone built into the device activates and captures the user's speech as an audio signal. The captured audio signal is converted into digital audio data and recorded in memory.

[0681] Step 3:

[0682] The device converts the audio data into text data.

[0683] The device's speech recognition engine analyzes the captured audio data and converts it into corresponding text data. For example, the audio data "What's the weather like today?" is converted into the text data "What's the weather like today?".

[0684] Step 4:

[0685] The device converts voice data into emotion data.

[0686] Simultaneously, the device's emotion engine analyzes the voice data to recognize the user's emotions. For example, it identifies that the user is feeling neutral based on factors such as voice tone, word choice, and volume.

[0687] Step 5:

[0688] The device sends text data and sentiment data to the server.

[0689] The device sends the converted text data and recognized sentiment data to the server via the network connection. For example, an HTTP request sends the text data "What's the weather like today?" and sentiment data (neutral) to the server.

[0690] Step 6:

[0691] The server receives text data and sentiment data.

[0692] The server parses the received HTTP request and retrieves text data and sentiment data. The server receives the text data "What's the weather like today?" and sentiment data (neutral).

[0693] Step 7:

[0694] The server generates responses using an AI model.

[0695] The server uses a generative AI model to generate a response based on the received text data. For example, in response to "What's the weather like today?", it generates the response "It's sunny today."

[0696] Step 8:

[0697] The server adjusts its response based on emotion.

[0698] The server then adjusts the content and tone of its response based on the recognized sentiment data. For example, if the user is perceived as having a neutral sentiment, it may respond with "It's sunny today," or it may adjust it to be a little more detailed, such as "It's sunny today. It's a nice day to go out."

[0699] Step 9:

[0700] The server sends the response text data to the terminal.

[0701] The server creates an HTTP response containing the adjusted response and sends it to the terminal. For example, the text data "It's sunny today. It's a good day to go outside." is sent to the terminal.

[0702] Step 10:

[0703] The device converts the received response text data into audio data.

[0704] The device sends the received text data to the speech synthesis engine, which converts it into speech data. The speech synthesis engine converts the text "It's sunny today. It's a good day to go outside." into the corresponding speech data.

[0705] Step 11:

[0706] The device plays audio data.

[0707] The device's speaker plays the generated audio data and responds to the user. For example, the speaker might play a voice message saying, "It's sunny today. It's a good day to go outside."

[0708] This system provides appropriate responses tailored to the user's emotions, thereby improving the user experience. This invention is particularly expected to alleviate feelings of isolation among the elderly and promote communication.

[0709] (Example 2)

[0710] 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".

[0711] In recent years, the increasing sense of isolation and dementia risk among the elderly has attracted attention as a social problem. In response, there is a growing need for dialogue support systems that the elderly can use on a daily basis. However, existing dialogue systems provide uniform responses without considering the user's emotions, making it difficult to provide appropriate support tailored to individual situations. Therefore, there is a need to develop dialogue support systems that provide personalized responses that respond to the user's emotions.

[0712] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for generating a response based on received text data and emotion data and adjusting it according to emotion, means for transmitting the generated response text data to a terminal, and means for converting the received response text data into voice data. This enables personalized responses that correspond to the user's emotional state.

[0713] A "user" refers to a person who uses a system.

[0714] "Means of capturing audio" refers to devices or processes for converting user speech into digital data.

[0715] "Means of converting audio data into text data" refers to the process or technology of analyzing audio signals and converting them into textual information.

[0716] "Emotional data" refers to emotional information extracted from the user's voice and behavior.

[0717] "Means of transmission over a network" refers to the processes and technologies used to transmit data over the internet or other communication networks.

[0718] A "server" refers to a computer system that performs data processing and provides services over a network.

[0719] "Means for generating a response" refers to the process or technology for generating an appropriate response based on input data.

[0720] A "generative AI model" refers to a model or algorithm that uses artificial intelligence technology to generate text or decisions.

[0721] A "terminal" refers to a computer device or equipment that a user directly operates.

[0722] "Means of playing audio data" refers to the process or technology of playing audio signals through output devices such as speakers.

[0723] "Acoustic acquisition device" refers to hardware or a device used to capture sound.

[0724] A "speech recognition device" refers to a technology or device that analyzes speech data and converts it into text data.

[0725] An "emotion recognition device" refers to technology or equipment that analyzes and identifies emotions from a user's voice and behavior.

[0726] "Communication equipment" refers to devices and technologies used for sending and receiving data.

[0727] This invention provides a system that allows users to obtain information and receive appropriate responses through voice interaction. The system aims to provide a more personalized experience by offering responses that take into account the user's emotional state.

[0728] The system consists of three main components: the user, the terminal, and the server. The user speaks to the terminal using their voice. A microphone built into the terminal captures the user's voice and converts it into digital data. This uses a microphone as an acoustic data acquisition device. For example, if the user says, "What's the weather like today?", the terminal's microphone captures this voice and converts it into digital data.

[0729] The device converts the acquired audio data into text data using a speech recognition engine (e.g., Google Cloud Speech-to-Text). This speech recognition process converts the audio "What's the weather like today?" into the text data "What's the weather like today?". Simultaneously, an emotion recognition device (e.g., Affectiva's SDK) is used to extract emotion data from the user's voice. For example, based on the tone and speed of the user's voice, it is identified that the user is in a neutral emotional state.

[0730] The converted text data and sentiment data are sent from the terminal to the server via a communication device. Specifically, they are sent as an HTTP POST request. The request payload includes the text data "What's the weather like today?" and the sentiment data "Neutral".

[0731] The server generates a response based on the received text data and sentiment data. This uses a generative AI model (e.g., OpenAI's GPT-4). This model takes a prompt as input and generates an appropriate response. For example, if the server receives the prompt "The user asked 'What's the weather like today?' The sentiment is neutral," it will generate the response "It's sunny today." Then, taking the sentiment data into consideration, the response is adjusted to produce the final text "It's sunny today. I hope that makes you feel a little better."

[0732] The generated response text data is sent from the server to the terminal. This is also sent as an HTTP response. The terminal converts the received response text data into audio data using a speech synthesis engine (for example, Amazon Polly). Finally, the terminal's speaker plays the audio data and responds to the user. Specifically, the audio plays, "It's sunny today. I hope this makes you feel a little better."

[0733] Examples of prompt messages include the following:

[0734] "The user asked, 'What's the weather like today?' The emotion is neutral."

[0735] This system provides personalized responses tailored to the user's emotional state, resulting in a more natural and comfortable conversational experience. This is expected to contribute to reducing feelings of isolation among the elderly and lowering the risk of dementia.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] The user inputs voice.

[0739] The user speaks into the device. At this time, the user's voice is captured by the microphone. For example, the user says, "What's the weather like today?" The input is the user's voice, and the output is the digital audio data captured by the microphone.

[0740] Step 2:

[0741] The device converts the audio data into text data.

[0742] The device sends the captured audio data to a speech recognition engine (e.g., Google Cloud Speech-to-Text). This engine analyzes the audio data and converts it into text data. For example, the audio data "What's the weather like today?" is converted to the text "What's the weather like today?". The input is digital audio data, and the output is text data.

[0743] Step 3:

[0744] The device extracts emotional data from the voice data.

[0745] The device sends the captured audio data to an emotion recognition engine (e.g., Affectiva's SDK). This engine analyzes the voice tone, speed, and other parameters to identify emotion data. For example, a neutral tone would be recognized as "neutral." The input is digital audio data, and the output is emotion data.

[0746] Step 4:

[0747] The device sends text data and sentiment data to the server.

[0748] The converted text data and sentiment data are sent to the server via the terminal's communication device. Specifically, they are sent as an HTTP POST request. For example, the text "What's the weather like today?" and the sentiment "Neutral" are sent to the server. The input is text data and sentiment data, and the output is an HTTP request.

[0749] Step 5:

[0750] The server generates and adjusts the response.

[0751] Based on the text data and sentiment data received by the server, a prompt is input to a generative AI model (e.g., GPT-4) to generate a response. For example, if the prompt "The user asked 'What's the weather like today?' The sentiment is neutral." is input, the response "It's sunny today." is generated. Then, the response is adjusted considering the sentiment data, and the text "It's sunny today. I hope you feel a little better." is generated. The input is text data and sentiment data, and the output is the adjusted response text.

[0752] Step 6:

[0753] The server sends response data to the terminal.

[0754] The server sends the generated response text data to the terminal. This is also sent as an HTTP response. For example, the response text "It's sunny today. I hope this makes you feel a little better." is sent to the terminal. The input is the response text data, and the output is the HTTP response.

[0755] Step 7:

[0756] The device converts the response text data into speech data.

[0757] The device sends the received response text data to a speech synthesis engine (e.g., Amazon Polly) and converts it into speech data. For example, the text data "It's sunny today. I hope that makes you feel a little better." is converted into speech data. The input is response text data, and the output is speech data.

[0758] Step 8:

[0759] The device plays audio data.

[0760] The device's speaker plays audio data. For example, the user might hear the voice say, "It's sunny today. I hope it makes you feel a little better." The input is audio data, and the output is the played audio.

[0761] By clearly indicating the specific actions, inputs, and outputs performed at each step, the overall flow of the system can be understood more concretely. The entire process, from the user inputting voice commands to the terminal playing back the response in audio, becomes clear.

[0762] (Application Example 2)

[0763] 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."

[0764] When elderly people use food delivery services, they often encounter problems such as complicated operation or a lack of emotionally sensitive responses, which reduces convenience and satisfaction. Furthermore, because elderly people are more likely to be isolated and at risk of dementia, they require support in their daily lives.

[0765] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for capturing the user's voice, means for converting the captured voice data into text data, means for recognizing the user's emotions based on the converted text data, means for transmitting the converted text data and recognized emotion information to the server via a network, means for generating a response based on the received text data and emotion information at the server and adjusting the generated response text data based on the emotion information, means for transmitting the adjusted response text data to a terminal, means for converting the received response text data into voice data, and means for playing back the converted voice data. As a result, elderly people can easily use the food delivery service through voice interaction and obtain appropriate responses according to their emotions, thereby improving the convenience and satisfaction of use and contributing to a reduction in feelings of isolation.

[0766] "Means for capturing user voice" refers to a device that acquires voice input from a user as digital data.

[0767] "Means for converting captured audio data into text data" refers to a device or software that analyzes audio data and converts it into corresponding textual information.

[0768] "Means for recognizing user emotions based on converted text data" refers to a device or software that identifies user emotions from text data and extracts them as emotional information.

[0769] "Means for transmitting converted text data and recognized sentiment information to a server via a network" refers to a device or software that transmits text data and sentiment information to a server via the internet or other communication networks.

[0770] "Means for generating a response based on received text data and sentiment information, and for adjusting the generated response text data based on sentiment information" refers to a device or software that generates an appropriate response based on data received on a server and modifies the content and expression of the response according to the user's sentiment.

[0771] "Means for sending adjusted response text data to a terminal" refers to a device or software that sends a response generated and adjusted on a server to a user's terminal via a network.

[0772] "Means for converting received response text data into audio data" refers to a device or software that outputs a response received as text data as audio data.

[0773] "Means for playing back converted audio data" refers to a device that plays back audio data through a speaker or similar device, allowing the user to listen to it.

[0774] A "generative AI model" is an algorithm or system that uses machine learning to generate responses from data.

[0775] A "prompt sentence" is the text input to a generative AI model, and it forms the basis for response generation.

[0776] A "sensing device" is a hardware device or sensor used to capture audio or other input data.

[0777] A "speech recognition engine" is software or hardware that converts speech into text in real time or from recorded data.

[0778] "Communication means" refers to the entire set of hardware and software used to send and receive data.

[0779] To implement this invention, a system will be constructed using the following hardware and software: a system that processes a series of operations from the user inputting voice into a terminal until an appropriate response is finally returned in voice.

[0780] Hardware to use

[0781] Terminal: This refers to devices that serve as the user interface, such as smartphones, smart glasses, and head-mounted displays.

[0782] Microphone: A sensing device used to capture the user's voice.

[0783] Server: A central processing unit that handles voice data conversion, emotion recognition, and response generation.

[0784] Speaker: An output device for playing back generated audio data.

[0785] Software to use

[0786] Speech recognition engine: Used to convert speech data into text data. For example, the Google Speech-to-Text API.

[0787] Emotion recognition engine: Used to identify user emotions from text data. For example, IBM Watson Tone Analyzer.

[0788] Generative AI models: Used to customize generated responses according to the user's emotions. For example, OpenAI GPT-3.

[0789] Speech synthesis engine: Used to generate speech data from text data. For example, Amazon Polly.

[0790] Processing flow

[0791] The device's microphone captures the user's voice, acquiring the digital data. Then, a speech recognition engine converts this digital data into text. The converted text data is sent to an emotion recognition engine, which identifies the user's emotions.

[0792] Text data and sentiment information are transmitted to a server via the network. The server uses a generative AI model based on the received data to generate an appropriate response. Next, the generated response is refined based on the sentiment information. The refined response text data is sent to the terminal and converted into speech data using a speech synthesis engine. Finally, the response is played back to the user through the terminal's speaker.

[0793] Specific example

[0794] When a user asks the device, "What do you recommend for lunch?", the audio is captured and converted into text data. The emotion recognition engine detects that the user's voice sounds a little tired. Based on this text data ("What do you recommend for lunch?") and the emotion data (a little tired), the server generates and sends the response, "Curry rice is recommended. It's easy to prepare." The device then converts this response back into speech and plays it through its speaker.

[0795] Examples of prompt statements that are input to the generative AI model are as follows:

[0796] User said: "What would you recommend for lunch?" (Emotion: A little tired). Generate a response.

[0797] By using such a system, elderly people can easily use food delivery services through voice interaction and receive appropriate responses that reflect their emotions.

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] The user speaks into the device. The user says, "What do you recommend for lunch?" The device's microphone captures the user's voice and obtains it as digital audio data.

[0801] Step 2:

[0802] The device converts the captured audio data into text data. At this stage, a speech recognition engine (for example, Google Speech-to-Text API) converts the digital audio data into text data such as "What dishes would you recommend for lunch?". This process outputs the audio data as text information.

[0803] Step 3:

[0804] The terminal sends the converted text data to the emotion recognition engine. The emotion recognition engine (e.g., IBM Watson Tone Analyzer) analyzes the text data and identifies the user's emotion (in this case, slightly tired). The output of this step includes emotion data along with the text data.

[0805] Step 4:

[0806] The device sends the converted text data and recognized sentiment data to the server over the network. The transmitted data arrives at the server as an HTTP request. In this step, the input text data "What do you recommend for lunch?" and sentiment data (slightly tired) are sent.

[0807] Step 5:

[0808] The server generates a response based on the text and sentiment data it receives. The server uses a generative AI model (e.g., OpenAI GPT-3) to create a prompt: "User said: "What do you recommend for lunch?" (Emotion: A little tired). Generate a response." Based on this, it generates the response "Curry rice is recommended. It's easy to cook." In this step, the prompt is taken as input, an appropriate response is generated, and it is output as text data.

[0809] Step 6:

[0810] The server adjusts the generated response data based on the user's mood. The generated response text data is appropriately adjusted in tone, content, and other aspects based on the user's mood (e.g., slightly tired). For example, if the user is tired, the response tone will be set to a gentler one. This results in the final adjusted response text data being output.

[0811] Step 7:

[0812] The server sends the pre-arranged response text data to the terminal over the network. This also arrives at the terminal as an HTTP response. The output of this step is the pre-arranged response text data "Curry rice is recommended. It's easy to cook."

[0813] Step 8:

[0814] The terminal converts the received response text data into speech data. A speech synthesis engine (e.g., Amazon Polly) analyzes the text data and generates the corresponding speech data. In this step, text data is input and speech data is output.

[0815] Step 9:

[0816] The device's speaker plays the converted audio data. Finally, the device's speaker outputs the message, "Curry rice is recommended. It's easy to cook."

[0817] 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.

[0818] 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.

[0819] 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.

[0820] [Third Embodiment]

[0821] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0822] 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.

[0823] 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).

[0824] 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.

[0825] 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.

[0826] 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).

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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".

[0833] This invention relates to a system that captures user voice data, converts it into text data, and sends it to a server via a network. The server generates and sends back a response, and the terminal converts that response back into voice data and plays it back. The aim of this system is to provide a conversational support system that elderly people can use on a daily basis, thereby reducing the risk of isolation and dementia.

[0834] Program processing details

[0835] 1. Audio Capture

[0836] The device captures the user's voice.

[0837] The microphone built into the device activates and receives voice input from the user. For example, if the user says, "What's the weather like today?", the device captures this voice as digital data. This digital data is then sent to the subsequent speech recognition process.

[0838] 2. Speech Recognition

[0839] The device converts the audio data into text data.

[0840] The captured audio data is sent to a speech recognition engine and converted into text data. For example, the captured audio "What's the weather like today?" is converted into the text "What's the weather like today?" using speech recognition technology.

[0841] 3. Sending text data

[0842] The device sends text data to the server.

[0843] The converted text data is sent to the server over the network. For example, text data can be sent to the server using an HTTP request.

[0844] 4. Response generation

[0845] The server generates responses using an AI model.

[0846] The server generates a response based on the received text data. Specifically, it uses a generative AI model to generate an appropriate response, such as "It's sunny today," in response to the text "What's the weather like today?". This response text data is then sent from the server to the terminal.

[0847] 5. Send response data

[0848] The server sends the generated response data to the terminal.

[0849] The response data generated by the server is then sent back to the terminal via the network. For example, it may be returned to the terminal as an HTTP response.

[0850] 6. Speech synthesis

[0851] The device converts received text data into audio data.

[0852] The terminal invokes a speech synthesis engine to convert the received response text data into speech. This converts the text "It's sunny today." into corresponding speech data.

[0853] 7. Audio Playback

[0854] The device plays audio data to respond to the user.

[0855] The device's speaker plays audio data and responds to the user. For example, the speaker might play the message, "It's sunny today."

[0856] Specific Scenario Examples

[0857] 1. The user asks the device, "What day is it today?"

[0858] 2. The device captures the user's voice and converts it into text, "What day is it today?", using a speech recognition engine.

[0859] 3. The converted text is sent to the server.

[0860] 4. The server uses a generative AI model to generate the response "Today is October 5th."

[0861] 5. The server sends a response back to the terminal.

[0862] 6. The terminal receives the response and converts it into speech data using a speech synthesis engine.

[0863] 7. The user hears the audio message "Today is October 5th" through the device's speaker.

[0864] Thus, the present invention provides a system that supports the resolution of questions and dialogues in the daily lives of elderly people, and contributes to maintaining cognitive function and improving quality of life.

[0865] The following describes the processing flow.

[0866] Step 1:

[0867] The user speaks to the device.

[0868] The user speaks and asks a question to the device. For example, the user might say, "What's the weather like today?"

[0869] Step 2:

[0870] The device captures the user's voice.

[0871] The device uses its built-in microphone to capture the user's speech as audio data. The captured audio data is temporarily stored in memory.

[0872] Step 3:

[0873] The device converts the audio data into text data.

[0874] The device activates its speech recognition engine (e.g., speech recognition software) and converts the captured audio data into text data. For example, the audio "What's the weather like today?" is converted into the text data "What's the weather like today?".

[0875] Step 4:

[0876] The device sends text data to the server.

[0877] The terminal uses its network connection to send the converted text data to the server. The text data "What's the weather like today?" is sent to the server as an HTTP request.

[0878] Step 5:

[0879] The server receives text data.

[0880] The server parses the received HTTP request and retrieves the text data it contains. The server receives the text data "What's the weather like today?".

[0881] Step 6:

[0882] The server generates responses using an AI model.

[0883] The server inputs the received text data into a generative AI model (e.g., a natural language processing model). The AI ​​model analyzes the text "What's the weather like today?" and generates the response "It's sunny today."

[0884] Step 7:

[0885] The server sends the response text data to the terminal.

[0886] The server creates an HTTP response to send the generated response text data back to the terminal. The text data "It's sunny today." is sent to the terminal.

[0887] Step 8:

[0888] The device converts the received response text data into audio data.

[0889] The terminal activates a speech synthesis engine to convert the received text data into speech. The speech synthesis engine converts the text "It's sunny today." into speech data.

[0890] Step 9:

[0891] The device plays audio data.

[0892] The device plays the generated audio data through its speaker. The user can hear the message "It's sunny today." coming from the speaker.

[0893] In this way, this system contributes to improving the quality of life for the elderly by providing intuitive and rapid responses to users' questions.

[0894] (Example 1)

[0895] 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."

[0896] In modern society, it is important to provide means for elderly people to engage in daily conversations without becoming isolated. Elderly people often have difficulty with youthful conversations and access to information, which increases the risk of dementia. This invention aims to solve these problems and provide a conversation support system that is easy for elderly people to use.

[0897] 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.

[0898] In this invention, the server includes means for capturing audio, means for converting the captured audio data into text data, means for transmitting the converted text data to the server via a network, means for generating a response based on the text data received by the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into audio data, and means for playing back the converted audio data. This enables elderly people to interact with the system using their own voice and obtain information and responses in real time.

[0899] "Means for capturing speech" refers to a device or technology that converts a user's speech into digital speech data.

[0900] "Means for converting audio data into text data" refers to a technology or device that analyzes captured audio data and generates a corresponding string of characters.

[0901] "Means for sending text data to a server over a network" refers to a device or technology that sends generated text data to a remote server using a communication protocol.

[0902] "Means for generating a response based on text data received on the server" refers to a device or technology that generates an appropriate response based on text data received on the server side.

[0903] "Means for sending generated response text data to a terminal" refers to a device or technology that sends the response generated from the server back to the terminal via the network.

[0904] "Means for converting received response text data into audio data" refers to a device or technology that converts received text data back into audio data.

[0905] "Means for playing back converted audio data" refers to a device or technology that plays back converted audio data as physical sound.

[0906] A "generative AI model" is an artificial intelligence model that generates responses in natural language based on input data.

[0907] A "prompt statement" is an input statement given to a generative AI model, and it is an instruction statement that the model uses to generate a response.

[0908] This invention relates to a system that captures user voice data, converts it into text data, and sends it to a server via a network. The server generates and sends back a response, and the terminal converts that response back into voice data and plays it back. This system aims to provide a conversational support system that elderly people can use on a daily basis, thereby reducing the risk of isolation and dementia.

[0909] The system configuration includes terminals and servers. Specific hardware and software examples include terminals equipped with a microphone, speaker, speech recognition engine (e.g., Google Cloud Speech-to-Text API), and speech synthesis engine (e.g., Amazon Polly API). The server is equipped with a generative AI model (e.g., OpenAI GPT-3) that generates responses based on text data.

[0910] Audio Capture

[0911] The device captures the user's voice. Specifically, the microphone built into the device operates and captures the user's voice input as digital data. For example, if the user says, "What's the weather like today?", the device's microphone picks up the voice and converts it into a digital signal.

[0912] Speech recognition

[0913] The device converts the captured audio data into text data. This process uses a speech recognition engine (e.g., Google Cloud Speech-to-Text API). For example, the captured audio "What's the weather like today?" is converted into the text "What's the weather like today?" by speech recognition technology.

[0914] Sending text data

[0915] The terminal sends the converted text data to the server. Specifically, it sends the text data to the server using an HTTP request.

[0916] Response generation

[0917] The server generates a response based on the text data it receives. A generative AI model (e.g., OpenAI GPT-3) is used. For example, in response to the text "What's the weather like today?", the generative AI model generates the response "It's sunny today."

[0918] Send response data

[0919] The server sends the generated response data to the terminal. Specifically, the response data is returned to the terminal as an HTTP response.

[0920] Speech synthesis

[0921] The device calls a speech synthesis engine (e.g., Amazon Polly API) to convert the received response text data into speech data. For example, the text "It's sunny today." is converted into the corresponding speech data.

[0922] Audio playback

[0923] The device's speaker plays audio data and responds to the user. For example, the speaker might play the message, "It's sunny today."

[0924] Specific Scenario Examples

[0925] 1. The user speaks to the device saying, "What day is it today?" The spoken words are captured by the device's microphone.

[0926] 2. The device captures the user's voice as digital data and converts it into text, "What day is it today?", using a speech recognition engine.

[0927] 3. The terminal sends the converted text data to the server as an HTTP POST request.

[0928] 4. The server uses a generative AI model (e.g., OpenAI GPT-3) to generate the response "Today is October 5th."

[0929] 5. The server sends the generated response data to the terminal as an HTTP response.

[0930] 6. The terminal converts the received response into audio data, "Today is October 5th," using a speech synthesis engine (e.g., Amazon Polly API).

[0931] 7. The device's speaker plays a voice message saying, "Today is October 5th," informing the user.

[0932] Example of a prompt:

[0933] "What's the weather like today?"

[0934] "What day is it today?"

[0935] "Please tell me today's news."

[0936] Thus, the present invention provides a system that makes it easier for elderly people to resolve questions in their daily lives, contributing to the maintenance of cognitive function and improvement of quality of life.

[0937] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0938] Step 1:

[0939] Audio Capture

[0940] The device captures the user's voice. Specifically, the microphone built into the device operates and captures the user's speech as digital data. For example, if the user says, "What's the weather like today?", the device's microphone picks up the voice and converts it into a digital signal.

[0941] Input: User's voice

[0942] Output: Digital audio data (audio waveform data)

[0943] Step 2:

[0944] Speech recognition

[0945] The device converts the audio data into text data. The captured audio data is sent to a speech recognition engine (e.g., Google Cloud Speech-to-Text API) for analysis. For example, the speech recognition engine analyzes the digital audio and converts it into text such as "What's the weather like today?". This process involves noise filtering and speech feature extraction.

[0946] Input: Digital audio data

[0947] Output: Recognized text data ("What's the weather like today?")

[0948] Step 3:

[0949] Sending text data

[0950] The device sends text data to the server. The device's application constructs an HTTP POST request and includes the text data as a payload. Specifically, the converted text data is sent to the server as an HTTP request.

[0951] Input: Recognized text data

[0952] Output: HTTP POST request (including text data)

[0953] Step 4:

[0954] Response generation

[0955] The server generates a response based on the text data it receives. Specifically, the server inputs text data into a generative AI model (e.g., OpenAI GPT-3) to generate an appropriate response. For example, in response to the text "What's the weather like today?", the generative AI model generates the response "It's sunny today." This process involves natural language processing and contextual analysis.

[0956] Input: HTTP POST request (including text data)

[0957] Output: Generated response text data ("It's sunny today.")

[0958] Step 5:

[0959] Send response data

[0960] The server sends the generated response data to the terminal. The server constructs an HTTP response, including the generated response text data as the payload. This response reaches the terminal via the internet.

[0961] Input: Generated response text data

[0962] Output: HTTP response (including response text data)

[0963] Step 6:

[0964] Speech synthesis

[0965] The device calls a speech synthesis engine (e.g., Amazon Polly API) to convert the received response text data into speech data. The device's application sends the text data to the speech synthesis API and receives the speech data returned by the API. This speech data is saved on the device as a corresponding audio file.

[0966] Input: Received response text data

[0967] Output: Synthesized audio data (audio file)

[0968] Step 7:

[0969] Audio playback

[0970] The device's speaker plays audio data and responds to the user. The device's audio playback function reads the audio file and plays the message "It's sunny today." through the speaker. During this process, volume adjustment and playback timing control are also performed.

[0971] Input: Synthesized audio data (audio file)

[0972] Output: Audio playback from the speaker ("It's sunny today.")

[0973] (Application Example 1)

[0974] 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."

[0975] In traditional factory operations, a major challenge was the time and effort required for workers to perform complex procedures and check situations. Furthermore, it was difficult for workers to give instructions using voice commands without using their hands and to receive real-time responses. This resulted in decreased work efficiency and an increased risk of errors and accidents. A simple and effective support system was particularly needed for elderly workers.

[0976] 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.

[0977] In this invention, the server includes means for capturing the user's voice, means for converting the captured voice data into text data, means for transmitting the converted text data to the server via a network, means for generating a response based on the text data received by the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into voice data, means for playing back the converted voice data, means for capturing the worker's voice using smart glasses and converting work instructions into text data using a voice recognition engine, means for converting the generated response text data into voice data using a speech synthesis engine, and means for playing back the voice data from the smart glasses' speaker. This makes it possible for workers to easily give instructions using only their voice without using their hands and to obtain appropriate responses in real time.

[0978] A "user" refers to a person who utilizes the system of the present invention, and who wears smart glasses at the work site and gives instructions to the robot or system using voice.

[0979] "Means of capturing audio" refers to devices such as microphones used to capture the voice spoken by a user as digital data.

[0980] "Means of converting audio data into text data" refers to a speech recognition engine that analyzes captured audio data and converts the words and phrases within it into text format.

[0981] "Means of sending text data to a server over a network" refers to communication devices or software used to send converted text data to a remote server using the internet or other networks.

[0982] "Means for generating a response based on text data received by the server" refers to generative AI models or response generation algorithms that analyze the text data received by the server and generate an appropriate response.

[0983] "Means for sending generated response text data to the terminal" refers to a communication function for sending text data generated on the server back to the user's terminal via the network.

[0984] "Means for converting received response text data into speech data" refers to the speech synthesis engine used by the terminal to convert received text data into speech.

[0985] "Means for playing back converted audio data" refers to speakers or other sound devices used by the terminal to play back the converted audio data in a format that can be heard by the user.

[0986] "Smart glasses" refer to wearable devices equipped with the ability to capture, play back, and perform other data processing.

[0987] "Means of converting work instructions into text data" refers to speech recognition technology that recognizes work instructions spoken by a user into smart glasses and converts them into text format.

[0988] A "speech synthesis engine" refers to software or algorithms used to convert text data into natural-sounding speech.

[0989] A "speaker" refers to an audio device that converts audio signals into sound waves and reproduces them in a format that can be heard by the user.

[0990] This invention relates to a voice interaction system using smart glasses for use in factories. This system captures the voice of workers and enables real-time instruction, aiming to improve work efficiency and safety. Specific embodiments for carrying out this invention are described below.

[0991] 1. System Configuration

[0992] The system of this invention consists of smart glasses worn by the user (worker), a speech recognition engine, a server, a generative AI model, a speech synthesis engine, and communication means.

[0993] 2. Smart Glasses

[0994] Smart glasses have a built-in microphone that captures the user's voice. This voice data is initially processed within the smart glasses and then sent to the voice recognition engine.

[0995] 3. Speech Recognition Engine

[0996] The speech recognition engine converts captured audio data into text data. Specifically, it uses technologies such as Google Cloud Speech-to-Text and Amazon Transcribe. This engine analyzes the received audio data and generates the corresponding string.

[0997] 4. Servers and Generative AI Models

[0998] The server receives text data sent from the speech recognition engine and generates an appropriate response using a generative AI model (e.g., GPT-4). An example of a prompt is shown below.

[0999] Example of a prompt:

[1000] Please generate the appropriate response to the following sentence: 'Check the placement of the parts'

[1001] 5. Means of communication

[1002] Text data and response data are sent and received between smart glasses and servers via the internet or other networks. Therefore, communication capabilities using HTTP requests and responses are required.

[1003] 6. Speech synthesis engine

[1004] The received response text data is converted into speech data by a speech synthesis engine (e.g., Amazon Polly or Google Text-to-Speech).

[1005] 7. Speakers

[1006] The converted audio data is played back through the speaker built into the smart glasses, and the user receives an audio response. For example, a message such as "The component placement is normal" might be played.

[1007] Specific example

[1008] When a user speaks to the smart glasses, "How is the engine doing?", the smart glasses capture the voice and convert it into text data, "How is the engine doing?", using a speech recognition engine. The converted text is sent to a server, where a generative AI model (GPT-4) generates the response, "The engine is doing well." The response is sent back to the smart glasses, converted into audio data by a speech synthesis engine, and played back as "The engine is doing well." from the smart glasses' speaker.

[1009] The above describes a specific embodiment for carrying out the present invention. With this system, workers can easily give instructions by voice without using their hands and receive appropriate responses in real time.

[1010] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1011] Step 1:

[1012] The user speaks into the smart glasses (e.g., "How is the engine?"). The smart glasses' built-in microphone captures the user's voice and records it as digital audio data.

[1013] Input: User's voice

[1014] Output: Digital audio data

[1015] Step 2:

[1016] The captured digital audio data is sent to the speech recognition engine in the smart glasses. The speech recognition engine (e.g., Google Cloud Speech-to-Text) analyzes the digital audio and converts it into corresponding text data (e.g., "How is the engine doing?").

[1017] Input: Digital audio data

[1018] Output: Text data

[1019] Step 3:

[1020] Text data is sent to the server via the network. Communication takes place through HTTP requests, and the text data reaches the server.

[1021] Input: Text data

[1022] Output: HTTP request to the server

[1023] Step 4:

[1024] The server processes the received text data and passes it to a generative AI model (e.g., GPT-4). The generative AI model generates an appropriate response (e.g., "The engine is functioning normally.") based on the text data.

[1025] Input: Text data

[1026] Output: Response text data

[1027] Step 5:

[1028] The server sends the generated response text data back to the smart glasses over the network. The response text data is sent as an HTTP response.

[1029] Input: Response text data

[1030] Output: HTTP response to the terminal (smart glasses)

[1031] Step 6:

[1032] The smart glasses pass the received response text data to a speech synthesis engine (e.g., Amazon Polly) and convert it into speech data.

[1033] Input: Response text data

[1034] Output: Audio data

[1035] Step 7:

[1036] The speaker in the smart glasses plays audio data and responds to the user verbally (e.g., "The engine is functioning normally.").

[1037] Input: Audio data

[1038] Output: Voice response to the user

[1039] 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.

[1040] This invention relates to a system that captures a user's voice, converts it into text data, sends it to a server, and the server generates a response which is then sent back to the terminal. This system further aims to provide more appropriate and personalized responses by incorporating an emotion engine that recognizes the user's emotions. This system provides conversational support in a form that elderly people can use on a daily basis, aiming to reduce the risk of isolation and dementia.

[1041] Program processing details

[1042] 1. Audio Capture

[1043] The device captures the user's voice.

[1044] The microphone built into the device activates and receives voice input from the user. For example, if the user says, "What's the weather like today?", the device captures this voice as digital data. This digital data is stored in memory and sent to subsequent voice recognition and emotion recognition processes.

[1045] 2. Speech Recognition and Emotion Recognition

[1046] The device converts voice data into text data and recognizes emotions.

[1047] The captured audio data is sent to a speech recognition engine and converted into text data. For example, the voice "What's the weather like today?" is converted into the text "What's the weather like today?". At the same time, an emotion engine analyzes the audio data to identify the user's emotions (e.g., joy, anger, sadness, etc.).

[1048] 3. Sending text data and sentiment data

[1049] The device sends text data and sentiment data to the server.

[1050] The converted text data and recognized sentiment information are sent to the server over the network. For example, the text data "What's the weather like today?" and sentiment data (e.g., neutral) are sent to the server as an HTTP request.

[1051] 4. Response generation

[1052] The server uses an AI model to generate responses and adjusts them based on emotions.

[1053] The server generates a response based on the received text data and sentiment data. Specifically, it uses a generative AI model to generate an appropriate response to "What's the weather like today?" such as "It's sunny today." Furthermore, it adjusts the tone and content of the response based on the sentiment data. For example, if the user seems sad, it will generate a response such as "It's sunny today. I hope that makes you feel a little better."

[1054] 5. Send response data

[1055] The server sends the generated response data to the terminal.

[1056] The response data generated by the server is then sent back to the terminal via the network. For example, the adjusted response text data, "It's sunny today. I hope this makes you feel a little better," is sent to the terminal as an HTTP response.

[1057] 6. Speech synthesis

[1058] The device converts received text data into audio data.

[1059] The terminal activates a speech synthesis engine to convert the received response text data into speech. The speech synthesis engine converts the received text "It's sunny today. I hope you feel a little better." into speech data.

[1060] 7. Audio Playback

[1061] The device plays audio data.

[1062] The device's speaker plays audio data and responds to the user. For example, it might play a message like, "It's sunny today. I hope it makes you feel a little better."

[1063] Specific Scenario Examples

[1064] 1. The user asks the device, "What day is it today?"

[1065] 2. The device captures the user's voice and converts it into text, "What day is it today?", using a speech recognition engine. Simultaneously, an emotion engine analyzes the user's tone and speed of voice and recognizes that the user is slightly tired.

[1066] 3. The converted text and sentiment data are sent to the server.

[1067] 4. The server uses a generative AI model to generate the response, "Today is October 5th," but adjusts the response to the user's fatigue level, changing it to, "Today is October 5th. How about taking a short break?"

[1068] 5. The server sends the adjusted response to the terminal.

[1069] 6. The terminal receives the response and converts it into speech data using a speech synthesis engine.

[1070] 7. A voice message plays from the device's speaker saying, "Today is October 5th. How about taking a short break?"

[1071] Thus, by providing responses that correspond to the user's emotional state, the present invention improves the user experience and contributes to reducing feelings of isolation and stress among the elderly.

[1072] The following describes the processing flow.

[1073] Step 1:

[1074] The user speaks to the device.

[1075] The user speaks and asks a question to the device. For example, if the user says, "What's the weather like today?", the spoken content is captured as audio data.

[1076] Step 2:

[1077] The device captures the user's voice.

[1078] The microphone built into the device activates and captures the user's speech as an audio signal. The captured audio signal is converted into digital audio data and recorded in memory.

[1079] Step 3:

[1080] The device converts the audio data into text data.

[1081] The device's speech recognition engine analyzes the captured audio data and converts it into corresponding text data. For example, the audio data "What's the weather like today?" is converted into the text data "What's the weather like today?".

[1082] Step 4:

[1083] The device converts voice data into emotion data.

[1084] Simultaneously, the device's emotion engine analyzes the voice data to recognize the user's emotions. For example, it identifies that the user is feeling neutral based on factors such as voice tone, word choice, and volume.

[1085] Step 5:

[1086] The device sends text data and sentiment data to the server.

[1087] The device sends the converted text data and recognized sentiment data to the server via the network connection. For example, an HTTP request sends the text data "What's the weather like today?" and sentiment data (neutral) to the server.

[1088] Step 6:

[1089] The server receives text data and sentiment data.

[1090] The server parses the received HTTP request and retrieves text data and sentiment data. The server receives the text data "What's the weather like today?" and sentiment data (neutral).

[1091] Step 7:

[1092] The server generates responses using an AI model.

[1093] The server uses a generative AI model to generate a response based on the received text data. For example, in response to "What's the weather like today?", it generates the response "It's sunny today."

[1094] Step 8:

[1095] The server adjusts its response based on emotion.

[1096] The server then adjusts the content and tone of its response based on the recognized sentiment data. For example, if the user is perceived as having a neutral sentiment, it may respond with "It's sunny today," or it may adjust it to be a little more detailed, such as "It's sunny today. It's a nice day to go out."

[1097] Step 9:

[1098] The server sends the response text data to the terminal.

[1099] The server creates an HTTP response containing the adjusted response and sends it to the terminal. For example, the text data "It's sunny today. It's a good day to go outside." is sent to the terminal.

[1100] Step 10:

[1101] The device converts the received response text data into audio data.

[1102] The device sends the received text data to the speech synthesis engine, which converts it into speech data. The speech synthesis engine converts the text "It's sunny today. It's a good day to go outside." into the corresponding speech data.

[1103] Step 11:

[1104] The device plays audio data.

[1105] The device's speaker plays the generated audio data and responds to the user. For example, the speaker might play a voice message saying, "It's sunny today. It's a good day to go outside."

[1106] This system provides appropriate responses tailored to the user's emotions, thereby improving the user experience. This invention is particularly expected to alleviate feelings of isolation among the elderly and promote communication.

[1107] (Example 2)

[1108] 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."

[1109] In recent years, the increasing sense of isolation and dementia risk among the elderly has attracted attention as a social problem. In response, there is a growing need for dialogue support systems that the elderly can use on a daily basis. However, existing dialogue systems provide uniform responses without considering the user's emotions, making it difficult to provide appropriate support tailored to individual situations. Therefore, there is a need to develop dialogue support systems that provide personalized responses that respond to the user's emotions.

[1110] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for generating a response based on received text data and emotion data and adjusting it according to emotion, means for transmitting the generated response text data to a terminal, and means for converting the received response text data into voice data. This enables personalized responses that correspond to the user's emotional state.

[1111] A "user" refers to a person who uses a system.

[1112] "Means of capturing audio" refers to devices or processes for converting user speech into digital data.

[1113] "Means of converting audio data into text data" refers to the process or technology of analyzing audio signals and converting them into textual information.

[1114] "Emotional data" refers to emotional information extracted from the user's voice and behavior.

[1115] "Means of transmission over a network" refers to the processes and technologies used to transmit data over the internet or other communication networks.

[1116] A "server" refers to a computer system that performs data processing and provides services over a network.

[1117] "Means for generating a response" refers to the process or technology for generating an appropriate response based on input data.

[1118] A "generative AI model" refers to a model or algorithm that uses artificial intelligence technology to generate text or decisions.

[1119] A "terminal" refers to a computer device or equipment that a user directly operates.

[1120] "Means of playing audio data" refers to the process or technology of playing audio signals through output devices such as speakers.

[1121] "Acoustic acquisition device" refers to hardware or a device used to capture sound.

[1122] A "speech recognition device" refers to a technology or device that analyzes speech data and converts it into text data.

[1123] An "emotion recognition device" refers to technology or equipment that analyzes and identifies emotions from a user's voice and behavior.

[1124] "Communication equipment" refers to devices and technologies used for sending and receiving data.

[1125] This invention provides a system that allows users to obtain information and receive appropriate responses through voice interaction. The system aims to provide a more personalized experience by offering responses that take into account the user's emotional state.

[1126] The system consists of three main components: the user, the terminal, and the server. The user speaks to the terminal using their voice. A microphone built into the terminal captures the user's voice and converts it into digital data. This uses a microphone as an acoustic data acquisition device. For example, if the user says, "What's the weather like today?", the terminal's microphone captures this voice and converts it into digital data.

[1127] The device converts the acquired audio data into text data using a speech recognition engine (e.g., Google Cloud Speech-to-Text). This speech recognition process converts the audio "What's the weather like today?" into the text data "What's the weather like today?". Simultaneously, an emotion recognition device (e.g., Affectiva's SDK) is used to extract emotion data from the user's voice. For example, based on the tone and speed of the user's voice, it is identified that the user is in a neutral emotional state.

[1128] The converted text data and sentiment data are sent from the terminal to the server via a communication device. Specifically, they are sent as an HTTP POST request. The request payload includes the text data "What's the weather like today?" and the sentiment data "Neutral".

[1129] The server generates a response based on the received text data and sentiment data. This uses a generative AI model (e.g., OpenAI's GPT-4). This model takes a prompt as input and generates an appropriate response. For example, if the server receives the prompt "The user asked 'What's the weather like today?' The sentiment is neutral," it will generate the response "It's sunny today." Then, taking the sentiment data into consideration, the response is adjusted to produce the final text "It's sunny today. I hope that makes you feel a little better."

[1130] The generated response text data is sent from the server to the terminal. This is also sent as an HTTP response. The terminal converts the received response text data into audio data using a speech synthesis engine (for example, Amazon Polly). Finally, the terminal's speaker plays the audio data and responds to the user. Specifically, the audio plays, "It's sunny today. I hope this makes you feel a little better."

[1131] Examples of prompt messages include the following:

[1132] "The user asked, 'What's the weather like today?' The emotion is neutral."

[1133] This system provides personalized responses tailored to the user's emotional state, resulting in a more natural and comfortable conversational experience. This is expected to contribute to reducing feelings of isolation among the elderly and lowering the risk of dementia.

[1134] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1135] Step 1:

[1136] The user inputs voice.

[1137] The user speaks into the device. At this time, the user's voice is captured by the microphone. For example, the user says, "What's the weather like today?" The input is the user's voice, and the output is the digital audio data captured by the microphone.

[1138] Step 2:

[1139] The device converts the audio data into text data.

[1140] The device sends the captured audio data to a speech recognition engine (e.g., Google Cloud Speech-to-Text). This engine analyzes the audio data and converts it into text data. For example, the audio data "What's the weather like today?" is converted to the text "What's the weather like today?". The input is digital audio data, and the output is text data.

[1141] Step 3:

[1142] The device extracts emotional data from the voice data.

[1143] The device sends the captured audio data to an emotion recognition engine (e.g., Affectiva's SDK). This engine analyzes the voice tone, speed, and other parameters to identify emotion data. For example, a neutral tone would be recognized as "neutral." The input is digital audio data, and the output is emotion data.

[1144] Step 4:

[1145] The device sends text data and sentiment data to the server.

[1146] The converted text data and sentiment data are sent to the server via the terminal's communication device. Specifically, they are sent as an HTTP POST request. For example, the text "What's the weather like today?" and the sentiment "Neutral" are sent to the server. The input is text data and sentiment data, and the output is an HTTP request.

[1147] Step 5:

[1148] The server generates and adjusts the response.

[1149] Based on the text data and sentiment data received by the server, a prompt is input to a generative AI model (e.g., GPT-4) to generate a response. For example, if the prompt "The user asked 'What's the weather like today?' The sentiment is neutral." is input, the response "It's sunny today." is generated. Then, the response is adjusted considering the sentiment data, and the text "It's sunny today. I hope you feel a little better." is generated. The input is text data and sentiment data, and the output is the adjusted response text.

[1150] Step 6:

[1151] The server sends response data to the terminal.

[1152] The server sends the generated response text data to the terminal. This is also sent as an HTTP response. For example, the response text "It's sunny today. I hope this makes you feel a little better." is sent to the terminal. The input is the response text data, and the output is the HTTP response.

[1153] Step 7:

[1154] The device converts the response text data into speech data.

[1155] The device sends the received response text data to a speech synthesis engine (e.g., Amazon Polly) and converts it into speech data. For example, the text data "It's sunny today. I hope that makes you feel a little better." is converted into speech data. The input is response text data, and the output is speech data.

[1156] Step 8:

[1157] The device plays audio data.

[1158] The device's speaker plays audio data. For example, the user might hear the voice say, "It's sunny today. I hope it makes you feel a little better." The input is audio data, and the output is the played audio.

[1159] By clearly indicating the specific actions, inputs, and outputs performed at each step, the overall flow of the system can be understood more concretely. The entire process, from the user inputting voice commands to the terminal playing back the response in audio, becomes clear.

[1160] (Application Example 2)

[1161] 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."

[1162] When elderly people use food delivery services, they often encounter problems such as complicated operation or a lack of emotionally sensitive responses, which reduces convenience and satisfaction. Furthermore, because elderly people are more likely to be isolated and at risk of dementia, they require support in their daily lives.

[1163] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for capturing the user's voice, means for converting the captured voice data into text data, means for recognizing the user's emotions based on the converted text data, means for transmitting the converted text data and recognized emotion information to the server via a network, means for generating a response based on the received text data and emotion information at the server and adjusting the generated response text data based on the emotion information, means for transmitting the adjusted response text data to a terminal, means for converting the received response text data into voice data, and means for playing back the converted voice data. As a result, elderly people can easily use the food delivery service through voice interaction and obtain appropriate responses according to their emotions, thereby improving the convenience and satisfaction of use and contributing to a reduction in feelings of isolation.

[1164] "Means for capturing user voice" refers to a device that acquires voice input from a user as digital data.

[1165] "Means for converting captured audio data into text data" refers to a device or software that analyzes audio data and converts it into corresponding textual information.

[1166] "Means for recognizing user emotions based on converted text data" refers to a device or software that identifies user emotions from text data and extracts them as emotional information.

[1167] "Means for transmitting converted text data and recognized sentiment information to a server via a network" refers to a device or software that transmits text data and sentiment information to a server via the internet or other communication networks.

[1168] "Means for generating a response based on received text data and sentiment information, and for adjusting the generated response text data based on sentiment information" refers to a device or software that generates an appropriate response based on data received on a server and modifies the content and expression of the response according to the user's sentiment.

[1169] "Means for sending adjusted response text data to a terminal" refers to a device or software that sends a response generated and adjusted on a server to a user's terminal via a network.

[1170] "Means for converting received response text data into audio data" refers to a device or software that outputs a response received as text data as audio data.

[1171] "Means for playing back converted audio data" refers to a device that plays back audio data through a speaker or similar device, allowing the user to listen to it.

[1172] A "generative AI model" is an algorithm or system that uses machine learning to generate responses from data.

[1173] A "prompt sentence" is the text input to a generative AI model, and it forms the basis for response generation.

[1174] A "sensing device" is a hardware device or sensor used to capture audio or other input data.

[1175] A "speech recognition engine" is software or hardware that converts speech into text in real time or from recorded data.

[1176] "Communication means" refers to the entire set of hardware and software used to send and receive data.

[1177] To implement this invention, a system will be constructed using the following hardware and software: a system that processes a series of operations from the user inputting voice into a terminal until an appropriate response is finally returned in voice.

[1178] Hardware to use

[1179] Terminal: This refers to devices that serve as the user interface, such as smartphones, smart glasses, and head-mounted displays.

[1180] Microphone: A sensing device used to capture the user's voice.

[1181] Server: A central processing unit that handles voice data conversion, emotion recognition, and response generation.

[1182] Speaker: An output device for playing back generated audio data.

[1183] Software to use

[1184] Speech recognition engine: Used to convert speech data into text data. For example, the Google Speech-to-Text API.

[1185] Emotion recognition engine: Used to identify user emotions from text data. For example, IBM Watson Tone Analyzer.

[1186] Generative AI models: Used to customize generated responses according to the user's emotions. For example, OpenAI GPT-3.

[1187] Speech synthesis engine: Used to generate speech data from text data. For example, Amazon Polly.

[1188] Processing flow

[1189] The device's microphone captures the user's voice, acquiring the digital data. Then, a speech recognition engine converts this digital data into text. The converted text data is sent to an emotion recognition engine, which identifies the user's emotions.

[1190] Text data and sentiment information are transmitted to a server via the network. The server uses a generative AI model based on the received data to generate an appropriate response. Next, the generated response is refined based on the sentiment information. The refined response text data is sent to the terminal and converted into speech data using a speech synthesis engine. Finally, the response is played back to the user through the terminal's speaker.

[1191] Specific example

[1192] When a user asks the device, "What do you recommend for lunch?", the audio is captured and converted into text data. The emotion recognition engine detects that the user's voice sounds a little tired. Based on this text data ("What do you recommend for lunch?") and the emotion data (a little tired), the server generates and sends the response, "Curry rice is recommended. It's easy to prepare." The device then converts this response back into speech and plays it through its speaker.

[1193] Examples of prompt statements that are input to the generative AI model are as follows:

[1194] User said: "What would you recommend for lunch?" (Emotion: A little tired). Generate a response.

[1195] By using such a system, elderly people can easily use food delivery services through voice interaction and receive appropriate responses that reflect their emotions.

[1196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1197] Step 1:

[1198] The user speaks into the device. The user says, "What do you recommend for lunch?" The device's microphone captures the user's voice and obtains it as digital audio data.

[1199] Step 2:

[1200] The device converts the captured audio data into text data. At this stage, a speech recognition engine (for example, Google Speech-to-Text API) converts the digital audio data into text data such as "What dishes would you recommend for lunch?". This process outputs the audio data as text information.

[1201] Step 3:

[1202] The terminal sends the converted text data to the emotion recognition engine. The emotion recognition engine (e.g., IBM Watson Tone Analyzer) analyzes the text data and identifies the user's emotion (in this case, slightly tired). The output of this step includes emotion data along with the text data.

[1203] Step 4:

[1204] The device sends the converted text data and recognized sentiment data to the server over the network. The transmitted data arrives at the server as an HTTP request. In this step, the input text data "What do you recommend for lunch?" and sentiment data (slightly tired) are sent.

[1205] Step 5:

[1206] The server generates a response based on the text and sentiment data it receives. The server uses a generative AI model (e.g., OpenAI GPT-3) to create a prompt: "User said: "What do you recommend for lunch?" (Emotion: A little tired). Generate a response." Based on this, it generates the response "Curry rice is recommended. It's easy to cook." In this step, the prompt is taken as input, an appropriate response is generated, and it is output as text data.

[1207] Step 6:

[1208] The server adjusts the generated response data based on the user's mood. The generated response text data is appropriately adjusted in tone, content, and other aspects based on the user's mood (e.g., slightly tired). For example, if the user is tired, the response tone will be set to a gentler one. This results in the final adjusted response text data being output.

[1209] Step 7:

[1210] The server sends the pre-arranged response text data to the terminal over the network. This also arrives at the terminal as an HTTP response. The output of this step is the pre-arranged response text data "Curry rice is recommended. It's easy to cook."

[1211] Step 8:

[1212] The terminal converts the received response text data into speech data. A speech synthesis engine (e.g., Amazon Polly) analyzes the text data and generates the corresponding speech data. In this step, text data is input and speech data is output.

[1213] Step 9:

[1214] The device's speaker plays the converted audio data. Finally, the device's speaker outputs the message, "Curry rice is recommended. It's easy to cook."

[1215] 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.

[1216] 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.

[1217] 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.

[1218] [Fourth Embodiment]

[1219] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1220] 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.

[1221] 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).

[1222] 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.

[1223] 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.

[1224] 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).

[1225] 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.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] 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.

[1230] 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.

[1231] 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".

[1232] This invention relates to a system that captures user voice data, converts it into text data, and sends it to a server via a network. The server generates and sends back a response, and the terminal converts that response back into voice data and plays it back. The aim of this system is to provide a conversational support system that elderly people can use on a daily basis, thereby reducing the risk of isolation and dementia.

[1233] Program processing details

[1234] 1. Audio Capture

[1235] The device captures the user's voice.

[1236] The microphone built into the device activates and receives voice input from the user. For example, if the user says, "What's the weather like today?", the device captures this voice as digital data. This digital data is then sent to the subsequent speech recognition process.

[1237] 2. Speech Recognition

[1238] The device converts the audio data into text data.

[1239] The captured audio data is sent to a speech recognition engine and converted into text data. For example, the captured audio "What's the weather like today?" is converted into the text "What's the weather like today?" using speech recognition technology.

[1240] 3. Sending text data

[1241] The device sends text data to the server.

[1242] The converted text data is sent to the server over the network. For example, text data can be sent to the server using an HTTP request.

[1243] 4. Response generation

[1244] The server generates responses using an AI model.

[1245] The server generates a response based on the received text data. Specifically, it uses a generative AI model to generate an appropriate response, such as "It's sunny today," in response to the text "What's the weather like today?". This response text data is then sent from the server to the terminal.

[1246] 5. Send response data

[1247] The server sends the generated response data to the terminal.

[1248] The response data generated by the server is then sent back to the terminal via the network. For example, it may be returned to the terminal as an HTTP response.

[1249] 6. Speech synthesis

[1250] The device converts received text data into audio data.

[1251] The terminal invokes a speech synthesis engine to convert the received response text data into speech. This converts the text "It's sunny today." into corresponding speech data.

[1252] 7. Audio Playback

[1253] The device plays audio data to respond to the user.

[1254] The device's speaker plays audio data and responds to the user. For example, the speaker might play the message, "It's sunny today."

[1255] Specific Scenario Examples

[1256] 1. The user asks the device, "What day is it today?"

[1257] 2. The device captures the user's voice and converts it into text, "What day is it today?", using a speech recognition engine.

[1258] 3. The converted text is sent to the server.

[1259] 4. The server uses a generative AI model to generate the response "Today is October 5th."

[1260] 5. The server sends a response back to the terminal.

[1261] 6. The terminal receives the response and converts it into speech data using a speech synthesis engine.

[1262] 7. The user hears the audio message "Today is October 5th" through the device's speaker.

[1263] Thus, the present invention provides a system that supports the resolution of questions and dialogues in the daily lives of elderly people, and contributes to maintaining cognitive function and improving quality of life.

[1264] The following describes the processing flow.

[1265] Step 1:

[1266] The user speaks to the device.

[1267] The user speaks and asks a question to the device. For example, the user might say, "What's the weather like today?"

[1268] Step 2:

[1269] The device captures the user's voice.

[1270] The device uses its built-in microphone to capture the user's speech as audio data. The captured audio data is temporarily stored in memory.

[1271] Step 3:

[1272] The device converts the audio data into text data.

[1273] The device activates its speech recognition engine (e.g., speech recognition software) and converts the captured audio data into text data. For example, the audio "What's the weather like today?" is converted into the text data "What's the weather like today?".

[1274] Step 4:

[1275] The device sends text data to the server.

[1276] The terminal uses its network connection to send the converted text data to the server. The text data "What's the weather like today?" is sent to the server as an HTTP request.

[1277] Step 5:

[1278] The server receives text data.

[1279] The server parses the received HTTP request and retrieves the text data it contains. The server receives the text data "What's the weather like today?".

[1280] Step 6:

[1281] The server generates responses using an AI model.

[1282] The server inputs the received text data into a generative AI model (e.g., a natural language processing model). The AI ​​model analyzes the text "What's the weather like today?" and generates the response "It's sunny today."

[1283] Step 7:

[1284] The server sends the response text data to the terminal.

[1285] The server creates an HTTP response to send the generated response text data back to the terminal. The text data "It's sunny today." is sent to the terminal.

[1286] Step 8:

[1287] The device converts the received response text data into audio data.

[1288] The terminal activates a speech synthesis engine to convert the received text data into speech. The speech synthesis engine converts the text "It's sunny today." into speech data.

[1289] Step 9:

[1290] The device plays audio data.

[1291] The device plays the generated audio data through its speaker. The user can hear the message "It's sunny today." coming from the speaker.

[1292] In this way, this system contributes to improving the quality of life for the elderly by providing intuitive and rapid responses to users' questions.

[1293] (Example 1)

[1294] 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".

[1295] In modern society, it is important to provide means for elderly people to engage in daily conversations without becoming isolated. Elderly people often have difficulty with youthful conversations and access to information, which increases the risk of dementia. This invention aims to solve these problems and provide a conversation support system that is easy for elderly people to use.

[1296] 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.

[1297] In this invention, the server includes means for capturing audio, means for converting the captured audio data into text data, means for transmitting the converted text data to the server via a network, means for generating a response based on the text data received by the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into audio data, and means for playing back the converted audio data. This enables elderly people to interact with the system using their own voice and obtain information and responses in real time.

[1298] "Means for capturing speech" refers to a device or technology that converts a user's speech into digital speech data.

[1299] "Means for converting audio data into text data" refers to a technology or device that analyzes captured audio data and generates a corresponding string of characters.

[1300] "Means for sending text data to a server over a network" refers to a device or technology that sends generated text data to a remote server using a communication protocol.

[1301] "Means for generating a response based on text data received on the server" refers to a device or technology that generates an appropriate response based on text data received on the server side.

[1302] "Means for sending generated response text data to a terminal" refers to a device or technology that sends the response generated from the server back to the terminal via the network.

[1303] "Means for converting received response text data into audio data" refers to a device or technology that converts received text data back into audio data.

[1304] "Means for playing back converted audio data" refers to a device or technology that plays back converted audio data as physical sound.

[1305] A "generative AI model" is an artificial intelligence model that generates responses in natural language based on input data.

[1306] A "prompt statement" is an input statement given to a generative AI model, and it is an instruction statement that the model uses to generate a response.

[1307] This invention relates to a system that captures user voice data, converts it into text data, and sends it to a server via a network. The server generates and sends back a response, and the terminal converts that response back into voice data and plays it back. This system aims to provide a conversational support system that elderly people can use on a daily basis, thereby reducing the risk of isolation and dementia.

[1308] The system configuration includes terminals and servers. Specific hardware and software examples include terminals equipped with a microphone, speaker, speech recognition engine (e.g., Google Cloud Speech-to-Text API), and speech synthesis engine (e.g., Amazon Polly API). The server is equipped with a generative AI model (e.g., OpenAI GPT-3) that generates responses based on text data.

[1309] Audio Capture

[1310] The device captures the user's voice. Specifically, the microphone built into the device operates and captures the user's voice input as digital data. For example, if the user says, "What's the weather like today?", the device's microphone picks up the voice and converts it into a digital signal.

[1311] Speech recognition

[1312] The device converts the captured audio data into text data. This process uses a speech recognition engine (e.g., Google Cloud Speech-to-Text API). For example, the captured audio "What's the weather like today?" is converted into the text "What's the weather like today?" by speech recognition technology.

[1313] Sending text data

[1314] The terminal sends the converted text data to the server. Specifically, it sends the text data to the server using an HTTP request.

[1315] Response generation

[1316] The server generates a response based on the text data it receives. A generative AI model (e.g., OpenAI GPT-3) is used. For example, in response to the text "What's the weather like today?", the generative AI model generates the response "It's sunny today."

[1317] Send response data

[1318] The server sends the generated response data to the terminal. Specifically, the response data is returned to the terminal as an HTTP response.

[1319] Speech synthesis

[1320] The device calls a speech synthesis engine (e.g., Amazon Polly API) to convert the received response text data into speech data. For example, the text "It's sunny today." is converted into the corresponding speech data.

[1321] Audio playback

[1322] The device's speaker plays audio data and responds to the user. For example, the speaker might play the message, "It's sunny today."

[1323] Specific Scenario Examples

[1324] 1. The user speaks to the device saying, "What day is it today?" The spoken words are captured by the device's microphone.

[1325] 2. The device captures the user's voice as digital data and converts it into text, "What day is it today?", using a speech recognition engine.

[1326] 3. The terminal sends the converted text data to the server as an HTTP POST request.

[1327] 4. The server uses a generative AI model (e.g., OpenAI GPT-3) to generate the response "Today is October 5th."

[1328] 5. The server sends the generated response data to the terminal as an HTTP response.

[1329] 6. The terminal converts the received response into audio data, "Today is October 5th," using a speech synthesis engine (e.g., Amazon Polly API).

[1330] 7. The device's speaker plays a voice message saying, "Today is October 5th," informing the user.

[1331] Example of a prompt:

[1332] "What's the weather like today?"

[1333] "What day is it today?"

[1334] "Please tell me today's news."

[1335] Thus, the present invention provides a system that makes it easier for elderly people to resolve questions in their daily lives, contributing to the maintenance of cognitive function and improvement of quality of life.

[1336] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1337] Step 1:

[1338] Audio Capture

[1339] The device captures the user's voice. Specifically, the microphone built into the device operates and captures the user's speech as digital data. For example, if the user says, "What's the weather like today?", the device's microphone picks up the voice and converts it into a digital signal.

[1340] Input: User's voice

[1341] Output: Digital audio data (audio waveform data)

[1342] Step 2:

[1343] Speech recognition

[1344] The device converts the audio data into text data. The captured audio data is sent to a speech recognition engine (e.g., Google Cloud Speech-to-Text API) for analysis. For example, the speech recognition engine analyzes the digital audio and converts it into text such as "What's the weather like today?". This process involves noise filtering and speech feature extraction.

[1345] Input: Digital audio data

[1346] Output: Recognized text data ("What's the weather like today?")

[1347] Step 3:

[1348] Sending text data

[1349] The device sends text data to the server. The device's application constructs an HTTP POST request and includes the text data as a payload. Specifically, the converted text data is sent to the server as an HTTP request.

[1350] Input: Recognized text data

[1351] Output: HTTP POST request (including text data)

[1352] Step 4:

[1353] Response generation

[1354] The server generates a response based on the text data it receives. Specifically, the server inputs text data into a generative AI model (e.g., OpenAI GPT-3) to generate an appropriate response. For example, in response to the text "What's the weather like today?", the generative AI model generates the response "It's sunny today." This process involves natural language processing and contextual analysis.

[1355] Input: HTTP POST request (including text data)

[1356] Output: Generated response text data ("It's sunny today.")

[1357] Step 5:

[1358] Send response data

[1359] The server sends the generated response data to the terminal. The server constructs an HTTP response, including the generated response text data as the payload. This response reaches the terminal via the internet.

[1360] Input: Generated response text data

[1361] Output: HTTP response (including response text data)

[1362] Step 6:

[1363] Speech synthesis

[1364] The device calls a speech synthesis engine (e.g., Amazon Polly API) to convert the received response text data into speech data. The device's application sends the text data to the speech synthesis API and receives the speech data returned by the API. This speech data is saved on the device as a corresponding audio file.

[1365] Input: Received response text data

[1366] Output: Synthesized audio data (audio file)

[1367] Step 7:

[1368] Audio playback

[1369] The device's speaker plays audio data and responds to the user. The device's audio playback function reads the audio file and plays the message "It's sunny today." through the speaker. During this process, volume adjustment and playback timing control are also performed.

[1370] Input: Synthesized audio data (audio file)

[1371] Output: Audio playback from the speaker ("It's sunny today.")

[1372] (Application Example 1)

[1373] 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".

[1374] In traditional factory operations, a major challenge was the time and effort required for workers to perform complex procedures and check situations. Furthermore, it was difficult for workers to give instructions using voice commands without using their hands and to receive real-time responses. This resulted in decreased work efficiency and an increased risk of errors and accidents. A simple and effective support system was particularly needed for elderly workers.

[1375] 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.

[1376] In this invention, the server includes means for capturing the user's voice, means for converting the captured voice data into text data, means for transmitting the converted text data to the server via a network, means for generating a response based on the text data received by the server, means for transmitting the generated response text data to a terminal, means for converting the received response text data into voice data, means for playing back the converted voice data, means for capturing the worker's voice using smart glasses and converting work instructions into text data using a voice recognition engine, means for converting the generated response text data into voice data using a speech synthesis engine, and means for playing back the voice data from the smart glasses' speaker. This makes it possible for workers to easily give instructions using only their voice without using their hands and to obtain appropriate responses in real time.

[1377] A "user" refers to a person who utilizes the system of the present invention, and who wears smart glasses at the work site and gives instructions to the robot or system using voice.

[1378] "Means of capturing audio" refers to devices such as microphones used to capture the voice spoken by a user as digital data.

[1379] "Means of converting audio data into text data" refers to a speech recognition engine that analyzes captured audio data and converts the words and phrases within it into text format.

[1380] "Means of sending text data to a server over a network" refers to communication devices or software used to send converted text data to a remote server using the internet or other networks.

[1381] "Means for generating a response based on text data received by the server" refers to generative AI models or response generation algorithms that analyze the text data received by the server and generate an appropriate response.

[1382] "Means for sending generated response text data to the terminal" refers to a communication function for sending text data generated on the server back to the user's terminal via the network.

[1383] "Means for converting received response text data into speech data" refers to the speech synthesis engine used by the terminal to convert received text data into speech.

[1384] "Means for playing back converted audio data" refers to speakers or other sound devices used by the terminal to play back the converted audio data in a format that can be heard by the user.

[1385] "Smart glasses" refer to wearable devices equipped with the ability to capture, play back, and perform other data processing.

[1386] "Means of converting work instructions into text data" refers to speech recognition technology that recognizes work instructions spoken by a user into smart glasses and converts them into text format.

[1387] A "speech synthesis engine" refers to software or algorithms used to convert text data into natural-sounding speech.

[1388] A "speaker" refers to an audio device that converts audio signals into sound waves and reproduces them in a format that can be heard by the user.

[1389] This invention relates to a voice interaction system using smart glasses for use in factories. This system captures the voice of workers and enables real-time instruction, aiming to improve work efficiency and safety. Specific embodiments for carrying out this invention are described below.

[1390] 1. System Configuration

[1391] The system of this invention consists of smart glasses worn by the user (worker), a speech recognition engine, a server, a generative AI model, a speech synthesis engine, and communication means.

[1392] 2. Smart Glasses

[1393] Smart glasses have a built-in microphone that captures the user's voice. This voice data is initially processed within the smart glasses and then sent to the voice recognition engine.

[1394] 3. Speech Recognition Engine

[1395] The speech recognition engine converts captured audio data into text data. Specifically, it uses technologies such as Google Cloud Speech-to-Text and Amazon Transcribe. This engine analyzes the received audio data and generates the corresponding string.

[1396] 4. Servers and Generative AI Models

[1397] The server receives text data sent from the speech recognition engine and generates an appropriate response using a generative AI model (e.g., GPT-4). An example of a prompt is shown below.

[1398] Example of a prompt:

[1399] Please generate the appropriate response to the following sentence: 'Check the placement of the parts'

[1400] 5. Means of communication

[1401] Text data and response data are sent and received between smart glasses and servers via the internet or other networks. Therefore, communication capabilities using HTTP requests and responses are required.

[1402] 6. Speech synthesis engine

[1403] The received response text data is converted into speech data by a speech synthesis engine (e.g., Amazon Polly or Google Text-to-Speech).

[1404] 7. Speakers

[1405] The converted audio data is played back through the speaker built into the smart glasses, and the user receives an audio response. For example, a message such as "The component placement is normal" might be played.

[1406] Specific example

[1407] When a user speaks to the smart glasses, "How is the engine doing?", the smart glasses capture the voice and convert it into text data, "How is the engine doing?", using a speech recognition engine. The converted text is sent to a server, where a generative AI model (GPT-4) generates the response, "The engine is doing well." The response is sent back to the smart glasses, converted into audio data by a speech synthesis engine, and played back as "The engine is doing well." from the smart glasses' speaker.

[1408] The above describes a specific embodiment for carrying out the present invention. With this system, workers can easily give instructions by voice without using their hands and receive appropriate responses in real time.

[1409] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1410] Step 1:

[1411] The user speaks into the smart glasses (e.g., "How is the engine?"). The smart glasses' built-in microphone captures the user's voice and records it as digital audio data.

[1412] Input: User's voice

[1413] Output: Digital audio data

[1414] Step 2:

[1415] The captured digital audio data is sent to the speech recognition engine in the smart glasses. The speech recognition engine (e.g., Google Cloud Speech-to-Text) analyzes the digital audio and converts it into corresponding text data (e.g., "How is the engine doing?").

[1416] Input: Digital audio data

[1417] Output: Text data

[1418] Step 3:

[1419] Text data is sent to the server via the network. Communication takes place through HTTP requests, and the text data reaches the server.

[1420] Input: Text data

[1421] Output: HTTP request to the server

[1422] Step 4:

[1423] The server processes the received text data and passes it to a generative AI model (e.g., GPT-4). The generative AI model generates an appropriate response (e.g., "The engine is functioning normally.") based on the text data.

[1424] Input: Text data

[1425] Output: Response text data

[1426] Step 5:

[1427] The server sends the generated response text data back to the smart glasses over the network. The response text data is sent as an HTTP response.

[1428] Input: Response text data

[1429] Output: HTTP response to the terminal (smart glasses)

[1430] Step 6:

[1431] The smart glasses pass the received response text data to a speech synthesis engine (e.g., Amazon Polly) and convert it into speech data.

[1432] Input: Response text data

[1433] Output: Audio data

[1434] Step 7:

[1435] The speaker in the smart glasses plays audio data and responds to the user verbally (e.g., "The engine is functioning normally.").

[1436] Input: Audio data

[1437] Output: Voice response to the user

[1438] 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.

[1439] This invention relates to a system that captures a user's voice, converts it into text data, sends it to a server, and the server generates a response which is then sent back to the terminal. This system further aims to provide more appropriate and personalized responses by incorporating an emotion engine that recognizes the user's emotions. This system provides conversational support in a form that elderly people can use on a daily basis, aiming to reduce the risk of isolation and dementia.

[1440] Program processing details

[1441] 1. Audio Capture

[1442] The device captures the user's voice.

[1443] The microphone built into the device activates and receives voice input from the user. For example, if the user says, "What's the weather like today?", the device captures this voice as digital data. This digital data is stored in memory and sent to subsequent voice recognition and emotion recognition processes.

[1444] 2. Speech Recognition and Emotion Recognition

[1445] The device converts voice data into text data and recognizes emotions.

[1446] The captured audio data is sent to a speech recognition engine and converted into text data. For example, the voice "What's the weather like today?" is converted into the text "What's the weather like today?". At the same time, an emotion engine analyzes the audio data to identify the user's emotions (e.g., joy, anger, sadness, etc.).

[1447] 3. Sending text data and sentiment data

[1448] The device sends text data and sentiment data to the server.

[1449] The converted text data and recognized sentiment information are sent to the server over the network. For example, the text data "What's the weather like today?" and sentiment data (e.g., neutral) are sent to the server as an HTTP request.

[1450] 4. Response generation

[1451] The server uses an AI model to generate responses and adjusts them based on emotions.

[1452] The server generates a response based on the received text data and sentiment data. Specifically, it uses a generative AI model to generate an appropriate response to "What's the weather like today?" such as "It's sunny today." Furthermore, it adjusts the tone and content of the response based on the sentiment data. For example, if the user seems sad, it will generate a response such as "It's sunny today. I hope that makes you feel a little better."

[1453] 5. Send response data

[1454] The server sends the generated response data to the terminal.

[1455] The response data generated by the server is then sent back to the terminal via the network. For example, the adjusted response text data, "It's sunny today. I hope this makes you feel a little better," is sent to the terminal as an HTTP response.

[1456] 6. Speech synthesis

[1457] The device converts received text data into audio data.

[1458] The terminal activates a speech synthesis engine to convert the received response text data into speech. The speech synthesis engine converts the received text "It's sunny today. I hope you feel a little better." into speech data.

[1459] 7. Audio Playback

[1460] The device plays audio data.

[1461] The device's speaker plays audio data and responds to the user. For example, it might play a message like, "It's sunny today. I hope it makes you feel a little better."

[1462] Specific Scenario Examples

[1463] 1. The user asks the device, "What day is it today?"

[1464] 2. The device captures the user's voice and converts it into text, "What day is it today?", using a speech recognition engine. Simultaneously, an emotion engine analyzes the user's tone and speed of voice and recognizes that the user is slightly tired.

[1465] 3. The converted text and sentiment data are sent to the server.

[1466] 4. The server uses a generative AI model to generate the response, "Today is October 5th," but adjusts the response to the user's fatigue level, changing it to, "Today is October 5th. How about taking a short break?"

[1467] 5. The server sends the adjusted response to the terminal.

[1468] 6. The terminal receives the response and converts it into speech data using a speech synthesis engine.

[1469] 7. A voice message plays from the device's speaker saying, "Today is October 5th. How about taking a short break?"

[1470] Thus, by providing responses that correspond to the user's emotional state, the present invention improves the user experience and contributes to reducing feelings of isolation and stress among the elderly.

[1471] The following describes the processing flow.

[1472] Step 1:

[1473] The user speaks to the device.

[1474] The user speaks and asks a question to the device. For example, if the user says, "What's the weather like today?", the spoken content is captured as audio data.

[1475] Step 2:

[1476] The device captures the user's voice.

[1477] The microphone built into the device activates and captures the user's speech as an audio signal. The captured audio signal is converted into digital audio data and recorded in memory.

[1478] Step 3:

[1479] The device converts the audio data into text data.

[1480] The device's speech recognition engine analyzes the captured audio data and converts it into corresponding text data. For example, the audio data "What's the weather like today?" is converted into the text data "What's the weather like today?".

[1481] Step 4:

[1482] The device converts voice data into emotion data.

[1483] Simultaneously, the device's emotion engine analyzes the voice data to recognize the user's emotions. For example, it identifies that the user is feeling neutral based on factors such as voice tone, word choice, and volume.

[1484] Step 5:

[1485] The device sends text data and sentiment data to the server.

[1486] The device sends the converted text data and recognized sentiment data to the server via the network connection. For example, an HTTP request sends the text data "What's the weather like today?" and sentiment data (neutral) to the server.

[1487] Step 6:

[1488] The server receives text data and sentiment data.

[1489] The server parses the received HTTP request and retrieves text data and sentiment data. The server receives the text data "What's the weather like today?" and sentiment data (neutral).

[1490] Step 7:

[1491] The server generates responses using an AI model.

[1492] The server uses a generative AI model to generate a response based on the received text data. For example, in response to "What's the weather like today?", it generates the response "It's sunny today."

[1493] Step 8:

[1494] The server adjusts its response based on emotion.

[1495] The server then adjusts the content and tone of its response based on the recognized sentiment data. For example, if the user is perceived as having a neutral sentiment, it may respond with "It's sunny today," or it may adjust it to be a little more detailed, such as "It's sunny today. It's a nice day to go out."

[1496] Step 9:

[1497] The server sends the response text data to the terminal.

[1498] The server creates an HTTP response containing the adjusted response and sends it to the terminal. For example, the text data "It's sunny today. It's a good day to go outside." is sent to the terminal.

[1499] Step 10:

[1500] The device converts the received response text data into audio data.

[1501] The device sends the received text data to the speech synthesis engine, which converts it into speech data. The speech synthesis engine converts the text "It's sunny today. It's a good day to go outside." into the corresponding speech data.

[1502] Step 11:

[1503] The device plays audio data.

[1504] The device's speaker plays the generated audio data and responds to the user. For example, the speaker might play a voice message saying, "It's sunny today. It's a good day to go outside."

[1505] This system provides appropriate responses tailored to the user's emotions, thereby improving the user experience. This invention is particularly expected to alleviate feelings of isolation among the elderly and promote communication.

[1506] (Example 2)

[1507] 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".

[1508] In recent years, the increasing sense of isolation and dementia risk among the elderly has attracted attention as a social problem. In response, there is a growing need for dialogue support systems that the elderly can use on a daily basis. However, existing dialogue systems provide uniform responses without considering the user's emotions, making it difficult to provide appropriate support tailored to individual situations. Therefore, there is a need to develop dialogue support systems that provide personalized responses that respond to the user's emotions.

[1509] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for generating a response based on received text data and emotion data and adjusting it according to emotion, means for transmitting the generated response text data to a terminal, and means for converting the received response text data into voice data. This enables personalized responses that correspond to the user's emotional state.

[1510] A "user" refers to a person who uses a system.

[1511] "Means of capturing audio" refers to devices or processes for converting user speech into digital data.

[1512] "Means of converting audio data into text data" refers to the process or technology of analyzing audio signals and converting them into textual information.

[1513] "Emotional data" refers to emotional information extracted from the user's voice and behavior.

[1514] "Means of transmission over a network" refers to the processes and technologies used to transmit data over the internet or other communication networks.

[1515] A "server" refers to a computer system that performs data processing and provides services over a network.

[1516] "Means for generating a response" refers to the process or technology for generating an appropriate response based on input data.

[1517] A "generative AI model" refers to a model or algorithm that uses artificial intelligence technology to generate text or decisions.

[1518] A "terminal" refers to a computer device or equipment that a user directly operates.

[1519] "Means of playing audio data" refers to the process or technology of playing audio signals through output devices such as speakers.

[1520] "Acoustic acquisition device" refers to hardware or a device used to capture sound.

[1521] A "speech recognition device" refers to a technology or device that analyzes speech data and converts it into text data.

[1522] An "emotion recognition device" refers to technology or equipment that analyzes and identifies emotions from a user's voice and behavior.

[1523] "Communication equipment" refers to devices and technologies used for sending and receiving data.

[1524] This invention provides a system that allows users to obtain information and receive appropriate responses through voice interaction. The system aims to provide a more personalized experience by offering responses that take into account the user's emotional state.

[1525] The system consists of three main components: the user, the terminal, and the server. The user speaks to the terminal using their voice. A microphone built into the terminal captures the user's voice and converts it into digital data. This uses a microphone as an acoustic data acquisition device. For example, if the user says, "What's the weather like today?", the terminal's microphone captures this voice and converts it into digital data.

[1526] The device converts the acquired audio data into text data using a speech recognition engine (e.g., Google Cloud Speech-to-Text). This speech recognition process converts the audio "What's the weather like today?" into the text data "What's the weather like today?". Simultaneously, an emotion recognition device (e.g., Affectiva's SDK) is used to extract emotion data from the user's voice. For example, based on the tone and speed of the user's voice, it is identified that the user is in a neutral emotional state.

[1527] The converted text data and sentiment data are sent from the terminal to the server via a communication device. Specifically, they are sent as an HTTP POST request. The request payload includes the text data "What's the weather like today?" and the sentiment data "Neutral".

[1528] The server generates a response based on the received text data and sentiment data. This uses a generative AI model (e.g., OpenAI's GPT-4). This model takes a prompt as input and generates an appropriate response. For example, if the server receives the prompt "The user asked 'What's the weather like today?' The sentiment is neutral," it will generate the response "It's sunny today." Then, taking the sentiment data into consideration, the response is adjusted to produce the final text "It's sunny today. I hope that makes you feel a little better."

[1529] The generated response text data is sent from the server to the terminal. This is also sent as an HTTP response. The terminal converts the received response text data into audio data using a speech synthesis engine (for example, Amazon Polly). Finally, the terminal's speaker plays the audio data and responds to the user. Specifically, the audio plays, "It's sunny today. I hope this makes you feel a little better."

[1530] Examples of prompt messages include the following:

[1531] "The user asked, 'What's the weather like today?' The emotion is neutral."

[1532] This system provides personalized responses tailored to the user's emotional state, resulting in a more natural and comfortable conversational experience. This is expected to contribute to reducing feelings of isolation among the elderly and lowering the risk of dementia.

[1533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1534] Step 1:

[1535] The user inputs voice.

[1536] The user speaks into the device. At this time, the user's voice is captured by the microphone. For example, the user says, "What's the weather like today?" The input is the user's voice, and the output is the digital audio data captured by the microphone.

[1537] Step 2:

[1538] The device converts the audio data into text data.

[1539] The device sends the captured audio data to a speech recognition engine (e.g., Google Cloud Speech-to-Text). This engine analyzes the audio data and converts it into text data. For example, the audio data "What's the weather like today?" is converted to the text "What's the weather like today?". The input is digital audio data, and the output is text data.

[1540] Step 3:

[1541] The device extracts emotional data from the voice data.

[1542] The device sends the captured audio data to an emotion recognition engine (e.g., Affectiva's SDK). This engine analyzes the voice tone, speed, and other parameters to identify emotion data. For example, a neutral tone would be recognized as "neutral." The input is digital audio data, and the output is emotion data.

[1543] Step 4:

[1544] The device sends text data and sentiment data to the server.

[1545] The converted text data and sentiment data are sent to the server via the terminal's communication device. Specifically, they are sent as an HTTP POST request. For example, the text "What's the weather like today?" and the sentiment "Neutral" are sent to the server. The input is text data and sentiment data, and the output is an HTTP request.

[1546] Step 5:

[1547] The server generates and adjusts the response.

[1548] Based on the text data and sentiment data received by the server, a prompt is input to a generative AI model (e.g., GPT-4) to generate a response. For example, if the prompt "The user asked 'What's the weather like today?' The sentiment is neutral." is input, the response "It's sunny today." is generated. Then, the response is adjusted considering the sentiment data, and the text "It's sunny today. I hope you feel a little better." is generated. The input is text data and sentiment data, and the output is the adjusted response text.

[1549] Step 6:

[1550] The server sends response data to the terminal.

[1551] The server sends the generated response text data to the terminal. This is also sent as an HTTP response. For example, the response text "It's sunny today. I hope this makes you feel a little better." is sent to the terminal. The input is the response text data, and the output is the HTTP response.

[1552] Step 7:

[1553] The device converts the response text data into speech data.

[1554] The device sends the received response text data to a speech synthesis engine (e.g., Amazon Polly) and converts it into speech data. For example, the text data "It's sunny today. I hope that makes you feel a little better." is converted into speech data. The input is response text data, and the output is speech data.

[1555] Step 8:

[1556] The device plays audio data.

[1557] The device's speaker plays audio data. For example, the user might hear the voice say, "It's sunny today. I hope it makes you feel a little better." The input is audio data, and the output is the played audio.

[1558] By clearly indicating the specific actions, inputs, and outputs performed at each step, the overall flow of the system can be understood more concretely. The entire process, from the user inputting voice commands to the terminal playing back the response in audio, becomes clear.

[1559] (Application Example 2)

[1560] 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".

[1561] When elderly people use food delivery services, they often encounter problems such as complicated operation or a lack of emotionally sensitive responses, which reduces convenience and satisfaction. Furthermore, because elderly people are more likely to be isolated and at risk of dementia, they require support in their daily lives.

[1562] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for capturing the user's voice, means for converting the captured voice data into text data, means for recognizing the user's emotions based on the converted text data, means for transmitting the converted text data and recognized emotion information to the server via a network, means for generating a response based on the received text data and emotion information at the server and adjusting the generated response text data based on the emotion information, means for transmitting the adjusted response text data to a terminal, means for converting the received response text data into voice data, and means for playing back the converted voice data. As a result, elderly people can easily use the food delivery service through voice interaction and obtain appropriate responses according to their emotions, thereby improving the convenience and satisfaction of use and contributing to a reduction in feelings of isolation.

[1563] "Means for capturing user voice" refers to a device that acquires voice input from a user as digital data.

[1564] "Means for converting captured audio data into text data" refers to a device or software that analyzes audio data and converts it into corresponding textual information.

[1565] "Means for recognizing user emotions based on converted text data" refers to a device or software that identifies user emotions from text data and extracts them as emotional information.

[1566] "Means for transmitting converted text data and recognized sentiment information to a server via a network" refers to a device or software that transmits text data and sentiment information to a server via the internet or other communication networks.

[1567] "Means for generating a response based on received text data and sentiment information, and for adjusting the generated response text data based on sentiment information" refers to a device or software that generates an appropriate response based on data received on a server and modifies the content and expression of the response according to the user's sentiment.

[1568] "Means for sending adjusted response text data to a terminal" refers to a device or software that sends a response generated and adjusted on a server to a user's terminal via a network.

[1569] "Means for converting received response text data into audio data" refers to a device or software that outputs a response received as text data as audio data.

[1570] "Means for playing back converted audio data" refers to a device that plays back audio data through a speaker or similar device, allowing the user to listen to it.

[1571] A "generative AI model" is an algorithm or system that uses machine learning to generate responses from data.

[1572] A "prompt sentence" is the text input to a generative AI model, and it forms the basis for response generation.

[1573] A "sensing device" is a hardware device or sensor used to capture audio or other input data.

[1574] A "speech recognition engine" is software or hardware that converts speech into text in real time or from recorded data.

[1575] "Communication means" refers to the entire set of hardware and software used to send and receive data.

[1576] To implement this invention, a system will be constructed using the following hardware and software: a system that processes a series of operations from the user inputting voice into a terminal until an appropriate response is finally returned in voice.

[1577] Hardware to use

[1578] Terminal: This refers to devices that serve as the user interface, such as smartphones, smart glasses, and head-mounted displays.

[1579] Microphone: A sensing device used to capture the user's voice.

[1580] Server: A central processing unit that handles voice data conversion, emotion recognition, and response generation.

[1581] Speaker: An output device for playing back generated audio data.

[1582] Software to use

[1583] Speech recognition engine: Used to convert speech data into text data. For example, the Google Speech-to-Text API.

[1584] Emotion recognition engine: Used to identify user emotions from text data. For example, IBM Watson Tone Analyzer.

[1585] Generative AI models: Used to customize generated responses according to the user's emotions. For example, OpenAI GPT-3.

[1586] Speech synthesis engine: Used to generate speech data from text data. For example, Amazon Polly.

[1587] Processing flow

[1588] The device's microphone captures the user's voice, acquiring the digital data. Then, a speech recognition engine converts this digital data into text. The converted text data is sent to an emotion recognition engine, which identifies the user's emotions.

[1589] Text data and sentiment information are transmitted to a server via the network. The server uses a generative AI model based on the received data to generate an appropriate response. Next, the generated response is refined based on the sentiment information. The refined response text data is sent to the terminal and converted into speech data using a speech synthesis engine. Finally, the response is played back to the user through the terminal's speaker.

[1590] Specific example

[1591] When a user asks the device, "What do you recommend for lunch?", the audio is captured and converted into text data. The emotion recognition engine detects that the user's voice sounds a little tired. Based on this text data ("What do you recommend for lunch?") and the emotion data (a little tired), the server generates and sends the response, "Curry rice is recommended. It's easy to prepare." The device then converts this response back into speech and plays it through its speaker.

[1592] Examples of prompt statements that are input to the generative AI model are as follows:

[1593] User said: "What would you recommend for lunch?" (Emotion: A little tired). Generate a response.

[1594] By using such a system, elderly people can easily use food delivery services through voice interaction and receive appropriate responses that reflect their emotions.

[1595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1596] Step 1:

[1597] The user speaks into the device. The user says, "What do you recommend for lunch?" The device's microphone captures the user's voice and obtains it as digital audio data.

[1598] Step 2:

[1599] The device converts the captured audio data into text data. At this stage, a speech recognition engine (for example, Google Speech-to-Text API) converts the digital audio data into text data such as "What dishes would you recommend for lunch?". This process outputs the audio data as text information.

[1600] Step 3:

[1601] The terminal sends the converted text data to the emotion recognition engine. The emotion recognition engine (e.g., IBM Watson Tone Analyzer) analyzes the text data and identifies the user's emotion (in this case, slightly tired). The output of this step includes emotion data along with the text data.

[1602] Step 4:

[1603] The device sends the converted text data and recognized sentiment data to the server over the network. The transmitted data arrives at the server as an HTTP request. In this step, the input text data "What do you recommend for lunch?" and sentiment data (slightly tired) are sent.

[1604] Step 5:

[1605] The server generates a response based on the text and sentiment data it receives. The server uses a generative AI model (e.g., OpenAI GPT-3) to create a prompt: "User said: "What do you recommend for lunch?" (Emotion: A little tired). Generate a response." Based on this, it generates the response "Curry rice is recommended. It's easy to cook." In this step, the prompt is taken as input, an appropriate response is generated, and it is output as text data.

[1606] Step 6:

[1607] The server adjusts the generated response data based on the user's mood. The generated response text data is appropriately adjusted in tone, content, and other aspects based on the user's mood (e.g., slightly tired). For example, if the user is tired, the response tone will be set to a gentler one. This results in the final adjusted response text data being output.

[1608] Step 7:

[1609] The server sends the pre-arranged response text data to the terminal over the network. This also arrives at the terminal as an HTTP response. The output of this step is the pre-arranged response text data "Curry rice is recommended. It's easy to cook."

[1610] Step 8:

[1611] The terminal converts the received response text data into speech data. A speech synthesis engine (e.g., Amazon Polly) analyzes the text data and generates the corresponding speech data. In this step, text data is input and speech data is output.

[1612] Step 9:

[1613] The device's speaker plays the converted audio data. Finally, the device's speaker outputs the message, "Curry rice is recommended. It's easy to cook."

[1614] 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.

[1615] 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.

[1616] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] 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."

[1623] 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.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] The following is further disclosed regarding the embodiments described above.

[1636] (Claim 1)

[1637] A means of capturing the user's voice,

[1638] A means of converting captured audio data into text data,

[1639] A means for sending the converted text data to a server over a network,

[1640] A means for generating a response based on text data received by the server,

[1641] A means for sending the generated response text data to the terminal,

[1642] A means for converting received response text data into audio data,

[1643] A means of playing back the converted audio data,

[1644] A system that includes this.

[1645] (Claim 2)

[1646] The system according to claim 1, which creates generated response text data using a generative AI model.

[1647] (Claim 3)

[1648] A microphone that captures the user's voice data,

[1649] A speech recognition engine that converts captured audio data into text data,

[1650] A communication means for sending the converted text data to the server,

[1651] The system according to claim 1, comprising:

[1652] "Example 1"

[1653] (Claim 1)

[1654] A means of capturing audio,

[1655] A means of converting captured audio data into text data,

[1656] A means for sending the converted text data to a server over a network,

[1657] A means for generating a response based on text data received by the server,

[1658] A means for sending the generated response text data to the terminal,

[1659] A means for converting received response text data into audio data,

[1660] A means of playing back the converted audio data,

[1661] A system that includes this.

[1662] (Claim 2)

[1663] The system according to claim 1, which creates generated response text data using a generative AI model.

[1664] (Claim 3)

[1665] A speech recognition engine that includes a microphone and converts captured audio data into text data,

[1666] A communication means for sending the converted text data to the server,

[1667] The system according to claim 1, comprising means for a server to generate a response, send the response to a terminal, and for the terminal to convert it into audio data and play it back.

[1668] "Application Example 1"

[1669] (Claim 1)

[1670] A means of capturing the user's voice,

[1671] A means of converting captured audio data into text data,

[1672] A means for sending the converted text data to a server over a network,

[1673] A means for generating a response based on text data received by the server,

[1674] A means for sending the generated response text data to the terminal,

[1675] A means for converting received response text data into audio data,

[1676] A means of playing back the converted audio data,

[1677] A means of capturing the worker's voice using smart glasses and converting work instructions into text data using a speech recognition engine,

[1678] A means for converting the generated response text data into speech data using a speech synthesis engine,

[1679] A method for playing audio data from the speaker of smart glasses,

[1680] A system that includes this.

[1681] (Claim 2)

[1682] The system according to claim 1, which creates generated response text data using a generative AI model.

[1683] (Claim 3)

[1684] A microphone that captures the user's voice data,

[1685] A speech recognition engine that converts captured audio data into text data,

[1686] A communication means for sending the converted text data to the server,

[1687] A speech synthesis engine that converts the generated response text data into speech data,

[1688] The system according to claim 1, comprising:

[1689] "Example 2 of combining an emotion engine"

[1690] (Claim 1)

[1691] A means of capturing the user's voice,

[1692] A means of converting captured audio data into text data,

[1693] Means for transmitting converted text data and sentiment data to a server via a network,

[1694] A means for generating a response based on text data and sentiment data received on the server, and adjusting it according to the sentiment,

[1695] A means for sending the generated response text data to the terminal,

[1696] A means for converting received response text data into audio data,

[1697] A means of playing back the converted audio data,

[1698] A system that includes this.

[1699] (Claim 2)

[1700] The system according to claim 1, which creates generated response text data using a generative AI model and adjusts it taking sentiment data into consideration.

[1701] (Claim 3)

[1702] An acoustic acquisition device that captures the user's voice data,

[1703] A speech recognition device that converts captured audio data into text data,

[1704] An emotion recognition device that recognizes emotions in voice data,

[1705] A communication device that transmits converted text data and sentiment data to a server,

[1706] The system according to claim 1, comprising:

[1707] "Application example 2 when combining with an emotional engine"

[1708] (Claim 1)

[1709] A means of capturing the user's voice,

[1710] A means of converting captured audio data into text data,

[1711] A means of recognizing the user's emotions based on the converted text data,

[1712] Means for transmitting converted text data and recognized sentiment information to a server via a network,

[1713] A means for generating a response based on text data and sentiment information received on the server, and for adjusting the generated response text data based on sentiment information,

[1714] A means for sending the adjusted response text data to the terminal,

[1715] A means for converting received response text data into audio data,

[1716] A means of playing back the converted audio data,

[1717] A system that includes this.

[1718] (Claim 2)

[1719] The system according to claim 1, which creates generated response text data using a generative AI model and adjusts the prompt sentence based on sentiment information.

[1720] (Claim 3)

[1721] A sensing device that captures the user's voice data,

[1722] A speech recognition engine that converts captured audio data into text data,

[1723] A communication means for transmitting converted text data and recognized emotion information to a server,

[1724] The system according to claim 1, comprising: [Explanation of symbols]

[1725] 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. A means of capturing the user's voice, A means of converting captured audio data into text data, A means for sending the converted text data to a server over a network, A means for generating a response based on text data received by the server, A means for sending the generated response text data to the terminal, A means for converting received response 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 generated response text data is created using a generative AI model.

3. A microphone that captures the user's voice data, A speech recognition engine that converts captured audio data into text data, A communication means for sending the converted text data to the server, The system according to claim 1, comprising:

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