system

Abstract

We provide the system. [Solution] A voice input means that receives voice data input via a user interface, A speech recognition means that converts received audio data into text data, A translation means for translating the text data into multiple languages, A speech synthesis method that converts translated text into speech, Cultural adaptation means to adjust the translation based on cultural background and manners, An output method for outputting the translation result to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern global society, multilingual communication is essential. However, not only translation between different languages, but misunderstandings and frictions caused by differences in cultural backgrounds and manners are major obstacles. Also, in language learning, there is a problem that it is difficult to maintain learning efficiency and motivation because there is a lack of support specialized for the difficult parts and the content that individual users want to learn.

Means for Solving the Problems

[0005] This invention provides a system that inputs voice data using a user interface, converts it into text using speech recognition means, and translates it into multiple languages ​​in real time. Furthermore, by incorporating a cultural adaptation means that converts the translated text back into speech using speech synthesis means and adjusts it considering cultural background and manners, misunderstandings can be minimized. In addition, by analyzing the user's usage history and providing learning support information, the system provides learning support tailored to individual needs and improves learning efficiency.

[0006] A "user interface" is a device or software that serves as a point of contact for a user to input or output data to and from a system.

[0007] "Voice input means" refers to a device or process for capturing the voice spoken by the user into the system.

[0008] "Speech recognition means" refers to a technology or device for capturing speech data and converting it into corresponding text data.

[0009] "Translation means" refers to a technology or device that has the function of converting input text data into another language.

[0010] "Speech synthesis means" refers to a technology or device that receives text data and outputs it as natural-sounding speech.

[0011] "Cultural adaptation tools" are techniques or devices that adjust translation results and communication content to take cultural backgrounds and manners into consideration in order to avoid misunderstandings.

[0012] "Output means" refers to a device or process for providing translated text or audio to the user.

[0013] "Learning support means" refers to technologies or devices that analyze a user's learning history and provide information and functions to support efficient learning.

[0014] An "advice generation method" is a technology or device that generates appropriate advice to prevent cultural misunderstandings that may arise during intercultural communication. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for users to engage in real-time multilingual communication, enabling smooth communication across language barriers. The system is comprised of a combination of means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, and learning support.

[0037] User interface:

[0038] Users can operate the system using apps that run on smartphones, PCs, or wearable devices. This allows for voice and text input, as well as verification of translation results.

[0039] Voice input and speech recognition:

[0040] When a user starts a conversation, the device collects voice data via the microphone. The collected voice data is sent to a server, where it is converted into text data using ASR (Automatic Speech Recognition) technology.

[0041] translation:

[0042] The server uses neural machine translation technology to translate the converted text into multiple languages ​​in real time. This provides highly accurate and contextually appropriate translations.

[0043] Cultural adaptation:

[0044] The translated results are adjusted by the server based on cultural context and business etiquette. This process enables translations that avoid misunderstandings between different cultures.

[0045] Speech synthesis and output:

[0046] The translated text is sent from the server to the terminal and either synthesized into speech using TTS technology or displayed as text. Users can then listen to it or view it on the screen to continue communicating.

[0047] Learning support:

[0048] The server analyzes the user's usage history and generates individually optimized learning plans and learning support information. The terminal provides this to the user, who can then use it for regular learning or learning a new language.

[0049] Specific example:

[0050] For example, suppose a native Japanese speaker uses this system when participating in a business meeting conducted in English. When the user starts a conversation using their smartphone, the device collects the user's voice and sends it to the server. The server converts the voice into Japanese text in real time, then translates it into English, and adjusts it to account for cultural differences. Finally, the translated result is returned to the device as either voice or text, allowing the user to continue the conversation while reviewing it. This entire process enables smooth communication even with people who speak different languages.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] The user launches the smartphone app and selects their preferred language setting. This changes the user interface to an input mode corresponding to the user's selection.

[0054] Step 2:

[0055] When the user begins speaking, the device uses its microphone to collect audio data. The collected audio data is then sent from the device to the server.

[0056] Step 3:

[0057] The server processes the received audio data through an ASR (Automatic Speech Recognition) module, converting the audio into corresponding text data. This converted text data is then sent to the next processing stage.

[0058] Step 4:

[0059] The server sends text data to a neural machine translation (NMT) module, which translates it into the selected target language in real time. During translation, the system takes into account context and cultural differences between languages.

[0060] Step 5:

[0061] The translated text is subjected to cultural adaptation measures. The server converts it into appropriate expressions that reflect cultural background and business etiquette, and adjusts the translation results.

[0062] Step 6:

[0063] The adjusted text data is then sent to a TTS (Text-to-Speech) module, where the server converts it into speech data.

[0064] Step 7:

[0065] The server sends the synthesized speech data or translated text back to the terminal, which then provides it to the user. The user can listen to the audio through the speaker or view the text displayed on the screen.

[0066] Step 8:

[0067] If the user continues communicating, the process from step 2 is repeated. Upon completion, the user's translation and usage history are recorded on the server and used as material for future learning support.

[0068] (Example 1)

[0069] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0070] There is a need to efficiently overcome language barriers and cultural misunderstandings that arise when people with different languages ​​communicate in real time. Conventional translation systems have had factors that hinder intercultural communication, such as insufficient translation accuracy and speed, and inappropriate cultural adaptation. Furthermore, there has been a lack of adaptive information provision to support user learning.

[0071] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0072] In this invention, the server includes a voice input means, a voice recognition means, a translation means, a cultural adaptation means, a voice synthesis means, an output means, a learning support means, and an advice generation means. This enables smooth real-time communication between people who speak different languages, prevents cultural misunderstandings, and achieves highly accurate and contextual translation. Furthermore, by providing individually optimized learning support information, it is possible to promote the user's language acquisition.

[0073] "Voice input means" refers to a device or function that acquires voice data input through a user interface.

[0074] "Speech recognition means" refers to a device or process for converting acquired speech data into text data.

[0075] "Translation means" refers to a device or technology for converting text data into multiple languages.

[0076] "Cultural adaptation tools" refer to devices or processes for adjusting translated content based on cultural background and customs.

[0077] "Speech synthesis means" refers to a technology or device that converts translated text data into speech.

[0078] "Output means" refers to a device or function for providing the translation result to the user.

[0079] "Learning support means" refers to a device or process for analyzing a user's usage history and generating and providing learning support information.

[0080] An "advice generation tool" refers to a device or function used to provide guidance to prevent cultural misunderstandings during intercultural communication.

[0081] The system based on this invention supports real-time communication between people who speak different languages ​​and can automatically handle everything from input to output of voice data. Specifically, users operate the system using terminals such as smartphones, personal computers, and wearable devices. These terminals have dedicated application software installed that supports input and output of voice and text information.

[0082] When a user starts a conversation, the device uses the microphone to capture audio data. This audio data is sent to a server via the internet. The server is equipped with speech recognition software (e.g., Google® Cloud Speech-to-Text or Amazon Transcribe) that converts the audio into text data.

[0083] The converted text data is translated into multiple languages ​​by neural machine translation software (e.g., Google Translate API or DeepL API) on the server. Then, cultural adaptation tools are applied, and the translated content is adjusted based on cultural context and business etiquette.

[0084] The edited text data is sent back from the server to the device and either output as speech using speech synthesis technology on the device (e.g., Amazon Polly or Google Cloud Text-to-Speech) or displayed as text on the screen. The user can listen to or confirm this and continue communicating.

[0085] Furthermore, the server analyzes the user's usage history and provides individually optimized learning support information. This support information is designed to help users efficiently acquire a new language and is displayed on the device as vocabulary lists and practice exercises.

[0086] As a concrete example, a native Japanese speaker can use this system to communicate smoothly when participating in a business meeting conducted in English. An example of a prompt in this case would be, "Please help me communicate in this English business meeting." In this way, users can engage in smooth communication regardless of language.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The user launches an application on their smartphone, PC, or wearable device and begins voice input. The device uses its built-in microphone to collect the voice signal. The collected voice signal is acquired as digital audio data. This becomes the input data for the next step.

[0090] Step 2:

[0091] The terminal transmits the acquired digital audio data to the server via the internet. The server uses speech recognition software to convert this audio data into text data. Specifically, it uses real-time speech recognition technology to analyze the sound waveform and convert it into a string of characters. This converted text data becomes the input data for the next step.

[0092] Step 3:

[0093] The server receives text data and translates it into multiple languages ​​using neural machine translation technology. The server leverages generative AI models to provide context-aware, highly accurate translations. This process generates translated text data, which then serves as input for the next step.

[0094] Step 4:

[0095] The server adjusts the translated text data using cultural adaptation tools. This step involves appropriately modifying the translated content based on cultural context and conventions. For example, it may avoid expressions that are sensitive in a particular culture. The adjusted text data then becomes the input data for the next step.

[0096] Step 5:

[0097] The adjusted text data is sent from the server to the terminal. The terminal uses speech synthesis technology to convert this text data into speech. Specifically, it converts the string of characters into synthesized speech and outputs it through the speaker. It is also possible to display the text information on the screen. The synthesized speech or displayed text is the final output.

[0098] Step 6:

[0099] The server analyzes the user's usage history and generates learning support information using a generative AI model. The information generated by the server is provided as an individually optimized learning plan. This plan is presented to the user via their device, offering concrete opportunities to support the acquisition of a new language. Users can then utilize this plan to improve their daily learning.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] Smooth, real-time communication in a multilingual environment is difficult due to language barriers and cultural differences. In particular, linguistic and cultural misunderstandings are common in intercultural interactions within families and tourist destinations, hindering smooth communication. A system is needed to address these challenges and allow visitors and residents to enjoy communication without being concerned about language or cultural differences.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes information input means for receiving audio data input via a user interface; information recognition means for converting the received audio data into text data; language processing means for translating the text data into multiple languages; audio conversion means for converting the translated text into audio; cultural adaptation means for adjusting the translation based on cultural background and manners; display means for outputting the translation results or generated learning support information to the user; and visitor response means for automatically recognizing the visitor's language and supporting communication. This enables real-time and culturally adaptive communication in a multilingual environment.

[0105] "Information input means" refers to a device or module equipped with the function of receiving voice data via a user interface.

[0106] "Information recognition means" refers to a device or module that executes technologies or algorithms for converting received audio data into text data.

[0107] "Language processing means" refers to a device or module that executes techniques and algorithms for translating text data into multiple languages.

[0108] "Speech conversion means" refers to a device or module that executes technologies or algorithms for converting translated text into speech.

[0109] "Cultural adaptation tools" are devices or modules that implement technologies or algorithms to adjust translations based on cultural background and customs.

[0110] "Display means" refers to a device or module equipped with the function of outputting translation results or generated learning support information to the user.

[0111] "Visitor interaction means" refers to a device or module equipped with the function to automatically recognize a visitor's language and support communication.

[0112] This invention aims to implement multilingual real-time communication capabilities in consumer robots. This will enable communication that transcends language barriers even in cross-cultural environments.

[0113] The server uses a microphone mounted on the robot as a voice input method to capture the user's speech. As a means of information recognition, the voice data is sent to the server via the network and converted into text by advanced speech recognition software (e.g., Google Cloud Speech-to-Text).

[0114] The converted text data is translated into multiple languages ​​by language processing tools. This process utilizes translation software that leverages the latest neural network technology (e.g., Google Cloud Translate API).

[0115] Furthermore, cultural adaptation measures adjust translation results based on cultural background and customs, reducing intercultural misunderstandings. This adjustment uses custom algorithms that adhere to specific cultural codes and practices.

[0116] The server uses a speech conversion means to convert the adjusted translated text into speech using speech synthesis software (e.g., Amazon Polly), which is then output through the robot's speaker. A display means allows the text to be displayed along with the audio on devices capable of displaying text.

[0117] As a concrete example, when a visitor greets the robot with "Hello," the robot automatically recognizes the language and responds with "Konnichiwa," thus facilitating communication.

[0118] Example of a prompt:

[0119] Translate the following sentence while considering Japanese cultural norms: "How are you?"

[0120] Expected Output: "How are you?"

[0121] In this way, the present invention provides an effective means for users to naturally enjoy communication in a multilingual environment.

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The user begins speaking to the robot. A microphone mounted on the robot captures the voice data and sends it to the server as input. At this point, the input is raw voice data.

[0125] Step 2:

[0126] The server performs speech recognition processing using the received audio data. Specifically, it uses services such as Google Cloud Speech-to-Text to convert the audio data into text. This process yields text data from the audio data. The output is text data that transcribes what was spoken.

[0127] Step 3:

[0128] The server translates the text data generated by speech recognition using language processing tools. It uses the Google Cloud Translate API to translate from one language to another. In this process, the input is text data, and the output is the translated text data in the other language.

[0129] Step 4:

[0130] The generated translated text is optimized on the server using cultural adaptation tools. Based on a custom algorithm, adjustments are made to account for cultural backgrounds and manners, reducing cross-cultural misunderstandings. The input is translated text data, and the output is culturally adapted text data.

[0131] Step 5:

[0132] The server is a speech conversion device that synthesizes speech from culturally adapted text and generates audio data using tools such as Amazon Polly. The input is culturally adapted text data, and the output is synthesized speech data. This audio data is output to the user through the robot's speaker.

[0133] Step 6:

[0134] Finally, the robot uses visitor interaction tools to automatically recognize the visitor's language and respond appropriately. This process utilizes generated voice and text information to continue communication with the visitor. This allows users to engage in natural conversations through the robot.

[0135] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0136] This invention is a system that facilitates multilingual communication and provides a more personalized communication experience by recognizing user emotions. The system is comprised of a combination of means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, learning support, and an emotion engine.

[0137] User interface and voice input:

[0138] Users operate the system via smartphones or wearable devices and configure settings according to their preferred language and purpose. When a user begins a conversation, the device collects their speech as audio data through the microphone.

[0139] Speech recognition and emotion recognition:

[0140] This audio data is sent from the terminal to the server, which uses ASR (Automatic Speech Recognition) technology to convert the audio into text data. Simultaneously, an emotion engine analyzes features such as tone and rhythm of the voice to identify the user's emotional state.

[0141] Translation and cultural adaptation:

[0142] The converted text data is translated into multiple languages ​​in real time by translation tools, and cultural adaptation tools make adjustments that take into account cultural backgrounds and manners. The results of the emotion engine are also taken into consideration, and the translation results and output format are adjusted to suit the user's emotions.

[0143] Speech synthesis and output:

[0144] The final translation result is either converted to speech using speech synthesis technology or sent to the device as text. This allows the user to receive appropriate feedback that reflects their emotions.

[0145] Learning support and emotional feedback:

[0146] The server analyzes the user's usage history and emotional data to provide personalized learning support information. Learning plans are dynamically optimized based on changes in the user's emotions, facilitating personalized learning.

[0147] Specific example:

[0148] For example, if a Japanese-speaking user is conducting a business negotiation in English, the system instantly collects their speech and translates it into English. If the emotion engine detects emotions such as tension or anxiety from the user's voice, the server adjusts the translation to more polite language through cultural adaptation mechanisms and provides feedback to alleviate stress. This allows users to participate in international communication with confidence.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The user opens the smartphone app and selects the language and translation purpose. This sets the user interface to speech input mode.

[0152] Step 2:

[0153] When the user begins speaking, the device collects audio data using its built-in microphone. This audio data is then compressed in real time and sent to the server.

[0154] Step 3:

[0155] The server processes the received audio data through an ASR (Automatic Speech Recognition) module, converting the audio into corresponding text data.

[0156] Step 4:

[0157] Simultaneously, the server uses an emotion engine to analyze the audio data and determine the user's emotional state based on factors such as tone and intonation.

[0158] Step 5:

[0159] The server sends the speech-recognized text to a translation device, which translates it into the configured target language. Here, neural machine translation technology is used to provide fast and accurate translations.

[0160] Step 6:

[0161] The translated text is sent to a cultural adaptation system. The server takes the user's emotions into consideration and adjusts the expression and tone to be appropriate.

[0162] Step 7:

[0163] The server converts the adjusted translated text into speech data using speech synthesis technology, or sends it to the terminal in text format.

[0164] Step 8:

[0165] The device provides the user with the received audio or text. The user listens to the translated audio through the speaker or views the text on the screen.

[0166] Step 9:

[0167] When a user's session ends, the server records their usage history and analyzed sentiment data. This data is used to support subsequent learning. The server generates a learning plan that reflects the user's emotional changes, which can be used to assist them in their next session.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] In intercultural communication, language barriers and cultural misunderstandings can lead to communication obstacles, resulting in users experiencing stress and anxiety. To address this, a system is needed that provides real-time, emotionally conscious, and culturally adaptable translation. Furthermore, the ability to provide learning support based on user emotions and usage history is also a key challenge.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes: a voice input means for receiving voice data input via a user interface; a voice recognition means for converting the received voice data into text data; an emotion recognition means for analyzing the text data and identifying emotions based on the tone and rhythm of the voice; a translation means for translating the text data into multiple languages; a cultural adaptation means for adjusting the translation based on cultural background and manners; a voice synthesis means for converting the translation results, according to the cultural background and the user's emotional state, into speech; and an output means for outputting the translation results to the user. This enables the user to communicate smoothly and confidently even in cross-cultural situations.

[0173] "User interface means" refers to devices and applications that allow users to input voice data, settings information, and other data.

[0174] "Voice input means" refers to a device or process for receiving voice data from a user and converting it into a format that can be processed within the system.

[0175] "Speech recognition means" refers to a technology or system that converts speech data acquired by a speech input means into text data.

[0176] "Emotion recognition means" refers to a technology or method that analyzes the tone and rhythm of voice data to identify the user's emotional state.

[0177] "Translation means" refers to a function or system that converts text data recorded in one language into another language.

[0178] "Cultural adaptation measures" refer to techniques or processes for adjusting translated data to suit the cultural background, customs, and manners of the recipient.

[0179] "Speech synthesis means" refers to a technology or system that converts translated text data back into speech data so that users can receive it as natural speech.

[0180] "Output means" refers to a method or device for providing the user with the results processed by the system in the form of audio or text.

[0181] "Learning support means" refers to technologies or systems that analyze users' emotional data and usage history to provide individually optimized learning support information.

[0182] "Advice generation methods" refer to processes or techniques for generating and providing advice to prevent cultural misunderstandings in intercultural communication.

[0183] This invention is a system for achieving smooth communication between multiple languages. This system is comprised of a combination of technical means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, learning support, and an emotion engine.

[0184] Users launch the system's application using their smartphone or wearable device and configure settings according to their preferred language and purpose. Voice data is collected using the device's microphone and sent to the server. The server utilizes a common speech recognition service, known as ASR (Automatic Speech Recognition) technology, to convert the voice data into text. Furthermore, the server uses an emotion recognition engine to identify the user's emotional state from the voice data. This involves techniques that analyze the tone and rhythm of the voice.

[0185] Next, the server translates the text data into multiple languages ​​in real time. This translation process utilizes known translation APIs. The translated text is then adjusted using cultural adaptation tools to suit the background and manners of the target culture. It is also adjusted to provide appropriate feedback based on the user's emotional state. The final translation result is converted into speech using speech synthesis technology and output to the user's device.

[0186] As a concrete example, when a Japanese-speaking user conducts negotiations in English, the user's Japanese speech is sent to the server in real time via a smartphone app. The server instantly translates it into English, generates an English audio transcript that incorporates cultural adaptation and emotional feedback, and plays it back on the device. If the emotion engine detects that the user is under stress, the translation is adjusted to use softer language.

[0187] For example, you can ask the system a question like, "If someone is nervous during a meeting, explain how you can provide feedback that will help alleviate that nervousness," and then observe the system's response.

[0188] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0189] Step 1:

[0190] The user launches an application on their smartphone or wearable device and enters their preferred language, purpose, privacy settings, etc. The device receives this information and verifies the settings. Specifically, the selected language set and voice input mode are activated.

[0191] Step 2:

[0192] When a user begins a conversation, the device uses its microphone to collect audio data in real time and converts it into a digital audio file. This audio data then becomes input data for subsequent analysis. The device receives the audio data and prepares to send it to the server.

[0193] Step 3:

[0194] The server receives the audio data sent from the terminal and starts speech recognition processing. Using speech recognition technology, the audio data is converted into text data. ASR (Automatic Speech Recognition) is a typical algorithm used. The converted text data is used in the next processing step.

[0195] Step 4:

[0196] The server uses an emotion recognition engine to analyze the tone and rhythm of the text data and the original audio data. This identifies the user's emotional state. The results of the emotion recognition are then reflected in subsequent processing for translation and cultural adaptation.

[0197] Step 5:

[0198] The translation mechanism allows the server to automatically translate text data into the user-defined target language. This translation is performed using a common translation API, and the translated text is generated as intermediate output.

[0199] Step 6:

[0200] The server uses cultural adaptation tools to adjust the translated text according to the cultural background and customs of the target language. The user's emotional state is also considered at this stage, ensuring that feedback is presented in the most appropriate way. The adjusted text forms the basis of the final output.

[0201] Step 7:

[0202] Using speech synthesis technology, the server converts the adjusted text into digital speech. This speech data is then prepared as user-readable audio and sent to the terminal.

[0203] Step 8:

[0204] The audio data sent to the terminal as the final output is played back by the terminal for the user. Through this, the user can understand what the other party is saying and respond appropriately according to the situation.

[0205] (Application Example 2)

[0206] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0207] In modern society, effective communication using multiple languages ​​is essential, but language and cultural barriers often hinder this. In particular, when people from different cultural backgrounds interact, language differences, coupled with a lack of emotional understanding, further complicate mutual understanding. Therefore, there is a need for a system that can appropriately recognize the emotional state of users and provide culture-appropriate feedback.

[0208] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0209] In this invention, the server includes acoustic recognition means for converting acoustic information into text data, translation means for translating the text data into multiple natural languages, and emotion recognition means for identifying the user's emotional state and providing adjusted feedback. This enables users to communicate smoothly while understanding the emotions of others, transcending language and cultural differences.

[0210] "Acoustic information" refers to data relating to speech or other sounds, which is collected and analyzed by digital devices.

[0211] "Character data" refers to text-based data obtained by processing acoustic information, and is considered fundamental to information processing.

[0212] "Natural language" refers to the language that humans use on a daily basis, and is the subject of translation tools.

[0213] "Acoustic recognition means" refers to a technological device that performs the process of converting acoustic information into text data.

[0214] "Translation means" refers to machine technology that has the function of converting text data into multiple languages.

[0215] "Sound synthesis means" refers to a technical device that performs a process to convert text data back into a speech format.

[0216] "Cultural adaptation techniques" are technologies that involve a process of adjusting the content of a translation based on its cultural background and customs.

[0217] An "emotion recognition device" is a technological device that identifies and analyzes emotions from the user's acoustic information.

[0218] "Output means" refers to a device that provides the converted and analyzed information to the user via display or audio.

[0219] To implement this invention, the user first prepares a device for inputting acoustic information (for example, a smartphone or a home robot). The acoustic information is transmitted to a server through a built-in microphone. The server converts the acoustic information into text data using acoustic recognition technology. In this process, general ASR (Automatic Speech Recognition) software can be used as the acoustic recognition technology.

[0220] The converted text data is translated into multiple natural languages ​​through translation algorithms on the server. Using a translation API (e.g., a public API service) enables rapid and efficient translation. Subsequently, cultural adaptation tools adjust the translation based on cultural background and customs. This results in expressions suitable for a specific cultural sphere.

[0221] The server identifies the user's emotional state using emotion recognition technology based on acoustic information and converted text data. This process employs emotion analysis algorithms that infer emotions from factors such as tone and patterns of speech.

[0222] Finally, sound synthesis technology converts text data back into sound, providing feedback to the user through output devices (e.g., speakers or text displays). Through this entire process, users can achieve effective communication across language and cultural differences.

[0223] A concrete example is the role a home robot can play in situations where visitors speak a different language. The robot instantly translates the visitor's words and provides comments that respond to the visitor's emotions, thereby supporting a smooth conversation.

[0224] An example of a prompt message is: "A home robot will assist with conversations with Chinese-speaking guests. Please utilize emotion recognition to create a friendly atmosphere that puts visitors at ease. Please provide support so that the host can enjoy the conversation comfortably."

[0225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0226] Step 1:

[0227] The device collects the user's voice as acoustic information via a microphone. The input is the user's voice, and the output is digitized acoustic information data. The voice is processed as a digital signal, and noise reduction and volume adjustment are performed.

[0228] Step 2:

[0229] The server receives the collected acoustic information and converts it into text data using an acoustic recognition system. The input is acoustic information data, and the output is the converted text data. The software used here is ASR (Automatic Speech Recognition), which uses a specific algorithm to analyze the patterns in the speech and convert them into text.

[0230] Step 3:

[0231] The server translates text data into multiple natural languages ​​using translation tools. The input is text data, and the output is translated text data. Multilingual support is achieved by utilizing a translation API, enabling accurate and speedy translation.

[0232] Step 4:

[0233] The server uses cultural adaptation tools to adjust the translated text data according to cultural background and customs. The input is the translated text data, and the output is the culturally adapted text data. The server adjusts the details of wording and expression based on the culture and etiquette policies of each country.

[0234] Step 5:

[0235] The server identifies the user's emotional state using emotion recognition means based on acoustic information. The input is acoustic information data, and the output is identified emotional state data. An emotion analysis algorithm infers and digitizes emotions based on factors such as tone and rhythm of the voice.

[0236] Step 6:

[0237] The server generates adjusted feedback based on the output of the emotion recognition system and outputs it as speech using the sound synthesis system. The input is culture-adapted text data and emotion state data, and the output is adjusted speech feedback. It is synthesized as natural speech using sound synthesis technology and provided to the user through a speaker.

[0238] 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.

[0239] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0240] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0241] [Second Embodiment]

[0242] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0243] 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.

[0244] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0245] 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.

[0246] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0247] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0248] 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.

[0249] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0250] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0251] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0252] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0253] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0254] This invention is a system for users to engage in real-time multilingual communication, enabling smooth communication across language barriers. The system is comprised of a combination of means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, and learning support.

[0255] User interface:

[0256] Users can operate the system using apps that run on smartphones, PCs, or wearable devices. This allows for voice and text input, as well as verification of translation results.

[0257] Voice input and speech recognition:

[0258] When a user starts a conversation, the device collects voice data via the microphone. The collected voice data is sent to a server, where it is converted into text data using ASR (Automatic Speech Recognition) technology.

[0259] translation:

[0260] The server uses neural machine translation technology to translate the converted text into multiple languages ​​in real time. This provides highly accurate and contextually appropriate translations.

[0261] Cultural adaptation:

[0262] The translated results are adjusted by the server based on cultural context and business etiquette. This process enables translations that avoid misunderstandings between different cultures.

[0263] Speech synthesis and output:

[0264] The translated text is sent from the server to the terminal and either synthesized into speech using TTS technology or displayed as text. Users can then listen to it or view it on the screen to continue communicating.

[0265] Learning support:

[0266] The server analyzes the user's usage history and generates individually optimized learning plans and learning support information. The terminal provides this to the user, who can then use it for regular learning or learning a new language.

[0267] Specific example:

[0268] For example, suppose a native Japanese speaker uses this system when participating in a business meeting conducted in English. When the user starts a conversation using their smartphone, the device collects the user's voice and sends it to the server. The server converts the voice into Japanese text in real time, then translates it into English, and adjusts it to account for cultural differences. Finally, the translated result is returned to the device as either voice or text, allowing the user to continue the conversation while reviewing it. This entire process enables smooth communication even with people who speak different languages.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] The user launches the smartphone app and selects their preferred language setting. This changes the user interface to an input mode corresponding to the user's selection.

[0272] Step 2:

[0273] When the user begins speaking, the device uses its microphone to collect audio data. The collected audio data is then sent from the device to the server.

[0274] Step 3:

[0275] The server processes the received audio data through an ASR (Automatic Speech Recognition) module, converting the audio into corresponding text data. This converted text data is then sent to the next processing stage.

[0276] Step 4:

[0277] The server sends text data to a neural machine translation (NMT) module, which translates it into the selected target language in real time. During translation, the system takes into account context and cultural differences between languages.

[0278] Step 5:

[0279] The translated text is subjected to cultural adaptation means. The server converts it into appropriate expressions reflecting the cultural background and business manners, and adjusts the translation result.

[0280] Step 6:

[0281] The adjusted text data is then sent to the TTS (Text-to-Speech) module, and the server converts it into audio data.

[0282] Step 7:

[0283] The server returns the audio synthesized data or the translated text to the terminal, and the terminal provides it to the user. The user can listen to the audio through the speaker or check the text displayed on the screen.

[0284] Step 8:

[0285] If the user continues the communication, the process from Step 2 is repeated. At the end, the user's translation and usage history are recorded on the server and used as materials for future learning support.

[0286] (Example 1)

[0287] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] When people with different languages communicate in real time, it is required to efficiently eliminate language barriers and cultural misunderstandings they face. In conventional translation systems, there are factors that hinder cross-cultural communication, such as insufficient translation accuracy and speed, or inappropriate cultural adaptation. Also, there has been a lack of adaptive information provision regarding user learning support.

[0289] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0290] In this invention, the server includes a voice input means, a voice recognition means, a translation means, a cultural adaptation means, a voice synthesis means, an output means, a learning support means, and an advice generation means. This enables smooth real-time communication between people who speak different languages, prevents cultural misunderstandings, and achieves highly accurate and contextual translation. Furthermore, by providing individually optimized learning support information, it is possible to promote the user's language acquisition.

[0291] "Voice input means" refers to a device or function that acquires voice data input through a user interface.

[0292] "Speech recognition means" refers to a device or process for converting acquired speech data into text data.

[0293] "Translation means" refers to a device or technology for converting text data into multiple languages.

[0294] "Cultural adaptation tools" refer to devices or processes for adjusting translated content based on cultural background and customs.

[0295] "Speech synthesis means" refers to a technology or device that converts translated text data into speech.

[0296] "Output means" refers to a device or function for providing the translation result to the user.

[0297] "Learning support means" refers to a device or process for analyzing a user's usage history and generating and providing learning support information.

[0298] An "advice generation tool" refers to a device or function used to provide guidance to prevent cultural misunderstandings during intercultural communication.

[0299] The system based on this invention supports real-time communication between people who speak different languages ​​and can automatically handle everything from input to output of voice data. Specifically, users operate the system using terminals such as smartphones, personal computers, and wearable devices. These terminals have dedicated application software installed that supports input and output of voice and text information.

[0300] When a user starts a conversation, the device uses the microphone to capture audio data. This audio data is sent to a server via the internet. The server is equipped with speech recognition software (e.g., Google Cloud Speech-to-Text or Amazon Transcribe) that converts the audio into text data.

[0301] The converted text data is translated into multiple languages ​​by neural machine translation software (e.g., Google Translate API or DeepL API) on the server. Then, cultural adaptation tools are applied, and the translated content is adjusted based on cultural context and business etiquette.

[0302] The edited text data is sent back from the server to the device and either output as speech using speech synthesis technology on the device (e.g., Amazon Polly or Google Cloud Text-to-Speech) or displayed as text on the screen. The user can listen to or confirm this and continue communicating.

[0303] Furthermore, the server analyzes the user's usage history and provides individually optimized learning support information. This support information is designed to help users efficiently acquire a new language and is displayed on the device as vocabulary lists and practice exercises.

[0304] As a specific example, when a user whose native language is Japanese participates in a business meeting in English, this system can be utilized to communicate smoothly. An example of a prompt sentence in this case is "Help with communication in an English business meeting." In this way, the user can conduct smooth interactions regardless of the language.

[0305] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0306] Step 1:

[0307] The user launches an application on a smartphone, a personal computer, or a wearable device and starts voice input. The terminal collects voice signals using a built-in microphone. The collected voice signals are obtained as digital voice data. This becomes the input data for the next step.

[0308] Step 2:

[0309] The terminal transmits the obtained digital voice data to the server via the Internet. The server converts this voice data into character data using voice recognition software. Specifically, it utilizes real-time voice recognition technology to analyze the waveform of the sound and convert it into a character string. This converted character data becomes the input data for the next step.

[0310] Step 3:

[0311] The server receives the character data and translates it into multiple languages using neural machine translation technology. The server utilizes a generative AI model to provide highly accurate translations considering the context. Through this process, the translated character data is generated and becomes the input data for the next step.

[0312] Step 4:

[0313] The server adjusts the translated text data using cultural adaptation tools. This step involves appropriately modifying the translated content based on cultural context and conventions. For example, it may avoid expressions that are sensitive in a particular culture. The adjusted text data then becomes the input data for the next step.

[0314] Step 5:

[0315] The adjusted text data is sent from the server to the terminal. The terminal uses speech synthesis technology to convert this text data into speech. Specifically, it converts the string of characters into synthesized speech and outputs it through the speaker. It is also possible to display the text information on the screen. The synthesized speech or displayed text is the final output.

[0316] Step 6:

[0317] The server analyzes the user's usage history and generates learning support information using a generative AI model. The information generated by the server is provided as an individually optimized learning plan. This plan is presented to the user via their device, offering concrete opportunities to support the acquisition of a new language. Users can then utilize this plan to improve their daily learning.

[0318] (Application Example 1)

[0319] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0320] Smooth, real-time communication in a multilingual environment is difficult due to language barriers and cultural differences. In particular, linguistic and cultural misunderstandings are common in intercultural interactions within families and tourist destinations, hindering smooth communication. A system is needed to address these challenges and allow visitors and residents to enjoy communication without being concerned about language or cultural differences.

[0321] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0322] In this invention, the server includes information input means for receiving audio data input via a user interface; information recognition means for converting the received audio data into text data; language processing means for translating the text data into multiple languages; audio conversion means for converting the translated text into audio; cultural adaptation means for adjusting the translation based on cultural background and manners; display means for outputting the translation results or generated learning support information to the user; and visitor response means for automatically recognizing the visitor's language and supporting communication. This enables real-time and culturally adaptive communication in a multilingual environment.

[0323] "Information input means" refers to a device or module equipped with the function of receiving voice data via a user interface.

[0324] "Information recognition means" refers to a device or module that executes technologies or algorithms for converting received audio data into text data.

[0325] "Language processing means" refers to a device or module that executes techniques and algorithms for translating text data into multiple languages.

[0326] "Speech conversion means" refers to a device or module that executes technologies or algorithms for converting translated text into speech.

[0327] "Cultural adaptation tools" are devices or modules that implement technologies or algorithms to adjust translations based on cultural background and customs.

[0328] "Display means" refers to a device or module equipped with the function of outputting translation results or generated learning support information to the user.

[0329] "Visitor interaction means" refers to a device or module equipped with the function to automatically recognize a visitor's language and support communication.

[0330] This invention aims to implement multilingual real-time communication capabilities in consumer robots. This will enable communication that transcends language barriers even in cross-cultural environments.

[0331] The server uses a microphone mounted on the robot as a voice input method to capture the user's speech. As a means of information recognition, the voice data is sent to the server via the network and converted into text by advanced speech recognition software (e.g., Google Cloud Speech-to-Text).

[0332] The converted text data is translated into multiple languages ​​by language processing tools. This process utilizes translation software that leverages the latest neural network technology (e.g., Google Cloud Translate API).

[0333] Furthermore, cultural adaptation measures adjust translation results based on cultural background and customs, reducing intercultural misunderstandings. This adjustment uses custom algorithms that adhere to specific cultural codes and practices.

[0334] The server uses a speech conversion means to convert the adjusted translated text into speech using speech synthesis software (e.g., Amazon Polly), which is then output through the robot's speaker. A display means allows the text to be displayed along with the audio on devices capable of displaying text.

[0335] As a concrete example, when a visitor greets the robot with "Hello," the robot automatically recognizes the language and responds with "Konnichiwa," thus facilitating communication.

[0336] Example of a prompt:

[0337] Translate the following sentence while considering Japanese cultural norms: "How are you?"

[0338] Expected Output: "How are you?"

[0339] In this way, the present invention provides an effective means for users to naturally enjoy communication in a multilingual environment.

[0340] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0341] Step 1:

[0342] The user begins speaking to the robot. A microphone mounted on the robot captures the voice data and sends it to the server as input. At this point, the input is raw voice data.

[0343] Step 2:

[0344] The server performs speech recognition processing using the received audio data. Specifically, it uses services such as Google Cloud Speech-to-Text to convert the audio data into text. This process yields text data from the audio data. The output is text data that transcribes what was spoken.

[0345] Step 3:

[0346] The server translates the text data generated by speech recognition using language processing tools. It uses the Google Cloud Translate API to translate from one language to another. In this process, the input is text data, and the output is the translated text data in the other language.

[0347] Step 4:

[0348] The generated translated text is optimized on the server using cultural adaptation tools. Based on a custom algorithm, adjustments are made to account for cultural backgrounds and manners, reducing cross-cultural misunderstandings. The input is translated text data, and the output is culturally adapted text data.

[0349] Step 5:

[0350] The server is a speech conversion device that synthesizes speech from culturally adapted text and generates audio data using tools such as Amazon Polly. The input is culturally adapted text data, and the output is synthesized speech data. This audio data is output to the user through the robot's speaker.

[0351] Step 6:

[0352] Finally, the robot uses visitor interaction tools to automatically recognize the visitor's language and respond appropriately. This process utilizes generated voice and text information to continue communication with the visitor. This allows users to engage in natural conversations through the robot.

[0353] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0354] This invention is a system that facilitates multilingual communication and provides a more personalized communication experience by recognizing user emotions. The system is comprised of a combination of means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, learning support, and an emotion engine.

[0355] User interface and voice input:

[0356] Users operate the system via smartphones or wearable devices and configure settings according to their preferred language and purpose. When a user begins a conversation, the device collects their speech as audio data through the microphone.

[0357] Speech recognition and emotion recognition:

[0358] This audio data is sent from the terminal to the server, which uses ASR (Automatic Speech Recognition) technology to convert the audio into text data. Simultaneously, an emotion engine analyzes features such as tone and rhythm of the voice to identify the user's emotional state.

[0359] Translation and cultural adaptation:

[0360] The converted text data is translated into multiple languages ​​in real time by translation tools, and cultural adaptation tools make adjustments that take into account cultural backgrounds and manners. The results of the emotion engine are also taken into consideration, and the translation results and output format are adjusted to suit the user's emotions.

[0361] Speech synthesis and output:

[0362] The final translation result is either converted to speech using speech synthesis technology or sent to the device as text. This allows the user to receive appropriate feedback that reflects their emotions.

[0363] Learning support and emotional feedback:

[0364] The server analyzes the user's usage history and emotional data to provide personalized learning support information. Learning plans are dynamically optimized based on changes in the user's emotions, facilitating personalized learning.

[0365] Specific example:

[0366] For example, if a Japanese-speaking user is conducting a business negotiation in English, the system instantly collects their speech and translates it into English. If the emotion engine detects emotions such as tension or anxiety from the user's voice, the server adjusts the translation to more polite language through cultural adaptation mechanisms and provides feedback to alleviate stress. This allows users to participate in international communication with confidence.

[0367] The following describes the processing flow.

[0368] Step 1:

[0369] The user opens the smartphone app and selects the language and translation purpose. This sets the user interface to speech input mode.

[0370] Step 2:

[0371] When the user begins speaking, the device collects audio data using its built-in microphone. This audio data is then compressed in real time and sent to the server.

[0372] Step 3:

[0373] The server processes the received audio data through an ASR (Automatic Speech Recognition) module, converting the audio into corresponding text data.

[0374] Step 4:

[0375] Simultaneously, the server uses an emotion engine to analyze the audio data and determine the user's emotional state based on factors such as tone and intonation.

[0376] Step 5:

[0377] The server sends the speech-recognized text to a translation device, which translates it into the configured target language. Here, neural machine translation technology is used to provide fast and accurate translations.

[0378] Step 6:

[0379] The translated text is sent to a cultural adaptation system. The server takes the user's emotions into consideration and adjusts the expression and tone to be appropriate.

[0380] Step 7:

[0381] The server converts the adjusted translated text into speech data using speech synthesis technology, or sends it to the terminal in text format.

[0382] Step 8:

[0383] The device provides the user with the received audio or text. The user listens to the translated audio through the speaker or views the text on the screen.

[0384] Step 9:

[0385] When a user's session ends, the server records their usage history and analyzed sentiment data. This data is used to support subsequent learning. The server generates a learning plan that reflects the user's emotional changes, which can be used to assist them in their next session.

[0386] (Example 2)

[0387] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0388] In intercultural communication, language barriers and cultural misunderstandings can lead to communication obstacles, resulting in users experiencing stress and anxiety. To address this, a system is needed that provides real-time, emotionally conscious, and culturally adaptable translation. Furthermore, the ability to provide learning support based on user emotions and usage history is also a key challenge.

[0389] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0390] In this invention, the server includes: a voice input means for receiving voice data input via a user interface; a voice recognition means for converting the received voice data into text data; an emotion recognition means for analyzing the text data and identifying emotions based on the tone and rhythm of the voice; a translation means for translating the text data into multiple languages; a cultural adaptation means for adjusting the translation based on cultural background and manners; a voice synthesis means for converting the translation results, according to the cultural background and the user's emotional state, into speech; and an output means for outputting the translation results to the user. This enables the user to communicate smoothly and confidently even in cross-cultural situations.

[0391] "User interface means" refers to devices and applications that allow users to input voice data, settings information, and other data.

[0392] "Voice input means" refers to a device or process for receiving voice data from a user and converting it into a format that can be processed within the system.

[0393] "Speech recognition means" refers to a technology or system that converts speech data acquired by a speech input means into text data.

[0394] "Emotion recognition means" refers to a technology or method that analyzes the tone and rhythm of voice data to identify the user's emotional state.

[0395] "Translation means" refers to a function or system that converts text data recorded in one language into another language.

[0396] "Cultural adaptation measures" refer to techniques or processes for adjusting translated data to suit the cultural background, customs, and manners of the recipient.

[0397] "Speech synthesis means" refers to a technology or system that converts translated text data back into speech data so that users can receive it as natural speech.

[0398] "Output means" refers to a method or device for providing the user with the results processed by the system in the form of audio or text.

[0399] "Learning support means" refers to technologies or systems that analyze users' emotional data and usage history to provide individually optimized learning support information.

[0400] "Advice generation methods" refer to processes or techniques for generating and providing advice to prevent cultural misunderstandings in intercultural communication.

[0401] This invention is a system for achieving smooth communication between multiple languages. This system is comprised of a combination of technical means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, learning support, and an emotion engine.

[0402] Users launch the system's application using their smartphone or wearable device and configure settings according to their preferred language and purpose. Voice data is collected using the device's microphone and sent to the server. The server utilizes a common speech recognition service, known as ASR (Automatic Speech Recognition) technology, to convert the voice data into text. Furthermore, the server uses an emotion recognition engine to identify the user's emotional state from the voice data. This involves techniques that analyze the tone and rhythm of the voice.

[0403] Next, the server translates the text data into multiple languages ​​in real time. This translation process utilizes known translation APIs. The translated text is then adjusted using cultural adaptation tools to suit the background and manners of the target culture. It is also adjusted to provide appropriate feedback based on the user's emotional state. The final translation result is converted into speech using speech synthesis technology and output to the user's device.

[0404] As a concrete example, when a Japanese-speaking user conducts negotiations in English, the user's Japanese speech is sent to the server in real time via a smartphone app. The server instantly translates it into English, generates an English audio transcript that incorporates cultural adaptation and emotional feedback, and plays it back on the device. If the emotion engine detects that the user is under stress, the translation is adjusted to use softer language.

[0405] For example, you can ask the system a question like, "If someone is nervous during a meeting, explain how you can provide feedback that will help alleviate that nervousness," and then observe the system's response.

[0406] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0407] Step 1:

[0408] The user launches an application on their smartphone or wearable device and enters their preferred language, purpose, privacy settings, etc. The device receives this information and verifies the settings. Specifically, the selected language set and voice input mode are activated.

[0409] Step 2:

[0410] When a user begins a conversation, the device uses its microphone to collect audio data in real time and converts it into a digital audio file. This audio data then becomes input data for subsequent analysis. The device receives the audio data and prepares to send it to the server.

[0411] Step 3:

[0412] The server receives the audio data sent from the terminal and starts speech recognition processing. Using speech recognition technology, the audio data is converted into text data. ASR (Automatic Speech Recognition) is a typical algorithm used. The converted text data is used in the next processing step.

[0413] Step 4:

[0414] The server uses an emotion recognition engine to analyze the tone and rhythm of the text data and the original audio data. This identifies the user's emotional state. The results of the emotion recognition are then reflected in subsequent processing for translation and cultural adaptation.

[0415] Step 5:

[0416] The translation mechanism allows the server to automatically translate text data into the user-defined target language. This translation is performed using a common translation API, and the translated text is generated as intermediate output.

[0417] Step 6:

[0418] The server uses cultural adaptation tools to adjust the translated text according to the cultural background and customs of the target language. The user's emotional state is also considered at this stage, ensuring that feedback is presented in the most appropriate way. The adjusted text forms the basis of the final output.

[0419] Step 7:

[0420] Using speech synthesis technology, the server converts the adjusted text into digital speech. This speech data is then prepared as user-readable audio and sent to the terminal.

[0421] Step 8:

[0422] The audio data sent to the terminal as the final output is played back by the terminal for the user. Through this, the user can understand what the other party is saying and respond appropriately according to the situation.

[0423] (Application Example 2)

[0424] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0425] In modern society, effective communication using multiple languages ​​is essential, but language and cultural barriers often hinder this. In particular, when people from different cultural backgrounds interact, language differences, coupled with a lack of emotional understanding, further complicate mutual understanding. Therefore, there is a need for a system that can appropriately recognize the emotional state of users and provide culture-appropriate feedback.

[0426] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0427] In this invention, the server includes acoustic recognition means for converting acoustic information into text data, translation means for translating the text data into multiple natural languages, and emotion recognition means for identifying the user's emotional state and providing adjusted feedback. This enables users to communicate smoothly while understanding the emotions of others, transcending language and cultural differences.

[0428] "Acoustic information" refers to data relating to speech or other sounds, which is collected and analyzed by digital devices.

[0429] "Character data" refers to text-based data obtained by processing acoustic information, and is considered fundamental to information processing.

[0430] "Natural language" refers to the language that humans use on a daily basis, and is the subject of translation tools.

[0431] "Acoustic recognition means" refers to a technological device that performs the process of converting acoustic information into text data.

[0432] "Translation means" refers to machine technology that has the function of converting text data into multiple languages.

[0433] "Sound synthesis means" refers to a technical device that performs a process to convert text data back into a speech format.

[0434] "Cultural adaptation techniques" are technologies that involve a process of adjusting the content of a translation based on its cultural background and customs.

[0435] An "emotion recognition device" is a technological device that identifies and analyzes emotions from the user's acoustic information.

[0436] "Output means" refers to a device that provides the converted and analyzed information to the user via display or audio.

[0437] To implement this invention, the user first prepares a device for inputting acoustic information (for example, a smartphone or a home robot). The acoustic information is transmitted to a server through a built-in microphone. The server converts the acoustic information into text data using acoustic recognition technology. In this process, general ASR (Automatic Speech Recognition) software can be used as the acoustic recognition technology.

[0438] The converted text data is translated into multiple natural languages ​​through translation algorithms on the server. Using a translation API (e.g., a public API service) enables rapid and efficient translation. Subsequently, cultural adaptation tools adjust the translation based on cultural background and customs. This results in expressions suitable for a specific cultural sphere.

[0439] The server identifies the user's emotional state using emotion recognition technology based on acoustic information and converted text data. This process employs emotion analysis algorithms that infer emotions from factors such as tone and patterns of speech.

[0440] Finally, sound synthesis technology converts text data back into sound, providing feedback to the user through output devices (e.g., speakers or text displays). Through this entire process, users can achieve effective communication across language and cultural differences.

[0441] A concrete example is the role a home robot can play in situations where visitors speak a different language. The robot instantly translates the visitor's words and provides comments that respond to the visitor's emotions, thereby supporting a smooth conversation.

[0442] An example of a prompt message is: "A home robot will assist with conversations with Chinese-speaking guests. Please utilize emotion recognition to create a friendly atmosphere that puts visitors at ease. Please provide support so that the host can enjoy the conversation comfortably."

[0443] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0444] Step 1:

[0445] The device collects the user's voice as acoustic information via a microphone. The input is the user's voice, and the output is digitized acoustic information data. The voice is processed as a digital signal, and noise reduction and volume adjustment are performed.

[0446] Step 2:

[0447] The server receives the collected acoustic information and converts it into text data using an acoustic recognition system. The input is acoustic information data, and the output is the converted text data. The software used here is ASR (Automatic Speech Recognition), which uses a specific algorithm to analyze the patterns in the speech and convert them into text.

[0448] Step 3:

[0449] The server translates text data into multiple natural languages ​​using translation tools. The input is text data, and the output is translated text data. Multilingual support is achieved by utilizing a translation API, enabling accurate and speedy translation.

[0450] Step 4:

[0451] The server uses cultural adaptation tools to adjust the translated text data according to cultural background and customs. The input is the translated text data, and the output is the culturally adapted text data. The server adjusts the details of wording and expression based on the culture and etiquette policies of each country.

[0452] Step 5:

[0453] The server identifies the user's emotional state using emotion recognition means based on acoustic information. The input is acoustic information data, and the output is identified emotional state data. An emotion analysis algorithm infers and digitizes emotions based on factors such as tone and rhythm of the voice.

[0454] Step 6:

[0455] The server generates adjusted feedback based on the output of the emotion recognition system and outputs it as speech using the sound synthesis system. The input is culture-adapted text data and emotion state data, and the output is adjusted speech feedback. It is synthesized as natural speech using sound synthesis technology and provided to the user through a speaker.

[0456] 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.

[0457] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0458] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0459] [Third Embodiment]

[0460] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0461] 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.

[0462] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0463] 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.

[0464] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0465] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0466] 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.

[0467] 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.

[0468] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0469] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0470] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0471] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0472] This invention is a system for users to engage in real-time multilingual communication, enabling smooth communication across language barriers. The system is comprised of a combination of means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, and learning support.

[0473] User interface:

[0474] Users can operate the system using apps that run on smartphones, PCs, or wearable devices. This allows for voice and text input, as well as verification of translation results.

[0475] Voice input and speech recognition:

[0476] When a user starts a conversation, the device collects voice data via the microphone. The collected voice data is sent to a server, where it is converted into text data using ASR (Automatic Speech Recognition) technology.

[0477] translation:

[0478] The server uses neural machine translation technology to translate the converted text into multiple languages ​​in real time. This provides highly accurate and contextually appropriate translations.

[0479] Cultural adaptation:

[0480] The translated results are adjusted by the server based on cultural context and business etiquette. This process enables translations that avoid misunderstandings between different cultures.

[0481] Speech synthesis and output:

[0482] The translated text is sent from the server to the terminal and either synthesized into speech using TTS technology or displayed as text. Users can then listen to it or view it on the screen to continue communicating.

[0483] Learning support:

[0484] The server analyzes the user's usage history and generates individually optimized learning plans and learning support information. The terminal provides this to the user, who can then use it for regular learning or learning a new language.

[0485] Specific example:

[0486] For example, suppose a native Japanese speaker uses this system when participating in a business meeting conducted in English. When the user starts a conversation using their smartphone, the device collects the user's voice and sends it to the server. The server converts the voice into Japanese text in real time, then translates it into English, and adjusts it to account for cultural differences. Finally, the translated result is returned to the device as either voice or text, allowing the user to continue the conversation while reviewing it. This entire process enables smooth communication even with people who speak different languages.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The user launches the smartphone app and selects their preferred language setting. This changes the user interface to an input mode corresponding to the user's selection.

[0490] Step 2:

[0491] When the user begins speaking, the device uses its microphone to collect audio data. The collected audio data is then sent from the device to the server.

[0492] Step 3:

[0493] The server processes the received audio data through an ASR (Automatic Speech Recognition) module, converting the audio into corresponding text data. This converted text data is then sent to the next processing stage.

[0494] Step 4:

[0495] The server sends text data to a neural machine translation (NMT) module, which translates it into the selected target language in real time. During translation, the system takes into account context and cultural differences between languages.

[0496] Step 5:

[0497] The translated text is subjected to cultural adaptation measures. The server converts it into appropriate expressions that reflect cultural background and business etiquette, and adjusts the translation results.

[0498] Step 6:

[0499] The adjusted text data is then sent to a TTS (Text-to-Speech) module, where the server converts it into speech data.

[0500] Step 7:

[0501] The server sends the synthesized speech data or translated text back to the terminal, which then provides it to the user. The user can listen to the audio through the speaker or view the text displayed on the screen.

[0502] Step 8:

[0503] If the user continues communicating, the process from step 2 is repeated. Upon completion, the user's translation and usage history are recorded on the server and used as material for future learning support.

[0504] (Example 1)

[0505] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0506] There is a need to efficiently overcome language barriers and cultural misunderstandings that arise when people with different languages ​​communicate in real time. Conventional translation systems have had factors that hinder intercultural communication, such as insufficient translation accuracy and speed, and inappropriate cultural adaptation. Furthermore, there has been a lack of adaptive information provision to support user learning.

[0507] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0508] In this invention, the server includes a voice input means, a voice recognition means, a translation means, a cultural adaptation means, a voice synthesis means, an output means, a learning support means, and an advice generation means. This enables smooth real-time communication between people who speak different languages, prevents cultural misunderstandings, and achieves highly accurate and contextual translation. Furthermore, by providing individually optimized learning support information, it is possible to promote the user's language acquisition.

[0509] "Voice input means" refers to a device or function that acquires voice data input through a user interface.

[0510] "Speech recognition means" refers to a device or process for converting acquired speech data into text data.

[0511] "Translation means" refers to a device or technology for converting text data into multiple languages.

[0512] "Cultural adaptation tools" refer to devices or processes for adjusting translated content based on cultural background and customs.

[0513] "Speech synthesis means" refers to a technology or device that converts translated text data into speech.

[0514] "Output means" refers to a device or function for providing the translation result to the user.

[0515] "Learning support means" refers to a device or process for analyzing a user's usage history and generating and providing learning support information.

[0516] An "advice generation tool" refers to a device or function used to provide guidance to prevent cultural misunderstandings during intercultural communication.

[0517] The system based on this invention supports real-time communication between people who speak different languages ​​and can automatically handle everything from input to output of voice data. Specifically, users operate the system using terminals such as smartphones, personal computers, and wearable devices. These terminals have dedicated application software installed that supports input and output of voice and text information.

[0518] When a user starts a conversation, the device uses the microphone to capture audio data. This audio data is sent to a server via the internet. The server is equipped with speech recognition software (e.g., Google Cloud Speech-to-Text or Amazon Transcribe) that converts the audio into text data.

[0519] The converted text data is translated into multiple languages ​​by neural machine translation software (e.g., Google Translate API or DeepL API) on the server. Then, cultural adaptation tools are applied, and the translated content is adjusted based on cultural context and business etiquette.

[0520] The edited text data is sent back from the server to the device and either output as speech using speech synthesis technology on the device (e.g., Amazon Polly or Google Cloud Text-to-Speech) or displayed as text on the screen. The user can listen to or confirm this and continue communicating.

[0521] Furthermore, the server analyzes the user's usage history and provides individually optimized learning support information. This support information is designed to help users efficiently acquire a new language and is displayed on the device as vocabulary lists and practice exercises.

[0522] As a concrete example, a native Japanese speaker can use this system to communicate smoothly when participating in a business meeting conducted in English. An example of a prompt in this case would be, "Please help me communicate in this English business meeting." In this way, users can engage in smooth communication regardless of language.

[0523] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0524] Step 1:

[0525] The user launches an application on their smartphone, PC, or wearable device and begins voice input. The device uses its built-in microphone to collect the voice signal. The collected voice signal is acquired as digital audio data. This becomes the input data for the next step.

[0526] Step 2:

[0527] The terminal transmits the acquired digital audio data to the server via the internet. The server uses speech recognition software to convert this audio data into text data. Specifically, it uses real-time speech recognition technology to analyze the sound waveform and convert it into a string of characters. This converted text data becomes the input data for the next step.

[0528] Step 3:

[0529] The server receives text data and translates it into multiple languages ​​using neural machine translation technology. The server leverages generative AI models to provide context-aware, highly accurate translations. This process generates translated text data, which then serves as input for the next step.

[0530] Step 4:

[0531] The server adjusts the translated text data using cultural adaptation tools. This step involves appropriately modifying the translated content based on cultural context and conventions. For example, it may avoid expressions that are sensitive in a particular culture. The adjusted text data then becomes the input data for the next step.

[0532] Step 5:

[0533] The adjusted text data is sent from the server to the terminal. The terminal uses speech synthesis technology to convert this text data into speech. Specifically, it converts the string of characters into synthesized speech and outputs it through the speaker. It is also possible to display the text information on the screen. The synthesized speech or displayed text is the final output.

[0534] Step 6:

[0535] The server analyzes the user's usage history and generates learning support information using a generative AI model. The information generated by the server is provided as an individually optimized learning plan. This plan is presented to the user via their device, offering concrete opportunities to support the acquisition of a new language. Users can then utilize this plan to improve their daily learning.

[0536] (Application Example 1)

[0537] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0538] Smooth, real-time communication in a multilingual environment is difficult due to language barriers and cultural differences. In particular, linguistic and cultural misunderstandings are common in intercultural interactions within families and tourist destinations, hindering smooth communication. A system is needed to address these challenges and allow visitors and residents to enjoy communication without being concerned about language or cultural differences.

[0539] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0540] In this invention, the server includes information input means for receiving audio data input via a user interface; information recognition means for converting the received audio data into text data; language processing means for translating the text data into multiple languages; audio conversion means for converting the translated text into audio; cultural adaptation means for adjusting the translation based on cultural background and manners; display means for outputting the translation results or generated learning support information to the user; and visitor response means for automatically recognizing the visitor's language and supporting communication. This enables real-time and culturally adaptive communication in a multilingual environment.

[0541] "Information input means" refers to a device or module equipped with the function of receiving voice data via a user interface.

[0542] "Information recognition means" refers to a device or module that executes technologies or algorithms for converting received audio data into text data.

[0543] "Language processing means" refers to a device or module that executes techniques and algorithms for translating text data into multiple languages.

[0544] "Speech conversion means" refers to a device or module that executes technologies or algorithms for converting translated text into speech.

[0545] "Cultural adaptation tools" are devices or modules that implement technologies or algorithms to adjust translations based on cultural background and customs.

[0546] "Display means" refers to a device or module equipped with the function of outputting translation results or generated learning support information to the user.

[0547] "Visitor interaction means" refers to a device or module equipped with the function to automatically recognize a visitor's language and support communication.

[0548] This invention aims to implement multilingual real-time communication capabilities in consumer robots. This will enable communication that transcends language barriers even in cross-cultural environments.

[0549] The server uses a microphone mounted on the robot as a voice input method to capture the user's speech. As a means of information recognition, the voice data is sent to the server via the network and converted into text by advanced speech recognition software (e.g., Google Cloud Speech-to-Text).

[0550] The converted text data is translated into multiple languages ​​by language processing tools. This process utilizes translation software that leverages the latest neural network technology (e.g., Google Cloud Translate API).

[0551] Furthermore, cultural adaptation measures adjust translation results based on cultural background and customs, reducing intercultural misunderstandings. This adjustment uses custom algorithms that adhere to specific cultural codes and practices.

[0552] The server uses a speech conversion means to convert the adjusted translated text into speech using speech synthesis software (e.g., Amazon Polly), which is then output through the robot's speaker. A display means allows the text to be displayed along with the audio on devices capable of displaying text.

[0553] As a concrete example, when a visitor greets the robot with "Hello," the robot automatically recognizes the language and responds with "Konnichiwa," thus facilitating communication.

[0554] Example of a prompt:

[0555] Translate the following sentence while considering Japanese cultural norms: "How are you?"

[0556] Expected Output: "How are you?"

[0557] In this way, the present invention provides an effective means for users to naturally enjoy communication in a multilingual environment.

[0558] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0559] Step 1:

[0560] The user begins speaking to the robot. A microphone mounted on the robot captures the voice data and sends it to the server as input. At this point, the input is raw voice data.

[0561] Step 2:

[0562] The server performs speech recognition processing using the received audio data. Specifically, it uses services such as Google Cloud Speech-to-Text to convert the audio data into text. This process yields text data from the audio data. The output is text data that transcribes what was spoken.

[0563] Step 3:

[0564] The server translates the text data generated by speech recognition using language processing tools. It uses the Google Cloud Translate API to translate from one language to another. In this process, the input is text data, and the output is the translated text data in the other language.

[0565] Step 4:

[0566] The generated translated text is optimized on the server using cultural adaptation tools. Based on a custom algorithm, adjustments are made to account for cultural backgrounds and manners, reducing cross-cultural misunderstandings. The input is translated text data, and the output is culturally adapted text data.

[0567] Step 5:

[0568] The server is a speech conversion device that synthesizes speech from culturally adapted text and generates audio data using tools such as Amazon Polly. The input is culturally adapted text data, and the output is synthesized speech data. This audio data is output to the user through the robot's speaker.

[0569] Step 6:

[0570] Finally, the robot uses visitor interaction tools to automatically recognize the visitor's language and respond appropriately. This process utilizes generated voice and text information to continue communication with the visitor. This allows users to engage in natural conversations through the robot.

[0571] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0572] This invention is a system that facilitates multilingual communication and provides a more personalized communication experience by recognizing user emotions. The system is comprised of a combination of means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, learning support, and an emotion engine.

[0573] User interface and voice input:

[0574] Users operate the system via smartphones or wearable devices and configure settings according to their preferred language and purpose. When a user begins a conversation, the device collects their speech as audio data through the microphone.

[0575] Speech recognition and emotion recognition:

[0576] This audio data is sent from the terminal to the server, which uses ASR (Automatic Speech Recognition) technology to convert the audio into text data. Simultaneously, an emotion engine analyzes features such as tone and rhythm of the voice to identify the user's emotional state.

[0577] Translation and cultural adaptation:

[0578] The converted text data is translated into multiple languages ​​in real time by translation tools, and cultural adaptation tools make adjustments that take into account cultural backgrounds and manners. The results of the emotion engine are also taken into consideration, and the translation results and output format are adjusted to suit the user's emotions.

[0579] Speech synthesis and output:

[0580] The final translation result is either converted to speech using speech synthesis technology or sent to the device as text. This allows the user to receive appropriate feedback that reflects their emotions.

[0581] Learning support and emotional feedback:

[0582] The server analyzes the user's usage history and emotional data to provide personalized learning support information. Learning plans are dynamically optimized based on changes in the user's emotions, facilitating personalized learning.

[0583] Specific example:

[0584] For example, if a Japanese-speaking user is conducting a business negotiation in English, the system instantly collects their speech and translates it into English. If the emotion engine detects emotions such as tension or anxiety from the user's voice, the server adjusts the translation to more polite language through cultural adaptation mechanisms and provides feedback to alleviate stress. This allows users to participate in international communication with confidence.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The user opens the smartphone app and selects the language and translation purpose. This sets the user interface to speech input mode.

[0588] Step 2:

[0589] When the user begins speaking, the device collects audio data using its built-in microphone. This audio data is then compressed in real time and sent to the server.

[0590] Step 3:

[0591] The server processes the received audio data through an ASR (Automatic Speech Recognition) module, converting the audio into corresponding text data.

[0592] Step 4:

[0593] Simultaneously, the server uses an emotion engine to analyze the audio data and determine the user's emotional state based on factors such as tone and intonation.

[0594] Step 5:

[0595] The server sends the speech-recognized text to a translation device, which translates it into the configured target language. Here, neural machine translation technology is used to provide fast and accurate translations.

[0596] Step 6:

[0597] The translated text is sent to a cultural adaptation system. The server takes the user's emotions into consideration and adjusts the expression and tone to be appropriate.

[0598] Step 7:

[0599] The server converts the adjusted translated text into speech data using speech synthesis technology, or sends it to the terminal in text format.

[0600] Step 8:

[0601] The device provides the user with the received audio or text. The user listens to the translated audio through the speaker or views the text on the screen.

[0602] Step 9:

[0603] When a user's session ends, the server records their usage history and analyzed sentiment data. This data is used to support subsequent learning. The server generates a learning plan that reflects the user's emotional changes, which can be used to assist them in their next session.

[0604] (Example 2)

[0605] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0606] In intercultural communication, language barriers and cultural misunderstandings can lead to communication obstacles, resulting in users experiencing stress and anxiety. To address this, a system is needed that provides real-time, emotionally conscious, and culturally adaptable translation. Furthermore, the ability to provide learning support based on user emotions and usage history is also a key challenge.

[0607] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0608] In this invention, the server includes: a voice input means for receiving voice data input via a user interface; a voice recognition means for converting the received voice data into text data; an emotion recognition means for analyzing the text data and identifying emotions based on the tone and rhythm of the voice; a translation means for translating the text data into multiple languages; a cultural adaptation means for adjusting the translation based on cultural background and manners; a voice synthesis means for converting the translation results, according to the cultural background and the user's emotional state, into speech; and an output means for outputting the translation results to the user. This enables the user to communicate smoothly and confidently even in cross-cultural situations.

[0609] "User interface means" refers to devices and applications that allow users to input voice data, settings information, and other data.

[0610] "Voice input means" refers to a device or process for receiving voice data from a user and converting it into a format that can be processed within the system.

[0611] "Speech recognition means" refers to a technology or system that converts speech data acquired by a speech input means into text data.

[0612] "Emotion recognition means" refers to a technology or method that analyzes the tone and rhythm of voice data to identify the user's emotional state.

[0613] "Translation means" refers to a function or system that converts text data recorded in one language into another language.

[0614] "Cultural adaptation measures" refer to techniques or processes for adjusting translated data to suit the cultural background, customs, and manners of the recipient.

[0615] "Speech synthesis means" refers to a technology or system that converts translated text data back into speech data so that users can receive it as natural speech.

[0616] "Output means" refers to a method or device for providing the user with the results processed by the system in the form of audio or text.

[0617] "Learning support means" refers to technologies or systems that analyze users' emotional data and usage history to provide individually optimized learning support information.

[0618] "Advice generation methods" refer to processes or techniques for generating and providing advice to prevent cultural misunderstandings in intercultural communication.

[0619] This invention is a system for achieving smooth communication between multiple languages. This system is comprised of a combination of technical means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, learning support, and an emotion engine.

[0620] Users launch the system's application using their smartphone or wearable device and configure settings according to their preferred language and purpose. Voice data is collected using the device's microphone and sent to the server. The server utilizes a common speech recognition service, known as ASR (Automatic Speech Recognition) technology, to convert the voice data into text. Furthermore, the server uses an emotion recognition engine to identify the user's emotional state from the voice data. This involves techniques that analyze the tone and rhythm of the voice.

[0621] Next, the server translates the text data into multiple languages ​​in real time. This translation process utilizes known translation APIs. The translated text is then adjusted using cultural adaptation tools to suit the background and manners of the target culture. It is also adjusted to provide appropriate feedback based on the user's emotional state. The final translation result is converted into speech using speech synthesis technology and output to the user's device.

[0622] As a concrete example, when a Japanese-speaking user conducts negotiations in English, the user's Japanese speech is sent to the server in real time via a smartphone app. The server instantly translates it into English, generates an English audio transcript that incorporates cultural adaptation and emotional feedback, and plays it back on the device. If the emotion engine detects that the user is under stress, the translation is adjusted to use softer language.

[0623] For example, you can ask the system a question like, "If someone is nervous during a meeting, explain how you can provide feedback that will help alleviate that nervousness," and then observe the system's response.

[0624] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0625] Step 1:

[0626] The user launches an application on their smartphone or wearable device and enters their preferred language, purpose, privacy settings, etc. The device receives this information and verifies the settings. Specifically, the selected language set and voice input mode are activated.

[0627] Step 2:

[0628] When a user begins a conversation, the device uses its microphone to collect audio data in real time and converts it into a digital audio file. This audio data then becomes input data for subsequent analysis. The device receives the audio data and prepares to send it to the server.

[0629] Step 3:

[0630] The server receives the audio data sent from the terminal and starts speech recognition processing. Using speech recognition technology, the audio data is converted into text data. ASR (Automatic Speech Recognition) is a typical algorithm used. The converted text data is used in the next processing step.

[0631] Step 4:

[0632] The server uses an emotion recognition engine to analyze the tone and rhythm of the text data and the original audio data. This identifies the user's emotional state. The results of the emotion recognition are then reflected in subsequent processing for translation and cultural adaptation.

[0633] Step 5:

[0634] The translation mechanism allows the server to automatically translate text data into the user-defined target language. This translation is performed using a common translation API, and the translated text is generated as intermediate output.

[0635] Step 6:

[0636] The server uses cultural adaptation tools to adjust the translated text according to the cultural background and customs of the target language. The user's emotional state is also considered at this stage, ensuring that feedback is presented in the most appropriate way. The adjusted text forms the basis of the final output.

[0637] Step 7:

[0638] Using speech synthesis technology, the server converts the adjusted text into digital speech. This speech data is then prepared as user-readable audio and sent to the terminal.

[0639] Step 8:

[0640] The audio data sent to the terminal as the final output is played back by the terminal for the user. Through this, the user can understand what the other party is saying and respond appropriately according to the situation.

[0641] (Application Example 2)

[0642] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0643] In modern society, effective communication using multiple languages ​​is essential, but language and cultural barriers often hinder this. In particular, when people from different cultural backgrounds interact, language differences, coupled with a lack of emotional understanding, further complicate mutual understanding. Therefore, there is a need for a system that can appropriately recognize the emotional state of users and provide culture-appropriate feedback.

[0644] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0645] In this invention, the server includes acoustic recognition means for converting acoustic information into text data, translation means for translating the text data into multiple natural languages, and emotion recognition means for identifying the user's emotional state and providing adjusted feedback. This enables users to communicate smoothly while understanding the emotions of others, transcending language and cultural differences.

[0646] "Acoustic information" refers to data relating to speech or other sounds, which is collected and analyzed by digital devices.

[0647] "Character data" refers to text-based data obtained by processing acoustic information, and is considered fundamental to information processing.

[0648] "Natural language" refers to the language that humans use on a daily basis, and is the subject of translation tools.

[0649] "Acoustic recognition means" refers to a technological device that performs the process of converting acoustic information into text data.

[0650] "Translation means" refers to machine technology that has the function of converting text data into multiple languages.

[0651] "Sound synthesis means" refers to a technical device that performs a process to convert text data back into a speech format.

[0652] "Cultural adaptation techniques" are technologies that involve a process of adjusting the content of a translation based on its cultural background and customs.

[0653] An "emotion recognition device" is a technological device that identifies and analyzes emotions from the user's acoustic information.

[0654] "Output means" refers to a device that provides the converted and analyzed information to the user via display or audio.

[0655] To implement this invention, the user first prepares a device for inputting acoustic information (for example, a smartphone or a home robot). The acoustic information is transmitted to a server through a built-in microphone. The server converts the acoustic information into text data using acoustic recognition technology. In this process, general ASR (Automatic Speech Recognition) software can be used as the acoustic recognition technology.

[0656] The converted text data is translated into multiple natural languages ​​through translation algorithms on the server. Using a translation API (e.g., a public API service) enables rapid and efficient translation. Subsequently, cultural adaptation tools adjust the translation based on cultural background and customs. This results in expressions suitable for a specific cultural sphere.

[0657] The server identifies the user's emotional state using emotion recognition technology based on acoustic information and converted text data. This process employs emotion analysis algorithms that infer emotions from factors such as tone and patterns of speech.

[0658] Finally, sound synthesis technology converts text data back into sound, providing feedback to the user through output devices (e.g., speakers or text displays). Through this entire process, users can achieve effective communication across language and cultural differences.

[0659] A concrete example is the role a home robot can play in situations where visitors speak a different language. The robot instantly translates the visitor's words and provides comments that respond to the visitor's emotions, thereby supporting a smooth conversation.

[0660] An example of a prompt message is: "A home robot will assist with conversations with Chinese-speaking guests. Please utilize emotion recognition to create a friendly atmosphere that puts visitors at ease. Please provide support so that the host can enjoy the conversation comfortably."

[0661] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0662] Step 1:

[0663] The device collects the user's voice as acoustic information via a microphone. The input is the user's voice, and the output is digitized acoustic information data. The voice is processed as a digital signal, and noise reduction and volume adjustment are performed.

[0664] Step 2:

[0665] The server receives the collected acoustic information and converts it into text data using an acoustic recognition system. The input is acoustic information data, and the output is the converted text data. The software used here is ASR (Automatic Speech Recognition), which uses a specific algorithm to analyze the patterns in the speech and convert them into text.

[0666] Step 3:

[0667] The server translates text data into multiple natural languages ​​using translation tools. The input is text data, and the output is translated text data. Multilingual support is achieved by utilizing a translation API, enabling accurate and speedy translation.

[0668] Step 4:

[0669] The server uses cultural adaptation tools to adjust the translated text data according to cultural background and customs. The input is the translated text data, and the output is the culturally adapted text data. The server adjusts the details of wording and expression based on the culture and etiquette policies of each country.

[0670] Step 5:

[0671] The server identifies the user's emotional state using emotion recognition means based on acoustic information. The input is acoustic information data, and the output is identified emotional state data. An emotion analysis algorithm infers and digitizes emotions based on factors such as tone and rhythm of the voice.

[0672] Step 6:

[0673] The server generates adjusted feedback based on the output of the emotion recognition system and outputs it as speech using the sound synthesis system. The input is culture-adapted text data and emotion state data, and the output is adjusted speech feedback. It is synthesized as natural speech using sound synthesis technology and provided to the user through a speaker.

[0674] 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.

[0675] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0676] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0677] [Fourth Embodiment]

[0678] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0679] 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.

[0680] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0681] 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.

[0682] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0683] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0684] 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.

[0685] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0686] 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.

[0687] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0688] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0689] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0690] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] This invention is a system for users to engage in real-time multilingual communication, enabling smooth communication across language barriers. The system is comprised of a combination of means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, and learning support.

[0692] User interface:

[0693] Users can operate the system using apps that run on smartphones, PCs, or wearable devices. This allows for voice and text input, as well as verification of translation results.

[0694] Voice input and speech recognition:

[0695] When a user starts a conversation, the device collects voice data via the microphone. The collected voice data is sent to a server, where it is converted into text data using ASR (Automatic Speech Recognition) technology.

[0696] translation:

[0697] The server uses neural machine translation technology to translate the converted text into multiple languages ​​in real time. This provides highly accurate and contextually appropriate translations.

[0698] Cultural adaptation:

[0699] The translated results are adjusted by the server based on cultural context and business etiquette. This process enables translations that avoid misunderstandings between different cultures.

[0700] Speech synthesis and output:

[0701] The translated text is sent from the server to the terminal and either synthesized into speech using TTS technology or displayed as text. Users can then listen to it or view it on the screen to continue communicating.

[0702] Learning support:

[0703] The server analyzes the user's usage history and generates individually optimized learning plans and learning support information. The terminal provides this to the user, who can then use it for regular learning or learning a new language.

[0704] Specific example:

[0705] For example, suppose a native Japanese speaker uses this system when participating in a business meeting conducted in English. When the user starts a conversation using their smartphone, the device collects the user's voice and sends it to the server. The server converts the voice into Japanese text in real time, then translates it into English, and adjusts it to account for cultural differences. Finally, the translated result is returned to the device as either voice or text, allowing the user to continue the conversation while reviewing it. This entire process enables smooth communication even with people who speak different languages.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The user launches the smartphone app and selects their preferred language setting. This changes the user interface to an input mode corresponding to the user's selection.

[0709] Step 2:

[0710] When the user begins speaking, the device uses its microphone to collect audio data. The collected audio data is then sent from the device to the server.

[0711] Step 3:

[0712] The server processes the received audio data through an ASR (Automatic Speech Recognition) module, converting the audio into corresponding text data. This converted text data is then sent to the next processing stage.

[0713] Step 4:

[0714] The server sends text data to a neural machine translation (NMT) module, which translates it into the selected target language in real time. During translation, the system takes into account context and cultural differences between languages.

[0715] Step 5:

[0716] The translated text is subjected to cultural adaptation measures. The server converts it into appropriate expressions that reflect cultural background and business etiquette, and adjusts the translation results.

[0717] Step 6:

[0718] The adjusted text data is then sent to a TTS (Text-to-Speech) module, where the server converts it into speech data.

[0719] Step 7:

[0720] The server sends the synthesized speech data or translated text back to the terminal, which then provides it to the user. The user can listen to the audio through the speaker or view the text displayed on the screen.

[0721] Step 8:

[0722] If the user continues communicating, the process from step 2 is repeated. Upon completion, the user's translation and usage history are recorded on the server and used as material for future learning support.

[0723] (Example 1)

[0724] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] There is a need to efficiently overcome language barriers and cultural misunderstandings that arise when people with different languages ​​communicate in real time. Conventional translation systems have had factors that hinder intercultural communication, such as insufficient translation accuracy and speed, and inappropriate cultural adaptation. Furthermore, there has been a lack of adaptive information provision to support user learning.

[0726] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0727] In this invention, the server includes a voice input means, a voice recognition means, a translation means, a cultural adaptation means, a voice synthesis means, an output means, a learning support means, and an advice generation means. This enables smooth real-time communication between people who speak different languages, prevents cultural misunderstandings, and achieves highly accurate and contextual translation. Furthermore, by providing individually optimized learning support information, it is possible to promote the user's language acquisition.

[0728] "Voice input means" refers to a device or function that acquires voice data input through a user interface.

[0729] "Speech recognition means" refers to a device or process for converting acquired speech data into text data.

[0730] "Translation means" refers to a device or technology for converting text data into multiple languages.

[0731] "Cultural adaptation tools" refer to devices or processes for adjusting translated content based on cultural background and customs.

[0732] "Speech synthesis means" refers to a technology or device that converts translated text data into speech.

[0733] "Output means" refers to a device or function for providing the translation result to the user.

[0734] "Learning support means" refers to a device or process for analyzing a user's usage history and generating and providing learning support information.

[0735] An "advice generation tool" refers to a device or function used to provide guidance to prevent cultural misunderstandings during intercultural communication.

[0736] The system based on this invention supports real-time communication between people who speak different languages ​​and can automatically handle everything from input to output of voice data. Specifically, users operate the system using terminals such as smartphones, personal computers, and wearable devices. These terminals have dedicated application software installed that supports input and output of voice and text information.

[0737] When a user starts a conversation, the device uses the microphone to capture audio data. This audio data is sent to a server via the internet. The server is equipped with speech recognition software (e.g., Google Cloud Speech-to-Text or Amazon Transcribe) that converts the audio into text data.

[0738] The converted text data is translated into multiple languages ​​by neural machine translation software (e.g., Google Translate API or DeepL API) on the server. Then, cultural adaptation tools are applied, and the translated content is adjusted based on cultural context and business etiquette.

[0739] The edited text data is sent back from the server to the device and either output as speech using speech synthesis technology on the device (e.g., Amazon Polly or Google Cloud Text-to-Speech) or displayed as text on the screen. The user can listen to or confirm this and continue communicating.

[0740] Furthermore, the server analyzes the user's usage history and provides individually optimized learning support information. This support information is designed to help users efficiently acquire a new language and is displayed on the device as vocabulary lists and practice exercises.

[0741] As a concrete example, a native Japanese speaker can use this system to communicate smoothly when participating in a business meeting conducted in English. An example of a prompt in this case would be, "Please help me communicate in this English business meeting." In this way, users can engage in smooth communication regardless of language.

[0742] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0743] Step 1:

[0744] The user launches an application on their smartphone, PC, or wearable device and begins voice input. The device uses its built-in microphone to collect the voice signal. The collected voice signal is acquired as digital audio data. This becomes the input data for the next step.

[0745] Step 2:

[0746] The terminal transmits the acquired digital audio data to the server via the internet. The server uses speech recognition software to convert this audio data into text data. Specifically, it uses real-time speech recognition technology to analyze the sound waveform and convert it into a string of characters. This converted text data becomes the input data for the next step.

[0747] Step 3:

[0748] The server receives text data and translates it into multiple languages ​​using neural machine translation technology. The server leverages generative AI models to provide context-aware, highly accurate translations. This process generates translated text data, which then serves as input for the next step.

[0749] Step 4:

[0750] The server adjusts the translated text data using cultural adaptation tools. This step involves appropriately modifying the translated content based on cultural context and conventions. For example, it may avoid expressions that are sensitive in a particular culture. The adjusted text data then becomes the input data for the next step.

[0751] Step 5:

[0752] The adjusted text data is sent from the server to the terminal. The terminal uses speech synthesis technology to convert this text data into speech. Specifically, it converts the string of characters into synthesized speech and outputs it through the speaker. It is also possible to display the text information on the screen. The synthesized speech or displayed text is the final output.

[0753] Step 6:

[0754] The server analyzes the user's usage history and generates learning support information using a generative AI model. The information generated by the server is provided as an individually optimized learning plan. This plan is presented to the user via their device, offering concrete opportunities to support the acquisition of a new language. Users can then utilize this plan to improve their daily learning.

[0755] (Application Example 1)

[0756] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0757] Smooth, real-time communication in a multilingual environment is difficult due to language barriers and cultural differences. In particular, linguistic and cultural misunderstandings are common in intercultural interactions within families and tourist destinations, hindering smooth communication. A system is needed to address these challenges and allow visitors and residents to enjoy communication without being concerned about language or cultural differences.

[0758] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0759] In this invention, the server includes information input means for receiving audio data input via a user interface; information recognition means for converting the received audio data into text data; language processing means for translating the text data into multiple languages; audio conversion means for converting the translated text into audio; cultural adaptation means for adjusting the translation based on cultural background and manners; display means for outputting the translation results or generated learning support information to the user; and visitor response means for automatically recognizing the visitor's language and supporting communication. This enables real-time and culturally adaptive communication in a multilingual environment.

[0760] "Information input means" refers to a device or module equipped with the function of receiving voice data via a user interface.

[0761] "Information recognition means" refers to a device or module that executes technologies or algorithms for converting received audio data into text data.

[0762] "Language processing means" refers to a device or module that executes techniques and algorithms for translating text data into multiple languages.

[0763] "Speech conversion means" refers to a device or module that executes technologies or algorithms for converting translated text into speech.

[0764] "Cultural adaptation tools" are devices or modules that implement technologies or algorithms to adjust translations based on cultural background and customs.

[0765] "Display means" refers to a device or module equipped with the function of outputting translation results or generated learning support information to the user.

[0766] "Visitor interaction means" refers to a device or module equipped with the function to automatically recognize a visitor's language and support communication.

[0767] This invention aims to implement multilingual real-time communication capabilities in consumer robots. This will enable communication that transcends language barriers even in cross-cultural environments.

[0768] The server uses a microphone mounted on the robot as a voice input method to capture the user's speech. As a means of information recognition, the voice data is sent to the server via the network and converted into text by advanced speech recognition software (e.g., Google Cloud Speech-to-Text).

[0769] The converted text data is translated into multiple languages ​​by language processing tools. This process utilizes translation software that leverages the latest neural network technology (e.g., Google Cloud Translate API).

[0770] Furthermore, cultural adaptation measures adjust translation results based on cultural background and customs, reducing intercultural misunderstandings. This adjustment uses custom algorithms that adhere to specific cultural codes and practices.

[0771] The server uses a speech conversion means to convert the adjusted translated text into speech using speech synthesis software (e.g., Amazon Polly), which is then output through the robot's speaker. A display means allows the text to be displayed along with the audio on devices capable of displaying text.

[0772] As a concrete example, when a visitor greets the robot with "Hello," the robot automatically recognizes the language and responds with "Konnichiwa," thus facilitating communication.

[0773] Example of a prompt:

[0774] Translate the following sentence while considering Japanese cultural norms: "How are you?"

[0775] Expected Output: "How are you?"

[0776] In this way, the present invention provides an effective means for users to naturally enjoy communication in a multilingual environment.

[0777] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0778] Step 1:

[0779] The user begins speaking to the robot. A microphone mounted on the robot captures the voice data and sends it to the server as input. At this point, the input is raw voice data.

[0780] Step 2:

[0781] The server performs speech recognition processing using the received audio data. Specifically, it uses services such as Google Cloud Speech-to-Text to convert the audio data into text. This process yields text data from the audio data. The output is text data that transcribes what was spoken.

[0782] Step 3:

[0783] The server translates the text data generated by speech recognition using language processing tools. It uses the Google Cloud Translate API to translate from one language to another. In this process, the input is text data, and the output is the translated text data in the other language.

[0784] Step 4:

[0785] The generated translated text is optimized on the server using cultural adaptation tools. Based on a custom algorithm, adjustments are made to account for cultural backgrounds and manners, reducing cross-cultural misunderstandings. The input is translated text data, and the output is culturally adapted text data.

[0786] Step 5:

[0787] The server is a speech conversion device that synthesizes speech from culturally adapted text and generates audio data using tools such as Amazon Polly. The input is culturally adapted text data, and the output is synthesized speech data. This audio data is output to the user through the robot's speaker.

[0788] Step 6:

[0789] Finally, the robot uses visitor interaction tools to automatically recognize the visitor's language and respond appropriately. This process utilizes generated voice and text information to continue communication with the visitor. This allows users to engage in natural conversations through the robot.

[0790] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0791] This invention is a system that facilitates multilingual communication and provides a more personalized communication experience by recognizing user emotions. The system is comprised of a combination of means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, learning support, and an emotion engine.

[0792] User interface and voice input:

[0793] Users operate the system via smartphones or wearable devices and configure settings according to their preferred language and purpose. When a user begins a conversation, the device collects their speech as audio data through the microphone.

[0794] Speech recognition and emotion recognition:

[0795] This audio data is sent from the terminal to the server, which uses ASR (Automatic Speech Recognition) technology to convert the audio into text data. Simultaneously, an emotion engine analyzes features such as tone and rhythm of the voice to identify the user's emotional state.

[0796] Translation and cultural adaptation:

[0797] The converted text data is translated into multiple languages ​​in real time by translation tools, and cultural adaptation tools make adjustments that take into account cultural backgrounds and manners. The results of the emotion engine are also taken into consideration, and the translation results and output format are adjusted to suit the user's emotions.

[0798] Speech synthesis and output:

[0799] The final translation result is either converted to speech using speech synthesis technology or sent to the device as text. This allows the user to receive appropriate feedback that reflects their emotions.

[0800] Learning support and emotional feedback:

[0801] The server analyzes the user's usage history and emotional data to provide personalized learning support information. Learning plans are dynamically optimized based on changes in the user's emotions, facilitating personalized learning.

[0802] Specific example:

[0803] For example, if a Japanese-speaking user is conducting a business negotiation in English, the system instantly collects their speech and translates it into English. If the emotion engine detects emotions such as tension or anxiety from the user's voice, the server adjusts the translation to more polite language through cultural adaptation mechanisms and provides feedback to alleviate stress. This allows users to participate in international communication with confidence.

[0804] The following describes the processing flow.

[0805] Step 1:

[0806] The user opens the smartphone app and selects the language and translation purpose. This sets the user interface to speech input mode.

[0807] Step 2:

[0808] When the user begins speaking, the device collects audio data using its built-in microphone. This audio data is then compressed in real time and sent to the server.

[0809] Step 3:

[0810] The server processes the received audio data through an ASR (Automatic Speech Recognition) module, converting the audio into corresponding text data.

[0811] Step 4:

[0812] Simultaneously, the server uses an emotion engine to analyze the audio data and determine the user's emotional state based on factors such as tone and intonation.

[0813] Step 5:

[0814] The server sends the speech-recognized text to a translation device, which translates it into the configured target language. Here, neural machine translation technology is used to provide fast and accurate translations.

[0815] Step 6:

[0816] The translated text is sent to a cultural adaptation system. The server takes the user's emotions into consideration and adjusts the expression and tone to be appropriate.

[0817] Step 7:

[0818] The server converts the adjusted translated text into speech data using speech synthesis technology, or sends it to the terminal in text format.

[0819] Step 8:

[0820] The device provides the user with the received audio or text. The user listens to the translated audio through the speaker or views the text on the screen.

[0821] Step 9:

[0822] When a user's session ends, the server records their usage history and analyzed sentiment data. This data is used to support subsequent learning. The server generates a learning plan that reflects the user's emotional changes, which can be used to assist them in their next session.

[0823] (Example 2)

[0824] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0825] In intercultural communication, language barriers and cultural misunderstandings can lead to communication obstacles, resulting in users experiencing stress and anxiety. To address this, a system is needed that provides real-time, emotionally conscious, and culturally adaptable translation. Furthermore, the ability to provide learning support based on user emotions and usage history is also a key challenge.

[0826] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0827] In this invention, the server includes: a voice input means for receiving voice data input via a user interface; a voice recognition means for converting the received voice data into text data; an emotion recognition means for analyzing the text data and identifying emotions based on the tone and rhythm of the voice; a translation means for translating the text data into multiple languages; a cultural adaptation means for adjusting the translation based on cultural background and manners; a voice synthesis means for converting the translation results, according to the cultural background and the user's emotional state, into speech; and an output means for outputting the translation results to the user. This enables the user to communicate smoothly and confidently even in cross-cultural situations.

[0828] "User interface means" refers to devices and applications that allow users to input voice data, settings information, and other data.

[0829] "Voice input means" refers to a device or process for receiving voice data from a user and converting it into a format that can be processed within the system.

[0830] "Speech recognition means" refers to a technology or system that converts speech data acquired by a speech input means into text data.

[0831] "Emotion recognition means" refers to a technology or method that analyzes the tone and rhythm of voice data to identify the user's emotional state.

[0832] "Translation means" refers to a function or system that converts text data recorded in one language into another language.

[0833] "Cultural adaptation measures" refer to techniques or processes for adjusting translated data to suit the cultural background, customs, and manners of the recipient.

[0834] "Speech synthesis means" refers to a technology or system that converts translated text data back into speech data so that users can receive it as natural speech.

[0835] "Output means" refers to a method or device for providing the user with the results processed by the system in the form of audio or text.

[0836] "Learning support means" refers to technologies or systems that analyze users' emotional data and usage history to provide individually optimized learning support information.

[0837] "Advice generation methods" refer to processes or techniques for generating and providing advice to prevent cultural misunderstandings in intercultural communication.

[0838] This invention is a system for achieving smooth communication between multiple languages. This system is comprised of a combination of technical means including a user interface, voice input, speech recognition, translation, speech synthesis, cultural adaptation, output, learning support, and an emotion engine.

[0839] Users launch the system's application using their smartphone or wearable device and configure settings according to their preferred language and purpose. Voice data is collected using the device's microphone and sent to the server. The server utilizes a common speech recognition service, known as ASR (Automatic Speech Recognition) technology, to convert the voice data into text. Furthermore, the server uses an emotion recognition engine to identify the user's emotional state from the voice data. This involves techniques that analyze the tone and rhythm of the voice.

[0840] Next, the server translates the text data into multiple languages ​​in real time. This translation process utilizes known translation APIs. The translated text is then adjusted using cultural adaptation tools to suit the background and manners of the target culture. It is also adjusted to provide appropriate feedback based on the user's emotional state. The final translation result is converted into speech using speech synthesis technology and output to the user's device.

[0841] As a concrete example, when a Japanese-speaking user conducts negotiations in English, the user's Japanese speech is sent to the server in real time via a smartphone app. The server instantly translates it into English, generates an English audio transcript that incorporates cultural adaptation and emotional feedback, and plays it back on the device. If the emotion engine detects that the user is under stress, the translation is adjusted to use softer language.

[0842] For example, you can ask the system a question like, "If someone is nervous during a meeting, explain how you can provide feedback that will help alleviate that nervousness," and then observe the system's response.

[0843] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0844] Step 1:

[0845] The user launches an application on their smartphone or wearable device and enters their preferred language, purpose, privacy settings, etc. The device receives this information and verifies the settings. Specifically, the selected language set and voice input mode are activated.

[0846] Step 2:

[0847] When a user begins a conversation, the device uses its microphone to collect audio data in real time and converts it into a digital audio file. This audio data then becomes input data for subsequent analysis. The device receives the audio data and prepares to send it to the server.

[0848] Step 3:

[0849] The server receives the audio data sent from the terminal and starts speech recognition processing. Using speech recognition technology, the audio data is converted into text data. ASR (Automatic Speech Recognition) is a typical algorithm used. The converted text data is used in the next processing step.

[0850] Step 4:

[0851] The server uses an emotion recognition engine to analyze the tone and rhythm of the text data and the original audio data. This identifies the user's emotional state. The results of the emotion recognition are then reflected in subsequent processing for translation and cultural adaptation.

[0852] Step 5:

[0853] The translation mechanism allows the server to automatically translate text data into the user-defined target language. This translation is performed using a common translation API, and the translated text is generated as intermediate output.

[0854] Step 6:

[0855] The server uses cultural adaptation tools to adjust the translated text according to the cultural background and customs of the target language. The user's emotional state is also considered at this stage, ensuring that feedback is presented in the most appropriate way. The adjusted text forms the basis of the final output.

[0856] Step 7:

[0857] Using speech synthesis technology, the server converts the adjusted text into digital speech. This speech data is then prepared as user-readable audio and sent to the terminal.

[0858] Step 8:

[0859] The audio data sent to the terminal as the final output is played back by the terminal for the user. Through this, the user can understand what the other party is saying and respond appropriately according to the situation.

[0860] (Application Example 2)

[0861] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0862] In modern society, effective communication using multiple languages ​​is essential, but language and cultural barriers often hinder this. In particular, when people from different cultural backgrounds interact, language differences, coupled with a lack of emotional understanding, further complicate mutual understanding. Therefore, there is a need for a system that can appropriately recognize the emotional state of users and provide culture-appropriate feedback.

[0863] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0864] In this invention, the server includes acoustic recognition means for converting acoustic information into text data, translation means for translating the text data into multiple natural languages, and emotion recognition means for identifying the user's emotional state and providing adjusted feedback. This enables users to communicate smoothly while understanding the emotions of others, transcending language and cultural differences.

[0865] "Acoustic information" refers to data relating to speech or other sounds, which is collected and analyzed by digital devices.

[0866] "Character data" refers to text-based data obtained by processing acoustic information, and is considered fundamental to information processing.

[0867] "Natural language" refers to the language that humans use on a daily basis, and is the subject of translation tools.

[0868] "Acoustic recognition means" refers to a technological device that performs the process of converting acoustic information into text data.

[0869] "Translation means" refers to machine technology that has the function of converting text data into multiple languages.

[0870] "Sound synthesis means" refers to a technical device that performs a process to convert text data back into a speech format.

[0871] "Cultural adaptation techniques" are technologies that involve a process of adjusting the content of a translation based on its cultural background and customs.

[0872] An "emotion recognition device" is a technological device that identifies and analyzes emotions from the user's acoustic information.

[0873] "Output means" refers to a device that provides the converted and analyzed information to the user via display or audio.

[0874] To implement this invention, the user first prepares a device for inputting acoustic information (for example, a smartphone or a home robot). The acoustic information is transmitted to a server through a built-in microphone. The server converts the acoustic information into text data using acoustic recognition technology. In this process, general ASR (Automatic Speech Recognition) software can be used as the acoustic recognition technology.

[0875] The converted text data is translated into multiple natural languages ​​through translation algorithms on the server. Using a translation API (e.g., a public API service) enables rapid and efficient translation. Subsequently, cultural adaptation tools adjust the translation based on cultural background and customs. This results in expressions suitable for a specific cultural sphere.

[0876] The server identifies the user's emotional state using emotion recognition technology based on acoustic information and converted text data. This process employs emotion analysis algorithms that infer emotions from factors such as tone and patterns of speech.

[0877] Finally, sound synthesis technology converts text data back into sound, providing feedback to the user through output devices (e.g., speakers or text displays). Through this entire process, users can achieve effective communication across language and cultural differences.

[0878] A concrete example is the role a home robot can play in situations where visitors speak a different language. The robot instantly translates the visitor's words and provides comments that respond to the visitor's emotions, thereby supporting a smooth conversation.

[0879] An example of a prompt message is: "A home robot will assist with conversations with Chinese-speaking guests. Please utilize emotion recognition to create a friendly atmosphere that puts visitors at ease. Please provide support so that the host can enjoy the conversation comfortably."

[0880] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0881] Step 1:

[0882] The device collects the user's voice as acoustic information via a microphone. The input is the user's voice, and the output is digitized acoustic information data. The voice is processed as a digital signal, and noise reduction and volume adjustment are performed.

[0883] Step 2:

[0884] The server receives the collected acoustic information and converts it into text data using an acoustic recognition system. The input is acoustic information data, and the output is the converted text data. The software used here is ASR (Automatic Speech Recognition), which uses a specific algorithm to analyze the patterns in the speech and convert them into text.

[0885] Step 3:

[0886] The server translates text data into multiple natural languages ​​using translation tools. The input is text data, and the output is translated text data. Multilingual support is achieved by utilizing a translation API, enabling accurate and speedy translation.

[0887] Step 4:

[0888] The server uses cultural adaptation tools to adjust the translated text data according to cultural background and customs. The input is the translated text data, and the output is the culturally adapted text data. The server adjusts the details of wording and expression based on the culture and etiquette policies of each country.

[0889] Step 5:

[0890] The server identifies the user's emotional state using emotion recognition means based on acoustic information. The input is acoustic information data, and the output is identified emotional state data. An emotion analysis algorithm infers and digitizes emotions based on factors such as tone and rhythm of the voice.

[0891] Step 6:

[0892] The server generates adjusted feedback based on the output of the emotion recognition system and outputs it as speech using the sound synthesis system. The input is culture-adapted text data and emotion state data, and the output is adjusted speech feedback. It is synthesized as natural speech using sound synthesis technology and provided to the user through a speaker.

[0893] 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.

[0894] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0895] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0896] 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.

[0897] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0898] 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.

[0899] 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.

[0900] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0901] 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."

[0902] 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.

[0903] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0904] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0913] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0914] The following is further disclosed regarding the embodiments described above.

[0915] (Claim 1)

[0916] A voice input means that receives voice data input via a user interface,

[0917] A speech recognition means that converts received audio data into text data,

[0918] A translation means for translating the text data into multiple languages,

[0919] A speech synthesis method that converts translated text into speech,

[0920] Cultural adaptation means to adjust the translation based on cultural background and manners,

[0921] An output method for outputting the translation result to the user,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, comprising a learning support means for analyzing the user's usage history and providing learning support information.

[0925] (Claim 3)

[0926] The system according to claim 1, comprising an advice generation means for providing advice to prevent cultural misunderstandings during intercultural communication.

[0927] "Example 1"

[0928] (Claim 1)

[0929] A voice input means that acquires voice data input via a user interface,

[0930] A speech recognition means that converts acquired audio data into text data,

[0931] A translation means for translating the text data into multiple languages,

[0932] A speech synthesis means for converting translated text data into speech,

[0933] Cultural adaptation measures that adjust the translated content based on cultural background and customs,

[0934] An output means for providing the translation result to the user,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, comprising a learning support means that analyzes the user's usage history and generates and provides learning support information.

[0938] (Claim 3)

[0939] The system according to claim 1, comprising an advice generation means for providing guidance to prevent cultural misunderstandings during intercultural communication.

[0940] "Application Example 1"

[0941] (Claim 1)

[0942] An information input means for receiving audio data entered via a user interface,

[0943] Information recognition means for converting received audio data into text data,

[0944] A language processing means for translating the text data into multiple languages,

[0945] A speech conversion method that converts the translated text into speech,

[0946] Cultural adaptation means to adjust the translation based on cultural background and manners,

[0947] A display means for outputting translation results or generated learning support information to the user,

[0948] A visitor support system that automatically recognizes the visitor's language and supports communication,

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, comprising a learning support means that analyzes the user's usage history and provides learning support information.

[0952] (Claim 3)

[0953] The system according to claim 1, comprising an advice generation means for providing advice to prevent cultural misunderstandings during intercultural information exchange.

[0954] "Example 2 of combining an emotion engine"

[0955] (Claim 1)

[0956] A voice input means that receives voice data input via a user interface,

[0957] A speech recognition means that converts received audio data into text data,

[0958] An emotion recognition means that analyzes the text data and identifies emotions based on the tone and rhythm of the voice,

[0959] A translation means for translating the text data into multiple languages,

[0960] Cultural adaptation means to adjust the translation based on cultural background and manners,

[0961] A speech synthesis means that converts translation results into speech according to cultural background and the user's emotional state,

[0962] An output method for outputting the translation result to the user,

[0963] A system that includes this.

[0964] (Claim 2)

[0965] The system according to claim 1, comprising a learning support means that analyzes the user's usage history and emotional data and provides personalized learning support information.

[0966] (Claim 3)

[0967] The system according to claim 1, comprising an advice generation means for preventing cultural misunderstandings during intercultural communication and providing advice based on the user's emotional state.

[0968] "Application example 2 when combining with an emotional engine"

[0969] (Claim 1)

[0970] An acoustic input means for receiving acoustic information input via a user interface,

[0971] A sound recognition means that converts received acoustic information into text data,

[0972] A translation means for translating the character data into multiple natural languages,

[0973] A sound synthesis means for converting translated text data into sound,

[0974] Cultural adaptation means for adjusting the translation based on cultural background and customs,

[0975] An output method for outputting the translation result to the user,

[0976] An emotion recognition means that identifies the user's emotional state and provides adjusted feedback,

[0977] A system that includes this.

[0978] (Claim 2)

[0979] The system according to claim 1, comprising a learning promotion means that analyzes the user's usage history and emotional data and provides learning support information.

[0980] (Claim 3)

[0981] The system according to claim 1, comprising an advice generation means for providing advice to prevent cultural misunderstandings during intercultural communication between regions. [Explanation of Symbols]

[0982] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A voice input means that receives voice data input via a user interface, A speech recognition means that converts received audio data into text data, A translation means for translating the text data into multiple languages, A speech synthesis method that converts translated text into speech, Cultural adaptation means to adjust the translation based on cultural background and manners, An output method for outputting the translation result to the user, A system that includes this.

2. The system according to claim 1, comprising a learning support means for analyzing the user's usage history and providing learning support information.

3. The system according to claim 1, comprising an advice generation means for providing advice to prevent cultural misunderstandings during intercultural communication.

Images