system

Abstract

The system according to this embodiment aims to enable people with speech impairments to engage in natural conversation. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an output unit, a learning unit, and a conversion unit. The reception unit receives text input from the user. The generation unit analyzes the text received by the reception unit and generates natural-sounding speech. The output unit outputs the speech generated by the generation unit. The learning unit adjusts the speech generated by the generation unit based on the user's speech characteristics. The conversion unit converts the speech of others into text.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult for people with speech disorders to have natural conversations, and there is room for improvement.

[0005] The system according to the embodiment aims to enable people with speech disorders to have natural conversations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an output unit, a learning unit, and a conversion unit. The reception unit receives text input from the user. The generation unit analyzes the text received by the reception unit and generates natural-sounding speech. The output unit outputs the speech generated by the generation unit. The learning unit adjusts the speech generated by the generation unit based on the user's speech characteristics. The conversion unit converts the speech of others into text. [Effects of the Invention]

[0007] The system according to this embodiment enables people with speech impairments to engage in natural conversations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 3, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The dialogue support system according to an embodiment of the present invention is a system for enabling people with speech disorders to engage in natural conversations. The dialogue support system allows users to input text, which is then converted into natural speech, enabling real-time conversations with others. The dialogue support system learns the user's voice characteristics through machine learning to provide more natural speech. The dialogue support system is available on smartphones and tablets. Specifically, it consists of the following steps: First, the user inputs text into the dialogue support system. This text is input into a generating AI. The generating AI analyzes the input text and converts it into natural speech. Next, the generating AI learns the user's voice characteristics. This allows the generating AI to generate natural speech that reflects the user's voice characteristics. Furthermore, the generating AI converts the statements of others into text in real time. This makes it easier for the user to understand what others are saying. This mechanism enables people with speech disorders to engage in natural conversations, lowering communication barriers. It also expands opportunities for social participation and improves quality of life. For example, the dialogue support system can be used in medical institutions, special needs schools, and welfare facilities to support people with speech disorders. This allows the dialogue support system to provide a system that enables people with speech impairments to engage in natural conversations.

[0029] The dialogue support system according to the embodiment comprises a reception unit, a generation unit, an output unit, a learning unit, and a conversion unit. The reception unit receives text input from the user. The reception unit receives, for example, text entered by the user. The generation unit uses a generation AI to analyze the text received by the reception unit and generate natural speech. The generation unit uses a generation AI to analyze the input text and generate natural speech. The generation unit uses a generation AI to learn the user's voice characteristics and adjusts the generated speech based on the user's voice characteristics. The output unit outputs the speech generated by the generation unit. The output unit outputs, for example, the generated speech through speakers or headphones. The output unit adjusts the generated speech based on the user's voice characteristics. The output unit adjusts, for example, the pitch and speed of the generated speech. The learning unit adjusts the speech generated by the generation unit based on the user's voice characteristics. The learning unit, for example, uses a generation AI to learn the user's voice characteristics and adjusts the generated speech based on the user's voice characteristics. The conversion unit converts the utterances of others into text. The conversion unit converts the utterances of others into text in real time, for example. As a result, the dialogue support system according to this embodiment can provide a system for people with speech disorders to engage in natural dialogue.

[0030] The reception unit receives text input from the user. For example, the reception unit receives text entered by the user. Specifically, the reception unit provides an interface for receiving text data entered by the user using a keyboard or touchscreen. Furthermore, it is possible to incorporate speech recognition technology that converts voice input into text, allowing the user to also input by voice. The reception unit processes the entered text data in real time and sends it to the generation unit. The reception unit also has the function to analyze the user's input, check grammar and spelling, and make automatic corrections as needed. This allows the user to input text accurately and quickly, improving the overall efficiency of the system.

[0031] The generation unit uses a generation AI to analyze the text received by the reception unit and generate natural-sounding speech. Specifically, the generation AI analyzes the input text using natural language processing techniques to determine appropriate speech synthesis parameters. The generation AI learns the user's voice characteristics and adjusts the generated speech based on these characteristics. For example, the generation AI learns the user's past voice data and extracts features such as tone, pitch, speed, and accent. This results in generated speech that is very close to the user's natural voice. The generation unit trains a speech synthesis model using deep learning techniques to generate high-quality speech. This allows the generation unit to provide customized speech tailored to the user's individual voice characteristics.

[0032] The output unit outputs the audio generated by the generation unit. For example, the output unit outputs the generated audio through speakers or headphones. Specifically, the output unit uses digital signal processing technology to transmit the generated audio data to speakers or headphones, reproducing clear and natural sound. The output unit adjusts the generated audio based on the user's voice characteristics. For example, by adjusting the pitch and speed of the generated audio, it reproduces a voice that is closer to the user's voice. The output unit also has a function to customize the volume and sound quality of the audio to the user's preferences. This allows the user to listen to the audio comfortably and improves the quality of the conversation. Furthermore, the output unit supports multiple output devices, allowing the user to select the optimal device according to their environment. This enables the output unit to provide the user with high-quality audio output, maximizing the effectiveness of the dialogue support system.

[0033] The learning unit adjusts the speech generated by the generation unit based on the user's voice characteristics. For example, the generation AI learns the user's voice characteristics, and the learning unit adjusts the generated speech based on those characteristics. Specifically, the learning unit collects the user's voice data and extracts voice features using deep learning technology. This allows the generation AI to learn features such as the tone, pitch, speed, and accent of the user's voice, and to make the generated speech more like the user's voice. The learning unit receives user feedback and continuously improves the quality of the generated speech. For example, the user evaluates their satisfaction with the generated speech, and the speech synthesis model is retrained based on that evaluation. This allows the learning unit to provide customized speech that meets the user's individual needs. Furthermore, the learning unit can improve the versatility of the speech synthesis model by comparing the voice characteristics of different users and extracting common features. This allows the learning unit to enhance the speech generation capabilities of the dialogue support system and provide high-quality speech to more users.

[0034] The conversion unit converts other people's speech into text. For example, the conversion unit converts other people's speech into text in real time. Specifically, the conversion unit uses high-precision speech recognition technology to convert other people's speech into text data in real time. The conversion unit uses noise reduction technology to remove background noise and accurately recognize the content of speech. This allows the conversion unit to achieve high-precision speech recognition even in noisy environments. The conversion unit displays the recognized text data in real time, allowing users to immediately check the content of other people's speech. Furthermore, the conversion unit also has a function to save the recognized text data for later reference. This allows users to easily check past conversations and manage their conversation history. The conversion unit supports multiple languages ​​and also has a function to automatically translate speech from different languages. This allows the conversion unit to support conversations between users who speak different languages ​​and remove communication barriers.

[0035] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display phrases that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest phrases that the user will use at specific times of day based on their past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0036] The reception desk can provide input assistance based on the user's current situation and environment when they are entering text. For example, if the user is in a public place, the reception desk can suggest an input method that takes privacy into consideration. It can also provide detailed input options if the user is at home. Furthermore, if the user is on the move, the reception desk can suggest a simple and quick input method. This improves the convenience of input by providing input assistance tailored to the user's situation and environment. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0037] The input system can present highly relevant input suggestions when a user is entering text, taking into account their geographical location. For example, if the user is in a specific location, the input system can display phrases related to that location as suggestions. Furthermore, if the user is traveling, the input system can display phrases related to their travel destination as suggestions. Additionally, if the user is at home, the input system can display phrases related to their home as suggestions. This improves input efficiency by presenting input suggestions based on geographical location. Some or all of the above processing in the input system may be performed using AI, for example, or without AI.

[0038] The input section can analyze the user's social media activity during text input and suggest relevant input options. For example, the input section can display relevant phrases as suggestions based on the user's recent posts. It can also display relevant phrases as suggestions based on the posts of accounts the user follows. Furthermore, it can display relevant phrases as suggestions based on the activities of groups the user participates in. This improves input efficiency by providing input suggestions based on social media activity. Some or all of the above processing in the input section may be performed using AI, for example, or without AI.

[0039] The generation unit can apply different speech generation algorithms depending on the context during text analysis. For example, in a formal context, the generation unit can apply an algorithm that generates a polite tone of voice. In a casual context, it can also apply an algorithm that generates a friendly tone of voice. Furthermore, in a technical context, the generation unit can apply an algorithm that accurately pronounces technical terms. By applying context-appropriate speech generation algorithms, more appropriate speech can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate speech using a generation AI that applies different speech generation algorithms depending on the context during text analysis.

[0040] The generation unit can improve the accuracy of speech generation by referring to the user's past speech patterns during text analysis. For example, the generation unit generates speech by referring to phrases and expressions the user has used in the past. The generation unit can also generate speech by referring to the user's past speech speed and tone. Furthermore, the generation unit can analyze the content of the user's past speech and generate speech appropriate to the context. This allows for improved accuracy of speech generation by referring to past speech patterns. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate speech using a generation AI that generates speech by referring to the user's past speech patterns.

[0041] The output unit can select the optimal output method by referring to the user's past voice output history when outputting voice. For example, the output unit can output voice by referring to the tone and volume that the user has previously preferred to use. The output unit can also select the optimal output method according to a specific situation from the user's past voice output history. Furthermore, the output unit can analyze the user's past voice output history and select the optimal voice output method according to the context. In this way, the optimal output method can be selected by referring to the past voice output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without using AI.

[0042] The output unit can customize the output method based on the user's current environment when outputting audio. For example, if the user is in a quiet environment, the output unit can output audio at a lower volume. Conversely, if the user is in a noisy environment, the output unit can output audio at a higher volume. Furthermore, if the user is moving, the output unit can output audio at an appropriate volume and tone. This allows for optimal audio output by customizing the output method based on the current environment. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI.

[0043] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on data that the user has previously learned. The learning unit can also analyze the user's past learning history and adjust the learning algorithm. Furthermore, the learning unit can improve the accuracy of learning by referring to the user's past learning data. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0044] The learning unit can improve the accuracy of learning by analyzing the user's speech patterns during training. For example, the learning unit can analyze the user's speech speed and tone and adjust the learning algorithm. The learning unit can also analyze the content of the user's speech and select learning data appropriate to the context. Furthermore, the learning unit can improve the accuracy of learning by referring to the user's speech patterns. In this way, the accuracy of learning can be improved by analyzing speech patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0045] The conversion unit can apply different conversion algorithms depending on the context when converting someone else's speech into text. For example, in a formal context, the conversion unit can apply a conversion algorithm that preserves polite phrasing. In a casual context, it can also apply a conversion algorithm that preserves friendly phrasing. Furthermore, in a technical context, it can apply an algorithm that accurately converts technical terms. By applying a context-appropriate conversion algorithm, the accuracy of the conversion can be improved. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI.

[0046] The conversion unit can improve the accuracy of the conversion by referring to the user's past dialogue history when converting other people's statements into text. For example, the conversion unit can accurately convert specific phrases or expressions by referring to the user's past dialogue history. The conversion unit can also analyze the user's past dialogue history and perform context-appropriate conversions. Furthermore, the conversion unit can improve the accuracy of the conversion by referring to the user's past dialogue history. In this way, the accuracy of the conversion can be improved by referring to past dialogue history. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] Dialogue support systems can analyze a user's past dialogue history and suggest optimal dialogue patterns. For example, they can automatically display phrases frequently used by the user in the past as suggestions. They can also prioritize suggesting dialogue progression methods (such as the order and content of questions) that the user has used in the past. Furthermore, they can predict and suggest phrases that the user will use at specific times based on their past dialogue history. In this way, by suggesting optimal dialogue patterns based on past dialogue history, the efficiency of dialogue can be improved.

[0049] Dialogue support systems can adjust the content of conversations based on the user's current situation and environment. For example, if the user is in a public place, the system can suggest conversations that take privacy into consideration. If the user is at home, it can provide more detailed conversations. Furthermore, if the user is on the go, it can suggest simple and quick conversations. In this way, the convenience of conversations can be improved by providing conversations that are tailored to the user's situation and environment.

[0050] The dialogue support system can present highly relevant dialogue content by considering the user's geographical location. For example, if the user is in a specific location, it can suggest topics related to that location. If the user is traveling, it can suggest topics related to their travel destination. Furthermore, if the user is at home, it can suggest topics related to their home. This improves the efficiency of dialogue by presenting dialogue content based on geographical location information.

[0051] The dialogue support system can analyze a user's social media activity and suggest relevant dialogue topics. For example, it can suggest relevant topics based on the user's recent posts. It can also suggest relevant topics based on the posts of accounts the user follows. Furthermore, it can suggest relevant topics based on the activities of groups the user participates in. By suggesting dialogue topics based on social media activity, the system can improve the efficiency of conversations.

[0052] Dialogue support systems can improve the accuracy of conversations by referencing the user's past speech patterns. For example, they can conduct conversations by referring to phrases and expressions the user has used in the past. They can also conduct conversations by referring to the user's past speaking speed and tone. Furthermore, they can analyze the content of the user's past speech and conduct conversations that are appropriate to the context. In this way, the accuracy of conversations can be improved by referring to past speech patterns.

[0053] The dialogue support system can customize the content of the conversation based on the user's current environment. For example, if the user is in a quiet environment, it can provide detailed dialogue. Conversely, if the user is in a noisy environment, it can provide concise dialogue. Furthermore, if the user is on the move, it can provide appropriate dialogue. In this way, by customizing the dialogue content based on the current environment, the optimal conversation can be achieved.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk receives text input from the user. For example, it receives text entered by the user. Step 2: The generation unit uses a generation AI to analyze the text received by the reception unit and generate natural-sounding speech. For example, the generation AI analyzes the input text and generates natural-sounding speech. Step 3: The output unit outputs the audio generated by the generation unit. For example, the generated audio is output through speakers or headphones. Step 4: The learning unit adjusts the speech generated by the generation unit based on the user's voice characteristics. For example, the generation AI learns the user's voice characteristics and adjusts the generated speech based on those characteristics. Step 5: The conversion unit converts other people's statements into text. For example, it converts other people's statements into text in real time.

[0056] (Example of form 2) The dialogue support system according to an embodiment of the present invention is a system for enabling people with speech disorders to engage in natural conversations. The dialogue support system allows users to input text, which is then converted into natural speech, enabling real-time conversations with others. The dialogue support system learns the user's voice characteristics through machine learning to provide more natural speech. The dialogue support system is available on smartphones and tablets. Specifically, it consists of the following steps: First, the user inputs text into the dialogue support system. This text is input into a generating AI. The generating AI analyzes the input text and converts it into natural speech. Next, the generating AI learns the user's voice characteristics. This allows the generating AI to generate natural speech that reflects the user's voice characteristics. Furthermore, the generating AI converts the statements of others into text in real time. This makes it easier for the user to understand what others are saying. This mechanism enables people with speech disorders to engage in natural conversations, lowering communication barriers. It also expands opportunities for social participation and improves quality of life. For example, the dialogue support system can be used in medical institutions, special needs schools, and welfare facilities to support people with speech disorders. This allows the dialogue support system to provide a system that enables people with speech impairments to engage in natural conversations.

[0057] The dialogue support system according to the embodiment comprises a reception unit, a generation unit, an output unit, a learning unit, and a conversion unit. The reception unit receives text input from the user. The reception unit receives, for example, text entered by the user. The generation unit uses a generation AI to analyze the text received by the reception unit and generate natural speech. The generation unit uses a generation AI to analyze the input text and generate natural speech. The generation unit uses a generation AI to learn the user's voice characteristics and adjusts the generated speech based on the user's voice characteristics. The output unit outputs the speech generated by the generation unit. The output unit outputs, for example, the generated speech through speakers or headphones. The output unit adjusts the generated speech based on the user's voice characteristics. The output unit adjusts, for example, the pitch and speed of the generated speech. The learning unit adjusts the speech generated by the generation unit based on the user's voice characteristics. The learning unit, for example, uses a generation AI to learn the user's voice characteristics and adjusts the generated speech based on the user's voice characteristics. The conversion unit converts the utterances of others into text. The conversion unit converts the utterances of others into text in real time, for example. As a result, the dialogue support system according to this embodiment can provide a system for people with speech disorders to engage in natural dialogue.

[0058] The reception unit receives text input from the user. For example, the reception unit receives text entered by the user. Specifically, the reception unit provides an interface for receiving text data entered by the user using a keyboard or touchscreen. Furthermore, it is possible to incorporate speech recognition technology that converts voice input into text, allowing the user to also input by voice. The reception unit processes the entered text data in real time and sends it to the generation unit. The reception unit also has the function to analyze the user's input, check grammar and spelling, and make automatic corrections as needed. This allows the user to input text accurately and quickly, improving the overall efficiency of the system.

[0059] The generation unit uses a generation AI to analyze the text received by the reception unit and generate natural-sounding speech. Specifically, the generation AI analyzes the input text using natural language processing techniques to determine appropriate speech synthesis parameters. The generation AI learns the user's voice characteristics and adjusts the generated speech based on these characteristics. For example, the generation AI learns the user's past voice data and extracts features such as tone, pitch, speed, and accent. This results in generated speech that is very close to the user's natural voice. The generation unit trains a speech synthesis model using deep learning techniques to generate high-quality speech. This allows the generation unit to provide customized speech tailored to the user's individual voice characteristics.

[0060] The output unit outputs the audio generated by the generation unit. For example, the output unit outputs the generated audio through speakers or headphones. Specifically, the output unit uses digital signal processing technology to transmit the generated audio data to speakers or headphones, reproducing clear and natural sound. The output unit adjusts the generated audio based on the user's voice characteristics. For example, by adjusting the pitch and speed of the generated audio, it reproduces a voice that is closer to the user's voice. The output unit also has a function to customize the volume and sound quality of the audio to the user's preferences. This allows the user to listen to the audio comfortably and improves the quality of the conversation. Furthermore, the output unit supports multiple output devices, allowing the user to select the optimal device according to their environment. This enables the output unit to provide the user with high-quality audio output, maximizing the effectiveness of the dialogue support system.

[0061] The learning unit adjusts the speech generated by the generation unit based on the user's voice characteristics. For example, the generation AI learns the user's voice characteristics, and the learning unit adjusts the generated speech based on those characteristics. Specifically, the learning unit collects the user's voice data and extracts voice features using deep learning technology. This allows the generation AI to learn features such as the tone, pitch, speed, and accent of the user's voice, and to make the generated speech more like the user's voice. The learning unit receives user feedback and continuously improves the quality of the generated speech. For example, the user evaluates their satisfaction with the generated speech, and the speech synthesis model is retrained based on that evaluation. This allows the learning unit to provide customized speech that meets the user's individual needs. Furthermore, the learning unit can improve the versatility of the speech synthesis model by comparing the voice characteristics of different users and extracting common features. This allows the learning unit to enhance the speech generation capabilities of the dialogue support system and provide high-quality speech to more users.

[0062] The conversion unit converts other people's speech into text. For example, the conversion unit converts other people's speech into text in real time. Specifically, the conversion unit uses high-precision speech recognition technology to convert other people's speech into text data in real time. The conversion unit uses noise reduction technology to remove background noise and accurately recognize the content of speech. This allows the conversion unit to achieve high-precision speech recognition even in noisy environments. The conversion unit displays the recognized text data in real time, allowing users to immediately check the content of other people's speech. Furthermore, the conversion unit also has a function to save the recognized text data for later reference. This allows users to easily check past conversations and manage their conversation history. The conversion unit supports multiple languages ​​and also has a function to automatically translate speech from different languages. This allows the conversion unit to support conversations between users who speak different languages ​​and remove communication barriers.

[0063] The reception desk can estimate the user's emotions and adjust the text input interface based on those emotions. For example, if the user is nervous, the reception desk can provide a simple and intuitive interface to reduce the effort required for input. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable interface. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick text entry. This reduces the effort required for input by providing an interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0064] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display phrases that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest phrases that the user will use at specific times of day based on their past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0065] The reception desk can provide input assistance based on the user's current situation and environment when they are entering text. For example, if the user is in a public place, the reception desk can suggest an input method that takes privacy into consideration. It can also provide detailed input options if the user is at home. Furthermore, if the user is on the move, the reception desk can suggest a simple and quick input method. This improves the convenience of input by providing input assistance tailored to the user's situation and environment. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0066] The reception desk can estimate the user's emotions and determine the priority of the text to be entered based on the estimated emotions. For example, if the user is nervous, the reception desk may suggest prioritizing the input of important text. It may also suggest prioritizing detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the reception desk may suggest prioritizing short, concise text. This allows for the priority of important information to be entered by determining the text priority according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The input system can present highly relevant input suggestions when a user is entering text, taking into account their geographical location. For example, if the user is in a specific location, the input system can display phrases related to that location as suggestions. Furthermore, if the user is traveling, the input system can display phrases related to their travel destination as suggestions. Additionally, if the user is at home, the input system can display phrases related to their home as suggestions. This improves input efficiency by presenting input suggestions based on geographical location. Some or all of the above processing in the input system may be performed using AI, for example, or without AI.

[0068] The input section can analyze the user's social media activity during text input and suggest relevant input options. For example, the input section can display relevant phrases as suggestions based on the user's recent posts. It can also display relevant phrases as suggestions based on the posts of accounts the user follows. Furthermore, it can display relevant phrases as suggestions based on the activities of groups the user participates in. This improves input efficiency by providing input suggestions based on social media activity. Some or all of the above processing in the input section may be performed using AI, for example, or without AI.

[0069] The generation unit can estimate the user's emotions and adjust the tone and speed of speech generation based on the estimated emotions. For example, if the user is nervous, the generation unit can generate speech in a calm tone and at a slow speed. If the user is relaxed, the generation unit can also generate speech in a bright tone and at a natural speed. Furthermore, if the user is in a hurry, the generation unit can generate speech in a quick and concise tone. This allows for more natural dialogue by generating speech in a tone and speed that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The generation unit can apply different speech generation algorithms depending on the context during text analysis. For example, in a formal context, the generation unit can apply an algorithm that generates a polite tone of voice. In a casual context, it can also apply an algorithm that generates a friendly tone of voice. Furthermore, in a technical context, the generation unit can apply an algorithm that accurately pronounces technical terms. By applying context-appropriate speech generation algorithms, more appropriate speech can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate speech using a generation AI that applies different speech generation algorithms depending on the context during text analysis.

[0071] The generation unit can improve the accuracy of speech generation by referring to the user's past speech patterns during text analysis. For example, the generation unit generates speech by referring to phrases and expressions the user has used in the past. The generation unit can also generate speech by referring to the user's past speech speed and tone. Furthermore, the generation unit can analyze the content of the user's past speech and generate speech appropriate to the context. This allows for improved accuracy of speech generation by referring to past speech patterns. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate speech using a generation AI that generates speech by referring to the user's past speech patterns.

[0072] The output unit can estimate the user's emotions and adjust the volume and tone of the voice output based on the estimated emotions. For example, if the user is nervous, the output unit will output a calm tone and a low volume. If the user is relaxed, the output unit can also output a bright tone and an appropriate volume. Furthermore, if the user is in a hurry, the output unit can output a fast and concise tone. This allows for more natural dialogue by outputting voice at a volume and tone appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The output unit can select the optimal output method by referring to the user's past voice output history when outputting voice. For example, the output unit can output voice by referring to the tone and volume that the user has previously preferred to use. The output unit can also select the optimal output method according to a specific situation from the user's past voice output history. Furthermore, the output unit can analyze the user's past voice output history and select the optimal voice output method according to the context. In this way, the optimal output method can be selected by referring to the past voice output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without using AI.

[0074] The output unit can customize the output method based on the user's current environment when outputting audio. For example, if the user is in a quiet environment, the output unit can output audio at a lower volume. Conversely, if the user is in a noisy environment, the output unit can output audio at a higher volume. Furthermore, if the user is moving, the output unit can output audio at an appropriate volume and tone. This allows for optimal audio output by customizing the output method based on the current environment. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI.

[0075] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is tense, the learning unit will select training data that promotes relaxation. If the user is relaxed, the learning unit can also select training data with more detailed content. Furthermore, if the user is in a hurry, the learning unit can select training data that can be learned in a short amount of time. By selecting training data that matches the user's emotions, the accuracy of learning can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on data that the user has previously learned. The learning unit can also analyze the user's past learning history and adjust the learning algorithm. Furthermore, the learning unit can improve the accuracy of learning by referring to the user's past learning data. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI.

[0077] The learning unit can improve the accuracy of learning by analyzing the user's speech patterns during training. For example, the learning unit can analyze the user's speech speed and tone and adjust the learning algorithm. The learning unit can also analyze the content of the user's speech and select learning data appropriate to the context. Furthermore, the learning unit can improve the accuracy of learning by referring to the user's speech patterns. In this way, the accuracy of learning can be improved by analyzing speech patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI.

[0078] The conversion unit can estimate the user's emotions and adjust the text conversion method based on the estimated emotions. For example, if the user is nervous, the conversion unit can provide a simple and intuitive conversion method. It can also provide detailed conversion options if the user is relaxed. Furthermore, if the user is in a hurry, the conversion unit can provide a quick and concise conversion method. This improves the accuracy of the conversion by providing a conversion method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The conversion unit can apply different conversion algorithms depending on the context when converting someone else's speech into text. For example, in a formal context, the conversion unit can apply a conversion algorithm that preserves polite phrasing. In a casual context, it can also apply a conversion algorithm that preserves friendly phrasing. Furthermore, in a technical context, it can apply an algorithm that accurately converts technical terms. By applying a context-appropriate conversion algorithm, the accuracy of the conversion can be improved. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI.

[0080] The conversion unit can improve the accuracy of the conversion by referring to the user's past dialogue history when converting other people's statements into text. For example, the conversion unit can accurately convert specific phrases or expressions by referring to the user's past dialogue history. The conversion unit can also analyze the user's past dialogue history and perform context-appropriate conversions. Furthermore, the conversion unit can improve the accuracy of the conversion by referring to the user's past dialogue history. In this way, the accuracy of the conversion can be improved by referring to past dialogue history. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] A dialogue support system can estimate the user's emotions and adjust the flow of the conversation based on those emotions. For example, if the user is nervous, the system can slow down the conversation and ask questions that help them relax. If the user is relaxed, the system can facilitate a smooth conversation and elicit detailed information. Furthermore, if the user is in a hurry, the system can provide concise and to-the-point answers. By adjusting the conversation flow according to the user's emotions, a more natural dialogue can be achieved.

[0083] Dialogue support systems can analyze a user's past dialogue history and suggest optimal dialogue patterns. For example, they can automatically display phrases frequently used by the user in the past as suggestions. They can also prioritize suggesting dialogue progression methods (such as the order and content of questions) that the user has used in the past. Furthermore, they can predict and suggest phrases that the user will use at specific times based on their past dialogue history. In this way, by suggesting optimal dialogue patterns based on past dialogue history, the efficiency of dialogue can be improved.

[0084] Dialogue support systems can adjust the content of conversations based on the user's current situation and environment. For example, if the user is in a public place, the system can suggest conversations that take privacy into consideration. If the user is at home, it can provide more detailed conversations. Furthermore, if the user is on the go, it can suggest simple and quick conversations. In this way, the convenience of conversations can be improved by providing conversations that are tailored to the user's situation and environment.

[0085] A dialogue support system can estimate the user's emotions and adjust the tone and content of the dialogue based on those emotions. For example, if the user is nervous, the system can use a calm tone and engage in relaxing conversation. If the user is relaxed, it can use a bright tone and engage in detailed conversation. Furthermore, if the user is in a hurry, it can engage in conversation in a quick and concise tone. By tailoring the tone and content of the dialogue to the user's emotions, it can achieve a more natural dialogue.

[0086] The dialogue support system can present highly relevant dialogue content by considering the user's geographical location. For example, if the user is in a specific location, it can suggest topics related to that location. If the user is traveling, it can suggest topics related to their travel destination. Furthermore, if the user is at home, it can suggest topics related to their home. This improves the efficiency of dialogue by presenting dialogue content based on geographical location information.

[0087] The dialogue support system can analyze a user's social media activity and suggest relevant dialogue topics. For example, it can suggest relevant topics based on the user's recent posts. It can also suggest relevant topics based on the posts of accounts the user follows. Furthermore, it can suggest relevant topics based on the activities of groups the user participates in. By suggesting dialogue topics based on social media activity, the system can improve the efficiency of conversations.

[0088] The dialogue support system can estimate the user's emotions and determine the priority of the conversation based on those emotions. For example, if the user is nervous, it can suggest prioritizing important topics in the conversation. If the user is relaxed, it can suggest discussing detailed information. Furthermore, if the user is in a hurry, it can suggest prioritizing short, concise topics in the conversation. In this way, by determining the priority of the conversation according to the user's emotions, important information can be prioritized.

[0089] Dialogue support systems can improve the accuracy of conversations by referencing the user's past speech patterns. For example, they can conduct conversations by referring to phrases and expressions the user has used in the past. They can also conduct conversations by referring to the user's past speaking speed and tone. Furthermore, they can analyze the content of the user's past speech and conduct conversations that are appropriate to the context. In this way, the accuracy of conversations can be improved by referring to past speech patterns.

[0090] The dialogue support system can estimate the user's emotions and adjust the pace of the conversation based on those emotions. For example, if the user is nervous, the conversation pace can be slowed down to help them relax. If the user is relaxed, the conversation pace can be adjusted to a natural speed. Furthermore, if the user is in a hurry, the conversation pace can be accelerated. In this way, by adjusting the conversation pace according to the user's emotions, a more natural conversation can be achieved.

[0091] The dialogue support system can customize the content of the conversation based on the user's current environment. For example, if the user is in a quiet environment, it can provide detailed dialogue. Conversely, if the user is in a noisy environment, it can provide concise dialogue. Furthermore, if the user is on the move, it can provide appropriate dialogue. In this way, by customizing the dialogue content based on the current environment, the optimal conversation can be achieved.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The reception desk receives text input from the user. For example, it receives text entered by the user. Step 2: The generation unit uses a generation AI to analyze the text received by the reception unit and generate natural-sounding speech. For example, the generation AI analyzes the input text and generates natural-sounding speech. Step 3: The output unit outputs the audio generated by the generation unit. For example, the generated audio is output through speakers or headphones. Step 4: The learning unit adjusts the speech generated by the generation unit based on the user's voice characteristics. For example, the generation AI learns the user's voice characteristics and adjusts the generated speech based on those characteristics. Step 5: The conversion unit converts other people's statements into text. For example, it converts other people's statements into text in real time.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0097] Each of the multiple elements described above, including the reception unit, generation unit, output unit, learning unit, and conversion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives text input from the user using the touch panel 38A or microphone 38B of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the text using a generation AI and generates natural-sounding speech. The output unit outputs the generated speech through the speaker 40B of the smart device 14. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's voice characteristics and adjusts the generated speech. The conversion unit converts the speech of others into text using the camera 42 or microphone 38B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, generation unit, output unit, learning unit, and conversion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives text input from the user using the microphone 238 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the text using a generation AI and generates natural-sounding speech. The output unit outputs the generated speech through the speaker 240 of the smart glasses 214. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's voice characteristics and adjusts the generated speech. The conversion unit converts the speech of others into text using the camera 42 and microphone 238 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, generation unit, output unit, learning unit, and conversion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives text input from the user using the microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the text using a generation AI and generates natural-sounding speech. The output unit outputs the generated speech through the speaker 240 of the headset terminal 314. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's voice characteristics and adjusts the generated speech. The conversion unit converts the speech of others into text using the camera 42 and microphone 238 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, generation unit, output unit, learning unit, and conversion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives text input from the user using the microphone 238 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the text using a generation AI and generates natural-sounding speech. The output unit outputs the generated speech through the speaker 240 of the robot 414. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's speech characteristics and adjusts the generated speech. The conversion unit converts the speech of others into text using the camera 42 and microphone 238 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] 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.

[0157] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] (Note 1) A reception area that accepts text input from the user, A generation unit analyzes the text received by the reception unit and generates natural-sounding speech, An output unit that outputs the sound generated by the generation unit, A learning unit adjusts the voice generated by the generation unit based on the user's voice characteristics, It comprises a conversion unit that converts the statements of others into text. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the text input interface based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering text, provide input assistance based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the text to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering text, the system will suggest highly relevant input options while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering text, the system analyzes the user's social media activity and suggests relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and adjusts the tone and speed of voice generation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When analyzing text, different speech generation algorithms are applied depending on the context. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During text analysis, the system improves the accuracy of speech generation by referencing the user's past speech patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The output unit is, It estimates the user's emotions and adjusts the volume and tone of the audio output based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The output unit is, When outputting audio, the system selects the optimal output method by referring to the user's past audio output history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The output unit is, When outputting audio, the output method is customized based on the user's current environment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During training, the system analyzes the user's speech patterns to improve the accuracy of the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is It estimates the user's emotions and adjusts the text conversion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is When converting someone else's speech into text, different conversion algorithms are applied depending on the context. The system described in Appendix 1, characterized by the features described herein. (Note 19) The conversion unit is When converting other people's statements into text, the system improves conversion accuracy by referencing the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts text input from the user, A generation unit analyzes the text received by the reception unit and generates natural-sounding speech, An output unit that outputs the sound generated by the generation unit, A learning unit adjusts the voice generated by the generation unit based on the user's voice characteristics, It comprises a conversion unit that converts the statements of others into text. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the text input interface based on those emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When entering text, provide input assistance based on the user's current situation and environment. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the text to be entered based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When entering text, the system will suggest highly relevant input options while considering the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When entering text, the system analyzes the user's social media activity and suggests relevant input options. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts the tone and speed of voice generation based on those estimated emotions. The system according to feature 1.

9. The generating unit is When analyzing text, different speech generation algorithms are applied depending on the context. The system according to feature 1.

10. The generating unit is During text analysis, the system improves the accuracy of speech generation by referencing the user's past speech patterns. The system according to feature 1.

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