System

Abstract

An object of the system according to the embodiment is to translate daily conversation into natural English and support learning.SOLUTION: A system according to an embodiment includes a recording unit, an analysis unit, a translation unit, and a providing unit. The recording unit automatically records a daily conversation. The analysis unit analyzes the conversation recorded by the recording unit and understands the context. The translation unit translates the conversation analyzed by the analysis unit into natural English. The providing unit provides the user with the English translated by the translation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to translate everyday conversations into natural English and learn from them.

[0005] The system according to the embodiment aims to translate everyday conversations into natural English and support learning. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, a translation unit, and a provision unit. The recording unit automatically records everyday conversations. The analysis unit analyzes the conversations recorded by the recording unit and understands the context. The translation unit translates the conversations analyzed by the analysis unit into natural English. The provision unit provides the English translated by the translation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can translate everyday conversations into natural English and assist in learning. [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) An English conversation learning system according to an embodiment of the present invention automatically records everyday conversations, analyzes them using a generation AI, translates them into natural English, and provides them to users. The English conversation learning system automatically records everyday conversations, analyzes them using a generation AI, understands the context, and translates them into natural English for users. For example, the English conversation learning system automatically records everyday conversations as users engage in them. Conversations in various situations, such as conversations with family and friends or business meetings, are covered. Recordings are made using a recording device. The English conversation learning system then sends the recorded conversation to a generation AI. The generation AI analyzes the recorded conversation and understands the context. For example, it analyzes the words and phrases used in the conversation, the speaker's intentions, and so on. This allows the generation AI to accurately grasp the content of the conversation. The English conversation learning system then uses the generation AI to translate the analyzed conversation into natural English. For example, if a conversation in Japanese is recorded, the conversation is translated into English. The generation AI generates natural English expressions while taking the context into account. This allows users to see how their conversation will be translated into English. The English conversation learning system then provides the translated English to the user. The user can check the translation results through a recording device. For example, the conversation content may be displayed as text or played back as audio. This allows the user to see how their conversation is translated into English and acquire natural English conversation skills. This allows the English conversation learning system to acquire natural English conversation through everyday conversations. The English conversation learning system automatically records the user's everyday conversations, analyzes them with a generative AI, and translates them into natural English for the user. For example, unlike English classes or English conversation books, learning through actual conversations can develop more practical English skills. Furthermore, because the generative AI understands context and generates natural English expressions, the user can learn accurate and natural English conversation. For example, by learning phrases and expressions used in business meetings and everyday conversations, users can acquire English skills that are useful in real situations.

[0029] The English conversation learning system according to the embodiment includes a recording unit, an analysis unit, a translation unit, and a provision unit. The recording unit automatically records everyday conversations. Daily conversations include, but are not limited to, conversations at home, at work, and with friends. The recording unit records the conversations using, for example, a recording device. The recording unit can also record conversations using a mobile device such as a smartphone or tablet. The recording unit can also record conversations using voice recognition technology. For example, the recording unit records conversations at home using a recording device and saves them as audio data. Conversations at work are recorded using a smartphone and saved as audio data. Conversations with friends are recorded using a tablet and saved as audio data. The analysis unit uses a generation AI to analyze the conversations recorded by the recording unit and understand the context. Understanding the context includes, for example, the context of the conversation, the speaker's intention, and the meaning of the words used, but is not limited to, for example. For example, the analysis unit uses a generation AI to analyze the words and phrases used in the conversation. The analysis unit can also use the generation AI to analyze the speaker's intention. The analysis unit can also use the generation AI to analyze the meaning of the words used. For example, the analysis unit uses the generation AI to analyze words and phrases used in the conversation and understand the context. The analysis unit analyzes the speaker's intention and understands the context. The analysis unit analyzes the meaning of the words used and understands the context. The translation unit uses the generation AI to translate the conversation analyzed by the analysis unit into natural English. Examples of natural English include, but are not limited to, grammatically correct expressions and expressions used by native speakers. For example, the translation unit can use the generation AI to generate natural English expressions based on the context. The translation unit can also use the generation AI to generate grammatically correct English expressions. The translation unit can also use the generation AI to generate English expressions used by native speakers. For example, the translation unit uses the generation AI to generate natural English expressions based on the context. The translation unit can generate grammatically correct English expressions. The translation unit can also use the generation AI to generate English expressions used by native speakers. The provision unit provides the English translated by the translation unit to the user.Methods of providing the translation result include, but are not limited to, displaying the translation result as text or playing it as audio. For example, the providing unit displays the translation result as text. The providing unit can also play the translation result as audio. The providing unit can also display the translation result as text and play it as audio. For example, the providing unit displays the translation result as text. Plays the translation result as audio. The translation result is displayed as text and played as audio. In this way, the English conversation learning system according to the embodiment can automatically record everyday conversations, have the generation AI analyze them, translate them into natural English, and provide them to the user. For example, the user can see how their own conversations are translated into English and acquire natural English conversation skills.

[0030] The recording unit can record conversations in a variety of situations, such as conversations with family and friends and business meetings. The recording unit, for example, records conversations with family and friends. For example, conversations at home are recorded using a recording device. The recording unit can also record business meetings. For example, conversations at work are recorded using a smartphone. The recording unit can also record conversations in public places. For example, conversations in a cafe are recorded using a tablet. This makes it possible to learn practical English conversation by recording conversations in a variety of situations. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input voice data acquired by the recording device into a generation AI and have the generation AI analyze the voice data.

[0031] The analysis unit can analyze words and phrases used in a conversation, the speaker's intention, etc. The analysis unit, for example, analyzes words and phrases used in a conversation. For example, it analyzes expressions commonly used in everyday conversation. The analysis unit can also analyze the speaker's intention. For example, it can analyze the speaker's intention using sentiment analysis. The analysis unit can also analyze the speaker's intention using context analysis. The analysis unit can also analyze the meaning of words used. For example, it can analyze technical terms. This makes it possible to accurately grasp the content of the conversation and generate natural English expressions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input words and phrases used in a conversation into a generation AI and have the generation AI analyze the words and phrases.

[0032] The translation unit can generate natural English expressions based on the context. The translation unit, for example, generates natural English expressions based on the context. For example, the translation unit generates English expressions taking into account the content of the preceding and following conversation. The translation unit can also generate grammatically correct English expressions. For example, it generates expressions used by native speakers. The translation unit can also generate English expressions taking into account the speaker's intention. For example, it generates English expressions that reflect emotions. In this way, by taking the context into consideration, more natural English expressions can be generated. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI. For example, in order to generate natural English expressions based on the context, the translation unit can input context information to the generation AI and cause the generation AI to generate English expressions.

[0033] The providing unit can have the function of displaying the translation result as text as well as playing it as audio. The providing unit, for example, displays the translation result as text. For example, it displays the translation result on a screen. The providing unit can also play the translation result as audio. For example, it uses speech synthesis technology to play the translation result as audio. The providing unit can also display the translation result as text and play it as audio. For example, it displays the translation result on a screen and plays it as audio at the same time. This allows the translation result to be provided as text and audio, allowing the user to check it in a variety of ways. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the translation result to a generation AI and have the generation AI perform the text display and audio playback processes.

[0034] The providing unit can have a feedback function and a learning history storage function. The providing unit, for example, has a feedback function. For example, the user can rate and comment on the translation result. The providing unit can also have a learning history storage function. For example, past translation results and learning progress are stored. This makes it possible to improve the user's learning effectiveness through the feedback function and learning history storage function. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback and learning history into the generation AI, and have the generation AI analyze the feedback and store the learning history.

[0035] The recording unit may have a function to automatically remove background noise during recording. For example, the recording unit may filter ambient noise in real time during recording to record clear audio. The recording unit may also analyze background noise after recording and apply noise reduction to make the audio clearer. The recording unit may also automatically detect and remove noise in a specific frequency band during recording. This allows for clear audio recording by removing background noise. Some or all of the above-described processing in the recording unit may be performed using, or without, AI, for example. For example, the recording unit may input audio data acquired during recording to a generation AI and have the generation AI perform noise removal processing.

[0036] The recording unit can adjust the quality of the recording based on the importance of the conversation during recording. For example, the recording unit applies high-quality recording settings for important conversations. The recording unit can also apply standard recording settings for everyday conversations. The recording unit can also automatically adjust the recording bit rate and sampling rate according to the content of the conversation. This allows important conversations to be recorded with high quality by adjusting the recording quality according to the importance of the conversation. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the importance of the conversation to a generation AI and have the generation AI adjust the recording quality.

[0037] During recording, the recording unit can analyze the characteristics of the speaker's voice and apply different recording settings to each speaker. For example, the recording unit can analyze the tone and pitch of the speaker's voice and apply optimal recording settings. The recording unit can also automatically adjust the recording gain according to the speaker's voice volume. The recording unit can also analyze the frequency characteristics of the speaker's voice and apply optimal equalization settings. This allows for recording with better sound quality by applying optimal recording settings to each speaker. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI, for example. For example, the recording unit can input the characteristics of the speaker's voice to a generation AI and have the generation AI adjust the recording settings.

[0038] The recording unit can customize recording settings based on the user's geographical location information when recording. For example, when the user is in a quiet location, the recording unit can apply standard recording settings. When the user is in a noisy location, the recording unit can also apply recording settings with enhanced noise reduction. When the user is moving, the recording unit can also apply settings that improve recording stability. This enables optimal recording by customizing the recording settings based on the geographical location information. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's geographical location information to the generation AI and have the generation AI customize the recording settings.

[0039] When recording, the recording unit can analyze the user's social media activity and prioritize recording related conversations. For example, the recording unit can prioritize recording conversations related to topics the user is talking about on social media. The recording unit can also analyze the content of the user's social media posts and prioritize recording related conversations. The recording unit can also prioritize recording conversations with the user's friends on social media. This allows for prioritizing recording of related conversations based on social media activity, so important conversations are not missed. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's social media activity data into a generation AI and have the generation AI prioritize related conversations.

[0040] The recording unit can customize the recording method by reflecting the user's past feedback during recording. For example, the recording unit can adjust the recording settings based on the user's feedback on the quality of conversations recorded in the past. The recording unit can also adjust the recording priority based on the user's feedback on the content of conversations recorded in the past. The recording unit can also adjust the start and end timing of recording based on the user's feedback on the length of conversations recorded in the past. This allows the user to be provided with an optimal recording method by reflecting past feedback. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's past feedback data into a generation AI and have the generation AI customize the recording method.

[0041] During analysis, the analysis unit can automatically recognize technical terms used in the conversation and provide appropriate translations. For example, the analysis unit can automatically detect technical terms in the conversation and translate them into appropriate English technical terms. The analysis unit can also automatically recognize abbreviations and slang in the conversation and translate them into appropriate English expressions. The analysis unit can also automatically recognize industry-specific terms in the conversation and translate them into appropriate English expressions. This enables accurate translation by automatically recognizing technical terms and providing appropriate translations. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input technical terms in the conversation into a generation AI and have the generation AI recognize and translate the technical terms.

[0042] During analysis, the analysis unit can understand the flow of the conversation and take into account the context before and after the conversation to perform the analysis. The analysis unit, for example, takes into account the context before and after the conversation to perform an appropriate translation. The analysis unit can also understand the flow of the conversation and generate natural English expressions. The analysis unit can also take into account the intention of the speaker in the conversation to perform an appropriate translation. This enables a more natural translation by understanding the flow of the conversation and taking into account the context before and after the conversation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input context information before and after the conversation into the generation AI and have the generation AI perform an analysis taking the context into account.

[0043] The analysis unit can estimate the speaker's intention during analysis and adjust the analysis result based on the intention. For example, the analysis unit can estimate the speaker's intention and perform an appropriate translation. The analysis unit can also adjust the nuance of the translation based on the speaker's intention. The analysis unit can also generate an appropriate English expression taking the speaker's intention into consideration. This enables more appropriate translation by taking the speaker's intention into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can input the speaker's intention into the generation AI and have the generation AI perform analysis based on the intention.

[0044] The analysis unit can perform analysis based on the geographical background of the conversation during analysis. For example, the analysis unit can automatically recognize place names and locations mentioned in the conversation and perform an appropriate translation. The analysis unit can also generate appropriate English expressions taking the geographical background of the conversation into account. The analysis unit can also understand the geographical context in the conversation and perform an appropriate translation. This enables more appropriate translation by taking the geographical background into account. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI, for example. For example, the analysis unit can input geographical background information of the conversation into the generation AI and cause the generation AI to perform an analysis taking the geographical background into account.

[0045] The analysis unit can improve the accuracy of the analysis by referring to related literature and materials during analysis. For example, the analysis unit refers to related literature and materials for technical terms and phrases mentioned in a conversation and performs an appropriate translation. The analysis unit can also refer to related literature and materials to understand the speaker's intention in the conversation. The analysis unit can also refer to related literature and materials to understand the context in the conversation. By referring to related literature and materials, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input related literature and materials into the generation AI and cause the generation AI to perform analysis referring to the literature and materials.

[0046] The analysis unit can perform analysis based on the market value of the conversation during analysis. The analysis unit, for example, takes into account the market value of products or services mentioned in the conversation and performs an appropriate translation. The analysis unit can also understand the business context in the conversation and generate appropriate English expressions. The analysis unit can also take into account the market value in the conversation and perform an appropriate translation. This enables translations that are useful in business by taking market value into account. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input market value information of the conversation into the generation AI and cause the generation AI to perform an analysis that takes market value into account.

[0047] The translation unit can adjust the level of detail of the translation based on the importance of the conversation during translation. For example, the translation unit provides a detailed translation for an important conversation. The translation unit can also provide a concise translation for an everyday conversation. The translation unit can also automatically adjust the level of detail of the translation according to the content of the conversation. This allows important conversations to be translated in detail by adjusting the level of detail of the translation according to the importance of the conversation. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input importance information of the conversation into the generation AI and cause the generation AI to adjust the level of detail of the translation based on the importance.

[0048] The translation unit can apply different translation algorithms depending on the category of the conversation during translation. For example, in the case of a business conversation, the translation unit applies a translation algorithm that emphasizes technical terminology. In addition, in the case of an everyday conversation, the translation unit can also apply a translation algorithm that emphasizes natural expressions. In addition, in the case of a technical conversation, the translation unit can also apply a translation algorithm that emphasizes accurate terminology. In this way, applying a translation algorithm according to the category of the conversation enables more appropriate translation. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input conversation category information into the generation AI and cause the generation AI to apply a translation algorithm according to the category.

[0049] The translation unit can improve the accuracy of translation by referring to the user's past translation results during translation. The translation unit performs appropriate translation based on, for example, the user's past translation results. The translation unit can also analyze the user's past translation history to improve the accuracy of translation. The translation unit can also generate natural English expressions by referring to the user's past translation results. In this way, by referring to the past translation results, the accuracy of translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input the user's past translation results into the generation AI and have the generation AI perform translation by referring to the past translation results.

[0050] The translation unit can determine the priority of translation based on the submission time of the conversation during translation. For example, the translation unit gives top priority to translation in the case of an urgent conversation. The translation unit can also translate regularly scheduled conversations at normal priority. The translation unit can also adjust the priority of translation based on a deadline specified by the user. In this way, by determining the priority of translation based on the submission time, urgent conversations can be translated preferentially. Some or all of the above-described processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the translation unit can input information about the submission time of the conversation into the generation AI and have the generation AI determine the priority of translation based on the submission time.

[0051] The translation unit can adjust the order of translation based on the relevance of the conversations during translation. For example, the translation unit prioritizes translation of highly relevant conversations. The translation unit can also postpone translation of less relevant conversations. The translation unit can also automatically adjust the order of translation according to the content of the conversation. In this way, by adjusting the order of translation based on the relevance of the conversations, highly relevant conversations can be translated with priority. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input relevance information of the conversations into the generation AI and cause the generation AI to adjust the order of translation based on the relevance.

[0052] During translation, the translation unit can adjust the use of technical terms in the translation according to the user's level of expertise. For example, if the user has technical expertise, the translation unit can provide a translation that makes extensive use of technical terms. Furthermore, if the user has general knowledge, the translation unit can also provide a concise and easy-to-understand translation. The translation unit can also automatically adjust the use of technical terms in the translation according to the user's level of expertise. This allows for more appropriate translation by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input the user's level of expertise information into the generation AI and have the generation AI adjust the translation based on the level of expertise.

[0053] When providing the display method, the providing unit can select an appropriate display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also analyze the user's past operation history and suggest an optimal display method. The providing unit can also customize the display method based on the user's past operation history. In this way, the optimal display method can be provided to the user by referring to the past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past operation history data to the generation AI and cause the generation AI to select a display method based on the operation history.

[0054] The providing unit can customize the display content according to the user's current task when providing the display content. For example, when the user is at work, the providing unit can provide a translation result for business use. Furthermore, when the user is relaxed, the providing unit can also provide a translation result for everyday conversation. Furthermore, the providing unit can automatically customize the display content according to the user's current task. In this way, by customizing the display content according to the current task, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task information to the generation AI and cause the generation AI to customize the display content according to the task.

[0055] The providing unit can improve the provision method by reflecting user feedback when providing the translation result. For example, when a user provides feedback on the provided translation result, the providing unit improves the provision method based on the feedback. The providing unit can also analyze the user feedback and optimize the provision method. The providing unit can also customize the provision method by reflecting the user feedback. In this way, the provision method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the provision method based on the feedback.

[0056] The providing unit can select an appropriate display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This improves user convenience by providing an optimal display method based on the device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generation AI and cause the generation AI to select a display method based on the device information.

[0057] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit, for example, automatically sets the language of the translation result based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the translation result in a specific language when the user selects that language. This improves user convenience by providing a multilingual display based on the language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting information to the generation AI and cause the generation AI to perform multilingual support of the display content based on the language setting.

[0058] At the time of providing, the providing unit can analyze the user's social media activity and provide relevant translation results. The providing unit can provide, for example, translation results related to places the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide relevant translation results. The providing unit can also provide relevant translation results by referring to the activities of the user's friends on social media. This improves user convenience by providing relevant translation results based on social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide relevant translation results.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can analyze the frequency of words and phrases used in conversations and infer a user's interests. For example, if a user frequently talks about a particular topic, the analysis unit will prioritize analyzing information related to that topic. Also, if a user shows interest in a new topic, the analysis unit can quickly analyze information related to that topic. Furthermore, if a user repeatedly uses a particular phrase, the analysis unit can gain a deeper understanding of the meaning of that phrase and provide more appropriate translations. This allows for improved analysis accuracy based on the user's interests.

[0061] The provider can track the user's learning progress and provide an individualized learning plan. For example, if the user is taking a long time to master a particular phrase, the provider can provide additional practice questions related to that phrase. Also, if the user shows interest in a particular topic, the provider can prioritize the provision of learning content related to that topic. Furthermore, based on the user's learning progress, the provider can regularly update the learning plan to provide an optimal learning experience. This can maximize the user's learning effectiveness.

[0062] The providing unit can analyze the user's learning history and provide personalized feedback. For example, if the user cannot pronounce a particular phrase correctly, the providing unit can suggest pronunciation practice for that phrase. Also, if the user does not understand a particular grammar rule, the providing unit can provide additional learning content related to that rule. Furthermore, based on the user's learning history, the providing unit can evaluate the user's learning progress and suggest next steps. This can improve the user's learning effectiveness.

[0063] The analyzer can analyze the cultural context of words and phrases used in a conversation. For example, it can analyze how a particular word or phrase is used in a particular culture. The analyzer can also understand the cultural nuances in a conversation and provide an appropriate translation. Furthermore, the analyzer can adjust the translation to take into account differences in word usage between different cultures. This allows the analyzer to provide a more appropriate translation by taking cultural context into account.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The recording unit automatically records everyday conversations. Daily conversations include conversations at home, at work, and with friends. The recording unit can record conversations using a recording device, a mobile device such as a smartphone or a tablet, or it can record conversations using voice recognition technology. Step 2: The analysis unit uses the generative AI to analyze the conversation recorded by the recording unit and understand the context. Understanding the context includes the content of the conversation before and after, the speaker's intention, and the meaning of the words used. The analysis unit analyzes the words and phrases used in the conversation, the speaker's intention, and the meaning of the words used. Step 3: The translation unit uses generation AI to translate the conversation analyzed by the analysis unit into natural English. Natural English includes grammatically correct expressions and expressions used by native speakers. The translation unit generates natural English expressions based on the context, generating grammatically correct English expressions and English expressions used by native speakers. Step 4: The providing unit provides the English translation by the translation unit to the user. Methods of providing include displaying the translation as text and playing it aloud. The providing unit can display the translation result as text and play it aloud.

[0066] (Example 2) An English conversation learning system according to an embodiment of the present invention automatically records everyday conversations, analyzes them using a generation AI, translates them into natural English, and provides them to users. The English conversation learning system automatically records everyday conversations, analyzes them using a generation AI, understands the context, and translates them into natural English for users. For example, the English conversation learning system automatically records everyday conversations as users engage in them. Conversations in various situations, such as conversations with family and friends or business meetings, are covered. Recordings are made using a recording device. The English conversation learning system then sends the recorded conversation to a generation AI. The generation AI analyzes the recorded conversation and understands the context. For example, it analyzes the words and phrases used in the conversation, the speaker's intentions, and so on. This allows the generation AI to accurately grasp the content of the conversation. The English conversation learning system then uses the generation AI to translate the analyzed conversation into natural English. For example, if a conversation in Japanese is recorded, the conversation is translated into English. The generation AI generates natural English expressions while taking the context into account. This allows users to see how their conversation will be translated into English. The English conversation learning system then provides the translated English to the user. The user can check the translation results through a recording device. For example, the conversation content may be displayed as text or played back as audio. This allows the user to see how their conversation is translated into English and acquire natural English conversation skills. This allows the English conversation learning system to acquire natural English conversation through everyday conversations. The English conversation learning system automatically records the user's everyday conversations, analyzes them with a generative AI, and translates them into natural English for the user. For example, unlike English classes or English conversation books, learning through actual conversations can develop more practical English skills. Furthermore, because the generative AI understands context and generates natural English expressions, the user can learn accurate and natural English conversation. For example, by learning phrases and expressions used in business meetings and everyday conversations, users can acquire English skills that are useful in real situations.

[0067] The English conversation learning system according to the embodiment includes a recording unit, an analysis unit, a translation unit, and a provision unit. The recording unit automatically records everyday conversations. Daily conversations include, but are not limited to, conversations at home, at work, and with friends. The recording unit records the conversations using, for example, a recording device. The recording unit can also record conversations using a mobile device such as a smartphone or tablet. The recording unit can also record conversations using voice recognition technology. For example, the recording unit records conversations at home using a recording device and saves them as audio data. Conversations at work are recorded using a smartphone and saved as audio data. Conversations with friends are recorded using a tablet and saved as audio data. The analysis unit uses a generation AI to analyze the conversations recorded by the recording unit and understand the context. Understanding the context includes, for example, the context of the conversation, the speaker's intention, and the meaning of the words used, but is not limited to, for example. For example, the analysis unit uses a generation AI to analyze the words and phrases used in the conversation. The analysis unit can also use the generation AI to analyze the speaker's intention. The analysis unit can also use the generation AI to analyze the meaning of the words used. For example, the analysis unit uses the generation AI to analyze words and phrases used in the conversation and understand the context. The analysis unit analyzes the speaker's intention and understands the context. The analysis unit analyzes the meaning of the words used and understands the context. The translation unit uses the generation AI to translate the conversation analyzed by the analysis unit into natural English. Examples of natural English include, but are not limited to, grammatically correct expressions and expressions used by native speakers. For example, the translation unit can use the generation AI to generate natural English expressions based on the context. The translation unit can also use the generation AI to generate grammatically correct English expressions. The translation unit can also use the generation AI to generate English expressions used by native speakers. For example, the translation unit uses the generation AI to generate natural English expressions based on the context. The translation unit can generate grammatically correct English expressions. The translation unit can also use the generation AI to generate English expressions used by native speakers. The provision unit provides the English translated by the translation unit to the user.Methods of providing the translation result include, but are not limited to, displaying the translation result as text or playing it as audio. For example, the providing unit displays the translation result as text. The providing unit can also play the translation result as audio. The providing unit can also display the translation result as text and play it as audio. For example, the providing unit displays the translation result as text. Plays the translation result as audio. The translation result is displayed as text and played as audio. In this way, the English conversation learning system according to the embodiment can automatically record everyday conversations, have the generation AI analyze them, translate them into natural English, and provide them to the user. For example, the user can see how their own conversations are translated into English and acquire natural English conversation skills.

[0068] The recording unit can record conversations in a variety of situations, such as conversations with family and friends and business meetings. The recording unit, for example, records conversations with family and friends. For example, conversations at home are recorded using a recording device. The recording unit can also record business meetings. For example, conversations at work are recorded using a smartphone. The recording unit can also record conversations in public places. For example, conversations in a cafe are recorded using a tablet. This makes it possible to learn practical English conversation by recording conversations in a variety of situations. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input voice data acquired by the recording device into a generation AI and have the generation AI analyze the voice data.

[0069] The analysis unit can analyze words and phrases used in a conversation, the speaker's intention, etc. The analysis unit, for example, analyzes words and phrases used in a conversation. For example, it analyzes expressions commonly used in everyday conversation. The analysis unit can also analyze the speaker's intention. For example, it can analyze the speaker's intention using sentiment analysis. The analysis unit can also analyze the speaker's intention using context analysis. The analysis unit can also analyze the meaning of words used. For example, it can analyze technical terms. This makes it possible to accurately grasp the content of the conversation and generate natural English expressions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input words and phrases used in a conversation into a generation AI and have the generation AI analyze the words and phrases.

[0070] The translation unit can generate natural English expressions based on the context. The translation unit, for example, generates natural English expressions based on the context. For example, the translation unit generates English expressions taking into account the content of the preceding and following conversation. The translation unit can also generate grammatically correct English expressions. For example, it generates expressions used by native speakers. The translation unit can also generate English expressions taking into account the speaker's intention. For example, it generates English expressions that reflect emotions. In this way, by taking the context into consideration, more natural English expressions can be generated. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI. For example, in order to generate natural English expressions based on the context, the translation unit can input context information to the generation AI and cause the generation AI to generate English expressions.

[0071] The providing unit can have the function of displaying the translation result as text as well as playing it as audio. The providing unit, for example, displays the translation result as text. For example, it displays the translation result on a screen. The providing unit can also play the translation result as audio. For example, it uses speech synthesis technology to play the translation result as audio. The providing unit can also display the translation result as text and play it as audio. For example, it displays the translation result on a screen and plays it as audio at the same time. This allows the translation result to be provided as text and audio, allowing the user to check it in a variety of ways. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the translation result to a generation AI and have the generation AI perform the text display and audio playback processes.

[0072] The providing unit can have a feedback function and a learning history storage function. The providing unit, for example, has a feedback function. For example, the user can rate and comment on the translation result. The providing unit can also have a learning history storage function. For example, past translation results and learning progress are stored. This makes it possible to improve the user's learning effectiveness through the feedback function and learning history storage function. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback and learning history into the generation AI, and have the generation AI analyze the feedback and store the learning history.

[0073] The recording unit can estimate the user's emotions and adjust the start timing of recording based on the estimated user emotions. For example, if the user is relaxed, the recording unit can start recording to maintain a natural flow of conversation. Furthermore, if the user is nervous, the recording unit can start recording when the conversation has calmed down. Furthermore, if the user is excited, the recording unit can start recording immediately to avoid missing important points. This allows for natural conversation recording by adjusting the start timing of recording according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or without AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0074] The recording unit may have a function to automatically remove background noise during recording. For example, the recording unit may filter ambient noise in real time during recording to record clear audio. The recording unit may also analyze background noise after recording and apply noise reduction to make the audio clearer. The recording unit may also automatically detect and remove noise in a specific frequency band during recording. This allows for clear audio recording by removing background noise. Some or all of the above-described processing in the recording unit may be performed using, or without, AI, for example. For example, the recording unit may input audio data acquired during recording to a generation AI and have the generation AI perform noise removal processing.

[0075] The recording unit can adjust the quality of the recording based on the importance of the conversation during recording. For example, the recording unit applies high-quality recording settings for important conversations. The recording unit can also apply standard recording settings for everyday conversations. The recording unit can also automatically adjust the recording bit rate and sampling rate according to the content of the conversation. This allows important conversations to be recorded with high quality by adjusting the recording quality according to the importance of the conversation. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the importance of the conversation to a generation AI and have the generation AI adjust the recording quality.

[0076] During recording, the recording unit can analyze the characteristics of the speaker's voice and apply different recording settings to each speaker. For example, the recording unit can analyze the tone and pitch of the speaker's voice and apply optimal recording settings. The recording unit can also automatically adjust the recording gain according to the speaker's voice volume. The recording unit can also analyze the frequency characteristics of the speaker's voice and apply optimal equalization settings. This allows for recording with better sound quality by applying optimal recording settings to each speaker. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI, for example. For example, the recording unit can input the characteristics of the speaker's voice to a generation AI and have the generation AI adjust the recording settings.

[0077] The recording unit can estimate the user's emotions and determine the priority of conversations to be recorded based on the estimated user emotions. For example, when the user is relaxed, the recording unit can prioritize recording everyday conversations. Furthermore, when the user is nervous, the recording unit can prioritize recording important conversations. Furthermore, when the user is excited, the recording unit can prioritize recording emotionally charged conversations. In this way, by determining the priority of conversations to be recorded according to the user's emotions, important conversations can be prioritized for recording. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or without AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] The recording unit can customize recording settings based on the user's geographical location information when recording. For example, when the user is in a quiet location, the recording unit can apply standard recording settings. When the user is in a noisy location, the recording unit can also apply recording settings with enhanced noise reduction. When the user is moving, the recording unit can also apply settings that improve recording stability. This enables optimal recording by customizing the recording settings based on the geographical location information. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's geographical location information to the generation AI and have the generation AI customize the recording settings.

[0079] When recording, the recording unit can analyze the user's social media activity and prioritize recording related conversations. For example, the recording unit can prioritize recording conversations related to topics the user is talking about on social media. The recording unit can also analyze the content of the user's social media posts and prioritize recording related conversations. The recording unit can also prioritize recording conversations with the user's friends on social media. This allows for prioritizing recording of related conversations based on social media activity, so important conversations are not missed. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's social media activity data into a generation AI and have the generation AI prioritize related conversations.

[0080] The recording unit can customize the recording method by reflecting the user's past feedback during recording. For example, the recording unit can adjust the recording settings based on the user's feedback on the quality of conversations recorded in the past. The recording unit can also adjust the recording priority based on the user's feedback on the content of conversations recorded in the past. The recording unit can also adjust the start and end timing of recording based on the user's feedback on the length of conversations recorded in the past. This allows the user to be provided with an optimal recording method by reflecting past feedback. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's past feedback data into a generation AI and have the generation AI customize the recording method.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a quick analysis when the user is nervous. The analysis unit can also perform an analysis that emphasizes emotional changes when the user is excited. This enables more accurate analysis by adjusting the accuracy of the analysis according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the accuracy of the analysis.

[0082] During analysis, the analysis unit can automatically recognize technical terms used in the conversation and provide appropriate translations. For example, the analysis unit can automatically detect technical terms in the conversation and translate them into appropriate English technical terms. The analysis unit can also automatically recognize abbreviations and slang in the conversation and translate them into appropriate English expressions. The analysis unit can also automatically recognize industry-specific terms in the conversation and translate them into appropriate English expressions. This enables accurate translation by automatically recognizing technical terms and providing appropriate translations. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input technical terms in the conversation into a generation AI and have the generation AI recognize and translate the technical terms.

[0083] During analysis, the analysis unit can understand the flow of the conversation and take into account the context before and after the conversation to perform the analysis. The analysis unit, for example, takes into account the context before and after the conversation to perform an appropriate translation. The analysis unit can also understand the flow of the conversation and generate natural English expressions. The analysis unit can also take into account the intention of the speaker in the conversation to perform an appropriate translation. This enables a more natural translation by understanding the flow of the conversation and taking into account the context before and after the conversation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input context information before and after the conversation into the generation AI and have the generation AI perform an analysis taking the context into account.

[0084] The analysis unit can estimate the speaker's intention during analysis and adjust the analysis result based on the intention. For example, the analysis unit can estimate the speaker's intention and perform an appropriate translation. The analysis unit can also adjust the nuance of the translation based on the speaker's intention. The analysis unit can also generate an appropriate English expression taking the speaker's intention into consideration. This enables more appropriate translation by taking the speaker's intention into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can input the speaker's intention into the generation AI and have the generation AI perform analysis based on the intention.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is nervous, the analysis unit can also display concise analysis results. If the user is excited, the analysis unit can also display analysis results that are visually easy to understand. This allows for adjusting the display method of the analysis results according to the user's emotions, making the display easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0086] The analysis unit can perform analysis based on the geographical background of the conversation during analysis. For example, the analysis unit can automatically recognize place names and locations mentioned in the conversation and perform an appropriate translation. The analysis unit can also generate appropriate English expressions taking the geographical background of the conversation into account. The analysis unit can also understand the geographical context in the conversation and perform an appropriate translation. This enables more appropriate translation by taking the geographical background into account. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI, for example. For example, the analysis unit can input geographical background information of the conversation into the generation AI and cause the generation AI to perform an analysis taking the geographical background into account.

[0087] The analysis unit can improve the accuracy of the analysis by referring to related literature and materials during analysis. For example, the analysis unit refers to related literature and materials for technical terms and phrases mentioned in a conversation and performs an appropriate translation. The analysis unit can also refer to related literature and materials to understand the speaker's intention in the conversation. The analysis unit can also refer to related literature and materials to understand the context in the conversation. By referring to related literature and materials, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input related literature and materials into the generation AI and cause the generation AI to perform analysis referring to the literature and materials.

[0088] The analysis unit can perform analysis based on the market value of the conversation during analysis. The analysis unit, for example, takes into account the market value of products or services mentioned in the conversation and performs an appropriate translation. The analysis unit can also understand the business context in the conversation and generate appropriate English expressions. The analysis unit can also take into account the market value in the conversation and perform an appropriate translation. This enables translations that are useful in business by taking market value into account. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input market value information of the conversation into the generation AI and cause the generation AI to perform an analysis that takes market value into account.

[0089] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is relaxed, the translation unit can use natural and soft expressions. If the user is nervous, the translation unit can use concise and clear expressions. If the user is excited, the translation unit can use expressions that emphasize the emotion. This allows for more natural translation by adjusting the translation expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression based on the emotion.

[0090] The translation unit can adjust the level of detail of the translation based on the importance of the conversation during translation. For example, the translation unit provides a detailed translation for an important conversation. The translation unit can also provide a concise translation for an everyday conversation. The translation unit can also automatically adjust the level of detail of the translation according to the content of the conversation. This allows important conversations to be translated in detail by adjusting the level of detail of the translation according to the importance of the conversation. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input importance information of the conversation into the generation AI and cause the generation AI to adjust the level of detail of the translation based on the importance.

[0091] The translation unit can apply different translation algorithms depending on the category of the conversation during translation. For example, in the case of a business conversation, the translation unit applies a translation algorithm that emphasizes technical terminology. In addition, in the case of an everyday conversation, the translation unit can also apply a translation algorithm that emphasizes natural expressions. In addition, in the case of a technical conversation, the translation unit can also apply a translation algorithm that emphasizes accurate terminology. In this way, applying a translation algorithm according to the category of the conversation enables more appropriate translation. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input conversation category information into the generation AI and cause the generation AI to apply a translation algorithm according to the category.

[0092] The translation unit can improve the accuracy of translation by referring to the user's past translation results during translation. The translation unit performs appropriate translation based on, for example, the user's past translation results. The translation unit can also analyze the user's past translation history to improve the accuracy of translation. The translation unit can also generate natural English expressions by referring to the user's past translation results. In this way, by referring to the past translation results, the accuracy of translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input the user's past translation results into the generation AI and have the generation AI perform translation by referring to the past translation results.

[0093] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. For example, the translation unit can provide a detailed translation when the user is relaxed. The translation unit can also provide a concise translation when the user is nervous. The translation unit can also provide a translation that emphasizes the user's emotions when the user is excited. This allows for more appropriate translation by adjusting the length of the translation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the translation based on the emotion.

[0094] The translation unit can determine the priority of translation based on the submission time of the conversation during translation. For example, the translation unit gives top priority to translation in the case of an urgent conversation. The translation unit can also translate regularly scheduled conversations at normal priority. The translation unit can also adjust the priority of translation based on a deadline specified by the user. In this way, by determining the priority of translation based on the submission time, urgent conversations can be translated preferentially. Some or all of the above-described processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the translation unit can input information about the submission time of the conversation into the generation AI and have the generation AI determine the priority of translation based on the submission time.

[0095] The translation unit can adjust the order of translation based on the relevance of the conversations during translation. For example, the translation unit prioritizes translation of highly relevant conversations. The translation unit can also postpone translation of less relevant conversations. The translation unit can also automatically adjust the order of translation according to the content of the conversation. In this way, by adjusting the order of translation based on the relevance of the conversations, highly relevant conversations can be translated with priority. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input relevance information of the conversations into the generation AI and cause the generation AI to adjust the order of translation based on the relevance.

[0096] During translation, the translation unit can adjust the use of technical terms in the translation according to the user's level of expertise. For example, if the user has technical expertise, the translation unit can provide a translation that makes extensive use of technical terms. Furthermore, if the user has general knowledge, the translation unit can also provide a concise and easy-to-understand translation. The translation unit can also automatically adjust the use of technical terms in the translation according to the user's level of expertise. This allows for more appropriate translation by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input the user's level of expertise information into the generation AI and have the generation AI adjust the translation based on the level of expertise.

[0097] The providing unit can estimate the user's emotions and adjust the display method of the translation result to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can display a detailed translation result. Furthermore, when the user is nervous, the providing unit can display a concise translation result. Furthermore, when the user is excited, the providing unit can display a visually easy-to-understand translation result. This allows for a more easily understandable display by adjusting the display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0098] When providing the display method, the providing unit can select an appropriate display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also analyze the user's past operation history and suggest an optimal display method. The providing unit can also customize the display method based on the user's past operation history. In this way, the optimal display method can be provided to the user by referring to the past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past operation history data to the generation AI and cause the generation AI to select a display method based on the operation history.

[0099] The providing unit can customize the display content according to the user's current task when providing the display content. For example, when the user is at work, the providing unit can provide a translation result for business use. Furthermore, when the user is relaxed, the providing unit can also provide a translation result for everyday conversation. Furthermore, the providing unit can automatically customize the display content according to the user's current task. In this way, by customizing the display content according to the current task, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task information to the generation AI and cause the generation AI to customize the display content according to the task.

[0100] The providing unit can improve the provision method by reflecting user feedback when providing the translation result. For example, when a user provides feedback on the provided translation result, the providing unit improves the provision method based on the feedback. The providing unit can also analyze the user feedback and optimize the provision method. The providing unit can also customize the provision method by reflecting the user feedback. In this way, the provision method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the provision method based on the feedback.

[0101] The providing unit can estimate the user's emotions and determine the priority of translation results to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can prioritize providing detailed translation results. Furthermore, if the user is nervous, the providing unit can prioritize providing concise translation results. Furthermore, if the user is excited, the providing unit can prioritize providing visually easy-to-understand translation results. This allows important information to be provided preferentially by determining the priority of translation results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of translation results based on the emotion.

[0102] The providing unit can select an appropriate display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This improves user convenience by providing an optimal display method based on the device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generation AI and cause the generation AI to select a display method based on the device information.

[0103] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit, for example, automatically sets the language of the translation result based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the translation result in a specific language when the user selects that language. This improves user convenience by providing a multilingual display based on the language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting information to the generation AI and cause the generation AI to perform multilingual support of the display content based on the language setting.

[0104] At the time of providing, the providing unit can analyze the user's social media activity and provide relevant translation results. The providing unit can provide, for example, translation results related to places the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide relevant translation results. The providing unit can also provide relevant translation results by referring to the activities of the user's friends on social media. This improves user convenience by providing relevant translation results based on social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide relevant translation results. === Hard Collateral 1-1 === Each of the multiple elements including the recording unit, analysis unit, translation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recording unit records everyday conversations using the microphone 38B of the smart device 14 and saves them as audio data. The analysis unit analyzes the recorded conversations using a generative AI and understands the context using the specific processing unit 290 of the data processing device 12. The translation unit translates the analyzed conversations into natural English using the specific processing unit 290 of the data processing device 12. The provision unit displays the translation results as text or plays them as audio using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the recording unit, analysis unit, translation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recording unit records everyday conversations using the microphone 238 of the smart glasses 214 and saves them as audio data. The analysis unit analyzes the recorded conversations using a generative AI and understands the context using the specific processing unit 290 of the data processing device 12. The translation unit translates the analyzed conversations into natural English using the specific processing unit 290 of the data processing device 12. The provision unit plays back the translation results aloud using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the recording unit, analysis unit, translation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the recording unit records everyday conversations using the microphone 238 of the headset-type terminal 314 and saves them as audio data. The analysis unit analyzes the recorded conversations using a generative AI and understands the context using the specific processing unit 290 of the data processing device 12. The translation unit translates the analyzed conversations into natural English using the specific processing unit 290 of the data processing device 12. The provision unit displays the translation results as text or plays them as audio using the display 343 or speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the recording unit, analysis unit, translation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recording unit records everyday conversations using the microphone 238 of the robot 414 and saves them as audio data. The analysis unit analyzes the recorded conversations using a generative AI and understands the context using the specific processing unit 290 of the data processing device 12. The translation unit translates the analyzed conversations into natural English using the specific processing unit 290 of the data processing device 12. The provision unit plays back the translation results aloud using the speaker 240 of the robot 414.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The recording unit can analyze the tone and pitch of the user's voice to detect changes in emotion. For example, if the user is excited, the recording unit can detect this heightened emotion and prioritize recording important conversations. If the user is calm, the recording unit can apply normal recording settings. Furthermore, if the user is sad, the recording unit can adjust the start timing of recording to take into account changes in emotion. This allows for more natural conversations to be recorded by optimizing the recording settings according to the user's emotions.

[0107] The analysis unit can analyze the frequency of words and phrases used in conversations and infer a user's interests. For example, if a user frequently talks about a particular topic, the analysis unit will prioritize analyzing information related to that topic. Also, if a user shows interest in a new topic, the analysis unit can quickly analyze information related to that topic. Furthermore, if a user repeatedly uses a particular phrase, the analysis unit can gain a deeper understanding of the meaning of that phrase and provide more appropriate translations. This allows for improved analysis accuracy based on the user's interests.

[0108] The translation 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 relaxed, the translation unit can generate English expressions with a soft tone. If the user is nervous, the translation unit can generate English expressions with a concise and clear tone. Furthermore, if the user is excited, the translation unit can generate English expressions with a tone that emphasizes the emotion. In this way, by adjusting the tone of the translation according to the user's emotions, more natural English conversation can be provided.

[0109] The provider can track the user's learning progress and provide an individualized learning plan. For example, if the user is taking a long time to master a particular phrase, the provider can provide additional practice questions related to that phrase. Also, if the user shows interest in a particular topic, the provider can prioritize the provision of learning content related to that topic. Furthermore, based on the user's learning progress, the provider can regularly update the learning plan to provide an optimal learning experience. This can maximize the user's learning effectiveness.

[0110] The recording unit can estimate the user's emotions and adjust the recording quality based on the estimated emotions. For example, if the user is relaxed, the recording unit can apply high-quality recording settings. If the user is nervous, the recording unit can apply standard recording settings. Furthermore, if the user is excited, the recording unit can adjust the recording bit rate and sampling rate taking into account the user's heightened emotions. This allows important conversations to be recorded with high quality by optimizing the recording quality according to the user's emotions.

[0111] The analyzer can analyze the emotional nuances of words and phrases used in a conversation. For example, if a user has a positive emotion, the analyzer can provide a translation that reflects that emotion. If a user has a negative emotion, the analyzer can adjust the tone of the translation to take that emotion into account. Furthermore, if the user is confused, the analyzer can provide a concise and clear translation. This allows the system to provide a more appropriate translation by adjusting the nuances of the translation according to the user's emotion.

[0112] The providing unit can analyze the user's learning history and provide personalized feedback. For example, if the user cannot pronounce a particular phrase correctly, the providing unit can suggest pronunciation practice for that phrase. Also, if the user does not understand a particular grammar rule, the providing unit can provide additional learning content related to that rule. Furthermore, based on the user's learning history, the providing unit can evaluate the user's learning progress and suggest next steps. This can improve the user's learning effectiveness.

[0113] The recording unit can estimate the user's emotions and adjust the timing to start recording based on the estimated emotions. For example, if the user is relaxed, recording can be started to maintain a natural flow of conversation. If the user is nervous, recording can be started when the conversation has calmed down. If the user is excited, recording can be started immediately to avoid missing important points. In this way, natural conversations can be recorded by adjusting the timing to start recording according to the user's emotions.

[0114] The analyzer can analyze the cultural context of words and phrases used in a conversation. For example, it can analyze how a particular word or phrase is used in a particular culture. The analyzer can also understand the cultural nuances in a conversation and provide an appropriate translation. Furthermore, the analyzer can adjust the translation to take into account differences in word usage between different cultures. This allows the analyzer to provide a more appropriate translation by taking cultural context into account.

[0115] The providing unit can estimate the user's emotions and adjust the display method of the translation results provided based on the estimated emotions. For example, if the user is relaxed, a detailed translation result can be displayed. If the user is nervous, a concise translation result can be displayed. If the user is excited, a visually easy-to-understand translation result can be displayed. In this way, by adjusting the display method according to the user's emotions, a more understandable display can be achieved.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The recording unit automatically records everyday conversations. Daily conversations include conversations at home, at work, and with friends. The recording unit can record conversations using a recording device, a mobile device such as a smartphone or a tablet, or it can record conversations using voice recognition technology. Step 2: The analysis unit uses the generative AI to analyze the conversation recorded by the recording unit and understand the context. Understanding the context includes the content of the conversation before and after, the speaker's intention, and the meaning of the words used. The analysis unit analyzes the words and phrases used in the conversation, the speaker's intention, and the meaning of the words used. Step 3: The translation unit uses generation AI to translate the conversation analyzed by the analysis unit into natural English. Natural English includes grammatically correct expressions and expressions used by native speakers. The translation unit generates natural English expressions based on the context, generating grammatically correct English expressions and English expressions used by native speakers. Step 4: The providing unit provides the English translation by the translation unit to the user. Methods of providing include displaying the translation as text and playing it aloud. The providing unit can display the translation result as text and play it aloud.

[0118] 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.

[0119] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[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 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.

[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 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.

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

[0152] 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 recording section that automatically records everyday conversations, an analysis unit that analyzes the conversation recorded by the recording unit and understands the context; a translation unit that translates the conversation analyzed by the analysis unit into natural English; a providing unit that provides the English translated by the translation unit to a user. A system characterized by:

2. The recording unit Record conversations in multiple situations, such as conversations with family and friends, or business meetings 2. The system of claim 1.

3. The analysis unit Analyze the words and phrases used in conversations, as well as the speaker's intentions 2. The system of claim 1.

4. The translation unit Generate natural English expressions based on context 2. The system of claim 1.

5. The providing unit In addition to displaying the translation results as text, it also has the ability to play them back as audio.

2. The system of claim 1.

6. The providing unit Equipped with feedback function and learning history saving function 2. The system of claim 1.

7. The recording unit Estimate the user's emotion and adjust the start timing of recording based on the estimated user emotion.

2. The system of claim 1.

8. The recording unit Automatically removes background noise when recording 2. The system of claim 1.

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