system

Abstract

The system according to this embodiment is intended to translate and transmit messages in real time. [Solution] The system according to this embodiment comprises a receiving unit, a translation unit, and a transmission unit. The receiving unit receives a message. The translation unit translates the message received by the receiving unit. The transmission unit transmits the message translated by the translation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, real-time translation of messages is not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to translate and transmit messages in real time.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a translation unit, and a transmission unit. The reception unit receives a message. The translation unit translates the message received by the reception unit. The transmission unit transmits the message translated by the translation unit.

Effects of the Invention

[0007] The system according to this embodiment can translate and transmit messages in real time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a service that translates messages in real time using a generative AI, and a service that converts messages into images or videos. This system consists of the following steps. First, when a user sends a message, the message is sent to the generative AI and translated in real time. The translated message is sent to the recipient. This service enables smooth communication between users who speak different languages. Next, when a user sends a message, the message is sent to the generative AI and converted into images or videos. The converted images or videos are sent to the recipient. This service enables communication not only through text messages but also through visual content. As a result, the system enables smooth communication between users who speak different languages. Furthermore, the system enables communication not only through text messages but also through visual content.

[0029] The system according to this embodiment comprises a receiving unit, a translation unit, and a transmission unit. The receiving unit receives messages. The receiving unit can receive, for example, text messages, voice messages, image messages, etc. The translation unit translates the messages received by the receiving unit using a generative AI. The translation unit translates the messages using, for example, a neural network-based translation algorithm. The translation unit can understand the context of the message using a generative AI and perform an appropriate translation. The transmission unit transmits the messages translated by the translation unit. The transmission unit can transmit the messages by, for example, email, push notification, etc. This enables the system to efficiently receive, translate, and transmit messages.

[0030] The reception unit receives messages. The reception unit can receive various types of messages, such as text messages, voice messages, and image messages. Specifically, in the case of text messages, it receives the string entered by the user as is and processes it within the system. In the case of voice messages, it uses speech recognition technology to convert the voice data into text data for subsequent processing. In the case of image messages, it uses image recognition technology to extract text from the image and processes it as text data. This allows the reception unit to accept messages in various formats, improving user convenience. Furthermore, the reception unit also has pre-processing functions to perform appropriate processing according to the format and content of the received message. For example, in the case of voice messages, it performs noise reduction and voice normalization to improve the accuracy of speech recognition. In the case of image messages, it adjusts the image resolution and performs pre-processing for character recognition. This allows the reception unit to process received messages with high accuracy and provide accurate data to subsequent translation and transmission units.

[0031] The translation department uses generative AI to translate messages received by the reception department. For example, the translation department uses a neural network-based translation algorithm. Specifically, the generative AI uses a model trained on a large amount of translation data to understand the context of the input message and provide an appropriate translation. For example, in the case of text messages, the generative AI analyzes the context and accurately captures the meaning of words and phrases for translation. In the case of voice messages, it translates based on data transcribed using speech recognition technology. In the case of image messages, it translates text data extracted using image recognition technology. To understand the context, the generative AI considers the surrounding context and relevant information of the input message to provide a natural and accurate translation. Furthermore, the translation department has the ability to evaluate the output of the generative AI and make corrections as needed. For example, if the translation result provided by the generative AI is inappropriate, the system automatically generates other translation candidates and selects the best translation. The translation department can also continuously improve its translation model based on user feedback to enhance translation accuracy. This allows the translation department to consistently provide high-quality translations and increase user satisfaction.

[0032] The sending unit transmits messages translated by the translation unit. The sending unit can transmit messages via methods such as email or push notifications. Specifically, in the case of email transmission, the translated message is sent to a specified email address. In the case of push notifications, notifications are sent in real time to devices such as smartphones and tablets. The sending unit selects the appropriate transmission method depending on the recipient's device and application to ensure reliable delivery. The sending unit also has a function to monitor transmission status and confirm whether transmission was successful. For example, in the case of email transmission, it attempts to resend if a transmission error occurs. In the case of push notifications, it checks whether the notification was received and resends if necessary. Furthermore, the sending unit records the transmission history for later reference. This allows the sending unit to reliably transmit translated messages and provide users with quick and accurate information. The sending unit can combine multiple transmission methods to flexibly respond to user needs. For example, for important messages, it can use a combination of email and push notifications to ensure reliable delivery. The sending unit can also customize the transmission method based on user settings. This allows the transmission unit to send messages to users in the most optimal way, improving the overall reliability and usability of the system.

[0033] The system includes a conversion unit that converts messages into video or animated images. The conversion unit uses generative AI to convert messages into video or animated images. For example, the conversion unit can convert text messages into animations. Using generative AI, the conversion unit can understand the content of a message and generate an appropriate visual representation. Using generative AI, the conversion unit can generate video or animated images that reflect emotions and atmosphere. This allows the system to convert messages into visual content.

[0034] The transformation unit can understand the content of the message and generate an appropriate visual representation. For example, the transformation unit can adjust colors and shapes based on the content of the message. Using a generation AI, the transformation unit can understand the context of the message and generate an appropriate visual representation. Using a generation AI, the transformation unit can generate a visual representation that corresponds to the content of the message. As a result, the transformation unit generates a visual representation that corresponds to the content of the message.

[0035] The transmitting unit can send converted video or footage. The transmitting unit can send converted video or footage via methods such as email or push notification. The transmitting unit can use generation AI to send converted video or footage in an appropriate format. The transmitting unit can also use generation AI to select the optimal method for sending converted video or footage. As a result, the transmitting unit sends the converted video or footage.

[0036] The reception unit can analyze a user's past message sending history and select the optimal reception method. For example, the reception unit can analyze patterns of messages that a user has frequently sent in the past and prioritize receiving similar messages. The reception unit can analyze messages that a user sends during specific time periods and select the optimal reception method for that time period. The reception unit can prioritize receiving messages containing specific keywords from a user's past message sending history. This allows the reception unit to receive messages in the most optimal way based on past history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0037] The reception unit can filter messages based on the user's current status and areas of interest when receiving them. For example, if the user has set their current status to "busy," the reception unit can receive only important messages. The reception unit can prioritize receiving highly relevant messages based on the user's areas of interest. If the user is participating in a specific event, the reception unit can prioritize receiving messages related to that event. In this way, the reception unit can prioritize receiving messages that are appropriate to the user's status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI.

[0038] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location when receiving messages. For example, if the user is in a specific region, the reception unit can prioritize receiving messages related to that region. If the user is traveling, the reception unit can prioritize receiving messages related to their travel destination. If the user is at home, the reception unit can prioritize receiving messages related to information around their home. In this way, the reception unit can prioritize receiving messages that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0039] The reception unit can analyze the user's social media activity when receiving a message and receive relevant messages. For example, if the user posts about a specific topic on social media, the reception unit can prioritize receiving messages related to that topic. The reception unit can prioritize receiving messages from accounts that the user follows on social media. The reception unit can analyze the user's social media activity history and prioritize receiving highly relevant messages. This allows the reception unit to prioritize receiving relevant messages based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI.

[0040] The translation unit can adjust the level of detail in the translation based on the importance of the message. For example, the AI ​​can generate detailed translations for high-priority messages, simplified translations for low-priority messages, and rapid translations for urgent messages. This allows the translation unit to perform translations with a level of detail appropriate to the importance of the message.

[0041] The translation unit can apply different translation algorithms depending on the message category during translation. For example, the AI ​​can apply a formal translation algorithm to business-related messages. For casual messages, the AI ​​can apply a friendly translation algorithm. For technical messages, the AI ​​can apply an algorithm that appropriately translates technical terms. This ensures that the translation unit applies the appropriate translation algorithm for each message category.

[0042] The translation unit can determine translation priorities based on when the messages were sent. For example, the AI ​​can generate translations of urgent messages with the highest priority. The AI ​​can generate translations of regular messages with normal priority. The AI ​​can generate translations of older messages at a later date. This allows the translation unit to perform translations with priorities according to when the messages were sent.

[0043] The translation unit can adjust the order of translations based on the relevance of the messages during the translation process. For example, the AI ​​can generate translations of important messages first. The AI ​​can generate translations of less relevant messages later. The AI ​​can generate translations in an appropriate order according to the content of the messages. As a result, the translation unit can translate messages in an order that is relevant to their relevance.

[0044] The sending unit can determine the sending priority based on the importance of the message at the time of transmission. For example, the sending unit can send high-priority messages with the highest priority. The sending unit can send low-priority messages later. The sending unit can send urgent messages immediately. In this way, the sending unit can send messages with priority according to their importance. Some or all of the above processing in the sending unit may be performed using AI, for example, or without using AI.

[0045] The sending unit can apply different sending methods depending on the message category when sending. For example, the sending unit can apply a formal sending method to business-related messages. The sending unit can apply a friendly sending method to casual messages. The sending unit can apply a rapid sending method to urgent messages. In this way, the sending unit applies the appropriate sending method according to the message category. Some or all of the above processing in the sending unit may be performed using AI, for example, or without using AI.

[0046] The transmitting unit can select the optimal transmission method when transmitting a message, taking into account the geographical location information of the message recipient. For example, if the recipient is overseas, the transmitting unit can select a method suitable for international transmission. If the recipient is nearby, the transmitting unit can select a fast transmission method. If the recipient is in a specific region, the transmitting unit can select a transmission method suitable for that region. In this way, the transmitting unit selects the optimal transmission method based on the geographical location information of the recipient. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without using AI.

[0047] The transmitting unit can improve the accuracy of transmission by referring to relevant literature for the message during transmission. For example, the transmitting unit can refer to literature related to the content of the message and select an appropriate transmission method. The transmitting unit can add relevant information based on the content of the message and transmit it. The transmitting unit can refer to past transmission history related to the content of the message and select the optimal transmission method. As a result, the transmitting unit improves the accuracy of transmission by referring to relevant literature. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without using AI.

[0048] The conversion unit can adjust the level of detail in the video or image based on the message content during conversion. For example, for important messages, the conversion unit can generate detailed video using its AI. For simple messages, the conversion unit can generate simplified video using its AI. For technical messages, the conversion unit can generate video including specialized details using its AI. As a result, the conversion unit generates video or images with a level of detail appropriate to the message content.

[0049] The conversion unit can apply different conversion algorithms depending on the message category during conversion. For example, the conversion unit can generate formal videos for business-related messages using its AI. For casual messages, it can generate friendly videos using its AI. For technical messages, it can generate videos that appropriately represent technical terms using its AI. In this way, the conversion unit applies the appropriate conversion algorithm according to the message category.

[0050] The conversion unit can determine the priority of video and film based on the message's sending time during conversion. For example, the conversion unit can have the AI ​​generate video for urgent messages with the highest priority. The conversion unit can have the AI ​​generate video for regular messages with normal priority. The conversion unit can have the AI ​​generate video for older messages at a later date. As a result, the conversion unit generates video and film with a priority according to the message's sending time.

[0051] The conversion unit can adjust the order of videos and images based on the relevance of the messages during conversion. For example, the conversion unit can have the AI ​​generate videos for important messages first. The conversion unit can have the AI ​​generate videos for less relevant messages later. The conversion unit can have the AI ​​generate videos in an appropriate order according to the content of the messages. As a result, the conversion unit generates videos and images in an order that corresponds to the relevance of the messages.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The reception unit can analyze a user's past message sending history and select the optimal reception method. For example, it can analyze patterns in messages a user has frequently sent in the past and prioritize receiving similar messages. It can also analyze messages a user sends during specific time periods and select the most suitable reception method for that time period. Furthermore, it can prioritize receiving messages containing specific keywords from a user's past message sending history. This allows the reception unit to receive messages in the most optimal way based on past history.

[0054] The reception system can filter messages based on the user's current status and areas of interest. For example, if a user has set their current status to "busy," only important messages can be received. Relevant messages can be prioritized based on the user's areas of interest. If a user is participating in a specific event, messages related to that event can be prioritized. In this way, the reception system can prioritize messages that are appropriate to the user's status and areas of interest.

[0055] The reception system can prioritize receiving messages that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, messages related to that region can be prioritized. If the user is traveling, messages related to their travel destination can be prioritized. If the user is at home, messages related to information about their home area can be prioritized. In this way, the reception system can prioritize receiving messages that are highly relevant based on the user's geographical location.

[0056] The sending unit can determine the sending priority based on the importance of the message at the time of transmission. For example, highly important messages can be sent with the highest priority. Less important messages can be sent later. Highly urgent messages can be sent immediately. In this way, the sending unit can send messages with priority according to their importance.

[0057] The sending unit can apply different sending methods depending on the message category. For example, a formal sending method can be applied to business-related messages, a friendly sending method to casual messages, and a fast sending method to urgent messages. This ensures that the sending unit applies the appropriate sending method according to the message category.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives messages. The reception desk can receive messages such as text messages, voice messages, and image messages. Step 2: The translation unit uses generative AI to translate the message received by the reception unit. The translation unit translates the message using, for example, a neural network-based translation algorithm. The translation unit can use generative AI to understand the context of the message and provide an appropriate translation. Step 3: The sending unit sends the message translated by the translation unit. The sending unit can send the message by methods such as email or push notification.

[0060] (Example of form 2) The system according to an embodiment of the present invention is a service that translates messages in real time using a generative AI, and a service that converts messages into images or videos. This system consists of the following steps. First, when a user sends a message, the message is sent to the generative AI and translated in real time. The translated message is sent to the recipient. This service enables smooth communication between users who speak different languages. Next, when a user sends a message, the message is sent to the generative AI and converted into images or videos. The converted images or videos are sent to the recipient. This service enables communication not only through text messages but also through visual content. As a result, the system enables smooth communication between users who speak different languages. Furthermore, the system enables communication not only through text messages but also through visual content.

[0061] The system according to this embodiment comprises a receiving unit, a translation unit, and a transmission unit. The receiving unit receives messages. The receiving unit can receive, for example, text messages, voice messages, image messages, etc. The translation unit translates the messages received by the receiving unit using a generative AI. The translation unit translates the messages using, for example, a neural network-based translation algorithm. The translation unit can understand the context of the message using a generative AI and perform an appropriate translation. The transmission unit transmits the messages translated by the translation unit. The transmission unit can transmit the messages by, for example, email, push notification, etc. This enables the system to efficiently receive, translate, and transmit messages.

[0062] The reception unit receives messages. The reception unit can receive various types of messages, such as text messages, voice messages, and image messages. Specifically, in the case of text messages, it receives the string entered by the user as is and processes it within the system. In the case of voice messages, it uses speech recognition technology to convert the voice data into text data for subsequent processing. In the case of image messages, it uses image recognition technology to extract text from the image and processes it as text data. This allows the reception unit to accept messages in various formats, improving user convenience. Furthermore, the reception unit also has pre-processing functions to perform appropriate processing according to the format and content of the received message. For example, in the case of voice messages, it performs noise reduction and voice normalization to improve the accuracy of speech recognition. In the case of image messages, it adjusts the image resolution and performs pre-processing for character recognition. This allows the reception unit to process received messages with high accuracy and provide accurate data to subsequent translation and transmission units.

[0063] The translation department uses generative AI to translate messages received by the reception department. For example, the translation department uses a neural network-based translation algorithm. Specifically, the generative AI uses a model trained on a large amount of translation data to understand the context of the input message and provide an appropriate translation. For example, in the case of text messages, the generative AI analyzes the context and accurately captures the meaning of words and phrases for translation. In the case of voice messages, it translates based on data transcribed using speech recognition technology. In the case of image messages, it translates text data extracted using image recognition technology. To understand the context, the generative AI considers the surrounding context and relevant information of the input message to provide a natural and accurate translation. Furthermore, the translation department has the ability to evaluate the output of the generative AI and make corrections as needed. For example, if the translation result provided by the generative AI is inappropriate, the system automatically generates other translation candidates and selects the best translation. The translation department can also continuously improve its translation model based on user feedback to enhance translation accuracy. This allows the translation department to consistently provide high-quality translations and increase user satisfaction.

[0064] The sending unit transmits messages translated by the translation unit. The sending unit can transmit messages via methods such as email or push notifications. Specifically, in the case of email transmission, the translated message is sent to a specified email address. In the case of push notifications, notifications are sent in real time to devices such as smartphones and tablets. The sending unit selects the appropriate transmission method depending on the recipient's device and application to ensure reliable delivery. The sending unit also has a function to monitor transmission status and confirm whether transmission was successful. For example, in the case of email transmission, it attempts to resend if a transmission error occurs. In the case of push notifications, it checks whether the notification was received and resends if necessary. Furthermore, the sending unit records the transmission history for later reference. This allows the sending unit to reliably transmit translated messages and provide users with quick and accurate information. The sending unit can combine multiple transmission methods to flexibly respond to user needs. For example, for important messages, it can use a combination of email and push notifications to ensure reliable delivery. The sending unit can also customize the transmission method based on user settings. This allows the transmission unit to send messages to users in the most optimal way, improving the overall reliability and usability of the system.

[0065] The system includes a conversion unit that converts messages into video or animated images. The conversion unit uses generative AI to convert messages into video or animated images. For example, the conversion unit can convert text messages into animations. Using generative AI, the conversion unit can understand the content of a message and generate an appropriate visual representation. Using generative AI, the conversion unit can generate video or animated images that reflect emotions and atmosphere. This allows the system to convert messages into visual content.

[0066] The transformation unit can understand the content of the message and generate an appropriate visual representation. For example, the transformation unit can adjust colors and shapes based on the content of the message. Using a generation AI, the transformation unit can understand the context of the message and generate an appropriate visual representation. Using a generation AI, the transformation unit can generate a visual representation that corresponds to the content of the message. As a result, the transformation unit generates a visual representation that corresponds to the content of the message.

[0067] The transformation unit can generate images and videos that reflect emotions and atmosphere. For example, the transformation unit can generate colors and movements that reflect emotions based on the content of a message. The transformation unit can use a generation AI to understand the emotions and atmosphere of a message and generate images and videos accordingly. The transformation unit can use a generation AI to generate images and videos that correspond to emotions and atmosphere. As a result, the transformation unit generates images and videos that correspond to emotions and atmosphere. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0068] The transmitting unit can send converted video or footage. The transmitting unit can send converted video or footage via methods such as email or push notification. The transmitting unit can use generation AI to send converted video or footage in an appropriate format. The transmitting unit can also use generation AI to select the optimal method for sending converted video or footage. As a result, the transmitting unit sends the converted video or footage.

[0069] The reception system can estimate the user's emotions and adjust the timing of message reception based on the estimated emotions. For example, if the user is stressed, the reception system can delay message reception and wait until the user relaxes. If the user is excited, the reception system can quickly receive the message and begin processing immediately. If the user is tired, the reception system can temporarily stop message reception to allow the user time to rest. This allows the reception system to receive messages at an appropriate time according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The reception unit can analyze a user's past message sending history and select the optimal reception method. For example, the reception unit can analyze patterns of messages that a user has frequently sent in the past and prioritize receiving similar messages. The reception unit can analyze messages that a user sends during specific time periods and select the optimal reception method for that time period. The reception unit can prioritize receiving messages containing specific keywords from a user's past message sending history. This allows the reception unit to receive messages in the most optimal way based on past history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0071] The reception unit can filter messages based on the user's current status and areas of interest when receiving them. For example, if the user has set their current status to "busy," the reception unit can receive only important messages. The reception unit can prioritize receiving highly relevant messages based on the user's areas of interest. If the user is participating in a specific event, the reception unit can prioritize receiving messages related to that event. In this way, the reception unit can prioritize receiving messages that are appropriate to the user's status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI.

[0072] The reception system can estimate the user's emotions and determine the priority of messages to receive based on the estimated emotions. For example, if the user is stressed, the reception system can postpone less important messages. If the user is relaxed, the reception system can receive all messages equally. If the user is in a hurry, the reception system can prioritize receiving urgent messages. In this way, the reception system can receive messages with priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location when receiving messages. For example, if the user is in a specific region, the reception unit can prioritize receiving messages related to that region. If the user is traveling, the reception unit can prioritize receiving messages related to their travel destination. If the user is at home, the reception unit can prioritize receiving messages related to information around their home. In this way, the reception unit can prioritize receiving messages that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0074] The reception unit can analyze the user's social media activity when receiving a message and receive relevant messages. For example, if the user posts about a specific topic on social media, the reception unit can prioritize receiving messages related to that topic. The reception unit can prioritize receiving messages from accounts that the user follows on social media. The reception unit can analyze the user's social media activity history and prioritize receiving highly relevant messages. This allows the reception unit to prioritize receiving relevant messages based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI.

[0075] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is relaxed, the AI ​​can generate a translation using softer language. If the user is angry, the AI ​​can generate a translation using calmer language. If the user is excited, the AI ​​can generate a translation using energetic language. This allows the translation unit to translate using an appropriate expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The translation unit can adjust the level of detail in the translation based on the importance of the message. For example, the AI ​​can generate detailed translations for high-priority messages, simplified translations for low-priority messages, and rapid translations for urgent messages. This allows the translation unit to perform translations with a level of detail appropriate to the importance of the message.

[0077] The translation unit can apply different translation algorithms depending on the message category during translation. For example, the AI ​​can apply a formal translation algorithm to business-related messages. For casual messages, the AI ​​can apply a friendly translation algorithm. For technical messages, the AI ​​can apply an algorithm that appropriately translates technical terms. This ensures that the translation unit applies the appropriate translation algorithm for each message category.

[0078] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated emotions. For example, if the user is in a hurry, the AI ​​can generate a short, concise translation. If the user is relaxed, the AI ​​can generate a longer translation that includes detailed explanations. If the user is excited, the AI ​​can generate a translation that reflects those emotions. This allows the translation unit to produce translations of appropriate length according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs.

[0079] The translation unit can determine translation priorities based on when the messages were sent. For example, the AI ​​can generate translations of urgent messages with the highest priority. The AI ​​can generate translations of regular messages with normal priority. The AI ​​can generate translations of older messages at a later date. This allows the translation unit to perform translations with priorities according to when the messages were sent.

[0080] The translation unit can adjust the order of translations based on the relevance of the messages during the translation process. For example, the AI ​​can generate translations of important messages first. The AI ​​can generate translations of less relevant messages later. The AI ​​can generate translations in an appropriate order according to the content of the messages. As a result, the translation unit can translate messages in an order that is relevant to their relevance.

[0081] The sending unit can estimate the user's emotions and adjust the timing of transmission based on the estimated emotions. For example, if the user is relaxed, the sending unit can delay transmission to send it at an appropriate time. If the user is in a hurry, the sending unit can send it immediately. If the user is stressed, the sending unit can temporarily withhold transmission and wait until the user calms down. This allows the sending unit to send messages at an appropriate time according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The sending unit can determine the sending priority based on the importance of the message at the time of transmission. For example, the sending unit can send high-priority messages with the highest priority. The sending unit can send low-priority messages later. The sending unit can send urgent messages immediately. In this way, the sending unit can send messages with priority according to their importance. Some or all of the above processing in the sending unit may be performed using AI, for example, or without using AI.

[0083] The sending unit can apply different sending methods depending on the message category when sending. For example, the sending unit can apply a formal sending method to business-related messages. The sending unit can apply a friendly sending method to casual messages. The sending unit can apply a rapid sending method to urgent messages. In this way, the sending unit applies the appropriate sending method according to the message category. Some or all of the above processing in the sending unit may be performed using AI, for example, or without using AI.

[0084] The sending unit can estimate the user's emotions and adjust the order of messages based on the estimated emotions. For example, if the user is relaxed, the sending unit can send all messages evenly. If the user is in a hurry, the sending unit can prioritize sending urgent messages. If the user is stressed, the sending unit can postpone sending less important messages. This allows the sending unit to send messages in an order that suits the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The transmitting unit can select the optimal transmission method when transmitting a message, taking into account the geographical location information of the message recipient. For example, if the recipient is overseas, the transmitting unit can select a method suitable for international transmission. If the recipient is nearby, the transmitting unit can select a fast transmission method. If the recipient is in a specific region, the transmitting unit can select a transmission method suitable for that region. In this way, the transmitting unit selects the optimal transmission method based on the geographical location information of the recipient. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without using AI.

[0086] The transmitting unit can improve the accuracy of transmission by referring to relevant literature for the message during transmission. For example, the transmitting unit can refer to literature related to the content of the message and select an appropriate transmission method. The transmitting unit can add relevant information based on the content of the message and transmit it. The transmitting unit can refer to past transmission history related to the content of the message and select the optimal transmission method. As a result, the transmitting unit improves the accuracy of transmission by referring to relevant literature. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without using AI.

[0087] The transformation unit can estimate the user's emotions and adjust the presentation of the video or image based on the estimated emotions. For example, if the user is relaxed, the transformation unit can use a generating AI to create a video with calm colors and slow movements. If the user is excited, the transformation unit can use a generating AI to create a video with vivid colors and fast movements. If the user is sad, the transformation unit can use a generating AI to create a video with calm colors and slow movements. In this way, the transformation unit generates videos or images with appropriate presentation methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The conversion unit can adjust the level of detail in the video or image based on the message content during conversion. For example, for important messages, the conversion unit can generate detailed video using its AI. For simple messages, the conversion unit can generate simplified video using its AI. For technical messages, the conversion unit can generate video including specialized details using its AI. As a result, the conversion unit generates video or images with a level of detail appropriate to the message content.

[0089] The conversion unit can apply different conversion algorithms depending on the message category during conversion. For example, the conversion unit can generate formal videos for business-related messages using its AI. For casual messages, it can generate friendly videos using its AI. For technical messages, it can generate videos that appropriately represent technical terms using its AI. In this way, the conversion unit applies the appropriate conversion algorithm according to the message category.

[0090] The conversion unit can estimate the user's emotions and adjust the length of the video or footage based on the estimated emotions. For example, if the user is in a hurry, the conversion unit can use a generating AI to create a short, concise video. If the user is relaxed, the conversion unit can use a generating AI to create a longer video with detailed explanations. If the user is excited, the conversion unit can use a generating AI to create a video that reflects those emotions. As a result, the conversion unit generates videos or footage of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The conversion unit can determine the priority of video and film based on the message's sending time during conversion. For example, the conversion unit can have the AI ​​generate video for urgent messages with the highest priority. The conversion unit can have the AI ​​generate video for regular messages with normal priority. The conversion unit can have the AI ​​generate video for older messages at a later date. As a result, the conversion unit generates video and film with a priority according to the message's sending time.

[0092] The conversion unit can adjust the order of videos and images based on the relevance of the messages during conversion. For example, the conversion unit can have the AI ​​generate videos for important messages first. The conversion unit can have the AI ​​generate videos for less relevant messages later. The conversion unit can have the AI ​​generate videos in an appropriate order according to the content of the messages. As a result, the conversion unit generates videos and images in an order that corresponds to the relevance of the messages.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The reception system can estimate the user's emotions and adjust how messages are received based on that estimation. For example, if the user is stressed, the reception system can temporarily hold off on receiving the message and wait until the user relaxes. If the user is agitated, the reception system can quickly receive the message and begin processing it immediately. Furthermore, if the user is tired, the reception system can delay receiving the message to allow the user time to rest. This allows the reception system to receive messages in an appropriate manner according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI.

[0095] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, if the user is relaxed, the AI ​​can generate a translation using softer language. If the user is angry, the AI ​​can generate a translation using calmer language. If the user is excited, the AI ​​can generate a translation using more energetic language. This allows the translation unit to produce translations using appropriate expressions that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI.

[0096] The sending unit can estimate the user's emotions and adjust the timing of transmission based on the estimated emotions. For example, if the user is relaxed, the transmission can be delayed to send at an appropriate time. If the user is in a hurry, the transmission can be sent immediately. If the user is stressed, the transmission can be temporarily withheld and waited until the user calms down. This allows the sending unit to send messages at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI.

[0097] The conversion unit can estimate the user's emotions and adjust the presentation of the video or image based on the estimated emotions. For example, if the user is relaxed, the generating AI can create a video with calm colors and slow movements. If the user is excited, the generating AI can create a video with vivid colors and fast movements. If the user is sad, the generating AI can create a video with calm colors and slow movements. In this way, the conversion unit generates videos or images with appropriate presentation methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generating AI.

[0098] The reception system can estimate the user's emotions and prioritize messages based on those emotions. For example, if a user is stressed, less important messages can be postponed. If a user is relaxed, all messages can be processed equally. If a user is in a hurry, urgent messages can be prioritized. This allows the reception system to process messages with priorities that match the user's emotions. Emotion estimation can be achieved using emotion estimation functions, such as an emotion engine or generative AI.

[0099] The reception unit can analyze a user's past message sending history and select the optimal reception method. For example, it can analyze patterns in messages a user has frequently sent in the past and prioritize receiving similar messages. It can also analyze messages a user sends during specific time periods and select the most suitable reception method for that time period. Furthermore, it can prioritize receiving messages containing specific keywords from a user's past message sending history. This allows the reception unit to receive messages in the most optimal way based on past history.

[0100] The reception system can filter messages based on the user's current status and areas of interest. For example, if a user has set their current status to "busy," only important messages can be received. Relevant messages can be prioritized based on the user's areas of interest. If a user is participating in a specific event, messages related to that event can be prioritized. In this way, the reception system can prioritize messages that are appropriate to the user's status and areas of interest.

[0101] The reception system can prioritize receiving messages that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, messages related to that region can be prioritized. If the user is traveling, messages related to their travel destination can be prioritized. If the user is at home, messages related to information about their home area can be prioritized. In this way, the reception system can prioritize receiving messages that are highly relevant based on the user's geographical location.

[0102] The sending unit can determine the sending priority based on the importance of the message at the time of transmission. For example, highly important messages can be sent with the highest priority. Less important messages can be sent later. Highly urgent messages can be sent immediately. In this way, the sending unit can send messages with priority according to their importance.

[0103] The sending unit can apply different sending methods depending on the message category. For example, a formal sending method can be applied to business-related messages, a friendly sending method to casual messages, and a fast sending method to urgent messages. This ensures that the sending unit applies the appropriate sending method according to the message category.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The reception desk receives messages. The reception desk can receive messages such as text messages, voice messages, and image messages. Step 2: The translation unit uses generative AI to translate the message received by the reception unit. The translation unit translates the message using, for example, a neural network-based translation algorithm. The translation unit can use generative AI to understand the context of the message and provide an appropriate translation. Step 3: The sending unit sends the message translated by the translation unit. The sending unit can send the message by methods such as email or push notification.

[0106] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0109] Each of the multiple elements described above, including the receiving unit, translation unit, transmission unit, and conversion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the receiving unit is implemented by the receiving device 38 of the smart device 14 and receives messages. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates messages using generation AI. The transmission unit is implemented by the communication I / F 44 of the smart device 14 and transmits the translated messages. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts messages into images or videos. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the receiving unit, translation unit, transmission unit, and conversion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the receiving unit is implemented by the microphone 238 of the smart glasses 214 and receives the message. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates the message using a generation AI. The transmission unit is implemented by the communication I / F 44 of the smart glasses 214 and transmits the translated message. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the message into video or image. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the receiving unit, translation unit, transmission unit, and conversion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the receiving unit is implemented by the microphone 238 of the headset terminal 314 and receives messages. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates messages using generation AI. The transmission unit is implemented by the communication I / F 44 of the headset terminal 314 and transmits the translated messages. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts messages into images or videos. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the 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.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the receiving unit, translation unit, transmission unit, and conversion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the receiving unit is implemented by the microphone 238 of the robot 414 and receives messages. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates messages using generation AI. The transmission unit is implemented by the communication I / F 44 of the robot 414 and transmits the translated messages. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts messages into images or videos. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] 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.

[0169] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] (Note 1) The reception desk that receives messages, A translation unit that translates messages received by the aforementioned reception unit, A transmitting unit that transmits the message translated by the aforementioned translation unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with a conversion unit that converts messages into video or image format. The system described in Appendix 1, characterized by the features described herein. (Note 3) The conversion unit is Understand the message content and generate appropriate visual representations. The system described in Appendix 2, characterized by the features described herein. (Note 4) The conversion unit is Generates images and videos that reflect emotions and atmosphere. The system described in Appendix 2, characterized by the features described herein. (Note 5) The aforementioned transmitting unit Send the converted video or image. The system described in Appendix 2, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of message delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past message sending history and select the optimal receiving method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving messages, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a message, the system analyzes the user's social media activity and selects relevant messages. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned translation department, During translation, adjust the level of detail based on the importance of the message. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned translation department, During translation, different translation algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned translation department, During translation, the translation priority is determined based on when the message was sent. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned transmitting unit It estimates the user's emotions and adjusts the timing of sending messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned transmitting unit When sending a message, the system prioritizes sending based on the message's importance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned transmitting unit When sending a message, apply different sending methods depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned transmitting unit It estimates the user's emotions and adjusts the order of messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned transmitting unit When sending a message, the system selects the optimal sending method by considering the geographical location of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned transmitting unit When sending a message, we refer to related literature to improve the accuracy of the transmission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The conversion unit is It estimates the user's emotions and adjusts the way images and videos are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The conversion unit is During conversion, the level of detail in the video or image is adjusted based on the content of the message. The system described in Appendix 2, characterized by the features described herein. (Note 26) The conversion unit is During conversion, different conversion algorithms are applied depending on the message category. The system described in Appendix 2, characterized by the features described herein. (Note 27) The conversion unit is It estimates the user's emotions and adjusts the length of videos and images based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The conversion unit is During conversion, the priority of videos and images is determined based on when the messages were sent. The system described in Appendix 2, characterized by the features described herein. (Note 29) The conversion unit is During conversion, the order of videos and images is adjusted based on the relevance of the messages. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk that receives messages, A translation unit that translates messages received by the aforementioned reception unit, A transmitting unit that transmits the message translated by the aforementioned translation unit, Equipped with A system characterized by the following features.

2. It is equipped with a conversion unit that converts messages into video or image format. The system according to feature 1.

3. The conversion unit is Understand the message content and generate appropriate visual representations. The system according to feature 2.

4. The conversion unit is Generates images and videos that reflect emotions and atmosphere. The system according to feature 2.

5. The aforementioned transmitting unit Send the converted video or image. The system according to feature 2.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of message delivery based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past message sending history and select the optimal receiving method. The system according to feature 1.

8. The aforementioned reception unit is When receiving messages, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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