system

Abstract

Provide a system. 【Solution means】 Means for acquiring an audio signal, Means for converting the acquired audio signal into text data, Means for transmitting the converted text data to a server device via a communication network, Means for analyzing the text data in the server device and generating response data, Means for transmitting the generated response data to a client terminal device, Means for synthesizing the received response data into an audio signal, Means for playing back the synthesized audio signal, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] There is a need to develop a dedicated terminal for seamlessly integrating modern AI technology into daily life. However, existing systems have problems with communication stability and speech recognition efficiency, making it difficult for users to interact smoothly with AI. Furthermore, in a voice-based interface, it is required to maintain real-time performance while reducing communication load.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for acquiring an audio signal and converting it into text data, means for transmitting the text data to a server device via a communication network, means for analyzing the text data in the server device and generating response data, means for transmitting the generated response data to a client terminal device, means for synthesizing the received response data with an audio signal, and means for reproducing the synthesized audio signal. This system solves the problem by performing speech recognition and speech synthesis processing on dedicated hardware and using a mobile communication module to stabilize the communication network connection.

[0006] An "audio signal" is a signal obtained by electrically converting sound, and is used as input data for speech recognition and speech synthesis.

[0007] "Text data" refers to character information converted from audio signals, which is transmitted for analysis by a server device.

[0008] A "communication network" is an infrastructure for sending and receiving data between terminal devices and server devices, and it maintains connections efficiently using a dedicated protocol.

[0009] A "server device" is a computer system that analyzes received text data and generates appropriate response data using a generation AI model.

[0010] "Response data" refers to information generated by the server device, transmitted to the terminal device as text data, and then synthesized for voice output.

[0011] "Dedicated hardware" refers to a computing device designed for a specific purpose, and in this invention, it efficiently performs speech recognition and speech synthesis processing.

[0012] A "mobile communication module" is a module that uses wireless communication to access a communication network, and in this invention, it is used to improve the stability of communication. [Brief explanation of the drawing]

[0013] [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a system that uses a dedicated terminal device equipped with the function of interacting with AI using voice input. When a user speaks to the terminal, the terminal uses its built-in speech recognition system to convert the user's voice signal into text data. This speech recognition is performed on a dedicated hardware, an FPGA, and operates efficiently and in real time.

[0035] The generated text data is transmitted to the server via a communication network. A mobile communication module installed in the terminal is used for communication, and a protocol is implemented to maintain a stable connection. The server analyzes the received text data and utilizes an AI model to generate an appropriate response. Natural language processing techniques are used for processing, and data from external sources is referenced as needed.

[0036] The generated response data is transmitted from the server to the terminal via the communication network. The receiving terminal synthesizes this response data into speech and outputs it to the user as voice. Speech synthesis is also performed at high speed using an FPGA, resulting in a natural and smooth response. The terminal displays the obtained information on its screen, providing visual feedback as well.

[0037] As a concrete example, when a user speaks to the device saying, "Tell me the current time," the device recognizes the voice and sends it to the server as text. The server retrieves the current time and generates a response, for example, "It's 3:30 PM." This response is then sent back to the device and communicated to the user as voice. This allows the user to confirm the time both audibly and visually.

[0038] Thus, the present invention is a system that incorporates AI technology into daily life, enabling users to easily obtain information and receive practical support.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] When a user speaks into the microphone, the device captures the audio using a receiving device. The audio signal is digitized and subjected to noise reduction processing.

[0042] Step 2:

[0043] The FPGA installed in the device converts digitized audio signals into text data in real time. During this process, a speech recognition algorithm is applied to represent what is spoken as text.

[0044] Step 3:

[0045] The converted text data is compressed and efficiently transmitted to the server via the terminal's communication module. This ensures communication stability.

[0046] Step 4:

[0047] The server analyzes the received text data and generates an appropriate response using natural language processing techniques. A generative AI model supports this analysis and retrieves information from external data sources as needed.

[0048] Step 5:

[0049] The response data generated on the server is compressed again and sent to the terminal. The data is delivered quickly via the communication module.

[0050] Step 6:

[0051] The terminal uses an FPGA to synthesize the response data received from the server into an audio signal. The synthesized voice is then adjusted to enable high-quality audio output.

[0052] Step 7:

[0053] Synthesized voice is played to the user through the device's speaker. This allows the user to receive AI-generated responses in real time via voice.

[0054] Step 8:

[0055] The device visually displays the text obtained through speech recognition and the AI's responses on its screen. This provides feedback to the user in a way that complements the audio information.

[0056] (Example 1)

[0057] 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."

[0058] Currently, systems for efficiently converting voice information into text in real time, analyzing it with an information processing device, and generating appropriate responses are not sufficiently practical. Furthermore, communication stability and visual feedback of information are often lacking. This makes it difficult for users to smoothly obtain information and receive practical support.

[0059] 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.

[0060] In this invention, the server includes means for acquiring voice information, means for converting the acquired voice information into text data, and means for transmitting the converted text data to a data processing device via an information network. This enables the user to acquire information efficiently and stably in real time and to interact with it.

[0061] "Voice information" refers to the acquisition of voice signals uttered by the user as digital signals.

[0062] "Text data" refers to digital data obtained by converting audio information into a string of characters.

[0063] An "information network" refers to the network infrastructure used to send and receive data.

[0064] A "data processing device" is a device that analyzes received character data and generates response information.

[0065] A "communication terminal device" is a device that receives generated response information and provides it to the user.

[0066] A "dedicated computing device" is a computing unit designed to efficiently perform speech recognition and speech synthesis processing.

[0067] A "mobile communication device" is a device that stabilizes the connection to an information network using mobile communication technology.

[0068] "Means of visual display" refers to displays and display devices that provide information to users visually.

[0069] This invention relates to a system that uses a dedicated terminal device equipped with the function of interacting with AI using voice input. When a user speaks to this terminal, the terminal acquires voice information using a high-sensitivity microphone. This voice information is converted into text data in real time using a speech recognition system on an FPGA, which is a dedicated computing device. This conversion creates an environment in which users can easily input information using their voice.

[0070] The generated text data is transmitted via the mobile communication device in the terminal to a data processing server through the information network. The server analyzes this text data using natural language processing technology and generates response information using a generative AI model. By accessing external information sources, the server can provide the most appropriate and up-to-date response to the question.

[0071] The response information generated by the server is transmitted again to the communication terminal device via the information network. The terminal uses an FPGA to synthesize this response information into audio information and outputs it to the user as audio through a speaker. Furthermore, the terminal displays this information on a screen, providing the user with visual feedback, allowing them to confirm the operation using both audio and visual means.

[0072] As a concrete example, when a user speaks to their device saying, "What's the weather like today?", the device recognizes the speech, converts it to text, and sends it to the server. The server then refers to the latest weather information and generates a response such as, "It's sunny." This response is sent back to the device and provided to the user as audio. This allows the user to easily obtain weather information.

[0073] Examples of prompt messages include, "Terminal, tell me the current weather."

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user inputs voice information by speaking into the device. The device acquires this voice information using a high-sensitivity microphone. This acquired voice information is an analog signal and is first converted into a digital signal. Digital signal processing (DSP) technology is used for this process.

[0077] Step 2:

[0078] Digitized audio information is converted into text data using an FPGA, a dedicated computing device in the terminal. This process utilizes speech recognition technology, where speech patterns are analyzed and mapped to a language model. The input is a digital audio signal, and the output is the corresponding text data.

[0079] Step 3:

[0080] The generated character data is transmitted to the server via the information network through the terminal's mobile communication device. During this process, the data is compressed using a communication protocol to ensure efficient and error-free transmission to the server. The input is character data, and the output is the character data transmitted to the server.

[0081] Step 4:

[0082] The server analyzes the received text data. It interprets the meaning of the data using natural language processing techniques and generates appropriate response information using a generative AI model. This process can also refer to relevant external databases. The input is the text data received by the server, and the output is the generated response information.

[0083] Step 5:

[0084] The generated response information is sent back to the terminal from the server via the information network. This is done with data compression and error checking to ensure communication stability. The input is the generated response information, and the output is the response information sent to the terminal.

[0085] Step 6:

[0086] The terminal synthesizes the received response information into speech information using an FPGA. This speech synthesis employs algorithms to maintain the intonation and naturalness of the speech. The input is the received response information, and the output is the synthesized speech information.

[0087] Step 7:

[0088] The device plays synthesized audio information to the user through its speaker. Simultaneously, response information is displayed on the screen as text and images, allowing the user to visually confirm the information. The input consists of synthesized audio information and display data, while the output consists of audio playback from the speaker and display data.

[0089] (Application Example 1)

[0090] 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."

[0091] In autonomous vehicles, a challenge is to provide an interface that allows passengers to intuitively give and change route instructions. Conventional methods suffer from delays in recognizing and responding to voice commands, making it difficult to provide a comfortable and swift experience.

[0092] 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.

[0093] In this invention, the server includes a device for acquiring voice data, a device for converting the acquired voice data into text format, and a device for transmitting the converted text format to a computing device via a communication line. This enables passengers to specify routes in real time using their voice, and allows the autonomous vehicle to respond quickly.

[0094] A "device for acquiring audio data" is a device that electronically captures audio signals, such as human speech, and inputs them into a system.

[0095] A "device for converting to text format" is a processing device that analyzes acquired audio data and converts it into transcribed text data.

[0096] A "device that transmits data to a computing device via a communication line" is a communication device used to transfer converted text-formatted data to a remote computing device using a network.

[0097] A "device that analyzes text format and generates response data in a computing device" is a processing device that analyzes received text data using natural language processing technology and generates an appropriate response.

[0098] A "device that transmits to the user device" is a transmitting device that sends the generated response data to the appropriate output device via a communication line.

[0099] A "device for synthesizing audio data" is a device that converts text data into data that can be played back as audio based on the results of its analysis.

[0100] A "playback device" is a device that outputs synthesized audio data in a format audible to humans through an output device such as a speaker.

[0101] A "device that performs destination setting and route calculation via voice commands" is a control device for an autonomous vehicle that analyzes voice commands from passengers and uses them to set destinations and calculate optimal routes.

[0102] This invention relates to a voice interface system for autonomous vehicles that sets destinations and calculates routes in real time based on passenger voice commands. The system includes the following main components:

[0103] The server includes a device that acquires audio data, which obtains audio signals from microphones inside the vehicle. The terminal that acquires this audio data then converts it into text format using dedicated speech recognition software. Since dedicated hardware such as FPGAs is used for speech recognition, high-speed and highly accurate recognition is possible.

[0104] The acquired text data is transmitted to the server via a communication line. Within the server, this text data is analyzed by an AI model, and an appropriate response is generated using natural language processing techniques. This response includes instructions based on the optimal route to the destination and current traffic conditions.

[0105] The generated response data is sent back to the terminal and converted into an audio signal by a speech synthesis device. Dedicated speech synthesis hardware is used for speech synthesis, resulting in natural and smooth voice generation.

[0106] Users receive vehicle instructions through synthesized voice. Visual feedback is also provided via a display device, allowing for safe and secure vehicle use through both audio and visual interfaces.

[0107] For example, if a user says, "I want to go to a nearby cafe," the system will search for nearby cafes, calculate the shortest route, and guide the vehicle along that route. An example of a prompt used in this case would be, "Activate the in-car dialogue system to calculate the shortest route based on the user's voice command and coordinate with the autonomous driving system."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The terminal acquires voice data via the vehicle's microphone. The user specifies the destination by voice, and this voice signal is input as digital information from the microphone. The voice waveform is acquired as digital information.

[0111] Step 2:

[0112] The terminal uses speech recognition software mounted on an FPGA to convert the acquired speech waveform into text format. The speech waveform is provided as input, linguistic analysis and signal processing are performed, and user instructions in text format are output.

[0113] Step 3:

[0114] User instructions obtained in text format are sent to the server via a communication module. Stable data transfer was performed during transmission. Text data is input and transmitted to the remote server via network communication.

[0115] Step 4:

[0116] The server analyzes received text data using generative AI models and natural language processing techniques to generate appropriate responses. Upon receiving text instructions, the server compares them with a route search algorithm and an external database to output the optimal route information to the destination.

[0117] Step 5:

[0118] The generated response data is sent back from the server to the terminal. A communication protocol is used to transmit the data, and the response data is supplied to the vehicle's terminal as output.

[0119] Step 6:

[0120] The terminal performs speech synthesis using response data. An FPGA is used to convert the analysis results into speech, achieving fast and smooth speech. The synthesized speech output is generated and provided to the user via a speaker.

[0121] Step 7:

[0122] The user receives instructions via synthesized voice and confirms visual feedback on the display device. One such prompt is, "Activate the in-vehicle dialogue system to calculate the shortest route based on the user's voice instructions and coordinate with the autonomous driving system," with instructions displayed both verbally and on the screen.

[0123] 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.

[0124] In this invention, an emotion engine is integrated into a voice-based AI dialogue system to make the user interface more natural and effective. When a user speaks into the terminal, the voice signal is acquired and digitized. This digital voice is converted into text data by a speech recognition system running on an FPGA.

[0125] The converted text data is analyzed by an emotion engine, which evaluates the user's emotional state. The emotion engine analyzes the tone and manner of speaking and assigns emotion tags based on this analysis. These emotion tags indicate the type of emotion the user expressed (e.g., joy, sadness, anger).

[0126] The text data and sentiment tags sent to the server are analyzed by an AI model. The server uses this information to generate appropriate, context-sensitive response data. This response is adjusted in tone and content according to the sentiment tags, ensuring it matches the user's specific emotional state.

[0127] The generated response data is sent to the terminal, which then converts the response data into speech using a speech synthesis system. This speech synthesis is also performed on an FPGA, and an appropriate voice expression is selected based on the user's emotions, which have been recognized in advance. The synthesized voice is played back to the user through the speaker, and visual text is also displayed on the screen.

[0128] For example, if a user says, "I've been feeling down lately," the device converts this statement into text data, and the emotion engine tags it as "sadness." The server then considers this context and emotion to generate a response such as, "What's wrong? Tell me what happened." This response is then delivered to the user as synthesized speech in a gentle tone.

[0129] In this way, the present invention optimizes the AI's response to match the user's emotional state, providing a more personalized conversational experience.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user speaks into the device saying, "I've been feeling down lately." The device uses its voice input device to capture the user's voice and converts it into a digital signal.

[0133] Step 2:

[0134] The acquired audio signal is converted into text data in real time by a system that performs speech recognition on the terminal's FPGA. In this case, the content of the audio is transformed into text information such as "I've been feeling down lately."

[0135] Step 3:

[0136] The device uses an emotion engine to analyze the user's emotions from the acquired audio signal. Based on audio characteristics such as tone and volume, an emotion tag such as "sadness" is assigned.

[0137] Step 4:

[0138] The converted text data and assigned sentiment tags are sent to the server via a communication module. Here, data compression and protocol conversion are performed to ensure fast and reliable data transmission.

[0139] Step 5:

[0140] The server analyzes the received text data and emotion tags, and generates response data using an AI model. It selects a gentle expression corresponding to the emotion tag "sadness" and generates a refined response such as, "What's wrong? Please tell me what happened."

[0141] Step 6:

[0142] The response data generated by the server is sent back to the terminal. The terminal processes the received response data and inputs it into a system for synthesizing it into an audio signal.

[0143] Step 7:

[0144] The FPGA in the terminal synthesizes the response data with an appropriate voice tone, which is then played back to the user through the speaker. The synthesized voice is adjusted based on the emotional tone set on the server.

[0145] Step 8:

[0146] The device also displays the response as visual information on its screen. This allows the user to confirm the response by supplementing the audio information.

[0147] (Example 2)

[0148] 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".

[0149] Conventional voice dialogue systems often provide mechanical and emotionless responses, making it difficult for users to desire a more natural and emotionally resonant interface. Furthermore, responses that disregard emotions can lower user satisfaction and compromise the quality of communication. In addition, improving the efficiency of voice processing and ensuring connection stability remained challenges.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for analyzing text data and emotion tags and generating context-based response data, means for using a generative artificial intelligence model, and means for evaluating emotional states and assigning emotion tags. This enables natural and personalized dialogue that is attentive to the user's emotions.

[0152] An "audio signal" is information acquired as sound waves converted into digital data.

[0153] "Text data" refers to data in written form obtained by analyzing audio signals.

[0154] "Emotional state" refers to the user's psychological condition, as estimated through the analysis of voice and text.

[0155] An "emotion tag" is a label used to indicate an emotional state within a specific category (e.g., joy, sadness, anger, etc.).

[0156] A "communication network" refers to the network infrastructure used to send and receive data.

[0157] A "computing device" refers to a general-purpose electronic computing device such as a server or computer used for data processing and analysis.

[0158] A "generative artificial intelligence model" is a type of AI technology that generates appropriate responses based on input data.

[0159] "Context" refers to the background information and circumstances that help understand the meaning of the content of a conversation or text.

[0160] "Response data" refers to response information generated based on the input text and emotional state.

[0161] A "visual display device" refers to a display or monitor used to present information such as text and images to a user.

[0162] This invention is a voice dialogue system that makes the interaction between the user and the machine more natural and emotionally rich. The system begins by acquiring a voice signal and converting it into text data.

[0163] When a user speaks into the device, the device uses its built-in microphone to acquire the audio signal and digitizes it. The digitized audio signal is then converted into text data by a speech recognition engine running on an FPGA (Field-Programmable Gate Array).

[0164] The converted text data is analyzed by an emotion engine built into the device. This engine analyzes the tone, speed, and intonation of the voice to evaluate the user's emotional state. Based on this evaluation, emotion tags (e.g., joy, sadness, anger) are assigned.

[0165] Next, the device sends text data and sentiment tags to the server. The server analyzes the received data using a generative artificial intelligence model and generates contextually appropriate response data. The response data is adjusted to match the user's emotional state in terms of tone and content.

[0166] The generated response data is sent to the terminal, which uses a speech synthesis system to convert the data into speech. Speech synthesis is again performed on the FPGA, and an appropriate tone is selected according to the emotion tag. Finally, the synthesized speech is played back to the user through the speaker, and the text is also displayed on a visual display device.

[0167] For example, if a user says, "I'm really tired today," the device sends this statement to the server along with the emotion tag "tired." The server generates a gentle response such as, "You must be tired. Please take a short break," and this is conveyed to the user.

[0168] An example of a prompt message would be input to the generating AI model in the form of "Text: I'm very tired today. Emotion: Tired." In this way, the present invention can generate responses that match the user's emotions and provide a personalized conversational experience.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] When a user speaks into the device, the device uses its microphone to acquire the audio signal. The input is an analog audio signal, which the device converts into a digital signal. An AD converter is used to convert the analog audio signal into digital data, and the output is digital audio data.

[0172] Step 2:

[0173] The terminal passes the digitized audio signal to a speech recognition engine running on an FPGA. The input is digital audio data, and the speech recognition engine converts that audio into text. It analyzes the audio features using acoustic and language models and generates text data as output.

[0174] Step 3:

[0175] The device sends the converted text data to the emotion engine. The input is text data, and the emotion engine analyzes the tone and speed of the voice to evaluate the user's emotional state. It estimates the emotion using natural language processing technology and outputs an emotion tag (e.g., "tired," "joyful").

[0176] Step 4:

[0177] The terminal sends text data and sentiment tags to the server. The input consists of text data and sentiment tags, which the server analyzes using a generative AI model to generate response data. The generative AI model is input with the prompt "Text: [text data]. Sentiment: [sentiment tag]" and outputs response data based on the appropriate context.

[0178] Step 5:

[0179] The server sends the generated response data to the terminal. The terminal receives this data and inputs it into the speech synthesis system. The input is the response data, which the speech synthesis system converts into a speech signal. Using the speech synthesis engine, the system converts the text into speech, adjusts the tone based on sentiment tags, and generates speech data as output.

[0180] Step 6:

[0181] The device plays synthesized speech to the user through its speaker and also displays response text on a visual display. Input consists of speech and text data; the speaker outputs the speech data in real time, and the display shows the text. The user can receive both speech and visual information.

[0182] (Application Example 2)

[0183] 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 device 14 will be referred to as the "terminal."

[0184] In modern commercial settings, customer interaction is essential for a company's success. However, technologies that enable natural and effective face-to-face dialogue are limited, making it difficult to understand and respond appropriately to customer emotions. This hinders improvements in customer satisfaction and the maximization of sales opportunities.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for acquiring an audio signal, means for converting the audio signal into non-analog data, and means for analyzing the audio signal acquired from the user and evaluating their emotional state. This enables real-time analysis of the customer's emotions, adjustment of response data based on that analysis, and natural and effective dialogue.

[0187] An "audio signal" is input information used to record and process sound waveforms as electronic data.

[0188] "Non-analog data" refers to digital data converted from analog signals, and is a format suitable for information processing.

[0189] An "information network" is a communication network designed to efficiently transmit digital data.

[0190] An "information processing device" is a computer system used to analyze data and generate the necessary responses.

[0191] A "request terminal device" is a device that receives response data sent from a server and presents it to the user.

[0192] An "application-specific integrated circuit" is a specialized integrated circuit designed to efficiently perform a specific function.

[0193] A "mobile communication device" is a device that uses wireless technology to enable communication between devices while they are in motion.

[0194] "Emotional state" refers to the psychological state that is analyzed based on the information expressed by the user through voice.

[0195] "Response data" refers to response information generated based on user input, and is provided to the user in audio or visual format.

[0196] The system implementing this invention achieves natural interaction with the user through multi-stage processing. First, the terminal is equipped with a microphone for acquiring audio signals and a speech recognition system for converting audio signals into non-analog data. This system incorporates application-specific integrated circuits, enabling rapid data processing.

[0197] The converted non-analog data is transmitted to a server via a communication network. The server analyzes the received data and uses a generative AI model to evaluate the user's emotional state. The emotional state information is used to adjust the tone and content of the response data. During this process, emotional tags are assigned, and personalized responses are generated based on the user's emotions.

[0198] The generated response data is retransmitted to the requesting terminal device, where it is converted back into a speech signal by a speech synthesis system. This speech synthesis is controlled by an application-specific integrated circuit, which selects the appropriate speech representation to produce a more natural-sounding voice.

[0199] For example, if a user speaks to a store staff member through smart glasses and says, "I've been feeling down lately," the system will interpret that emotion as "sadness" and provide a gentle response. The response will be played back as audio, such as, "What's wrong? Please tell me what it is."

[0200] An example of a prompt for a generative AI model is, "Please show me an example of an AI model that analyzes voice data, generates customer emotion tags, and optimizes conversation content." Through this prompt, the system continues to learn to make interactions with users more natural and effective.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] When a user speaks into the terminal, the terminal acquires the audio signal using its microphone. A speech recognition system using application-specific integrated circuits is activated to convert the acquired audio signal into non-analog data. The input for this step is the user's voice, and the output is the digital data converted by speech recognition.

[0204] Step 2:

[0205] The terminal transmits the converted digital data to the server via a communication network. The server takes the received digital data as input and uses an emotion analysis engine to evaluate the user's emotional state. As part of the data processing, a generative AI model analyzes the intonation and rhythm of the voice, and an emotion tag is generated as output.

[0206] Step 3:

[0207] The server uses emotion tags to construct response data. The response data generation algorithm selects appropriate response content based on the prompt text and adjusts the tone of the response to match the emotion tag. The output of this step is a customized response that corresponds to the user's emotion.

[0208] Step 4:

[0209] The generated response data is retransmitted from the server to the requesting terminal device. The terminal activates the speech synthesis system and converts the digital response data into a speech signal. An application-specific integrated circuit selects and synthesizes the most natural-sounding speech representation. The input for this step is the digital response data from the server, and the output is the speech signal presented to the user.

[0210] Step 5:

[0211] The device plays synthesized audio signals through its speakers. This allows the user to hear natural responses that are adjusted based on emotion. Along with the audio, visual information may also be displayed on the screen. The output consists of the audio the user receives and, if necessary, the text displayed on the screen.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention relates to a system that uses a dedicated terminal device equipped with the function of interacting with AI using voice input. When a user speaks to the terminal, the terminal uses its built-in speech recognition system to convert the user's voice signal into text data. This speech recognition is performed on a dedicated hardware, an FPGA, and operates efficiently and in real time.

[0229] The generated text data is transmitted to the server via a communication network. A mobile communication module installed in the terminal is used for communication, and a protocol is implemented to maintain a stable connection. The server analyzes the received text data and utilizes an AI model to generate an appropriate response. Natural language processing techniques are used for processing, and data from external sources is referenced as needed.

[0230] The generated response data is transmitted from the server to the terminal via the communication network. The receiving terminal synthesizes this response data into speech and outputs it to the user as voice. Speech synthesis is also performed at high speed using an FPGA, resulting in a natural and smooth response. The terminal displays the obtained information on its screen, providing visual feedback as well.

[0231] As a concrete example, when a user speaks to the device saying, "Tell me the current time," the device recognizes the voice and sends it to the server as text. The server retrieves the current time and generates a response, for example, "It's 3:30 PM." This response is then sent back to the device and communicated to the user as voice. This allows the user to confirm the time both audibly and visually.

[0232] Thus, the present invention is a system that incorporates AI technology into daily life, enabling users to easily obtain information and receive practical support.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] When a user speaks into the microphone, the device captures the audio using a receiving device. The audio signal is digitized and subjected to noise reduction processing.

[0236] Step 2:

[0237] The FPGA installed in the device converts digitized audio signals into text data in real time. During this process, a speech recognition algorithm is applied to represent what is spoken as text.

[0238] Step 3:

[0239] The converted text data is compressed and efficiently transmitted to the server via the terminal's communication module. This ensures communication stability.

[0240] Step 4:

[0241] The server analyzes the received text data and generates an appropriate response using natural language processing techniques. A generative AI model supports this analysis and retrieves information from external data sources as needed.

[0242] Step 5:

[0243] The response data generated on the server is compressed again and sent to the terminal. The data is delivered quickly via the communication module.

[0244] Step 6:

[0245] The terminal uses an FPGA to synthesize the response data received from the server into an audio signal. The synthesized voice is then adjusted to enable high-quality audio output.

[0246] Step 7:

[0247] Synthesized voice is played to the user through the device's speaker. This allows the user to receive AI-generated responses in real time via voice.

[0248] Step 8:

[0249] The device visually displays the text obtained through speech recognition and the AI's responses on its screen. This provides feedback to the user in a way that complements the audio information.

[0250] (Example 1)

[0251] 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."

[0252] Currently, systems for efficiently converting voice information into text in real time, analyzing it with an information processing device, and generating appropriate responses are not sufficiently practical. Furthermore, communication stability and visual feedback of information are often lacking. This makes it difficult for users to smoothly obtain information and receive practical support.

[0253] 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.

[0254] In this invention, the server includes means for acquiring voice information, means for converting the acquired voice information into text data, and means for transmitting the converted text data to a data processing device via an information network. This enables the user to acquire information efficiently and stably in real time and to interact with it.

[0255] "Voice information" refers to the acquisition of voice signals uttered by the user as digital signals.

[0256] "Text data" refers to digital data obtained by converting audio information into a string of characters.

[0257] An "information network" refers to the network infrastructure used to send and receive data.

[0258] A "data processing device" is a device that analyzes received character data and generates response information.

[0259] A "communication terminal device" is a device that receives generated response information and provides it to the user.

[0260] A "dedicated computing device" is a computing unit designed to efficiently perform speech recognition and speech synthesis processing.

[0261] A "mobile communication device" is a device that stabilizes the connection to an information network using mobile communication technology.

[0262] "Means of visual display" refers to displays and display devices that provide information to users visually.

[0263] This invention relates to a system that uses a dedicated terminal device equipped with the function of interacting with AI using voice input. When a user speaks to this terminal, the terminal acquires voice information using a high-sensitivity microphone. This voice information is converted into text data in real time using a speech recognition system on an FPGA, which is a dedicated computing device. This conversion creates an environment in which users can easily input information using their voice.

[0264] The generated text data is transmitted via the mobile communication device in the terminal to a data processing server through the information network. The server analyzes this text data using natural language processing technology and generates response information using a generative AI model. By accessing external information sources, the server can provide the most appropriate and up-to-date response to the question.

[0265] The response information generated by the server is transmitted again to the communication terminal device via the information network. The terminal uses an FPGA to synthesize this response information into audio information and outputs it to the user as audio through a speaker. Furthermore, the terminal displays this information on a screen, providing the user with visual feedback, allowing them to confirm the operation using both audio and visual means.

[0266] As a concrete example, when a user speaks to their device saying, "What's the weather like today?", the device recognizes the speech, converts it to text, and sends it to the server. The server then refers to the latest weather information and generates a response such as, "It's sunny." This response is sent back to the device and provided to the user as audio. This allows the user to easily obtain weather information.

[0267] Examples of prompt messages include, "Terminal, tell me the current weather."

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user inputs voice information by speaking into the device. The device acquires this voice information using a high-sensitivity microphone. This acquired voice information is an analog signal and is first converted into a digital signal. Digital signal processing (DSP) technology is used for this process.

[0271] Step 2:

[0272] Digitized audio information is converted into text data using an FPGA, a dedicated computing device in the terminal. This process utilizes speech recognition technology, where speech patterns are analyzed and mapped to a language model. The input is a digital audio signal, and the output is the corresponding text data.

[0273] Step 3:

[0274] The generated character data is transmitted to the server via the information network through the terminal's mobile communication device. During this process, the data is compressed using a communication protocol to ensure efficient and error-free transmission to the server. The input is character data, and the output is the character data transmitted to the server.

[0275] Step 4:

[0276] The server analyzes the received character data. Using natural language processing technology, it interprets the meaning of the data and generates appropriate response information with a generative AI model. In this process, it is also possible to refer to relevant external databases. The input is the character data received by the server, and the output is the generated response information.

[0277] Step 5:

[0278] The generated response information is sent from the server back to the terminal via the information network. This is done while ensuring communication stability, with data compression and error checking. The input is the generated response information, and the output is the response information sent to the terminal.

[0279] Step 6:

[0280] The terminal synthesizes the received response information into voice information using an FPGA. For this voice synthesis, an algorithm for maintaining the intonation and naturalness of the voice is applied. The input is the received response information, and the output is the synthesized voice information.

[0281] Step 7:

[0282] The terminal plays the synthesized voice information back to the user through the speaker. At the same time, the response information is displayed as text or images on the display, allowing the user to visually confirm the information. The input is the synthesized voice information and the display data, and the output is the voice playback from the speaker and the display on the screen.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In an autonomous vehicle, it is an issue to provide an interface that allows passengers to intuitively give route instructions and make changes. In conventional methods, there are delays in voice instruction recognition and response, making it difficult to provide a comfortable and quick experience.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes a device for acquiring voice data, a device for converting the acquired voice data into text format, and a device for transmitting the converted text format to a computing device via a communication line. This enables a passenger to specify a route in real time using voice and the autonomous vehicle to respond quickly.

[0288] The "device for acquiring voice data" is a device for electronically capturing a voice signal such as a human voice and inputting it into the system.

[0289] The "device for converting to text format" is a processing device for analyzing the acquired voice data and changing it into text data in a characterized form.

[0290] The "device for transmitting to a computing device via a communication line" is a communication device for transferring the converted text format data to a remote computing device using a network.

[0291] The "device for analyzing the text format and generating response data in a computing device" is a processing device for analyzing the received text data using natural language processing technology and generating an appropriate response.

[0292] The "device for transmitting to a utilization device" is a transmitting device for transmitting the generated response data to an appropriate output device via a communication line.

[0293] A "device for synthesizing audio data" is a device that converts text data into data that can be played back as audio based on the results of its analysis.

[0294] A "playback device" is a device that outputs synthesized audio data in a format audible to humans through an output device such as a speaker.

[0295] A "device that performs destination setting and route calculation via voice commands" is a control device for an autonomous vehicle that analyzes voice commands from passengers and uses them to set destinations and calculate optimal routes.

[0296] This invention relates to a voice interface system for autonomous vehicles that sets destinations and calculates routes in real time based on passenger voice commands. The system includes the following main components:

[0297] The server includes a device that acquires audio data, which obtains audio signals from microphones inside the vehicle. The terminal that acquires this audio data then converts it into text format using dedicated speech recognition software. Since dedicated hardware such as FPGAs is used for speech recognition, high-speed and highly accurate recognition is possible.

[0298] The acquired text data is transmitted to the server via a communication line. Within the server, this text data is analyzed by an AI model, and an appropriate response is generated using natural language processing techniques. This response includes instructions based on the optimal route to the destination and current traffic conditions.

[0299] The generated response data is sent back to the terminal and converted into an audio signal by a speech synthesis device. Dedicated speech synthesis hardware is used for speech synthesis, resulting in natural and smooth voice generation.

[0300] The user receives vehicle instructions through the synthesized voice. Also, visual feedback is provided through the display device, so the user can use the vehicle with confidence through both audio and visual interfaces.

[0301] For example, when the user says "I want to go to a nearby café", the system searches for nearby cafés, calculates the shortest route, and guides the vehicle along that route. An example of the prompt text used in this case is "Activate the in-vehicle dialogue system, calculate the shortest route based on the user's voice instructions, and cooperate with the autonomous driving system."

[0302] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0303] Step 1:

[0304] The terminal acquires voice data via the microphone in the vehicle. The user specifies the destination by voice, and the voice signal is input from the microphone as digital information. The voice waveform is acquired as digital information.

[0305] Step 2:

[0306] The terminal uses the voice recognition software installed on the FPGA to convert the acquired voice waveform into text format. The voice waveform is given as input, undergoes linguistic analysis and signal processing, and a user instruction in text format is output.

[0307] Step 3:

[0308] The user instruction obtained in text format is transmitted to the server via the communication module. During transmission, stable data transfer is performed. The text data is input and transmitted to the remote server through network communication.

[0309] Step 4:

[0310] The server analyzes received text data using generative AI models and natural language processing techniques to generate appropriate responses. Upon receiving text instructions, the server compares them with a route search algorithm and an external database to output the optimal route information to the destination.

[0311] Step 5:

[0312] The generated response data is sent back from the server to the terminal. A communication protocol is used to transmit the data, and the response data is supplied to the vehicle's terminal as output.

[0313] Step 6:

[0314] The terminal performs speech synthesis using response data. An FPGA is used to convert the analysis results into speech, achieving fast and smooth speech. The synthesized speech output is generated and provided to the user via a speaker.

[0315] Step 7:

[0316] The user receives instructions via synthesized voice and confirms visual feedback on the display device. One such prompt is, "Activate the in-vehicle dialogue system to calculate the shortest route based on the user's voice instructions and coordinate with the autonomous driving system," with instructions displayed both verbally and on the screen.

[0317] 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.

[0318] In this invention, an emotion engine is integrated into a voice-based AI dialogue system to make the user interface more natural and effective. When a user speaks into the terminal, the voice signal is acquired and digitized. This digital voice is converted into text data by a speech recognition system running on an FPGA.

[0319] The converted text data is analyzed by an emotion engine, which evaluates the user's emotional state. The emotion engine analyzes the tone and manner of speaking and assigns emotion tags based on this analysis. These emotion tags indicate the type of emotion the user expressed (e.g., joy, sadness, anger).

[0320] The text data and sentiment tags sent to the server are analyzed by an AI model. The server uses this information to generate appropriate, context-sensitive response data. This response is adjusted in tone and content according to the sentiment tags, ensuring it matches the user's specific emotional state.

[0321] The generated response data is sent to the terminal, which then converts the response data into speech using a speech synthesis system. This speech synthesis is also performed on an FPGA, and an appropriate voice expression is selected based on the user's emotions, which have been recognized in advance. The synthesized voice is played back to the user through the speaker, and visual text is also displayed on the screen.

[0322] For example, if a user says, "I've been feeling down lately," the device converts this statement into text data, and the emotion engine tags it as "sadness." The server then considers this context and emotion to generate a response such as, "What's wrong? Tell me what happened." This response is then delivered to the user as synthesized speech in a gentle tone.

[0323] In this way, the present invention optimizes the AI's response to match the user's emotional state, providing a more personalized conversational experience.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The user speaks into the device saying, "I've been feeling down lately." The device uses its voice input device to capture the user's voice and converts it into a digital signal.

[0327] Step 2:

[0328] The acquired audio signal is converted into text data in real time by a system that performs speech recognition on the terminal's FPGA. In this case, the content of the audio is transformed into text information such as "I've been feeling down lately."

[0329] Step 3:

[0330] The device uses an emotion engine to analyze the user's emotions from the acquired audio signal. Based on audio characteristics such as tone and volume, an emotion tag such as "sadness" is assigned.

[0331] Step 4:

[0332] The converted text data and assigned sentiment tags are sent to the server via a communication module. Here, data compression and protocol conversion are performed to ensure fast and reliable data transmission.

[0333] Step 5:

[0334] The server analyzes the received text data and emotion tags, and generates response data using an AI model. It selects a gentle expression corresponding to the emotion tag "sadness" and generates a refined response such as, "What's wrong? Please tell me what happened."

[0335] Step 6:

[0336] The response data generated by the server is sent back to the terminal. The terminal processes the received response data and inputs it into a system for synthesizing it into an audio signal.

[0337] Step 7:

[0338] The FPGA in the terminal synthesizes the response data with an appropriate voice tone, which is then played back to the user through the speaker. The synthesized voice is adjusted based on the emotional tone set on the server.

[0339] Step 8:

[0340] The device also displays the response as visual information on its screen. This allows the user to confirm the response by supplementing the audio information.

[0341] (Example 2)

[0342] 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".

[0343] Conventional voice dialogue systems often provide mechanical and emotionless responses, making it difficult for users to desire a more natural and emotionally resonant interface. Furthermore, responses that disregard emotions can lower user satisfaction and compromise the quality of communication. In addition, improving the efficiency of voice processing and ensuring connection stability remained challenges.

[0344] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0345] In this invention, the server includes means for analyzing text data and emotion tags and generating context-based response data, means for using a generative artificial intelligence model, and means for evaluating emotional states and assigning emotion tags. This enables natural and personalized dialogue that is attentive to the user's emotions.

[0346] An "audio signal" is information acquired as sound waves converted into digital data.

[0347] "Text data" refers to data in written form obtained by analyzing audio signals.

[0348] "Emotional state" refers to the user's psychological condition, as estimated through the analysis of voice and text.

[0349] An "emotion tag" is a label used to indicate an emotional state within a specific category (e.g., joy, sadness, anger, etc.).

[0350] A "communication network" refers to the network infrastructure used to send and receive data.

[0351] A "computing device" refers to a general-purpose electronic computing device such as a server or computer used for data processing and analysis.

[0352] A "generative artificial intelligence model" is a type of AI technology that generates appropriate responses based on input data.

[0353] "Context" refers to the background information and circumstances that help understand the meaning of the content of a conversation or text.

[0354] "Response data" refers to response information generated based on the input text and emotional state.

[0355] A "visual display device" refers to a display or monitor used to present information such as text and images to a user.

[0356] This invention is a voice dialogue system that makes the interaction between the user and the machine more natural and emotionally rich. The system begins by acquiring a voice signal and converting it into text data.

[0357] When a user speaks into the device, the device uses its built-in microphone to acquire the audio signal and digitizes it. The digitized audio signal is then converted into text data by a speech recognition engine running on an FPGA (Field-Programmable Gate Array).

[0358] The converted text data is analyzed by an emotion engine built into the device. This engine analyzes the tone, speed, and intonation of the voice to evaluate the user's emotional state. Based on this evaluation, emotion tags (e.g., joy, sadness, anger) are assigned.

[0359] Next, the device sends text data and sentiment tags to the server. The server analyzes the received data using a generative artificial intelligence model and generates contextually appropriate response data. The response data is adjusted to match the user's emotional state in terms of tone and content.

[0360] The generated response data is sent to the terminal, which uses a speech synthesis system to convert the data into speech. Speech synthesis is again performed on the FPGA, and an appropriate tone is selected according to the emotion tag. Finally, the synthesized speech is played back to the user through the speaker, and the text is also displayed on a visual display device.

[0361] For example, if a user says, "I'm really tired today," the device sends this statement to the server along with the emotion tag "tired." The server generates a gentle response such as, "You must be tired. Please take a short break," and this is conveyed to the user.

[0362] An example of a prompt message would be input to the generating AI model in the form of "Text: I'm very tired today. Emotion: Tired." In this way, the present invention can generate responses that match the user's emotions and provide a personalized conversational experience.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] When a user speaks into the device, the device uses its microphone to acquire the audio signal. The input is an analog audio signal, which the device converts into a digital signal. An AD converter is used to convert the analog audio signal into digital data, and the output is digital audio data.

[0366] Step 2:

[0367] The terminal passes the digitized audio signal to a speech recognition engine running on an FPGA. The input is digital audio data, and the speech recognition engine converts that audio into text. It analyzes the audio features using acoustic and language models and generates text data as output.

[0368] Step 3:

[0369] The device sends the converted text data to the emotion engine. The input is text data, and the emotion engine analyzes the tone and speed of the voice to evaluate the user's emotional state. It estimates the emotion using natural language processing technology and outputs an emotion tag (e.g., "tired," "joyful").

[0370] Step 4:

[0371] The terminal sends text data and sentiment tags to the server. The input consists of text data and sentiment tags, which the server analyzes using a generative AI model to generate response data. The generative AI model is input with the prompt "Text: [text data]. Sentiment: [sentiment tag]" and outputs response data based on the appropriate context.

[0372] Step 5:

[0373] The server sends the generated response data to the terminal. The terminal receives this data and inputs it into the speech synthesis system. The input is the response data, which the speech synthesis system converts into a speech signal. Using the speech synthesis engine, the system converts the text into speech, adjusts the tone based on sentiment tags, and generates speech data as output.

[0374] Step 6:

[0375] The device plays synthesized speech to the user through its speaker and also displays response text on a visual display. Input consists of speech and text data; the speaker outputs the speech data in real time, and the display shows the text. The user can receive both speech and visual information.

[0376] (Application Example 2)

[0377] 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 as the "terminal".

[0378] In modern commercial settings, customer interaction is essential for a company's success. However, technologies that enable natural and effective face-to-face dialogue are limited, making it difficult to understand and respond appropriately to customer emotions. This hinders improvements in customer satisfaction and the maximization of sales opportunities.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for acquiring an audio signal, means for converting the audio signal into non-analog data, and means for analyzing the audio signal acquired from the user and evaluating their emotional state. This enables real-time analysis of the customer's emotions, adjustment of response data based on that analysis, and natural and effective dialogue.

[0381] An "audio signal" is input information used to record and process sound waveforms as electronic data.

[0382] "Non-analog data" refers to digital data converted from analog signals, and is a format suitable for information processing.

[0383] An "information network" is a communication network designed to efficiently transmit digital data.

[0384] An "information processing device" is a computer system used to analyze data and generate the necessary responses.

[0385] A "request terminal device" is a device that receives response data sent from a server and presents it to the user.

[0386] An "application-specific integrated circuit" is a specialized integrated circuit designed to efficiently perform a specific function.

[0387] A "mobile communication device" is a device that uses wireless technology to enable communication between devices while they are in motion.

[0388] "Emotional state" refers to the psychological state that is analyzed based on the information expressed by the user through voice.

[0389] "Response data" refers to response information generated based on user input, and is provided to the user in audio or visual format.

[0390] The system implementing this invention achieves natural interaction with the user through multi-stage processing. First, the terminal is equipped with a microphone for acquiring audio signals and a speech recognition system for converting audio signals into non-analog data. This system incorporates application-specific integrated circuits, enabling rapid data processing.

[0391] The converted non-analog data is transmitted to a server via a communication network. The server analyzes the received data and uses a generative AI model to evaluate the user's emotional state. The emotional state information is used to adjust the tone and content of the response data. During this process, emotional tags are assigned, and personalized responses are generated based on the user's emotions.

[0392] The generated response data is retransmitted to the requesting terminal device, where it is converted back into a speech signal by a speech synthesis system. This speech synthesis is controlled by an application-specific integrated circuit, which selects the appropriate speech representation to produce a more natural-sounding voice.

[0393] For example, if a user speaks to a store staff member through smart glasses and says, "I've been feeling down lately," the system will interpret that emotion as "sadness" and provide a gentle response. The response will be played back as audio, such as, "What's wrong? Please tell me what it is."

[0394] An example of a prompt for a generative AI model is, "Please show me an example of an AI model that analyzes voice data, generates customer emotion tags, and optimizes conversation content." Through this prompt, the system continues to learn to make interactions with users more natural and effective.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] When a user speaks into the terminal, the terminal acquires the audio signal using its microphone. A speech recognition system using application-specific integrated circuits is activated to convert the acquired audio signal into non-analog data. The input for this step is the user's voice, and the output is the digital data converted by speech recognition.

[0398] Step 2:

[0399] The terminal transmits the converted digital data to the server via a communication network. The server takes the received digital data as input and uses an emotion analysis engine to evaluate the user's emotional state. As part of the data processing, a generative AI model analyzes the intonation and rhythm of the voice, and an emotion tag is generated as output.

[0400] Step 3:

[0401] The server uses emotion tags to construct response data. The response data generation algorithm selects appropriate response content based on the prompt text and adjusts the tone of the response to match the emotion tag. The output of this step is a customized response that corresponds to the user's emotion.

[0402] Step 4:

[0403] The generated response data is retransmitted from the server to the requesting terminal device. The terminal activates the speech synthesis system and converts the digital response data into a speech signal. An application-specific integrated circuit selects and synthesizes the most natural-sounding speech representation. The input for this step is the digital response data from the server, and the output is the speech signal presented to the user.

[0404] Step 5:

[0405] The device plays synthesized audio signals through its speakers. This allows the user to hear natural responses that are adjusted based on emotion. Along with the audio, visual information may also be displayed on the screen. The output consists of the audio the user receives and, if necessary, the text displayed on the screen.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] This invention relates to a system that uses a dedicated terminal device equipped with the function of interacting with AI using voice input. When a user speaks to the terminal, the terminal uses its built-in speech recognition system to convert the user's voice signal into text data. This speech recognition is performed on a dedicated hardware, an FPGA, and operates efficiently and in real time.

[0423] The generated text data is transmitted to the server via a communication network. A mobile communication module installed in the terminal is used for communication, and a protocol is implemented to maintain a stable connection. The server analyzes the received text data and utilizes an AI model to generate an appropriate response. Natural language processing techniques are used for processing, and data from external sources is referenced as needed.

[0424] The generated response data is transmitted from the server to the terminal via the communication network. The receiving terminal synthesizes this response data into speech and outputs it to the user as voice. Speech synthesis is also performed at high speed using an FPGA, resulting in a natural and smooth response. The terminal displays the obtained information on its screen, providing visual feedback as well.

[0425] As a concrete example, when a user speaks to the device saying, "Tell me the current time," the device recognizes the voice and sends it to the server as text. The server retrieves the current time and generates a response, for example, "It's 3:30 PM." This response is then sent back to the device and communicated to the user as voice. This allows the user to confirm the time both audibly and visually.

[0426] Thus, the present invention is a system that incorporates AI technology into daily life, enabling users to easily obtain information and receive practical support.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] When a user speaks into the microphone, the device captures the audio using a receiving device. The audio signal is digitized and subjected to noise reduction processing.

[0430] Step 2:

[0431] The FPGA installed in the device converts digitized audio signals into text data in real time. During this process, a speech recognition algorithm is applied to represent what is spoken as text.

[0432] Step 3:

[0433] The converted text data is compressed and efficiently transmitted to the server via the terminal's communication module. This ensures communication stability.

[0434] Step 4:

[0435] The server analyzes the received text data and generates an appropriate response using natural language processing techniques. A generative AI model supports this analysis and retrieves information from external data sources as needed.

[0436] Step 5:

[0437] The response data generated on the server is compressed again and sent to the terminal. The data is delivered quickly via the communication module.

[0438] Step 6:

[0439] The terminal uses an FPGA to synthesize the response data received from the server into an audio signal. The synthesized voice is then adjusted to enable high-quality audio output.

[0440] Step 7:

[0441] Synthesized voice is played to the user through the device's speaker. This allows the user to receive AI-generated responses in real time via voice.

[0442] Step 8:

[0443] The device visually displays the text obtained through speech recognition and the AI's responses on its screen. This provides feedback to the user in a way that complements the audio information.

[0444] (Example 1)

[0445] 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."

[0446] Currently, systems for efficiently converting voice information into text in real time, analyzing it with an information processing device, and generating appropriate responses are not sufficiently practical. Furthermore, communication stability and visual feedback of information are often lacking. This makes it difficult for users to smoothly obtain information and receive practical support.

[0447] 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.

[0448] In this invention, the server includes means for acquiring voice information, means for converting the acquired voice information into text data, and means for transmitting the converted text data to a data processing device via an information network. This enables the user to acquire information efficiently and stably in real time and to interact with it.

[0449] "Voice information" refers to the acquisition of voice signals uttered by the user as digital signals.

[0450] "Text data" refers to digital data obtained by converting audio information into a string of characters.

[0451] An "information network" refers to the network infrastructure used to send and receive data.

[0452] A "data processing device" is a device that analyzes received character data and generates response information.

[0453] A "communication terminal device" is a device that receives generated response information and provides it to the user.

[0454] A "dedicated computing device" is a computing unit designed to efficiently perform speech recognition and speech synthesis processing.

[0455] A "mobile communication device" is a device that stabilizes the connection to an information network using mobile communication technology.

[0456] "Means of visual display" refers to displays and display devices that provide information to users visually.

[0457] This invention relates to a system that uses a dedicated terminal device equipped with the function of interacting with AI using voice input. When a user speaks to this terminal, the terminal acquires voice information using a high-sensitivity microphone. This voice information is converted into text data in real time using a speech recognition system on an FPGA, which is a dedicated computing device. This conversion creates an environment in which users can easily input information using their voice.

[0458] The generated text data is transmitted via the mobile communication device in the terminal to a data processing server through the information network. The server analyzes this text data using natural language processing technology and generates response information using a generative AI model. By accessing external information sources, the server can provide the most appropriate and up-to-date response to the question.

[0459] The response information generated by the server is transmitted again to the communication terminal device via the information network. The terminal uses an FPGA to synthesize this response information into audio information and outputs it to the user as audio through a speaker. Furthermore, the terminal displays this information on a screen, providing the user with visual feedback, allowing them to confirm the operation using both audio and visual means.

[0460] As a concrete example, when a user speaks to their device saying, "What's the weather like today?", the device recognizes the speech, converts it to text, and sends it to the server. The server then refers to the latest weather information and generates a response such as, "It's sunny." This response is sent back to the device and provided to the user as audio. This allows the user to easily obtain weather information.

[0461] Examples of prompt messages include, "Terminal, tell me the current weather."

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The user inputs voice information by speaking into the device. The device acquires this voice information using a high-sensitivity microphone. This acquired voice information is an analog signal and is first converted into a digital signal. Digital signal processing (DSP) technology is used for this process.

[0465] Step 2:

[0466] Digitized audio information is converted into text data using an FPGA, a dedicated computing device in the terminal. This process utilizes speech recognition technology, where speech patterns are analyzed and mapped to a language model. The input is a digital audio signal, and the output is the corresponding text data.

[0467] Step 3:

[0468] The generated character data is transmitted to the server via the information network through the terminal's mobile communication device. During this process, the data is compressed using a communication protocol to ensure efficient and error-free transmission to the server. The input is character data, and the output is the character data transmitted to the server.

[0469] Step 4:

[0470] The server analyzes the received text data. It interprets the meaning of the data using natural language processing techniques and generates appropriate response information using a generative AI model. This process can also refer to relevant external databases. The input is the text data received by the server, and the output is the generated response information.

[0471] Step 5:

[0472] The generated response information is sent back to the terminal from the server via the information network. This is done with data compression and error checking to ensure communication stability. The input is the generated response information, and the output is the response information sent to the terminal.

[0473] Step 6:

[0474] The terminal synthesizes the received response information into speech information using an FPGA. This speech synthesis employs algorithms to maintain the intonation and naturalness of the speech. The input is the received response information, and the output is the synthesized speech information.

[0475] Step 7:

[0476] The device plays synthesized audio information to the user through its speaker. Simultaneously, response information is displayed on the screen as text and images, allowing the user to visually confirm the information. The input consists of synthesized audio information and display data, while the output consists of audio playback from the speaker and display data.

[0477] (Application Example 1)

[0478] 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."

[0479] In autonomous vehicles, a challenge is to provide an interface that allows passengers to intuitively give and change route instructions. Conventional methods suffer from delays in recognizing and responding to voice commands, making it difficult to provide a comfortable and swift experience.

[0480] 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.

[0481] In this invention, the server includes a device for acquiring voice data, a device for converting the acquired voice data into text format, and a device for transmitting the converted text format to a computing device via a communication line. This enables passengers to specify routes in real time using their voice, and allows the autonomous vehicle to respond quickly.

[0482] A "device for acquiring audio data" is a device that electronically captures audio signals, such as human speech, and inputs them into a system.

[0483] A "device for converting to text format" is a processing device that analyzes acquired audio data and converts it into transcribed text data.

[0484] A "device that transmits data to a computing device via a communication line" is a communication device used to transfer converted text-formatted data to a remote computing device using a network.

[0485] A "device that analyzes text format and generates response data in a computing device" is a processing device that analyzes received text data using natural language processing technology and generates an appropriate response.

[0486] A "device that transmits to the user device" is a transmitting device that sends the generated response data to the appropriate output device via a communication line.

[0487] A "device for synthesizing audio data" is a device that converts text data into data that can be played back as audio based on the results of its analysis.

[0488] A "playback device" is a device that outputs synthesized audio data in a format audible to humans through an output device such as a speaker.

[0489] A "device that performs destination setting and route calculation via voice commands" is a control device for an autonomous vehicle that analyzes voice commands from passengers and uses them to set destinations and calculate optimal routes.

[0490] This invention relates to a voice interface system for autonomous vehicles that sets destinations and calculates routes in real time based on passenger voice commands. The system includes the following main components:

[0491] The server includes a device that acquires audio data, which obtains audio signals from microphones inside the vehicle. The terminal that acquires this audio data then converts it into text format using dedicated speech recognition software. Since dedicated hardware such as FPGAs is used for speech recognition, high-speed and highly accurate recognition is possible.

[0492] The acquired text data is transmitted to the server via a communication line. Within the server, this text data is analyzed by an AI model, and an appropriate response is generated using natural language processing techniques. This response includes instructions based on the optimal route to the destination and current traffic conditions.

[0493] The generated response data is sent back to the terminal and converted into an audio signal by a speech synthesis device. Dedicated speech synthesis hardware is used for speech synthesis, resulting in natural and smooth voice generation.

[0494] Users receive vehicle instructions through synthesized voice. Visual feedback is also provided via a display device, allowing for safe and secure vehicle use through both audio and visual interfaces.

[0495] For example, if a user says, "I want to go to a nearby cafe," the system will search for nearby cafes, calculate the shortest route, and guide the vehicle along that route. An example of a prompt used in this case would be, "Activate the in-car dialogue system to calculate the shortest route based on the user's voice command and coordinate with the autonomous driving system."

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The terminal acquires voice data via the vehicle's microphone. The user specifies the destination by voice, and this voice signal is input as digital information from the microphone. The voice waveform is acquired as digital information.

[0499] Step 2:

[0500] The terminal uses speech recognition software mounted on an FPGA to convert the acquired speech waveform into text format. The speech waveform is provided as input, linguistic analysis and signal processing are performed, and user instructions in text format are output.

[0501] Step 3:

[0502] User instructions obtained in text format are sent to the server via a communication module. Stable data transfer was performed during transmission. Text data is input and transmitted to the remote server via network communication.

[0503] Step 4:

[0504] The server analyzes received text data using generative AI models and natural language processing techniques to generate appropriate responses. Upon receiving text instructions, the server compares them with a route search algorithm and an external database to output the optimal route information to the destination.

[0505] Step 5:

[0506] The generated response data is sent back from the server to the terminal. A communication protocol is used to transmit the data, and the response data is supplied to the vehicle's terminal as output.

[0507] Step 6:

[0508] The terminal performs speech synthesis using response data. An FPGA is used to convert the analysis results into speech, achieving fast and smooth speech. The synthesized speech output is generated and provided to the user via a speaker.

[0509] Step 7:

[0510] The user receives instructions via synthesized voice and confirms visual feedback on the display device. One such prompt is, "Activate the in-vehicle dialogue system to calculate the shortest route based on the user's voice instructions and coordinate with the autonomous driving system," with instructions displayed both verbally and on the screen.

[0511] 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.

[0512] In this invention, an emotion engine is integrated into a voice-based AI dialogue system to make the user interface more natural and effective. When a user speaks into the terminal, the voice signal is acquired and digitized. This digital voice is converted into text data by a speech recognition system running on an FPGA.

[0513] The converted text data is analyzed by an emotion engine, which evaluates the user's emotional state. The emotion engine analyzes the tone and manner of speaking and assigns emotion tags based on this analysis. These emotion tags indicate the type of emotion the user expressed (e.g., joy, sadness, anger).

[0514] The text data and sentiment tags sent to the server are analyzed by an AI model. The server uses this information to generate appropriate, context-sensitive response data. This response is adjusted in tone and content according to the sentiment tags, ensuring it matches the user's specific emotional state.

[0515] The generated response data is sent to the terminal, which then converts the response data into speech using a speech synthesis system. This speech synthesis is also performed on an FPGA, and an appropriate voice expression is selected based on the user's emotions, which have been recognized in advance. The synthesized voice is played back to the user through the speaker, and visual text is also displayed on the screen.

[0516] For example, if a user says, "I've been feeling down lately," the device converts this statement into text data, and the emotion engine tags it as "sadness." The server then considers this context and emotion to generate a response such as, "What's wrong? Tell me what happened." This response is then delivered to the user as synthesized speech in a gentle tone.

[0517] In this way, the present invention optimizes the AI's response to match the user's emotional state, providing a more personalized conversational experience.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user speaks into the device saying, "I've been feeling down lately." The device uses its voice input device to capture the user's voice and converts it into a digital signal.

[0521] Step 2:

[0522] The acquired audio signal is converted into text data in real time by a system that performs speech recognition on the terminal's FPGA. In this case, the content of the audio is transformed into text information such as "I've been feeling down lately."

[0523] Step 3:

[0524] The device uses an emotion engine to analyze the user's emotions from the acquired audio signal. Based on audio characteristics such as tone and volume, an emotion tag such as "sadness" is assigned.

[0525] Step 4:

[0526] The converted text data and assigned sentiment tags are sent to the server via a communication module. Here, data compression and protocol conversion are performed to ensure fast and reliable data transmission.

[0527] Step 5:

[0528] The server analyzes the received text data and emotion tags, and generates response data using an AI model. It selects a gentle expression corresponding to the emotion tag "sadness" and generates a refined response such as, "What's wrong? Please tell me what happened."

[0529] Step 6:

[0530] The response data generated by the server is sent back to the terminal. The terminal processes the received response data and inputs it into a system for synthesizing it into an audio signal.

[0531] Step 7:

[0532] The FPGA in the terminal synthesizes the response data with an appropriate voice tone, which is then played back to the user through the speaker. The synthesized voice is adjusted based on the emotional tone set on the server.

[0533] Step 8:

[0534] The device also displays the response as visual information on its screen. This allows the user to confirm the response by supplementing the audio information.

[0535] (Example 2)

[0536] 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."

[0537] Conventional voice dialogue systems often provide mechanical and emotionless responses, making it difficult for users to desire a more natural and emotionally resonant interface. Furthermore, responses that disregard emotions can lower user satisfaction and compromise the quality of communication. In addition, improving the efficiency of voice processing and ensuring connection stability remained challenges.

[0538] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0539] In this invention, the server includes means for analyzing text data and emotion tags and generating context-based response data, means for using a generative artificial intelligence model, and means for evaluating emotional states and assigning emotion tags. This enables natural and personalized dialogue that is attentive to the user's emotions.

[0540] An "audio signal" is information acquired as sound waves converted into digital data.

[0541] "Text data" refers to data in written form obtained by analyzing audio signals.

[0542] "Emotional state" refers to the user's psychological condition, as estimated through the analysis of voice and text.

[0543] An "emotion tag" is a label used to indicate an emotional state within a specific category (e.g., joy, sadness, anger, etc.).

[0544] A "communication network" refers to the network infrastructure used to send and receive data.

[0545] A "computing device" refers to a general-purpose electronic computing device such as a server or computer used for data processing and analysis.

[0546] A "generative artificial intelligence model" is a type of AI technology that generates appropriate responses based on input data.

[0547] "Context" refers to the background information and circumstances that help understand the meaning of the content of a conversation or text.

[0548] "Response data" refers to response information generated based on the input text and emotional state.

[0549] A "visual display device" refers to a display or monitor used to present information such as text and images to a user.

[0550] This invention is a voice dialogue system that makes the interaction between the user and the machine more natural and emotionally rich. The system begins by acquiring a voice signal and converting it into text data.

[0551] When a user speaks into the device, the device uses its built-in microphone to acquire the audio signal and digitizes it. The digitized audio signal is then converted into text data by a speech recognition engine running on an FPGA (Field-Programmable Gate Array).

[0552] The converted text data is analyzed by an emotion engine built into the device. This engine analyzes the tone, speed, and intonation of the voice to evaluate the user's emotional state. Based on this evaluation, emotion tags (e.g., joy, sadness, anger) are assigned.

[0553] Next, the device sends text data and sentiment tags to the server. The server analyzes the received data using a generative artificial intelligence model and generates contextually appropriate response data. The response data is adjusted to match the user's emotional state in terms of tone and content.

[0554] The generated response data is sent to the terminal, which uses a speech synthesis system to convert the data into speech. Speech synthesis is again performed on the FPGA, and an appropriate tone is selected according to the emotion tag. Finally, the synthesized speech is played back to the user through the speaker, and the text is also displayed on a visual display device.

[0555] For example, if a user says, "I'm really tired today," the device sends this statement to the server along with the emotion tag "tired." The server generates a gentle response such as, "You must be tired. Please take a short break," and this is conveyed to the user.

[0556] An example of a prompt message would be input to the generating AI model in the form of "Text: I'm very tired today. Emotion: Tired." In this way, the present invention can generate responses that match the user's emotions and provide a personalized conversational experience.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] When a user speaks into the device, the device uses its microphone to acquire the audio signal. The input is an analog audio signal, which the device converts into a digital signal. An AD converter is used to convert the analog audio signal into digital data, and the output is digital audio data.

[0560] Step 2:

[0561] The terminal passes the digitized audio signal to a speech recognition engine running on an FPGA. The input is digital audio data, and the speech recognition engine converts that audio into text. It analyzes the audio features using acoustic and language models and generates text data as output.

[0562] Step 3:

[0563] The device sends the converted text data to the emotion engine. The input is text data, and the emotion engine analyzes the tone and speed of the voice to evaluate the user's emotional state. It estimates the emotion using natural language processing technology and outputs an emotion tag (e.g., "tired," "joyful").

[0564] Step 4:

[0565] The terminal sends text data and sentiment tags to the server. The input consists of text data and sentiment tags, which the server analyzes using a generative AI model to generate response data. The generative AI model is input with the prompt "Text: [text data]. Sentiment: [sentiment tag]" and outputs response data based on the appropriate context.

[0566] Step 5:

[0567] The server sends the generated response data to the terminal. The terminal receives this data and inputs it into the speech synthesis system. The input is the response data, which the speech synthesis system converts into a speech signal. Using the speech synthesis engine, the system converts the text into speech, adjusts the tone based on sentiment tags, and generates speech data as output.

[0568] Step 6:

[0569] The device plays synthesized speech to the user through its speaker and also displays response text on a visual display. Input consists of speech and text data; the speaker outputs the speech data in real time, and the display shows the text. The user can receive both speech and visual information.

[0570] (Application Example 2)

[0571] 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."

[0572] In modern commercial settings, customer interaction is essential for a company's success. However, technologies that enable natural and effective face-to-face dialogue are limited, making it difficult to understand and respond appropriately to customer emotions. This hinders improvements in customer satisfaction and the maximization of sales opportunities.

[0573] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0574] In this invention, the server includes means for acquiring an audio signal, means for converting the audio signal into non-analog data, and means for analyzing the audio signal acquired from the user and evaluating their emotional state. This enables real-time analysis of the customer's emotions, adjustment of response data based on that analysis, and natural and effective dialogue.

[0575] An "audio signal" is input information used to record and process sound waveforms as electronic data.

[0576] "Non-analog data" refers to digital data converted from analog signals, and is a format suitable for information processing.

[0577] An "information network" is a communication network designed to efficiently transmit digital data.

[0578] An "information processing device" is a computer system used to analyze data and generate the necessary responses.

[0579] A "request terminal device" is a device that receives response data sent from a server and presents it to the user.

[0580] An "application-specific integrated circuit" is a specialized integrated circuit designed to efficiently perform a specific function.

[0581] A "mobile communication device" is a device that uses wireless technology to enable communication between devices while they are in motion.

[0582] "Emotional state" refers to the psychological state that is analyzed based on the information expressed by the user through voice.

[0583] "Response data" refers to response information generated based on user input, and is provided to the user in audio or visual format.

[0584] The system implementing this invention achieves natural interaction with the user through multi-stage processing. First, the terminal is equipped with a microphone for acquiring audio signals and a speech recognition system for converting audio signals into non-analog data. This system incorporates application-specific integrated circuits, enabling rapid data processing.

[0585] The converted non-analog data is transmitted to a server via a communication network. The server analyzes the received data and uses a generative AI model to evaluate the user's emotional state. The emotional state information is used to adjust the tone and content of the response data. During this process, emotional tags are assigned, and personalized responses are generated based on the user's emotions.

[0586] The generated response data is retransmitted to the requesting terminal device, where it is converted back into a speech signal by a speech synthesis system. This speech synthesis is controlled by an application-specific integrated circuit, which selects the appropriate speech representation to produce a more natural-sounding voice.

[0587] For example, if a user speaks to a store staff member through smart glasses and says, "I've been feeling down lately," the system will interpret that emotion as "sadness" and provide a gentle response. The response will be played back as audio, such as, "What's wrong? Please tell me what it is."

[0588] An example of a prompt for a generative AI model is, "Please show me an example of an AI model that analyzes voice data, generates customer emotion tags, and optimizes conversation content." Through this prompt, the system continues to learn to make interactions with users more natural and effective.

[0589] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0590] Step 1:

[0591] When a user speaks into the terminal, the terminal acquires the audio signal using its microphone. A speech recognition system using application-specific integrated circuits is activated to convert the acquired audio signal into non-analog data. The input for this step is the user's voice, and the output is the digital data converted by speech recognition.

[0592] Step 2:

[0593] The terminal transmits the converted digital data to the server via a communication network. The server takes the received digital data as input and uses an emotion analysis engine to evaluate the user's emotional state. As part of the data processing, a generative AI model analyzes the intonation and rhythm of the voice, and an emotion tag is generated as output.

[0594] Step 3:

[0595] The server uses emotion tags to construct response data. The response data generation algorithm selects appropriate response content based on the prompt text and adjusts the tone of the response to match the emotion tag. The output of this step is a customized response that corresponds to the user's emotion.

[0596] Step 4:

[0597] The generated response data is retransmitted from the server to the requesting terminal device. The terminal activates the speech synthesis system and converts the digital response data into a speech signal. An application-specific integrated circuit selects and synthesizes the most natural-sounding speech representation. The input for this step is the digital response data from the server, and the output is the speech signal presented to the user.

[0598] Step 5:

[0599] The device plays synthesized audio signals through its speakers. This allows the user to hear natural responses that are adjusted based on emotion. Along with the audio, visual information may also be displayed on the screen. The output consists of the audio the user receives and, if necessary, the text displayed on the screen.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] This invention relates to a system that uses a dedicated terminal device equipped with the function of interacting with AI using voice input. When a user speaks to the terminal, the terminal uses its built-in speech recognition system to convert the user's voice signal into text data. This speech recognition is performed on a dedicated hardware, an FPGA, and operates efficiently and in real time.

[0618] The generated text data is transmitted to the server via a communication network. A mobile communication module installed in the terminal is used for communication, and a protocol is implemented to maintain a stable connection. The server analyzes the received text data and utilizes an AI model to generate an appropriate response. Natural language processing techniques are used for processing, and data from external sources is referenced as needed.

[0619] The generated response data is transmitted from the server to the terminal via the communication network. The receiving terminal synthesizes this response data into speech and outputs it to the user as voice. Speech synthesis is also performed at high speed using an FPGA, resulting in a natural and smooth response. The terminal displays the obtained information on its screen, providing visual feedback as well.

[0620] As a concrete example, when a user speaks to the device saying, "Tell me the current time," the device recognizes the voice and sends it to the server as text. The server retrieves the current time and generates a response, for example, "It's 3:30 PM." This response is then sent back to the device and communicated to the user as voice. This allows the user to confirm the time both audibly and visually.

[0621] Thus, the present invention is a system that incorporates AI technology into daily life, enabling users to easily obtain information and receive practical support.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] When a user speaks into the microphone, the device captures the audio using a receiving device. The audio signal is digitized and subjected to noise reduction processing.

[0625] Step 2:

[0626] The FPGA installed in the device converts digitized audio signals into text data in real time. During this process, a speech recognition algorithm is applied to represent what is spoken as text.

[0627] Step 3:

[0628] The converted text data is compressed and efficiently transmitted to the server via the terminal's communication module. This ensures communication stability.

[0629] Step 4:

[0630] The server analyzes the received text data and generates an appropriate response using natural language processing techniques. A generative AI model supports this analysis and retrieves information from external data sources as needed.

[0631] Step 5:

[0632] The response data generated on the server is compressed again and sent to the terminal. The data is delivered quickly via the communication module.

[0633] Step 6:

[0634] The terminal uses an FPGA to synthesize the response data received from the server into an audio signal. The synthesized voice is then adjusted to enable high-quality audio output.

[0635] Step 7:

[0636] Synthesized voice is played to the user through the device's speaker. This allows the user to receive AI-generated responses in real time via voice.

[0637] Step 8:

[0638] The device visually displays the text obtained through speech recognition and the AI's responses on its screen. This provides feedback to the user in a way that complements the audio information.

[0639] (Example 1)

[0640] 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".

[0641] Currently, systems for efficiently converting voice information into text in real time, analyzing it with an information processing device, and generating appropriate responses are not sufficiently practical. Furthermore, communication stability and visual feedback of information are often lacking. This makes it difficult for users to smoothly obtain information and receive practical support.

[0642] 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.

[0643] In this invention, the server includes means for acquiring voice information, means for converting the acquired voice information into text data, and means for transmitting the converted text data to a data processing device via an information network. This enables the user to acquire information efficiently and stably in real time and to interact with it.

[0644] "Voice information" refers to the acquisition of voice signals uttered by the user as digital signals.

[0645] "Text data" refers to digital data obtained by converting audio information into a string of characters.

[0646] An "information network" refers to the network infrastructure used to send and receive data.

[0647] A "data processing device" is a device that analyzes received character data and generates response information.

[0648] A "communication terminal device" is a device that receives generated response information and provides it to the user.

[0649] A "dedicated computing device" is a computing unit designed to efficiently perform speech recognition and speech synthesis processing.

[0650] A "mobile communication device" is a device that stabilizes the connection to an information network using mobile communication technology.

[0651] "Means of visual display" refers to displays and display devices that provide information to users visually.

[0652] This invention relates to a system that uses a dedicated terminal device equipped with the function of interacting with AI using voice input. When a user speaks to this terminal, the terminal acquires voice information using a high-sensitivity microphone. This voice information is converted into text data in real time using a speech recognition system on an FPGA, which is a dedicated computing device. This conversion creates an environment in which users can easily input information using their voice.

[0653] The generated text data is transmitted via the mobile communication device in the terminal to a data processing server through the information network. The server analyzes this text data using natural language processing technology and generates response information using a generative AI model. By accessing external information sources, the server can provide the most appropriate and up-to-date response to the question.

[0654] The response information generated by the server is transmitted again to the communication terminal device via the information network. The terminal uses an FPGA to synthesize this response information into audio information and outputs it to the user as audio through a speaker. Furthermore, the terminal displays this information on a screen, providing the user with visual feedback, allowing them to confirm the operation using both audio and visual means.

[0655] As a concrete example, when a user speaks to their device saying, "What's the weather like today?", the device recognizes the speech, converts it to text, and sends it to the server. The server then refers to the latest weather information and generates a response such as, "It's sunny." This response is sent back to the device and provided to the user as audio. This allows the user to easily obtain weather information.

[0656] Examples of prompt messages include, "Terminal, tell me the current weather."

[0657] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0658] Step 1:

[0659] The user inputs voice information by speaking into the device. The device acquires this voice information using a high-sensitivity microphone. This acquired voice information is an analog signal and is first converted into a digital signal. Digital signal processing (DSP) technology is used for this process.

[0660] Step 2:

[0661] Digitized audio information is converted into text data using an FPGA, a dedicated computing device in the terminal. This process utilizes speech recognition technology, where speech patterns are analyzed and mapped to a language model. The input is a digital audio signal, and the output is the corresponding text data.

[0662] Step 3:

[0663] The generated character data is transmitted to the server via the information network through the terminal's mobile communication device. During this process, the data is compressed using a communication protocol to ensure efficient and error-free transmission to the server. The input is character data, and the output is the character data transmitted to the server.

[0664] Step 4:

[0665] The server analyzes the received text data. It interprets the meaning of the data using natural language processing techniques and generates appropriate response information using a generative AI model. This process can also refer to relevant external databases. The input is the text data received by the server, and the output is the generated response information.

[0666] Step 5:

[0667] The generated response information is sent back to the terminal from the server via the information network. This is done with data compression and error checking to ensure communication stability. The input is the generated response information, and the output is the response information sent to the terminal.

[0668] Step 6:

[0669] The terminal synthesizes the received response information into speech information using an FPGA. This speech synthesis employs algorithms to maintain the intonation and naturalness of the speech. The input is the received response information, and the output is the synthesized speech information.

[0670] Step 7:

[0671] The device plays synthesized audio information to the user through its speaker. Simultaneously, response information is displayed on the screen as text and images, allowing the user to visually confirm the information. The input consists of synthesized audio information and display data, while the output consists of audio playback from the speaker and display data.

[0672] (Application Example 1)

[0673] 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".

[0674] In autonomous vehicles, a challenge is to provide an interface that allows passengers to intuitively give and change route instructions. Conventional methods suffer from delays in recognizing and responding to voice commands, making it difficult to provide a comfortable and swift experience.

[0675] 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.

[0676] In this invention, the server includes a device for acquiring voice data, a device for converting the acquired voice data into text format, and a device for transmitting the converted text format to a computing device via a communication line. This enables passengers to specify routes in real time using their voice, and allows the autonomous vehicle to respond quickly.

[0677] A "device for acquiring audio data" is a device that electronically captures audio signals, such as human speech, and inputs them into a system.

[0678] A "device for converting to text format" is a processing device that analyzes acquired audio data and converts it into transcribed text data.

[0679] A "device that transmits data to a computing device via a communication line" is a communication device used to transfer converted text-formatted data to a remote computing device using a network.

[0680] A "device that analyzes text format and generates response data in a computing device" is a processing device that analyzes received text data using natural language processing technology and generates an appropriate response.

[0681] A "device that transmits to the user device" is a transmitting device that sends the generated response data to the appropriate output device via a communication line.

[0682] A "device for synthesizing audio data" is a device that converts text data into data that can be played back as audio based on the results of its analysis.

[0683] A "playback device" is a device that outputs synthesized audio data in a format audible to humans through an output device such as a speaker.

[0684] A "device that performs destination setting and route calculation via voice commands" is a control device for an autonomous vehicle that analyzes voice commands from passengers and uses them to set destinations and calculate optimal routes.

[0685] This invention relates to a voice interface system for autonomous vehicles that sets destinations and calculates routes in real time based on passenger voice commands. The system includes the following main components:

[0686] The server includes a device that acquires audio data, which obtains audio signals from microphones inside the vehicle. The terminal that acquires this audio data then converts it into text format using dedicated speech recognition software. Since dedicated hardware such as FPGAs is used for speech recognition, high-speed and highly accurate recognition is possible.

[0687] The acquired text data is transmitted to the server via a communication line. Within the server, this text data is analyzed by an AI model, and an appropriate response is generated using natural language processing techniques. This response includes instructions based on the optimal route to the destination and current traffic conditions.

[0688] The generated response data is sent back to the terminal and converted into an audio signal by a speech synthesis device. Dedicated speech synthesis hardware is used for speech synthesis, resulting in natural and smooth voice generation.

[0689] Users receive vehicle instructions through synthesized voice. Visual feedback is also provided via a display device, allowing for safe and secure vehicle use through both audio and visual interfaces.

[0690] For example, if a user says, "I want to go to a nearby cafe," the system will search for nearby cafes, calculate the shortest route, and guide the vehicle along that route. An example of a prompt used in this case would be, "Activate the in-car dialogue system to calculate the shortest route based on the user's voice command and coordinate with the autonomous driving system."

[0691] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0692] Step 1:

[0693] The terminal acquires voice data via the vehicle's microphone. The user specifies the destination by voice, and this voice signal is input as digital information from the microphone. The voice waveform is acquired as digital information.

[0694] Step 2:

[0695] The terminal uses speech recognition software mounted on an FPGA to convert the acquired speech waveform into text format. The speech waveform is provided as input, linguistic analysis and signal processing are performed, and user instructions in text format are output.

[0696] Step 3:

[0697] User instructions obtained in text format are sent to the server via a communication module. Stable data transfer was performed during transmission. Text data is input and transmitted to the remote server via network communication.

[0698] Step 4:

[0699] The server analyzes received text data using generative AI models and natural language processing techniques to generate appropriate responses. Upon receiving text instructions, the server compares them with a route search algorithm and an external database to output the optimal route information to the destination.

[0700] Step 5:

[0701] The generated response data is sent back from the server to the terminal. A communication protocol is used to transmit the data, and the response data is supplied to the vehicle's terminal as output.

[0702] Step 6:

[0703] The terminal performs speech synthesis using response data. An FPGA is used to convert the analysis results into speech, achieving fast and smooth speech. The synthesized speech output is generated and provided to the user via a speaker.

[0704] Step 7:

[0705] The user receives instructions via synthesized voice and confirms visual feedback on the display device. One such prompt is, "Activate the in-vehicle dialogue system to calculate the shortest route based on the user's voice instructions and coordinate with the autonomous driving system," with instructions displayed both verbally and on the screen.

[0706] 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.

[0707] In this invention, an emotion engine is integrated into a voice-based AI dialogue system to make the user interface more natural and effective. When a user speaks into the terminal, the voice signal is acquired and digitized. This digital voice is converted into text data by a speech recognition system running on an FPGA.

[0708] The converted text data is analyzed by an emotion engine, which evaluates the user's emotional state. The emotion engine analyzes the tone and manner of speaking and assigns emotion tags based on this analysis. These emotion tags indicate the type of emotion the user expressed (e.g., joy, sadness, anger).

[0709] The text data and sentiment tags sent to the server are analyzed by an AI model. The server uses this information to generate appropriate, context-sensitive response data. This response is adjusted in tone and content according to the sentiment tags, ensuring it matches the user's specific emotional state.

[0710] The generated response data is sent to the terminal, which then converts the response data into speech using a speech synthesis system. This speech synthesis is also performed on an FPGA, and an appropriate voice expression is selected based on the user's emotions, which have been recognized in advance. The synthesized voice is played back to the user through the speaker, and visual text is also displayed on the screen.

[0711] For example, if a user says, "I've been feeling down lately," the device converts this statement into text data, and the emotion engine tags it as "sadness." The server then considers this context and emotion to generate a response such as, "What's wrong? Tell me what happened." This response is then delivered to the user as synthesized speech in a gentle tone.

[0712] In this way, the present invention optimizes the AI's response to match the user's emotional state, providing a more personalized conversational experience.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] The user speaks into the device saying, "I've been feeling down lately." The device uses its voice input device to capture the user's voice and converts it into a digital signal.

[0716] Step 2:

[0717] The acquired audio signal is converted into text data in real time by a system that performs speech recognition on the terminal's FPGA. In this case, the content of the audio is transformed into text information such as "I've been feeling down lately."

[0718] Step 3:

[0719] The device uses an emotion engine to analyze the user's emotions from the acquired audio signal. Based on audio characteristics such as tone and volume, an emotion tag such as "sadness" is assigned.

[0720] Step 4:

[0721] The converted text data and assigned sentiment tags are sent to the server via a communication module. Here, data compression and protocol conversion are performed to ensure fast and reliable data transmission.

[0722] Step 5:

[0723] The server analyzes the received text data and emotion tags, and generates response data using an AI model. It selects a gentle expression corresponding to the emotion tag "sadness" and generates a refined response such as, "What's wrong? Please tell me what happened."

[0724] Step 6:

[0725] The response data generated by the server is sent back to the terminal. The terminal processes the received response data and inputs it into a system for synthesizing it into an audio signal.

[0726] Step 7:

[0727] The FPGA in the terminal synthesizes the response data with an appropriate voice tone, which is then played back to the user through the speaker. The synthesized voice is adjusted based on the emotional tone set on the server.

[0728] Step 8:

[0729] The device also displays the response as visual information on its screen. This allows the user to confirm the response by supplementing the audio information.

[0730] (Example 2)

[0731] 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".

[0732] Conventional voice dialogue systems often provide mechanical and emotionless responses, making it difficult for users to desire a more natural and emotionally resonant interface. Furthermore, responses that disregard emotions can lower user satisfaction and compromise the quality of communication. In addition, improving the efficiency of voice processing and ensuring connection stability remained challenges.

[0733] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0734] In this invention, the server includes means for analyzing text data and emotion tags and generating context-based response data, means for using a generative artificial intelligence model, and means for evaluating emotional states and assigning emotion tags. This enables natural and personalized dialogue that is attentive to the user's emotions.

[0735] An "audio signal" is information acquired as sound waves converted into digital data.

[0736] "Text data" refers to data in written form obtained by analyzing audio signals.

[0737] "Emotional state" refers to the user's psychological condition, as estimated through the analysis of voice and text.

[0738] An "emotion tag" is a label used to indicate an emotional state within a specific category (e.g., joy, sadness, anger, etc.).

[0739] A "communication network" refers to the network infrastructure used to send and receive data.

[0740] A "computing device" refers to a general-purpose electronic computing device such as a server or computer used for data processing and analysis.

[0741] A "generative artificial intelligence model" is a type of AI technology that generates appropriate responses based on input data.

[0742] "Context" refers to the background information and circumstances that help understand the meaning of the content of a conversation or text.

[0743] "Response data" refers to response information generated based on the input text and emotional state.

[0744] A "visual display device" refers to a display or monitor used to present information such as text and images to a user.

[0745] This invention is a voice dialogue system that makes the interaction between the user and the machine more natural and emotionally rich. The system begins by acquiring a voice signal and converting it into text data.

[0746] When a user speaks into the device, the device uses its built-in microphone to acquire the audio signal and digitizes it. The digitized audio signal is then converted into text data by a speech recognition engine running on an FPGA (Field-Programmable Gate Array).

[0747] The converted text data is analyzed by an emotion engine built into the device. This engine analyzes the tone, speed, and intonation of the voice to evaluate the user's emotional state. Based on this evaluation, emotion tags (e.g., joy, sadness, anger) are assigned.

[0748] Next, the device sends text data and sentiment tags to the server. The server analyzes the received data using a generative artificial intelligence model and generates contextually appropriate response data. The response data is adjusted to match the user's emotional state in terms of tone and content.

[0749] The generated response data is sent to the terminal, which uses a speech synthesis system to convert the data into speech. Speech synthesis is again performed on the FPGA, and an appropriate tone is selected according to the emotion tag. Finally, the synthesized speech is played back to the user through the speaker, and the text is also displayed on a visual display device.

[0750] For example, if a user says, "I'm really tired today," the device sends this statement to the server along with the emotion tag "tired." The server generates a gentle response such as, "You must be tired. Please take a short break," and this is conveyed to the user.

[0751] An example of a prompt message would be input to the generating AI model in the form of "Text: I'm very tired today. Emotion: Tired." In this way, the present invention can generate responses that match the user's emotions and provide a personalized conversational experience.

[0752] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0753] Step 1:

[0754] When a user speaks into the device, the device uses its microphone to acquire the audio signal. The input is an analog audio signal, which the device converts into a digital signal. An AD converter is used to convert the analog audio signal into digital data, and the output is digital audio data.

[0755] Step 2:

[0756] The terminal passes the digitized audio signal to a speech recognition engine running on an FPGA. The input is digital audio data, and the speech recognition engine converts that audio into text. It analyzes the audio features using acoustic and language models and generates text data as output.

[0757] Step 3:

[0758] The device sends the converted text data to the emotion engine. The input is text data, and the emotion engine analyzes the tone and speed of the voice to evaluate the user's emotional state. It estimates the emotion using natural language processing technology and outputs an emotion tag (e.g., "tired," "joyful").

[0759] Step 4:

[0760] The terminal sends text data and sentiment tags to the server. The input consists of text data and sentiment tags, which the server analyzes using a generative AI model to generate response data. The generative AI model is input with the prompt "Text: [text data]. Sentiment: [sentiment tag]" and outputs response data based on the appropriate context.

[0761] Step 5:

[0762] The server sends the generated response data to the terminal. The terminal receives this data and inputs it into the speech synthesis system. The input is the response data, which the speech synthesis system converts into a speech signal. Using the speech synthesis engine, the system converts the text into speech, adjusts the tone based on sentiment tags, and generates speech data as output.

[0763] Step 6:

[0764] The device plays synthesized speech to the user through its speaker and also displays response text on a visual display. Input consists of speech and text data; the speaker outputs the speech data in real time, and the display shows the text. The user can receive both speech and visual information.

[0765] (Application Example 2)

[0766] 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".

[0767] In modern commercial settings, customer interaction is essential for a company's success. However, technologies that enable natural and effective face-to-face dialogue are limited, making it difficult to understand and respond appropriately to customer emotions. This hinders improvements in customer satisfaction and the maximization of sales opportunities.

[0768] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0769] In this invention, the server includes means for acquiring an audio signal, means for converting the audio signal into non-analog data, and means for analyzing the audio signal acquired from the user and evaluating their emotional state. This enables real-time analysis of the customer's emotions, adjustment of response data based on that analysis, and natural and effective dialogue.

[0770] An "audio signal" is input information used to record and process sound waveforms as electronic data.

[0771] "Non-analog data" refers to digital data converted from analog signals, and is a format suitable for information processing.

[0772] An "information network" is a communication network designed to efficiently transmit digital data.

[0773] An "information processing device" is a computer system used to analyze data and generate the necessary responses.

[0774] A "request terminal device" is a device that receives response data sent from a server and presents it to the user.

[0775] An "application-specific integrated circuit" is a specialized integrated circuit designed to efficiently perform a specific function.

[0776] A "mobile communication device" is a device that uses wireless technology to enable communication between devices while they are in motion.

[0777] "Emotional state" refers to the psychological state that is analyzed based on the information expressed by the user through voice.

[0778] "Response data" refers to response information generated based on user input, and is provided to the user in audio or visual format.

[0779] The system implementing this invention achieves natural interaction with the user through multi-stage processing. First, the terminal is equipped with a microphone for acquiring audio signals and a speech recognition system for converting audio signals into non-analog data. This system incorporates application-specific integrated circuits, enabling rapid data processing.

[0780] The converted non-analog data is transmitted to a server via a communication network. The server analyzes the received data and uses a generative AI model to evaluate the user's emotional state. The emotional state information is used to adjust the tone and content of the response data. During this process, emotional tags are assigned, and personalized responses are generated based on the user's emotions.

[0781] The generated response data is retransmitted to the requesting terminal device, where it is converted back into a speech signal by a speech synthesis system. This speech synthesis is controlled by an application-specific integrated circuit, which selects the appropriate speech representation to produce a more natural-sounding voice.

[0782] For example, if a user speaks to a store staff member through smart glasses and says, "I've been feeling down lately," the system will interpret that emotion as "sadness" and provide a gentle response. The response will be played back as audio, such as, "What's wrong? Please tell me what it is."

[0783] An example of a prompt for a generative AI model is, "Please show me an example of an AI model that analyzes voice data, generates customer emotion tags, and optimizes conversation content." Through this prompt, the system continues to learn to make interactions with users more natural and effective.

[0784] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0785] Step 1:

[0786] When a user speaks into the terminal, the terminal acquires the audio signal using its microphone. A speech recognition system using application-specific integrated circuits is activated to convert the acquired audio signal into non-analog data. The input for this step is the user's voice, and the output is the digital data converted by speech recognition.

[0787] Step 2:

[0788] The terminal transmits the converted digital data to the server via a communication network. The server takes the received digital data as input and uses an emotion analysis engine to evaluate the user's emotional state. As part of the data processing, a generative AI model analyzes the intonation and rhythm of the voice, and an emotion tag is generated as output.

[0789] Step 3:

[0790] The server uses emotion tags to construct response data. The response data generation algorithm selects appropriate response content based on the prompt text and adjusts the tone of the response to match the emotion tag. The output of this step is a customized response that corresponds to the user's emotion.

[0791] Step 4:

[0792] The generated response data is retransmitted from the server to the requesting terminal device. The terminal activates the speech synthesis system and converts the digital response data into a speech signal. An application-specific integrated circuit selects and synthesizes the most natural-sounding speech representation. The input for this step is the digital response data from the server, and the output is the speech signal presented to the user.

[0793] Step 5:

[0794] The device plays synthesized audio signals through its speakers. This allows the user to hear natural responses that are adjusted based on emotion. Along with the audio, visual information may also be displayed on the screen. The output consists of the audio the user receives and, if necessary, the text displayed on the screen.

[0795] 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.

[0796] 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.

[0797] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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."

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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 as being incorporated by reference.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] Means for acquiring audio signals,

[0819] A means for converting acquired audio signals into text data,

[0820] Means for transmitting the converted text data to a server device via a communication network,

[0821] A server device provides means for analyzing text data and generating response data,

[0822] A means for transmitting the generated response data to a contracted terminal device,

[0823] A means for combining the received response data into an audio signal,

[0824] A means for reproducing the synthesized audio signal,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, further comprising means for performing speech recognition and speech synthesis processing using dedicated hardware.

[0828] (Claim 3)

[0829] The system according to claim 1, further comprising means for using a mobile communication module to stabilize the connection with a communication network.

[0830] "Example 1"

[0831] (Claim 1)

[0832] Means for acquiring audio information,

[0833] A means of converting acquired audio information into text data,

[0834] A means for transmitting the converted character data to a data processing device via an information network,

[0835] A data processing device includes means for analyzing character data and generating response information,

[0836] Means for transmitting the generated response information to a communication terminal device,

[0837] A means for synthesizing the received response information into audio information,

[0838] A means for playing back synthesized audio information,

[0839] Means of visually displaying information,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, further comprising means for performing speech recognition and speech synthesis processing using a dedicated computing device.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising means of using a mobile communication device to stabilize the connection with a communication network.

[0845] "Application Example 1"

[0846] (Claim 1)

[0847] A device for acquiring audio data,

[0848] A device that converts acquired audio data into text format,

[0849] A device that transmits the converted text format to a computer via a communication line,

[0850] A computing device that analyzes text format and generates response data,

[0851] A device that transmits the generated response data to the user device,

[0852] A device that synthesizes received response data into voice data,

[0853] A device for playing back synthesized audio data,

[0854] A device that performs destination selection and route calculation using voice commands,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising a device using a dedicated electronic circuit for high-speed processing of speech recognition and speech synthesis.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising a device that uses a mobile communication device to stabilize the connection with a communication line.

[0860] "Example 2 of combining an emotion engine"

[0861] (Claim 1)

[0862] Means for acquiring audio signals,

[0863] A means for converting acquired audio signals into text data,

[0864] A means of analyzing the converted text data to evaluate the emotional state and assigning emotional tags,

[0865] Means for transmitting text data and sentiment tags to a computing device via a communication network,

[0866] A means for analyzing text data and sentiment tags using a generative artificial intelligence model in a computing device and generating context-based response data,

[0867] A means for transmitting the generated response data to a contracted terminal device,

[0868] A means for synthesizing received response data into an audio signal and adjusting the tone based on emotion tags,

[0869] A means for reproducing a synthesized audio signal and displaying the response on a visual display device,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for performing speech recognition and speech synthesis processing by a dedicated electronic circuit.

[0873] (Claim 3)

[0874] The system according to claim 1, further comprising means for using a mobile communication unit to stabilize the connection with the communication network.

[0875] "Application example 2 of combining emotional engines"

[0876] (Claim 1)

[0877] Means for acquiring audio signals,

[0878] A means for converting the acquired audio signal into non-analog data,

[0879] A means for transmitting the converted non-analog data to an information processing device via an information network,

[0880] A means for analyzing non-analog data and generating response data in an information processing device,

[0881] A means for transmitting the generated response data to the request terminal device,

[0882] A means for combining the received response data into an audio signal,

[0883] A means for reproducing the synthesized audio signal,

[0884] A means of analyzing audio signals obtained from users to evaluate their emotional state,

[0885] A means of adjusting response data based on emotional state,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, further comprising means for performing speech recognition and speech synthesis processing using an application-specific integrated circuit.

[0889] (Claim 3)

[0890] The system according to claim 1, further comprising means of using mobile communication equipment to stabilize the connection with an information network. [Explanation of Symbols]

[0891] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring audio signals, A means for converting acquired audio signals into text data, Means for transmitting the converted text data to a server device via a communication network, A server device provides means for analyzing text data and generating response data, A means for transmitting the generated response data to a contracted terminal device, A means for combining the received response data into an audio signal, A means for reproducing the synthesized audio signal, A system that includes this.

2. The system according to claim 1, further comprising means for performing speech recognition and speech synthesis processing using dedicated hardware.

3. The system according to claim 1, further comprising means for using a mobile communication module to stabilize the connection with a communication network.

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