System

Abstract

An object of a system according to an embodiment is to consistently perform processing from voice input to dialogue text generation and voice output.SOLUTION: A system according to an embodiment includes a voice input unit, a voice recognition unit, a dialogue text generator, and a voice output unit. The voice input unit inputs a voice of a user. The voice recognition unit converts the voice input by the voice input unit into a text. The dialogue text generation unit generates a dialogue text based on the text converted by the speech recognition unit. The voice output unit outputs the dialogue text generated by the dialogue text generation unit as voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide systems that perform the entire process from voice input to dialogue generation, and there is room for improvement.

[0005] The system according to the embodiment aims to consistently perform processes from voice input to dialogue generation and voice output. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech input unit, a speech recognition unit, a dialogue generation unit, and a speech output unit. The speech input unit inputs a user's speech. The speech recognition unit converts the speech input by the speech input unit into text. The dialogue generation unit generates a dialogue based on the text converted by the speech recognition unit. The speech output unit outputs the dialogue generated by the dialogue generation unit as speech. [Effects of the Invention]

[0007] The system according to the embodiment can consistently perform processes from voice input to dialogue generation and voice output. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The dialogue system according to an embodiment of the present invention is a system in which a user's voice is automatically input, a generation AI generates a dialogue, and the dialogue is output in CoeFont AI voice. This allows the dialogue system to automatically recognize the user's voice, and the generation AI generates a dialogue and outputs it in CoeFont AI voice.

[0029] The dialogue system according to the embodiment includes a voice input unit, a voice recognition unit, a dialogue generation unit, and a voice output unit. The voice input unit inputs a user's voice. For example, the voice is input using a microphone. Alternatively, the voice input function of a smartphone can be used. The voice input unit transmits voice data to the voice recognition unit. For example, the voice input unit transmits the voice data to the voice recognition unit in real time. The voice recognition unit converts the voice input by the voice input unit into text. For example, deep learning can be used to convert the voice into text. Alternatively, a hidden Markov model (HMM) can be used. The voice recognition unit transmits the converted text to the dialogue generation unit. For example, the voice recognition unit transmits the converted text to the dialogue generation unit in real time. The dialogue generation unit generates a dialogue based on the text converted by the voice recognition unit. For example, a natural language generation technique is used to generate the dialogue. Alternatively, a template-based generation method can be used. The dialogue generation unit transmits the generated dialogue to the voice output unit. For example, the dialogue generation unit transmits the generated dialogue to the voice output unit in real time. The audio output unit outputs the dialogue generated by the dialogue generation unit as audio. For example, the audio is output using speakers. Alternatively, headphones can be used. The audio output unit also converts the dialogue into audio using CoeFont AI voice. For example, the dialogue is converted into natural-sounding audio using CoeFont AI voice. This allows the dialogue system according to the embodiment to automatically recognize the user's voice, have the generation AI generate dialogue, and output it in CoeFont AI voice. For example, when a user asks everyday questions or has a conversation with AIChat, the generation AI generates an appropriate response and outputs it in CoeFont AI voice, thereby realizing natural dialogue.

[0030] The speech recognition unit can learn the user's past speech data and recognize individual speech patterns. For example, the speech recognition unit collects the user's past speech data and trains the speech recognition system. For example, it makes it easier to recognize phrases and expressions that the user uses frequently. The speech recognition system also learns the user's speech patterns and responds to individual speaking styles. For example, if the user speaks quickly, the recognition accuracy is adjusted to match the speed. The speech recognition system also recognizes individual speech patterns based on the user's past speech data. For example, it accurately recognizes the speech of a user with a specific accent or dialect. In this way, the accuracy of speech recognition is improved by learning the user's past speech data and recognizing individual speech patterns.

[0031] The speech recognition unit can automatically filter background and environmental sounds to acquire speech data with less noise. For example, the speech recognition unit may incorporate a system that automatically filters background and environmental sounds during speech input. For example, clear speech data can be acquired even in noisy environments. The speech recognition unit also uses noise canceling technology to remove background sounds during speech input. For example, accurate speech recognition can be performed even in noisy places such as inside a car or a cafe. The speech recognition unit also analyzes environmental sounds in real time during speech input and filters out noise. For example, wind noise and traffic noise can be removed to recognize only the user's voice. This automatically filters background and environmental sounds and acquires speech data with less noise, improving the accuracy of speech recognition.

[0032] The speech recognition unit can support different languages ​​and dialects, enabling the construction of a multilingual dialogue system. For example, the speech recognition unit trains data on different languages ​​and dialects to make the speech input and recognition system multilingual. For example, it supports multiple languages ​​such as English, Japanese, and Spanish. The speech recognition unit also adds regional data to the speech recognition system to recognize dialects and expressions specific to a region. For example, it accurately recognizes Kansai dialect and New York accents. The speech recognition unit also constructs a multilingual dialogue system and performs speech recognition according to the language and dialect selected by the user. For example, if the user selects French, it performs French speech recognition. This allows the construction of a multilingual dialogue system that supports different languages ​​and dialects, thereby enabling the system to accommodate a greater number of users.

[0033] The speech recognition unit can acquire user location information and generate a response that takes into account the context based on the location information. For example, the speech recognition unit acquires the user's location information when a user inputs voice, and generates a response that takes into account the context based on that information. For example, if the user is in a park, information related to the park is provided. The speech recognition unit also provides information related to the user's current location to the speech recognition system based on the location information. For example, if the user is in a restaurant, information about the menu and business hours is provided. The speech recognition unit also acquires the user's location information in real time and generates a response based on that information. For example, if the user is traveling, information about tourist spots and transportation is provided. In this way, by acquiring the user's location information and generating a response that takes into account the context based on the location information, a more appropriate response can be provided.

[0034] The dialogue generation unit can refer to the user's past dialogue history and generate a consistent response. The dialogue generation unit, for example, refers to the user's past dialogue history and generates a consistent response. For example, it revisits topics that were discussed in previous conversations. In addition, the dialogue generation unit analyzes the user's past dialogue history when generating a dialogue and generates a response that includes related information. For example, it talks about topics that the user showed interest in in previous conversations. In addition, the dialogue generation unit generates a consistent dialogue based on the user's past dialogue history. For example, it generates a response that confirms what was promised in the previous conversation. In this way, by referring to the user's past dialogue history and generating a consistent response, the continuity of the dialogue is improved.

[0035] The dialogue generation unit can build a dialogue system with specialized knowledge that can handle different themes and topics. For example, the dialogue generation unit builds a database with specialized knowledge to adapt the dialogue generation process to different themes and topics. For example, it can handle specialized fields such as medicine, law, and technology. The dialogue generation unit also builds a dialogue system with specialized knowledge and generates specialized responses to user questions. For example, it can provide accurate information in response to questions about medicine. The dialogue generation unit also customizes the dialogue generation process to handle different themes and topics. For example, it generates dialogue with specialized knowledge according to a theme selected by the user. In this way, a dialogue system with specialized knowledge that can handle different themes and topics can be built, thereby meeting the diverse needs of users.

[0036] The dialogue generation unit can automatically search for related images and videos based on the user's input and provide visual information. The dialogue generation unit, for example, builds a system that automatically searches for related images and videos based on the user's input when generating a dialogue. For example, if the user says, "Show me pictures of cats," an image of a cat is displayed. The dialogue generation unit also analyzes the user's input and provides related visual information. For example, if the user says, "Tell me about tourist spots in Paris," an image or video of tourist spots in Paris is displayed. The dialogue generation unit also searches for related images and videos based on the user's input when generating a dialogue and provides visual information. For example, if the user says, "Tell me a cooking recipe," a video showing cooking steps is displayed. In this way, by automatically searching for related images and videos based on the user's input and providing visual information, the realism of the dialogue is improved.

[0037] The voice output unit can add a function that allows users to select a voice character according to their preferences. For example, when CoeFont's AI voice output is used, the voice output unit adds a function that allows users to select their preferred voice character. For example, a male voice, a female voice, a young voice, or an older voice can be selected. The voice output unit also builds a system that automatically suggests voice characters according to preferences based on the user's profile data. For example, it suggests the most suitable voice character based on past selection history. The voice output unit also increases the number of voice character options, allowing users to freely customize them. For example, it provides a function that allows users to adjust the tone of voice, accent, and speaking style. This improves the personalization of dialogue by adding a function that allows users to select a voice character according to their preferences.

[0038] The audio output unit can add background music and sound effects when outputting AI voice, providing a more immersive dialogue experience. For example, the audio output unit can add background music when outputting CoeFont AI voice, making the dialogue experience more immersive. For example, it can play calm music to create a relaxing atmosphere. The audio output unit also adds sound effects and provides sound effects according to the content of the dialogue. For example, when a user asks a question, it can play sound effects to indicate the start of the response. The audio output unit can also adjust the background music and sound effects in real time to change the atmosphere of the dialogue. For example, if the user is excited, it can play fast-paced music. In this way, adding background music and sound effects when outputting AI voice improves the immersive dialogue experience.

[0039] The voice output unit can support different devices (smart speakers, in-car systems, etc.), enabling use across multiple devices. For example, the voice output unit can make CoeFont's AI voice output compatible with smart speakers, enabling use within the home. For example, voice dialogue can be performed on devices such as Amazon Echo and Google Home. The voice output unit can also be made compatible with in-car systems, enabling AI voice output to be used even while driving. For example, it can work with a car navigation system to support dialogue while driving. The voice output unit can also build a multi-device compatible system, allowing users to have seamless voice dialogue across different devices. For example, the same dialogue can be continued on a smartphone, tablet, PC, etc. This allows it to support different devices and enable use across multiple devices, improving user convenience.

[0040] The voice output unit can adjust the voice to match the user's speaking speed and rhythm, enabling natural dialogue. The voice output unit, for example, analyzes the user's speaking speed and rhythm and adjusts the AI ​​voice output accordingly. For example, if the user speaks slowly, the AI ​​voice will also respond slowly. The voice output unit also adjusts the intonation and pauses of the AI ​​voice according to the speaking speed and rhythm. For example, if the user speaks quickly, the AI ​​voice will also speed up the tempo. The voice output unit also learns the user's speaking patterns and optimizes the AI ​​voice output based on them. For example, if the user speaks with a specific rhythm, the AI ​​voice will respond in accordance with that rhythm. In this way, natural dialogue is achieved by adjusting the voice to match the user's speaking speed and rhythm.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The voice input unit inputs the user's voice. For example, the voice is input using a microphone. Alternatively, the voice input function of a smartphone can be used. The voice input unit also transmits voice data to the voice recognition unit. For example, the voice input unit transmits the voice data to the voice recognition unit in real time. The voice recognition unit converts the voice input by the voice input unit into text. For example, deep learning is used to convert voice to text. Alternatively, an HMM (Hidden Markov Model) can be used. The voice recognition unit also transmits the converted text to a dialogue generation unit. For example, the voice recognition unit transmits the converted text to the dialogue generation unit in real time. The dialogue generation unit generates a dialogue based on the text converted by the voice recognition unit. For example, a natural language generation technique is used to generate the dialogue. Alternatively, a template-based generation method can be used. The dialogue generation unit also transmits the generated dialogue to an audio output unit. For example, the dialogue generation unit transmits the generated dialogue to the audio output unit in real time. The audio output unit outputs the dialogue generated by the dialogue generation unit as audio. For example, the audio is output using a speaker. Headphones can also be used. The audio output unit converts the dialogue into speech using CoeFont AI voice. For example, the dialogue is converted into natural speech using CoeFont AI voice. This allows the dialogue system according to the embodiment to automatically recognize the user's voice, and the generation AI generates dialogue and outputs it in CoeFont AI voice. For example, when a user asks everyday questions or has a conversation with AIChat, the generation AI generates an appropriate response and outputs it in CoeFont AI voice, thereby realizing natural dialogue.

[0043] The speech recognition unit can learn the user's past speech data and recognize individual speech patterns. For example, the speech recognition system can collect the user's past speech data and learn it. For example, it can make it easier to recognize phrases and expressions that the user uses frequently. The speech recognition system can also learn the user's speech patterns and accommodate individual speaking styles. For example, if the user speaks quickly, the recognition accuracy can be adjusted to match the speed. The speech recognition system can also recognize individual speech patterns based on the user's past speech data. For example, it can accurately recognize the speech of a user with a specific accent or dialect. In this way, the accuracy of speech recognition can be improved by learning the user's past speech data and recognizing individual speech patterns.

[0044] The speech recognition unit can automatically filter background and environmental sounds to acquire speech data with less noise. For example, a system that automatically filters background and environmental sounds during speech input can be introduced. For example, clear speech data can be acquired even in noisy environments. The speech recognition unit also uses noise canceling technology to remove background sounds during speech input. For example, accurate speech recognition can be performed even in noisy places such as inside a car or a cafe. The speech recognition unit also analyzes environmental sounds in real time during speech input and filters out noise. For example, wind noise and traffic noise can be removed to recognize only the user's voice. This automatically filters background and environmental sounds and acquires speech data with less noise, improving the accuracy of speech recognition.

[0045] The speech recognition unit can support different languages ​​and dialects, building a multilingual dialogue system. For example, to make the speech input and recognition system multilingual, data for different languages ​​and dialects is trained. For example, it can support multiple languages ​​such as English, Japanese, and Spanish. The speech recognition unit also adds regional data to the speech recognition system to recognize dialects and expressions specific to a region. For example, it can accurately recognize Kansai dialect and New York accents. The speech recognition unit also builds a multilingual dialogue system and performs speech recognition according to the language and dialect selected by the user. For example, if the user selects French, it performs French speech recognition. This allows the system to support different languages ​​and dialects, building a multilingual dialogue system that can accommodate a wider range of users.

[0046] The speech recognition unit can acquire user location information and generate a response that takes into account the context based on the location information. For example, when a user inputs voice, the speech recognition unit acquires the user's location information and generates a response that takes into account the context based on that information. For example, if the user is in a park, information related to the park is provided. The speech recognition unit also provides information related to the user's current location to the speech recognition system based on the location information. For example, if the user is in a restaurant, information about the menu and business hours is provided. The speech recognition unit also acquires the user's location information in real time and generates a response based on that information. For example, if the user is traveling, information about tourist spots and transportation is provided. In this way, by acquiring the user's location information and generating a response that takes into account the context based on the location information, a more appropriate response can be provided.

[0047] The dialogue generation unit can refer to the user's past dialogue history to generate a consistent response. For example, it may refer to the user's past dialogue history to generate a consistent response. For example, it may revisit topics that were discussed in previous conversations. In addition, when generating a dialogue, the dialogue generation unit analyzes the user's past dialogue history to generate a response that includes related information. For example, it may discuss topics that the user showed interest in in previous conversations. In addition, the dialogue generation unit generates a consistent dialogue based on the user's past dialogue history. For example, it may generate a response that confirms what was promised in the previous conversation. In this way, by referring to the user's past dialogue history and generating a consistent response, the continuity of the dialogue is improved.

[0048] The dialogue generation unit can build a dialogue system with specialized knowledge that can handle different themes and topics. For example, a database with specialized knowledge can be built to adapt the dialogue generation process to different themes and topics. For example, it can handle specialized fields such as medicine, law, and technology. The dialogue generation unit can also build a dialogue system with specialized knowledge and generate specialized responses to user questions. For example, it can provide accurate information in response to questions about medicine. The dialogue generation unit can also customize the dialogue generation process to handle different themes and topics. For example, it can generate dialogue with specialized knowledge depending on the theme selected by the user. This allows a dialogue system with specialized knowledge that can handle different themes and topics to be built, thereby meeting the diverse needs of users.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The voice input unit inputs the user's voice. For example, the voice is input using a microphone or the voice input function of a smartphone. The voice input unit also transmits the voice data to the voice recognition unit in real time. Step 2: The speech recognition unit converts the speech input by the speech input unit into text. For example, it converts the speech into text using deep learning or HMM (Hidden Markov Model). The speech recognition unit then sends the converted text to the dialogue generation unit in real time. Step 3: The dialogue generation unit generates dialogue based on the text converted by the speech recognition unit. For example, the dialogue is generated using natural language generation technology or a template-based generation method. The dialogue generation unit then transmits the generated dialogue to the speech output unit in real time. Step 4: The voice output unit outputs the dialogue generated by the dialogue generation unit as voice. For example, the voice is output using speakers or headphones. The voice output unit also converts the dialogue into natural-sounding voice using CoeFont's AI voice.

[0051] (Example 2) The dialogue system according to an embodiment of the present invention is a system in which a user's voice is automatically input, a generation AI generates a dialogue, and the dialogue is output in CoeFont AI voice. This allows the dialogue system to automatically recognize the user's voice, and the generation AI generates a dialogue and outputs it in CoeFont AI voice.

[0052] The dialogue system according to the embodiment includes a voice input unit, a voice recognition unit, a dialogue generation unit, and a voice output unit. The voice input unit inputs a user's voice. For example, the voice is input using a microphone. Alternatively, the voice input function of a smartphone can be used. The voice input unit transmits voice data to the voice recognition unit. For example, the voice input unit transmits the voice data to the voice recognition unit in real time. The voice recognition unit converts the voice input by the voice input unit into text. For example, deep learning can be used to convert the voice into text. Alternatively, a hidden Markov model (HMM) can be used. The voice recognition unit transmits the converted text to the dialogue generation unit. For example, the voice recognition unit transmits the converted text to the dialogue generation unit in real time. The dialogue generation unit generates a dialogue based on the text converted by the voice recognition unit. For example, a natural language generation technique is used to generate the dialogue. Alternatively, a template-based generation method can be used. The dialogue generation unit transmits the generated dialogue to the voice output unit. For example, the dialogue generation unit transmits the generated dialogue to the voice output unit in real time. The audio output unit outputs the dialogue generated by the dialogue generation unit as audio. For example, the audio is output using speakers. Alternatively, headphones can be used. The audio output unit also converts the dialogue into audio using CoeFont AI voice. For example, the dialogue is converted into natural-sounding audio using CoeFont AI voice. This allows the dialogue system according to the embodiment to automatically recognize the user's voice, have the generation AI generate dialogue, and output it in CoeFont AI voice. For example, when a user asks everyday questions or has a conversation with AIChat, the generation AI generates an appropriate response and outputs it in CoeFont AI voice, thereby realizing natural dialogue.

[0053] The voice input unit uses an emotion engine to estimate the user's emotion and generates feedback according to the emotion. For example, when the user speaks, the voice input unit activates the emotion engine simultaneously with the voice input to estimate the user's emotion in real time. For example, if the user is excited, the emotion is reflected in the generated dialogue. Furthermore, when the voice input unit receives voice, the emotion engine analyzes the tone and pitch of the user's voice to estimate the emotion. For example, if the user speaks in a sad voice, the generated dialogue includes a comforting element. Furthermore, the voice input unit uses the emotion engine to estimate the user's emotion and generates feedback according to the emotion. For example, if the user is angry, the voice input unit generates a dialogue that responds in a calm tone. In this way, feedback according to the user's emotion is generated, improving the naturalness of the dialogue.

[0054] The speech recognition unit can learn the user's past speech data and recognize individual speech patterns. For example, the speech recognition unit collects the user's past speech data and trains the speech recognition system. For example, it makes it easier to recognize phrases and expressions that the user uses frequently. The speech recognition system also learns the user's speech patterns and responds to individual speaking styles. For example, if the user speaks quickly, the recognition accuracy is adjusted to match the speed. The speech recognition system also recognizes individual speech patterns based on the user's past speech data. For example, it accurately recognizes the speech of a user with a specific accent or dialect. In this way, the accuracy of speech recognition is improved by learning the user's past speech data and recognizing individual speech patterns.

[0055] The speech recognition unit can automatically filter background and environmental sounds to acquire speech data with less noise. For example, the speech recognition unit may incorporate a system that automatically filters background and environmental sounds during speech input. For example, clear speech data can be acquired even in noisy environments. The speech recognition unit also uses noise canceling technology to remove background sounds during speech input. For example, accurate speech recognition can be performed even in noisy places such as inside a car or a cafe. The speech recognition unit also analyzes environmental sounds in real time during speech input and filters out noise. For example, wind noise and traffic noise can be removed to recognize only the user's voice. This automatically filters background and environmental sounds and acquires speech data with less noise, improving the accuracy of speech recognition.

[0056] The speech recognition unit can support different languages ​​and dialects, enabling the construction of a multilingual dialogue system. For example, the speech recognition unit trains data on different languages ​​and dialects to make the speech input and recognition system multilingual. For example, it supports multiple languages ​​such as English, Japanese, and Spanish. The speech recognition unit also adds regional data to the speech recognition system to recognize dialects and expressions specific to a region. For example, it accurately recognizes Kansai dialect and New York accents. The speech recognition unit also constructs a multilingual dialogue system and performs speech recognition according to the language and dialect selected by the user. For example, if the user selects French, it performs French speech recognition. This allows the construction of a multilingual dialogue system that supports different languages ​​and dialects, thereby enabling the system to accommodate a greater number of users.

[0057] The speech recognition unit can acquire user location information and generate a response that takes into account the context based on the location information. For example, the speech recognition unit acquires the user's location information when a user inputs voice, and generates a response that takes into account the context based on that information. For example, if the user is in a park, information related to the park is provided. The speech recognition unit also provides information related to the user's current location to the speech recognition system based on the location information. For example, if the user is in a restaurant, information about the menu and business hours is provided. The speech recognition unit also acquires the user's location information in real time and generates a response based on that information. For example, if the user is traveling, information about tourist spots and transportation is provided. In this way, by acquiring the user's location information and generating a response that takes into account the context based on the location information, a more appropriate response can be provided.

[0058] The voice recognition unit uses the emotion estimation function to provide voice feedback according to the user's emotion in real time, thereby improving the naturalness of the dialogue. The voice recognition unit, for example, uses the emotion estimation function to provide voice feedback according to the user's emotion in real time. For example, if the user is happy, the voice recognition unit responds in a bright tone. The voice recognition unit also analyzes the user's emotion in real time and generates feedback according to the emotion. For example, if the user is tired, the voice recognition unit responds in a gentle tone. The voice recognition unit also uses the emotion estimation function to provide voice feedback according to the user's emotion, thereby improving the naturalness of the dialogue. For example, if the user is excited, the voice recognition unit responds in a similarly excited tone. In this way, the emotion estimation function is used to provide voice feedback according to the user's emotion in real time, thereby improving the naturalness of the dialogue.

[0059] The dialogue generation unit can take the user's emotions into consideration and use appropriate tones and expressions according to the emotions. For example, when generating a dialogue, the dialogue generation unit takes the user's emotions into consideration and uses tones and expressions according to the emotions. For example, if the user is sad, it uses comforting expressions. The dialogue generation unit also performs emotion analysis and generates dialogue according to the user's emotions. For example, if the user is angry, it generates dialogue with a calm and collected tone. The dialogue generation unit also analyzes the user's emotions in real time and uses appropriate tones and expressions according to the emotions. For example, if the user is happy, it uses bright and positive expressions. In this way, by taking the user's emotions into consideration and using appropriate tones and expressions according to the emotions, the naturalness of the dialogue is improved.

[0060] The dialogue generation unit can refer to the user's past dialogue history and generate a consistent response. The dialogue generation unit, for example, refers to the user's past dialogue history and generates a consistent response. For example, it revisits topics that were discussed in previous conversations. In addition, the dialogue generation unit analyzes the user's past dialogue history when generating a dialogue and generates a response that includes related information. For example, it talks about topics that the user showed interest in in previous conversations. In addition, the dialogue generation unit generates a consistent dialogue based on the user's past dialogue history. For example, it generates a response that confirms what was promised in the previous conversation. In this way, by referring to the user's past dialogue history and generating a consistent response, the continuity of the dialogue is improved.

[0061] The dialogue generation unit can build a dialogue system with specialized knowledge that can handle different themes and topics. For example, the dialogue generation unit builds a database with specialized knowledge to adapt the dialogue generation process to different themes and topics. For example, it can handle specialized fields such as medicine, law, and technology. The dialogue generation unit also builds a dialogue system with specialized knowledge and generates specialized responses to user questions. For example, it can provide accurate information in response to questions about medicine. The dialogue generation unit also customizes the dialogue generation process to handle different themes and topics. For example, it generates dialogue with specialized knowledge according to a theme selected by the user. In this way, a dialogue system with specialized knowledge that can handle different themes and topics can be built, thereby meeting the diverse needs of users.

[0062] The dialogue generation unit can automatically search for related images and videos based on the user's input and provide visual information. The dialogue generation unit, for example, builds a system that automatically searches for related images and videos based on the user's input when generating a dialogue. For example, if the user says, "Show me pictures of cats," an image of a cat is displayed. The dialogue generation unit also analyzes the user's input and provides related visual information. For example, if the user says, "Tell me about tourist spots in Paris," an image or video of tourist spots in Paris is displayed. The dialogue generation unit also searches for related images and videos based on the user's input when generating a dialogue and provides visual information. For example, if the user says, "Tell me a cooking recipe," a video showing cooking steps is displayed. In this way, by automatically searching for related images and videos based on the user's input and providing visual information, the realism of the dialogue is improved.

[0063] The dialogue generation unit uses the emotion estimation function to generate dialogue that corresponds to the user's emotion, thereby realizing a dialogue that elicits emotional empathy. The dialogue generation unit, for example, uses the emotion estimation function to generate dialogue that corresponds to the user's emotion. For example, if the user is sad, it generates a comforting dialogue. The dialogue generation unit also analyzes the user's emotion in real time and generates dialogue that corresponds to the emotion. For example, if the user is happy, it generates a dialogue that shows empathy. The dialogue generation unit also uses the emotion estimation function to generate dialogue that corresponds to the user's emotion, thereby realizing a dialogue that elicits emotional empathy. For example, if the user is angry, it generates a calm and collected dialogue. In this way, the emotion estimation function is used to generate dialogue that corresponds to the user's emotion, thereby realizing a dialogue that elicits emotional empathy, thereby improving the quality of the dialogue.

[0064] The voice output unit can adjust the voice tone and intonation to reflect the user's emotions. For example, when outputting CoeFont's AI voice, the voice output unit adjusts the voice tone and intonation to reflect the user's emotions. For example, if the user is happy, the voice output unit outputs a bright tone. The voice output unit also analyzes the user's emotions in real time and adjusts the voice tone and intonation according to the emotion. For example, if the user is sad, the voice output unit outputs a gentle tone. The voice output unit also uses an emotion estimation function to adjust the voice tone and intonation to reflect the user's emotions. For example, if the user is angry, the voice output unit outputs a calm and composed tone. This adjusts the voice tone and intonation to reflect the user's emotions, improving the naturalness of the dialogue.

[0065] The voice output unit can add a function that allows users to select a voice character according to their preferences. For example, when CoeFont's AI voice output is used, the voice output unit adds a function that allows users to select their preferred voice character. For example, a male voice, a female voice, a young voice, or an older voice can be selected. The voice output unit also builds a system that automatically suggests voice characters according to preferences based on the user's profile data. For example, it suggests the most suitable voice character based on past selection history. The voice output unit also increases the number of voice character options, allowing users to freely customize them. For example, it provides a function that allows users to adjust the tone of voice, accent, and speaking style. This improves the personalization of dialogue by adding a function that allows users to select a voice character according to their preferences.

[0066] The audio output unit can add background music and sound effects when outputting AI voice, providing a more immersive dialogue experience. For example, the audio output unit can add background music when outputting CoeFont AI voice, making the dialogue experience more immersive. For example, it can play calm music to create a relaxing atmosphere. The audio output unit also adds sound effects and provides sound effects according to the content of the dialogue. For example, when a user asks a question, it can play sound effects to indicate the start of the response. The audio output unit can also adjust the background music and sound effects in real time to change the atmosphere of the dialogue. For example, if the user is excited, it can play fast-paced music. In this way, adding background music and sound effects when outputting AI voice improves the immersive dialogue experience.

[0067] The voice output unit can support different devices (smart speakers, in-car systems, etc.), enabling use across multiple devices. For example, the voice output unit can make CoeFont's AI voice output compatible with smart speakers, enabling use within the home. For example, voice dialogue can be performed on devices such as Amazon Echo and Google Home. The voice output unit can also be made compatible with in-car systems, enabling AI voice output to be used even while driving. For example, it can work with a car navigation system to support dialogue while driving. The voice output unit can also build a multi-device compatible system, allowing users to have seamless voice dialogue across different devices. For example, the same dialogue can be continued on a smartphone, tablet, PC, etc. This allows it to support different devices and enable use across multiple devices, improving user convenience.

[0068] The voice output unit can adjust the voice to match the user's speaking speed and rhythm, enabling natural dialogue. The voice output unit, for example, analyzes the user's speaking speed and rhythm and adjusts the AI ​​voice output accordingly. For example, if the user speaks slowly, the AI ​​voice will also respond slowly. The voice output unit also adjusts the intonation and pauses of the AI ​​voice according to the speaking speed and rhythm. For example, if the user speaks quickly, the AI ​​voice will also speed up the tempo. The voice output unit also learns the user's speaking patterns and optimizes the AI ​​voice output based on them. For example, if the user speaks with a specific rhythm, the AI ​​voice will respond in accordance with that rhythm. In this way, natural dialogue is achieved by adjusting the voice to match the user's speaking speed and rhythm.

[0069] The voice output unit uses the emotion estimation function to provide voice feedback according to the user's emotion, thereby realizing a dialogue that elicits emotional empathy. The voice output unit, for example, uses the emotion estimation function to provide voice feedback according to the user's emotion. For example, if the user is sad, the voice output unit responds in a comforting tone. The voice output unit also analyzes the user's emotion in real time and provides voice feedback according to the emotion. For example, if the user is happy, the voice output unit responds in a tone that shows empathy. The voice output unit also uses the emotion estimation function to provide voice feedback according to the user's emotion, thereby realizing a dialogue that elicits emotional empathy. For example, if the user is angry, the voice output unit responds in a calm and composed tone. In this way, the emotion estimation function is used to provide voice feedback according to the user's emotion, thereby realizing a dialogue that elicits emotional empathy, thereby improving the quality of the dialogue.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The voice input unit inputs the user's voice. For example, the voice is input using a microphone. Alternatively, the voice input function of a smartphone can be used. The voice input unit also transmits voice data to the voice recognition unit. For example, the voice input unit transmits the voice data to the voice recognition unit in real time. The voice recognition unit converts the voice input by the voice input unit into text. For example, deep learning is used to convert voice to text. Alternatively, an HMM (Hidden Markov Model) can be used. The voice recognition unit also transmits the converted text to a dialogue generation unit. For example, the voice recognition unit transmits the converted text to the dialogue generation unit in real time. The dialogue generation unit generates a dialogue based on the text converted by the voice recognition unit. For example, a natural language generation technique is used to generate the dialogue. Alternatively, a template-based generation method can be used. The dialogue generation unit also transmits the generated dialogue to an audio output unit. For example, the dialogue generation unit transmits the generated dialogue to the audio output unit in real time. The audio output unit outputs the dialogue generated by the dialogue generation unit as audio. For example, the audio is output using a speaker. Headphones can also be used. The audio output unit converts the dialogue into speech using CoeFont AI voice. For example, the dialogue is converted into natural speech using CoeFont AI voice. This allows the dialogue system according to the embodiment to automatically recognize the user's voice, and the generation AI generates dialogue and outputs it in CoeFont AI voice. For example, when a user asks everyday questions or has a conversation with AIChat, the generation AI generates an appropriate response and outputs it in CoeFont AI voice, thereby realizing natural dialogue.

[0072] The voice input unit uses an emotion engine to estimate the user's emotion and generates feedback according to the emotion. For example, when the user speaks, the emotion engine is activated simultaneously with the voice input to estimate the user's emotion in real time. For example, if the user is excited, the emotion is reflected in the generated dialogue. Furthermore, when the voice input unit receives voice, the emotion engine analyzes the tone and pitch of the user's voice to estimate the emotion. For example, if the user speaks in a sad voice, the generated dialogue includes a comforting element. Furthermore, the voice input unit uses the emotion engine to estimate the user's emotion and generates feedback according to the emotion. For example, if the user is angry, the dialogue is generated to respond in a calm tone. In this way, feedback according to the user's emotion is generated, improving the naturalness of the dialogue.

[0073] The speech recognition unit can learn the user's past speech data and recognize individual speech patterns. For example, the speech recognition system can collect the user's past speech data and learn it. For example, it can make it easier to recognize phrases and expressions that the user uses frequently. The speech recognition system can also learn the user's speech patterns and accommodate individual speaking styles. For example, if the user speaks quickly, the recognition accuracy can be adjusted to match the speed. The speech recognition system can also recognize individual speech patterns based on the user's past speech data. For example, it can accurately recognize the speech of a user with a specific accent or dialect. In this way, the accuracy of speech recognition can be improved by learning the user's past speech data and recognizing individual speech patterns.

[0074] The speech recognition unit can automatically filter background and environmental sounds to acquire speech data with less noise. For example, a system that automatically filters background and environmental sounds during speech input can be introduced. For example, clear speech data can be acquired even in noisy environments. The speech recognition unit also uses noise canceling technology to remove background sounds during speech input. For example, accurate speech recognition can be performed even in noisy places such as inside a car or a cafe. The speech recognition unit also analyzes environmental sounds in real time during speech input and filters out noise. For example, wind noise and traffic noise can be removed to recognize only the user's voice. This automatically filters background and environmental sounds and acquires speech data with less noise, improving the accuracy of speech recognition.

[0075] The speech recognition unit can support different languages ​​and dialects, building a multilingual dialogue system. For example, to make the speech input and recognition system multilingual, data for different languages ​​and dialects is trained. For example, it can support multiple languages ​​such as English, Japanese, and Spanish. The speech recognition unit also adds regional data to the speech recognition system to recognize dialects and expressions specific to a region. For example, it can accurately recognize Kansai dialect and New York accents. The speech recognition unit also builds a multilingual dialogue system and performs speech recognition according to the language and dialect selected by the user. For example, if the user selects French, it performs French speech recognition. This allows the system to support different languages ​​and dialects, building a multilingual dialogue system that can accommodate a wider range of users.

[0076] The speech recognition unit can acquire user location information and generate a response that takes into account the context based on the location information. For example, when a user inputs voice, the speech recognition unit acquires the user's location information and generates a response that takes into account the context based on that information. For example, if the user is in a park, information related to the park is provided. The speech recognition unit also provides information related to the user's current location to the speech recognition system based on the location information. For example, if the user is in a restaurant, information about the menu and business hours is provided. The speech recognition unit also acquires the user's location information in real time and generates a response based on that information. For example, if the user is traveling, information about tourist spots and transportation is provided. In this way, by acquiring the user's location information and generating a response that takes into account the context based on the location information, a more appropriate response can be provided.

[0077] The voice recognition unit uses the emotion estimation function to provide voice feedback in real time according to the user's emotion. For example, if the user is happy, the voice recognition unit responds in a bright tone. The voice recognition unit also analyzes the user's emotion in real time and generates feedback according to that emotion. For example, if the user is tired, the voice recognition unit responds in a gentle tone. The voice recognition unit also uses the emotion estimation function to provide voice feedback in accordance with the user's emotion, improving the naturalness of the dialogue. For example, if the user is excited, the voice recognition unit responds in a similarly excited tone. In this way, the emotion estimation function is used to provide voice feedback in real time according to the user's emotion, improving the naturalness of the dialogue.

[0078] The dialogue generation unit takes into account the user's emotions and can use appropriate tones and expressions according to the emotions. For example, when generating a dialogue, the unit takes into account the user's emotions and uses tones and expressions according to the emotions. For example, if the user is sad, it uses comforting expressions. The dialogue generation unit also performs emotion analysis and generates dialogue according to the user's emotions. For example, if the user is angry, it generates dialogue with a calm and collected tone. The dialogue generation unit also analyzes the user's emotions in real time and uses appropriate tones and expressions according to the emotions. For example, if the user is happy, it uses bright and positive expressions. In this way, by taking into account the user's emotions and using appropriate tones and expressions according to the emotions, the naturalness of the dialogue is improved.

[0079] The dialogue generation unit can refer to the user's past dialogue history to generate a consistent response. For example, it may refer to the user's past dialogue history to generate a consistent response. For example, it may revisit topics that were discussed in previous conversations. In addition, when generating a dialogue, the dialogue generation unit analyzes the user's past dialogue history to generate a response that includes related information. For example, it may discuss topics that the user showed interest in in previous conversations. In addition, the dialogue generation unit generates a consistent dialogue based on the user's past dialogue history. For example, it may generate a response that confirms what was promised in the previous conversation. In this way, by referring to the user's past dialogue history and generating a consistent response, the continuity of the dialogue is improved.

[0080] The dialogue generation unit can build a dialogue system with specialized knowledge that can handle different themes and topics. For example, a database with specialized knowledge can be built to adapt the dialogue generation process to different themes and topics. For example, it can handle specialized fields such as medicine, law, and technology. The dialogue generation unit can also build a dialogue system with specialized knowledge and generate specialized responses to user questions. For example, it can provide accurate information in response to questions about medicine. The dialogue generation unit can also customize the dialogue generation process to handle different themes and topics. For example, it can generate dialogue with specialized knowledge depending on the theme selected by the user. This allows a dialogue system with specialized knowledge that can handle different themes and topics to be built, thereby meeting the diverse needs of users.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The voice input unit inputs the user's voice. For example, the voice is input using a microphone or the voice input function of a smartphone. The voice input unit also transmits the voice data to the voice recognition unit in real time. Step 2: The speech recognition unit converts the speech input by the speech input unit into text. For example, it converts the speech into text using deep learning or HMM (Hidden Markov Model). The speech recognition unit then sends the converted text to the dialogue generation unit in real time. Step 3: The dialogue generation unit generates dialogue based on the text converted by the speech recognition unit. For example, the dialogue is generated using natural language generation technology or a template-based generation method. The dialogue generation unit then transmits the generated dialogue to the speech output unit in real time. Step 4: The voice output unit outputs the dialogue generated by the dialogue generation unit as voice. For example, the voice is output using speakers or headphones. The voice output unit also converts the dialogue into natural-sounding voice using CoeFont's AI voice.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice input unit for inputting a user's voice; a speech recognition unit that converts the speech input by the speech input unit into text; a dialogue generation unit that generates dialogue based on the text converted by the speech recognition unit; a voice output unit that outputs the dialogue generated by the dialogue generation unit as voice. A system characterized by:

2. The voice input unit Using an emotion engine to estimate a user's emotion, and generating feedback according to the emotion.

2. The system of claim 1.

3. The voice recognition unit Learns from the user's past speech data and recognizes individual speech patterns 2. The system of claim 1.

4. The voice recognition unit Automatically filters out background and environmental sounds to obtain less noisy audio data 2. The system of claim 1.

5. The voice recognition unit Develop a multilingual dialogue system that supports different languages ​​and dialects 2. The system of claim 1.

Images