system

Abstract

The system according to the embodiment aims to translate speech in real time and enable use in a stable communication environment. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a display unit, and a communication unit. The reception unit receives voice input. The analysis unit analyzes and translates the voice received by the reception unit. The display unit displays the translation result obtained by the analysis unit. The communication unit provides communication.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has difficulty translating speech in real time, making it difficult to use in places where a stable communication environment is required.

[0005] The system according to the embodiment aims to translate speech in real time and enable use in a stable communication environment. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a display unit, and a communication unit. The reception unit receives a voice input. The analysis unit analyzes and translates the voice received by the reception unit. The display unit displays the translation result obtained by the analysis unit. The communication unit provides communication. [Effects of the Invention]

[0007] The system according to the embodiment can translate speech in real time and can be used in a stable communication environment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Translator Glasses system according to an embodiment of the present invention is a glasses-type interpreter that provides real-time communication for people working overseas, studying abroad, and tourism workers. This system overcomes the challenges of conventional interpreters by translating languages ​​while preserving the nuances of conversation. It is particularly suitable for use in urban areas and tourist destinations where stable communication is required, eliminating language barriers and supporting international communication. For example, a user wears the glasses-type interpreter. The user's spoken language is then accepted as voice input. This voice input is performed through a microphone built into the glasses-type interpreter. The accepted voice input is analyzed and translated by the built-in AI. The translated result is displayed on the lenses of the glasses, allowing the user to check the translation results in real time. Furthermore, this glasses-type interpreter utilizes a specific telecommunications provider's network to provide stable communication. This ensures stable communication even in areas prone to network congestion, such as urban areas and tourist destinations. This system allows users to communicate smoothly with people who speak different languages. For example, it can be useful for people working overseas to converse with locals, or for tourism industry workers to guide foreign tourists. It can also be used by international students to understand local classes. This eyeglass-type interpreter can accurately translate while preserving the nuances of conversation, allowing users to enjoy natural conversations. Furthermore, by providing stable communication, real-time translation is performed without interruption, making it stress-free to use. As a result, the Translator Glasses system allows users to communicate smoothly with people who speak different languages.

[0029] The translator glasses system according to the embodiment includes a reception unit, an analysis unit, a display unit, and a communication unit. The reception unit receives a language spoken by a user as a voice input. For example, the reception unit receives the voice input through a microphone built into the eyeglass-type interpreter. The reception unit can also analyze the voice input using voice recognition technology. For example, the reception unit converts the voice input into text data using a voice recognition algorithm. The analysis unit uses AI to analyze the voice received by the reception unit and perform translation. For example, the analysis unit uses a machine learning algorithm to analyze the voice input and perform translation. The analysis unit can also improve the accuracy of the translation using training data. For example, the analysis unit learns from a large amount of voice data and has advanced translation capabilities. The display unit displays the results of the translation by the analysis unit on the lenses of the glasses. For example, the display unit displays the translation results using a see-through display technology. The display unit can also adjust the format of the displayed information. For example, the display unit adjusts font size and color to improve visibility. The communication unit provides stable communication using a specific communication provider's network. For example, the communication unit transmits and receives data using a communication protocol. The communication unit can also set up redundant paths to ensure communication stability. For example, the communication unit can set up multiple communication paths to prepare for communication interruptions. This allows the Translator Glass system according to the embodiment to smoothly communicate with people who speak different languages. Furthermore, the Translator Glass system can estimate the user's emotions and adjust the sensitivity of the voice input based on the estimated emotions. For example, if the user is nervous, the sensitivity of the voice input can be increased to capture the voice more clearly. Furthermore, if the user is relaxed, the sensitivity of the voice input can be returned to normal to promote natural conversation. This allows the Translator Glass system to provide appropriate voice input according to the user's emotions.

[0030] The reception unit can receive voice input through a microphone built into the eyeglass-type interpreter. The eyeglass-type interpreter is equipped with a built-in microphone and can receive the language spoken by the user as voice input. For example, the reception unit captures voice through a microphone built into the frame of the eyeglasses. The reception unit also has a function for adjusting the sensitivity of the voice input. For example, the reception unit filters out ambient noise to remove noise and achieve clear voice input. The reception unit can also estimate the user's emotions and adjust the sensitivity of the voice input based on the estimated emotions. For example, if the user is nervous, the sensitivity of the voice input can be increased to capture the voice more clearly. Furthermore, if the user is relaxed, the sensitivity of the voice input can be returned to normal to promote natural conversation. As a result, receiving voice input through the microphone built into the eyeglass-type interpreter allows the user to input voice naturally.

[0031] The analysis unit can analyze voice input using AI and perform translation. The analysis unit uses AI to analyze the voice received by the reception unit and perform translation. For example, the analysis unit uses a machine learning algorithm to analyze voice input and perform translation. The analysis unit can also improve the accuracy of the translation using training data. For example, the analysis unit learns from large amounts of voice data and has advanced translation capabilities. Furthermore, the analysis unit can improve the accuracy of the translation by taking into account the context of the voice. For example, the analysis unit analyzes the context before and after the conversation and provides an appropriate translation. The analysis unit can also use a dictionary to appropriately translate specific technical terms and slang. For example, in the case of a conversation that includes medical terminology, the analysis unit uses a medical terminology dictionary to perform translation. In this way, the analysis unit uses AI to improve the accuracy of the analysis and translation of voice input.

[0032] The display unit can display the translation result on the lenses of the glasses. The display unit displays the translation result obtained by the analysis unit on the lenses of the glasses. For example, the display unit displays the translation result using a transparent display technology. The display unit can also adjust the format of the information to be displayed. For example, the display unit can adjust the font size and color to improve visibility. Furthermore, the display unit can estimate the user's emotions and adjust the font size and color of the display based on the estimated emotions. For example, if the user is nervous, a font size and color with high visibility can be used. On the other hand, if the user is relaxed, a normal font size and color can be used. In this way, the display unit can display the translation result on the lenses of the glasses, allowing the user to check the translation result in real time.

[0033] The communication unit can provide stable communication by utilizing a communication provider's network. The communication unit provides stable communication by utilizing a specific communication provider's network. For example, the communication unit transmits and receives data using a communication protocol. The communication unit can also set redundant paths to ensure communication stability. For example, the communication unit sets multiple communication paths to prepare for communication interruptions. Furthermore, the communication unit can estimate the user's emotions and determine communication priorities based on the estimated emotions. For example, if the user is nervous, the communication priority can be increased to quickly transmit and receive data. Also, if the user is relaxed, the communication priority can be returned to normal. In this way, the communication unit ensures stable communication by utilizing a specific communication provider's network.

[0034] The reception unit can analyze the user's past voice input history and select the optimal voice input method. The reception unit analyzes the user's past voice input history and selects the optimal voice input method. For example, the reception unit preferentially selects a voice input method that the user has used in the past (e.g., a specific phrase or pronunciation). The reception unit can also suggest the optimal voice input method for a specific environment based on the user's past voice input history. Furthermore, the reception unit can analyze the user's past voice input history and select the voice input method with the highest success rate. In this way, the reception unit can select the optimal voice input method by analyzing the past voice input history.

[0035] The reception unit can filter surrounding environmental sounds to remove noise when inputting voice. The reception unit can filter surrounding environmental sounds to remove noise when inputting voice. For example, when the surrounding noise is loud, the reception unit can use noise canceling technology to make the voice input clear. Also, when the wind noise is strong, the reception unit can filter wind noise to make the voice input clear. Furthermore, when background music is playing, the reception unit can filter the music to make the voice input accurate. As a result, the reception unit can filter surrounding environmental sounds to remove noise and enable clear voice input.

[0036] The reception unit can preferentially accept highly relevant voices based on the user's geographical location information when inputting voice. The reception unit preferentially accepts highly relevant voices by taking into account the user's geographical location information when inputting voice. For example, if the user is in a tourist spot, the reception unit can preferentially accept voice input related to the location. Also, if the user is in a business area, the reception unit can preferentially accept business-related voice input. Furthermore, if the user is at home, the reception unit can preferentially accept voice input related to daily life. In this way, the reception unit can preferentially accept highly relevant voices by taking into account the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity and accept related voices when a voice is input. The reception unit analyzes the user's social media activity and accepts related voices when a voice is input. For example, the reception unit preferentially accepts voice inputs related to topics that the user has recently been talking about on social media. The reception unit can also analyze the content of the user's social media posts and accept related voice inputs. Furthermore, the reception unit can also accept related voice inputs by referring to the content of posts by the user's social media followers and friends. In this way, the reception unit can preferentially accept related voices by analyzing social media activity.

[0038] The analysis unit can improve the accuracy of the translation based on the context of the speech during translation. The analysis unit improves the accuracy of the translation by taking the context of the speech into account during translation. For example, the analysis unit analyzes the context before and after the conversation to provide an appropriate translation. The analysis unit can also take into account the context of specific technical terms and slang when translating. Furthermore, the analysis unit can take into account the tone and emotion of the conversation to provide an appropriate translation. In this way, the analysis unit improves the accuracy of the translation by taking the context of the speech into account.

[0039] The analysis unit can use a dictionary to properly translate specific technical terms and slang during translation. The analysis unit can use a dictionary to properly translate specific technical terms and slang during translation. For example, in the case of a conversation that includes medical terms, the analysis unit can use a medical terminology dictionary to perform the translation. In addition, in the case of a conversation that includes business terms, the analysis unit can also use a business terminology dictionary to perform the translation. Furthermore, in the case of a conversation that includes youth slang and slang, the analysis unit can also use a slang dictionary to perform the translation. In this way, the analysis unit can properly translate specific technical terms and slang, thereby improving the accuracy of the translation.

[0040] The analysis unit can perform translation based on attribute information of the speaker of the audio when translating. The analysis unit performs translation taking into account attribute information of the speaker of the audio when translating. For example, if the speaker is a child, the analysis unit provides a simple and easy-to-understand translation. Also, if the speaker is an expert, the analysis unit can provide a detailed translation including technical terms. Furthermore, if the speaker is a foreigner, the analysis unit can provide a translation that takes into account the cultural background. In this way, the analysis unit can provide a more appropriate translation by taking into account attribute information of the speaker of the audio.

[0041] The analysis unit can improve the accuracy of the translation by referring to literature related to the audio during translation. The analysis unit improves the accuracy of the translation by referring to literature related to the audio during translation. For example, in the case of a medical-related conversation, the analysis unit can perform the translation by referring to related medical literature. In addition, in the case of a legal-related conversation, the analysis unit can also perform the translation by referring to related legal literature. Furthermore, in the case of a technical-related conversation, the analysis unit can perform the translation by referring to related technical literature. In this way, the analysis unit can improve the accuracy of the translation by referring to related literature.

[0042] The display unit can adjust the level of detail of the display based on the importance of the translation result when displaying. The display unit adjusts the level of detail of the display based on the importance of the translation result when displaying. For example, the display unit displays a detailed translation result in the case of an important conversation. The display unit can also display a concise translation result in the case of an everyday conversation. Furthermore, the display unit can display a detailed translation result including technical terms in the case of a technical conversation. In this way, the display unit adjusts the level of detail of the display based on the importance of the translation result, thereby emphasizing important information.

[0043] The display unit can select the optimal display method based on the user's visual acuity information when displaying. The display unit selects the optimal display method taking the user's visual acuity information into consideration when displaying. For example, if the user has poor visual acuity, the display unit can display using a large font size. Also, if the user has color vision deficiency, the display unit can adjust the color contrast when displaying. Furthermore, if the user has good visual acuity, the display unit can display using a normal font size. In this way, the display unit provides the optimal display method by taking the user's visual acuity information into consideration.

[0044] The display unit can select the optimal display method based on the user's device information when displaying. The display unit selects the optimal display method by taking the user's device information into consideration when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. In this way, the display unit provides the optimal display method by taking the user's device information into consideration.

[0045] The display unit can adjust the display order based on the relevance of the translation results when displaying them. The display unit adjusts the display order based on the relevance of the translation results when displaying them. For example, the display unit displays important translation results first. The display unit can also display translation results for everyday conversations later. Furthermore, the display unit can also display specialized translation results preferentially. In this way, the display unit adjusts the display order based on the relevance of the translation results, thereby providing important information quickly.

[0046] The communication unit can select the optimal communication provider by referring to past communication history during communication. The communication unit selects the optimal communication provider by referring to past communication history during communication. For example, the communication unit selects the optimal provider based on the history of communication providers used by the user in the past. The communication unit can also select the most stable communication provider from the user's past communication history. Furthermore, the communication unit can analyze the user's past communication history and select the most efficient communication provider. In this way, the communication unit can select the optimal communication provider by referring to the past communication history.

[0047] The communication unit can set a redundant path to ensure communication stability during communication. The communication unit sets a redundant path to ensure communication stability during communication. For example, the communication unit sets multiple communication paths in preparation for a communication interruption. The communication unit can also set a backup communication provider to ensure communication stability. Furthermore, the communication unit can monitor communication paths in real time and switch as necessary to prevent communication interruptions. In this way, the communication unit can ensure communication stability by setting redundant paths.

[0048] The communication unit can select an optimal communication provider based on the user's geographical location information during communication. The communication unit selects an optimal communication provider taking into account the user's geographical location information during communication. For example, when the user is in an urban area, the communication unit selects a provider with strong communication strength in the urban area. Also, when the user is in a suburban area, the communication unit can select a provider with strong communication strength in the suburban area. Furthermore, when the user is overseas, the communication unit can select a provider that supports international roaming. In this way, the communication unit can select an optimal communication provider by taking into account the user's geographical location information.

[0049] The communication unit can analyze the user's social media activity during communication to determine the priority of the communication. The communication unit analyzes the user's social media activity during communication to determine the priority of the communication. For example, the communication unit can increase the priority of the communication when the user is making an important post on social media. The communication unit can also maximize the priority of the communication when the user is live streaming on social media. Furthermore, the communication unit can return the priority of the communication to normal when the user is making normal posts on social media. In this way, the communication unit can appropriately determine the priority of the communication by analyzing social media activity.

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

[0051] The translator glasses system may further include a health management unit that monitors the user's health condition. For example, the health management unit may measure the user's heart rate and blood pressure and display a warning if an abnormality is detected. The health management unit may also monitor the user's stress level and provide advice on how to relax if stress is high. The health management unit may also record the user's exercise volume and provide feedback to help the user achieve their daily exercise goals. This allows the translator glasses system to monitor the user's health condition in real time and provide appropriate advice.

[0052] The analysis unit can learn the user's past translation history and improve the accuracy of the translation. For example, the analysis unit can prioritize learning specific phrases and expressions used by the user in the past and reflect them in subsequent translations. The analysis unit can also learn the user's history of correcting past mistranslations and prevent similar mistranslations. Furthermore, the analysis unit can analyze the user's past translation history and improve the translation accuracy of specific technical terms and slang. In this way, the analysis unit can improve the accuracy of the translation by utilizing the user's past translation history.

[0053] The display unit can track the user's gaze and display the translation results according to the position of the gaze. For example, when the user directs their gaze at a specific word or phrase, the display unit highlights that part. The display unit can also automatically scroll the displayed content when the user moves their gaze. Furthermore, when the user fixates their gaze, detailed translation information can be displayed. By tracking the user's gaze, the display unit can improve visibility and enable more intuitive operation.

[0054] The communication unit can analyze the user's communication history and select the optimal communication protocol. For example, the communication unit selects the most stable protocol based on the history of communication protocols used by the user in the past. The communication unit can also select the most efficient protocol from the user's past communication history. Furthermore, the communication unit can analyze the user's past communication history and suggest the optimal protocol for a specific environment. In this way, the communication unit can select the optimal communication protocol by utilizing the past communication history.

[0055] The Translator Glasses system may further include an environmental recognition unit that acquires information about the user's surrounding environment. For example, the environmental recognition unit may acquire information about surrounding sounds and light to provide an appropriate translation environment. The environmental recognition unit may also monitor the surrounding temperature and humidity to provide a comfortable environment for the user. Furthermore, the environmental recognition unit may detect the movements of people around the user and display translation results at the appropriate time. This allows the Translator Glasses system to utilize information about the user's surrounding environment to provide a more comfortable and effective translation experience.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives the language spoken by the user as a voice input. For example, the reception unit receives the voice input through a microphone built into the eyeglass-type interpreter. The reception unit also analyzes the voice input using voice recognition technology and converts the voice input into text data using a voice recognition algorithm. Step 2: The analysis unit uses AI to analyze the speech received by the reception unit and perform translation. For example, the analysis unit uses a machine learning algorithm to analyze the speech input and perform translation. The analysis unit also learns from large amounts of speech data and uses the training data to improve the accuracy of the translation. Step 3: The display unit displays the translation results from the analysis unit on the lenses of the glasses. For example, the display unit may use a transparent display technology to display the translation results and adjust the font size and color to improve visibility. Step 4: The communication unit provides stable communication using a specific communication provider's network. For example, the communication unit transmits and receives data using a communication protocol and sets up redundant paths to ensure stable communication.

[0058] (Example 2) The Translator Glasses system according to an embodiment of the present invention is a glasses-type interpreter that provides real-time communication for people working overseas, studying abroad, and tourism workers. This system overcomes the challenges of conventional interpreters by translating languages ​​while preserving the nuances of conversation. It is particularly suitable for use in urban areas and tourist destinations where stable communication is required, eliminating language barriers and supporting international communication. For example, a user wears the glasses-type interpreter. The user's spoken language is then accepted as voice input. This voice input is performed through a microphone built into the glasses-type interpreter. The accepted voice input is analyzed and translated by the built-in AI. The translated result is displayed on the lenses of the glasses, allowing the user to check the translation results in real time. Furthermore, this glasses-type interpreter utilizes a specific telecommunications provider's network to provide stable communication. This ensures stable communication even in areas prone to network congestion, such as urban areas and tourist destinations. This system allows users to communicate smoothly with people who speak different languages. For example, it can be useful for people working overseas to converse with locals, or for tourism industry workers to guide foreign tourists. It can also be used by international students to understand local classes. This eyeglass-type interpreter can accurately translate while preserving the nuances of conversation, allowing users to enjoy natural conversations. Furthermore, by providing stable communication, real-time translation is performed without interruption, making it stress-free to use. As a result, the Translator Glasses system allows users to communicate smoothly with people who speak different languages.

[0059] The translator glasses system according to the embodiment includes a reception unit, an analysis unit, a display unit, and a communication unit. The reception unit receives a language spoken by a user as a voice input. For example, the reception unit receives the voice input through a microphone built into the eyeglass-type interpreter. The reception unit can also analyze the voice input using voice recognition technology. For example, the reception unit converts the voice input into text data using a voice recognition algorithm. The analysis unit uses AI to analyze the voice received by the reception unit and perform translation. For example, the analysis unit uses a machine learning algorithm to analyze the voice input and perform translation. The analysis unit can also improve the accuracy of the translation using training data. For example, the analysis unit learns from a large amount of voice data and has advanced translation capabilities. The display unit displays the results of the translation by the analysis unit on the lenses of the glasses. For example, the display unit displays the translation results using a see-through display technology. The display unit can also adjust the format of the displayed information. For example, the display unit adjusts font size and color to improve visibility. The communication unit provides stable communication using a specific communication provider's network. For example, the communication unit transmits and receives data using a communication protocol. The communication unit can also set up redundant paths to ensure communication stability. For example, the communication unit can set up multiple communication paths to prepare for communication interruptions. This allows the Translator Glass system according to the embodiment to smoothly communicate with people who speak different languages. Furthermore, the Translator Glass system can estimate the user's emotions and adjust the sensitivity of the voice input based on the estimated emotions. For example, if the user is nervous, the sensitivity of the voice input can be increased to capture the voice more clearly. Furthermore, if the user is relaxed, the sensitivity of the voice input can be returned to normal to promote natural conversation. This allows the Translator Glass system to provide appropriate voice input according to the user's emotions.

[0060] The reception unit can receive voice input through a microphone built into the eyeglass-type interpreter. The eyeglass-type interpreter is equipped with a built-in microphone and can receive the language spoken by the user as voice input. For example, the reception unit captures voice through a microphone built into the frame of the eyeglasses. The reception unit also has a function for adjusting the sensitivity of the voice input. For example, the reception unit filters out ambient noise to remove noise and achieve clear voice input. The reception unit can also estimate the user's emotions and adjust the sensitivity of the voice input based on the estimated emotions. For example, if the user is nervous, the sensitivity of the voice input can be increased to capture the voice more clearly. Furthermore, if the user is relaxed, the sensitivity of the voice input can be returned to normal to promote natural conversation. As a result, receiving voice input through the microphone built into the eyeglass-type interpreter allows the user to input voice naturally.

[0061] The analysis unit can analyze voice input using AI and perform translation. The analysis unit uses AI to analyze the voice received by the reception unit and perform translation. For example, the analysis unit uses a machine learning algorithm to analyze voice input and perform translation. The analysis unit can also improve the accuracy of the translation using training data. For example, the analysis unit learns from large amounts of voice data and has advanced translation capabilities. Furthermore, the analysis unit can improve the accuracy of the translation by taking into account the context of the voice. For example, the analysis unit analyzes the context before and after the conversation and provides an appropriate translation. The analysis unit can also use a dictionary to appropriately translate specific technical terms and slang. For example, in the case of a conversation that includes medical terminology, the analysis unit uses a medical terminology dictionary to perform translation. In this way, the analysis unit uses AI to improve the accuracy of the analysis and translation of voice input.

[0062] The display unit can display the translation result on the lenses of the glasses. The display unit displays the translation result obtained by the analysis unit on the lenses of the glasses. For example, the display unit displays the translation result using a transparent display technology. The display unit can also adjust the format of the information to be displayed. For example, the display unit can adjust the font size and color to improve visibility. Furthermore, the display unit can estimate the user's emotions and adjust the font size and color of the display based on the estimated emotions. For example, if the user is nervous, a font size and color with high visibility can be used. On the other hand, if the user is relaxed, a normal font size and color can be used. In this way, the display unit can display the translation result on the lenses of the glasses, allowing the user to check the translation result in real time.

[0063] The communication unit can provide stable communication by utilizing a communication provider's network. The communication unit provides stable communication by utilizing a specific communication provider's network. For example, the communication unit transmits and receives data using a communication protocol. The communication unit can also set redundant paths to ensure communication stability. For example, the communication unit sets multiple communication paths to prepare for communication interruptions. Furthermore, the communication unit can estimate the user's emotions and determine communication priorities based on the estimated emotions. For example, if the user is nervous, the communication priority can be increased to quickly transmit and receive data. Also, if the user is relaxed, the communication priority can be returned to normal. In this way, the communication unit ensures stable communication by utilizing a specific communication provider's network.

[0064] The reception unit can estimate the user's emotion and adjust the sensitivity of the voice input based on the estimated emotion. The reception unit estimates the user's emotion and adjusts the sensitivity of the voice input based on the estimated emotion. For example, if the user is nervous, the reception unit increases the sensitivity of the voice input to capture the voice more clearly. If the user is relaxed, the reception unit can return the sensitivity of the voice input to normal to promote natural conversation. Furthermore, if the user is in a hurry, the reception unit can maximize the sensitivity of the voice input to quickly recognize the voice. This allows the reception unit to adjust the sensitivity of the voice input according to the user's emotion, enabling more appropriate voice input. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The reception unit can analyze the user's past voice input history and select the optimal voice input method. The reception unit analyzes the user's past voice input history and selects the optimal voice input method. For example, the reception unit preferentially selects a voice input method that the user has used in the past (e.g., a specific phrase or pronunciation). The reception unit can also suggest the optimal voice input method for a specific environment based on the user's past voice input history. Furthermore, the reception unit can analyze the user's past voice input history and select the voice input method with the highest success rate. In this way, the reception unit can select the optimal voice input method by analyzing the past voice input history.

[0066] The reception unit can filter surrounding environmental sounds to remove noise when inputting voice. The reception unit can filter surrounding environmental sounds to remove noise when inputting voice. For example, when the surrounding noise is loud, the reception unit can use noise canceling technology to make the voice input clear. Also, when the wind noise is strong, the reception unit can filter wind noise to make the voice input clear. Furthermore, when background music is playing, the reception unit can filter the music to make the voice input accurate. As a result, the reception unit can filter surrounding environmental sounds to remove noise and enable clear voice input.

[0067] The reception unit can estimate the user's emotion and determine the priority of the voice input based on the estimated emotion. The reception unit estimates the user's emotion and determines the priority of the voice input based on the estimated emotion. For example, if the user is nervous, the reception unit can increase the priority of the voice input and process it quickly. Also, if the user is relaxed, the reception unit can return the priority of the voice input to normal. Furthermore, if the user is in a hurry, the reception unit can maximize the priority of the voice input and process it immediately. In this way, the reception unit can determine the priority of the voice input according to the user's emotion, thereby enabling rapid processing. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The reception unit can preferentially accept highly relevant voices based on the user's geographical location information when inputting voice. The reception unit preferentially accepts highly relevant voices by taking into account the user's geographical location information when inputting voice. For example, if the user is in a tourist spot, the reception unit can preferentially accept voice input related to the location. Also, if the user is in a business area, the reception unit can preferentially accept business-related voice input. Furthermore, if the user is at home, the reception unit can preferentially accept voice input related to daily life. In this way, the reception unit can preferentially accept highly relevant voices by taking into account the user's geographical location information.

[0069] The reception unit can analyze the user's social media activity and accept related voices when a voice is input. The reception unit analyzes the user's social media activity and accepts related voices when a voice is input. For example, the reception unit preferentially accepts voice inputs related to topics that the user has recently been talking about on social media. The reception unit can also analyze the content of the user's social media posts and accept related voice inputs. Furthermore, the reception unit can also accept related voice inputs by referring to the content of posts by the user's social media followers and friends. In this way, the reception unit can preferentially accept related voices by analyzing social media activity.

[0070] The analysis unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the translation expression based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a concise and clear translation. If the user is relaxed, the analysis unit can also provide a detailed and nuanced translation. If the user is in a hurry, the analysis unit can also provide a quick and to-the-point translation. In this way, the analysis unit can adjust the translation expression based on the user's emotions, thereby providing a more appropriate translation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The analysis unit can improve the accuracy of the translation based on the context of the speech during translation. The analysis unit improves the accuracy of the translation by taking the context of the speech into account during translation. For example, the analysis unit analyzes the context before and after the conversation to provide an appropriate translation. The analysis unit can also take into account the context of specific technical terms and slang when translating. Furthermore, the analysis unit can take into account the tone and emotion of the conversation to provide an appropriate translation. In this way, the analysis unit improves the accuracy of the translation by taking the context of the speech into account.

[0072] The analysis unit can use a dictionary to properly translate specific technical terms and slang during translation. The analysis unit can use a dictionary to properly translate specific technical terms and slang during translation. For example, in the case of a conversation that includes medical terms, the analysis unit can use a medical terminology dictionary to perform the translation. In addition, in the case of a conversation that includes business terms, the analysis unit can also use a business terminology dictionary to perform the translation. Furthermore, in the case of a conversation that includes youth slang and slang, the analysis unit can also use a slang dictionary to perform the translation. In this way, the analysis unit can properly translate specific technical terms and slang, thereby improving the accuracy of the translation.

[0073] The analysis unit can estimate the user's emotions and adjust the translation speed based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the translation speed based on the estimated emotions. For example, if the user is nervous, the analysis unit can speed up the translation speed to provide quick results. The analysis unit can also return the translation speed to normal if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can maximize the translation speed to provide quick results. In this way, the analysis unit can adjust the translation speed according to the user's emotions, thereby providing quick translation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The analysis unit can perform translation based on attribute information of the speaker of the audio when translating. The analysis unit performs translation taking into account attribute information of the speaker of the audio when translating. For example, if the speaker is a child, the analysis unit provides a simple and easy-to-understand translation. Also, if the speaker is an expert, the analysis unit can provide a detailed translation including technical terms. Furthermore, if the speaker is a foreigner, the analysis unit can provide a translation that takes into account the cultural background. In this way, the analysis unit can provide a more appropriate translation by taking into account attribute information of the speaker of the audio.

[0075] The analysis unit can improve the accuracy of the translation by referring to literature related to the audio during translation. The analysis unit improves the accuracy of the translation by referring to literature related to the audio during translation. For example, in the case of a medical-related conversation, the analysis unit can perform the translation by referring to related medical literature. In addition, in the case of a legal-related conversation, the analysis unit can also perform the translation by referring to related legal literature. Furthermore, in the case of a technical-related conversation, the analysis unit can perform the translation by referring to related technical literature. In this way, the analysis unit can improve the accuracy of the translation by referring to related literature.

[0076] The display unit can estimate the user's emotion and adjust the font size and color of the display based on the estimated emotion. The display unit can estimate the user's emotion and adjust the font size and color of the display based on the estimated emotion. For example, if the user is nervous, the display unit can use a font size and color that is highly visible. Alternatively, if the user is relaxed, the display unit can use a normal font size and color. Furthermore, if the user is in a hurry, the display unit can use a large font size and bright color to highlight important information. This allows the display unit to adjust the font size and color of the display according to the user's emotion, thereby improving visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The display unit can adjust the level of detail of the display based on the importance of the translation result when displaying. The display unit adjusts the level of detail of the display based on the importance of the translation result when displaying. For example, the display unit displays a detailed translation result in the case of an important conversation. The display unit can also display a concise translation result in the case of an everyday conversation. Furthermore, the display unit can display a detailed translation result including technical terms in the case of a technical conversation. In this way, the display unit adjusts the level of detail of the display based on the importance of the translation result, thereby emphasizing important information.

[0078] The display unit can select the optimal display method based on the user's visual acuity information when displaying. The display unit selects the optimal display method taking the user's visual acuity information into consideration when displaying. For example, if the user has poor visual acuity, the display unit can display using a large font size. Also, if the user has color vision deficiency, the display unit can adjust the color contrast when displaying. Furthermore, if the user has good visual acuity, the display unit can display using a normal font size. In this way, the display unit provides the optimal display method by taking the user's visual acuity information into consideration.

[0079] The display unit can estimate the user's emotion and adjust the display order based on the estimated user's emotion. The display unit estimates the user's emotion and adjusts the display order based on the estimated emotion. For example, if the user is nervous, the display unit displays important information first. Also, if the user is relaxed, the display unit can display information in a normal order. Furthermore, if the user is in a hurry, the display unit can display the main points first. In this way, the display unit adjusts the display order according to the user's emotion, thereby quickly providing important information. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The display unit can select the optimal display method based on the user's device information when displaying. The display unit selects the optimal display method by taking the user's device information into consideration when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. In this way, the display unit provides the optimal display method by taking the user's device information into consideration.

[0081] The display unit can adjust the display order based on the relevance of the translation results when displaying them. The display unit adjusts the display order based on the relevance of the translation results when displaying them. For example, the display unit displays important translation results first. The display unit can also display translation results for everyday conversations later. Furthermore, the display unit can also display specialized translation results preferentially. In this way, the display unit adjusts the display order based on the relevance of the translation results, thereby providing important information quickly.

[0082] The communication unit can estimate the user's emotion and determine the priority of communication based on the estimated emotion. The communication unit estimates the user's emotion and determines the priority of communication based on the estimated emotion. For example, if the user is nervous, the communication unit can increase the priority of communication and send and receive data quickly. Also, if the user is relaxed, the communication unit can return the priority of communication to normal. Furthermore, if the user is in a hurry, the communication unit can maximize the priority of communication and send and receive data immediately. In this way, the communication unit can determine the priority of communication according to the user's emotion, thereby enabling rapid data transmission and reception. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The communication unit can select the optimal communication provider by referring to past communication history during communication. The communication unit selects the optimal communication provider by referring to past communication history during communication. For example, the communication unit selects the optimal provider based on the history of communication providers used by the user in the past. The communication unit can also select the most stable communication provider from the user's past communication history. Furthermore, the communication unit can analyze the user's past communication history and select the most efficient communication provider. In this way, the communication unit can select the optimal communication provider by referring to the past communication history.

[0084] The communication unit can set a redundant path to ensure communication stability during communication. The communication unit sets a redundant path to ensure communication stability during communication. For example, the communication unit sets multiple communication paths in preparation for a communication interruption. The communication unit can also set a backup communication provider to ensure communication stability. Furthermore, the communication unit can monitor communication paths in real time and switch as necessary to prevent communication interruptions. In this way, the communication unit can ensure communication stability by setting redundant paths.

[0085] The communication unit can estimate the user's emotion and adjust the frequency of communication based on the estimated user's emotion. The communication unit can estimate the user's emotion and adjust the frequency of communication based on the estimated emotion. For example, if the user is nervous, the communication unit can increase the frequency of communication to quickly send and receive data. Also, if the user is relaxed, the communication unit can return the frequency of communication to normal. Furthermore, if the user is in a hurry, the communication unit can maximize the frequency of communication to quickly send and receive data. In this way, the communication unit can adjust the frequency of communication according to the user's emotion, thereby enabling rapid data transmission and reception. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The communication unit can select an optimal communication provider based on the user's geographical location information during communication. The communication unit selects an optimal communication provider taking into account the user's geographical location information during communication. For example, when the user is in an urban area, the communication unit selects a provider with strong communication strength in the urban area. Also, when the user is in a suburban area, the communication unit can select a provider with strong communication strength in the suburban area. Furthermore, when the user is overseas, the communication unit can select a provider that supports international roaming. In this way, the communication unit can select an optimal communication provider by taking into account the user's geographical location information.

[0087] The communication unit can analyze the user's social media activity during communication to determine the priority of the communication. The communication unit analyzes the user's social media activity during communication to determine the priority of the communication. For example, the communication unit can increase the priority of the communication when the user is making an important post on social media. The communication unit can also maximize the priority of the communication when the user is live streaming on social media. Furthermore, the communication unit can return the priority of the communication to normal when the user is making normal posts on social media. In this way, the communication unit can appropriately determine the priority of the communication by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, display unit, and communication unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14 and performs voice recognition using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice using a machine learning algorithm to perform translation. The display unit displays the translation result using the display 40A of the smart device 14. The communication unit provides stable communication using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, display unit, and communication unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214 and performs voice recognition using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice using a machine learning algorithm to perform translation. The display unit displays the translation result using the display of the smart glasses 214. The communication unit provides stable communication using the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, display unit, and communication unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the headset type terminal 314, and performs voice recognition using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice using a machine learning algorithm to perform translation. The display unit displays the translation result using the display 343 of the headset type terminal 314. The communication unit provides stable communication using the communication I / F 44 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, display unit, and communication unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414, and performs voice recognition using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice using a machine learning algorithm to perform translation. The display unit displays the translation result using a display of the robot 414. The communication unit provides stable communication using the communication I / F 44 of the robot 414.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The translator glasses system may further include a health management unit that monitors the user's health condition. For example, the health management unit may measure the user's heart rate and blood pressure and display a warning if an abnormality is detected. The health management unit may also monitor the user's stress level and provide advice on how to relax if stress is high. The health management unit may also record the user's exercise volume and provide feedback to help the user achieve their daily exercise goals. This allows the translator glasses system to monitor the user's health condition in real time and provide appropriate advice.

[0090] The reception unit can analyze the tone and speed of the user's voice and estimate the user's emotions. For example, if the user's voice is high and fast, the reception unit can estimate that the user is nervous and increase the sensitivity of the voice input. If the user's voice is low and slow, the reception unit can estimate that the user is relaxed and return the sensitivity of the voice input to normal. Furthermore, if the user's voice is trembling, the reception unit can estimate that the user is scared and maximize the sensitivity of the voice input. In this way, the reception unit can estimate the user's emotions by analyzing the characteristics of the user's voice and provide appropriate voice input.

[0091] The analysis unit can learn the user's past translation history and improve the accuracy of the translation. For example, the analysis unit can prioritize learning specific phrases and expressions used by the user in the past and reflect them in subsequent translations. The analysis unit can also learn the user's history of correcting past mistranslations and prevent similar mistranslations. Furthermore, the analysis unit can analyze the user's past translation history and improve the translation accuracy of specific technical terms and slang. In this way, the analysis unit can improve the accuracy of the translation by utilizing the user's past translation history.

[0092] The display unit can track the user's gaze and display the translation results according to the position of the gaze. For example, when the user directs their gaze at a specific word or phrase, the display unit highlights that part. The display unit can also automatically scroll the displayed content when the user moves their gaze. Furthermore, when the user fixates their gaze, detailed translation information can be displayed. By tracking the user's gaze, the display unit can improve visibility and enable more intuitive operation.

[0093] The communication unit can analyze the user's communication history and select the optimal communication protocol. For example, the communication unit selects the most stable protocol based on the history of communication protocols used by the user in the past. The communication unit can also select the most efficient protocol from the user's past communication history. Furthermore, the communication unit can analyze the user's past communication history and suggest the optimal protocol for a specific environment. In this way, the communication unit can select the optimal communication protocol by utilizing the past communication history.

[0094] The reception unit can estimate the user's emotion and provide feedback on the voice input based on the estimated emotion. For example, if the user is nervous, the reception unit can provide voice advice to relax. Also, if the user is relaxed, the reception unit can provide positive feedback. Furthermore, if the user is in a hurry, the reception unit can provide hints for quickly performing voice input. In this way, the reception unit can improve the accuracy of voice input by providing feedback according to the user's emotion.

[0095] The analysis unit can estimate the user's emotions and adjust the tone of the translation based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a translation in a calm tone. If the user is relaxed, the analysis unit can provide a translation in a friendly tone. Furthermore, if the user is angry, the analysis unit can provide a translation in a calm tone. In this way, the analysis unit can achieve more natural communication by providing a translation in a tone that corresponds to the user's emotions.

[0096] The display unit can estimate the user's emotion and adjust the display layout based on the estimated emotion. For example, if the user is nervous, the display unit can use a simple, highly visible layout. If the user is relaxed, the display unit can use a layout including detailed information. Furthermore, if the user is in a hurry, the display unit can use a layout that emphasizes important information. In this way, the display unit can improve visibility and convenience by providing a layout according to the user's emotion.

[0097] The communication unit can estimate the user's emotions and adjust the communication bandwidth based on the estimated emotions. For example, if the user is nervous, the communication unit can widen the communication bandwidth to send and receive data quickly. If the user is relaxed, the communication unit can return the communication bandwidth to normal. Furthermore, if the user is in a hurry, the communication bandwidth can be maximized to send and receive data immediately. In this way, the communication unit can adjust the communication bandwidth according to the user's emotions, enabling quick data transmission and reception.

[0098] The Translator Glasses system may further include an environmental recognition unit that acquires information about the user's surrounding environment. For example, the environmental recognition unit may acquire information about surrounding sounds and light to provide an appropriate translation environment. The environmental recognition unit may also monitor the surrounding temperature and humidity to provide a comfortable environment for the user. Furthermore, the environmental recognition unit may detect the movements of people around the user and display translation results at the appropriate time. This allows the Translator Glasses system to utilize information about the user's surrounding environment to provide a more comfortable and effective translation experience.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives the language spoken by the user as a voice input. For example, the reception unit receives the voice input through a microphone built into the eyeglass-type interpreter. The reception unit also analyzes the voice input using voice recognition technology and converts the voice input into text data using a voice recognition algorithm. Step 2: The analysis unit uses AI to analyze the speech received by the reception unit and perform translation. For example, the analysis unit uses a machine learning algorithm to analyze the speech input and perform translation. The analysis unit also learns from large amounts of speech data and uses the training data to improve the accuracy of the translation. Step 3: The display unit displays the translation results from the analysis unit on the lenses of the glasses. For example, the display unit may use a transparent display technology to display the translation results and adjust the font size and color to improve visibility. Step 4: The communication unit provides stable communication using a specific communication provider's network. For example, the communication unit transmits and receives data using a communication protocol and sets up redundant paths to ensure stable communication.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives voice input; an analysis unit that analyzes and translates the speech received by the reception unit; a display unit that displays the translation result obtained by the analysis unit; a communication unit that provides communication; A system characterized by:

2. The reception unit Accepts voice input through a microphone built into the eyeglass-type interpreter 2. The system of claim 1.

3. The analysis unit AI analyzes voice input and translates it 2. The system of claim 1.

4. The display unit The translated results are displayed on the lenses of the glasses.

2. The system of claim 1.

5. The communication unit Providing stable communications using the network of a communications provider 2. The system of claim 1.

6. The reception unit Estimate the user's emotion and adjust the sensitivity of the voice input based on the estimated user's emotion.

2. The system of claim 1.

7. The reception unit Analyze the user's past voice input history and select a voice input method 2. The system of claim 1.

8. The reception unit Filters out ambient noise when you input voice.

2. The system of claim 1.

Images