system

Abstract

The system according to this embodiment aims to enable people with speech impairments to communicate smoothly. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives text input. The analysis unit analyzes the text input by the reception unit. The generation unit generates natural-sounding speech based on the text analyzed by the analysis unit.

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, the method including the 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 conventional technology, there is a problem that it is difficult for people with speech disorders to communicate smoothly. [[ID=​​​​​​​​​​​​​

[0007] The system according to this embodiment can enable people with speech impairments to communicate smoothly. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 30, 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An AI agent system according to an embodiment of the present invention is a system that enables people with speech impairments to communicate smoothly. This AI agent system converts text input into natural speech in real time, learns the characteristics of each user's voice, and generates more natural and personalized responses. For example, when a user inputs text, the AI ​​agent system analyzes the text and converts it into natural speech using speech synthesis technology. In this process, the AI ​​agent system learns the characteristics of each user's voice and generates more natural and personalized responses. This enables people with speech impairments to communicate smoothly. For example, if a user inputs "Hello, how are you?", the AI ​​agent system will speak "Hello, how are you?" in real time. This enables people with speech impairments to communicate smoothly. Furthermore, the AI ​​agent system learns the characteristics of the user's voice and generates more natural and personalized responses. For example, if a user inputs "What's the weather like today?", the AI ​​agent system will speak "What's the weather like today?" in real time. This enables people with speech impairments to communicate smoothly. This improves the speed and efficiency of communication for people with speech impairments and increases their social participation. Furthermore, improved user satisfaction is expected, along with increased support for independence in daily life and a reduction in social isolation. For example, people with speech impairments will be able to converse more smoothly in their daily lives, reducing their sense of social isolation. This means that the AI ​​agent system will enable people with speech impairments to communicate more effectively.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives text input from the user. For example, the reception unit can receive text input using a keyboard or touchscreen. The reception unit can also receive text input using voice input. For example, the reception unit converts voice to text using speech recognition technology. The analysis unit analyzes the text input by the reception unit. For example, the analysis unit understands the meaning of the text using natural language processing technology. The analysis unit can analyze the text using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to break down the text into words, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the text. The generation unit generates natural speech based on the text analyzed by the analysis unit. For example, the generation unit generates natural speech using speech synthesis technology. The generation unit can generate speech using techniques such as waveform concatenation speech synthesis and statistical parametric speech synthesis. For example, the generation unit generates speech using waveform concatenation speech synthesis and improves the naturalness of the speech using statistical parametric speech synthesis. As a result, the AI ​​agent system according to this embodiment can convert text into natural speech in real time, enabling people with speech impairments to communicate smoothly.

[0030] The reception desk is where users input text. For example, the reception desk can input text using a keyboard or a touchscreen. With keyboard input, characters are entered by pressing physical keys, and with a touchscreen, characters are entered by tapping a virtual keyboard on the screen. The reception desk can also input text using voice input. With voice input, the user inputs voice by speaking into a microphone, and speech recognition technology is used to convert the voice into text. Speech recognition technology includes a process of extracting features from the voice, breaking them down into phonemes, and converting them into text. For example, the speech recognition engine analyzes the voice signal, identifies sequences of phonemes, and converts them into words and sentences. This allows the user to input text by voice without using their hands. Furthermore, the reception desk also plays a role in temporarily storing the entered text and voice data and sending it to the analysis unit. This allows the reception desk to efficiently process input from the user and smoothly hand it over to the next step, the analysis unit.

[0031] The analysis unit analyzes the text input by the reception unit. For example, the analysis unit understands the meaning of the text using natural language processing techniques. Natural language processing techniques include morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis breaks down the text into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of a sentence and identifies its elements such as subject, predicate, and object. Semantic analysis understands the meaning of a sentence and provides an appropriate interpretation based on the context. For example, the analysis unit uses morphological analysis to break down the text into words, grammatical analysis to analyze the structure of a sentence, and semantic analysis to understand the meaning of the text. Furthermore, the analysis unit identifies the intent of the input text and provides information to generate an appropriate response. For example, the analysis unit identifies the intent of a question and provides information to generate an appropriate answer. The analysis unit can also analyze the sentiment of the input text and generate a sentiment-appropriate response. This allows the analysis unit to analyze the input text quickly and accurately and provide the necessary information to the next step, the generation unit.

[0032] The generation unit generates natural-sounding speech based on the text analyzed by the analysis unit. For example, the generation unit uses speech synthesis technology to generate natural-sounding speech. Speech synthesis technologies include waveform concatenation speech synthesis and statistical parametric speech synthesis. Waveform concatenation speech synthesis generates natural-sounding speech by concatenating the waveforms of speech based on pre-recorded speech data. Statistical parametric speech synthesis generates natural-sounding speech by statistically modeling the characteristics of speech and adjusting parameters. For example, the generation unit generates speech using waveform concatenation speech synthesis and improves the naturalness of the speech using statistical parametric speech synthesis. Furthermore, the generation unit outputs the generated speech in real time and provides it to the user. This allows the generation unit to convert text into natural-sounding speech in real time, enabling people with speech impairments to communicate smoothly. The generation unit also has technologies to adjust the intonation and rhythm of speech to achieve more natural speech. For example, the generation unit can adjust the intonation of speech based on the structure and meaning of sentences to express emotions and emphasis. This allows the generation unit to achieve more human-like and natural speech, not just convert text into speech.

[0033] The generation unit can learn the characteristics of each user's voice and generate more natural and personalized responses. For example, the generation unit learns characteristics such as the pitch, timbre, and speech patterns of the user's voice. Based on these characteristics, the generation unit can generate natural voice responses tailored to the user. For example, the generation unit can learn the pitch of the user's voice and generate voices that are similar to the user's voice. The generation unit can also learn the timbre of the user's voice and generate voices with a timbre similar to the user's voice. Furthermore, the generation unit can learn the speech patterns of the user and generate voices that resemble the user's speech patterns. This allows the generation unit to generate natural voice responses tailored to each individual user. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input voice data into a generation AI to learn the characteristics of the user's voice, and have the generation AI extract the characteristics of the voice data. This allows the generation unit to generate more natural and personalized responses.

[0034] The analysis unit can understand the meaning of text using natural language processing techniques. For example, the analysis unit can break down text into words using morphological analysis. The analysis unit can analyze sentence structure using grammatical analysis. Furthermore, the analysis unit can understand the meaning of text using semantic analysis. For example, the analysis unit breaks down text into words using morphological analysis, analyzes sentence structure using grammatical analysis, and understands the meaning of text using semantic analysis. As a result, the analysis unit can accurately understand the meaning of text. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data into a generating AI and have the generating AI analyze the meaning of the text data. As a result, the analysis unit can generate a more appropriate speech response.

[0035] The generation unit can generate natural-sounding speech using speech synthesis technology. The generation unit generates speech using, for example, waveform concatenation speech synthesis. The generation unit can improve the naturalness of the speech using statistical parametric speech synthesis. For example, the generation unit generates speech using waveform concatenation speech synthesis and improves the naturalness of the speech using statistical parametric speech synthesis. This allows the generation unit to generate natural-sounding speech. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input speech data into a generation AI and have the generation AI generate speech data. This allows the generation unit to generate more natural-sounding speech.

[0036] The generation unit can convert text to speech in real time. For example, when text is entered, the generation unit instantly converts it to speech. By converting text to speech in real time, the generation unit can provide smooth communication. For example, when the user enters "Hello," the generation unit instantly responds with "Hello" in voice. In this way, the generation unit can provide smooth communication by converting text to speech in real time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input text data into a generation AI, and the generation AI can generate speech in real time. In this way, the generation unit can provide even smoother communication.

[0037] The generation unit can create personalized voice profiles. For example, the generation unit learns the characteristics of the user's voice and creates a personalized voice profile. This allows the generation unit to provide voice responses tailored to the user. For example, the generation unit learns characteristics such as the pitch, timbre, and speaking style of the user's voice and creates a personalized voice profile. This allows the generation unit to provide voice responses tailored to the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input voice data into a generation AI to learn the characteristics of the user's voice, and have the generation AI extract the characteristics of the voice data. This allows the generation unit to generate more natural and personalized responses.

[0038] 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 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 based on their past input history. This allows the reception desk to suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method. This allows the reception desk to suggest the optimal input method for the user.

[0039] The input unit can provide input assistance based on the user's current situation and environment when entering text. For example, if the user is in a noisy environment, the input unit can provide noise cancellation to improve the accuracy of voice input. If the user is on the move, the input unit can provide an interface that allows input with simple tap operations. Furthermore, if the user is in a quiet environment, the input unit can provide detailed input options to support highly accurate input. In this way, the input unit can improve the accuracy and efficiency of input by providing input assistance according to the user's situation and environment. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's current situation and environment data into a generating AI, and have the generating AI provide input assistance. In this way, the input unit can provide input assistance according to the user's situation and environment.

[0040] The input field 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 field can automatically display phrases related to that location as suggestions. If the user is traveling, the input field can suggest phrases related to their travel destination. Furthermore, if the user is at home, the input field can prioritize displaying phrases related to daily life. In this way, the input field can present highly relevant input suggestions by taking into account the user's geographical location. Some or all of the above processing in the input field may be performed using AI, for example, or without AI. For example, the input field can input the user's geographical location into a generating AI, which can then present highly relevant input suggestions. In this way, the input field can present highly relevant input suggestions by taking into account the user's geographical location.

[0041] The input field can analyze the user's social media activity when text is entered and suggest relevant input options. For example, the input field can suggest phrases related to topics the user has recently been discussing on social media. If the user is participating in a specific event, the input field can display phrases related to that event. Furthermore, the input field can automatically display phrases that the user frequently uses on social media as suggestions. In this way, the input field can suggest relevant input options by analyzing the user's social media activity. Some or all of the above processing in the input field may be performed using AI, for example, or not using AI. For example, the input field can input the user's social media activity data into a generating AI, which can then suggest relevant input options. In this way, the input field can analyze the user's social media activity and suggest relevant input options.

[0042] The analysis unit can improve the accuracy of its analysis by considering contextual information during text analysis. For example, the analysis unit can accurately understand the meaning by considering the context before and after the text. The analysis unit can refer to the user's past utterances to perform consistent analysis. Furthermore, the analysis unit can also extract keywords from the text and improve the accuracy of the analysis based on the context. In this way, the analysis unit can improve the accuracy of its analysis by considering contextual information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input contextual information into a generating AI and have the generating AI analyze the contextual information. In this way, the analysis unit can improve the accuracy of its analysis by considering contextual information.

[0043] The analysis unit can perform text analysis by referring to the user's past utterances. For example, the analysis unit can refer to phrases the user has used in the past to understand the meaning of the current text. The analysis unit can extract specific patterns from the user's past utterances and reflect them in the analysis. Furthermore, the analysis unit can perform contextual analysis based on the user's past utterances. This allows the analysis unit to perform contextual analysis by referring to past utterances. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past utterance data into a generating AI and have the generating AI analyze the past utterances. This allows the analysis unit to perform analysis by referring to past utterances.

[0044] The analysis unit can perform text analysis while considering the user's geographical background. For example, if the user is in a specific region, the analysis unit can prioritize analyzing information related to that region. If the user is traveling, the analysis unit can analyze information related to the travel destination. Furthermore, if the user is at home, the analysis unit can prioritize analyzing information related to daily life. In this way, the analysis unit can perform highly relevant analysis by considering the user's geographical background. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical background data into a generating AI and have the generating AI analyze the geographical background. In this way, the analysis unit can perform analysis while considering the user's geographical background.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases during text analysis. For example, the analysis unit performs analysis by referring to relevant literature based on keywords in the text. The analysis unit can accurately understand the meaning of the text by referring to information in databases. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to relevant research papers. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature and databases into a generating AI, and have the generating AI refer to the literature and databases. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases.

[0046] The generation unit can generate natural speech by learning the user's past speech patterns during speech generation. For example, the generation unit can generate natural speech based on phrases the user has used in the past. The generation unit can learn the user's past speech patterns and generate consistent speech. Furthermore, the generation unit can refer to the user's past speech content and generate contextually natural speech. In this way, the generation unit can generate consistent and natural speech by learning past speech patterns. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past speech data into a generation AI, allowing the generation AI to learn past speech patterns. In this way, the generation unit can learn past speech patterns and generate natural speech.

[0047] The generation unit can customize the content of the voice based on the user's current situation and environment when generating voice. For example, if the user is in a noisy environment, the generation unit can generate voice using a noise-canceling function. If the user is in a quiet environment, the generation unit can generate voice containing detailed information. Furthermore, if the user is on the move, the generation unit can generate concise and to-the-point voice. In this way, the generation unit can provide more appropriate voice responses by generating voice according to the user's situation and environment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current situation and environment data into a generation AI, and have the generation AI customize the content of the voice. In this way, the generation unit can customize the content of the voice based on the user's situation and environment.

[0048] The generation unit can adjust the accent and dialect of the voice when generating speech, taking into account the user's geographical background. For example, if the user is in a specific region, the generation unit can generate speech that reflects the accent and dialect of that region. If the user is traveling, the generation unit can generate speech that reflects the accent and dialect of the travel destination. Furthermore, if the user is at home, the generation unit can generate speech that reflects the accent and dialect appropriate for daily life. In this way, the generation unit can generate more familiar speech by reflecting the accent and dialect according to the user's geographical background. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical background data into a generation AI, and have the generation AI adjust the accent and dialect. In this way, the generation unit can adjust the accent and dialect of the voice, taking the user's geographical background into account.

[0049] The generation unit can improve the accuracy of speech generation by referring to the relevant speech database during speech generation. For example, the generation unit can refer to information in the speech database to generate natural-sounding speech. The generation unit can improve the accuracy of the speech based on the relevant speech data. Furthermore, the generation unit can also improve the quality of the generated speech by using samples in the speech database. In this way, the generation unit can improve the quality and accuracy of the generated speech by referring to the relevant speech database. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the speech database into a generation AI, and the generation AI can refer to the speech data. In this way, the generation unit can improve the accuracy of speech generation by referring to the relevant speech database.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The input unit can learn the user's input speed and provide an interface tailored to that speed. For example, if the user inputs quickly, the input unit can provide simple and quick input options. If the user inputs slowly, it can provide detailed input options to improve input accuracy. Furthermore, the input unit can adjust the display speed of input suggestions according to the user's input speed. In this way, the input unit can improve input efficiency and accuracy by providing an interface tailored to the user's input speed.

[0052] The generation unit can learn the user's past speech and adjust the tone and intonation of the voice based on that past speech. For example, it can learn the tone and intonation of phrases the user has used in the past and generate speech with a similar tone and intonation. This allows the generation unit to produce natural-sounding speech based on the user's past speech.

[0053] The reception desk can analyze the user's input in real time and suggest appropriate input options based on that input. For example, if the user inputs "Hello," the reception desk will suggest appropriate input options such as "How are you?". In this way, the reception desk can improve input efficiency by suggesting appropriate input options based on the user's input.

[0054] The reception desk can analyze the user's input and provide appropriate feedback based on that input. For example, if a user inputs "I'm tired today," the reception desk will provide feedback such as "Thank you for your hard work. Please rest well." In this way, the reception desk can improve user satisfaction by providing appropriate feedback based on the user's input.

[0055] The reception desk can analyze the user's input and suggest appropriate actions based on that input. For example, if a user inputs "I'm hungry," the reception desk might suggest an action such as "Would you like to search for nearby restaurants?" In this way, the reception desk can improve user convenience by suggesting appropriate actions based on the user's input.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives text input from the user. For example, the reception desk can receive text input using a keyboard or touchscreen. The reception desk can also receive text input using voice input. The reception desk converts speech to text using speech recognition technology. Step 2: The analysis unit analyzes the text input by the reception unit. For example, the analysis unit understands the meaning of the text using natural language processing techniques. The analysis unit analyzes the text using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The generation unit generates natural-sounding speech based on the text analyzed by the analysis unit. For example, the generation unit generates natural-sounding speech using speech synthesis technology. The generation unit generates speech using techniques such as waveform concatenation speech synthesis and statistical parametric speech synthesis.

[0058] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that enables people with speech impairments to communicate smoothly. This AI agent system converts text input into natural speech in real time, learns the characteristics of each user's voice, and generates more natural and personalized responses. For example, when a user inputs text, the AI ​​agent system analyzes the text and converts it into natural speech using speech synthesis technology. In this process, the AI ​​agent system learns the characteristics of each user's voice and generates more natural and personalized responses. This enables people with speech impairments to communicate smoothly. For example, if a user inputs "Hello, how are you?", the AI ​​agent system will speak "Hello, how are you?" in real time. This enables people with speech impairments to communicate smoothly. Furthermore, the AI ​​agent system learns the characteristics of the user's voice and generates more natural and personalized responses. For example, if a user inputs "What's the weather like today?", the AI ​​agent system will speak "What's the weather like today?" in real time. This enables people with speech impairments to communicate smoothly. This improves the speed and efficiency of communication for people with speech impairments and increases their social participation. Furthermore, improved user satisfaction is expected, along with increased support for independence in daily life and a reduction in social isolation. For example, people with speech impairments will be able to converse more smoothly in their daily lives, reducing their sense of social isolation. This means that the AI ​​agent system will enable people with speech impairments to communicate more effectively.

[0059] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives text input from the user. For example, the reception unit can receive text input using a keyboard or touchscreen. The reception unit can also receive text input using voice input. For example, the reception unit converts voice to text using speech recognition technology. The analysis unit analyzes the text input by the reception unit. For example, the analysis unit understands the meaning of the text using natural language processing technology. The analysis unit can analyze the text using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to break down the text into words, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the text. The generation unit generates natural speech based on the text analyzed by the analysis unit. For example, the generation unit generates natural speech using speech synthesis technology. The generation unit can generate speech using techniques such as waveform concatenation speech synthesis and statistical parametric speech synthesis. For example, the generation unit generates speech using waveform concatenation speech synthesis and improves the naturalness of the speech using statistical parametric speech synthesis. As a result, the AI ​​agent system according to this embodiment can convert text into natural speech in real time, enabling people with speech impairments to communicate smoothly.

[0060] The reception desk is where users input text. For example, the reception desk can input text using a keyboard or a touchscreen. With keyboard input, characters are entered by pressing physical keys, and with a touchscreen, characters are entered by tapping a virtual keyboard on the screen. The reception desk can also input text using voice input. With voice input, the user inputs voice by speaking into a microphone, and speech recognition technology is used to convert the voice into text. Speech recognition technology includes a process of extracting features from the voice, breaking them down into phonemes, and converting them into text. For example, the speech recognition engine analyzes the voice signal, identifies sequences of phonemes, and converts them into words and sentences. This allows the user to input text by voice without using their hands. Furthermore, the reception desk also plays a role in temporarily storing the entered text and voice data and sending it to the analysis unit. This allows the reception desk to efficiently process input from the user and smoothly hand it over to the next step, the analysis unit.

[0061] The analysis unit analyzes the text input by the reception unit. For example, the analysis unit understands the meaning of the text using natural language processing techniques. Natural language processing techniques include morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis breaks down the text into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of a sentence and identifies its elements such as subject, predicate, and object. Semantic analysis understands the meaning of a sentence and provides an appropriate interpretation based on the context. For example, the analysis unit uses morphological analysis to break down the text into words, grammatical analysis to analyze the structure of a sentence, and semantic analysis to understand the meaning of the text. Furthermore, the analysis unit identifies the intent of the input text and provides information to generate an appropriate response. For example, the analysis unit identifies the intent of a question and provides information to generate an appropriate answer. The analysis unit can also analyze the sentiment of the input text and generate a sentiment-appropriate response. This allows the analysis unit to analyze the input text quickly and accurately and provide the necessary information to the next step, the generation unit.

[0062] The generation unit generates natural-sounding speech based on the text analyzed by the analysis unit. For example, the generation unit uses speech synthesis technology to generate natural-sounding speech. Speech synthesis technologies include waveform concatenation speech synthesis and statistical parametric speech synthesis. Waveform concatenation speech synthesis generates natural-sounding speech by concatenating the waveforms of speech based on pre-recorded speech data. Statistical parametric speech synthesis generates natural-sounding speech by statistically modeling the characteristics of speech and adjusting parameters. For example, the generation unit generates speech using waveform concatenation speech synthesis and improves the naturalness of the speech using statistical parametric speech synthesis. Furthermore, the generation unit outputs the generated speech in real time and provides it to the user. This allows the generation unit to convert text into natural-sounding speech in real time, enabling people with speech impairments to communicate smoothly. The generation unit also has technologies to adjust the intonation and rhythm of speech to achieve more natural speech. For example, the generation unit can adjust the intonation of speech based on the structure and meaning of sentences to express emotions and emphasis. This allows the generation unit to achieve more human-like and natural speech, not just convert text into speech.

[0063] The generation unit can learn the characteristics of each user's voice and generate more natural and personalized responses. For example, the generation unit learns characteristics such as the pitch, timbre, and speech patterns of the user's voice. Based on these characteristics, the generation unit can generate natural voice responses tailored to the user. For example, the generation unit can learn the pitch of the user's voice and generate voices that are similar to the user's voice. The generation unit can also learn the timbre of the user's voice and generate voices with a timbre similar to the user's voice. Furthermore, the generation unit can learn the speech patterns of the user and generate voices that resemble the user's speech patterns. This allows the generation unit to generate natural voice responses tailored to each individual user. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input voice data into a generation AI to learn the characteristics of the user's voice, and have the generation AI extract the characteristics of the voice data. This allows the generation unit to generate more natural and personalized responses.

[0064] The analysis unit can understand the meaning of text using natural language processing techniques. For example, the analysis unit can break down text into words using morphological analysis. The analysis unit can analyze sentence structure using grammatical analysis. Furthermore, the analysis unit can understand the meaning of text using semantic analysis. For example, the analysis unit breaks down text into words using morphological analysis, analyzes sentence structure using grammatical analysis, and understands the meaning of text using semantic analysis. As a result, the analysis unit can accurately understand the meaning of text. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data into a generating AI and have the generating AI analyze the meaning of the text data. As a result, the analysis unit can generate a more appropriate speech response.

[0065] The generation unit can generate natural-sounding speech using speech synthesis technology. The generation unit generates speech using, for example, waveform concatenation speech synthesis. The generation unit can improve the naturalness of the speech using statistical parametric speech synthesis. For example, the generation unit generates speech using waveform concatenation speech synthesis and improves the naturalness of the speech using statistical parametric speech synthesis. This allows the generation unit to generate natural-sounding speech. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input speech data into a generation AI and have the generation AI generate speech data. This allows the generation unit to generate more natural-sounding speech.

[0066] The generation unit can convert text to speech in real time. For example, when text is entered, the generation unit instantly converts it to speech. By converting text to speech in real time, the generation unit can provide smooth communication. For example, when the user enters "Hello," the generation unit instantly responds with "Hello" in voice. In this way, the generation unit can provide smooth communication by converting text to speech in real time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input text data into a generation AI, and the generation AI can generate speech in real time. In this way, the generation unit can provide even smoother communication.

[0067] The generation unit can create personalized voice profiles. For example, the generation unit learns the characteristics of the user's voice and creates a personalized voice profile. This allows the generation unit to provide voice responses tailored to the user. For example, the generation unit learns characteristics such as the pitch, timbre, and speaking style of the user's voice and creates a personalized voice profile. This allows the generation unit to provide voice responses tailored to the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input voice data into a generation AI to learn the characteristics of the user's voice, and have the generation AI extract the characteristics of the voice data. This allows the generation unit to generate more natural and personalized responses.

[0068] The reception unit can estimate the user's emotions and adjust the text input interface based on the estimated emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface to reduce the burden of input. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable interface. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick text input. In this way, the reception unit can reduce the burden of input by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input facial expression data into a generative AI to estimate the user's emotions, and have the generative AI estimate the emotions. This allows the reception unit to adjust the interface based on the user's emotions.

[0069] 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 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 based on their past input history. This allows the reception desk to suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method. This allows the reception desk to suggest the optimal input method for the user.

[0070] The input unit can provide input assistance based on the user's current situation and environment when entering text. For example, if the user is in a noisy environment, the input unit can provide noise cancellation to improve the accuracy of voice input. If the user is on the move, the input unit can provide an interface that allows input with simple tap operations. Furthermore, if the user is in a quiet environment, the input unit can provide detailed input options to support highly accurate input. In this way, the input unit can improve the accuracy and efficiency of input by providing input assistance according to the user's situation and environment. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's current situation and environment data into a generating AI, and have the generating AI provide input assistance. In this way, the input unit can provide input assistance according to the user's situation and environment.

[0071] 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 stressed, the reception desk may suggest prioritizing important messages. If the user is relaxed, the reception desk may suggest prioritizing detailed information. Furthermore, if the user is in a hurry, the reception desk may suggest prioritizing short, concise messages. This allows the reception desk to prioritize important messages by determining the priority of text according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input facial expression data into a generative AI to estimate the user's emotions, allowing the generative AI to estimate the emotions. This allows the reception desk to determine the priority of text based on the user's emotions.

[0072] The input field 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 field can automatically display phrases related to that location as suggestions. If the user is traveling, the input field can suggest phrases related to their travel destination. Furthermore, if the user is at home, the input field can prioritize displaying phrases related to daily life. In this way, the input field can present highly relevant input suggestions by taking into account the user's geographical location. Some or all of the above processing in the input field may be performed using AI, for example, or without AI. For example, the input field can input the user's geographical location into a generating AI, which can then present highly relevant input suggestions. In this way, the input field can present highly relevant input suggestions by taking into account the user's geographical location.

[0073] The input field can analyze the user's social media activity when text is entered and suggest relevant input options. For example, the input field can suggest phrases related to topics the user has recently been discussing on social media. If the user is participating in a specific event, the input field can display phrases related to that event. Furthermore, the input field can automatically display phrases that the user frequently uses on social media as suggestions. In this way, the input field can suggest relevant input options by analyzing the user's social media activity. Some or all of the above processing in the input field may be performed using AI, for example, or not using AI. For example, the input field can input the user's social media activity data into a generating AI, which can then suggest relevant input options. In this way, the input field can analyze the user's social media activity and suggest relevant input options.

[0074] The analysis unit can estimate the user's emotions and adjust the text analysis method based on the estimated emotions. For example, if the user is tense, the analysis unit can use a simple and intuitive analysis method. If the user is relaxed, the analysis unit can perform a detailed analysis to provide a deeper understanding. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise results. In this way, the analysis unit can improve the accuracy of the analysis by providing an analysis method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input facial expression data into a generative AI to estimate the user's emotions, and have the generative AI estimate the emotions. In this way, the analysis unit can adjust the text analysis method based on the user's emotions.

[0075] The analysis unit can improve the accuracy of its analysis by considering contextual information during text analysis. For example, the analysis unit can accurately understand the meaning by considering the context before and after the text. The analysis unit can refer to the user's past utterances to perform consistent analysis. Furthermore, the analysis unit can also extract keywords from the text and improve the accuracy of the analysis based on the context. In this way, the analysis unit can improve the accuracy of its analysis by considering contextual information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input contextual information into a generating AI and have the generating AI analyze the contextual information. In this way, the analysis unit can improve the accuracy of its analysis by considering contextual information.

[0076] The analysis unit can perform text analysis by referring to the user's past utterances. For example, the analysis unit can refer to phrases the user has used in the past to understand the meaning of the current text. The analysis unit can extract specific patterns from the user's past utterances and reflect them in the analysis. Furthermore, the analysis unit can perform contextual analysis based on the user's past utterances. This allows the analysis unit to perform contextual analysis by referring to past utterances. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past utterance data into a generating AI and have the generating AI analyze the past utterances. This allows the analysis unit to perform analysis by referring to past utterances.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the analysis unit can facilitate the understanding of the analysis results by providing a display method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input facial expression data into a generative AI to estimate the user's emotions, and have the generative AI estimate the emotions. In this way, the analysis unit can adjust the display method of the analysis results based on the user's emotions.

[0078] The analysis unit can perform text analysis while considering the user's geographical background. For example, if the user is in a specific region, the analysis unit can prioritize analyzing information related to that region. If the user is traveling, the analysis unit can analyze information related to the travel destination. Furthermore, if the user is at home, the analysis unit can prioritize analyzing information related to daily life. In this way, the analysis unit can perform highly relevant analysis by considering the user's geographical background. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical background data into a generating AI and have the generating AI analyze the geographical background. In this way, the analysis unit can perform analysis while considering the user's geographical background.

[0079] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases during text analysis. For example, the analysis unit performs analysis by referring to relevant literature based on keywords in the text. The analysis unit can accurately understand the meaning of the text by referring to information in databases. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to relevant research papers. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature and databases into a generating AI, and have the generating AI refer to the literature and databases. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases.

[0080] The generation unit can estimate the user's emotions and adjust the tone and intonation of the generated speech based on the estimated emotions. For example, if the user is tense, the generation unit can generate speech in a calm tone. If the user is relaxed, the generation unit can generate speech in a bright tone. Furthermore, if the user is in a hurry, the generation unit can generate speech in a quick and concise tone. In this way, the generation unit can produce more natural speech by providing tone and intonation that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input facial expression data into the generation AI to estimate the user's emotions, and have the generation AI estimate the emotions. In this way, the generation unit can adjust the tone and intonation of the generated speech based on the user's emotions.

[0081] The generation unit can generate natural speech by learning the user's past speech patterns during speech generation. For example, the generation unit can generate natural speech based on phrases the user has used in the past. The generation unit can learn the user's past speech patterns and generate consistent speech. Furthermore, the generation unit can refer to the user's past speech content and generate contextually natural speech. In this way, the generation unit can generate consistent and natural speech by learning past speech patterns. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past speech data into a generation AI, allowing the generation AI to learn past speech patterns. In this way, the generation unit can learn past speech patterns and generate natural speech.

[0082] The generation unit can customize the content of the voice based on the user's current situation and environment when generating voice. For example, if the user is in a noisy environment, the generation unit can generate voice using a noise-canceling function. If the user is in a quiet environment, the generation unit can generate voice containing detailed information. Furthermore, if the user is on the move, the generation unit can generate concise and to-the-point voice. In this way, the generation unit can provide more appropriate voice responses by generating voice according to the user's situation and environment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current situation and environment data into a generation AI, and have the generation AI customize the content of the voice. In this way, the generation unit can customize the content of the voice based on the user's situation and environment.

[0083] The generation unit can estimate the user's emotions and adjust the speed of the generated speech based on the estimated emotions. For example, if the user is nervous, the generation unit can generate speech at a slow speed. If the user is relaxed, the generation unit can generate speech at a normal speed. Furthermore, if the user is in a hurry, the generation unit can generate speech at a rapid speed. This allows for more natural communication by generating speech at a speed appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input facial expression data into the generation AI to estimate the user's emotions, allowing the generation AI to estimate the emotions. This allows the generation unit to adjust the speed of the generated speech based on the user's emotions.

[0084] The generation unit can adjust the accent and dialect of the voice when generating speech, taking into account the user's geographical background. For example, if the user is in a specific region, the generation unit can generate speech that reflects the accent and dialect of that region. If the user is traveling, the generation unit can generate speech that reflects the accent and dialect of the travel destination. Furthermore, if the user is at home, the generation unit can generate speech that reflects the accent and dialect appropriate for daily life. In this way, the generation unit can generate more familiar speech by reflecting the accent and dialect according to the user's geographical background. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical background data into a generation AI, and have the generation AI adjust the accent and dialect. In this way, the generation unit can adjust the accent and dialect of the voice, taking the user's geographical background into account.

[0085] The generation unit can improve the accuracy of speech generation by referring to the relevant speech database during speech generation. For example, the generation unit can refer to information in the speech database to generate natural-sounding speech. The generation unit can improve the accuracy of the speech based on the relevant speech data. Furthermore, the generation unit can also improve the quality of the generated speech by using samples in the speech database. In this way, the generation unit can improve the quality and accuracy of the generated speech by referring to the relevant speech database. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the speech database into a generation AI, and the generation AI can refer to the speech data. In this way, the generation unit can improve the accuracy of speech generation by referring to the relevant speech database.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The input unit can learn the user's input speed and provide an interface tailored to that speed. For example, if the user inputs quickly, the input unit can provide simple and quick input options. If the user inputs slowly, it can provide detailed input options to improve input accuracy. Furthermore, the input unit can adjust the display speed of input suggestions according to the user's input speed. In this way, the input unit can improve input efficiency and accuracy by providing an interface tailored to the user's input speed.

[0088] The generation unit can estimate the user's emotions and adjust the intonation of the voice based on those emotions. For example, if the user is happy, it can generate a voice with a bright and cheerful intonation. If the user is sad, it can generate a voice with a calm intonation. Furthermore, if the user is angry, it can generate a voice with a strong intonation. In this way, the generation unit can produce more natural and emotionally rich voices by providing intonation that matches the user's emotions.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is tense, it can present simple and visually easy-to-understand results. If the user is relaxed, it can present detailed analysis results. Furthermore, if the user is in a hurry, it can present concise results that get straight to the point. In this way, the analysis unit can facilitate the understanding of the analysis results by providing a presentation method that is tailored to the user's emotions.

[0090] The generation unit can learn the user's past speech and adjust the tone and intonation of the voice based on that past speech. For example, it can learn the tone and intonation of phrases the user has used in the past and generate speech with a similar tone and intonation. This allows the generation unit to produce natural-sounding speech based on the user's past speech.

[0091] The reception desk can analyze the user's input in real time and suggest appropriate input options based on that input. For example, if the user inputs "Hello," the reception desk will suggest appropriate input options such as "How are you?". In this way, the reception desk can improve input efficiency by suggesting appropriate input options based on the user's input.

[0092] The generation unit can estimate the user's emotions and adjust the speed of the speech based on those emotions. For example, if the user is nervous, the speech can be generated at a slow speed. If the user is relaxed, the speech can be generated at a normal speed. Furthermore, if the user is in a hurry, the speech can be generated at a rapid speed. This allows the generation unit to generate speech at a speed appropriate to the user's emotions, enabling more natural communication.

[0093] The reception desk can analyze the user's input and provide appropriate feedback based on that input. For example, if a user inputs "I'm tired today," the reception desk will provide feedback such as "Thank you for your hard work. Please rest well." In this way, the reception desk can improve user satisfaction by providing appropriate feedback based on the user's input.

[0094] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is tense, a simple analysis is performed; if the user is relaxed, a detailed analysis is performed. This allows the analysis unit to improve the reliability of the analysis results by providing an accuracy of analysis that is appropriate to the user's emotions.

[0095] The generation unit can estimate the user's emotions and adjust the content of the voice based on those emotions. For example, if the user is sad, it can generate voice messages that include words of encouragement. If the user is happy, it can generate voice messages that include words of empathy. In this way, the generation unit can provide more personal communication by generating voice messages that match the user's emotions.

[0096] The reception desk can analyze the user's input and suggest appropriate actions based on that input. For example, if a user inputs "I'm hungry," the reception desk might suggest an action such as "Would you like to search for nearby restaurants?" In this way, the reception desk can improve user convenience by suggesting appropriate actions based on the user's input.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives text input from the user. For example, the reception desk can receive text input using a keyboard or touchscreen. The reception desk can also receive text input using voice input. The reception desk converts speech to text using speech recognition technology. Step 2: The analysis unit analyzes the text input by the reception unit. For example, the analysis unit understands the meaning of the text using natural language processing techniques. The analysis unit analyzes the text using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The generation unit generates natural-sounding speech based on the text analyzed by the analysis unit. For example, the generation unit generates natural-sounding speech using speech synthesis technology. The generation unit generates speech using techniques such as waveform concatenation speech synthesis and statistical parametric speech synthesis.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the reception unit, analysis unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input text using the touch panel 38A or keyboard of the smart device 14. The reception unit can also convert speech to text using the microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the text using natural language processing technology. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates natural speech using speech synthesis technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input text using the touch panel or keyboard of the smart glasses 214. The reception unit can also convert speech to text using the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the text using natural language processing technology. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates natural speech using speech synthesis technology. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input text using the touch panel or keyboard of the headset terminal 314. The reception unit can also convert speech to text using the microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the text using natural language processing technology. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates natural speech using speech synthesis technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, analysis unit, and generation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can input text using the robot 414's touch panel or keyboard. The reception unit can also convert speech to text using the robot 414's microphone 238. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the text using natural language processing technology. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates natural speech using speech synthesis technology. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A reception desk where you enter text, An analysis unit that analyzes the text input by the reception unit, The system comprises a generation unit that generates natural-sounding speech based on the text analyzed by the analysis unit. A system characterized by the following features. (Note 2) The generating unit is It learns the characteristics of each user's voice and generates more natural and personalized responses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Understanding the meaning of text using natural language processing technology The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Using speech synthesis technology to generate natural-sounding speech The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Convert text to speech in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Create an individualized voice profile The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the text input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned reception unit is When entering text, input assistance is provided based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering text, the system will suggest highly relevant input options, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the text analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing text, consider contextual information to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During text analysis, the analysis is performed by referring to the user's past speech. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing text, the analysis takes into account the user's geographical background. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When performing text analysis, we improve the accuracy of the analysis by referring to relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the tone and intonation of the generated voice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During speech generation, the system learns the user's past speech patterns to produce natural-sounding speech. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating speech, the content of the speech is customized based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the speed of the generated audio based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating speech, the system adjusts the accent and dialect of the voice to take into account the user's geographical background. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During speech generation, the system references relevant speech databases to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 desk where you enter text, An analysis unit that analyzes the text input by the reception unit, The system comprises a generation unit that generates natural-sounding speech based on the text analyzed by the analysis unit. A system characterized by the following features.

2. The generating unit is It learns the characteristics of each user's voice and generates more natural and personalized responses. The system according to feature 1.

3. The aforementioned analysis unit, Understanding the meaning of text using natural language processing technology The system according to feature 1.

4. The generating unit is Using speech synthesis technology to generate natural-sounding speech The system according to feature 1.

5. The generating unit is Convert text to speech in real time. The system according to feature 1.

6. The generating unit is Create an individualized voice profile The system according to feature 1.

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

8. 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.

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

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