System

Abstract

The system according to the embodiment aims to integrate and provide knowledge of different fields.SOLUTION: A system according to an embodiment includes a reception unit, a cultural insight unit, a technical insight unit, and a common understanding providing unit. The reception unit receives a question from a user. The cultural insight unit provides a cultural insight regarding classical literature of Japan based on the question received by the reception unit. The technical insight component provides technical insight based on the information provided by the cultural insight component. The common understanding providing unit provides a common understanding based on the information provided by the technical insight providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to integrate and provide knowledge from different fields, making it difficult for users to obtain a multifaceted perspective.

[0005] The system according to the embodiment aims to provide an integrated view of knowledge from different fields. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a cultural insight unit, a technical insight unit, and a common understanding providing unit. The reception unit receives questions from users. The cultural insight unit provides cultural insights about Japanese classical literature based on the questions received by the reception unit. The technical insight unit provides technical insights based on information provided by the cultural insight unit. The common understanding providing unit provides common understanding based on information provided by the technical insight unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide knowledge from different fields in an integrated manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that coordinates multiple generative AIs that have learned the languages ​​and cultures of countries around the world. This system accepts questions from users, and the generative AIs provide cultural insights into Japanese classical literature, technical insights from German-speaking engineering professionals, and a common understanding based on English-speaking global business data. This allows the system to provide answers to user questions from a multifaceted perspective. For example, a user can simultaneously obtain cultural insights into Japanese classical literature, technical insights from German-speaking engineering professionals, and a common understanding based on English-speaking business data, all through a single service. This allows users to utilize diverse knowledge in an integrated manner.

[0029] The system according to the embodiment includes a reception unit, a cultural insight unit, a technical insight unit, and a shared understanding unit. The reception unit receives questions from users. The questions may be in text, audio, or image format, but are not limited to these examples. The reception unit, for example, analyzes the user's questions and assigns them to an appropriate generation AI. The cultural insight unit uses the generation AI to provide cultural insights about Japanese classical literature. For example, in response to a question about Japanese classical literature, the generation AI explains its background and historical context. The cultural insight unit can also use the generation AI to provide literary interpretations and cultural influences. The technical insight unit uses the generation AI to provide technical insights about engineering. For example, in response to a question about engineering, the generation AI generates an answer based on German technical literature. The technical insight unit can also use the generation AI to provide examples of technology applications and the history of technology. The shared understanding unit uses the generation AI to provide a shared understanding about business. For example, in response to a question about business, the generation AI generates an answer based on global business data. The common understanding unit can also use generative AI to provide common recognition and consensus building. This allows the system according to the embodiment to provide answers to user questions from multiple perspectives. For example, a user can simultaneously obtain cultural insights into Japanese classical literature, technical insights from an engineering specialist who has studied German, and a common understanding based on English-based business data, all with a single service. This allows users to utilize diverse knowledge in an integrated manner.

[0030] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be used in a specific time period based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past question data into a generation AI and have the generation AI select the optimal reception method.

[0031] When receiving a question, the reception unit can filter the questions based on the user's current areas of interest. For example, the reception unit can preferentially receive questions related to topics that the user has recently been interested in. The reception unit can also analyze the user's social media activities and filter out related questions. The reception unit can also suggest questions related to the user's current areas of interest based on the user's past search history. In this way, by filtering questions based on the user's current areas of interest, it is possible to preferentially receive highly relevant questions. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI identify the user's areas of interest.

[0032] When receiving a question, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user uses voice input, the reception unit can receive the question using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also receive the question using text analysis technology. Furthermore, if the user uses images, the reception unit can also receive the question using image recognition technology. This allows the reception of questions to be made more efficient by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0033] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the area around the user's home. In this way, by taking the user's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to identify highly relevant questions.

[0034] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can preferentially receive questions related to topics the user is talking about on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. The reception unit can also receive related questions by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related questions can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to identify related questions.

[0035] When receiving a question, the reception unit can customize the reception method by reflecting the user's past feedback. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the reception method.

[0036] When providing cultural insights, the cultural insight unit can adjust the level of detail of the insights based on the importance of the question. For example, the cultural insight unit provides detailed cultural insights for questions with high importance. The cultural insight unit can also provide concise cultural insights for questions with low importance. The cultural insight unit can also provide cultural insights with an appropriate level of detail depending on the importance of the question. In this way, appropriate cultural insights can be provided by adjusting the level of detail of the insights depending on the importance of the question. Some or all of the above-described processing in the cultural insight unit may be performed using AI, for example, or may be performed without using AI. For example, the cultural insight unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the insights.

[0037] When providing cultural insights, the cultural insight unit can apply different insight algorithms depending on the category of the question. For example, the cultural insight unit can apply an insight algorithm dedicated to literature to a question about literature. The cultural insight unit can also apply an insight algorithm dedicated to history to a question about history. The cultural insight unit can also apply an insight algorithm dedicated to art to a question about art. This makes it possible to provide highly accurate cultural insights by applying an appropriate insight algorithm depending on the category of the question. Some or all of the above-mentioned processing in the cultural insight unit can be performed using AI, for example, or without using AI. For example, the cultural insight unit can input question category data into the generation AI and cause the generation AI to apply an appropriate insight algorithm.

[0038] When providing cultural insights, the cultural insight unit can improve the accuracy of the insights by referring to the user's past insight results. The cultural insight unit provides highly accurate cultural insights, for example, based on insight results the user has received in the past. The cultural insight unit can also preferentially apply a specific insight algorithm based on the user's past insight results. The cultural insight unit can also analyze the user's past insight results and improve the accuracy of the insights. In this way, by referring to the user's past insight results, highly accurate cultural insights can be provided. Some or all of the above-described processing in the cultural insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the cultural insight unit can input the user's past insight data into the generation AI and cause the generation AI to improve the accuracy of the insights.

[0039] When providing cultural insights, the cultural insight unit can determine the priority of the insights based on the time when the questions were submitted. The cultural insight unit determines the priority based on, for example, the time when the questions were submitted. The cultural insight unit can also provide cultural insights with an appropriate priority depending on the time when the questions were submitted. The cultural insight unit can also adjust the priority of the insights taking into account the time when the questions were submitted. In this way, by determining the priority of the insights based on the time when the questions were submitted, cultural insights can be provided at the appropriate time. Some or all of the above-mentioned processing in the cultural insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the cultural insight unit can input question submission time data into the generation AI and have the generation AI determine the priority of the insights.

[0040] When providing cultural insights, the cultural insight unit can adjust the order of insights based on the relevance of the questions. For example, the cultural insight unit adjusts the order of insights based on the relevance of the questions. The cultural insight unit can also provide cultural insights in an appropriate order taking into account the relevance of the questions. The cultural insight unit can also optimize the order of insights according to the relevance of the questions. In this way, by adjusting the order of insights based on the relevance of the questions, highly relevant insights can be provided preferentially. Some or all of the above-described processing in the cultural insight unit may be performed using AI, for example, or may be performed without using AI. For example, the cultural insight unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of insights.

[0041] When providing cultural insights, the cultural insight unit can adjust the use of technical terminology in the insights according to the user's level of expertise. For example, the cultural insight unit uses appropriate technical terminology according to the user's level of expertise. Furthermore, if the user is a beginner, the cultural insight unit can provide cultural insights using simple terminology. Furthermore, if the user is an expert, the cultural insight unit can provide cultural insights using detailed technical terminology. This makes it possible to provide easy-to-understand cultural insights by using appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the cultural insight unit may be performed using AI, for example, or may be performed without using AI. For example, the cultural insight unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0042] When providing technical insight, the technical insight unit can adjust the level of detail of the insight based on the importance of the question. For example, the technical insight unit provides detailed technical insight for a question with a high level of importance. The technical insight unit can also provide concise technical insight for a question with a low level of importance. The technical insight unit can also provide technical insight with an appropriate level of detail depending on the importance of the question. In this way, appropriate technical insight can be provided by adjusting the level of detail of the insight depending on the importance of the question. Some or all of the above-mentioned processing in the technical insight unit may be performed using AI, for example, or may be performed without using AI. For example, the technical insight unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the insight.

[0043] When providing technical insights, the technical insight unit can apply different insight algorithms depending on the category of the question. For example, the technical insight unit can apply an insight algorithm dedicated to engineering to a question related to engineering. The technical insight unit can also apply an insight algorithm dedicated to computer science to a question related to computer science. The technical insight unit can also apply an insight algorithm dedicated to mechanical engineering to a question related to mechanical engineering. In this way, by applying an appropriate insight algorithm depending on the category of the question, highly accurate technical insights can be provided. Some or all of the above-mentioned processing in the technical insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the technical insight unit can input question category data to the generation AI and cause the generation AI to apply an appropriate insight algorithm.

[0044] When providing technical insights, the technical insight unit can improve the accuracy of the insights by referring to the user's past insight results. The technical insight unit provides highly accurate technical insights, for example, based on insight results received by the user in the past. The technical insight unit can also preferentially apply a specific insight algorithm based on the user's past insight results. The technical insight unit can also analyze the user's past insight results and improve the accuracy of the insights. In this way, highly accurate technical insights can be provided by referring to the user's past insight results. Some or all of the above-described processing in the technical insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the technical insight unit can input the user's past insight data into the generation AI and cause the generation AI to improve the accuracy of the insights.

[0045] When providing technical insights, the technical insight unit can determine the priority of the insights based on the time when the questions were submitted. The technical insight unit determines the priority based on, for example, the time when the questions were submitted. The technical insight unit can also provide technical insights with appropriate priority depending on the time when the questions were submitted. The technical insight unit can also adjust the priority of the insights taking into account the time when the questions were submitted. In this way, by determining the priority of the insights based on the time when the questions were submitted, the technical insights can be provided at appropriate times. Some or all of the above-mentioned processing in the technical insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the technical insight unit can input question submission time data to the generation AI and cause the generation AI to determine the priority of the insights.

[0046] When providing technical insights, the technical insight unit can adjust the order of the insights based on the relevance of the questions. The technical insight unit adjusts the order of the insights, for example, based on the relevance of the questions. The technical insight unit can also provide technical insights in an appropriate order taking into account the relevance of the questions. The technical insight unit can also optimize the order of the insights according to the relevance of the questions. In this way, by adjusting the order of the insights based on the relevance of the questions, it is possible to provide highly relevant insights preferentially. Some or all of the above-described processing in the technical insight unit may be performed using AI, for example, or may be performed without using AI. For example, the technical insight unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of the insights.

[0047] When providing technical insights, the technical insight unit can adjust the use of technical terminology in the insights according to the user's level of expertise. The technical insight unit, for example, uses appropriate technical terminology according to the user's level of expertise. Furthermore, if the user is a beginner, the technical insight unit can provide the technical insights using simple terminology. Furthermore, if the user is an expert, the technical insight unit can provide the technical insights using detailed technical terminology. This makes it possible to provide technical insights that are easy to understand by using appropriate terminology according to the user's level of expertise. Some or all of the above-described processing in the technical insight unit may be performed using AI, for example, or may be performed without using AI. For example, the technical insight unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0048] When providing a common understanding, the common understanding unit can adjust the level of detail of the understanding based on the importance of the question. For example, the common understanding unit provides a detailed common understanding for a question with a high level of importance. The common understanding unit can also provide a concise common understanding for a question with a low level of importance. The common understanding unit can also provide a common understanding with an appropriate level of detail depending on the importance of the question. In this way, an appropriate common understanding can be provided by adjusting the level of detail of the understanding depending on the importance of the question. Some or all of the above-mentioned processing in the common understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the common understanding unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the understanding.

[0049] When providing a common understanding, the common understanding unit can apply different understanding algorithms depending on the category of the question. For example, the common understanding unit can apply a business-specific understanding algorithm to a question about business. The common understanding unit can also apply an economics-specific understanding algorithm to a question about economics. The common understanding unit can also apply a politics-specific understanding algorithm to a question about politics. This makes it possible to provide a highly accurate common understanding by applying an appropriate understanding algorithm depending on the question category. Some or all of the above-mentioned processing in the common understanding unit may be performed using, or without, AI, for example. For example, the common understanding unit can input question category data into a generation AI and cause the generation AI to apply an appropriate understanding algorithm.

[0050] When providing a common understanding, the common understanding unit can improve the accuracy of the understanding by referring to the user's past understanding results. The common understanding unit can provide a highly accurate common understanding, for example, based on the user's past understanding results. The common understanding unit can also preferentially apply a specific understanding algorithm based on the user's past understanding results. The common understanding unit can also analyze the user's past understanding results and improve the accuracy of the understanding. In this way, by referring to the user's past understanding results, a highly accurate common understanding can be provided. Some or all of the above-mentioned processing in the common understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the common understanding unit can input the user's past understanding data into the generation AI and cause the generation AI to improve the accuracy of the understanding.

[0051] When providing a common understanding, the common understanding unit can determine the priority of understandings based on the time when the question was submitted. The common understanding unit can determine the priority based on, for example, the time when the question was submitted. The common understanding unit can also provide a common understanding with an appropriate priority depending on the time when the question was submitted. The common understanding unit can also adjust the priority of understandings taking into account the time when the question was submitted. In this way, by determining the priority of understandings based on the time when the question was submitted, it is possible to provide a common understanding at an appropriate time. Some or all of the above-mentioned processing in the common understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the common understanding unit can input question submission time data into the generation AI and have the generation AI determine the priority of understandings.

[0052] When providing a common understanding, the common understanding unit can adjust the order of understandings based on the relevance of the questions. For example, the common understanding unit adjusts the order of understandings based on the relevance of the questions. The common understanding unit can also provide a common understanding in an appropriate order taking into account the relevance of the questions. The common understanding unit can also optimize the order of understandings according to the relevance of the questions. In this way, by adjusting the order of understandings based on the relevance of the questions, it is possible to provide highly relevant understandings preferentially. Some or all of the above-described processing in the common understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the common understanding unit can input question relevance data into a generation AI and cause the generation AI to adjust the order of understandings.

[0053] When providing a common understanding, the common understanding unit can adjust the use of technical terminology in the understanding according to the user's level of expertise. For example, the common understanding unit uses appropriate technical terminology according to the user's level of expertise. Furthermore, if the user is a beginner, the common understanding unit can provide the common understanding using simple terminology. Furthermore, if the user is an expert, the common understanding unit can provide the common understanding using detailed technical terminology. This makes it possible to provide an easy-to-understand common understanding by using appropriate technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the common understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the common understanding unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can also automatically search relevant external databases and provide supplemental information based on the user's question. For example, if a user asks a question about a specific technology, the reception unit can search relevant patent databases and provide related patent information. If a user asks a question about a historical event, the reception unit can search a history database to provide detailed background information. Furthermore, if a user asks a business question, the reception unit can search the latest market data and provide relevant statistical information. This allows users to obtain answers to their questions from multiple perspectives.

[0056] The reception unit can also analyze the user's past question history and customize questions based on the user's learning progress. For example, if the user has asked many questions about a particular topic in the past, the reception unit can suggest new questions related to that topic. It can also suggest questions about topics that the user struggled with in the past to encourage review. Furthermore, if the user shows progress in a particular area, it can suggest advanced questions related to that area. This makes it possible to support effective learning by providing questions that correspond to the user's learning progress.

[0057] The reception unit can also suggest related news articles and research papers based on the user's current areas of interest. For example, the reception unit can provide the latest news articles related to topics that the user has recently been interested in. If the user is interested in a particular field, the reception unit can also suggest the latest research papers related to that field. Furthermore, if the user is interested in a particular event, the reception unit can provide information related to that event. This allows the user to deepen their knowledge by providing related information based on the user's current areas of interest.

[0058] The reception unit can also provide appropriate feedback depending on the user's input method. For example, if the user uses voice input, the reception unit can provide voice feedback. If the user uses text input, the reception unit can provide text feedback. If the user uses images, the reception unit can provide image feedback. By providing appropriate feedback depending on the user's input method, convenience for the user can be improved.

[0059] The reception unit can also suggest related events and activities taking into account the user's geographical location information. For example, if the user is in a specific area, events held in that area can be suggested. If the user is traveling, tourist spots and activities at the travel destination can be suggested. Furthermore, if the user is at home, activities and events in the vicinity of the user's home can be suggested. In this way, related events and activities can be suggested by taking into account the user's geographical location information.

[0060] The reception unit can also analyze the user's social media activity and suggest relevant communities and groups based on the user's interests. For example, if the user is interested in a particular topic, online communities related to that topic can be suggested. Also, if the user has a particular hobby, groups related to that hobby can be suggested. Furthermore, if the user is interested in a particular event, forums and discussion groups related to that event can be suggested. In this way, relevant communities and groups can be suggested by analyzing the user's social media activity.

[0061] The reception unit can also customize the method for suggesting questions by reflecting the user's past feedback. For example, if the user has previously preferred questions of a specific type, questions of that type can be preferentially suggested. Also, if the user has previously shown interest in a specific topic, questions related to that topic can be suggested. Furthermore, if the user has previously asked questions during a specific time period, questions related to that time period can be suggested. In this way, the method for suggesting questions can be customized by reflecting the user's past feedback.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives questions from users. Questions can be in text, audio, or image format. The reception unit analyzes the user's question and assigns it to the appropriate generation AI. Step 2: The Cultural Insights Unit uses generative AI to provide cultural insights about classical Japanese literature. For example, in response to a question about classical Japanese literature, the generative AI explains its background and historical context. The Cultural Insights Unit can also use generative AI to provide literary interpretations and cultural influences. Step 3: The technical insights department uses generative AI to provide technical insights on engineering topics. For example, the technical insights department uses generative AI to generate answers to engineering questions based on German technical literature. The technical insights department can also use generative AI to provide examples of technology applications and the history of technology. Step 4: The shared understanding unit uses generative AI to provide a shared understanding of the business. For example, in the shared understanding unit, the generative AI generates answers to business questions based on business data from around the world. The shared understanding unit can also use generative AI to provide common recognition and consensus building.

[0064] (Example 2) A system according to an embodiment of the present invention is a system that coordinates multiple generative AIs that have learned the languages ​​and cultures of countries around the world. This system accepts questions from users, and the generative AIs provide cultural insights into Japanese classical literature, technical insights from German-speaking engineering professionals, and a common understanding based on English-speaking global business data. This allows the system to provide answers to user questions from a multifaceted perspective. For example, a user can simultaneously obtain cultural insights into Japanese classical literature, technical insights from German-speaking engineering professionals, and a common understanding based on English-speaking business data, all through a single service. This allows users to utilize diverse knowledge in an integrated manner.

[0065] The system according to the embodiment includes a reception unit, a cultural insight unit, a technical insight unit, and a shared understanding unit. The reception unit receives questions from users. The questions may be in text, audio, or image format, but are not limited to these examples. The reception unit, for example, analyzes the user's questions and assigns them to an appropriate generation AI. The cultural insight unit uses the generation AI to provide cultural insights about Japanese classical literature. For example, in response to a question about Japanese classical literature, the generation AI explains its background and historical context. The cultural insight unit can also use the generation AI to provide literary interpretations and cultural influences. The technical insight unit uses the generation AI to provide technical insights about engineering. For example, in response to a question about engineering, the generation AI generates an answer based on German technical literature. The technical insight unit can also use the generation AI to provide examples of technology applications and the history of technology. The shared understanding unit uses the generation AI to provide a shared understanding about business. For example, in response to a question about business, the generation AI generates an answer based on global business data. The common understanding unit can also use generative AI to provide common recognition and consensus building. This allows the system according to the embodiment to provide answers to user questions from multiple perspectives. For example, a user can simultaneously obtain cultural insights into Japanese classical literature, technical insights from an engineering specialist who has studied German, and a common understanding based on English-based business data, all with a single service. This allows users to utilize diverse knowledge in an integrated manner.

[0066] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can temporarily delay the reception of questions to provide time for the user to relax. Furthermore, if the user is excited, the reception unit can immediately accept questions and provide answers quickly. Furthermore, if the user is tired, the reception unit can simplify the question reception process and adjust it to complete it in a short time. By adjusting the timing of question reception according to the user's emotions, questions can be received at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0067] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be used in a specific time period based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past question data into a generation AI and have the generation AI select the optimal reception method.

[0068] When receiving a question, the reception unit can filter the questions based on the user's current areas of interest. For example, the reception unit can preferentially receive questions related to topics that the user has recently been interested in. The reception unit can also analyze the user's social media activities and filter out related questions. The reception unit can also suggest questions related to the user's current areas of interest based on the user's past search history. In this way, by filtering questions based on the user's current areas of interest, it is possible to preferentially receive highly relevant questions. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI identify the user's areas of interest.

[0069] When receiving a question, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user uses voice input, the reception unit can receive the question using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also receive the question using text analysis technology. Furthermore, if the user uses images, the reception unit can also receive the question using image recognition technology. This allows the reception of questions to be made more efficient by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0070] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, if the user has an urgent question, the reception unit can prioritize the question. Furthermore, if the user is relaxed, the reception unit can also prioritize the question of normal priority. Furthermore, if the user is feeling stressed, the reception unit can prioritize the question of high importance. Thus, by determining the priority of questions according to the user's emotions, important questions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0071] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the area around the user's home. In this way, by taking the user's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to identify highly relevant questions.

[0072] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can preferentially receive questions related to topics the user is talking about on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. The reception unit can also receive related questions by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related questions can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to identify related questions.

[0073] When receiving a question, the reception unit can customize the reception method by reflecting the user's past feedback. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the reception method.

[0074] The cultural insight unit can estimate the user's emotions and adjust the way the cultural insight is expressed based on the estimated user's emotions. For example, if the user is relaxed, the cultural insight unit can provide detailed cultural insights. If the user is in a hurry, the cultural insight unit can also provide concise cultural insights that focus on the main points. If the user is excited, the cultural insight unit can also provide cultural insights with visually stimulating effects. This allows for adjusting the way the cultural insight is expressed based on the user's emotions, thereby providing more appropriate cultural insights. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the cultural insight unit can be performed using AI, for example, or without AI. For example, the cultural insight unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0075] When providing cultural insights, the cultural insight unit can adjust the level of detail of the insights based on the importance of the question. For example, the cultural insight unit provides detailed cultural insights for questions with high importance. The cultural insight unit can also provide concise cultural insights for questions with low importance. The cultural insight unit can also provide cultural insights with an appropriate level of detail depending on the importance of the question. In this way, appropriate cultural insights can be provided by adjusting the level of detail of the insights depending on the importance of the question. Some or all of the above-described processing in the cultural insight unit may be performed using AI, for example, or may be performed without using AI. For example, the cultural insight unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the insights.

[0076] When providing cultural insights, the cultural insight unit can apply different insight algorithms depending on the category of the question. For example, the cultural insight unit can apply an insight algorithm dedicated to literature to a question about literature. The cultural insight unit can also apply an insight algorithm dedicated to history to a question about history. The cultural insight unit can also apply an insight algorithm dedicated to art to a question about art. This makes it possible to provide highly accurate cultural insights by applying an appropriate insight algorithm depending on the category of the question. Some or all of the above-mentioned processing in the cultural insight unit can be performed using AI, for example, or without using AI. For example, the cultural insight unit can input question category data into the generation AI and cause the generation AI to apply an appropriate insight algorithm.

[0077] When providing cultural insights, the cultural insight unit can improve the accuracy of the insights by referring to the user's past insight results. The cultural insight unit provides highly accurate cultural insights, for example, based on insight results the user has received in the past. The cultural insight unit can also preferentially apply a specific insight algorithm based on the user's past insight results. The cultural insight unit can also analyze the user's past insight results and improve the accuracy of the insights. In this way, by referring to the user's past insight results, highly accurate cultural insights can be provided. Some or all of the above-described processing in the cultural insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the cultural insight unit can input the user's past insight data into the generation AI and cause the generation AI to improve the accuracy of the insights.

[0078] The cultural insight unit can estimate the user's emotions and adjust the length of the cultural insight based on the estimated user's emotions. For example, if the user is relaxed, the cultural insight unit can provide detailed cultural insight. If the user is in a hurry, the cultural insight unit can provide concise cultural insight that focuses on the main points. If the user is excited, the cultural insight unit can provide cultural insight with visually stimulating effects. This allows for adjusting the length of the cultural insight according to the user's emotions, thereby providing more appropriate cultural insight. 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 cultural insight unit can be performed using AI, for example, or without AI. For example, the cultural insight unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0079] When providing cultural insights, the cultural insight unit can determine the priority of the insights based on the time when the questions were submitted. The cultural insight unit determines the priority based on, for example, the time when the questions were submitted. The cultural insight unit can also provide cultural insights with an appropriate priority depending on the time when the questions were submitted. The cultural insight unit can also adjust the priority of the insights taking into account the time when the questions were submitted. In this way, by determining the priority of the insights based on the time when the questions were submitted, cultural insights can be provided at the appropriate time. Some or all of the above-mentioned processing in the cultural insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the cultural insight unit can input question submission time data into the generation AI and have the generation AI determine the priority of the insights.

[0080] When providing cultural insights, the cultural insight unit can adjust the order of insights based on the relevance of the questions. For example, the cultural insight unit adjusts the order of insights based on the relevance of the questions. The cultural insight unit can also provide cultural insights in an appropriate order taking into account the relevance of the questions. The cultural insight unit can also optimize the order of insights according to the relevance of the questions. In this way, by adjusting the order of insights based on the relevance of the questions, highly relevant insights can be provided preferentially. Some or all of the above-described processing in the cultural insight unit may be performed using AI, for example, or may be performed without using AI. For example, the cultural insight unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of insights.

[0081] When providing cultural insights, the cultural insight unit can adjust the use of technical terminology in the insights according to the user's level of expertise. For example, the cultural insight unit uses appropriate technical terminology according to the user's level of expertise. Furthermore, if the user is a beginner, the cultural insight unit can provide cultural insights using simple terminology. Furthermore, if the user is an expert, the cultural insight unit can provide cultural insights using detailed technical terminology. This makes it possible to provide easy-to-understand cultural insights by using appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the cultural insight unit may be performed using AI, for example, or may be performed without using AI. For example, the cultural insight unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0082] The technical insight unit can estimate the user's emotions and adjust the way the technical insight is expressed based on the estimated user's emotions. For example, if the user is relaxed, the technical insight unit can provide detailed technical insight. If the user is in a hurry, the technical insight unit can also provide concise technical insight that focuses on the main points. If the user is excited, the technical insight unit can also provide technical insight with visually stimulating effects. This allows for adjusting the way the technical insight is expressed based on the user's emotions, thereby providing more appropriate technical insight. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the technical insight unit can be performed using, for example, an AI, or without an AI. For example, the technical insight unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] When providing technical insight, the technical insight unit can adjust the level of detail of the insight based on the importance of the question. For example, the technical insight unit provides detailed technical insight for a question with a high level of importance. The technical insight unit can also provide concise technical insight for a question with a low level of importance. The technical insight unit can also provide technical insight with an appropriate level of detail depending on the importance of the question. In this way, appropriate technical insight can be provided by adjusting the level of detail of the insight depending on the importance of the question. Some or all of the above-mentioned processing in the technical insight unit may be performed using AI, for example, or may be performed without using AI. For example, the technical insight unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the insight.

[0084] When providing technical insights, the technical insight unit can apply different insight algorithms depending on the category of the question. For example, the technical insight unit can apply an insight algorithm dedicated to engineering to a question related to engineering. The technical insight unit can also apply an insight algorithm dedicated to computer science to a question related to computer science. The technical insight unit can also apply an insight algorithm dedicated to mechanical engineering to a question related to mechanical engineering. In this way, by applying an appropriate insight algorithm depending on the category of the question, highly accurate technical insights can be provided. Some or all of the above-mentioned processing in the technical insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the technical insight unit can input question category data to the generation AI and cause the generation AI to apply an appropriate insight algorithm.

[0085] When providing technical insights, the technical insight unit can improve the accuracy of the insights by referring to the user's past insight results. The technical insight unit provides highly accurate technical insights, for example, based on insight results received by the user in the past. The technical insight unit can also preferentially apply a specific insight algorithm based on the user's past insight results. The technical insight unit can also analyze the user's past insight results and improve the accuracy of the insights. In this way, highly accurate technical insights can be provided by referring to the user's past insight results. Some or all of the above-described processing in the technical insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the technical insight unit can input the user's past insight data into the generation AI and cause the generation AI to improve the accuracy of the insights.

[0086] The technical insight unit can estimate the user's emotions and adjust the length of the technical insight based on the estimated user's emotions. For example, if the user is relaxed, the technical insight unit can provide detailed technical insight. If the user is in a hurry, the technical insight unit can provide concise technical insight that focuses on the main points. If the user is excited, the technical insight unit can provide technical insight with visually stimulating effects. This allows the length of the technical insight to be adjusted according to the user's emotions, thereby providing more appropriate technical insight. 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 technical insight unit can be performed using an AI, for example, or without an AI. For example, the technical insight unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0087] When providing technical insights, the technical insight unit can determine the priority of the insights based on the time when the questions were submitted. The technical insight unit determines the priority based on, for example, the time when the questions were submitted. The technical insight unit can also provide technical insights with appropriate priority depending on the time when the questions were submitted. The technical insight unit can also adjust the priority of the insights taking into account the time when the questions were submitted. In this way, by determining the priority of the insights based on the time when the questions were submitted, the technical insights can be provided at appropriate times. Some or all of the above-mentioned processing in the technical insight unit may be performed using, for example, AI, or may be performed without using AI. For example, the technical insight unit can input question submission time data to the generation AI and cause the generation AI to determine the priority of the insights.

[0088] When providing technical insights, the technical insight unit can adjust the order of the insights based on the relevance of the questions. The technical insight unit adjusts the order of the insights, for example, based on the relevance of the questions. The technical insight unit can also provide technical insights in an appropriate order taking into account the relevance of the questions. The technical insight unit can also optimize the order of the insights according to the relevance of the questions. In this way, by adjusting the order of the insights based on the relevance of the questions, it is possible to provide highly relevant insights preferentially. Some or all of the above-described processing in the technical insight unit may be performed using AI, for example, or may be performed without using AI. For example, the technical insight unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of the insights.

[0089] When providing technical insights, the technical insight unit can adjust the use of technical terminology in the insights according to the user's level of expertise. The technical insight unit, for example, uses appropriate technical terminology according to the user's level of expertise. Furthermore, if the user is a beginner, the technical insight unit can provide the technical insights using simple terminology. Furthermore, if the user is an expert, the technical insight unit can provide the technical insights using detailed technical terminology. This makes it possible to provide technical insights that are easy to understand by using appropriate terminology according to the user's level of expertise. Some or all of the above-described processing in the technical insight unit may be performed using AI, for example, or may be performed without using AI. For example, the technical insight unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0090] The common understanding unit can estimate the user's emotions and adjust the way the common understanding is expressed based on the estimated user's emotions. For example, if the user is relaxed, the common understanding unit can provide a detailed common understanding. If the user is in a hurry, the common understanding unit can also provide a concise common understanding that focuses on the main points. If the user is excited, the common understanding unit can also provide a common understanding that adds a visually stimulating effect. This allows for adjusting the way the common understanding is expressed based on the user's emotions, thereby providing a more appropriate common understanding. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the common understanding unit can be performed using AI, or without AI. For example, the common understanding unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0091] When providing a common understanding, the common understanding unit can adjust the level of detail of the understanding based on the importance of the question. For example, the common understanding unit provides a detailed common understanding for a question with a high level of importance. The common understanding unit can also provide a concise common understanding for a question with a low level of importance. The common understanding unit can also provide a common understanding with an appropriate level of detail depending on the importance of the question. In this way, an appropriate common understanding can be provided by adjusting the level of detail of the understanding depending on the importance of the question. Some or all of the above-mentioned processing in the common understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the common understanding unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the understanding.

[0092] When providing a common understanding, the common understanding unit can apply different understanding algorithms depending on the category of the question. For example, the common understanding unit can apply a business-specific understanding algorithm to a question about business. The common understanding unit can also apply an economics-specific understanding algorithm to a question about economics. The common understanding unit can also apply a politics-specific understanding algorithm to a question about politics. This makes it possible to provide a highly accurate common understanding by applying an appropriate understanding algorithm depending on the question category. Some or all of the above-mentioned processing in the common understanding unit may be performed using, or without, AI, for example. For example, the common understanding unit can input question category data into a generation AI and cause the generation AI to apply an appropriate understanding algorithm.

[0093] When providing a common understanding, the common understanding unit can improve the accuracy of the understanding by referring to the user's past understanding results. The common understanding unit can provide a highly accurate common understanding, for example, based on the user's past understanding results. The common understanding unit can also preferentially apply a specific understanding algorithm based on the user's past understanding results. The common understanding unit can also analyze the user's past understanding results and improve the accuracy of the understanding. In this way, by referring to the user's past understanding results, a highly accurate common understanding can be provided. Some or all of the above-mentioned processing in the common understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the common understanding unit can input the user's past understanding data into the generation AI and cause the generation AI to improve the accuracy of the understanding.

[0094] The shared understanding unit can estimate the user's emotions and adjust the length of the shared understanding based on the estimated user's emotions. For example, if the user is relaxed, the shared understanding unit can provide a detailed shared understanding. If the user is in a hurry, the shared understanding unit can also provide a concise shared understanding that focuses on the main points. If the user is excited, the shared understanding unit can also provide a shared understanding with a visually stimulating effect. This allows for adjusting the length of the shared understanding according to the user's emotions to provide a more appropriate shared understanding. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the shared understanding unit may be performed using AI, or may be performed without AI. For example, the shared understanding unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0095] When providing a common understanding, the common understanding unit can determine the priority of understandings based on the time when the question was submitted. The common understanding unit can determine the priority based on, for example, the time when the question was submitted. The common understanding unit can also provide a common understanding with an appropriate priority depending on the time when the question was submitted. The common understanding unit can also adjust the priority of understandings taking into account the time when the question was submitted. In this way, by determining the priority of understandings based on the time when the question was submitted, it is possible to provide a common understanding at an appropriate time. Some or all of the above-mentioned processing in the common understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the common understanding unit can input question submission time data into the generation AI and have the generation AI determine the priority of understandings.

[0096] When providing a common understanding, the common understanding unit can adjust the order of understandings based on the relevance of the questions. For example, the common understanding unit adjusts the order of understandings based on the relevance of the questions. The common understanding unit can also provide a common understanding in an appropriate order taking into account the relevance of the questions. The common understanding unit can also optimize the order of understandings according to the relevance of the questions. In this way, by adjusting the order of understandings based on the relevance of the questions, it is possible to provide highly relevant understandings preferentially. Some or all of the above-described processing in the common understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the common understanding unit can input question relevance data into a generation AI and cause the generation AI to adjust the order of understandings.

[0097] When providing a common understanding, the common understanding unit can adjust the use of technical terminology in the understanding according to the user's level of expertise. For example, the common understanding unit uses appropriate technical terminology according to the user's level of expertise. Furthermore, if the user is a beginner, the common understanding unit can provide the common understanding using simple terminology. Furthermore, if the user is an expert, the common understanding unit can provide the common understanding using detailed technical terminology. This makes it possible to provide an easy-to-understand common understanding by using appropriate technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the common understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the common understanding unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, cultural insight unit, technical insight unit, and shared understanding unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a question from a user via the control unit 46A of the smart device 14, and the question is analyzed by the specific processing unit 290 of the data processing device 12. The cultural insight unit provides cultural insight into Japanese classical literature via the specific processing unit 290 of the data processing device 12. The technical insight unit provides technical insight into engineering via the specific processing unit 290 of the data processing device 12. The shared understanding unit provides a shared understanding about business via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, cultural insight unit, technical insight unit, and shared understanding unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a question from a user via the control unit 46A of the smart glasses 214, and the question is analyzed by the specific processing unit 290 of the data processing device 12. The cultural insight unit provides cultural insight on Japanese classical literature via the specific processing unit 290 of the data processing device 12. The technical insight unit provides technical insight on engineering via the specific processing unit 290 of the data processing device 12. The shared understanding unit provides a shared understanding on business via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, cultural insight unit, technical insight unit, and shared understanding unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a question from a user via the control unit 46A of the headset type terminal 314, and the question is analyzed by the specific processing unit 290 of the data processing device 12. The cultural insight unit provides cultural insight into Japanese classical literature via the specific processing unit 290 of the data processing device 12. The technical insight unit provides technical insight into engineering via the specific processing unit 290 of the data processing device 12. The shared understanding unit provides a shared understanding about business via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, cultural insight unit, technical insight unit, and shared understanding unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a question from a user via the control unit 46A of the robot 414, which is analyzed by the specific processing unit 290 of the data processing device 12. The cultural insight unit provides cultural insight into Japanese classical literature via the specific processing unit 290 of the data processing device 12. The technical insight unit provides technical insight into engineering via the specific processing unit 290 of the data processing device 12. The shared understanding unit provides a shared understanding about business via the specific processing unit 290 of the data processing device 12.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can also automatically search relevant external databases and provide supplemental information based on the user's question. For example, if a user asks a question about a specific technology, the reception unit can search relevant patent databases and provide related patent information. If a user asks a question about a historical event, the reception unit can search a history database to provide detailed background information. Furthermore, if a user asks a business question, the reception unit can search the latest market data and provide relevant statistical information. This allows users to obtain answers to their questions from multiple perspectives.

[0100] The reception unit can also estimate the user's emotions and adjust the difficulty of the questions based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can preferentially receive easy questions. Also, if the user is relaxed, the reception unit can receive more difficult questions. Furthermore, if the user is excited, the reception unit can receive more challenging questions. In this way, the level of difficulty of the questions can be adjusted according to the user's emotions, thereby improving user satisfaction.

[0101] The reception unit can also analyze the user's past question history and customize questions based on the user's learning progress. For example, if the user has asked many questions about a particular topic in the past, the reception unit can suggest new questions related to that topic. It can also suggest questions about topics that the user struggled with in the past to encourage review. Furthermore, if the user shows progress in a particular area, it can suggest advanced questions related to that area. This makes it possible to support effective learning by providing questions that correspond to the user's learning progress.

[0102] The reception unit can also suggest related news articles and research papers based on the user's current areas of interest. For example, the reception unit can provide the latest news articles related to topics that the user has recently been interested in. If the user is interested in a particular field, the reception unit can also suggest the latest research papers related to that field. Furthermore, if the user is interested in a particular event, the reception unit can provide information related to that event. This allows the user to deepen their knowledge by providing related information based on the user's current areas of interest.

[0103] The reception unit can also provide appropriate feedback depending on the user's input method. For example, if the user uses voice input, the reception unit can provide voice feedback. If the user uses text input, the reception unit can provide text feedback. If the user uses images, the reception unit can provide image feedback. By providing appropriate feedback depending on the user's input method, convenience for the user can be improved.

[0104] The reception unit can also estimate the user's emotions and suggest question categories based on the estimated user emotions. For example, if the user is relaxed, questions related to hobbies and entertainment can be suggested. If the user is stressed, questions related to relaxation and stress relief can be suggested. Furthermore, if the user is excited, questions related to challenging topics can be suggested. In this way, the user's interest can be aroused by suggesting question categories according to the user's emotions.

[0105] The reception unit can also suggest related events and activities taking into account the user's geographical location information. For example, if the user is in a specific area, events held in that area can be suggested. If the user is traveling, tourist spots and activities at the travel destination can be suggested. Furthermore, if the user is at home, activities and events in the vicinity of the user's home can be suggested. In this way, related events and activities can be suggested by taking into account the user's geographical location information.

[0106] The reception unit can also analyze the user's social media activity and suggest relevant communities and groups based on the user's interests. For example, if the user is interested in a particular topic, online communities related to that topic can be suggested. Also, if the user has a particular hobby, groups related to that hobby can be suggested. Furthermore, if the user is interested in a particular event, forums and discussion groups related to that event can be suggested. In this way, relevant communities and groups can be suggested by analyzing the user's social media activity.

[0107] The reception unit can also customize the method for suggesting questions by reflecting the user's past feedback. For example, if the user has previously preferred questions of a specific type, questions of that type can be preferentially suggested. Also, if the user has previously shown interest in a specific topic, questions related to that topic can be suggested. Furthermore, if the user has previously asked questions during a specific time period, questions related to that time period can be suggested. In this way, the method for suggesting questions can be customized by reflecting the user's past feedback.

[0108] The cultural insight unit can also estimate the user's emotions and personalize the method of providing cultural insights based on the estimated user's emotions. For example, if the user is relaxed, detailed cultural insights can be provided. If the user is in a hurry, concise cultural insights that focus on the main points can be provided. Furthermore, if the user is excited, cultural insights with visually stimulating effects can be provided. In this way, by personalizing the method of providing cultural insights according to the user's emotions, more appropriate cultural insights can be provided.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit receives questions from users. Questions can be in text, audio, or image format. The reception unit analyzes the user's question and assigns it to the appropriate generation AI. Step 2: The Cultural Insights Unit uses generative AI to provide cultural insights about classical Japanese literature. For example, in response to a question about classical Japanese literature, the generative AI explains its background and historical context. The Cultural Insights Unit can also use generative AI to provide literary interpretations and cultural influences. Step 3: The technical insights department uses generative AI to provide technical insights on engineering topics. For example, the technical insights department uses generative AI to generate answers to engineering questions based on German technical literature. The technical insights department can also use generative AI to provide examples of technology applications and the history of technology. Step 4: The shared understanding unit uses generative AI to provide a shared understanding of the business. For example, in the shared understanding unit, the generative AI generates answers to business questions based on business data from around the world. The shared understanding unit can also use generative AI to provide common recognition and consensus building.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; a cultural insight section that provides cultural insights on Japanese classical literature based on the questions received by the reception section; a technical insight unit that provides technical insights based on the information provided by the cultural insight unit; a unit for providing a common understanding based on the information provided by the technical insight unit. A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

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

4. The reception unit When questions are asked, they are filtered based on the user's current interests.

2. The system of claim 1.

5. The reception unit When accepting a question, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the questions to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting questions, the system takes into account the user's geographic location information to prioritize relevant questions.

2. The system of claim 1.

8. The reception unit When a question is received, the social media activity of the user is analyzed and related questions are received.

2. The system of claim 1.

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