system

Abstract

The system according to this embodiment aims to provide travelers with warnings and instructions in a comic book format in multiple languages. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit receives input for notices and instructions. The generation unit analyzes the information input by the reception unit and converts the notices and instructions appropriate to the situation into a comic strip format in multiple languages. The display unit displays the comic strip generated by the generation 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, including 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] [[ID=3B]]In the conventional technology, it is difficult to accurately convey precautions and guidance to passengers traveling from overseas, and there is a risk of communication loss. <000D027> The system according to the embodiment aims to convert precautions and guidance into a multi-language comic format and provide them to passengers.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit inputs precautions and guidance. The generation unit analyzes the information input by the reception unit and converts precautions and guidance according to the situation into a multi-language comic format. The display unit displays the comic generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can convert notices and instructions into a comic book format in multiple languages ​​and provide them to travelers. [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) An autonomous AI agent system according to an embodiment of the present invention is a system that provides travelers from overseas with real-time, situation-appropriate notices and guidance in multiple languages ​​in comic book format. This system is designed to solve the communication problems caused by differences in pronunciation and language. The autonomous AI agent system allows users to input notices and guidance, and in real time, it converts situation-appropriate information into multiple languages ​​in comic book format and provides it to travelers. For example, users can input information such as airport check-in procedures or guidance at tourist destinations. This information is input into the autonomous AI agent. Next, the autonomous AI agent analyzes the input information and converts situation-appropriate notices and guidance into multiple languages ​​in comic book format. For example, guidance on airport check-in procedures is converted into comic book format in multiple languages ​​such as English, Chinese, and Korean. This comic is displayed on the user's smartphone or tablet. Furthermore, a cross-cultural understanding agent suggests the most appropriate expression for each country and region. For example, the gesture of making a circle with the thumb and index finger, which means "OK" in Japan, may mean "worthless, zero" in France. By considering such cross-cultural differences and suggesting the most appropriate expression, misunderstandings can be prevented. This system reduces communication losses in inbound tourism, improves visitor satisfaction, and alleviates the burden on receiving organizations. Furthermore, manga is a source of Japanese culture and an excellent manual format that combines visuals and text, ensuring smooth and effective information transmission. For example, providing information in manga format at tourist destinations and public facilities makes it more approachable and easier to understand. This allows the autonomous AI agent system to provide travelers with rapid and accurate information, facilitating understanding across language barriers.

[0029] The autonomous AI agent system according to this embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit receives input for notes and guidance. These notes and guidance include, but are not limited to, safety precautions, user instructions, and emergency instructions. For example, the reception unit can receive input from a user regarding airport check-in procedures or guidance at a tourist destination. The generation unit analyzes the information entered by the reception unit and converts the appropriate notes and guidance into a comic book format in multiple languages. For example, the generation unit uses a generation AI to analyze the input information and convert it into a comic book format in multiple languages ​​such as English, Spanish, and Chinese. For example, the generation unit can convert airport check-in procedure guidance into a comic book format in multiple languages ​​such as English, Chinese, and Korean. The generation unit can also use a generation AI to extract important parts of text and generate a comic book based on them. The display unit displays the comic book generated by the generation unit. For example, the display unit can display the generated comic book on the user's smartphone or tablet. The display unit can also display the generated comic book in real time. The display unit can, for example, display the generated comics in real time on the user's smartphone or tablet. This allows the autonomous AI agent system according to the embodiment to convert warnings and instructions into comic format in multiple languages ​​and display them, thereby facilitating understanding across language barriers.

[0030] The reception unit inputs notes and instructions. These notes and instructions include, but are not limited to, safety precautions, user guides, and emergency instructions. For example, users can input information such as airport check-in procedures or tourist information. Specifically, the reception unit receives text data entered by users and handles the initial stages of processing it within the system. Users can provide information via touch panels or voice input, allowing the reception unit to support diverse input methods. For example, when inputting instructions regarding airport check-in procedures, users input flight information and boarding procedure details, which the reception unit receives accurately. When inputting instructions for tourist destinations, users input descriptions and notes about the tourist spots, which the reception unit sends to the generation unit for analysis. The reception unit appropriately classifies the input information and filters it as needed, enabling the generation unit to process the information efficiently. The reception unit also has a feedback function to verify the accuracy of the information entered by users, prompting users to check and correct their input. This allows the reception unit to collect information from users accurately and quickly, improving the overall efficiency of the system.

[0031] The generation unit analyzes the information entered by the reception unit and converts situation-appropriate notes and instructions into a comic format in multiple languages. For example, the generation unit uses a generation AI to analyze the entered information and convert it into a comic format in multiple languages ​​such as English, Spanish, and Chinese. Specifically, the generation unit uses natural language processing technology to analyze the entered text data and extract important information. The generation AI identifies keywords and important phrases from the entered text and uses them to construct the comic's storyline. Furthermore, the generation AI uses translation models corresponding to each language to translate the extracted information into multiple languages. For example, when converting instructions for airport check-in procedures into a comic format in multiple languages ​​such as English, Chinese, and Korean, the generation AI first analyzes the details of the entered procedure, translates it into each language, and then arranges it as comic panels and character dialogue. The generation unit can also use the generation AI to extract important parts of a text and generate a comic based on that. For example, if instructions regarding emergency procedures are entered, the generation AI extracts the necessary actions and precautions in an emergency and expresses them in a visually easy-to-understand comic format. The generation unit provides a preview of the generated comic to ensure it is easy for the user to understand, and allows for modifications as needed. This enables the generation unit to convert user-inputted information into an easy-to-understand comic format in multiple languages, thereby communicating information across language barriers.

[0032] The display unit displays the manga generated by the generation unit. For example, the display unit can display the generated manga on the user's smartphone or tablet. Specifically, the display unit receives the generated manga data and displays it in a format suitable for the user's device. The display unit can also display the generated manga in real time. For example, if instructions for airport check-in procedures are generated, the display unit can immediately display the manga on the user's smartphone, allowing the user to visually confirm the procedure flow. The display unit can display the generated manga on the user's smartphone or tablet in real time. This allows the user to quickly obtain the necessary information and take appropriate action. Furthermore, the display unit employs responsive design to provide the optimal display method according to the user's device. This optimizes the display on different devices such as smartphones, tablets, and PCs, allowing users to comfortably view information on any device. The display unit also has a function that automatically displays the manga in the appropriate language according to the user's language settings, saving the user the trouble of changing language settings. This allows the display unit to quickly and appropriately display the generated manga, making it easy for users to obtain the necessary information.

[0033] The generation unit includes a suggestion unit that proposes appropriate expressions tailored to each country and region. The suggestion unit can, for example, use generation AI to propose the most suitable expressions for each country and region. For example, the suggestion unit can propose an appropriate expression considering that the gesture of making a circle with the thumb and index finger, which means "OK" in Japan, means "worthless, zero" in France. For example, the suggestion unit can propose an expression considering that a "thumbs up" has a positive meaning in the United States, but may have a different meaning in other countries. For example, the suggestion unit can consider cultural background when selecting colors, considering that red symbolizes good luck in China, but may have a different meaning in other countries. By proposing the most suitable expressions for each country and region, misunderstandings can be prevented.

[0034] The display unit can display the generated manga on the user's smartphone or tablet. For example, the display unit can display the generated manga on the user's smartphone. For example, the display unit can display the generated manga on the user's tablet. The display unit can display the generated manga on various devices, such as iOS devices and Android devices. This allows users to easily access the information by displaying the generated manga on their smartphones or tablets.

[0035] The proposal unit can suggest appropriate expressions using a cross-cultural understanding agent. For example, the proposal unit can suggest the optimal expression considering cultural background using a cross-cultural understanding agent. The cross-cultural understanding agent can, for example, refer to a database of cultural backgrounds to suggest appropriate expressions. The cross-cultural understanding agent can, for example, use a language translation function to suggest appropriate expressions. Therefore, by using a cross-cultural understanding agent, the optimal expression considering cultural background can be suggested.

[0036] The generation unit can convert airport check-in procedures or tourist information into a comic book format in multiple languages. For example, the generation unit can convert airport check-in procedure instructions into a comic book format in multiple languages. For example, the generation unit can convert tourist information into a comic book format in multiple languages. For example, the generation unit can convert directions to check-in counters into a comic book format. For example, the generation unit can convert introductions to tourist attractions into a comic book format in multiple languages. By converting airport check-in procedures and tourist information into a comic book format in multiple languages, it makes the information easier for travelers to understand.

[0037] The display unit can display the generated manga in real time. For example, the display unit can display the generated manga in real time. For example, the display unit can display the generated manga on the user's smartphone or tablet in real time. For example, the display unit can update the generated manga in real time and display the latest information. This allows users to instantly check the latest information by displaying the generated manga in real time.

[0038] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). For example, the reception desk can predict and suggest input methods that the user will use during specific time periods based on their past input history. For example, the reception desk can customize input methods by referring to content that the user has entered in the past. In this way, by analyzing the user's past input history, the optimal input method can be suggested.

[0039] The reception system can filter the input of notices and information based on the user's current situation and areas of interest. For example, if the user is at an airport, the reception system can prioritize displaying airport-related notices and information. For example, if the user is at a tourist destination, the reception system can prioritize displaying information related to that tourist destination. The reception system can also filter and display relevant notices and information based on the user's areas of interest. This allows the system to provide highly relevant information by filtering information based on the user's current situation and areas of interest.

[0040] The reception system can prioritize inputting highly relevant information by considering the user's geographical location when entering notices and instructions. For example, if the user is in a specific region, the reception system can prioritize displaying notices and instructions relevant to that region. For example, if the user is on the move, the reception system can prioritize displaying relevant information based on their current location. For example, if the user is in a specific facility, the reception system can prioritize displaying information related to that facility. In this way, highly relevant information can be provided by considering the user's geographical location.

[0041] The reception desk can analyze the user's social media activity and input relevant information when entering notices and instructions. For example, the reception desk can display relevant notices and instructions based on information the user has shared on social media. For example, the reception desk can analyze the content of the user's social media posts and display information based on their areas of interest. For example, the reception desk can display relevant notices and instructions based on information about accounts the user follows. In this way, relevant information can be provided by analyzing the user's social media activity.

[0042] The generation unit can adjust the level of detail in the comic based on the importance of the warnings and instructions. For example, in the case of important warnings, the generation unit can generate a comic with a detailed explanation. For example, in the case of general instructions, the generation unit can generate a comic with a concise explanation. For example, in the case of emergency instructions, the generation unit can generate a comic that focuses on the key points so that they can be quickly understood. In this way, by adjusting the level of detail in the comic based on the importance of the warnings and instructions, important information can be conveyed in detail.

[0043] The generation unit can apply different generation algorithms depending on the category of the warnings or instructions when generating comics. For example, in the case of safety warnings, the generation unit can generate comics with visual emphasis. For example, in the case of tourist information, the generation unit can generate comics that include attractive visuals. For example, in the case of transportation information, the generation unit can generate comics that include maps and route information. In this way, by applying different generation algorithms depending on the category of warnings or instructions, more appropriate comics can be generated.

[0044] The generation unit can determine the priority of comics based on the submission timing of notices and announcements. For example, in the case of urgent notices, the generation unit can generate the comic with the highest priority. For example, in the case of regular announcements, the generation unit can generate the comic with the normal priority. For example, in the case of future notices, the generation unit can postpone the generation of the comic. This allows for the priority of providing highly urgent information by determining the priority of comics based on the submission timing of notices and announcements.

[0045] The generation unit can adjust the order of comics based on the relevance of notes and instructions during comic generation. For example, the generation unit can generate comics that prioritize the display of highly relevant notes. For example, the generation unit can generate comics that display less relevant instructions later. For example, if there are multiple notes or instructions, the generation unit can generate comics that adjust the order based on relevance. This allows for the priority provision of highly relevant information by adjusting the order of comics based on the relevance of notes and instructions.

[0046] The display unit can select the optimal display method when displaying comics by referring to the user's past operation history. For example, the display unit can prioritize providing display methods that the user has previously preferred. For example, the display unit can predict and provide display methods to be used during specific time periods based on the user's past operation history. For example, the display unit can customize the display method by referring to content that the user has previously displayed. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0047] The display unit can select an appropriate display method when displaying comics, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information.

[0048] The display unit can prioritize displaying manga 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, the display unit can prioritize displaying manga related to that region. For example, if the user is on the move, the display unit can prioritize displaying manga related to the user's current location. For example, if the user is in a specific facility, the display unit can prioritize displaying manga related to that facility. In this way, by taking the user's geographical location into consideration, the display unit can provide highly relevant manga.

[0049] The display unit can analyze the user's social media activity when displaying manga and show relevant manga. For example, the display unit can show relevant manga based on information the user has shared on social media. For example, the display unit can analyze the content of the user's social media posts and show manga based on their areas of interest. For example, the display unit can show relevant manga based on information about accounts the user follows. In this way, relevant manga can be provided by analyzing the user's social media activity.

[0050] The proposal team can select appropriate expressions when making proposals, taking into account the cultural background of each country and region. For example, the proposal team can propose an appropriate expression considering that the gesture of making a circle with the thumb and index finger, which means "OK" in Japan, means "worthless, zero" in France. For example, the proposal team can propose an expression considering that a "thumbs up" has a positive meaning in the United States, but may have a different meaning in other countries. For example, the proposal team can consider the cultural background when selecting colors, considering that red symbolizes good luck in China, but may have a different meaning in other countries. By considering the cultural background of each country and region, misunderstandings can be prevented.

[0051] The proposal unit can select appropriate expressions by referring to past proposal history when making a proposal. For example, the proposal unit can prioritize suggesting expressions that the user has preferred to use in the past. For example, the proposal unit can predict and suggest expressions suitable for a specific situation based on the user's past proposal history. For example, the proposal unit can suggest the optimal expression while avoiding expressions that the user has rejected in the past. In this way, the optimal expression can be suggested by referring to past proposal history.

[0052] The suggestion function can propose appropriate expressions while considering the user's geographical location information. For example, if the user is in a specific region, the suggestion function can prioritize suggesting expressions related to that region. For example, if the user is on the move, the suggestion function can prioritize suggesting expressions related to the user's current location. For example, if the user is in a specific facility, the suggestion function can prioritize suggesting expressions related to that facility. In this way, by considering the user's geographical location information, it is possible to provide highly relevant expressions.

[0053] The proposal department can analyze a user's social media activity and suggest relevant expressions when making a proposal. For example, the proposal department can suggest relevant expressions based on information shared by the user on social media. For example, the proposal department can analyze the content of a user's social media posts and suggest expressions based on their areas of interest. For example, the proposal department can suggest relevant expressions based on information about accounts the user follows. In this way, relevant expressions can be provided by analyzing the user's social media activity.

[0054] Intercultural understanding agents can select appropriate methods of understanding by considering the cultural background of each country and region. For example, an intercultural understanding agent can provide appropriate methods of understanding, taking into account that the gesture of making a circle with the thumb and index finger, which means "OK" in Japan, means "worthless, zero" in France. For example, an intercultural understanding agent can provide methods of understanding by considering that a "thumbs up" has a positive meaning in the United States, but may have a different meaning in other countries. For example, an intercultural understanding agent can consider cultural background when selecting colors, as red symbolizes good luck in China, but may have a different meaning in other countries. In this way, misunderstandings can be prevented by considering the cultural background of each country and region.

[0055] The intercultural understanding agent can select an appropriate method of understanding by referring to the user's past history of intercultural understanding. For example, the agent can prioritize providing understanding methods that the user has preferred in the past. For example, the agent can predict and provide an understanding method suitable for a specific situation based on the user's past history of intercultural understanding. For example, the agent can provide the optimal understanding method by avoiding understanding methods that the user has rejected in the past. In this way, the agent can provide the optimal understanding method by referring to the user's past history of intercultural understanding.

[0056] The intercultural understanding agent can select an appropriate understanding method when understanding different cultures, taking into account the user's geographical location. For example, if the user is in a specific region, the agent can prioritize providing intercultural understanding relevant to that region. For example, if the user is on the move, the agent can prioritize providing intercultural understanding relevant to their current location. For example, if the user is in a specific facility, the agent can prioritize providing intercultural understanding relevant to that facility. In this way, by considering the user's geographical location, it can provide a more relevant understanding method.

[0057] The intercultural understanding agent can analyze a user's social media activity and suggest relevant methods of understanding when providing intercultural insights. For example, the agent can provide relevant intercultural insights based on information shared by the user on social media. It can also analyze the content of a user's social media posts and provide insights based on their areas of interest. Furthermore, it can provide relevant intercultural insights based on information from accounts the user follows. In this way, by analyzing the user's social media activity, it can provide relevant methods of understanding.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze user voice input and convert instructions and information into text using speech recognition technology. For example, if a user voice-instructs about airport check-in procedures, the system can convert that voice into text and send it to the generation unit. Furthermore, it can take into account the user's pronunciation and accent during voice input to ensure accurate text conversion. In addition, a function to remove background noise during voice input enables even more accurate speech recognition. This allows users to input instructions and information via voice, providing information more intuitively and quickly.

[0060] The display unit can optimize the generated comics to fit the screen size of the user's device. For example, on a small smartphone screen, the font size can be enlarged and the layout adjusted for easy scrolling. On a large tablet screen, multiple panels can be displayed at once to make it easier to grasp the overall flow. Furthermore, on a desktop computer screen, full-screen display is possible, allowing detailed information to be viewed at a glance. In this way, by providing the optimal display method according to the user's device, the visibility of the information can be improved.

[0061] The suggestion function can analyze a user's past behavior history and propose the most appropriate expressions. For example, if a user has previously preferred using a particular expression, it can prioritize suggesting that expression. It can also eliminate expressions the user has avoided in the past and suggest more appropriate ones. Furthermore, it can predict and suggest expressions appropriate for specific times of day or situations based on the user's behavior history. In this way, by considering the user's past behavior history, it can propose more personalized expressions.

[0062] The generation unit can prioritize displaying relevant information by considering the user's current location when generating manga. For example, if the user is at an airport, airport-related notices and information can be prioritized. If the user is at a tourist destination, information related to that destination can be prioritized. Furthermore, if the user is at a specific facility, information related to that facility can be prioritized. In this way, by considering the user's current location, highly relevant information can be provided.

[0063] The generation unit can prioritize displaying relevant information when generating manga, taking into account the user's areas of interest. For example, if the user is interested in history, information about the historical background of tourist destinations can be prioritized. If the user is interested in food, information about local specialty dishes can be prioritized. Also, if the user is interested in shopping, information about shopping spots can be prioritized. In this way, by considering the user's areas of interest, more engaging information can be provided.

[0064] The proposal department can analyze users' social media activity and suggest relevant expressions. For example, it can suggest relevant expressions based on information users share on social media. It can analyze the content of users' social media posts and suggest expressions based on their areas of interest. It can also suggest relevant expressions based on information about accounts users follow. In this way, by analyzing users' social media activity, it can provide relevant expressions.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk enters important notes and instructions. These notes and instructions include safety precautions, usage guidelines, and emergency instructions. For example, users can enter information about airport check-in procedures or directions at tourist destinations. Step 2: The generation unit analyzes the information entered by the reception unit and converts situation-appropriate notices and instructions into a comic format in multiple languages. The generation unit uses a generation AI to analyze the entered information and convert it into a comic format in multiple languages ​​such as English, Spanish, and Chinese. The generation unit can also extract important parts of the text and generate a comic based on that. Step 3: The display unit displays the manga generated by the generation unit. The display unit can display the generated manga on the user's smartphone or tablet. The display unit can also display the generated manga in real time.

[0067] (Example of form 2) An autonomous AI agent system according to an embodiment of the present invention is a system that provides travelers from overseas with real-time, situation-appropriate notices and guidance in multiple languages ​​in comic book format. This system is designed to solve the communication problems caused by differences in pronunciation and language. The autonomous AI agent system allows users to input notices and guidance, and in real time, it converts situation-appropriate information into multiple languages ​​in comic book format and provides it to travelers. For example, users can input information such as airport check-in procedures or guidance at tourist destinations. This information is input into the autonomous AI agent. Next, the autonomous AI agent analyzes the input information and converts situation-appropriate notices and guidance into multiple languages ​​in comic book format. For example, guidance on airport check-in procedures is converted into comic book format in multiple languages ​​such as English, Chinese, and Korean. This comic is displayed on the user's smartphone or tablet. Furthermore, a cross-cultural understanding agent suggests the most appropriate expression for each country and region. For example, the gesture of making a circle with the thumb and index finger, which means "OK" in Japan, may mean "worthless, zero" in France. By considering such cross-cultural differences and suggesting the most appropriate expression, misunderstandings can be prevented. This system reduces communication losses in inbound tourism, improves visitor satisfaction, and alleviates the burden on receiving organizations. Furthermore, manga is a source of Japanese culture and an excellent manual format that combines visuals and text, ensuring smooth and effective information transmission. For example, providing information in manga format at tourist destinations and public facilities makes it more approachable and easier to understand. This allows the autonomous AI agent system to provide travelers with rapid and accurate information, facilitating understanding across language barriers.

[0068] The autonomous AI agent system according to this embodiment comprises a reception unit, a generation unit, and a display unit. The reception unit receives input for notes and guidance. These notes and guidance include, but are not limited to, safety precautions, user instructions, and emergency instructions. For example, the reception unit can receive input from a user regarding airport check-in procedures or guidance at a tourist destination. The generation unit analyzes the information entered by the reception unit and converts the appropriate notes and guidance into a comic book format in multiple languages. For example, the generation unit uses a generation AI to analyze the input information and convert it into a comic book format in multiple languages ​​such as English, Spanish, and Chinese. For example, the generation unit can convert airport check-in procedure guidance into a comic book format in multiple languages ​​such as English, Chinese, and Korean. The generation unit can also use a generation AI to extract important parts of text and generate a comic book based on them. The display unit displays the comic book generated by the generation unit. For example, the display unit can display the generated comic book on the user's smartphone or tablet. The display unit can also display the generated comic book in real time. The display unit can, for example, display the generated comics in real time on the user's smartphone or tablet. This allows the autonomous AI agent system according to the embodiment to convert warnings and instructions into comic format in multiple languages ​​and display them, thereby facilitating understanding across language barriers.

[0069] The reception unit inputs notes and instructions. These notes and instructions include, but are not limited to, safety precautions, user guides, and emergency instructions. For example, users can input information such as airport check-in procedures or tourist information. Specifically, the reception unit receives text data entered by users and handles the initial stages of processing it within the system. Users can provide information via touch panels or voice input, allowing the reception unit to support diverse input methods. For example, when inputting instructions regarding airport check-in procedures, users input flight information and boarding procedure details, which the reception unit receives accurately. When inputting instructions for tourist destinations, users input descriptions and notes about the tourist spots, which the reception unit sends to the generation unit for analysis. The reception unit appropriately classifies the input information and filters it as needed, enabling the generation unit to process the information efficiently. The reception unit also has a feedback function to verify the accuracy of the information entered by users, prompting users to check and correct their input. This allows the reception unit to collect information from users accurately and quickly, improving the overall efficiency of the system.

[0070] The generation unit analyzes the information entered by the reception unit and converts situation-appropriate notes and instructions into a comic format in multiple languages. For example, the generation unit uses a generation AI to analyze the entered information and convert it into a comic format in multiple languages ​​such as English, Spanish, and Chinese. Specifically, the generation unit uses natural language processing technology to analyze the entered text data and extract important information. The generation AI identifies keywords and important phrases from the entered text and uses them to construct the comic's storyline. Furthermore, the generation AI uses translation models corresponding to each language to translate the extracted information into multiple languages. For example, when converting instructions for airport check-in procedures into a comic format in multiple languages ​​such as English, Chinese, and Korean, the generation AI first analyzes the details of the entered procedure, translates it into each language, and then arranges it as comic panels and character dialogue. The generation unit can also use the generation AI to extract important parts of a text and generate a comic based on that. For example, if instructions regarding emergency procedures are entered, the generation AI extracts the necessary actions and precautions in an emergency and expresses them in a visually easy-to-understand comic format. The generation unit provides a preview of the generated comic to ensure it is easy for the user to understand, and allows for modifications as needed. This enables the generation unit to convert user-inputted information into an easy-to-understand comic format in multiple languages, thereby communicating information across language barriers.

[0071] The display unit displays the manga generated by the generation unit. For example, the display unit can display the generated manga on the user's smartphone or tablet. Specifically, the display unit receives the generated manga data and displays it in a format suitable for the user's device. The display unit can also display the generated manga in real time. For example, if instructions for airport check-in procedures are generated, the display unit can immediately display the manga on the user's smartphone, allowing the user to visually confirm the procedure flow. The display unit can display the generated manga on the user's smartphone or tablet in real time. This allows the user to quickly obtain the necessary information and take appropriate action. Furthermore, the display unit employs responsive design to provide the optimal display method according to the user's device. This optimizes the display on different devices such as smartphones, tablets, and PCs, allowing users to comfortably view information on any device. The display unit also has a function that automatically displays the manga in the appropriate language according to the user's language settings, saving the user the trouble of changing language settings. This allows the display unit to quickly and appropriately display the generated manga, making it easy for users to obtain the necessary information.

[0072] The generation unit includes a suggestion unit that proposes appropriate expressions tailored to each country and region. The suggestion unit can, for example, use generation AI to propose the most suitable expressions for each country and region. For example, the suggestion unit can propose an appropriate expression considering that the gesture of making a circle with the thumb and index finger, which means "OK" in Japan, means "worthless, zero" in France. For example, the suggestion unit can propose an expression considering that a "thumbs up" has a positive meaning in the United States, but may have a different meaning in other countries. For example, the suggestion unit can consider cultural background when selecting colors, considering that red symbolizes good luck in China, but may have a different meaning in other countries. By proposing the most suitable expressions for each country and region, misunderstandings can be prevented.

[0073] The display unit can display the generated manga on the user's smartphone or tablet. For example, the display unit can display the generated manga on the user's smartphone. For example, the display unit can display the generated manga on the user's tablet. The display unit can display the generated manga on various devices, such as iOS devices and Android devices. This allows users to easily access the information by displaying the generated manga on their smartphones or tablets.

[0074] The proposal unit can suggest appropriate expressions using a cross-cultural understanding agent. For example, the proposal unit can suggest the optimal expression considering cultural background using a cross-cultural understanding agent. The cross-cultural understanding agent can, for example, refer to a database of cultural backgrounds to suggest appropriate expressions. The cross-cultural understanding agent can, for example, use a language translation function to suggest appropriate expressions. Therefore, by using a cross-cultural understanding agent, the optimal expression considering cultural background can be suggested.

[0075] The generation unit can convert airport check-in procedures or tourist information into a comic book format in multiple languages. For example, the generation unit can convert airport check-in procedure instructions into a comic book format in multiple languages. For example, the generation unit can convert tourist information into a comic book format in multiple languages. For example, the generation unit can convert directions to check-in counters into a comic book format. For example, the generation unit can convert introductions to tourist attractions into a comic book format in multiple languages. By converting airport check-in procedures and tourist information into a comic book format in multiple languages, it makes the information easier for travelers to understand.

[0076] The display unit can display the generated manga in real time. For example, the display unit can display the generated manga in real time. For example, the display unit can display the generated manga on the user's smartphone or tablet in real time. For example, the display unit can update the generated manga in real time and display the latest information. This allows users to instantly check the latest information by displaying the generated manga in real time.

[0077] The reception desk can estimate the user's emotions and adjust the timing of inputting notices and instructions based on the estimated emotions. For example, if the user is nervous, the reception desk can delay the input timing to help them relax. For example, if the user is in a hurry, the reception desk can speed up the input timing to help them input quickly. For example, if the user is excited, the reception desk can adjust the input timing to help them input calmly. In this way, by adjusting the input timing according to the user's emotions, the user can input information in a relaxed state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). For example, the reception desk can predict and suggest input methods that the user will use during specific time periods based on their past input history. For example, the reception desk can customize input methods by referring to content that the user has entered in the past. In this way, by analyzing the user's past input history, the optimal input method can be suggested.

[0079] The reception system can filter the input of notices and information based on the user's current situation and areas of interest. For example, if the user is at an airport, the reception system can prioritize displaying airport-related notices and information. For example, if the user is at a tourist destination, the reception system can prioritize displaying information related to that tourist destination. The reception system can also filter and display relevant notices and information based on the user's areas of interest. This allows the system to provide highly relevant information by filtering information based on the user's current situation and areas of interest.

[0080] The reception desk can estimate the user's emotions and determine the priority of the instructions and guidance to be entered based on the estimated emotions. For example, if the user is nervous, the reception desk can prioritize displaying important instructions. For example, if the user is relaxed, the reception desk can prioritize displaying detailed guidance. For example, if the user is in a hurry, the reception desk can prioritize displaying instructions that can be quickly understood. In this way, important information can be provided preferentially by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception system can prioritize inputting highly relevant information by considering the user's geographical location when entering notices and instructions. For example, if the user is in a specific region, the reception system can prioritize displaying notices and instructions relevant to that region. For example, if the user is on the move, the reception system can prioritize displaying relevant information based on their current location. For example, if the user is in a specific facility, the reception system can prioritize displaying information related to that facility. In this way, highly relevant information can be provided by considering the user's geographical location.

[0082] The reception desk can analyze the user's social media activity and input relevant information when entering notices and instructions. For example, the reception desk can display relevant notices and instructions based on information the user has shared on social media. For example, the reception desk can analyze the content of the user's social media posts and display information based on their areas of interest. For example, the reception desk can display relevant notices and instructions based on information about accounts the user follows. In this way, relevant information can be provided by analyzing the user's social media activity.

[0083] The generation unit can estimate the user's emotions and adjust the manga's expression based on those emotions. For example, if the user is relaxed, the generation unit can generate a manga with soft colors. If the user is tense, the generation unit can generate a simple and highly visual manga. If the user is excited, the generation unit can generate a manga with visually stimulating effects. This allows for more appropriate expression by adjusting the manga's expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The generation unit can adjust the level of detail in the comic based on the importance of the warnings and instructions. For example, in the case of important warnings, the generation unit can generate a comic with a detailed explanation. For example, in the case of general instructions, the generation unit can generate a comic with a concise explanation. For example, in the case of emergency instructions, the generation unit can generate a comic that focuses on the key points so that they can be quickly understood. In this way, by adjusting the level of detail in the comic based on the importance of the warnings and instructions, important information can be conveyed in detail.

[0085] The generation unit can apply different generation algorithms depending on the category of the warnings or instructions when generating comics. For example, in the case of safety warnings, the generation unit can generate comics with visual emphasis. For example, in the case of tourist information, the generation unit can generate comics that include attractive visuals. For example, in the case of transportation information, the generation unit can generate comics that include maps and route information. In this way, by applying different generation algorithms depending on the category of warnings or instructions, more appropriate comics can be generated.

[0086] The generation unit can estimate the user's emotions and adjust the length of the comic based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise comic. For example, if the user is relaxed, the generation unit can generate a longer comic with detailed explanations. For example, if the user is excited, the generation unit can generate a comic with visually stimulating effects. This allows the user to receive a comic of the optimal length by adjusting the length according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The generation unit can determine the priority of comics based on the submission timing of notices and announcements. For example, in the case of urgent notices, the generation unit can generate the comic with the highest priority. For example, in the case of regular announcements, the generation unit can generate the comic with the normal priority. For example, in the case of future notices, the generation unit can postpone the generation of the comic. This allows for the priority of providing highly urgent information by determining the priority of comics based on the submission timing of notices and announcements.

[0088] The generation unit can adjust the order of comics based on the relevance of notes and instructions during comic generation. For example, the generation unit can generate comics that prioritize the display of highly relevant notes. For example, the generation unit can generate comics that display less relevant instructions later. For example, if there are multiple notes or instructions, the generation unit can generate comics that adjust the order based on relevance. This allows for the priority provision of highly relevant information by adjusting the order of comics based on the relevance of notes and instructions.

[0089] The display unit can estimate the user's emotions and adjust the way the comic is displayed based on the estimated emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display method. For example, if the user is relaxed, the display unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the display unit can provide a display method that gets straight to the point. By adjusting the way the comic is displayed according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The display unit can select the optimal display method when displaying comics by referring to the user's past operation history. For example, the display unit can prioritize providing display methods that the user has previously preferred. For example, the display unit can predict and provide display methods to be used during specific time periods based on the user's past operation history. For example, the display unit can customize the display method by referring to content that the user has previously displayed. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0091] The display unit can select an appropriate display method when displaying comics, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information.

[0092] The display unit can estimate the user's emotions and adjust the display order of the comics based on the estimated emotions. For example, if the user is tense, the display unit can prioritize displaying important information. For example, if the user is relaxed, the display unit can prioritize displaying detailed information. For example, if the user is in a hurry, the display unit can prioritize displaying concise information. In this way, by adjusting the display order of the comics according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The display unit can prioritize displaying manga 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, the display unit can prioritize displaying manga related to that region. For example, if the user is on the move, the display unit can prioritize displaying manga related to the user's current location. For example, if the user is in a specific facility, the display unit can prioritize displaying manga related to that facility. In this way, by taking the user's geographical location into consideration, the display unit can provide highly relevant manga.

[0094] The display unit can analyze the user's social media activity when displaying manga and show relevant manga. For example, the display unit can show relevant manga based on information the user has shared on social media. For example, the display unit can analyze the content of the user's social media posts and show manga based on their areas of interest. For example, the display unit can show relevant manga based on information about accounts the user follows. In this way, relevant manga can be provided by analyzing the user's social media activity.

[0095] The suggestion unit can estimate the user's emotions and propose the most appropriate expression based on the estimated emotions. For example, if the user is tense, the suggestion unit can propose a calm expression. For example, if the user is relaxed, the suggestion unit can propose a cheerful expression. For example, if the user is excited, the suggestion unit can propose a visually stimulating expression. This allows for more appropriate expressions by proposing the most appropriate expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The proposal team can select appropriate expressions when making proposals, taking into account the cultural background of each country and region. For example, the proposal team can propose an appropriate expression considering that the gesture of making a circle with the thumb and index finger, which means "OK" in Japan, means "worthless, zero" in France. For example, the proposal team can propose an expression considering that a "thumbs up" has a positive meaning in the United States, but may have a different meaning in other countries. For example, the proposal team can consider the cultural background when selecting colors, considering that red symbolizes good luck in China, but may have a different meaning in other countries. By considering the cultural background of each country and region, misunderstandings can be prevented.

[0097] The proposal unit can select appropriate expressions by referring to past proposal history when making a proposal. For example, the proposal unit can prioritize suggesting expressions that the user has preferred to use in the past. For example, the proposal unit can predict and suggest expressions suitable for a specific situation based on the user's past proposal history. For example, the proposal unit can suggest the optimal expression while avoiding expressions that the user has rejected in the past. In this way, the optimal expression can be suggested by referring to past proposal history.

[0098] The suggestion section can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion section can prioritize important suggestions. For example, if the user is relaxed, the suggestion section can prioritize detailed suggestions. For example, if the user is in a hurry, the suggestion section can prioritize suggestions that can be quickly understood. This allows for the priority of important suggestions by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The suggestion function can propose appropriate expressions while considering the user's geographical location information. For example, if the user is in a specific region, the suggestion function can prioritize suggesting expressions related to that region. For example, if the user is on the move, the suggestion function can prioritize suggesting expressions related to the user's current location. For example, if the user is in a specific facility, the suggestion function can prioritize suggesting expressions related to that facility. In this way, by considering the user's geographical location information, it is possible to provide highly relevant expressions.

[0100] The proposal department can analyze a user's social media activity and suggest relevant expressions when making a proposal. For example, the proposal department can suggest relevant expressions based on information shared by the user on social media. For example, the proposal department can analyze the content of a user's social media posts and suggest expressions based on their areas of interest. For example, the proposal department can suggest relevant expressions based on information about accounts the user follows. In this way, relevant expressions can be provided by analyzing the user's social media activity.

[0101] An intercultural understanding agent can estimate a user's emotions and adjust its method of intercultural understanding based on those emotions. For example, if a user is tense, the agent can adjust its method of intercultural understanding to help them relax. If a user is relaxed, the agent can provide a more detailed method of intercultural understanding. If a user is excited, the agent can provide a visually stimulating method of intercultural understanding. By adjusting the method of intercultural understanding according to the user's emotions, a more appropriate understanding becomes possible. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] Intercultural understanding agents can select appropriate methods of understanding by considering the cultural background of each country and region. For example, an intercultural understanding agent can provide appropriate methods of understanding, taking into account that the gesture of making a circle with the thumb and index finger, which means "OK" in Japan, means "worthless, zero" in France. For example, an intercultural understanding agent can provide methods of understanding by considering that a "thumbs up" has a positive meaning in the United States, but may have a different meaning in other countries. For example, an intercultural understanding agent can consider cultural background when selecting colors, as red symbolizes good luck in China, but may have a different meaning in other countries. In this way, misunderstandings can be prevented by considering the cultural background of each country and region.

[0103] The intercultural understanding agent can select an appropriate method of understanding by referring to the user's past history of intercultural understanding. For example, the agent can prioritize providing understanding methods that the user has preferred in the past. For example, the agent can predict and provide an understanding method suitable for a specific situation based on the user's past history of intercultural understanding. For example, the agent can provide the optimal understanding method by avoiding understanding methods that the user has rejected in the past. In this way, the agent can provide the optimal understanding method by referring to the user's past history of intercultural understanding.

[0104] An intercultural understanding agent can estimate a user's emotions and prioritize intercultural understanding based on those emotions. For example, if a user is stressed, the agent can prioritize providing important intercultural understanding. If a user is relaxed, the agent can prioritize providing detailed intercultural understanding. If a user is in a hurry, the agent can prioritize providing quickly understandable intercultural understanding. This allows for the prioritization of important understanding based on 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.

[0105] The intercultural understanding agent can select an appropriate understanding method when understanding different cultures, taking into account the user's geographical location. For example, if the user is in a specific region, the agent can prioritize providing intercultural understanding relevant to that region. For example, if the user is on the move, the agent can prioritize providing intercultural understanding relevant to their current location. For example, if the user is in a specific facility, the agent can prioritize providing intercultural understanding relevant to that facility. In this way, by considering the user's geographical location, it can provide a more relevant understanding method.

[0106] The intercultural understanding agent can analyze a user's social media activity and suggest relevant methods of understanding when providing intercultural insights. For example, the agent can provide relevant intercultural insights based on information shared by the user on social media. It can also analyze the content of a user's social media posts and provide insights based on their areas of interest. Furthermore, it can provide relevant intercultural insights based on information from accounts the user follows. In this way, by analyzing the user's social media activity, it can provide relevant methods of understanding.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze user voice input and convert instructions and information into text using speech recognition technology. For example, if a user voice-instructs about airport check-in procedures, the system can convert that voice into text and send it to the generation unit. Furthermore, it can take into account the user's pronunciation and accent during voice input to ensure accurate text conversion. In addition, a function to remove background noise during voice input enables even more accurate speech recognition. This allows users to input instructions and information via voice, providing information more intuitively and quickly.

[0109] The generation unit can estimate the user's emotions and adjust the facial expressions and tone of the manga characters based on those estimated emotions. For example, if the user is tense, the character's facial expression can be softened to convey a sense of reassurance. If the user is relaxed, the character's facial expression can be brightened and the tone can be made more approachable. If the user is excited, the character's facial expression can be made more lively and the tone can be made more energetic. By adjusting the facial expressions and tone of the manga characters according to the user's emotions, it becomes possible to create expressions that are more likely to evoke empathy.

[0110] The display unit can optimize the generated comics to fit the screen size of the user's device. For example, on a small smartphone screen, the font size can be enlarged and the layout adjusted for easy scrolling. On a large tablet screen, multiple panels can be displayed at once to make it easier to grasp the overall flow. Furthermore, on a desktop computer screen, full-screen display is possible, allowing detailed information to be viewed at a glance. In this way, by providing the optimal display method according to the user's device, the visibility of the information can be improved.

[0111] The suggestion function can analyze a user's past behavior history and propose the most appropriate expressions. For example, if a user has previously preferred using a particular expression, it can prioritize suggesting that expression. It can also eliminate expressions the user has avoided in the past and suggest more appropriate ones. Furthermore, it can predict and suggest expressions appropriate for specific times of day or situations based on the user's behavior history. In this way, by considering the user's past behavior history, it can propose more personalized expressions.

[0112] The generation unit can prioritize displaying relevant information by considering the user's current location when generating manga. For example, if the user is at an airport, airport-related notices and information can be prioritized. If the user is at a tourist destination, information related to that destination can be prioritized. Furthermore, if the user is at a specific facility, information related to that facility can be prioritized. In this way, by considering the user's current location, highly relevant information can be provided.

[0113] The reception desk can estimate the user's emotions and adjust the content of the instructions and guidance provided based on those estimates. For example, if the user is nervous, important instructions can be displayed concisely and easily understood. If the user is relaxed, detailed guidance can be provided to promote a deeper understanding. If the user is in a hurry, concise information can be provided for quick comprehension. By adjusting the content of information according to the user's emotions, more effective information delivery becomes possible.

[0114] The generation unit can prioritize displaying relevant information when generating manga, taking into account the user's areas of interest. For example, if the user is interested in history, information about the historical background of tourist destinations can be prioritized. If the user is interested in food, information about local specialty dishes can be prioritized. Also, if the user is interested in shopping, information about shopping spots can be prioritized. In this way, by considering the user's areas of interest, more engaging information can be provided.

[0115] The display unit can estimate the user's emotions and adjust the display speed of the comic strip based on those emotions. For example, if the user is nervous, the display speed can be slowed down so that they can slowly review the information. If the user is relaxed, the display speed can be set to normal so that they can review the information smoothly. If the user is in a hurry, the display speed can be increased so that they can review the information quickly. By adjusting the display speed according to the user's emotions, it becomes possible to provide more appropriate information.

[0116] The proposal department can analyze users' social media activity and suggest relevant expressions. For example, it can suggest relevant expressions based on information users share on social media. It can analyze the content of users' social media posts and suggest expressions based on their areas of interest. It can also suggest relevant expressions based on information about accounts users follow. In this way, by analyzing users' social media activity, it can provide relevant expressions.

[0117] The generation unit can estimate the user's emotions and adjust the colors of the manga based on those emotions. For example, if the user is relaxed, it can generate a manga with soft colors. If the user is tense, it can generate a manga with simple, highly visible colors. If the user is excited, it can generate a manga using visually stimulating colors. By adjusting the colors of the manga according to the user's emotions, more appropriate expression becomes possible.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk enters important notes and instructions. These notes and instructions include safety precautions, usage guidelines, and emergency instructions. For example, users can enter information about airport check-in procedures or directions at tourist destinations. Step 2: The generation unit analyzes the information entered by the reception unit and converts situation-appropriate notices and instructions into a comic format in multiple languages. The generation unit uses a generation AI to analyze the entered information and convert it into a comic format in multiple languages ​​such as English, Spanish, and Chinese. The generation unit can also extract important parts of the text and generate a comic based on that. Step 3: The display unit displays the manga generated by the generation unit. The display unit can display the generated manga on the user's smartphone or tablet. The display unit can also display the generated manga in real time.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, generation unit, display unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input information for airport check-in procedures or guidance at tourist destinations. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and converts situation-appropriate notices and guidance into a comic book format in multiple languages. The display unit is implemented, for example, by the output device 40 of the smart device 14, which displays the generated comic book in real time on the user's smartphone or tablet. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the most appropriate expression for the country or region. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, generation unit, display unit, and suggestion unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input information such as airport check-in procedures or directions at tourist destinations. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and converts situation-appropriate notices and directions into a comic book format in multiple languages. The display unit is implemented, for example, by the speaker 240 of the smart glasses 214, which displays the generated comic book in real time on the user's smartphone or tablet. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the most appropriate expression for the country or region. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, generation unit, display unit, and suggestion unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input information such as airport check-in procedures or directions at tourist destinations. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input information and converts situation-appropriate notices and directions into a comic book format in multiple languages. The display unit is implemented by, for example, the display 343 of the headset terminal 314, which displays the generated comic book in real time on the user's smartphone or tablet. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most appropriate expression for the country or region. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, generation unit, display unit, and suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input information for airport check-in procedures or directions at tourist destinations. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input information and converts situation-appropriate notices and directions into a comic book format in multiple languages. The display unit is implemented by, for example, the speaker 240 of the robot 414, which displays the generated comic book in real time on the user's smartphone or tablet. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most appropriate expression for the country or region. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) The reception area where you enter notes and instructions, A generation unit analyzes the information entered by the reception unit and converts the situation-appropriate notices and instructions into a comic book format in multiple languages. A system comprising: a display unit that displays the manga generated by the generation unit; (Note 2) The generating unit is We have a proposal department that suggests appropriate expressions tailored to each country and region. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is The generated comic will be displayed on the user's smartphone or tablet. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose appropriate expressions using an intercultural understanding agent. The system described in Appendix 2, characterized by the features described herein. (Note 5) The generating unit is Convert airport check-in procedures or tourist information into a comic book format in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Display the generated comic in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting warnings and instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the appropriate input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering notes or instructions, filter them based on the user's current situation or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the warnings and instructions to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering notes and instructions, the system prioritizes inputting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering notes or instructions, analyze the user's social media activity and enter relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the manga's style of expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating the comic, the level of detail in the comic is adjusted based on the importance of the notes and instructions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating comics, different generation algorithms are applied depending on the category of notes and instructions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the comic based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When creating comics, the priority of the comics is determined based on the submission timing of notes and instructions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating the comic, the order of the comics will be adjusted based on the relevance of the notes and instructions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is The system estimates the user's emotions and adjusts how the comics are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying comics, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying comics, the system selects the appropriate display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is The system estimates the user's emotions and adjusts the display order of comics based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying comics, the system prioritizes showing comics that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying comics, the system analyzes the user's social media activity and displays relevant comics. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and suggests appropriate expressions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, select appropriate expressions that take into account the cultural background of the country or region. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, refer to past proposal history to select appropriate wording. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we will suggest appropriate wording that takes into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant expressions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned cross-cultural understanding agent, It estimates user emotions and adjusts methods of cross-cultural understanding based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned cross-cultural understanding agent, When understanding different cultures, it is important to select an appropriate method of understanding by considering the cultural background of each country and region. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned cross-cultural understanding agent, When understanding different cultures, refer to past experiences of understanding different cultures to select the appropriate method of understanding. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned cross-cultural understanding agent, It estimates user emotions and determines the priority of cross-cultural understanding based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned cross-cultural understanding agent, When understanding different cultures, the appropriate method of understanding should be selected considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned cross-cultural understanding agent, When understanding different cultures, we analyze users' social media activity and suggest relevant methods of understanding. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0192] 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 area where you enter notes and instructions, A generation unit analyzes the information entered by the reception unit and converts the situation-appropriate notices and instructions into a comic book format in multiple languages. A system comprising: a display unit that displays the manga generated by the generation unit;

2. The generating unit is We have a proposal department that suggests appropriate expressions tailored to each country and region. The system according to feature 1.

3. The aforementioned display unit is The generated comic will be displayed on the user's smartphone or tablet. The system according to feature 1.

4. The aforementioned proposal section is, We propose appropriate expressions using an intercultural understanding agent. The system according to feature 2.

5. The generating unit is Convert airport check-in procedures or tourist information into a comic book format in multiple languages. The system according to feature 1.

6. The aforementioned display unit is Display the generated comic in real time. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting warnings and instructions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the appropriate input method. The system according to feature 1.

9. The aforementioned reception unit is When entering notes or instructions, filter them based on the user's current situation or areas of interest. The system according to feature 1.

Images