system

Abstract

The system according to the embodiment aims to realize seamless communication between users who speak different languages. [Solution] A system according to an embodiment includes a reception unit, a translation unit, and a display unit. The reception unit receives posts from users. The translation unit translates the posts received by the reception unit into a language of another country. The display unit displays the posts translated by the translation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to achieve seamless communication between users who speak different languages.

[0005] The system according to the embodiment aims to realize seamless communication between users who speak different languages. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a translation unit, and a display unit. The reception unit receives posts from users. The translation unit translates the posts received by the reception unit into a language of another country. The display unit displays the posts translated by the translation unit. [Effects of the Invention]

[0007] The system according to the embodiment can realize seamless communication between users who speak different languages. [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 system according to an embodiment of the present invention combines natural language translation and social networking using a generative AI. In this system, when a user posts in their own language, the post is automatically translated into the language of that language when displayed to users in other countries. Furthermore, all exchanges in the comments section are automatically translated, enabling natural conversations. This system eliminates language barriers and enables seamless communication. For example, a user posts in their own language. The post is automatically translated into another language by a generative AI. For example, a post written in Japanese is displayed in English to English-speaking users. In this case, the generative AI analyzes the post content and provides an appropriate translation. Next, the exchanges in the comments section are automatically translated. For example, a comment written in Japanese is displayed in English to English-speaking users, and a comment written in English is displayed in Japanese to Japanese-speaking users. In this way, users who speak different languages ​​can have natural conversations. This system eliminates language barriers and enables seamless communication between users who speak different languages. For example, in business situations, partners from different countries can smoothly communicate without experiencing language barriers. It is also useful when communicating with local people while traveling. Furthermore, this system uses generative AI to achieve high translation accuracy and natural expressions. For example, nuances such as slang and dialects are translated appropriately, allowing users to enjoy conversations without any discomfort. In this way, the present invention is a system that uses generative AI to combine natural language translation and SNS, eliminating language barriers and realizing seamless communication. As a result, the system can automatically translate and display user posts into other languages, eliminating language barriers and realizing seamless communication.

[0029] A communication system according to an embodiment includes a reception unit, a translation unit, and a display unit. The reception unit receives posts from users. Posts from users include, but are not limited to, text, images, and videos. For example, the reception unit receives text data entered by users and converts it into a format that can be processed within the system. The reception unit can also receive media data such as images and videos. For example, the reception unit receives image data uploaded by users and converts it into a format that can be displayed within the system. The translation unit uses a generation AI to translate the posts received by the reception unit into other languages. The translation is performed based on, for example, a translation algorithm used and translation accuracy, but is not limited to, these examples. For example, the translation unit translates text data using a neural network. The translation unit can also translate text data using a rule-based translation algorithm. Furthermore, the translation unit can use a generation AI to understand context and perform an appropriate translation. For example, the translation unit analyzes the content of the post using the generation AI and performs an appropriate translation based on the context. The display unit displays the post translated by the translation unit. The display is performed based on, for example, a display format or a display device, but is not limited to such examples. For example, the display unit displays the translated text data on a web browser. The display unit can also display the translated text data on a mobile application. Furthermore, the display unit can send the translated text data by email. For example, the display unit sends the translated text data as the body of an email. In this way, the communication system according to the embodiment can automatically translate and display user posts into other languages, thereby eliminating language barriers and realizing seamless communication.

[0030] The communication system includes a comment translation unit that automatically translates exchanges in the comment section. The comment translation unit automatically translates exchanges in the comment section. The comment section may be, for example, text-only or may include attachments of images and videos, but is not limited to these examples. The comment translation unit, for example, receives comments entered by a user and translates them into another language using a generation AI. For example, the comment translation unit translates comments entered by a user in Japanese into English and displays them to English-speaking users. The comment translation unit can also translate comments entered by a user in English into Japanese and display them to Japanese-speaking users. Furthermore, the comment translation unit can appropriately translate nuances such as slang and dialects using a generation AI. For example, the comment translation unit analyzes slang and dialects and performs appropriate translations. This allows the exchanges in the comment section to be automatically translated, enabling natural conversations. Some or all of the above-described processing in the comment translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the comment translation unit can translate comments by inputting user comments into a generation AI model that outputs the translation results. This allows the communication system to automatically translate exchanges in the comment section, enabling natural conversations.

[0031] The translation unit can analyze the posted content using a generation AI and provide an appropriate translation. The translation unit can analyze the posted content using a generation AI and provide an appropriate translation. The generation AI can, for example, analyze text data using a neural network and perform translation. For example, the generation AI can analyze the posted content and provide an appropriate translation based on the context. The generation AI can also analyze text data using a rule-based translation algorithm and perform translation. For example, the generation AI can analyze the posted content based on specific rules and provide an appropriate translation. Furthermore, the generation AI can also appropriately translate nuances such as slang and dialects. For example, the generation AI can analyze slang and dialects and provide an appropriate translation. This allows the generation AI to analyze the posted content and provide an appropriate translation, thereby improving the accuracy of the translation. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the translation unit can input the posted content entered by the user into the generation AI and perform translation using a generation AI model that outputs the translation result. This allows the translation unit to analyze the posted content using the generation AI and provide an appropriate translation.

[0032] The display unit can display the translated post to users in other countries. The display unit displays the post translated by the translation unit to users in other countries. The display is performed based on, for example, a display format or a display device, but is not limited to such examples. For example, the display unit displays the translated text data on a web browser. The display unit can also display the translated text data on a mobile application. Furthermore, the display unit can send the translated text data by email. For example, the display unit sends the translated text data as the body of an email. In this way, by displaying the translated post to users in other countries, users who speak different languages ​​can have a natural conversation. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the translated text data to the generation AI and have the generation AI determine the display format. In this way, the display unit can display the translated post to users in other countries.

[0033] The comment translation unit can translate comments into other languages ​​and display them. The comment translation unit can translate comments into other languages ​​and display them. For example, the comment translation unit receives comments entered by a user and translates them into other languages ​​using a generation AI. For example, the comment translation unit can translate comments entered in Japanese by a user into English and display them to English-speaking users. The comment translation unit can also translate comments entered in English by a user into Japanese and display them to Japanese-speaking users. Furthermore, the comment translation unit can appropriately translate nuances such as slang and dialects using a generation AI. For example, the comment translation unit uses a generation AI to analyze slang and dialects and perform appropriate translations. This allows users who speak different languages ​​to have natural conversations by translating and displaying comments into other languages. Some or all of the above-described processing in the comment translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the comment translation unit can translate comments entered by a user using a generation AI model that inputs comments entered by a user into a generation AI and outputs the translation results. This allows the comment translation unit to translate the comment into a foreign language and display it.

[0034] The translation unit can also appropriately translate the nuances of slang and dialects. The translation unit can also appropriately translate the nuances of slang and dialects. For example, the translation unit uses a generation AI to analyze slang and dialects and perform an appropriate translation. For example, the translation unit uses a generation AI to analyze slang and dialects and perform an appropriate translation based on the context. The translation unit can also analyze slang and dialects using a rule-based translation algorithm and perform an appropriate translation. For example, the translation unit analyzes slang and dialects based on specific rules and performs an appropriate translation. This allows the user to enjoy conversations without feeling uncomfortable by appropriately translating the nuances of slang and dialects. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can perform translation using a generation AI model that inputs slang and dialects entered by a user into a generation AI and outputs the translation result. This allows the translation unit to appropriately translate the nuances of slang and dialects.

[0035] The reception unit can analyze the user's past posting history and select the optimal reception method. The reception unit uses a generation AI to analyze the user's past posting history and select the optimal reception method. For example, the reception unit can analyze the time periods during which the user frequently posted in the past and prioritize reception of posts from those time periods. The reception unit can also analyze the content of the user's past posts and prioritize reception of posts related to a specific theme. Furthermore, the reception unit can analyze the frequency of the user's past posts and prioritize reception of posts from users who post frequently. In this way, the optimal reception method can be selected by analyzing the user's past posting history. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input the user's past posting history data into the generation AI and have the generation AI select the optimal reception method. In this way, the reception unit can analyze the user's past posting history and select the optimal reception method.

[0036] The reception unit can perform filtering based on the user's current areas of interest when receiving a post. The reception unit can use the generation AI to perform filtering based on the user's current areas of interest when receiving a post. For example, the reception unit can preferentially accept posts related to topics in which the user is currently interested. The reception unit can also preferentially accept posts from accounts the user follows. Furthermore, the reception unit can preferentially accept posts related to keywords recently searched by the user. In this way, by filtering based on the user's current areas of interest, highly relevant posts can be preferentially accepted. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's area of ​​interest data into the generation AI and have the generation AI perform filtering. In this way, the reception unit can perform filtering based on the user's current areas of interest when receiving a post.

[0037] The reception unit can prioritize receiving highly relevant posts based on the user's geographical location information when receiving posts. The reception unit uses the generation AI to prioritize receiving highly relevant posts based on the user's geographical location information when receiving posts. For example, the reception unit prioritizes receiving posts related to the area where the user is currently located. The reception unit can also prioritize receiving posts related to places the user has visited in the past. The reception unit can also prioritize receiving posts related to places the user plans to visit in the future. In this way, by taking the user's geographical location information into consideration, highly relevant posts can be prioritized. Some or all of the above-described processing in the reception unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant posts. In this way, the reception unit can prioritize receiving highly relevant posts based on the user's geographical location information when receiving posts.

[0038] The reception unit can analyze the user's social media activity when receiving a post and receive related posts. The reception unit can use the generation AI to analyze the user's social media activity when receiving a post and receive related posts. For example, the reception unit can preferentially receive content related to posts that the user has recently "liked." It can also preferentially receive content related to posts that the user has recently commented on. It can also preferentially receive content related to posts that the user has recently shared. In this way, by analyzing the user's social media activity, related posts can be preferentially received. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related posts. In this way, the reception unit can analyze the user's social media activity when receiving a post and receive related posts.

[0039] The translation unit can adjust the level of detail of the translation based on the importance of the posted content during translation. The translation unit uses the generation AI to adjust the level of detail of the translation based on the importance of the posted content during translation. For example, posts containing important information are translated in detail. Posts containing general information can also be translated concisely. Furthermore, content that is of high interest to users can be translated in detail. In this way, by adjusting the level of detail of the translation based on the importance of the posted content, important information can be appropriately conveyed. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the translation unit can input importance data of the posted content into the generation AI and have the generation AI adjust the level of detail of the translation. In this way, the translation unit can adjust the level of detail of the translation based on the importance of the posted content during translation.

[0040] The translation unit can apply different translation algorithms depending on the category of the post during translation. The translation unit uses the generation AI to apply different translation algorithms depending on the category of the post during translation. For example, an algorithm that properly translates technical terms can be applied to business-related posts. An algorithm that uses casual expressions can also be applied to entertainment-related posts. Furthermore, an algorithm that provides accurate translation can be applied to academic-related posts. In this way, applying different translation algorithms depending on the category of the post enables more appropriate translation. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the translation unit can input category data of the post into the generation AI and have the generation AI select the translation algorithm to apply. In this way, the translation unit can apply different translation algorithms depending on the category of the post during translation.

[0041] The translation unit can determine the priority of translation based on the time of submission of the post during translation. The translation unit can use the generation AI to determine the priority of translation based on the time of submission of the post during translation. For example, the most recent post can be translated with priority. Posts related to a specific event can also be translated with priority. Furthermore, content posted during a time period specified by the user can be translated with priority. In this way, by determining the priority of translation based on the time of submission of the post, the latest information can be translated quickly. Some or all of the above-described processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input data on the time of submission of the post into the generation AI and have the generation AI determine the priority of translation. In this way, the translation unit can determine the priority of translation based on the time of submission of the post during translation.

[0042] The translation unit can adjust the order of translations based on the relevance of posts during translation. The translation unit uses the generation AI to adjust the order of translations based on the relevance of posts during translation. For example, content that is of high interest to the user can be translated preferentially. Posts from accounts that the user follows can also be translated preferentially. Furthermore, posts related to keywords recently searched by the user can also be translated preferentially. By adjusting the order of translations based on the relevance of posts, information that is important to the user can be translated preferentially. Some or all of the above-described processing in the translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the translation unit can input relevance data of posts into the generation AI and have the generation AI adjust the order of translations. This allows the translation unit to adjust the order of translations based on the relevance of posts during translation.

[0043] The display unit can select the optimal display method by referring to the user's past browsing history when displaying content. The display unit uses the generation AI to select the optimal display method by referring to the user's past browsing history when displaying content. For example, content that the user has frequently viewed in the past can be preferentially displayed. Content that the user is most interested in can also be preferentially displayed based on the user's past browsing history. Furthermore, posts that the user has "liked" in the past can be preferentially displayed. In this way, content that the user is most interested in can be preferentially displayed by referring to the user's past browsing history. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's past browsing history data into the generation AI and have the generation AI select the optimal display method. In this way, the display unit can select the optimal display method by referring to the user's past browsing history when displaying content.

[0044] The display unit can customize the display content based on the user's areas of interest when displaying the content. The display unit uses a generation AI to customize the display content based on the user's areas of interest when displaying the content. For example, the display unit can prioritize displaying content related to topics in which the user is currently interested. The display unit can also prioritize displaying posts from accounts the user follows. Furthermore, the display unit can prioritize displaying content related to keywords recently searched by the user. This allows the display unit to provide more relevant information by customizing the display content based on the user's areas of interest. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's areas of interest data into the generation AI and have the generation AI customize the display content. This allows the display unit to customize the display content based on the user's areas of interest when displaying the content.

[0045] The display unit can select the optimal display method by taking into account the user's geographical location information when displaying. The display unit uses the generation AI to select the optimal display method by taking into account the user's geographical location information when displaying. For example, content related to the area where the user is currently located can be preferentially displayed. Content related to places the user has visited in the past can also be preferentially displayed. Content related to places the user plans to visit in the future can also be preferentially displayed. In this way, highly relevant information can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal display method when displaying. In this way, the display unit can select the optimal display method by taking into account the user's geographical location information when displaying.

[0046] The display unit can select the optimal display method by taking into account the user's device information when displaying. The display unit uses the generation AI to select the optimal display method by taking into account the user's device information when displaying. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. This allows the optimal display method to be provided by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the display unit can input the user's device information data into the generation AI and have the generation AI select the optimal display method. This allows the display unit to select the optimal display method by taking into account the user's device information when displaying.

[0047] The comment translation unit can adjust the level of detail of the translation based on the importance of the comment content when translating a comment. The comment translation unit uses the generation AI to adjust the level of detail of the translation based on the importance of the comment content when translating a comment. For example, comments containing important information are translated in detail. Comments containing general information can also be translated concisely. Furthermore, content that is of great interest to the user can be translated in detail. In this way, by adjusting the level of detail of the translation based on the importance of the comment content, important information can be appropriately conveyed. Some or all of the above-mentioned processing in the comment translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the comment translation unit can input importance data of the comment content into the generation AI and cause the generation AI to adjust the level of detail of the translation. In this way, the comment translation unit can adjust the level of detail of the translation based on the importance of the comment content when translating a comment.

[0048] The comment translation unit can apply different translation algorithms depending on the comment category when translating comments. The comment translation unit uses the generation AI to apply different translation algorithms depending on the comment category when translating comments. For example, an algorithm that appropriately translates technical terms can be applied to business-related comments. An algorithm that uses casual expressions can be applied to entertainment-related comments. Furthermore, an algorithm that performs accurate translation can be applied to academic-related comments. In this way, applying different translation algorithms depending on the comment category enables more appropriate translation. Some or all of the above-mentioned processing in the comment translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the comment translation unit can input comment category data into the generation AI and have the generation AI select the translation algorithm to be applied. In this way, the comment translation unit can apply different translation algorithms depending on the comment category when translating comments.

[0049] The comment translation unit can determine the translation priority based on the time the comment was submitted when translating the comment. The comment translation unit can use the generation AI to determine the translation priority based on the time the comment was submitted when translating the comment. For example, the most recent comment can be translated with priority. Comments related to a specific event can also be translated with priority. Furthermore, comments posted during a time period specified by the user can be translated with priority. In this way, by determining the translation priority based on the time the comment was submitted, the latest information can be translated quickly. Some or all of the above-described processing in the comment translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the comment translation unit can input comment submission time data into the generation AI and have the generation AI determine the translation priority. In this way, the comment translation unit can determine the translation priority based on the time the comment was submitted when translating the comment.

[0050] The comment translation unit can adjust the order of translations based on the relevance of comments when translating comments. The comment translation unit adjusts the order of translations based on the relevance of comments when translating comments using a generation AI. For example, content that the user is most interested in can be translated preferentially. Comments from accounts the user follows can also be translated preferentially. Furthermore, comments related to keywords recently searched by the user can also be translated preferentially. In this way, by adjusting the order of translations based on the relevance of comments, information that is important to the user can be translated preferentially. Some or all of the above-described processing in the comment translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the comment translation unit can input comment relevance data into the generation AI and have the generation AI adjust the order of translations. In this way, the comment translation unit can adjust the order of translations based on the relevance of comments when translating comments.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can automatically categorize posts based on the content of the user's post. For example, if a post is business-related, it is categorized into the business category, and if it is entertainment-related, it is categorized into the entertainment category. If a user uses a specific hashtag, the category can also be determined based on that hashtag. Furthermore, the reception unit can analyze the user's past posting history and categorize similar posts into the same category. By automatically categorizing posts based on the content of the post, the reception unit can efficiently manage posts in categories that interest the user.

[0053] The translation department can evaluate the importance of posts based on the content of the user's posts and determine the priority of translation according to the importance. For example, posts containing important business information can be translated first, while posts containing general information can be translated later. In addition, if a user uses a specific keyword, the importance can be evaluated based on that keyword. Furthermore, the importance of posts can be evaluated based on the number of followers and influence of the user, and posts by influential users can be translated first. In this way, the translation department can determine the priority of translation according to the importance of the post, thereby enabling important information to be provided quickly.

[0054] The comment translation unit can automatically categorize comments based on the content of the user's comment. For example, if a comment is business-related, it is categorized as a business category, and if it is entertainment-related, it is categorized as an entertainment category. If a user uses a specific hashtag, the category can also be determined based on that hashtag. Furthermore, the comment translation unit can analyze the user's past comment history and categorize similar comments into the same category. By automatically categorizing comments based on their content, the comment translation unit can efficiently manage comments in categories that interest the user.

[0055] The reception unit can evaluate the relevance of posts based on the content of the user's posts and prioritize the reception of highly relevant posts. For example, it can prioritize the reception of posts related to topics in which the user is currently interested. It can also prioritize the reception of posts from accounts the user follows. It can also prioritize the reception of posts related to keywords recently searched by the user. In this way, the reception unit can efficiently manage information that is important to the user by determining the priority based on the relevance of the posts.

[0056] The reception unit can evaluate the importance of a post based on the content of the user's post and select a reception method according to the importance. For example, posts containing important business information can be received with priority, while posts containing general information can be postponed. Also, if a user uses a specific keyword, the importance can be evaluated based on that keyword. Furthermore, the importance of a post can be evaluated based on the number of followers and influence of the user, and posts by influential users can be received with priority. In this way, the reception unit can efficiently manage important information by selecting a reception method according to the importance of the post.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives posts from users. Posts from users include text, images, videos, etc. The reception unit receives text data entered by users and converts it into a format that can be processed within the system. It can also receive media data such as images and videos. For example, it receives image data uploaded by users and converts it into a format that can be displayed within the system. Step 2: The translation unit uses the generation AI to translate the posts received by the reception unit into other languages. The translation is performed based on the translation algorithm used and the accuracy of the translation. For example, a neural network is used to translate the text data. Alternatively, a rule-based translation algorithm can be used to translate the text data. Furthermore, the generation AI can be used to understand the context and provide an appropriate translation. Step 3: The display unit displays the post translated by the translation unit. The display is performed based on the display format and display device. For example, the translated text data may be displayed on a web browser. The translated text data may also be displayed on a mobile application. The translated text data may also be sent by email.

[0059] (Example 2) A system according to an embodiment of the present invention combines natural language translation and social networking using a generative AI. In this system, when a user posts in their own language, the post is automatically translated into the language of that language when displayed to users in other countries. Furthermore, all exchanges in the comments section are automatically translated, enabling natural conversations. This system eliminates language barriers and enables seamless communication. For example, a user posts in their own language. The post is automatically translated into another language by a generative AI. For example, a post written in Japanese is displayed in English to English-speaking users. In this case, the generative AI analyzes the post content and provides an appropriate translation. Next, the exchanges in the comments section are automatically translated. For example, a comment written in Japanese is displayed in English to English-speaking users, and a comment written in English is displayed in Japanese to Japanese-speaking users. In this way, users who speak different languages ​​can have natural conversations. This system eliminates language barriers and enables seamless communication between users who speak different languages. For example, in business situations, partners from different countries can smoothly communicate without experiencing language barriers. It is also useful when communicating with local people while traveling. Furthermore, this system uses generative AI to achieve high translation accuracy and natural expressions. For example, nuances such as slang and dialects are translated appropriately, allowing users to enjoy conversations without any discomfort. In this way, the present invention is a system that uses generative AI to combine natural language translation and SNS, eliminating language barriers and realizing seamless communication. As a result, the system can automatically translate and display user posts into other languages, eliminating language barriers and realizing seamless communication.

[0060] A communication system according to an embodiment includes a reception unit, a translation unit, and a display unit. The reception unit receives posts from users. Posts from users include, but are not limited to, text, images, and videos. For example, the reception unit receives text data entered by users and converts it into a format that can be processed within the system. The reception unit can also receive media data such as images and videos. For example, the reception unit receives image data uploaded by users and converts it into a format that can be displayed within the system. The translation unit uses a generation AI to translate the posts received by the reception unit into other languages. The translation is performed based on, for example, a translation algorithm used and translation accuracy, but is not limited to, these examples. For example, the translation unit translates text data using a neural network. The translation unit can also translate text data using a rule-based translation algorithm. Furthermore, the translation unit can use a generation AI to understand context and perform an appropriate translation. For example, the translation unit analyzes the content of the post using the generation AI and performs an appropriate translation based on the context. The display unit displays the post translated by the translation unit. The display is performed based on, for example, a display format or a display device, but is not limited to such examples. For example, the display unit displays the translated text data on a web browser. The display unit can also display the translated text data on a mobile application. Furthermore, the display unit can send the translated text data by email. For example, the display unit sends the translated text data as the body of an email. In this way, the communication system according to the embodiment can automatically translate and display user posts into other languages, thereby eliminating language barriers and realizing seamless communication.

[0061] The communication system includes a comment translation unit that automatically translates exchanges in the comment section. The comment translation unit automatically translates exchanges in the comment section. The comment section may be, for example, text-only or may include attachments of images and videos, but is not limited to these examples. The comment translation unit, for example, receives comments entered by a user and translates them into another language using a generation AI. For example, the comment translation unit translates comments entered by a user in Japanese into English and displays them to English-speaking users. The comment translation unit can also translate comments entered by a user in English into Japanese and display them to Japanese-speaking users. Furthermore, the comment translation unit can appropriately translate nuances such as slang and dialects using a generation AI. For example, the comment translation unit analyzes slang and dialects and performs appropriate translations. This allows the exchanges in the comment section to be automatically translated, enabling natural conversations. Some or all of the above-described processing in the comment translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the comment translation unit can translate comments by inputting user comments into a generation AI model that outputs the translation results. This allows the communication system to automatically translate exchanges in the comment section, enabling natural conversations.

[0062] The translation unit can analyze the posted content using a generation AI and provide an appropriate translation. The translation unit can analyze the posted content using a generation AI and provide an appropriate translation. The generation AI can, for example, analyze text data using a neural network and perform translation. For example, the generation AI can analyze the posted content and provide an appropriate translation based on the context. The generation AI can also analyze text data using a rule-based translation algorithm and perform translation. For example, the generation AI can analyze the posted content based on specific rules and provide an appropriate translation. Furthermore, the generation AI can also appropriately translate nuances such as slang and dialects. For example, the generation AI can analyze slang and dialects and provide an appropriate translation. This allows the generation AI to analyze the posted content and provide an appropriate translation, thereby improving the accuracy of the translation. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the translation unit can input the posted content entered by the user into the generation AI and perform translation using a generation AI model that outputs the translation result. This allows the translation unit to analyze the posted content using the generation AI and provide an appropriate translation.

[0063] The display unit can display the translated post to users in other countries. The display unit displays the post translated by the translation unit to users in other countries. The display is performed based on, for example, a display format or a display device, but is not limited to such examples. For example, the display unit displays the translated text data on a web browser. The display unit can also display the translated text data on a mobile application. Furthermore, the display unit can send the translated text data by email. For example, the display unit sends the translated text data as the body of an email. In this way, by displaying the translated post to users in other countries, users who speak different languages ​​can have a natural conversation. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the translated text data to the generation AI and have the generation AI determine the display format. In this way, the display unit can display the translated post to users in other countries.

[0064] The comment translation unit can translate comments into other languages ​​and display them. The comment translation unit can, for example, receive comments entered by a user and translate them into other languages ​​using a generation AI. For example, the comment translation unit can translate comments entered in Japanese by a user into English and display them to English-speaking users. The comment translation unit can also translate comments entered in English by a user into Japanese and display them to Japanese-speaking users. Furthermore, the comment translation unit can appropriately translate nuances such as slang and dialects using a generation AI. For example, the comment translation unit can analyze slang and dialects and perform appropriate translations. This allows users who speak different languages ​​to have natural conversations by translating and displaying comments into other languages. Some or all of the above-described processing in the comment translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the comment translation unit can translate comments entered by a user using a generation AI model that inputs comments entered by a user into a generation AI and outputs translation results. This allows the comment translation unit to translate the comment into a foreign language and display it.

[0065] The translation unit can also appropriately translate the nuances of slang and dialects. The translation unit can also appropriately translate the nuances of slang and dialects. For example, the translation unit uses a generation AI to analyze slang and dialects and perform an appropriate translation. For example, the translation unit uses a generation AI to analyze slang and dialects and perform an appropriate translation based on the context. The translation unit can also analyze slang and dialects using a rule-based translation algorithm and perform an appropriate translation. For example, the translation unit analyzes slang and dialects based on specific rules and performs an appropriate translation. This allows the user to enjoy conversations without feeling uncomfortable by appropriately translating the nuances of slang and dialects. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can perform translation using a generation AI model that inputs slang and dialects entered by a user into a generation AI and outputs the translation result. This allows the translation unit to appropriately translate the nuances of slang and dialects.

[0066] The reception unit can estimate the user's emotions and adjust the timing of post acceptance based on the estimated user emotions. The reception unit can estimate the user's emotions using a generation AI and adjust the timing of post acceptance based on the estimated user emotions. For example, if the user is excited, the reception unit can immediately accept the post and quickly display it to other users. Alternatively, if the user is calm, the reception unit can allow the user time to reconfirm the content of the post before accepting it. Furthermore, if the user is stressed, the reception unit can temporarily suspend the acceptance of the post until the user relaxes. This allows the reception timing of posts to be adjusted according to the user's emotions, allowing posts to be accepted at a more appropriate time. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the timing of post acceptance. This allows the reception unit to estimate the user's emotions and adjust the timing of post acceptance based on the estimated user emotions.

[0067] The reception unit can analyze the user's past posting history and select the optimal reception method. The reception unit uses a generation AI to analyze the user's past posting history and select the optimal reception method. For example, the reception unit can analyze the time periods during which the user frequently posted in the past and prioritize reception of posts from those time periods. The reception unit can also analyze the content of the user's past posts and prioritize reception of posts related to a specific theme. Furthermore, the reception unit can analyze the frequency of the user's past posts and prioritize reception of posts from users who post frequently. In this way, the optimal reception method can be selected by analyzing the user's past posting history. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input the user's past posting history data into the generation AI and have the generation AI select the optimal reception method. In this way, the reception unit can analyze the user's past posting history and select the optimal reception method.

[0068] The reception unit can perform filtering based on the user's current areas of interest when receiving a post. The reception unit can use the generation AI to perform filtering based on the user's current areas of interest when receiving a post. For example, the reception unit can preferentially accept posts related to topics in which the user is currently interested. The reception unit can also preferentially accept posts from accounts the user follows. Furthermore, the reception unit can preferentially accept posts related to keywords recently searched by the user. In this way, by filtering based on the user's current areas of interest, highly relevant posts can be preferentially accepted. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's area of ​​interest data into the generation AI and have the generation AI perform filtering. In this way, the reception unit can perform filtering based on the user's current areas of interest when receiving a post.

[0069] The reception unit can estimate the user's emotions and determine the priority of posts to be received based on the estimated user emotions. The reception unit can estimate the user's emotions using a generation AI and determine the priority of posts to be received based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize posts to be received and quickly display them to other users. If the user is calm, the reception unit can allow the user time to reconfirm the content of the post before receiving it. Furthermore, if the user is stressed, the reception unit can temporarily suspend the reception of posts until the user relaxes. This allows posts to be prioritized based on the user's emotions, allowing more appropriate posts to be received preferentially. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input user emotion data into the generation AI and have the generation AI execute emotion estimation and post priority determination. This allows the reception unit to estimate the user's emotions and determine the priority of posts to be received based on the estimated user emotions.

[0070] The reception unit can prioritize receiving highly relevant posts based on the user's geographical location information when receiving posts. The reception unit uses the generation AI to prioritize receiving highly relevant posts based on the user's geographical location information when receiving posts. For example, the reception unit prioritizes receiving posts related to the area where the user is currently located. The reception unit can also prioritize receiving posts related to places the user has visited in the past. The reception unit can also prioritize receiving posts related to places the user plans to visit in the future. In this way, by taking the user's geographical location information into consideration, highly relevant posts can be prioritized. Some or all of the above-described processing in the reception unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant posts. In this way, the reception unit can prioritize receiving highly relevant posts based on the user's geographical location information when receiving posts.

[0071] The reception unit can analyze the user's social media activity when receiving a post and receive related posts. The reception unit can use the generation AI to analyze the user's social media activity when receiving a post and receive related posts. For example, the reception unit can preferentially receive content related to posts that the user has recently "liked." It can also preferentially receive content related to posts that the user has recently commented on. It can also preferentially receive content related to posts that the user has recently shared. In this way, by analyzing the user's social media activity, related posts can be preferentially received. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related posts. In this way, the reception unit can analyze the user's social media activity when receiving a post and receive related posts.

[0072] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. The translation unit can estimate the user's emotions using a generation AI and adjust the translation expression based on the estimated user's emotions. For example, if the user is excited, the translation can emphasize the emotion. If the user is calm, the translation can use calm expressions. Furthermore, if the user is stressed, the translation can use gentle expressions. This allows for more natural translation by adjusting the translation expression based on the user's emotions. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, a generation AI. For example, the translation unit can input user emotion data into the generation AI and have the generation AI estimate the emotion and adjust the translation expression. This allows the translation unit to estimate the user's emotions and adjust the translation expression based on the estimated user's emotions.

[0073] The translation unit can adjust the level of detail of the translation based on the importance of the posted content during translation. The translation unit uses the generation AI to adjust the level of detail of the translation based on the importance of the posted content during translation. For example, posts containing important information are translated in detail. Posts containing general information can also be translated concisely. Furthermore, content that is of high interest to users can be translated in detail. In this way, by adjusting the level of detail of the translation based on the importance of the posted content, important information can be appropriately conveyed. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the translation unit can input importance data of the posted content into the generation AI and have the generation AI adjust the level of detail of the translation. In this way, the translation unit can adjust the level of detail of the translation based on the importance of the posted content during translation.

[0074] The translation unit can apply different translation algorithms depending on the category of the post during translation. The translation unit uses the generation AI to apply different translation algorithms depending on the category of the post during translation. For example, an algorithm that properly translates technical terms can be applied to business-related posts. An algorithm that uses casual expressions can also be applied to entertainment-related posts. Furthermore, an algorithm that provides accurate translation can be applied to academic-related posts. In this way, applying different translation algorithms depending on the category of the post enables more appropriate translation. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the translation unit can input category data of the post into the generation AI and have the generation AI select the translation algorithm to apply. In this way, the translation unit can apply different translation algorithms depending on the category of the post during translation.

[0075] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. The translation unit can estimate the user's emotions using a generation AI and adjust the length of the translation based on the estimated user emotions. For example, if the user is in a hurry, a short, to-the-point translation can be provided. If the user is relaxed, a longer translation with detailed explanations can be provided. Furthermore, if the user is excited, a translation that emphasizes the emotions can be provided. This allows for more appropriate translation by adjusting the length of the translation according to the user's emotions. Some or all of the above-described processing in the translation unit can be performed using, or without, the generation AI. For example, the translation unit can input user emotion data into the generation AI and have the generation AI estimate the emotions and adjust the length of the translation. This allows the translation unit to estimate the user's emotions and adjust the length of the translation based on the estimated user emotions.

[0076] The translation unit can determine the priority of translation based on the time of submission of the post during translation. The translation unit can use the generation AI to determine the priority of translation based on the time of submission of the post during translation. For example, the most recent post can be translated with priority. Posts related to a specific event can also be translated with priority. Furthermore, content posted during a time period specified by the user can be translated with priority. In this way, by determining the priority of translation based on the time of submission of the post, the latest information can be translated quickly. Some or all of the above-described processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input data on the time of submission of the post into the generation AI and have the generation AI determine the priority of translation. In this way, the translation unit can determine the priority of translation based on the time of submission of the post during translation.

[0077] The translation unit can adjust the order of translations based on the relevance of posts during translation. The translation unit uses the generation AI to adjust the order of translations based on the relevance of posts during translation. For example, content that is of high interest to the user can be translated preferentially. Posts from accounts that the user follows can also be translated preferentially. Furthermore, posts related to keywords recently searched by the user can also be translated preferentially. By adjusting the order of translations based on the relevance of posts, information that is important to the user can be translated preferentially. Some or all of the above-described processing in the translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the translation unit can input relevance data of posts into the generation AI and have the generation AI adjust the order of translations. This allows the translation unit to adjust the order of translations based on the relevance of posts during translation.

[0078] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. The display unit can estimate the user's emotions using a generation AI and adjust the display method based on the estimated user's emotions. For example, if the user is excited, a visually stimulating display method can be provided. If the user is calm, a simple and highly visible display method can be provided. Furthermore, if the user is stressed, a display method with a calming color scheme can be provided. This allows for a more appropriate display by adjusting the display method according to the user's emotions. Some or all of the above-described processing in the display unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the display unit can input user emotion data into the generation AI and have the generation AI estimate the emotions and adjust the display method. This allows the display unit to estimate the user's emotions and adjust the display method based on the estimated user's emotions.

[0079] The display unit can select the optimal display method by referring to the user's past browsing history when displaying content. The display unit uses the generation AI to select the optimal display method by referring to the user's past browsing history when displaying content. For example, content that the user has frequently viewed in the past can be preferentially displayed. Content that the user is most interested in can also be preferentially displayed based on the user's past browsing history. Furthermore, posts that the user has "liked" in the past can be preferentially displayed. In this way, content that the user is most interested in can be preferentially displayed by referring to the user's past browsing history. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's past browsing history data into the generation AI and have the generation AI select the optimal display method. In this way, the display unit can select the optimal display method by referring to the user's past browsing history when displaying content.

[0080] The display unit can customize the display content based on the user's areas of interest when displaying the content. The display unit uses a generation AI to customize the display content based on the user's areas of interest when displaying the content. For example, the display unit can prioritize displaying content related to topics in which the user is currently interested. The display unit can also prioritize displaying posts from accounts the user follows. Furthermore, the display unit can prioritize displaying content related to keywords recently searched by the user. This allows the display unit to provide more relevant information by customizing the display content based on the user's areas of interest. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's areas of interest data into the generation AI and have the generation AI customize the display content. This allows the display unit to customize the display content based on the user's areas of interest when displaying the content.

[0081] The display unit can estimate the user's emotions and determine display priorities based on the estimated user emotions. The display unit can estimate the user's emotions using a generation AI and determine display priorities based on the estimated user emotions. For example, if the user is excited, content related to that emotion can be displayed preferentially. Also, if the user is calm, calming content can be displayed preferentially. Furthermore, if the user is stressed, relaxing content can be displayed preferentially. This allows for more appropriate information to be provided by determining display priorities according to the user's emotions. Some or all of the above-described processing in the display unit can be performed using, or without, the generation AI. For example, the display unit can input user emotion data into the generation AI and have the generation AI execute emotion estimation and display priority determination. This allows the display unit to estimate the user's emotions and determine display priorities based on the estimated user emotions.

[0082] The display unit can select the optimal display method by taking into account the user's geographical location information when displaying. The display unit uses the generation AI to select the optimal display method by taking into account the user's geographical location information when displaying. For example, content related to the area where the user is currently located can be preferentially displayed. Content related to places the user has visited in the past can also be preferentially displayed. Content related to places the user plans to visit in the future can also be preferentially displayed. In this way, highly relevant information can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal display method when displaying. In this way, the display unit can select the optimal display method by taking into account the user's geographical location information when displaying.

[0083] The display unit can select the optimal display method by taking into account the user's device information when displaying. The display unit uses the generation AI to select the optimal display method by taking into account the user's device information when displaying. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. This allows the optimal display method to be provided by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the display unit can input the user's device information data into the generation AI and have the generation AI select the optimal display method. This allows the display unit to select the optimal display method by taking into account the user's device information when displaying.

[0084] The comment translation unit can estimate the user's emotions and adjust the comment translation method based on the estimated user's emotions. The comment translation unit can estimate the user's emotions using a generation AI and adjust the comment translation method based on the estimated user's emotions. For example, if the user is excited, the translation can emphasize the emotion. If the user is calm, the translation can be performed using calm expressions. Furthermore, if the user is stressed, the translation can be performed using gentle expressions. This allows for a more natural translation by adjusting the comment translation method according to the user's emotions. Some or all of the above-described processing in the comment translation unit can be performed using, for example, a generation AI. For example, the comment translation unit can input user emotion data into the generation AI and have the generation AI estimate the emotion and adjust the translation method. This allows the comment translation unit to estimate the user's emotions and adjust the comment translation method based on the estimated user's emotions.

[0085] The comment translation unit can adjust the level of detail of the translation based on the importance of the comment content when translating a comment. The comment translation unit uses the generation AI to adjust the level of detail of the translation based on the importance of the comment content when translating a comment. For example, comments containing important information are translated in detail. Comments containing general information can also be translated concisely. Furthermore, content that is of great interest to the user can be translated in detail. In this way, by adjusting the level of detail of the translation based on the importance of the comment content, important information can be appropriately conveyed. Some or all of the above-mentioned processing in the comment translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the comment translation unit can input importance data of the comment content into the generation AI and cause the generation AI to adjust the level of detail of the translation. In this way, the comment translation unit can adjust the level of detail of the translation based on the importance of the comment content when translating a comment.

[0086] The comment translation unit can apply different translation algorithms depending on the comment category when translating comments. The comment translation unit uses the generation AI to apply different translation algorithms depending on the comment category when translating comments. For example, an algorithm that appropriately translates technical terms can be applied to business-related comments. An algorithm that uses casual expressions can be applied to entertainment-related comments. Furthermore, an algorithm that performs accurate translation can be applied to academic-related comments. In this way, applying different translation algorithms depending on the comment category enables more appropriate translation. Some or all of the above-mentioned processing in the comment translation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the comment translation unit can input comment category data into the generation AI and have the generation AI select the translation algorithm to be applied. In this way, the comment translation unit can apply different translation algorithms depending on the comment category when translating comments.

[0087] The comment translation unit can estimate a user's emotions and adjust the display method of comments based on the estimated user emotions. The comment translation unit can estimate a user's emotions using a generation AI and adjust the display method of comments based on the estimated user emotions. For example, if a user is excited, a visually stimulating display method can be provided. If a user is calm, a simple, highly visible display method can be provided. Furthermore, if a user is stressed, a display method using subdued colors can be provided. This allows for more appropriate display by adjusting the display method of comments according to the user's emotions. Some or all of the above-described processing in the comment translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the comment translation unit can input user emotion data into the generation AI and have the generation AI estimate emotions and adjust the display method. This allows the comment translation unit to estimate a user's emotions and adjust the display method of comments based on the estimated user emotions.

[0088] The comment translation unit can determine the translation priority based on the time the comment was submitted when translating the comment. The comment translation unit can use the generation AI to determine the translation priority based on the time the comment was submitted when translating the comment. For example, the most recent comment can be translated with priority. Comments related to a specific event can also be translated with priority. Furthermore, comments posted during a time period specified by the user can be translated with priority. In this way, by determining the translation priority based on the time the comment was submitted, the latest information can be translated quickly. Some or all of the above-described processing in the comment translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the comment translation unit can input comment submission time data into the generation AI and have the generation AI determine the translation priority. In this way, the comment translation unit can determine the translation priority based on the time the comment was submitted when translating the comment.

[0089] The comment translation unit can adjust the order of translations based on the relevance of comments when translating comments. The comment translation unit adjusts the order of translations based on the relevance of comments when translating comments using a generation AI. For example, content that the user is most interested in can be translated preferentially. Comments from accounts the user follows can also be translated preferentially. Furthermore, comments related to keywords recently searched by the user can also be translated preferentially. In this way, by adjusting the order of translations based on the relevance of comments, information that is important to the user can be translated preferentially. Some or all of the above-described processing in the comment translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the comment translation unit can input comment relevance data into the generation AI and have the generation AI adjust the order of translations. In this way, the comment translation unit can adjust the order of translations based on the relevance of comments when translating comments. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, translation unit, display unit, and comment translation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives posts from users. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the posts into other languages ​​using a generation AI. The display unit is realized by the output device 40 of the smart device 14 and displays the translated posts. The comment translation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically translates exchanges in the comment section. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, translation unit, display unit, and comment translation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives posts from users. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the posts into other languages ​​using a generation AI. The display unit is realized by the speaker 240 of the smart glasses 214 and displays the translated posts. The comment translation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically translates exchanges in the comment section. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, translation unit, display unit, and comment translation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives posts from users. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the posts into other languages ​​using a generation AI. The display unit is realized by the display 343 of the headset type terminal 314 and displays the translated posts. The comment translation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically translates exchanges in the comment section. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, translation unit, display unit, and comment translation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives posts from users. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the posts into other languages ​​using a generation AI. The display unit is realized by the speaker 240 of the robot 414 and displays the translated posts. The comment translation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically translates exchanges in the comment section.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can automatically categorize posts based on the content of the user's post. For example, if a post is business-related, it is categorized into the business category, and if it is entertainment-related, it is categorized into the entertainment category. If a user uses a specific hashtag, the category can also be determined based on that hashtag. Furthermore, the reception unit can analyze the user's past posting history and categorize similar posts into the same category. By automatically categorizing posts based on the content of the post, the reception unit can efficiently manage posts in categories that interest the user.

[0092] The comment translation unit can estimate the emotion of the comment based on the content of the user's comment and translate according to that emotion. For example, if the user is expressing joy, the translation can emphasize positive expressions. If the user is expressing anger, the translation can use calm expressions. If the user is expressing sadness, the translation can use gentle expressions. In this way, the comment translation unit can achieve more natural communication by translating according to the user's emotions.

[0093] The translation department can evaluate the importance of posts based on the content of the user's posts and determine the priority of translation according to the importance. For example, posts containing important business information can be translated first, while posts containing general information can be translated later. In addition, if a user uses a specific keyword, the importance can be evaluated based on that keyword. Furthermore, the importance of posts can be evaluated based on the number of followers and influence of the user, and posts by influential users can be translated first. In this way, the translation department can determine the priority of translation according to the importance of the post, thereby enabling important information to be provided quickly.

[0094] The display unit can estimate the user's emotions and customize the display content based on the estimated emotions. For example, if the user is excited, visually stimulating content can be displayed preferentially. If the user is calm, simple, highly visible content can be displayed. Furthermore, if the user is stressed, relaxing content can be displayed. In this way, the display unit can provide more appropriate information by customizing the display content according to the user's emotions.

[0095] The comment translation unit can automatically categorize comments based on the content of the user's comment. For example, if a comment is business-related, it is categorized as a business category, and if it is entertainment-related, it is categorized as an entertainment category. If a user uses a specific hashtag, the category can also be determined based on that hashtag. Furthermore, the comment translation unit can analyze the user's past comment history and categorize similar comments into the same category. By automatically categorizing comments based on their content, the comment translation unit can efficiently manage comments in categories that interest the user.

[0096] 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 excited, the translation can emphasize the emotion. If the user is calm, the translation can be performed in a calm tone. Furthermore, if the user is stressed, the translation can be performed in a gentle tone. This allows the translation unit to translate in a tone that matches the user's emotions, thereby achieving more natural communication.

[0097] The reception unit can evaluate the relevance of posts based on the content of the user's posts and prioritize the reception of highly relevant posts. For example, it can prioritize the reception of posts related to topics in which the user is currently interested. It can also prioritize the reception of posts from accounts the user follows. It can also prioritize the reception of posts related to keywords recently searched by the user. In this way, the reception unit can efficiently manage information that is important to the user by determining the priority based on the relevance of the posts.

[0098] The display unit can estimate the user's emotion and adjust the display layout based on the estimated emotion. For example, if the user is excited, a visually stimulating layout can be provided. If the user is calm, a simple, highly visible layout can be provided. Furthermore, if the user is stressed, a layout with subdued colors can be provided. In this way, the display unit can display more appropriate information by providing a layout according to the user's emotion.

[0099] The reception unit can evaluate the importance of a post based on the content of the user's post and select a reception method according to the importance. For example, posts containing important business information can be received with priority, while posts containing general information can be postponed. Also, if a user uses a specific keyword, the importance can be evaluated based on that keyword. Furthermore, the importance of a post can be evaluated based on the number of followers and influence of the user, and posts by influential users can be received with priority. In this way, the reception unit can efficiently manage important information by selecting a reception method according to the importance of the post.

[0100] The display unit can estimate the user's emotions and determine display priorities based on the estimated emotions. For example, if the user is excited, content related to that emotion can be displayed with priority. Also, if the user is calm, calming content can be displayed with priority. Furthermore, if the user is stressed, relaxing content can be displayed with priority. In this way, the display unit can provide more appropriate information by determining display priorities according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives posts from users. Posts from users include text, images, videos, etc. The reception unit receives text data entered by users and converts it into a format that can be processed within the system. It can also receive media data such as images and videos. For example, it receives image data uploaded by users and converts it into a format that can be displayed within the system. Step 2: The translation unit uses the generation AI to translate the posts received by the reception unit into other languages. The translation is performed based on the translation algorithm used and the accuracy of the translation. For example, a neural network is used to translate the text data. Alternatively, a rule-based translation algorithm can be used to translate the text data. Furthermore, the generation AI can be used to understand the context and provide an appropriate translation. Step 3: The display unit displays the post translated by the translation unit. The display is performed based on the display format and display device. For example, the translated text data may be displayed on a web browser. The translated text data may also be displayed on a mobile application. The translated text data may also be sent by email.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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, in order to avoid confusion and to 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives posts from users; a translation unit that translates the posts received by the reception unit into other languages; a display unit that displays the post translated by the translation unit. A system characterized by:

2. Equipped with a comment translation section that automatically translates exchanges in the comment section 2. The system of claim 1.

3. The translation unit Analyze the post content using generative AI and provide an appropriate translation 2. The system of claim 1.

4. The display unit Displaying translated posts to users in other countries 2. The system of claim 1.

5. The comment translation unit: Translate and display comments in other languages 3. The system of claim 2.

6. The translation unit Properly translating slang and dialect nuances 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the timing of posting based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past posting history and select the appropriate reception method 2. The system of claim 1.

9. The reception unit As posts are accepted, they are filtered based on the user's current interests.

2. The system of claim 1.

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