system

Abstract

The system according to the embodiment aims to facilitate communication between foreign crew members and Japanese people and promote cultural understanding. [Solution] The system according to the embodiment includes a translation unit, an education unit, and a monitoring unit. The translation unit performs translation to facilitate communication between foreign crew members and Japanese people. The education unit provides information about Japanese culture and customs based on the information translated by the translation unit. The monitoring unit monitors discriminatory remarks and hate speech based on the information provided by the education unit and takes appropriate measures.

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] With conventional technology, communication between foreign crew members and Japanese people is not smooth, which can lead to cultural misunderstandings and discriminatory remarks.

[0005] The system according to the embodiment aims to facilitate communication between foreign crew members and Japanese people and promote cultural understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a translation unit, an education unit, and a monitoring unit. The translation unit performs translation to facilitate communication between foreign crew members and Japanese people. The education unit provides information about Japanese culture and customs based on the information translated by the translation unit. The monitoring unit monitors discriminatory remarks and hate speech based on the information provided by the education unit and takes appropriate measures. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate communication between foreign crew members and Japanese people and promote cultural understanding. [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) A support system according to an embodiment of the present invention provides support to foreign crew members working on-site to prevent them from imposing Japan's unique culture and customs. This support system improves automatic language translation AI and provides a mechanism for appropriately conveying cultural nuances, thereby reducing communication barriers and promoting intercultural understanding and mutual respect. First, the automatic language translation AI translates languages ​​in real time to facilitate communication between foreign crew members and Japanese personnel. For example, foreign crew members can translate messages sent in Japanese using communication tools such as Slack into their native language. Furthermore, to properly convey cultural nuances, the AI ​​performs translations based on specific cultural backgrounds and customs. For example, it converts the Japanese expression "otsukaresama" (thank you for your hard work) into appropriate English expressions. Next, the cultural education AI provides information about Japanese culture and customs to promote intercultural understanding and mutual respect. For example, it provides information on Japanese etiquette and business manners to help foreign crew members behave appropriately in the Japanese workplace. Furthermore, it provides an automatic monitoring and reporting system that uses AI to monitor discriminatory remarks and hate speech and take prompt action. For example, if discriminatory remarks are detected on communication tools such as Slack, the AI ​​will automatically report them and take appropriate measures. This system prevents foreign crew members from becoming isolated and discourages them from quitting. In this way, the present invention provides support AI that prevents foreign crew members from being forced to accept Japan's unique culture and customs, promoting understanding and mutual respect between different cultures. This allows the support system to facilitate communication between foreign crew members and Japanese people, promoting understanding and mutual respect between different cultures.

[0029] The support system according to the embodiment includes a translation unit, an education unit, and a monitoring unit. The translation unit translates languages ​​in real time to facilitate communication between foreign crew members and Japanese crew members. For example, the translation unit can translate messages sent in Japanese by foreign crew members into their native language using a communication tool such as Slack. The translation unit can also perform translations based on specific cultural backgrounds and customs. For example, it can convert the Japanese expression "otsukaresama" (thank you for your hard work) into an appropriate English expression. The education unit provides information about Japanese culture and customs based on the information translated by the translation unit. For example, the education unit provides information about Japanese etiquette and business manners to help foreign crew members behave appropriately in a Japanese workplace. Furthermore, the education unit can provide guidelines for foreign crew members to behave appropriately in a Japanese workplace. The monitoring unit monitors discriminatory remarks and hate speech based on the information provided by the education unit and takes prompt measures. For example, if discriminatory remarks are detected using a communication tool such as Slack, the monitoring unit automatically reports the remarks and takes appropriate measures. This allows the support system to prevent foreign crew members from becoming isolated and discourage them from quitting. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without AI. For example, the monitoring unit could input comments made on communication tools such as Slack into AI, and have the AI ​​detect and report discriminatory comments.

[0030] The translation unit can translate languages ​​in real time. Real-time translation includes, but is not limited to, a tolerance for delay time. For example, the translation unit can translate messages sent in Japanese by foreign crew members into their native language using a communication tool such as Slack. The translation unit can also perform translations based on specific cultural backgrounds and customs. For example, the translation unit can convert the Japanese expression "otsukaresama" (thank you for your hard work) into an appropriate English expression. This reduces communication delays through real-time translation. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit can input messages sent through a communication tool such as Slack into AI and have the AI ​​perform real-time translation.

[0031] The training department can provide information about Japanese etiquette and business manners. For example, the training department can provide information about Japanese etiquette and business manners to enable foreign crew members to behave appropriately in a Japanese workplace. Examples of etiquette and business manners include, but are not limited to, how to hand over a business card and how to speak in a meeting. This enables foreign crew members to behave appropriately in a Japanese workplace. Some or all of the above-mentioned processing in the training department can be performed, for example, using AI or without AI. For example, the training department can input information about Japanese etiquette and business manners into AI and have the AI ​​generate information to provide to foreign crew members.

[0032] The monitoring unit can automatically report discriminatory remarks detected in a specific communication tool and take appropriate measures. For example, the monitoring unit can automatically report discriminatory remarks detected in a communication tool such as Slack and take appropriate measures. Specific communication tools include, but are not limited to, Slack, email, and chat apps. This allows discriminatory remarks and hate speech to be dealt with quickly and prevents foreign crew members from being isolated. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input remarks made in a communication tool such as Slack into AI and have the AI ​​detect and report discriminatory remarks.

[0033] The translation unit can perform translations based on specific cultural backgrounds and customs. For example, the translation unit converts the Japanese expression "otsukaresama" (thank you for your hard work) into an appropriate English expression. Examples of specific cultural backgrounds and customs include, but are not limited to, religious customs and regional cultures. This allows cultural nuances to be conveyed appropriately. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can have AI perform translations based on Japanese cultural backgrounds and customs.

[0034] The training department can provide guidelines for foreign crew members to behave appropriately in the Japanese workplace. For example, the training department provides guidelines for foreign crew members to behave appropriately in the Japanese workplace. Guidelines include, but are not limited to, for example, a code of conduct and a dress code. This allows foreign crew members to behave appropriately in the Japanese workplace. Some or all of the above-mentioned processing in the training department may be performed using, or without using, AI. For example, the training department can have AI generate guidelines for foreign crew members to behave appropriately in the Japanese workplace.

[0035] The translation unit can improve the accuracy of translation by taking into account specific industry terminology and technical terms during translation. For example, the translation unit can refer to a technical terminology dictionary to accurately translate terminology in the medical industry. The translation unit can also use the latest technical terminology database to accurately translate technical terminology in the IT industry. The translation unit can also refer to legal document translation guidelines to accurately translate technical terminology in the legal industry. This enables accurate translation even in specific industries and fields of expertise. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or can be performed without using AI. For example, the translation unit can input specific industry terminology and technical terms into AI to improve the accuracy of the translation.

[0036] When translating, the translation unit can select the optimal translation method by referring to the user's past translation history. For example, the translation unit provides a translation in a similar style based on a translation style used by the user in the past. The translation unit can also prioritize translation expressions that the user has preferred in the past. The translation unit can also adjust the translation to avoid specific expressions based on the user's past translation history. This makes it possible to provide a more appropriate translation based on the user's past translation history. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input the user's past translation history into AI and have the AI ​​select the optimal translation method.

[0037] When translating, the translation unit can prioritize highly relevant translations by taking into account the user's geographical location information. For example, if the user is in a specific region, the translation unit can translate by taking into account the dialect and cultural background of that region. Furthermore, if the user is traveling, the translation unit can provide translations based on the culture and customs of the destination. Furthermore, if the user is on a business trip, the translation unit can prioritize translations of business-related terms and expressions. This makes it possible to provide appropriate translations based on the user's geographical location. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input the user's geographical location information into AI and have the AI ​​prioritize highly relevant translations.

[0038] The translation unit can analyze the user's social media activity during translation and provide relevant translations. For example, the translation unit reflects expressions frequently used by the user on social media in the translation. The translation unit can also provide relevant translations based on the user's social media interests. The translation unit can also analyze the user's social media activity history and select an appropriate translation style. This makes it possible to provide appropriate translations based on the user's social media activity. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the user's social media activity into AI and have the AI ​​provide relevant translations.

[0039] During training, the training department can customize the training content based on specific cultural backgrounds and customs. For example, when explaining Japanese etiquette, the training department can use appropriate examples taking into account the cultural background of the foreign crew. Furthermore, when explaining Japanese business etiquette, the training department can also provide explanations tailored to the customs of the foreign crew. Furthermore, when explaining Japanese cultural events, the training department can make them easier to understand by comparing them with the culture of the foreign crew. This allows for the provision of appropriate training content based on specific cultural backgrounds and customs. Some or all of the above-described processing in the training department may be performed using, for example, AI, or may be performed without using AI. For example, the training department can have AI customize the training content based on specific cultural backgrounds and customs.

[0040] During training, the training unit can select the optimal training method by referring to the user's past learning history. For example, the training unit can emphasize parts that need to be reviewed based on the content the user has previously studied. The training unit can also re-explain parts that were difficult to understand based on the user's past learning history. The training unit can also analyze the user's learning history and select the most effective training method. This makes it possible to provide an appropriate training method based on the user's past learning history. Some or all of the above-mentioned processing in the training unit can be performed using, for example, AI, or can be performed without using AI. For example, the training unit can input the user's past learning history into AI and have the AI ​​select the optimal training method.

[0041] During education, the education module can provide highly relevant educational content by taking into account the user's geographical location information. For example, if the user is in a specific region, the education module can provide educational content based on the culture and customs of that region. Furthermore, if the user is traveling, the education module can provide educational content based on the culture and customs of the travel destination. Furthermore, if the user is on a business trip, the education module can prioritize providing business-related educational content. This allows appropriate educational content to be provided based on the user's geographical location. Some or all of the above-described processing in the education module may be performed using, or without, AI. For example, the education module can input the user's geographical location information into AI and have the AI ​​provide highly relevant educational content.

[0042] During education, the education department can analyze the user's social media activities and provide relevant educational content. For example, the education department can provide educational content based on topics the user frequently shows interest in on social media. The education department can also analyze the user's social media activity history and select appropriate educational content. The education department can also provide relevant educational content based on the user's social media interests. This makes it possible to provide appropriate educational content based on the user's social media activities. Some or all of the above-mentioned processing in the education department can be performed using, for example, AI, or can be performed without using AI. For example, the education department can input the user's social media activities into AI and have the AI ​​provide relevant educational content.

[0043] The monitoring unit can improve the accuracy of monitoring by taking into account specific keywords and phrases during monitoring. For example, the monitoring unit can list discriminatory keywords and perform monitoring based on those keywords. The monitoring unit can also identify phrases related to hate speech and improve the accuracy of monitoring. The monitoring unit can also monitor discriminatory expressions related to specific cultures or backgrounds. This allows for appropriate monitoring based on specific keywords and phrases. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input specific keywords and phrases into AI to improve the accuracy of monitoring.

[0044] During monitoring, the monitoring unit can select the optimal monitoring method by referring to the user's past speech history. The monitoring unit, for example, analyzes discriminatory tendencies based on the user's past speech history. The monitoring unit can also target specific keywords from the user's past speech history for monitoring. The monitoring unit can also analyze the user's speech history and select the most effective monitoring method. This makes it possible to provide appropriate monitoring based on the user's past speech history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past speech history into AI and have the AI ​​select the optimal monitoring method.

[0045] During monitoring, the monitoring unit can prioritize highly relevant monitoring by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit can perform monitoring based on the culture and background of that area. Furthermore, if the user is traveling, the monitoring unit can also perform monitoring based on the culture and background of the travel destination. Furthermore, if the user is on a business trip, the monitoring unit can prioritize monitoring of business-related comments. This makes it possible to provide appropriate monitoring based on the user's geographical location. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into AI and have the AI ​​prioritize highly relevant monitoring.

[0046] During monitoring, the monitoring unit can analyze the user's social media activities and perform related monitoring. For example, the monitoring unit can monitor expressions frequently used by the user on social media. The monitoring unit can also analyze the user's social media activity history and set appropriate monitoring criteria. The monitoring unit can also perform related monitoring based on the user's social media interests. This makes it possible to provide appropriate monitoring based on the user's social media activities. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's social media activities into AI and have the AI ​​perform related monitoring.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The support system may further include a feedback unit. The feedback unit collects feedback from foreign crew members and uses it to improve the system. For example, if foreign crew members provide feedback on the quality of translation, the translation unit can adjust the translation algorithm based on that feedback. The education unit can receive feedback on cultural education from foreign crew members and customize the education content more effectively. Furthermore, the monitoring unit can review the monitoring standards based on feedback on discriminatory remarks from foreign crew members. This allows the support system to flexibly respond to the needs of foreign crew members.

[0049] The education department can provide customized educational content according to the user's learning style. For example, for users who prefer visual learning, content that makes extensive use of videos and illustrations can be provided. For users who prefer auditory learning, audio guides and podcast-style content can be provided. Furthermore, for users who prefer hands-on learning, interactive simulations and quizzes can be provided. This maximizes the effectiveness of user learning.

[0050] The translation unit can analyze the user's translation history and learn the user's individual translation style. For example, it can prioritize translation expressions that the user has previously preferred. It can also learn expressions that the user wants to avoid and adjust the translation to avoid them. Furthermore, it can learn specific industry terms and technical terms from the user's translation history to improve the accuracy of translations. This allows the system to provide the most suitable translation for the user.

[0051] The translation department can receive real-time feedback from users and improve the quality of translations on the spot. For example, if a user expresses dissatisfaction with the translation results, the translation algorithm can be adjusted based on that feedback. Also, if a user prefers a particular expression, it can prioritize the use of that expression. Furthermore, if a user has a particular cultural background, it can provide translations based on that background. This allows the optimal translation for the user to be provided in real time.

[0052] The monitoring unit can analyze a user's past behavior history and predict future behavior to improve the accuracy of monitoring. For example, if a user has made discriminatory remarks during a specific time period in the past, monitoring can be strengthened during that time period. Also, if a user has a tendency to make extreme remarks in specific situations, monitoring can be performed according to those situations. Furthermore, the monitoring unit can learn the user's behavior patterns and perform monitoring based on predictions. This allows for more effective monitoring.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The translation department translates languages ​​in real time to facilitate communication between foreign crew members and Japanese. For example, they can use communication tools such as Slack to translate messages sent in Japanese by foreign crew members into their own native language. They also translate based on specific cultural backgrounds and customs. For example, they convert the Japanese expression "otsukaresama" (thank you for your hard work) into the appropriate English expression. Step 2: The Education Department will provide information about Japanese culture and customs based on the information translated by the Translation Department. For example, they will provide information about Japanese etiquette and business manners to help foreign crew members behave appropriately in the Japanese workplace. They will also provide guidelines for foreign crew members to behave appropriately in the Japanese workplace. Step 3: The monitoring unit will monitor discriminatory remarks and hate speech based on the information provided by the Ministry of Education and take prompt action. For example, if discriminatory remarks are detected on communication tools such as Slack, they will be automatically reported and appropriate measures will be taken. The monitoring unit's processing may be carried out using AI or without AI.

[0055] (Example 2) A support system according to an embodiment of the present invention provides support to foreign crew members working on-site to prevent them from imposing Japan's unique culture and customs. This support system improves automatic language translation AI and provides a mechanism for appropriately conveying cultural nuances, thereby reducing communication barriers and promoting intercultural understanding and mutual respect. First, the automatic language translation AI translates languages ​​in real time to facilitate communication between foreign crew members and Japanese personnel. For example, foreign crew members can translate messages sent in Japanese using communication tools such as Slack into their native language. Furthermore, to properly convey cultural nuances, the AI ​​performs translations based on specific cultural backgrounds and customs. For example, it converts the Japanese expression "otsukaresama" (thank you for your hard work) into appropriate English expressions. Next, the cultural education AI provides information about Japanese culture and customs to promote intercultural understanding and mutual respect. For example, it provides information on Japanese etiquette and business manners to help foreign crew members behave appropriately in the Japanese workplace. Furthermore, it provides an automatic monitoring and reporting system that uses AI to monitor discriminatory remarks and hate speech and take prompt action. For example, if discriminatory remarks are detected on communication tools such as Slack, the AI ​​will automatically report them and take appropriate measures. This system prevents foreign crew members from becoming isolated and discourages them from quitting. In this way, the present invention provides support AI that prevents foreign crew members from being forced to accept Japan's unique culture and customs, promoting understanding and mutual respect between different cultures. This allows the support system to facilitate communication between foreign crew members and Japanese people, promoting understanding and mutual respect between different cultures.

[0056] The support system according to the embodiment includes a translation unit, an education unit, and a monitoring unit. The translation unit translates languages ​​in real time to facilitate communication between foreign crew members and Japanese crew members. For example, the translation unit can translate messages sent in Japanese by foreign crew members into their native language using a communication tool such as Slack. The translation unit can also perform translations based on specific cultural backgrounds and customs. For example, it can convert the Japanese expression "otsukaresama" (thank you for your hard work) into an appropriate English expression. The education unit provides information about Japanese culture and customs based on the information translated by the translation unit. For example, the education unit provides information about Japanese etiquette and business manners to help foreign crew members behave appropriately in a Japanese workplace. Furthermore, the education unit can provide guidelines for foreign crew members to behave appropriately in a Japanese workplace. The monitoring unit monitors discriminatory remarks and hate speech based on the information provided by the education unit and takes prompt measures. For example, if discriminatory remarks are detected using a communication tool such as Slack, the monitoring unit automatically reports the remarks and takes appropriate measures. This allows the support system to prevent foreign crew members from becoming isolated and discourage them from quitting. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without AI. For example, the monitoring unit could input comments made on communication tools such as Slack into AI, and have the AI ​​detect and report discriminatory comments.

[0057] The translation unit can translate languages ​​in real time. Real-time translation includes, but is not limited to, a tolerance for delay time. For example, the translation unit can translate messages sent in Japanese by foreign crew members into their native language using a communication tool such as Slack. The translation unit can also perform translations based on specific cultural backgrounds and customs. For example, the translation unit can convert the Japanese expression "otsukaresama" (thank you for your hard work) into an appropriate English expression. This reduces communication delays through real-time translation. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit can input messages sent through a communication tool such as Slack into AI and have the AI ​​perform real-time translation.

[0058] The training department can provide information about Japanese etiquette and business manners. For example, the training department can provide information about Japanese etiquette and business manners to enable foreign crew members to behave appropriately in a Japanese workplace. Examples of etiquette and business manners include, but are not limited to, how to hand over a business card and how to speak in a meeting. This enables foreign crew members to behave appropriately in a Japanese workplace. Some or all of the above-mentioned processing in the training department can be performed, for example, using AI or without AI. For example, the training department can input information about Japanese etiquette and business manners into AI and have the AI ​​generate information to provide to foreign crew members.

[0059] The monitoring unit can automatically report discriminatory remarks detected in a specific communication tool and take appropriate measures. For example, the monitoring unit can automatically report discriminatory remarks detected in a communication tool such as Slack and take appropriate measures. Specific communication tools include, but are not limited to, Slack, email, and chat apps. This allows discriminatory remarks and hate speech to be dealt with quickly and prevents foreign crew members from being isolated. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input remarks made in a communication tool such as Slack into AI and have the AI ​​detect and report discriminatory remarks.

[0060] The translation unit can perform translations based on specific cultural backgrounds and customs. For example, the translation unit converts the Japanese expression "otsukaresama" (thank you for your hard work) into an appropriate English expression. Examples of specific cultural backgrounds and customs include, but are not limited to, religious customs and regional cultures. This allows cultural nuances to be conveyed appropriately. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can have AI perform translations based on Japanese cultural backgrounds and customs.

[0061] The training department can provide guidelines for foreign crew members to behave appropriately in the Japanese workplace. For example, the training department provides guidelines for foreign crew members to behave appropriately in the Japanese workplace. Guidelines include, but are not limited to, for example, a code of conduct and a dress code. This allows foreign crew members to behave appropriately in the Japanese workplace. Some or all of the above-mentioned processing in the training department may be performed using, or without using, AI. For example, the training department can have AI generate guidelines for foreign crew members to behave appropriately in the Japanese workplace.

[0062] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is stressed, the translation unit can provide a concise and easy-to-understand translation. Furthermore, if the user is relaxed, the translation unit can provide a translation that includes detailed explanations. Furthermore, if the user is in a hurry, the translation unit can provide a translation that focuses on the main points so that the user can understand quickly. This allows for an appropriate translation to be provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit can estimate the user's emotions and have AI adjust the translation expression based on the estimated emotions.

[0063] The translation unit can improve the accuracy of translation by taking into account specific industry terminology and technical terms during translation. For example, the translation unit can refer to a technical terminology dictionary to accurately translate terminology in the medical industry. The translation unit can also use the latest technical terminology database to accurately translate technical terminology in the IT industry. The translation unit can also refer to legal document translation guidelines to accurately translate technical terminology in the legal industry. This enables accurate translation even in specific industries and fields of expertise. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or can be performed without using AI. For example, the translation unit can input specific industry terminology and technical terms into AI to improve the accuracy of the translation.

[0064] When translating, the translation unit can select the optimal translation method by referring to the user's past translation history. For example, the translation unit provides a translation in a similar style based on a translation style used by the user in the past. The translation unit can also prioritize translation expressions that the user has preferred in the past. The translation unit can also adjust the translation to avoid specific expressions based on the user's past translation history. This makes it possible to provide a more appropriate translation based on the user's past translation history. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input the user's past translation history into AI and have the AI ​​select the optimal translation method.

[0065] The translation unit can estimate the user's emotions and determine translation priorities based on the estimated user emotions. For example, when the user is in an emergency, the translation unit prioritizes translation of important messages. Furthermore, when the user is relaxed, the translation unit can equally translate all messages. Furthermore, when the user is stressed, the translation unit can prioritize translation of concise and important messages. This allows important messages to be translated preferentially according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit can estimate the user's emotions and have AI determine translation priorities based on the estimated emotions.

[0066] When translating, the translation unit can prioritize highly relevant translations by taking into account the user's geographical location information. For example, if the user is in a specific region, the translation unit can translate by taking into account the dialect and cultural background of that region. Furthermore, if the user is traveling, the translation unit can provide translations based on the culture and customs of the destination. Furthermore, if the user is on a business trip, the translation unit can prioritize translations of business-related terms and expressions. This makes it possible to provide appropriate translations based on the user's geographical location. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input the user's geographical location information into AI and have the AI ​​prioritize highly relevant translations.

[0067] The translation unit can analyze the user's social media activity during translation and provide relevant translations. For example, the translation unit reflects expressions frequently used by the user on social media in the translation. The translation unit can also provide relevant translations based on the user's social media interests. The translation unit can also analyze the user's social media activity history and select an appropriate translation style. This makes it possible to provide appropriate translations based on the user's social media activity. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the user's social media activity into AI and have the AI ​​provide relevant translations.

[0068] The education unit can estimate the user's emotions and adjust the way educational content is presented based on the estimated user emotions. For example, if the user is excited, the education unit can provide visually stimulating educational content. Furthermore, if the user is relaxed, the education unit can provide educational content with detailed explanations. Furthermore, if the user is stressed, the education unit can provide concise and easy-to-understand educational content. This allows appropriate educational content to be provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the education unit can be performed using, for example, an AI, or without an AI. For example, the education unit can estimate the user's emotions and have the AI ​​adjust the way educational content is presented based on the estimated emotions.

[0069] During training, the training department can customize the training content based on specific cultural backgrounds and customs. For example, when explaining Japanese etiquette, the training department can use appropriate examples taking into account the cultural background of the foreign crew. Furthermore, when explaining Japanese business etiquette, the training department can also provide explanations tailored to the customs of the foreign crew. Furthermore, when explaining Japanese cultural events, the training department can make them easier to understand by comparing them with the culture of the foreign crew. This allows for the provision of appropriate training content based on specific cultural backgrounds and customs. Some or all of the above-described processing in the training department may be performed using, for example, AI, or may be performed without using AI. For example, the training department can have AI customize the training content based on specific cultural backgrounds and customs.

[0070] During training, the training unit can select the optimal training method by referring to the user's past learning history. For example, the training unit can emphasize parts that need to be reviewed based on the content the user has previously studied. The training unit can also re-explain parts that were difficult to understand based on the user's past learning history. The training unit can also analyze the user's learning history and select the most effective training method. This makes it possible to provide an appropriate training method based on the user's past learning history. Some or all of the above-mentioned processing in the training unit can be performed using, for example, AI, or can be performed without using AI. For example, the training unit can input the user's past learning history into AI and have the AI ​​select the optimal training method.

[0071] The education unit can estimate the user's emotions and prioritize educational content based on the estimated user emotions. For example, when the user is in an emergency, the education unit can prioritize providing important educational content. The education unit can also provide all educational content equally when the user is relaxed. The education unit can also prioritize providing concise and important educational content when the user is stressed. This allows important educational content to be prioritized according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the education unit can be performed using, for example, an AI, or without an AI. For example, the education unit can estimate the user's emotions and have an AI prioritize educational content based on the estimated emotions.

[0072] During education, the education module can provide highly relevant educational content by taking into account the user's geographical location information. For example, if the user is in a specific region, the education module can provide educational content based on the culture and customs of that region. Furthermore, if the user is traveling, the education module can provide educational content based on the culture and customs of the travel destination. Furthermore, if the user is on a business trip, the education module can prioritize providing business-related educational content. This allows appropriate educational content to be provided based on the user's geographical location. Some or all of the above-described processing in the education module may be performed using, or without, AI. For example, the education module can input the user's geographical location information into AI and have the AI ​​provide highly relevant educational content.

[0073] During education, the education department can analyze the user's social media activities and provide relevant educational content. For example, the education department can provide educational content based on topics the user frequently shows interest in on social media. The education department can also analyze the user's social media activity history and select appropriate educational content. The education department can also provide relevant educational content based on the user's social media interests. This makes it possible to provide appropriate educational content based on the user's social media activities. Some or all of the above-mentioned processing in the education department can be performed using, for example, AI, or can be performed without using AI. For example, the education department can input the user's social media activities into AI and have the AI ​​provide relevant educational content.

[0074] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can strengthen monitoring of discriminatory remarks. Furthermore, if the user is relaxed, the monitoring unit can also apply normal monitoring criteria. Furthermore, if the user is excited, the monitoring unit can also strengthen monitoring of extreme remarks. This makes it possible to provide appropriate monitoring criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can estimate the user's emotions and have the AI ​​adjust the monitoring criteria based on the estimated emotions.

[0075] The monitoring unit can improve the accuracy of monitoring by taking into account specific keywords and phrases during monitoring. For example, the monitoring unit can list discriminatory keywords and perform monitoring based on those keywords. The monitoring unit can also identify phrases related to hate speech and improve the accuracy of monitoring. The monitoring unit can also monitor discriminatory expressions related to specific cultures or backgrounds. This allows for appropriate monitoring based on specific keywords and phrases. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input specific keywords and phrases into AI to improve the accuracy of monitoring.

[0076] During monitoring, the monitoring unit can select the optimal monitoring method by referring to the user's past speech history. The monitoring unit, for example, analyzes discriminatory tendencies based on the user's past speech history. The monitoring unit can also target specific keywords from the user's past speech history for monitoring. The monitoring unit can also analyze the user's speech history and select the most effective monitoring method. This makes it possible to provide appropriate monitoring based on the user's past speech history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past speech history into AI and have the AI ​​select the optimal monitoring method.

[0077] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. For example, when the user is in an emergency, the monitoring unit prioritizes monitoring important comments. Furthermore, when the user is relaxed, the monitoring unit can monitor all comments equally. Furthermore, when the user is stressed, the monitoring unit can prioritize monitoring discriminatory comments. This allows important comments to be prioritized according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can estimate the user's emotions and have an AI determine the monitoring priorities based on the estimated emotions.

[0078] During monitoring, the monitoring unit can prioritize highly relevant monitoring by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit can perform monitoring based on the culture and background of that area. Furthermore, if the user is traveling, the monitoring unit can also perform monitoring based on the culture and background of the travel destination. Furthermore, if the user is on a business trip, the monitoring unit can prioritize monitoring of business-related comments. This makes it possible to provide appropriate monitoring based on the user's geographical location. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into AI and have the AI ​​prioritize highly relevant monitoring.

[0079] During monitoring, the monitoring unit can analyze the user's social media activities and perform related monitoring. For example, the monitoring unit can monitor expressions frequently used by the user on social media. The monitoring unit can also analyze the user's social media activity history and set appropriate monitoring criteria. The monitoring unit can also perform related monitoring based on the user's social media interests. This makes it possible to provide appropriate monitoring based on the user's social media activities. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's social media activities into AI and have the AI ​​perform related monitoring. === Hard Collateral 1-1 === Each of the multiple elements, including the translation unit, education unit, and monitoring unit, described above, may be implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the translation unit may be implemented by the control unit 46A of the smart device 14 and translate languages ​​in real time. The education unit may be implemented by the specific processing unit 290 of the data processing device 12 and provide information about Japanese culture and customs. The monitoring unit may be implemented by the specific processing unit 290 of the data processing device 12 and monitor discriminatory remarks and hate speech and take prompt action. Some or all of the translation unit, education unit, and monitoring unit may be implemented, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the translation unit, education unit, and monitoring unit, described above, may be realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the translation unit may be realized by the control unit 46A of the smart glasses 214 and translate languages ​​in real time. The education unit may be realized by the specific processing unit 290 of the data processing device 12 and provide information about Japanese culture and customs. The monitoring unit may be realized by the specific processing unit 290 of the data processing device 12 and monitor discriminatory remarks and hate speech and take prompt action. Some or all of the translation unit, education unit, and monitoring unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the translation unit, education unit, and monitoring unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the translation unit is realized by the control unit 46A of the headset-type terminal 314 and translates languages ​​in real time. The education unit is realized by the specific processing unit 290 of the data processing device 12 and provides information about Japanese culture and customs. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors discriminatory remarks and hate speech and takes prompt action. Some or all of the translation unit, education unit, and monitoring unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the translation unit, education unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the translation unit is realized by the control unit 46A of the robot 414 and translates languages ​​in real time. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information about Japanese culture and customs. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors discriminatory remarks and hate speech and takes prompt action. Some or all of the translation unit, education unit, and monitoring unit may be realized, for example, by the control unit 46A of the robot 414.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The support system may further include a feedback unit. The feedback unit collects feedback from foreign crew members and uses it to improve the system. For example, if foreign crew members provide feedback on the quality of translation, the translation unit can adjust the translation algorithm based on that feedback. The education unit can receive feedback on cultural education from foreign crew members and customize the education content more effectively. Furthermore, the monitoring unit can review the monitoring standards based on feedback on discriminatory remarks from foreign crew members. This allows the support system to flexibly respond to the needs of foreign crew members.

[0082] The translation unit can convert a user's voice input into text and translate that text. For example, it can translate what a foreign crew member says in Japanese into their native language in real time. The translation unit can also handle different accents and dialects using speech recognition technology. Furthermore, the translation unit can estimate the emotion of the voice input and provide a translation that corresponds to that emotion. This facilitates smooth voice communication.

[0083] The education department can provide customized educational content according to the user's learning style. For example, for users who prefer visual learning, content that makes extensive use of videos and illustrations can be provided. For users who prefer auditory learning, audio guides and podcast-style content can be provided. Furthermore, for users who prefer hands-on learning, interactive simulations and quizzes can be provided. This maximizes the effectiveness of user learning.

[0084] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is feeling stressed, the monitoring frequency can be increased to detect discriminatory remarks early. Alternatively, if the user is relaxed, the normal monitoring frequency can be maintained. Furthermore, if the user is excited, monitoring for extreme remarks can be strengthened. In this way, appropriate monitoring can be performed according to the user's emotions.

[0085] The translation unit can analyze the user's translation history and learn the user's individual translation style. For example, it can prioritize translation expressions that the user has previously preferred. It can also learn expressions that the user wants to avoid and adjust the translation to avoid them. Furthermore, it can learn specific industry terms and technical terms from the user's translation history to improve the accuracy of translations. This allows the system to provide the most suitable translation for the user.

[0086] The education department can estimate the user's emotions and adjust the difficulty of the educational content based on the estimated emotions. For example, if the user is feeling stressed, simple and easy-to-understand content can be provided. If the user is relaxed, detailed and in-depth content can be provided. Furthermore, if the user is excited, interesting and stimulating content can be provided. In this way, appropriate education can be provided according to the user's emotions.

[0087] The translation department can receive real-time feedback from users and improve the quality of translations on the spot. For example, if a user expresses dissatisfaction with the translation results, the translation algorithm can be adjusted based on that feedback. Also, if a user prefers a particular expression, it can prioritize the use of that expression. Furthermore, if a user has a particular cultural background, it can provide translations based on that background. This allows the optimal translation for the user to be provided in real time.

[0088] The education unit can estimate the user's emotions and adjust the timing of education based on the estimated emotions. For example, if the user is feeling stressed, the timing of education can be delayed. Also, if the user is relaxed, the timing of education can be advanced. Furthermore, if the user is excited, the timing of education can be adjusted to increase concentration. In this way, education is provided at the optimal timing according to the user's emotions.

[0089] The monitoring unit can analyze a user's past behavior history and predict future behavior to improve the accuracy of monitoring. For example, if a user has made discriminatory remarks during a specific time period in the past, monitoring can be strengthened during that time period. Also, if a user has a tendency to make extreme remarks in specific situations, monitoring can be performed according to those situations. Furthermore, the monitoring unit can learn the user's behavior patterns and perform monitoring based on predictions. This allows for more effective monitoring.

[0090] The translation unit can estimate the user's emotions and adjust the tone of the translation based on the estimated emotions. For example, if the user is angry, a translation with a calm and composed tone can be provided. If the user is sad, a translation with a gentle tone can be provided. Furthermore, if the user is happy, a translation with a bright and positive tone can be provided. In this way, a translation with an appropriate tone can be provided according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The translation department translates languages ​​in real time to facilitate communication between foreign crew members and Japanese. For example, they can use communication tools such as Slack to translate messages sent in Japanese by foreign crew members into their own native language. They also translate based on specific cultural backgrounds and customs. For example, they convert the Japanese expression "otsukaresama" (thank you for your hard work) into the appropriate English expression. Step 2: The Education Department will provide information about Japanese culture and customs based on the information translated by the Translation Department. For example, they will provide information about Japanese etiquette and business manners to help foreign crew members behave appropriately in the Japanese workplace. They will also provide guidelines for foreign crew members to behave appropriately in the Japanese workplace. Step 3: The monitoring unit will monitor discriminatory remarks and hate speech based on the information provided by the Ministry of Education and take prompt action. For example, if discriminatory remarks are detected on communication tools such as Slack, they will be automatically reported and appropriate measures will be taken. The monitoring unit's processing may be carried out using AI or without AI.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 translation department to facilitate communication between foreign crew members and Japanese people, an education department that provides information about Japanese culture and customs based on the information translated by the translation department; a monitoring unit that monitors discriminatory remarks and hate speech and takes appropriate measures based on the information provided by the Ministry of Education. A system characterized by:

2. The translation unit Translate languages ​​in real time 2. The system of claim 1.

3. The Ministry of Education, Providing information about Japanese etiquette and business manners 2. The system of claim 1.

4. The monitoring unit If discriminatory remarks are detected in certain communication tools, they will be automatically reported and appropriate measures will be taken.

2. The system of claim 1.

5. The translation unit Translation based on specific cultural backgrounds and customs 2. The system of claim 1.

6. The Ministry of Education, Providing guidelines for foreign crew members to behave appropriately in the Japanese workplace 2. The system of claim 1.

7. The translation unit Estimate the user's emotions and adjust the translation style based on the estimated user emotions.

2. The system of claim 1.

8. The translation unit Improve translation accuracy by taking into account specific industry and technical terms during translation 2. The system of claim 1.

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