System

Abstract

An object of a system according to an embodiment is to convert different languages, dialects, and accents into a standard language to realize smooth communication.SOLUTION: A system includes an attachment part, a collection part, a translation part, an output part, and an analysis part. The earphone is attached to the attachment portion. The collection unit collects a speech of the user by the earphone attached by the attachment unit. The translation unit translates the speech collected by the collection unit. The output unit outputs the speech translated by the translation unit. The analysis unit analyzes the utterances collected by the collection unit and converts dialects and accents into a standard language.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 faced the challenge of communication barriers caused by different languages, dialects, and accents, making it difficult to communicate smoothly.

[0005] The system according to the embodiment aims to convert different languages, dialects, and accents into a standard language to achieve smooth communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a wearing unit, a collection unit, a translation unit, an output unit, and an analysis unit. The wearing unit wears earphones. The collection unit collects user utterances through the earphones worn by the wearing unit. The translation unit translates the utterances collected by the collection unit. The output unit outputs the utterances translated by the translation unit. The analysis unit analyzes the utterances collected by the collection unit and converts dialects and accents into standard language. [Effects of the Invention]

[0007] The system according to the embodiment can convert different languages, dialects, and accents into a standard language, thereby realizing smooth communication. [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 earphone system according to an embodiment of the present invention translates a user's speech in real time and converts dialects and accents into standard language. The earphone system allows a user to wear earphones, collect speech, and have a generation AI translate and output the speech. For example, when a user speaks in Japanese, the earphone system collects the speech, and the generation AI translates the speech into English and transmits it to the other party. Furthermore, the earphone system can also translate speech that is difficult for local people to understand, such as French with a southern accent. For example, the earphone system can translate French with a southern accent into standard French, which can then be further translated into a specified language. This allows the earphone system to translate a user's speech in real time and convert dialects and accents into standard language, thereby enabling smooth communication. This allows the earphone system to translate a user's speech in real time and convert dialects and accents into standard language, thereby providing smooth communication. For example, this system is extremely useful when conversing with local people while traveling or when conducting business meetings with people who speak different languages.

[0029] An earphone system according to an embodiment includes a wearing unit, a collecting unit, a translating unit, an output unit, and an analyzing unit. The wearing unit causes a user to wear the earphones. For example, the wearing unit has an adjustment function for fitting the earphones to the ears. The collecting unit collects user utterances. For example, the collecting unit collects user utterances in real time using a microphone. The translating unit translates the collected utterances into a specified language. For example, the translating unit analyzes the utterances using a generation AI and translates them into the specified language. The output unit communicates the translated utterances to the other party through the earphones. For example, the output unit outputs audio and communicates the translated utterances to the other party. The analyzing unit analyzes the collected utterances and converts dialects or accents into standard language. For example, the analyzing unit analyzes dialects or accents using a generation AI and converts them into standard language. As a result, the earphone system according to an embodiment can provide smooth communication by translating user utterances in real time and converting dialects or accents into standard language.

[0030] The wearing unit allows the earphones to be worn by the user or the other party. The wearing unit has, for example, an adjustment function for wearing the earphones in the user's ears. The wearing unit also has a function for wearing the earphones on the other party. For example, the wearing unit has an adjustment function for fitting the earphones to the other party's ears. This allows interpretation to be possible even if the other party does not have this product, by having them wear one of the earphones.

[0031] The collection unit can collect user utterances in real time. The collection unit collects user utterances in real time using, for example, a microphone. For example, the collection unit has a noise canceling function for collecting user utterances with high accuracy. The collection unit also uses technology for minimizing delays in collecting user utterances. For example, the collection unit transfers data in real time to minimize delays. This allows for real-time collection of user utterances, enabling instant translation.

[0032] The translation unit can translate the collected utterances into a specified language. For example, the translation unit translates the collected utterances into a specified language using a generation AI. For example, the translation unit analyzes the utterances using a text generation AI (e.g., LLM) and translates them into a specified language. The translation unit can also translate the content of the utterances using a multimodal generation AI. For example, the translation unit receives a prompt from the generation AI saying, "Please translate this utterance into English," and translates the utterance into English. This makes it possible to communicate with people who speak different languages ​​by translating the collected utterances into a specified language.

[0033] The output unit can transmit the translated utterance to the other party through earphones. The output unit, for example, outputs the translated utterance as audio. For example, the output unit adjusts the format of the audio output to transmit the utterance in a voice that is easy for the other party to hear. The output unit also has a function for adjusting sound quality. For example, the output unit adjusts the sound quality to transmit the utterance to the other party in clear voice. This allows for smooth communication by transmitting the translated utterance to the other party through earphones.

[0034] The analysis unit can analyze dialects and accents and convert them into standard language. For example, the analysis unit uses a generation AI to analyze dialects and accents and convert them into standard language. For example, the analysis unit uses speech analysis technology to analyze dialects and accents and convert them into standard language. The analysis unit can also convert dialects and accents into standard language using a conversion algorithm. For example, the analysis unit receives a prompt from the generation AI saying, "Convert this dialect into standard language," and converts the dialect into standard language. This enables accurate translation by converting dialects and accents into standard language.

[0035] The wearing unit can scan the shape of the user's ear when worn and provide an appropriate fit. For example, the earphones can 3D scan the shape of the user's ear and provide an optimal fit. For example, the wearing unit automatically adjusts the size of the earphone's silicone tip based on the shape of the user's ear. The wearing unit also has a function to save the user's ear shape data and provide an optimal fit the next time the earphones are worn. For example, the wearing unit saves the user's ear shape data and provides an optimal fit the next time the earphones are worn. This provides an optimal fit based on the shape of the user's ear, resulting in a comfortable fit.

[0036] The wearing unit can detect the user's movement when worn and adjust the stability of the wear according to the movement. The wearing unit uses, for example, a motion sensor to detect the user's movement. For example, when the user is walking, the wearing unit detects the movement and improves the stability of the wear. The wearing unit also has a function to detect the user's movement and strengthen the stability of the wear when the user is exercising. For example, when the user is exercising, the wearing unit detects the movement and strengthens the stability of the wear. This allows the stability of the wear to be adjusted according to the user's movement, providing a stable fit.

[0037] When wearing the device, the wearing unit can suggest an appropriate wearing method by referring to the user's past wearing history. The wearing unit, for example, has a data storage function for referring to the user's past wearing history. For example, the wearing unit suggests the optimal wearing method based on the wearing methods used by the user in the past. The wearing unit also has a function for suggesting the most comfortable wearing method based on the user's past wearing history. For example, the wearing unit analyzes the user's past wearing history and suggests a method to improve wearing stability. In this way, the optimal wearing method is suggested by referring to the user's past wearing history.

[0038] The wearing unit can provide an appropriate wearing method by taking into account the user's geographical location information when wearing the earphones. The wearing unit, for example, uses GPS technology to acquire the user's geographical location information. For example, the wearing unit provides a method to keep the earphones warm when the user is in a cold region. The wearing unit also has a function to provide a method to keep the earphones cool when the user is in a hot and humid region. For example, the wearing unit adjusts the earphones' fit to accommodate changes in air pressure when the user is at high altitude. This provides an optimal wearing method based on the user's geographical location information, thereby achieving a comfortable fit.

[0039] When worn, the wearing unit can analyze the user's social media activity and suggest a related wearing method. The wearing unit, for example, uses data analysis technology to analyze the user's social media activity. For example, if the user posts about exercise on social media, the wearing unit suggests the optimal wearing method for exercise. Furthermore, if the user posts about travel on social media, the wearing unit has a function to suggest the optimal wearing method for travel. For example, if the user posts about relaxation on social media, the wearing unit suggests the optimal wearing method for relaxation. In this way, the optimal wearing method is suggested based on the user's social media activity.

[0040] The wearing unit can customize the wearing method by reflecting the user's past feedback when wearing the device. The wearing unit, for example, has a data storage function for reflecting the user's past feedback. For example, the wearing unit customizes the optimal wearing method based on feedback provided by the user in the past. The wearing unit also has a function for customizing a method for improving the wearing comfort based on the user's past feedback. For example, the wearing unit analyzes the user's past feedback and customizes a method for improving the stability of the device. In this way, customizing the wearing method based on the user's past feedback provides a comfortable wearing comfort.

[0041] The collection unit can analyze the volume and tone of the user's speech during collection and select an appropriate collection method. The collection unit uses, for example, audio analysis technology to analyze the volume and tone of the user's speech. For example, if the volume of the user's speech is loud, the collection unit adjusts the collection method to collect clear audio. The collection unit also has a function to adjust the collection method to collect accurate audio if the tone of the user's speech is high. For example, the collection unit analyzes the volume and tone of the user's speech in real time and selects the optimal collection method. This makes it possible to collect accurate speech by selecting the optimal collection method based on the volume and tone of the user's speech.

[0042] The collection unit can filter the user's environmental sounds and collect only the utterances during collection. The collection unit uses, for example, noise canceling technology to filter the user's environmental sounds. For example, when the user is in a noisy environment, the collection unit filters the environmental sounds and collects only the utterances. In addition, when the user is in a quiet environment, the collection unit has a function to minimize the environmental sounds and collect only the utterances. For example, the collection unit filters the user's environmental sounds in real time and collects only the utterances. In this way, by filtering the user's environmental sounds, only the utterances can be accurately collected.

[0043] The collection unit can improve the accuracy of collection by referring to the user's past speech history when collecting data. The collection unit, for example, has a data storage function for referring to the user's past speech history. For example, the collection unit improves the accuracy of collection based on the user's past speech history. The collection unit also has a function for selecting the most accurate collection method from the user's past speech history. For example, the collection unit analyzes the user's past speech history and improves the accuracy of collection. In this way, the accuracy of collection is improved by referring to the user's past speech history.

[0044] When collecting data, the collection unit can prioritize collecting relevant comments by taking into account the user's geographical location information. The collection unit, for example, uses GPS technology to acquire the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting comments related to that area. Furthermore, when the user is traveling, the collection unit has a function of prioritized collection of comments related to the travel destination. For example, the collection unit analyzes the user's geographical location information in real time and prioritizes collection of highly relevant comments. In this way, highly relevant comments are prioritized and collected based on the user's geographical location information.

[0045] The collection unit can analyze the user's social media activities and collect related comments during collection. The collection unit, for example, uses data analysis technology to analyze the user's social media activities. For example, the collection unit collects related comments based on the content posted by the user on social media. The collection unit also has a function to analyze the user's social media activities and collect related comments. For example, the collection unit collects related comments with reference to the activities of the user's friends on social media. This makes it possible to collect more appropriate comments by collecting related comments based on the user's social media activities.

[0046] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, has a data storage function for reflecting the user's past feedback. For example, the collection unit customizes the optimal collection method based on feedback provided by the user in the past. The collection unit also has a function for customizing a method for improving the accuracy of collection based on the user's past feedback. For example, the collection unit analyzes the user's past feedback and customizes a method for improving the accuracy of collection. In this way, the accuracy of collection is improved by customizing the collection method based on the user's past feedback.

[0047] The translation unit can adjust the level of detail of the translation based on the importance of the statement during translation. For example, the translation unit uses an algorithm to evaluate the importance of the statement. For example, the translation unit analyzes the importance of the statement in real time and provides the optimal level of detail of the translation. The translation unit also has a function to provide a detailed translation for an important statement. For example, the translation unit provides a concise translation for a general statement. In this way, by adjusting the level of detail of the translation based on the importance of the statement, important statements are translated in more detail.

[0048] The translation unit can apply different translation algorithms depending on the category of the utterance during translation. The translation unit uses, for example, an algorithm to classify the category of the utterance. For example, the translation unit applies a translation algorithm that includes technical terms to business-related utterances. The translation unit also has the function of applying a simple and easy-to-understand translation algorithm to everyday conversations. For example, the translation unit analyzes the category of the utterance in real time and applies the optimal translation algorithm. This allows for more accurate translation by applying the optimal translation algorithm depending on the category of the utterance.

[0049] The translation unit can improve the accuracy of translation by referring to the user's past translation results when translating. The translation unit, for example, has a data storage function for referring to the user's past translation results. For example, the translation unit improves the accuracy of translation based on the user's past translation results. The translation unit also has a function for selecting the most accurate translation method from the user's past translation results. For example, the translation unit analyzes the user's past translation results and improves the accuracy of translation. In this way, the accuracy of translation is improved by referring to the user's past translation results.

[0050] The translation unit can determine the priority of translations based on the time of submission of comments during translation. The translation unit, for example, uses an algorithm to evaluate the time of submission of comments. For example, the translation unit analyzes the time of submission of comments in real time and determines the optimal translation priority. The translation unit also has a function to give top priority to translations of urgent comments. For example, the translation unit translates general comments with normal priority. In this way, by determining the priority of translations based on the time of submission of comments, urgent comments are translated with priority.

[0051] The translation unit can adjust the order of translation based on the relevance of statements during translation. The translation unit uses, for example, an algorithm for evaluating the relevance of statements. For example, the translation unit analyzes the relevance of statements in real time and determines the optimal translation order. The translation unit also has a function for giving top priority to translation of important statements. For example, the translation unit translates general statements in the normal order. In this way, by adjusting the order of translation based on the relevance of statements, important statements are given priority in translation.

[0052] The translation unit can adjust the use of technical terms in the translation according to the user's level of expertise during translation. For example, the translation unit uses an algorithm to evaluate the user's level of expertise. For example, the translation unit analyzes the user's level of expertise in real time and adjusts the use of technical terms in the translation to optimize it. The translation unit also has a function to provide a translation that uses a lot of technical terms when the user has specialized knowledge. For example, the translation unit provides a concise and easy-to-understand translation when the user has general knowledge. This allows the use of technical terms in the translation to be adjusted according to the user's level of expertise, thereby providing a more appropriate translation.

[0053] The output unit can analyze the user's hearing characteristics at the time of output and output with appropriate sound quality. The output unit uses, for example, an algorithm for analyzing the user's hearing characteristics. For example, the output unit scans the user's hearing characteristics and outputs with optimal sound quality. The output unit also has a function for automatically adjusting sound quality based on the user's hearing characteristics. For example, the output unit stores the user's hearing characteristic data and provides optimal sound quality the next time the output is performed. This allows for better sound quality to be provided by outputting with optimal sound quality based on the user's hearing characteristics.

[0054] The output unit can automatically adjust the volume of the output during output, taking into account the user's environmental sound. The output unit uses, for example, an environmental sound sensor for acquiring the user's environmental sound. For example, the output unit outputs at a higher volume when the user is in a noisy environment. The output unit also has a function of outputting at a lower volume when the user is in a quiet environment. For example, the output unit analyzes the user's environmental sound in real time and outputs at an optimal volume. In this way, the output volume is automatically adjusted based on the user's environmental sound, thereby outputting at a more appropriate volume.

[0055] The output unit can improve the accuracy of the output by referring to the user's past output history at the time of output. The output unit, for example, has a data storage function for referring to the user's past output history. For example, the output unit improves the accuracy of the output based on the user's past output history. The output unit also has a function for selecting the most accurate output method from the user's past output history. For example, the output unit analyzes the user's past output history and improves the accuracy of the output. In this way, the accuracy of the output is improved by referring to the user's past output history.

[0056] The output unit can provide an appropriate output method by taking into account the user's geographical location information when outputting information. The output unit, for example, uses GPS technology to acquire the user's geographical location information. For example, if the user is in a specific area, the output unit prioritizes outputting information related to that area. Also, if the user is traveling, the output unit has a function to prioritize outputting information related to the travel destination. For example, the output unit analyzes the user's geographical location information in real time and provides the optimal output method. This allows more appropriate information to be output by providing the optimal output method based on the user's geographical location information.

[0057] The output unit can analyze the user's social media activity at the time of output and suggest a related output method. The output unit uses, for example, data analysis technology to analyze the user's social media activity. For example, the output unit outputs related information based on the content posted by the user on social media. The output unit also has a function of analyzing the user's social media activity and outputting related information. For example, the output unit outputs related information by referring to the activity of the user's friends on social media. In this way, more appropriate information can be output by suggesting a related output method based on the user's social media activity.

[0058] The output unit can customize the output method by reflecting the user's past feedback at the time of output. The output unit, for example, has a data storage function for reflecting the user's past feedback. For example, the output unit customizes the optimal output method based on feedback provided by the user in the past. The output unit also has a function for customizing a method for improving output accuracy based on the user's past feedback. For example, the output unit analyzes the user's past feedback and customizes a method for improving output accuracy. In this way, by customizing the output method based on the user's past feedback, more appropriate information is output.

[0059] During analysis, the analysis unit can analyze the voice characteristics of the speech and select an appropriate analysis algorithm. The analysis unit uses, for example, voice analysis technology to analyze the voice characteristics of the speech. For example, the analysis unit scans the voice characteristics of the speech and selects the optimal analysis algorithm. The analysis unit also has a function to automatically adjust the analysis algorithm based on the voice characteristics of the speech. For example, the analysis unit stores the voice characteristic data of the speech and provides the optimal algorithm for the next and subsequent analyses. This allows for more accurate analysis by selecting the optimal analysis algorithm based on the voice characteristics of the speech.

[0060] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past comment history. The analysis unit, for example, has a data storage function for referring to the user's past comment history. For example, the analysis unit improves the accuracy of the analysis based on the user's past comment history. The analysis unit also has a function for selecting the most accurate analysis method from the user's past comment history. For example, the analysis unit analyzes the user's past comment history to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past comment history.

[0061] During analysis, the analysis unit can convert dialects and accents into standard language by taking into account the context of the utterance. The analysis unit uses, for example, context analysis technology to take into account the context of the utterance. For example, the analysis unit scans the context of the utterance and converts dialects and accents into standard language. The analysis unit also has a function to automatically convert dialects and accents based on the context of the utterance. For example, the analysis unit stores context data of the utterance and provides the optimal conversion method for the next and subsequent analyses. This enables more accurate conversion by converting dialects and accents into standard language by taking into account the context of the utterance.

[0062] The analysis unit can provide an appropriate analysis method by taking into account the user's geographical location information during analysis. The analysis unit, for example, uses GPS technology to acquire the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analyzing comments related to that area. Also, if the user is traveling, the analysis unit has a function to prioritize analyzing comments related to the travel destination. For example, the analysis unit analyzes the user's geographical location information in real time and provides the optimal analysis method. This provides a more accurate analysis by providing the optimal analysis method based on the user's geographical location information.

[0063] During analysis, the analysis unit can analyze the user's social media activity and suggest relevant analysis methods. The analysis unit, for example, uses data analysis technology to analyze the user's social media activity. For example, the analysis unit suggests relevant analysis methods based on the content posted by the user on social media. The analysis unit also has a function of analyzing the user's social media activity and suggesting relevant analysis methods. For example, the analysis unit suggests relevant analysis methods based on the activity of the user's friends on social media. In this way, more appropriate analysis can be provided by suggesting relevant analysis methods based on the user's social media activity.

[0064] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. The analysis unit, for example, has a data storage function for reflecting the user's past feedback. For example, the analysis unit customizes the optimal analysis method based on feedback provided by the user in the past. The analysis unit also has a function for customizing a method for improving the accuracy of the analysis based on the user's past feedback. For example, the analysis unit analyzes the user's past feedback and customizes a method for improving the accuracy of the analysis. In this way, the analysis method is customized based on the user's past feedback, thereby improving the accuracy of the analysis.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The earphone system may further include a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit may measure the user's heart rate and blood pressure and issue a warning if an abnormality is detected. The health monitoring unit may also measure the user's stress level and provide relaxing music if the stress level is high. Furthermore, the health monitoring unit may record the user's exercise volume and send a notification encouraging exercise if it detects a lack of exercise. This allows the earphone system to monitor the user's health condition in real time and provide appropriate measures.

[0067] The earphone system may further include an environment adaptation unit that uses the user's location information to provide audio feedback according to the surrounding environment. For example, the environment adaptation unit may enhance noise cancellation when the user is in a noisy place. Alternatively, the environment adaptation unit may provide a function to capture ambient sounds when the user is in a quiet place. Furthermore, when the user is in a specific tourist spot, audio guidance about the spot may be provided. This allows the earphone system to provide optimal audio feedback based on the user's location information.

[0068] The earphone system may further include a music recommendation unit that recommends optimal music based on the user's music preferences. For example, the music recommendation unit may analyze the user's past playback history and recommend music in the user's preferred genre. The music recommendation unit may also recommend appropriate music based on the user's current mood or activity. Furthermore, the music recommendation unit may recommend new music based on the music the user's friends are listening to. This allows the earphone system to provide optimal music based on the user's music preferences.

[0069] The earphone system may further include an information providing unit that provides appropriate information based on the content of a user's speech. For example, if the user is talking about a specific place, the information providing unit may provide detailed information about the place. If the user is talking about a specific product, the information providing unit may provide reviews and price information about the product. If the user is talking about a specific event, the information providing unit may provide schedules and ticket information for the event. This allows the earphone system to provide appropriate information based on the content of a user's speech.

[0070] The earphone system can further include a learning support unit that supports the user's learning. For example, if the user is learning a foreign language, the learning support unit can support pronunciation practice. Also, if the user is studying a specific subject, the learning support unit can provide quizzes and practice questions related to that subject. Furthermore, the learning support unit has a function to record the user's learning progress and evaluate the effectiveness of the learning. This allows the earphone system to effectively support the user's learning.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The wearing unit puts the earphones on the user. For example, the wearing unit has an adjustment function for fitting the earphones to the ears. Step 2: The collection unit collects user utterances. For example, the collection unit collects user utterances in real time using a microphone. Step 3: The analysis unit analyzes the collected utterances and converts dialects and accents into standard language. For example, the analysis unit uses generative AI to analyze dialects and accents and convert them into standard language. Step 4: The translation unit translates the collected utterances into the specified language. For example, the translation unit uses a generative AI to analyze the utterances and translate them into the specified language. Step 5: The output unit transmits the translated utterance to the other party through earphones. For example, the output unit outputs the translated utterance to the other party through audio output.

[0073] (Example 2) An earphone system according to an embodiment of the present invention translates a user's speech in real time and converts dialects and accents into standard language. The earphone system allows a user to wear earphones, collect speech, and have a generation AI translate and output the speech. For example, when a user speaks in Japanese, the earphone system collects the speech, and the generation AI translates the speech into English and transmits it to the other party. Furthermore, the earphone system can also translate speech that is difficult for local people to understand, such as French with a southern accent. For example, the earphone system can translate French with a southern accent into standard French, which can then be further translated into a specified language. This allows the earphone system to translate a user's speech in real time and convert dialects and accents into standard language, thereby enabling smooth communication. This allows the earphone system to translate a user's speech in real time and convert dialects and accents into standard language, thereby providing smooth communication. For example, this system is extremely useful when conversing with local people while traveling or when conducting business meetings with people who speak different languages.

[0074] An earphone system according to an embodiment includes a wearing unit, a collecting unit, a translating unit, an output unit, and an analyzing unit. The wearing unit causes a user to wear the earphones. For example, the wearing unit has an adjustment function for fitting the earphones to the ears. The collecting unit collects user utterances. For example, the collecting unit collects user utterances in real time using a microphone. The translating unit translates the collected utterances into a specified language. For example, the translating unit analyzes the utterances using a generation AI and translates them into the specified language. The output unit communicates the translated utterances to the other party through the earphones. For example, the output unit outputs audio and communicates the translated utterances to the other party. The analyzing unit analyzes the collected utterances and converts dialects or accents into standard language. For example, the analyzing unit analyzes dialects or accents using a generation AI and converts them into standard language. As a result, the earphone system according to an embodiment can provide smooth communication by translating user utterances in real time and converting dialects or accents into standard language.

[0075] The wearing unit allows the earphones to be worn by the user or the other party. The wearing unit has, for example, an adjustment function for wearing the earphones in the user's ears. The wearing unit also has a function for wearing the earphones on the other party. For example, the wearing unit has an adjustment function for fitting the earphones to the other party's ears. This allows interpretation to be possible even if the other party does not have this product, by having them wear one of the earphones.

[0076] The collection unit can collect user utterances in real time. The collection unit collects user utterances in real time using, for example, a microphone. For example, the collection unit has a noise canceling function for collecting user utterances with high accuracy. The collection unit also uses technology for minimizing delays in collecting user utterances. For example, the collection unit transfers data in real time to minimize delays. This allows for real-time collection of user utterances, enabling instant translation.

[0077] The translation unit can translate the collected utterances into a specified language. For example, the translation unit translates the collected utterances into a specified language using a generation AI. For example, the translation unit analyzes the utterances using a text generation AI (e.g., LLM) and translates them into a specified language. The translation unit can also translate the content of the utterances using a multimodal generation AI. For example, the translation unit receives a prompt from the generation AI saying, "Please translate this utterance into English," and translates the utterance into English. This makes it possible to communicate with people who speak different languages ​​by translating the collected utterances into a specified language.

[0078] The output unit can transmit the translated utterance to the other party through earphones. The output unit, for example, outputs the translated utterance as audio. For example, the output unit adjusts the format of the audio output to transmit the utterance in a voice that is easy for the other party to hear. The output unit also has a function for adjusting sound quality. For example, the output unit adjusts the sound quality to transmit the utterance to the other party in clear voice. This allows for smooth communication by transmitting the translated utterance to the other party through earphones.

[0079] The analysis unit can analyze dialects and accents and convert them into standard language. For example, the analysis unit uses a generation AI to analyze dialects and accents and convert them into standard language. For example, the analysis unit uses speech analysis technology to analyze dialects and accents and convert them into standard language. The analysis unit can also convert dialects and accents into standard language using a conversion algorithm. For example, the analysis unit receives a prompt from the generation AI saying, "Convert this dialect into standard language," and converts the dialect into standard language. This enables accurate translation by converting dialects and accents into standard language.

[0080] The wearing unit can estimate the user's emotion and adjust the wearing position of the earphones based on that emotion. The wearing unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the wearing unit analyzes the user's facial expressions and voice to estimate the emotion. The wearing unit also has a function to adjust the wearing position of the earphones based on the estimated emotion. For example, if the user is nervous, the wearing unit fine-tunes the wearing position of the earphones to provide a comfortable fit. In this way, adjusting the wearing position of the earphones according to the user's emotion provides a comfortable fit.

[0081] The wearing unit can scan the shape of the user's ear when worn and provide an appropriate fit. For example, the earphones can 3D scan the shape of the user's ear and provide an optimal fit. For example, the wearing unit automatically adjusts the size of the earphone's silicone tip based on the shape of the user's ear. The wearing unit also has a function to save the user's ear shape data and provide an optimal fit the next time the earphones are worn. For example, the wearing unit saves the user's ear shape data and provides an optimal fit the next time the earphones are worn. This provides an optimal fit based on the shape of the user's ear, resulting in a comfortable fit.

[0082] The wearing unit can detect the user's movement when worn and adjust the stability of the wear according to the movement. The wearing unit uses, for example, a motion sensor to detect the user's movement. For example, when the user is walking, the wearing unit detects the movement and improves the stability of the wear. The wearing unit also has a function to detect the user's movement and strengthen the stability of the wear when the user is exercising. For example, when the user is exercising, the wearing unit detects the movement and strengthens the stability of the wear. This allows the stability of the wear to be adjusted according to the user's movement, providing a stable fit.

[0083] When wearing the device, the wearing unit can suggest an appropriate wearing method by referring to the user's past wearing history. The wearing unit, for example, has a data storage function for referring to the user's past wearing history. For example, the wearing unit suggests the optimal wearing method based on the wearing methods used by the user in the past. The wearing unit also has a function for suggesting the most comfortable wearing method based on the user's past wearing history. For example, the wearing unit analyzes the user's past wearing history and suggests a method to improve wearing stability. In this way, the optimal wearing method is suggested by referring to the user's past wearing history.

[0084] The wearing unit can estimate the user's emotions and improve wearing comfort based on the emotions. The wearing unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. For example, the wearing unit analyzes the user's facial expressions and voice to estimate the emotions. The wearing unit also has a function to improve wearing comfort based on the estimated emotions. For example, if the user is feeling stressed, the wearing unit softens the fit of the earphones to improve comfort. In this way, the wearing comfort is improved in accordance with the user's emotions, providing a more comfortable fit.

[0085] The wearing unit can provide an appropriate wearing method by taking into account the user's geographical location information when wearing the earphones. The wearing unit, for example, uses GPS technology to acquire the user's geographical location information. For example, the wearing unit provides a method to keep the earphones warm when the user is in a cold region. The wearing unit also has a function to provide a method to keep the earphones cool when the user is in a hot and humid region. For example, the wearing unit adjusts the earphones' fit to accommodate changes in air pressure when the user is at high altitude. This provides an optimal wearing method based on the user's geographical location information, thereby achieving a comfortable fit.

[0086] When worn, the wearing unit can analyze the user's social media activity and suggest a related wearing method. The wearing unit, for example, uses data analysis technology to analyze the user's social media activity. For example, if the user posts about exercise on social media, the wearing unit suggests the optimal wearing method for exercise. Furthermore, if the user posts about travel on social media, the wearing unit has a function to suggest the optimal wearing method for travel. For example, if the user posts about relaxation on social media, the wearing unit suggests the optimal wearing method for relaxation. In this way, the optimal wearing method is suggested based on the user's social media activity.

[0087] The wearing unit can customize the wearing method by reflecting the user's past feedback when wearing the device. The wearing unit, for example, has a data storage function for reflecting the user's past feedback. For example, the wearing unit customizes the optimal wearing method based on feedback provided by the user in the past. The wearing unit also has a function for customizing a method for improving the wearing comfort based on the user's past feedback. For example, the wearing unit analyzes the user's past feedback and customizes a method for improving the stability of the device. In this way, customizing the wearing method based on the user's past feedback provides a comfortable wearing comfort.

[0088] The collection unit can estimate the user's emotions and adjust the timing of collecting utterances based on the emotions. The collection unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. For example, the collection unit analyzes the user's facial expressions and voice to estimate the emotions. The collection unit also has a function of adjusting the timing of collecting utterances based on the estimated emotions. For example, if the user is nervous, the collection unit delays the timing of collecting utterances to allow the user to relax. In this way, by adjusting the timing of collecting utterances according to the user's emotions, utterances can be collected at more appropriate times.

[0089] The collection unit can analyze the volume and tone of the user's speech during collection and select an appropriate collection method. The collection unit uses, for example, audio analysis technology to analyze the volume and tone of the user's speech. For example, if the volume of the user's speech is loud, the collection unit adjusts the collection method to collect clear audio. The collection unit also has a function to adjust the collection method to collect accurate audio if the tone of the user's speech is high. For example, the collection unit analyzes the volume and tone of the user's speech in real time and selects the optimal collection method. This makes it possible to collect accurate speech by selecting the optimal collection method based on the volume and tone of the user's speech.

[0090] The collection unit can filter the user's environmental sounds and collect only the utterances during collection. The collection unit uses, for example, noise canceling technology to filter the user's environmental sounds. For example, when the user is in a noisy environment, the collection unit filters the environmental sounds and collects only the utterances. In addition, when the user is in a quiet environment, the collection unit has a function to minimize the environmental sounds and collect only the utterances. For example, the collection unit filters the user's environmental sounds in real time and collects only the utterances. In this way, by filtering the user's environmental sounds, only the utterances can be accurately collected.

[0091] The collection unit can improve the accuracy of collection by referring to the user's past speech history when collecting data. The collection unit, for example, has a data storage function for referring to the user's past speech history. For example, the collection unit improves the accuracy of collection based on the user's past speech history. The collection unit also has a function for selecting the most accurate collection method from the user's past speech history. For example, the collection unit analyzes the user's past speech history and improves the accuracy of collection. In this way, the accuracy of collection is improved by referring to the user's past speech history.

[0092] The collection unit can estimate the user's emotions and determine the priority of utterances to be collected based on the emotions. The collection unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. For example, the collection unit analyzes the user's facial expressions and voice to estimate the emotions. The collection unit also has a function to determine the priority of utterances to be collected based on the estimated emotions. For example, if the user is nervous, the collection unit prioritizes collecting important utterances. In this way, important utterances are collected preferentially by determining the priority of utterances according to the user's emotions.

[0093] When collecting data, the collection unit can prioritize collecting relevant comments by taking into account the user's geographical location information. The collection unit, for example, uses GPS technology to acquire the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting comments related to that area. Furthermore, when the user is traveling, the collection unit has a function of prioritized collection of comments related to the travel destination. For example, the collection unit analyzes the user's geographical location information in real time and prioritizes collection of highly relevant comments. In this way, highly relevant comments are prioritized and collected based on the user's geographical location information.

[0094] The collection unit can analyze the user's social media activities and collect related comments during collection. The collection unit, for example, uses data analysis technology to analyze the user's social media activities. For example, the collection unit collects related comments based on the content posted by the user on social media. The collection unit also has a function to analyze the user's social media activities and collect related comments. For example, the collection unit collects related comments with reference to the activities of the user's friends on social media. This makes it possible to collect more appropriate comments by collecting related comments based on the user's social media activities.

[0095] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, has a data storage function for reflecting the user's past feedback. For example, the collection unit customizes the optimal collection method based on feedback provided by the user in the past. The collection unit also has a function for customizing a method for improving the accuracy of collection based on the user's past feedback. For example, the collection unit analyzes the user's past feedback and customizes a method for improving the accuracy of collection. In this way, the accuracy of collection is improved by customizing the collection method based on the user's past feedback.

[0096] The translation unit can estimate the user's emotions and adjust the translation expression method based on those emotions. The translation unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. For example, the translation unit analyzes the user's facial expressions and voice to estimate the emotions. The translation unit also has a function to adjust the translation expression method based on the estimated emotions. For example, if the user is nervous, the translation unit provides a simple and easy-to-understand expression method. This allows the translation unit to adjust the translation expression method according to the user's emotions and provide a more appropriate translation.

[0097] The translation unit can adjust the level of detail of the translation based on the importance of the statement during translation. For example, the translation unit uses an algorithm to evaluate the importance of the statement. For example, the translation unit analyzes the importance of the statement in real time and provides the optimal level of detail of the translation. The translation unit also has a function to provide a detailed translation for an important statement. For example, the translation unit provides a concise translation for a general statement. In this way, by adjusting the level of detail of the translation based on the importance of the statement, important statements are translated in more detail.

[0098] The translation unit can apply different translation algorithms depending on the category of the utterance during translation. The translation unit uses, for example, an algorithm to classify the category of the utterance. For example, the translation unit applies a translation algorithm that includes technical terms to business-related utterances. The translation unit also has the function of applying a simple and easy-to-understand translation algorithm to everyday conversations. For example, the translation unit analyzes the category of the utterance in real time and applies the optimal translation algorithm. This allows for more accurate translation by applying the optimal translation algorithm depending on the category of the utterance.

[0099] The translation unit can improve the accuracy of translation by referring to the user's past translation results when translating. The translation unit, for example, has a data storage function for referring to the user's past translation results. For example, the translation unit improves the accuracy of translation based on the user's past translation results. The translation unit also has a function for selecting the most accurate translation method from the user's past translation results. For example, the translation unit analyzes the user's past translation results and improves the accuracy of translation. In this way, the accuracy of translation is improved by referring to the user's past translation results.

[0100] The translation unit can estimate the user's emotions and adjust the length of the translation based on the emotions. The translation unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. For example, the translation unit analyzes the user's facial expressions and voice to estimate the emotions. The translation unit also has a function to adjust the length of the translation based on the estimated emotions. For example, if the user is in a hurry, the translation unit provides a short, concise translation. This allows the translation unit to adjust the length of the translation according to the user's emotions, thereby providing a more appropriate translation.

[0101] The translation unit can determine the priority of translations based on the time of submission of comments during translation. The translation unit, for example, uses an algorithm to evaluate the time of submission of comments. For example, the translation unit analyzes the time of submission of comments in real time and determines the optimal translation priority. The translation unit also has a function to give top priority to translations of urgent comments. For example, the translation unit translates general comments with normal priority. In this way, by determining the priority of translations based on the time of submission of comments, urgent comments are translated with priority.

[0102] The translation unit can adjust the order of translation based on the relevance of statements during translation. The translation unit uses, for example, an algorithm for evaluating the relevance of statements. For example, the translation unit analyzes the relevance of statements in real time and determines the optimal translation order. The translation unit also has a function for giving top priority to translation of important statements. For example, the translation unit translates general statements in the normal order. In this way, by adjusting the order of translation based on the relevance of statements, important statements are given priority in translation.

[0103] The translation unit can adjust the use of technical terms in the translation according to the user's level of expertise during translation. For example, the translation unit uses an algorithm to evaluate the user's level of expertise. For example, the translation unit analyzes the user's level of expertise in real time and adjusts the use of technical terms in the translation to optimize it. The translation unit also has a function to provide a translation that uses a lot of technical terms when the user has specialized knowledge. For example, the translation unit provides a concise and easy-to-understand translation when the user has general knowledge. This allows the use of technical terms in the translation to be adjusted according to the user's level of expertise, thereby providing a more appropriate translation.

[0104] The output unit can estimate the user's emotion and adjust the volume of the output based on the emotion. The output unit uses, for example, an emotion estimation algorithm to estimate the user's emotion. For example, the output unit analyzes the user's facial expression and voice to estimate the emotion. The output unit also has a function to adjust the volume of the output based on the estimated emotion. For example, if the user is nervous, the output unit outputs at a lower volume. In this way, the output volume is adjusted according to the user's emotion, thereby outputting at a more appropriate volume.

[0105] The output unit can analyze the user's hearing characteristics at the time of output and output with appropriate sound quality. The output unit uses, for example, an algorithm for analyzing the user's hearing characteristics. For example, the output unit scans the user's hearing characteristics and outputs with optimal sound quality. The output unit also has a function for automatically adjusting sound quality based on the user's hearing characteristics. For example, the output unit stores the user's hearing characteristic data and provides optimal sound quality the next time the output is performed. This allows for better sound quality to be provided by outputting with optimal sound quality based on the user's hearing characteristics.

[0106] The output unit can automatically adjust the volume of the output during output, taking into account the user's environmental sound. The output unit uses, for example, an environmental sound sensor for acquiring the user's environmental sound. For example, the output unit outputs at a higher volume when the user is in a noisy environment. The output unit also has a function of outputting at a lower volume when the user is in a quiet environment. For example, the output unit analyzes the user's environmental sound in real time and outputs at an optimal volume. In this way, the output volume is automatically adjusted based on the user's environmental sound, thereby outputting at a more appropriate volume.

[0107] The output unit can improve the accuracy of the output by referring to the user's past output history at the time of output. The output unit, for example, has a data storage function for referring to the user's past output history. For example, the output unit improves the accuracy of the output based on the user's past output history. The output unit also has a function for selecting the most accurate output method from the user's past output history. For example, the output unit analyzes the user's past output history and improves the accuracy of the output. In this way, the accuracy of the output is improved by referring to the user's past output history.

[0108] The output unit can estimate the user's emotion and adjust the timing of the output based on the emotion. The output unit uses, for example, an emotion estimation algorithm to estimate the user's emotion. For example, the output unit analyzes the user's facial expression and voice to estimate the emotion. The output unit also has a function to adjust the timing of the output based on the estimated emotion. For example, if the user is nervous, the output unit delays the timing of the output to relax the user. In this way, the output timing is adjusted according to the user's emotion, thereby outputting at a more appropriate timing.

[0109] The output unit can provide an appropriate output method by taking into account the user's geographical location information when outputting information. The output unit, for example, uses GPS technology to acquire the user's geographical location information. For example, if the user is in a specific area, the output unit prioritizes outputting information related to that area. Also, if the user is traveling, the output unit has a function to prioritize outputting information related to the travel destination. For example, the output unit analyzes the user's geographical location information in real time and provides the optimal output method. This allows more appropriate information to be output by providing the optimal output method based on the user's geographical location information.

[0110] The output unit can analyze the user's social media activity at the time of output and suggest a related output method. The output unit uses, for example, data analysis technology to analyze the user's social media activity. For example, the output unit outputs related information based on the content posted by the user on social media. The output unit also has a function of analyzing the user's social media activity and outputting related information. For example, the output unit outputs related information by referring to the activity of the user's friends on social media. In this way, more appropriate information can be output by suggesting a related output method based on the user's social media activity.

[0111] The output unit can customize the output method by reflecting the user's past feedback at the time of output. The output unit, for example, has a data storage function for reflecting the user's past feedback. For example, the output unit customizes the optimal output method based on feedback provided by the user in the past. The output unit also has a function for customizing a method for improving output accuracy based on the user's past feedback. For example, the output unit analyzes the user's past feedback and customizes a method for improving output accuracy. In this way, by customizing the output method based on the user's past feedback, more appropriate information is output.

[0112] The analysis unit can estimate the user's emotions and adjust the dialect and accent analysis method based on the emotions. The analysis unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions and voice to estimate the emotions. The analysis unit also has a function to adjust the dialect and accent analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-understand analysis method. This allows for more accurate analysis by adjusting the dialect and accent analysis method according to the user's emotions.

[0113] During analysis, the analysis unit can analyze the voice characteristics of the speech and select an appropriate analysis algorithm. The analysis unit uses, for example, voice analysis technology to analyze the voice characteristics of the speech. For example, the analysis unit scans the voice characteristics of the speech and selects the optimal analysis algorithm. The analysis unit also has a function to automatically adjust the analysis algorithm based on the voice characteristics of the speech. For example, the analysis unit stores the voice characteristic data of the speech and provides the optimal algorithm for the next and subsequent analyses. This allows for more accurate analysis by selecting the optimal analysis algorithm based on the voice characteristics of the speech.

[0114] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past comment history. The analysis unit, for example, has a data storage function for referring to the user's past comment history. For example, the analysis unit improves the accuracy of the analysis based on the user's past comment history. The analysis unit also has a function for selecting the most accurate analysis method from the user's past comment history. For example, the analysis unit analyzes the user's past comment history to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past comment history.

[0115] During analysis, the analysis unit can convert dialects and accents into standard language by taking into account the context of the utterance. The analysis unit uses, for example, context analysis technology to take into account the context of the utterance. For example, the analysis unit scans the context of the utterance and converts dialects and accents into standard language. The analysis unit also has a function to automatically convert dialects and accents based on the context of the utterance. For example, the analysis unit stores context data of the utterance and provides the optimal conversion method for the next and subsequent analyses. This enables more accurate conversion by converting dialects and accents into standard language by taking into account the context of the utterance.

[0116] The analysis unit can estimate the user's emotions and determine the analysis priority based on the emotions. The analysis unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions and voice to estimate the emotions. The analysis unit also has a function to determine the analysis priority based on the estimated emotions. For example, if the user is nervous, the analysis unit prioritizes analyzing important statements. In this way, by determining the analysis priority according to the user's emotions, important statements are prioritized in analysis.

[0117] The analysis unit can provide an appropriate analysis method by taking into account the user's geographical location information during analysis. The analysis unit, for example, uses GPS technology to acquire the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analyzing comments related to that area. Also, if the user is traveling, the analysis unit has a function to prioritize analyzing comments related to the travel destination. For example, the analysis unit analyzes the user's geographical location information in real time and provides the optimal analysis method. This provides a more accurate analysis by providing the optimal analysis method based on the user's geographical location information.

[0118] During analysis, the analysis unit can analyze the user's social media activity and suggest relevant analysis methods. The analysis unit, for example, uses data analysis technology to analyze the user's social media activity. For example, the analysis unit suggests relevant analysis methods based on the content posted by the user on social media. The analysis unit also has a function of analyzing the user's social media activity and suggesting relevant analysis methods. For example, the analysis unit suggests relevant analysis methods based on the activity of the user's friends on social media. In this way, more appropriate analysis can be provided by suggesting relevant analysis methods based on the user's social media activity.

[0119] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. The analysis unit, for example, has a data storage function for reflecting the user's past feedback. For example, the analysis unit customizes the optimal analysis method based on feedback provided by the user in the past. The analysis unit also has a function for customizing a method for improving the accuracy of the analysis based on the user's past feedback. For example, the analysis unit analyzes the user's past feedback and customizes a method for improving the accuracy of the analysis. In this way, the analysis method is customized based on the user's past feedback, thereby improving the accuracy of the analysis. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described attachment unit, collection unit, translation unit, output unit, and analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the attachment unit is realized by the reception device 38 of the smart device 14. For example, the collection unit collects user utterances in real time using the microphone 38B of the smart device 14. For example, the translation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the utterances using a generation AI, and translates them into a specified language. For example, the output unit transmits the translated utterances to the other party using the speaker 40B of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes dialects and accents using a generation AI and converts them into a standard language. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described wearing unit, collection unit, translation unit, output unit, and analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the wearing unit is realized by the microphone 238 of the smart glasses 214. For example, the collection unit collects a user's utterances in real time using the microphone 238 of the smart glasses 214. For example, the translation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the utterances using a generation AI and translates them into a specified language. For example, the output unit transmits the translated utterances to the other party using the speaker 240 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to analyze dialects and accents and convert them into a standard language. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned wearing unit, collection unit, translation unit, output unit, and analysis unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the wearing unit is realized by the microphone 238 of the headset-type terminal 314. For example, the collection unit collects user utterances in real time using the microphone 238 of the headset-type terminal 314. For example, the translation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the utterances using a generation AI, and translates them into a specified language. For example, the output unit transmits the translated utterances to the other party using the speaker 240 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes dialects and accents using a generation AI and converts them into a standard language. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned mounting unit, collection unit, translation unit, output unit, and analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the mounting unit is realized by the microphone 238 of the robot 414. For example, the collection unit collects user utterances in real time using the microphone 238 of the robot 414. For example, the translation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the utterances using a generation AI, and translates them into a specified language. For example, the output unit communicates the translated utterances to the other party using the speaker 240 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes dialects and accents using a generation AI and converts them into a standard language.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The earphone system may further include a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit may measure the user's heart rate and blood pressure and issue a warning if an abnormality is detected. The health monitoring unit may also measure the user's stress level and provide relaxing music if the stress level is high. Furthermore, the health monitoring unit may record the user's exercise volume and send a notification encouraging exercise if it detects a lack of exercise. This allows the earphone system to monitor the user's health condition in real time and provide appropriate measures.

[0122] The earphone system may further include an environment adaptation unit that uses the user's location information to provide audio feedback according to the surrounding environment. For example, the environment adaptation unit may enhance noise cancellation when the user is in a noisy place. Alternatively, the environment adaptation unit may provide a function to capture ambient sounds when the user is in a quiet place. Furthermore, when the user is in a specific tourist spot, audio guidance about the spot may be provided. This allows the earphone system to provide optimal audio feedback based on the user's location information.

[0123] The earphone system may further include a music recommendation unit that recommends optimal music based on the user's music preferences. For example, the music recommendation unit may analyze the user's past playback history and recommend music in the user's preferred genre. The music recommendation unit may also recommend appropriate music based on the user's current mood or activity. Furthermore, the music recommendation unit may recommend new music based on the music the user's friends are listening to. This allows the earphone system to provide optimal music based on the user's music preferences.

[0124] The earphone system may further include an information providing unit that provides appropriate information based on the content of a user's speech. For example, if the user is talking about a specific place, the information providing unit may provide detailed information about the place. If the user is talking about a specific product, the information providing unit may provide reviews and price information about the product. If the user is talking about a specific event, the information providing unit may provide schedules and ticket information for the event. This allows the earphone system to provide appropriate information based on the content of a user's speech.

[0125] The earphone system can further include a learning support unit that supports the user's learning. For example, if the user is learning a foreign language, the learning support unit can support pronunciation practice. Also, if the user is studying a specific subject, the learning support unit can provide quizzes and practice questions related to that subject. Furthermore, the learning support unit has a function to record the user's learning progress and evaluate the effectiveness of the learning. This allows the earphone system to effectively support the user's learning.

[0126] The earphone system may further include an emotional music recommendation unit that estimates the user's emotion and recommends music based on the emotion. For example, the emotional music recommendation unit may recommend relaxing music if the user is sad. Alternatively, the emotional music recommendation unit may recommend energetic music if the user is excited. Furthermore, the emotional music recommendation unit may recommend relaxing music if the user is stressed. This allows the earphone system to provide optimal music based on the user's emotion.

[0127] The earphone system may further include an emotional tone adjustment unit that estimates the user's emotion and adjusts the tone of speech based on the emotion. For example, the emotional tone adjustment unit may deliver speech in a calm tone if the user is angry. Alternatively, the emotional tone adjustment unit may deliver speech in a relaxed tone if the user is nervous. Furthermore, the emotional tone adjustment unit may deliver speech in a bright tone if the user is happy. This allows the earphone system to adjust the tone of speech based on the user's emotion.

[0128] The earphone system may further include an emotion notification prioritization unit that estimates the user's emotion and prioritizes notifications based on the estimated emotion. For example, the emotion notification prioritization unit may prioritize only important notifications when the user is stressed, or prioritize all notifications when the user is relaxed. Furthermore, the emotion notification prioritization unit may prioritize only urgent notifications when the user is busy. This allows the earphone system to prioritize notifications based on the user's emotion.

[0129] The earphone system may further include an emotional voice guidance unit that estimates the user's emotions and adjusts the content of the voice guidance based on the emotions. For example, the emotional voice guidance unit may provide a concise voice guidance when the user is tired, or a detailed voice guidance when the user is interested, or an easy-to-understand voice guidance when the user is confused. This allows the earphone system to adjust the content of the voice guidance based on the user's emotions.

[0130] The earphone system may further include an emotion feedback unit that estimates the user's emotion and provides feedback based on the estimated emotion. For example, the emotion feedback unit may provide an encouraging message if the user is depressed, a congratulatory message if the user is happy, or a reassuring message if the user is anxious. This allows the earphone system to provide appropriate feedback based on the user's emotion.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The wearing unit puts the earphones on the user. For example, the wearing unit has an adjustment function for fitting the earphones to the ears. Step 2: The collection unit collects user utterances. For example, the collection unit collects user utterances in real time using a microphone. Step 3: The analysis unit analyzes the collected utterances and converts dialects and accents into standard language. For example, the analysis unit uses generative AI to analyze dialects and accents and convert them into standard language. Step 4: The translation unit translates the collected utterances into the specified language. For example, the translation unit uses a generative AI to analyze the utterances and translate them into the specified language. Step 5: The output unit transmits the translated utterance to the other party through earphones. For example, the output unit outputs the translated utterance to the other party through audio output.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0140] The 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.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 AI 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 AI 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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 AI 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 mounting portion for mounting the earphones; a collection unit configured to collect user utterances by earphones attached by the attachment unit; a translation unit that translates the utterances collected by the collection unit; an output unit that outputs the utterance translated by the translation unit; An analysis unit that analyzes the utterances collected by the collection unit and converts dialects and accents into standard language A system characterized by:

2. The mounting portion is Putting the earphones on the user or the other party 2. The system of claim 1.

3. The collecting unit Collect user comments in real time 2. The system of claim 1.

4. The translation unit Translate collected utterances into a specified language 2. The system of claim 1.

5. The output unit Translate the message to the other person through earphones 2. The system of claim 1.

6. The analysis unit Analyze dialects and accents and convert them into standard language 2. The system of claim 1.

7. The mounting portion is Estimates the user's emotions and adjusts the earphone position based on those emotions 2. The system of claim 1.

8. The mounting portion is When worn, the headphones scan the shape of the user's ears to provide a perfect fit.

2. The system of claim 1.

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