system

Abstract

The system according to the embodiment aims to automatically generate quiz questions and provide an interactive quiz that supports multiple languages. [Solution] A system according to an embodiment includes a generation unit, an interactive quiz providing unit, and a multilingual support unit. The generation unit automatically generates questions. The interactive quiz providing unit provides interactive quizzes based on the questions generated by the generation unit. The multilingual support unit provides multilingual support based on the quiz provided by the interactive quiz providing unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that creating quiz questions is time-consuming, and it is difficult to provide multilingual support and interactive quizzes.

[0005] The system according to the embodiment aims to automatically generate quiz questions and provide an interactive quiz that supports multiple languages. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, an interactive quiz providing unit, and a multilingual support unit. The generation unit automatically generates questions. The interactive quiz providing unit provides an interactive quiz based on the questions generated by the generation unit. The multilingual support unit provides multilingual support based on the quiz provided by the interactive quiz providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate quiz questions and provide interactive quizzes in multiple languages. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A quiz creation system according to an embodiment of the present invention utilizes a generation AI to create quiz questions, provide interactive quizzes, and support multiple languages. The quiz creation system automatically generates questions without human intervention using the generation AI, generating questions based on a wide range of knowledge. The quiz creation system also generates global questions compatible with multiple languages ​​and provides interactive quizzes. For example, the quiz creation system uses the generation AI to generate questions across various genres, such as history, science, literature, and sports. Then, when a user asks a question in a quiz, the quiz creation system uses the generation AI to provide appropriate answers to the question. Furthermore, the quiz creation system uses the generation AI to provide quizzes compatible with multiple languages. This enables the quiz creation system to provide a global, online, and large-scale quiz service. For example, the quiz creation system can turn quizzes into competitions by assigning the number of correct answers, the rate of correct answers, genre, region, and so on. This allows users to compete against each other to prove their knowledge. Furthermore, the new interactive quiz format can increase the entertainment value. For example, when a user asks a question in a quiz, the generation AI provides appropriate answers to the question. In this way, the entertainment value of quizzes can be enhanced. Furthermore, the quiz creation system can establish and popularize competitive quizzes, which are typically only offered in local competitions or on television programs, as a general competition that tests human intelligence by using generative AI to provide them as an online service. For example, the quiz creation system can hold an online quiz competition, allowing users from all over the world to participate. In this way, quizzes can become more widespread and competitive. This allows the quiz creation system to use generative AI to create quiz questions, provide interactive quizzes, and support multiple languages. For example, the quiz creation system can quickly provide many questions, reducing the burden on quiz creators. Furthermore, the quiz creation system can provide questions from a variety of genres by generating questions based on a wide range of knowledge.Furthermore, the quiz creation system generates global questions that support multiple languages, allowing users all over the world to enjoy the same quiz. This makes it possible to provide a global, online, and large-scale quiz service.

[0029] A quiz creation system according to an embodiment includes a generation unit, an interactive quiz providing unit, and a multilingual support unit. The generation unit automatically generates questions using a generation AI. For example, the generation unit uses the generation AI to generate questions in various genres, such as history, science, literature, and sports. The generation unit can also use the generation AI to generate global questions that support multiple languages. For example, the generation unit uses the generation AI to generate questions in multiple languages, such as English, Japanese, and French. The interactive quiz providing unit provides an interactive quiz based on the questions generated by the generation unit. For example, when a user asks a question in a quiz, the generation AI provides an appropriate answer to the question. The interactive quiz providing unit can also interact with the user using the generation AI. For example, the interactive quiz providing unit provides answers to user questions in real time using the generation AI. The multilingual support unit supports multiple languages ​​based on the quiz provided by the interactive quiz providing unit. For example, the multilingual support unit translates the content of the quiz into multiple languages ​​using the generation AI. The multilingual support unit can also use a generation AI to provide a quiz according to the user's language setting. For example, the multilingual support unit automatically sets the quiz language based on the language setting of the user's device. This allows the quiz creation system according to the embodiment to utilize a generation AI to create quiz questions, provide an interactive quiz, and achieve multilingual support. For example, the quiz creation system can quickly provide many questions, reducing the burden on the quiz creator. Furthermore, the quiz creation system can provide questions in a variety of genres by generating questions based on a wide range of knowledge. Furthermore, the quiz creation system can generate global questions in multiple languages, allowing users around the world to enjoy the same quiz. This allows the quiz creation system to provide a global, online, and large-scale quiz service.

[0030] The generation unit can automatically generate questions using a generation AI without human intervention. The generation unit, for example, uses a generation AI to automatically generate questions in various genres such as history, science, literature, and sports. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate questions. The generation unit can also use the generation AI to generate questions in multiple languages. For example, the generation unit uses the generation AI to generate questions in multiple languages ​​such as English, Japanese, and French. This allows the generation AI to automatically generate questions, thereby reducing the burden on the quiz creator. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a prompt to the generation AI and output the generated question.

[0031] The generation unit can use the generation AI to generate questions based on a wider range of knowledge than a human quiz creator. The generation unit, for example, uses the generation AI to generate questions in various genres, such as history, science, literature, and sports. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate questions based on a wider range of knowledge. The generation unit can also use the generation AI to generate questions that support multiple languages. For example, the generation unit uses the generation AI to generate questions in multiple languages, such as English, Japanese, and French. This allows the generation AI to generate questions based on a wider range of knowledge, making it possible to provide questions in a variety of genres. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input prompts to the generation AI and output generated questions.

[0032] The generation unit can generate global questions that support multiple languages ​​using a generation AI. The generation unit generates global questions that support multiple languages ​​using, for example, a generation AI. The generation AI generates questions in multiple languages, such as English, Japanese, and French, using, for example, a text generation AI (e.g., LLM). The generation unit can also generate questions that support multiple languages ​​using the generation AI. For example, the generation unit generates questions in multiple languages, such as English, Japanese, and French, using the generation AI. This makes it possible to provide a global quiz service by generating questions that support multiple languages ​​using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a prompt to the generation AI and output the generated question.

[0033] The interactive quiz providing unit allows the generation AI to provide an appropriate answer to a question when a user asks a question in a quiz. For example, when a user asks a question in a quiz, the generation AI provides an appropriate answer to the question. The generation AI generates an answer to the user's question using, for example, a text generation AI (e.g., LLM). The interactive quiz providing unit can also interact with the user using the generation AI. For example, the interactive quiz providing unit provides an answer to a user's question in real time using the generation AI. In this way, when a user asks a question in a quiz, the generation AI can provide an appropriate answer, thereby increasing the entertainment value of the interactive quiz. Some or all of the above-mentioned processing in the interactive quiz providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input a user's question to the generation AI and output a generated answer.

[0034] The multilingual support unit can provide quizzes that support multiple languages ​​using a generation AI. The multilingual support unit, for example, uses a generation AI to translate the content of a quiz into multiple languages. The generation AI, for example, uses a text generation AI (e.g., LLM) to translate the content of a quiz into multiple languages, such as English, Japanese, and French. The multilingual support unit can also use the generation AI to provide a quiz that matches the user's language setting. For example, the multilingual support unit automatically sets the language of the quiz based on the language setting of the user's device. This allows users around the world to enjoy the same quiz by providing a quiz that supports multiple languages ​​using the generation AI. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input the content of a quiz into the generation AI and output the generated translation.

[0035] The generation unit can analyze past quiz results and generate questions taking into account the user's areas of strength and weakness. The generation unit, for example, uses a generation AI to analyze past quiz results and identify the user's areas of strength and weakness. The generation AI, for example, analyzes the user's quiz results using a data analysis algorithm. For example, the generation unit can increase the number of questions in areas in which the user has previously scored high. The generation unit can also reduce the number of questions in areas in which the user has previously scored low. The generation unit can also generate questions that balance the user's areas of strength and weakness. This makes it possible to provide questions that are suitable for the user by generating questions taking into account the user's areas of strength and weakness. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past quiz result data into the generation AI and output generated questions.

[0036] The generation unit can generate highly relevant questions based on the user's current learning situation and interests. The generation unit, for example, uses a generation AI to identify the user's current learning situation and interests and generates questions based on them. The generation AI, for example, analyzes the user's learning situation and interests using a learning history and interest identification algorithm. For example, the generation unit generates questions related to topics the user has recently studied. The generation unit can also generate questions in a genre that the user is interested in. The generation unit can also generate questions related to the content that the user should learn next, depending on the user's learning progress. This makes it possible to provide questions that are appropriate for the user by generating questions based on the user's learning situation and interests. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's learning history data into the generation AI and output generated questions.

[0037] When generating questions, the generation unit can select appropriate questions according to the user's age and educational level. The generation unit, for example, uses a generation AI to identify the user's age and educational level and selects questions based on that. The generation AI, for example, identifies the age and educational level using an algorithm that analyzes user profile information. For example, the generation unit generates easy questions for children. The generation unit can also generate questions of medium difficulty for university students. The generation unit can also generate advanced questions for experts. This makes it possible to provide questions appropriate for the user by selecting questions according to the user's age and educational level. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input user profile data into the generation AI and output generated questions.

[0038] The generation unit can generate region-related questions by taking into account the user's geographical location information. The generation unit, for example, uses a generation AI to identify the user's geographical location information and generates region-related questions based on the information. The generation AI, for example, identifies the user's geographical location information using a location information acquisition algorithm. For example, if the user is in Japan, the generation unit can generate questions about Japanese history. If the user is in the United States, the generation unit can generate questions about American culture. If the user is in Europe, the generation unit can generate questions about European geography. This makes it possible to provide region-related questions by generating questions by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's location information data into the generation AI and output the generated questions.

[0039] The generation unit can analyze the user's social media activity and generate interesting questions. The generation unit can, for example, use a generation AI to analyze the user's social media activity and generate interesting questions based on the analysis. The generation AI can, for example, use a social media analysis algorithm to analyze the user's activity. For example, the generation unit can generate questions related to topics the user frequently talks about on social media. The generation unit can also generate questions related to influencers the user follows. The generation unit can also generate questions related to online communities in which the user participates. In this way, by analyzing the user's social media activity and generating questions, it is possible to provide questions that are suitable for the user. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's social media data into the generation AI and output the generated questions.

[0040] The generation unit can customize the method for generating questions by reflecting the user's past feedback. The generation unit, for example, uses a generation AI to analyze the user's past feedback and customize the method for generating questions based on that. The generation AI analyzes the user's feedback using, for example, a feedback analysis algorithm. For example, the generation unit reuses question formats that users have previously found popular. The generation unit can also avoid question formats that users have previously dissatisfied with. The generation unit can also try new question formats based on the user's feedback. In this way, questions can be generated by reflecting the user's past feedback, thereby providing questions that are suitable for the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user feedback data into the generation AI and output generated questions.

[0041] The interactive quiz providing unit can analyze the user's past dialogue history and provide an optimal dialogue scenario. The interactive quiz providing unit can, for example, use a generation AI to analyze the user's past dialogue history and provide an optimal dialogue scenario based on the analysis. The generation AI can, for example, analyze the user's dialogue history using a dialogue history analysis algorithm. For example, the interactive quiz providing unit can reuse dialogue scenarios that the user previously preferred. The interactive quiz providing unit can also avoid dialogue scenarios that the user previously avoided. The interactive quiz providing unit can also propose new dialogue scenarios based on the user's dialogue history. In this way, by analyzing the user's past dialogue history and providing a dialogue scenario, an interactive quiz suitable for the user can be provided. Some or all of the above-mentioned processing in the interactive quiz providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the interactive quiz providing unit can input the user's dialogue history data to the generation AI and output a generated dialogue scenario.

[0042] The interactive quiz providing unit can customize the content of the dialogue according to the user's current knowledge level. The interactive quiz providing unit, for example, uses a generation AI to identify the user's current knowledge level and customizes the content of the dialogue based on that information. The generation AI, for example, identifies the user's knowledge level using a knowledge level evaluation algorithm. For example, if the user is a beginner, the interactive quiz providing unit can provide dialogue with basic content. If the user is an intermediate learner, the interactive quiz providing unit can also provide dialogue with applied content. If the user is an advanced learner, the interactive quiz providing unit can also provide dialogue with specialized content. This allows the content of the dialogue to be customized according to the user's knowledge level, thereby providing an interactive quiz suitable for the user. Some or all of the above-described processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input user knowledge level data to the generation AI and output a generated dialogue scenario.

[0043] The interactive quiz providing unit can improve the quality of the dialogue by reflecting user feedback. The interactive quiz providing unit, for example, uses a generation AI to analyze the user feedback and improve the quality of the dialogue based on the analysis. The generation AI analyzes the user feedback using, for example, a feedback analysis algorithm. For example, the interactive quiz providing unit reuses a dialogue format that was well-received by the user. The interactive quiz providing unit can also avoid a dialogue format that the user is dissatisfied with. The interactive quiz providing unit can also try a new dialogue format based on the user feedback. In this way, the quality of the dialogue can be improved by reflecting user feedback, thereby providing an interactive quiz that is suitable for the user. Some or all of the above-mentioned processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input user feedback data to the generation AI and output a generated dialogue scenario.

[0044] The interactive quiz providing unit can provide an optimal dialogue format by taking into account the user's device information. The interactive quiz providing unit, for example, uses a generation AI to identify the user's device information and provides an optimal dialogue format based on the identified device information. The generation AI, for example, identifies the user's device information using a device information acquisition algorithm. For example, if the user is using a smartphone, the interactive quiz providing unit can provide an dialogue format tailored to the screen size. Also, if the user is using a tablet, the interactive quiz providing unit can provide an dialogue format optimized for a large screen. Also, if the user is using a smartwatch, the interactive quiz providing unit can provide a concise and highly visible dialogue format. In this way, by providing a dialogue format by taking into account the user's device information, an interactive quiz suited to the user can be provided. Some or all of the above-described processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input the user's device information to the generation AI and output a generated dialogue scenario.

[0045] The interactive quiz providing unit can make the dialogue content multilingual according to the user's language setting. The interactive quiz providing unit, for example, uses a generation AI to identify the user's language setting and makes the dialogue content multilingual based on that setting. The generation AI, for example, identifies the user's language setting using a language setting acquisition algorithm. For example, the interactive quiz providing unit automatically sets the dialogue language based on the language setting of the user's device. The interactive quiz providing unit can also provide a language switching function when the user uses multiple languages. The interactive quiz providing unit can also provide the dialogue in a specific language if the user selects that language. This makes it possible to provide an interactive quiz suitable for the user by making the dialogue content multilingual according to the user's language setting. Some or all of the above-described processing in the interactive quiz providing unit can be performed, for example, using the generation AI or without the generation AI. For example, the interactive quiz providing unit can input the user's language setting data to the generation AI and output a generated dialogue scenario.

[0046] The interactive quiz providing unit can customize the content of the dialogue based on the user's occupation and lifestyle. The interactive quiz providing unit, for example, uses a generation AI to identify the user's occupation and lifestyle and customizes the content of the dialogue based on the identified occupation and lifestyle. The generation AI, for example, uses an occupation and lifestyle identification algorithm to identify the user's occupation and lifestyle. For example, if the user is a student, the interactive quiz providing unit can provide dialogue related to their studies. Also, if the user is a businessman, the interactive quiz providing unit can provide dialogue related to business. Also, if the user is a housewife, the interactive quiz providing unit can provide dialogue related to family life. In this way, by customizing the content of the dialogue based on the user's occupation and lifestyle, an interactive quiz suitable for the user can be provided. Some or all of the above-described processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input the user's occupation and lifestyle data into the generation AI and output a generated dialogue scenario.

[0047] The multilingual support unit can analyze past translation history and select the optimal translation algorithm. The multilingual support unit, for example, uses a generation AI to analyze past translation history and select the optimal translation algorithm based on the analysis. The generation AI analyzes past translation history, for example, using a translation history analysis algorithm. For example, the multilingual support unit reuses translation styles that the user previously preferred. The multilingual support unit can also avoid translation styles that the user previously avoided. The multilingual support unit can also suggest new translation styles based on the user's translation history. In this way, by analyzing past translation history and selecting the optimal translation algorithm, it is possible to provide translations that are suitable for the user. Some or all of the above-mentioned processing in the multilingual support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input past translation history data into a generation AI and output a generated translation.

[0048] The multilingual support unit can provide appropriate translations according to the user's language acquisition status. The multilingual support unit, for example, uses a generation AI to identify the user's language acquisition status and provides appropriate translations based on that status. The generation AI, for example, uses a language acquisition status evaluation algorithm to identify the user's language acquisition status. For example, if the user is a beginner, the multilingual support unit can provide translations using simple expressions. If the user is an intermediate learner, the multilingual support unit can also provide translations using applied expressions. If the user is an advanced learner, the multilingual support unit can also provide translations using specialized expressions. This makes it possible to provide translations suited to the user by providing translations according to the user's language acquisition status. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input the user's language acquisition status data into the generation AI and output a generated translation.

[0049] The multilingual support unit can improve the quality of the translation by reflecting user feedback. The multilingual support unit, for example, uses a generation AI to analyze user feedback and improve the quality of the translation based on that. The generation AI analyzes the user feedback using, for example, a feedback analysis algorithm. For example, the multilingual support unit reuses translation styles that users have found popular. The multilingual support unit can also avoid translation styles that users are dissatisfied with. The multilingual support unit can also try new translation styles based on user feedback. In this way, by reflecting user feedback and improving the quality of the translation, it is possible to provide a translation that is suitable for the user. Some or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input user feedback data into a generation AI and output a generated translation.

[0050] The multilingual support unit can provide translations relevant to a region by taking into account the user's geographical location information. The multilingual support unit, for example, uses a generation AI to identify the user's geographical location information and provides translations relevant to a region based on the identified location information. The generation AI, for example, uses a location information acquisition algorithm to identify the user's geographical location information. For example, if the user is in Japan, the multilingual support unit can provide translations relevant to Japanese culture and customs. If the user is in the United States, the multilingual support unit can provide translations relevant to American culture and customs. If the user is in Europe, the multilingual support unit can provide translations relevant to European culture and customs. This makes it possible to provide translations relevant to a region by taking into account the user's geographical location information. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, the generation AI. For example, the multilingual support unit can input the user's location information data into the generation AI and output the generated translation.

[0051] The multilingual support unit can analyze a user's social media activity and provide interesting translations. The multilingual support unit can, for example, use a generation AI to analyze a user's social media activity and provide interesting translations based on the analysis. The generation AI can, for example, use a social media analysis algorithm to analyze the user's activity. For example, the multilingual support unit can provide translations related to topics the user frequently discusses on social media. The multilingual support unit can also provide translations related to influencers the user follows. The multilingual support unit can also provide translations related to online communities in which the user participates. In this way, by analyzing the user's social media activity and providing translations, it is possible to provide translations that are suitable for the user. Some or all of the above-described processing in the multilingual support unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the multilingual support unit can input a user's social media data into a generation AI and output a generated translation.

[0052] The multilingual support unit can customize the translation method by reflecting the user's past feedback. The multilingual support unit, for example, uses a generation AI to analyze the user's past feedback and customize the translation method based on that. The generation AI analyzes the user's feedback using, for example, a feedback analysis algorithm. For example, the multilingual support unit reuses translation methods that users have previously found popular. The multilingual support unit can also avoid translation methods that users have previously dissatisfied with. The multilingual support unit can also try new translation methods based on the user's feedback. In this way, by customizing the translation by reflecting the user's past feedback, it is possible to provide a translation that is suitable for the user. Some or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input user feedback data into a generation AI and output a generated translation.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The generation unit can track the user's learning progress and adjust the difficulty of the questions according to the progress. For example, if the user scores high in a particular genre, the generation unit can increase the difficulty of questions in that genre. Conversely, if the user scores low, the generation unit can lower the difficulty of questions in that genre. Furthermore, the generation unit can add questions in new genres based on the user's learning progress. This makes it possible to provide appropriate questions according to the user's learning progress.

[0055] The interactive quiz providing unit can analyze the user's answer history and re-present questions that the user is likely to get wrong. For example, it can re-present questions that the user got wrong in the past to encourage review. It can also re-present questions that the user answered correctly to help solidify their memory. Furthermore, the interactive quiz providing unit can also present similar questions based on the user's answer history. This can improve the user's learning effect.

[0056] The multilingual support unit can provide questions in a specific language preferentially depending on the user's language acquisition goals. For example, if the user wants to learn English, it can provide many questions in English. Also, if the user wants to learn French, it can provide many questions in French. Furthermore, the multilingual support unit can provide questions in multiple languages ​​in a balanced manner based on the user's language acquisition goals. This makes it possible to provide appropriate questions according to the user's language acquisition goals.

[0057] The generator can generate questions based on topics that interest the user. For example, if the user is interested in sports, many questions related to sports can be generated. Also, if the user is interested in music, many questions related to music can be generated. Furthermore, the generator can suggest questions on new topics based on the user's interests. This makes it possible to provide appropriate questions that match the user's interests.

[0058] The generation unit can analyze the user's learning history and adjust the difficulty of the questions according to the user's learning progress. For example, if the user scores high in a particular genre, the difficulty of questions in that genre can be increased. Conversely, if the user scores low, the difficulty of questions in that genre can be decreased. Furthermore, the generation unit can add questions in new genres based on the user's learning history. This makes it possible to provide appropriate questions according to the user's learning progress.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The generator uses a generative AI to automatically generate questions. The generator generates questions in a variety of genres, including history, science, literature, and sports, and also generates global questions that support multiple languages. For example, the generator uses a generative AI to generate questions in multiple languages, including English, Japanese, and French. Step 2: The interactive quiz providing unit provides an interactive quiz based on the questions generated by the generating unit. When the user asks a question to the quiz, the generating AI provides an appropriate answer to the question. The interactive quiz providing unit also uses the generating AI to interact with the user and provide answers in real time. Step 3: The multilingual support unit provides multilingual support based on the quiz provided by the interactive quiz providing unit. The multilingual support unit uses a generation AI to translate the content of the quiz into multiple languages ​​and provides the quiz according to the user's language setting. For example, the multilingual support unit automatically sets the quiz language based on the language setting of the user's device.

[0061] (Example 2) A quiz creation system according to an embodiment of the present invention utilizes a generation AI to create quiz questions, provide interactive quizzes, and support multiple languages. The quiz creation system automatically generates questions without human intervention using the generation AI, generating questions based on a wide range of knowledge. The quiz creation system also generates global questions compatible with multiple languages ​​and provides interactive quizzes. For example, the quiz creation system uses the generation AI to generate questions across various genres, such as history, science, literature, and sports. Then, when a user asks a question in a quiz, the quiz creation system uses the generation AI to provide appropriate answers to the question. Furthermore, the quiz creation system uses the generation AI to provide quizzes compatible with multiple languages. This enables the quiz creation system to provide a global, online, and large-scale quiz service. For example, the quiz creation system can turn quizzes into competitions by assigning the number of correct answers, the rate of correct answers, genre, region, and so on. This allows users to compete against each other to prove their knowledge. Furthermore, the new interactive quiz format can increase the entertainment value. For example, when a user asks a question in a quiz, the generation AI provides appropriate answers to the question. In this way, the entertainment value of quizzes can be enhanced. Furthermore, the quiz creation system can establish and popularize competitive quizzes, which are typically only offered in local competitions or on television programs, as a general competition that tests human intelligence by using generative AI to provide them as an online service. For example, the quiz creation system can hold an online quiz competition, allowing users from all over the world to participate. In this way, quizzes can become more widespread and competitive. This allows the quiz creation system to use generative AI to create quiz questions, provide interactive quizzes, and support multiple languages. For example, the quiz creation system can quickly provide many questions, reducing the burden on quiz creators. Furthermore, the quiz creation system can provide questions from a variety of genres by generating questions based on a wide range of knowledge.Furthermore, the quiz creation system generates global questions that support multiple languages, allowing users all over the world to enjoy the same quiz. This makes it possible to provide a global, online, and large-scale quiz service.

[0062] A quiz creation system according to an embodiment includes a generation unit, an interactive quiz providing unit, and a multilingual support unit. The generation unit automatically generates questions using a generation AI. For example, the generation unit uses the generation AI to generate questions in various genres, such as history, science, literature, and sports. The generation unit can also use the generation AI to generate global questions that support multiple languages. For example, the generation unit uses the generation AI to generate questions in multiple languages, such as English, Japanese, and French. The interactive quiz providing unit provides an interactive quiz based on the questions generated by the generation unit. For example, when a user asks a question in a quiz, the generation AI provides an appropriate answer to the question. The interactive quiz providing unit can also interact with the user using the generation AI. For example, the interactive quiz providing unit provides answers to user questions in real time using the generation AI. The multilingual support unit supports multiple languages ​​based on the quiz provided by the interactive quiz providing unit. For example, the multilingual support unit translates the content of the quiz into multiple languages ​​using the generation AI. The multilingual support unit can also use a generation AI to provide a quiz according to the user's language setting. For example, the multilingual support unit automatically sets the quiz language based on the language setting of the user's device. This allows the quiz creation system according to the embodiment to utilize a generation AI to create quiz questions, provide an interactive quiz, and achieve multilingual support. For example, the quiz creation system can quickly provide many questions, reducing the burden on the quiz creator. Furthermore, the quiz creation system can provide questions in a variety of genres by generating questions based on a wide range of knowledge. Furthermore, the quiz creation system can generate global questions in multiple languages, allowing users around the world to enjoy the same quiz. This allows the quiz creation system to provide a global, online, and large-scale quiz service.

[0063] The generation unit can automatically generate questions using a generation AI without human intervention. The generation unit, for example, uses a generation AI to automatically generate questions in various genres such as history, science, literature, and sports. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate questions. The generation unit can also use the generation AI to generate questions in multiple languages. For example, the generation unit uses the generation AI to generate questions in multiple languages ​​such as English, Japanese, and French. This allows the generation AI to automatically generate questions, thereby reducing the burden on the quiz creator. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a prompt to the generation AI and output the generated question.

[0064] The generation unit can use the generation AI to generate questions based on a wider range of knowledge than a human quiz creator. The generation unit, for example, uses the generation AI to generate questions in various genres, such as history, science, literature, and sports. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate questions based on a wider range of knowledge. The generation unit can also use the generation AI to generate questions that support multiple languages. For example, the generation unit uses the generation AI to generate questions in multiple languages, such as English, Japanese, and French. This allows the generation AI to generate questions based on a wider range of knowledge, making it possible to provide questions in a variety of genres. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input prompts to the generation AI and output generated questions.

[0065] The generation unit can generate global questions that support multiple languages ​​using a generation AI. The generation unit generates global questions that support multiple languages ​​using, for example, a generation AI. The generation AI generates questions in multiple languages, such as English, Japanese, and French, using, for example, a text generation AI (e.g., LLM). The generation unit can also generate questions that support multiple languages ​​using the generation AI. For example, the generation unit generates questions in multiple languages, such as English, Japanese, and French, using the generation AI. This makes it possible to provide a global quiz service by generating questions that support multiple languages ​​using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a prompt to the generation AI and output the generated question.

[0066] The interactive quiz providing unit allows the generation AI to provide an appropriate answer to a question when a user asks a question in a quiz. For example, when a user asks a question in a quiz, the generation AI provides an appropriate answer to the question. The generation AI generates an answer to the user's question using, for example, a text generation AI (e.g., LLM). The interactive quiz providing unit can also interact with the user using the generation AI. For example, the interactive quiz providing unit provides an answer to a user's question in real time using the generation AI. In this way, when a user asks a question in a quiz, the generation AI can provide an appropriate answer, thereby increasing the entertainment value of the interactive quiz. Some or all of the above-mentioned processing in the interactive quiz providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input a user's question to the generation AI and output a generated answer.

[0067] The multilingual support unit can provide quizzes that support multiple languages ​​using a generation AI. The multilingual support unit, for example, uses a generation AI to translate the content of a quiz into multiple languages. The generation AI, for example, uses a text generation AI (e.g., LLM) to translate the content of a quiz into multiple languages, such as English, Japanese, and French. The multilingual support unit can also use the generation AI to provide a quiz that matches the user's language setting. For example, the multilingual support unit automatically sets the language of the quiz based on the language setting of the user's device. This allows users around the world to enjoy the same quiz by providing a quiz that supports multiple languages ​​using the generation AI. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input the content of a quiz into the generation AI and output the generated translation.

[0068] The generation unit can estimate the user's emotions and adjust the difficulty of the generated questions based on the estimated user emotions. The generation unit, for example, uses a generation AI to estimate the user's emotions and adjust the difficulty of the questions based on the estimated emotions. The generation AI, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, if the user is feeling stressed, the generation AI can generate easy questions. Also, if the user is relaxed, the generation unit can generate medium-difficulty questions. Also, if the user is feeling challenged, the generation unit can generate high-difficulty questions. This allows the difficulty of questions to be adjusted according to the user's emotions, thereby providing questions suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and output generated questions.

[0069] The generation unit can analyze past quiz results and generate questions taking into account the user's areas of strength and weakness. The generation unit, for example, uses a generation AI to analyze past quiz results and identify the user's areas of strength and weakness. The generation AI, for example, analyzes the user's quiz results using a data analysis algorithm. For example, the generation unit can increase the number of questions in areas in which the user has previously scored high. The generation unit can also reduce the number of questions in areas in which the user has previously scored low. The generation unit can also generate questions that balance the user's areas of strength and weakness. This makes it possible to provide questions that are suitable for the user by generating questions taking into account the user's areas of strength and weakness. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past quiz result data into the generation AI and output generated questions.

[0070] The generation unit can generate highly relevant questions based on the user's current learning situation and interests. The generation unit, for example, uses a generation AI to identify the user's current learning situation and interests and generates questions based on them. The generation AI, for example, analyzes the user's learning situation and interests using a learning history and interest identification algorithm. For example, the generation unit generates questions related to topics the user has recently studied. The generation unit can also generate questions in a genre that the user is interested in. The generation unit can also generate questions related to the content that the user should learn next, depending on the user's learning progress. This makes it possible to provide questions that are appropriate for the user by generating questions based on the user's learning situation and interests. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's learning history data into the generation AI and output generated questions.

[0071] When generating questions, the generation unit can select appropriate questions according to the user's age and educational level. The generation unit, for example, uses a generation AI to identify the user's age and educational level and selects questions based on that. The generation AI, for example, identifies the age and educational level using an algorithm that analyzes user profile information. For example, the generation unit generates easy questions for children. The generation unit can also generate questions of medium difficulty for university students. The generation unit can also generate advanced questions for experts. This makes it possible to provide questions appropriate for the user by selecting questions according to the user's age and educational level. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input user profile data into the generation AI and output generated questions.

[0072] The generation unit can estimate the user's emotions and select the genre of the questions to be generated based on the estimated user emotions. The generation unit, for example, uses a generation AI to estimate the user's emotions and select the genre of the questions based on the estimated emotions. The generation AI, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, if the user is relaxed, the generation unit can generate entertainment-related questions. If the user is concentrating, the generation unit can also generate science or mathematics-related questions. If the user is excited, the generation unit can also generate sports-related questions. This allows the selection of the genre of questions according to the user's emotions to provide questions appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and output the generated questions.

[0073] The generation unit can generate region-related questions by taking into account the user's geographical location information. The generation unit, for example, uses a generation AI to identify the user's geographical location information and generates region-related questions based on the information. The generation AI, for example, identifies the user's geographical location information using a location information acquisition algorithm. For example, if the user is in Japan, the generation unit can generate questions about Japanese history. If the user is in the United States, the generation unit can generate questions about American culture. If the user is in Europe, the generation unit can generate questions about European geography. This makes it possible to provide region-related questions by generating questions by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's location information data into the generation AI and output the generated questions.

[0074] The generation unit can analyze the user's social media activity and generate interesting questions. The generation unit can, for example, use a generation AI to analyze the user's social media activity and generate interesting questions based on the analysis. The generation AI can, for example, use a social media analysis algorithm to analyze the user's activity. For example, the generation unit can generate questions related to topics the user frequently talks about on social media. The generation unit can also generate questions related to influencers the user follows. The generation unit can also generate questions related to online communities in which the user participates. In this way, by analyzing the user's social media activity and generating questions, it is possible to provide questions that are suitable for the user. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's social media data into the generation AI and output the generated questions.

[0075] The generation unit can customize the method for generating questions by reflecting the user's past feedback. The generation unit, for example, uses a generation AI to analyze the user's past feedback and customize the method for generating questions based on that. The generation AI analyzes the user's feedback using, for example, a feedback analysis algorithm. For example, the generation unit reuses question formats that users have previously found popular. The generation unit can also avoid question formats that users have previously dissatisfied with. The generation unit can also try new question formats based on the user's feedback. In this way, questions can be generated by reflecting the user's past feedback, thereby providing questions that are suitable for the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user feedback data into the generation AI and output generated questions.

[0076] The interactive quiz providing unit can estimate the user's emotions and adjust the speed of the dialogue based on the estimated user emotions. The interactive quiz providing unit can estimate the user's emotions using, for example, a generation AI and adjust the speed of the dialogue based on the estimated emotions. The generation AI can estimate the user's emotions using, for example, an emotion estimation algorithm. For example, the interactive quiz providing unit can proceed with the dialogue at a slow pace if the user is relaxed. Furthermore, the interactive quiz providing unit can also proceed with the dialogue quickly if the user is in a hurry. Furthermore, the interactive quiz providing unit can also proceed with the dialogue at a good tempo if the user is excited. This allows the dialogue speed to be adjusted according to the user's emotions, thereby providing an interactive quiz suitable for the user. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the interactive quiz providing unit can be performed using, for example, the generation AI, or without the generation AI. For example, the interactive quiz providing unit can input user emotion data to the generation AI and output the generated dialogue scenario.

[0077] The interactive quiz providing unit can analyze the user's past dialogue history and provide an optimal dialogue scenario. The interactive quiz providing unit can, for example, use a generation AI to analyze the user's past dialogue history and provide an optimal dialogue scenario based on the analysis. The generation AI can, for example, analyze the user's dialogue history using a dialogue history analysis algorithm. For example, the interactive quiz providing unit can reuse dialogue scenarios that the user previously preferred. The interactive quiz providing unit can also avoid dialogue scenarios that the user previously avoided. The interactive quiz providing unit can also propose new dialogue scenarios based on the user's dialogue history. In this way, by analyzing the user's past dialogue history and providing a dialogue scenario, an interactive quiz suitable for the user can be provided. Some or all of the above-mentioned processing in the interactive quiz providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the interactive quiz providing unit can input the user's dialogue history data to the generation AI and output a generated dialogue scenario.

[0078] The interactive quiz providing unit can customize the content of the dialogue according to the user's current knowledge level. The interactive quiz providing unit, for example, uses a generation AI to identify the user's current knowledge level and customizes the content of the dialogue based on that information. The generation AI, for example, identifies the user's knowledge level using a knowledge level evaluation algorithm. For example, if the user is a beginner, the interactive quiz providing unit can provide dialogue with basic content. If the user is an intermediate learner, the interactive quiz providing unit can also provide dialogue with applied content. If the user is an advanced learner, the interactive quiz providing unit can also provide dialogue with specialized content. This allows the content of the dialogue to be customized according to the user's knowledge level, thereby providing an interactive quiz suitable for the user. Some or all of the above-described processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input user knowledge level data to the generation AI and output a generated dialogue scenario.

[0079] The interactive quiz providing unit can improve the quality of the dialogue by reflecting user feedback. The interactive quiz providing unit, for example, uses a generation AI to analyze the user feedback and improve the quality of the dialogue based on the analysis. The generation AI analyzes the user feedback using, for example, a feedback analysis algorithm. For example, the interactive quiz providing unit reuses a dialogue format that was well-received by the user. The interactive quiz providing unit can also avoid a dialogue format that the user is dissatisfied with. The interactive quiz providing unit can also try a new dialogue format based on the user feedback. In this way, the quality of the dialogue can be improved by reflecting user feedback, thereby providing an interactive quiz that is suitable for the user. Some or all of the above-mentioned processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input user feedback data to the generation AI and output a generated dialogue scenario.

[0080] The interactive quiz providing unit can estimate the user's emotions and adjust the tone of the dialogue based on the estimated user emotions. The interactive quiz providing unit, for example, uses a generation AI to estimate the user's emotions and adjust the tone of the dialogue based on the estimated emotions. The generation AI, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, if the user is nervous, the interactive quiz providing unit can proceed with the dialogue in a calm tone. Also, if the user is relaxed, the interactive quiz providing unit can proceed with the dialogue in a bright tone. Also, if the user is excited, the interactive quiz providing unit can proceed with the dialogue in an energetic tone. In this way, by adjusting the tone of the dialogue according to the user's emotions, an interactive quiz suitable for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input user emotion data to the generation AI and output the generated dialogue scenario.

[0081] The interactive quiz providing unit can provide an optimal dialogue format by taking into account the user's device information. The interactive quiz providing unit, for example, uses a generation AI to identify the user's device information and provides an optimal dialogue format based on the identified device information. The generation AI, for example, identifies the user's device information using a device information acquisition algorithm. For example, if the user is using a smartphone, the interactive quiz providing unit can provide an dialogue format tailored to the screen size. Also, if the user is using a tablet, the interactive quiz providing unit can provide an dialogue format optimized for a large screen. Also, if the user is using a smartwatch, the interactive quiz providing unit can provide a concise and highly visible dialogue format. In this way, by providing a dialogue format by taking into account the user's device information, an interactive quiz suited to the user can be provided. Some or all of the above-described processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input the user's device information to the generation AI and output a generated dialogue scenario.

[0082] The interactive quiz providing unit can make the dialogue content multilingual according to the user's language setting. The interactive quiz providing unit, for example, uses a generation AI to identify the user's language setting and makes the dialogue content multilingual based on that setting. The generation AI, for example, identifies the user's language setting using a language setting acquisition algorithm. For example, the interactive quiz providing unit automatically sets the dialogue language based on the language setting of the user's device. The interactive quiz providing unit can also provide a language switching function when the user uses multiple languages. The interactive quiz providing unit can also provide the dialogue in a specific language if the user selects that language. This makes it possible to provide an interactive quiz suitable for the user by making the dialogue content multilingual according to the user's language setting. Some or all of the above-described processing in the interactive quiz providing unit can be performed, for example, using the generation AI or without the generation AI. For example, the interactive quiz providing unit can input the user's language setting data to the generation AI and output a generated dialogue scenario.

[0083] The interactive quiz providing unit can customize the content of the dialogue based on the user's occupation and lifestyle. The interactive quiz providing unit, for example, uses a generation AI to identify the user's occupation and lifestyle and customizes the content of the dialogue based on the identified occupation and lifestyle. The generation AI, for example, uses an occupation and lifestyle identification algorithm to identify the user's occupation and lifestyle. For example, if the user is a student, the interactive quiz providing unit can provide dialogue related to their studies. Also, if the user is a businessman, the interactive quiz providing unit can provide dialogue related to business. Also, if the user is a housewife, the interactive quiz providing unit can provide dialogue related to family life. In this way, by customizing the content of the dialogue based on the user's occupation and lifestyle, an interactive quiz suitable for the user can be provided. Some or all of the above-described processing in the interactive quiz providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the interactive quiz providing unit can input the user's occupation and lifestyle data into the generation AI and output a generated dialogue scenario.

[0084] The multilingual support unit can estimate a user's emotions and adjust the accuracy of the translation based on the estimated user emotions. The multilingual support unit can estimate a user's emotions using, for example, a generation AI and adjust the accuracy of the translation based on the estimated emotions. The generation AI can estimate a user's emotions using, for example, an emotion estimation algorithm. For example, if the user is nervous, the multilingual support unit can provide a concise and easy-to-understand translation. Furthermore, if the user is relaxed, the multilingual support unit can also provide a detailed translation. Furthermore, if the user is excited, the multilingual support unit can also provide a translation including energetic expressions. This allows the translation accuracy to be adjusted according to the user's emotions, thereby providing a translation that is suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the multilingual support unit can input user emotional data into the generation AI and output the generated translation.

[0085] The multilingual support unit can analyze past translation history and select the optimal translation algorithm. The multilingual support unit, for example, uses a generation AI to analyze past translation history and select the optimal translation algorithm based on the analysis. The generation AI analyzes past translation history, for example, using a translation history analysis algorithm. For example, the multilingual support unit reuses translation styles that the user previously preferred. The multilingual support unit can also avoid translation styles that the user previously avoided. The multilingual support unit can also suggest new translation styles based on the user's translation history. In this way, by analyzing past translation history and selecting the optimal translation algorithm, it is possible to provide translations that are suitable for the user. Some or all of the above-mentioned processing in the multilingual support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input past translation history data into a generation AI and output a generated translation.

[0086] The multilingual support unit can provide appropriate translations according to the user's language acquisition status. The multilingual support unit, for example, uses a generation AI to identify the user's language acquisition status and provides appropriate translations based on that status. The generation AI, for example, uses a language acquisition status evaluation algorithm to identify the user's language acquisition status. For example, if the user is a beginner, the multilingual support unit can provide translations using simple expressions. If the user is an intermediate learner, the multilingual support unit can also provide translations using applied expressions. If the user is an advanced learner, the multilingual support unit can also provide translations using specialized expressions. This makes it possible to provide translations suited to the user by providing translations according to the user's language acquisition status. Some or all of the above-described processing in the multilingual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input the user's language acquisition status data into the generation AI and output a generated translation.

[0087] The multilingual support unit can improve the quality of the translation by reflecting user feedback. The multilingual support unit, for example, uses a generation AI to analyze user feedback and improve the quality of the translation based on that. The generation AI analyzes the user feedback using, for example, a feedback analysis algorithm. For example, the multilingual support unit reuses translation styles that users have found popular. The multilingual support unit can also avoid translation styles that users are dissatisfied with. The multilingual support unit can also try new translation styles based on user feedback. In this way, by reflecting user feedback and improving the quality of the translation, it is possible to provide a translation that is suitable for the user. Some or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input user feedback data into a generation AI and output a generated translation.

[0088] The multilingual support unit can estimate a user's emotions and determine translation priorities based on the estimated user emotions. The multilingual support unit can estimate a user's emotions using, for example, a generation AI and determine translation priorities based on the estimated emotions. The generation AI can estimate a user's emotions using, for example, an emotion estimation algorithm. For example, if a user is nervous, the multilingual support unit can prioritize translating important information. Also, if a user is relaxed, the multilingual support unit can prioritize translating detailed information. Also, if a user is excited, the multilingual support unit can prioritize translating energetic expressions. In this way, by determining translation priorities according to the user's emotions, it is possible to provide translations that are appropriate for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the multilingual support unit can input user emotional data into the generation AI and output the generated translation.

[0089] The multilingual support unit can provide translations relevant to a region by taking into account the user's geographical location information. The multilingual support unit, for example, uses a generation AI to identify the user's geographical location information and provides translations relevant to a region based on the identified location information. The generation AI, for example, uses a location information acquisition algorithm to identify the user's geographical location information. For example, if the user is in Japan, the multilingual support unit can provide translations relevant to Japanese culture and customs. If the user is in the United States, the multilingual support unit can provide translations relevant to American culture and customs. If the user is in Europe, the multilingual support unit can provide translations relevant to European culture and customs. This makes it possible to provide translations relevant to a region by taking into account the user's geographical location information. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, the generation AI. For example, the multilingual support unit can input the user's location information data into the generation AI and output the generated translation.

[0090] The multilingual support unit can analyze a user's social media activity and provide interesting translations. The multilingual support unit can, for example, use a generation AI to analyze a user's social media activity and provide interesting translations based on the analysis. The generation AI can, for example, use a social media analysis algorithm to analyze the user's activity. For example, the multilingual support unit can provide translations related to topics the user frequently discusses on social media. The multilingual support unit can also provide translations related to influencers the user follows. The multilingual support unit can also provide translations related to online communities in which the user participates. In this way, by analyzing the user's social media activity and providing translations, it is possible to provide translations that are suitable for the user. Some or all of the above-described processing in the multilingual support unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the multilingual support unit can input a user's social media data into a generation AI and output a generated translation.

[0091] The multilingual support unit can customize the translation method by reflecting the user's past feedback. The multilingual support unit, for example, uses a generation AI to analyze the user's past feedback and customize the translation method based on that. The generation AI analyzes the user's feedback using, for example, a feedback analysis algorithm. For example, the multilingual support unit reuses translation methods that users have previously found popular. The multilingual support unit can also avoid translation methods that users have previously dissatisfied with. The multilingual support unit can also try new translation methods based on the user's feedback. In this way, by customizing the translation by reflecting the user's past feedback, it is possible to provide a translation that is suitable for the user. Some or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the multilingual support unit can input user feedback data into a generation AI and output a generated translation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned generation unit, interactive quiz providing unit, and multilingual support unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The interactive quiz providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The multilingual support unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, interactive quiz providing unit, and multilingual support unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The interactive quiz providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The multilingual support unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, interactive quiz providing unit, and multilingual support unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The interactive quiz providing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The multilingual support unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, interactive quiz providing unit, and multilingual support unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The interactive quiz providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The multilingual support unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The generation unit can track the user's learning progress and adjust the difficulty of the questions according to the progress. For example, if the user scores high in a particular genre, the generation unit can increase the difficulty of questions in that genre. Conversely, if the user scores low, the generation unit can lower the difficulty of questions in that genre. Furthermore, the generation unit can add questions in new genres based on the user's learning progress. This makes it possible to provide appropriate questions according to the user's learning progress.

[0094] The interactive quiz providing unit can analyze the user's answer history and re-present questions that the user is likely to get wrong. For example, it can re-present questions that the user got wrong in the past to encourage review. It can also re-present questions that the user answered correctly to help solidify their memory. Furthermore, the interactive quiz providing unit can also present similar questions based on the user's answer history. This can improve the user's learning effect.

[0095] The multilingual support unit can provide questions in a specific language preferentially depending on the user's language acquisition goals. For example, if the user wants to learn English, it can provide many questions in English. Also, if the user wants to learn French, it can provide many questions in French. Furthermore, the multilingual support unit can provide questions in multiple languages ​​in a balanced manner based on the user's language acquisition goals. This makes it possible to provide appropriate questions according to the user's language acquisition goals.

[0096] The generator can generate questions based on topics that interest the user. For example, if the user is interested in sports, many questions related to sports can be generated. Also, if the user is interested in music, many questions related to music can be generated. Furthermore, the generator can suggest questions on new topics based on the user's interests. This makes it possible to provide appropriate questions that match the user's interests.

[0097] The interactive quiz providing unit can estimate the user's emotions and adjust the content of the dialogue based on the estimated emotions. For example, if the user is feeling stressed, it can provide relaxing dialogue content. If the user is excited, it can provide energetic dialogue content. Furthermore, if the user is concentrating, it can provide more difficult dialogue content. In this way, it is possible to provide appropriate dialogue content according to the user's emotions.

[0098] The generation unit can estimate the user's emotions and select a question genre based on the estimated emotions. For example, if the user is relaxed, entertainment-related questions can be generated. If the user is concentrating, science or mathematics-related questions can be generated. Furthermore, if the user is excited, sports-related questions can be generated. In this way, appropriate questions can be provided according to the user's emotions.

[0099] The interactive quiz providing unit can estimate the user's emotions and adjust the tone of the dialogue based on the estimated emotions. For example, if the user is nervous, the dialogue can be conducted in a calm tone. If the user is relaxed, the dialogue can be conducted in a bright tone. Furthermore, if the user is excited, the dialogue can be conducted in an energetic tone. In this way, an appropriate dialogue tone can be provided according to the user's emotions.

[0100] The multilingual support unit can estimate the user's emotions and adjust the accuracy of the translation based on the estimated emotions. For example, if the user is nervous, a concise and easy-to-understand translation can be provided. If the user is relaxed, a detailed translation can be provided. Furthermore, if the user is excited, a translation including energetic expressions can be provided. This makes it possible to provide an appropriate translation according to the user's emotions.

[0101] The multilingual support unit can estimate the user's emotions and determine translation priorities based on the estimated emotions. For example, if the user is nervous, important information can be prioritized in translation. If the user is relaxed, detailed information can be prioritized in translation. Furthermore, if the user is excited, energetic expressions can be prioritized in translation. This makes it possible to provide appropriate translations according to the user's emotions.

[0102] The generation unit can analyze the user's learning history and adjust the difficulty of the questions according to the user's learning progress. For example, if the user scores high in a particular genre, the difficulty of questions in that genre can be increased. Conversely, if the user scores low, the difficulty of questions in that genre can be decreased. Furthermore, the generation unit can add questions in new genres based on the user's learning history. This makes it possible to provide appropriate questions according to the user's learning progress.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The generator uses a generative AI to automatically generate questions. The generator generates questions in a variety of genres, including history, science, literature, and sports, and also generates global questions that support multiple languages. For example, the generator uses a generative AI to generate questions in multiple languages, including English, Japanese, and French. Step 2: The interactive quiz providing unit provides an interactive quiz based on the questions generated by the generating unit. When the user asks a question to the quiz, the generating AI provides an appropriate answer to the question. The interactive quiz providing unit also uses the generating AI to interact with the user and provide answers in real time. Step 3: The multilingual support unit provides multilingual support based on the quiz provided by the interactive quiz providing unit. The multilingual support unit uses a generation AI to translate the content of the quiz into multiple languages ​​and provides the quiz according to the user's language setting. For example, the multilingual support unit automatically sets the quiz language based on the language setting of the user's device.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that automatically generates questions; an interactive quiz providing unit that provides an interactive quiz based on the questions generated by the generating unit; a multilingual support unit that provides multilingual support based on the quiz provided by the interactive quiz providing unit. A system characterized by:

2. The generation unit Generative AI automatically generates questions without human intervention 2. The system of claim 1.

3. The generation unit Generative AI generates questions based on a broader range of knowledge than human quiz creators 2. The system of claim 1.

4. The generation unit Generative AI generates global problems that are multilingual 2. The system of claim 1.

5. The interactive quiz providing unit When a user asks a question in a quiz, the generative AI provides an appropriate answer to that question.

2. The system of claim 1.

6. The multilingual support unit Providing multilingual quizzes using generative AI 2. The system of claim 1.

7. The generation unit Estimate the user's emotions and adjust the difficulty of the generated questions based on the estimated user emotions.

2. The system of claim 1.

8. The generation unit Analyze past quiz results and generate questions that take into account the user's strengths and weaknesses 2. The system of claim 1.

Images