System

Abstract

A system according to an embodiment aims to enable an interactive dialogue between a visitor and an animal in an animal display area.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, a collection unit, and a translation unit. The reception unit receives a question from the visitor. The analyzer analyzes the question received by the receiver. The generation unit generates an answer based on the information analyzed by the analysis unit. The providing unit provides the answer generated by the generating unit. The collection unit collects animal information. The translation unit supports multiple languages.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 technology has had the problem that communication with visitors in animal exhibit areas is one-way, making two-way dialogue difficult.

[0005] The system according to the embodiment aims to enable two-way interaction between visitors and animals in the animal exhibit area. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, a collection unit, and a translation unit. The reception unit receives questions from visitors. The analysis unit analyzes the questions received by the reception unit. The generation unit generates answers based on information analyzed by the analysis unit. The provision unit provides the answers generated by the generation unit. The collection unit collects information about animals. The translation unit supports multiple languages. [Effects of the Invention]

[0007] Systems according to embodiments may enable two-way interaction between visitors and animals in animal exhibit areas. [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) An interactive experience system according to an embodiment of the present invention provides visitors with an interactive experience with animals through a device installed in an animal exhibition area. In this system, visitors input questions into the device, and a generation AI analyzes the questions and generates answers based on information about the animals. For example, if a visitor asks, "What does this animal eat?", the generation AI generates an answer based on information about the animal's diet and provides it to the visitor. Furthermore, this system supports multiple languages, making it accessible to international visitors. For example, if a visitor inputs a question in English, the generation AI generates an answer in English and provides it to the visitor. This allows visitors to deepen their understanding of the ecology and behavior of animals. This interactive experience system allows visitors to enjoy real-time, two-way dialogue with animals, deepening their understanding of the ecology and behavior of animals. Furthermore, the system supports multiple languages, making it accessible to international visitors.

[0029] An interactive experience system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, a collection unit, and a translation unit. The reception unit receives a visitor's question. The visitor's question may be, for example, in text format, audio format, or on a specific topic, but is not limited to these examples. The reception unit may receive, for example, a text-format question entered by the visitor into a device. The reception unit may also receive audio-format questions from the visitor using a voice input interface. The analysis unit analyzes the question received by the reception unit. The analysis may be performed using, for example, a natural language processing technique or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may analyze the content of the question using a natural language processing technique and extract information for generating an appropriate answer. The analysis unit may also analyze the intent of the question using a machine learning algorithm. The generation unit generates an answer based on the information analyzed by the analysis unit. The generation may be performed using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit may generate an answer to the visitor's question using a text generation AI. The generation unit can also use multimodal generation AI to generate answers that include not only text but also images and audio. The provision unit provides the answer generated by the generation unit to the visitor. For example, provision can be achieved by, but not limited to, providing an interface that displays the answer on a device display. For example, the provision unit can display the generated answer on a device display. The provision unit can also provide the generated answer by audio using an audio output interface. The collection unit collects information about the animals and updates the database. For example, collection can be achieved using observation data or sensor data, for example, but not limited to. For example, the collection unit can collect observation data on the ecology and behavior of animals and add it to the database. The collection unit can also monitor the health of animals using sensor data and update the database. The translation unit translates the generated answer into multiple languages. For example, translation can be achieved using machine translation or expert translation, for example, but not limited to. For example, the translation unit can use machine translation to translate the generated answer into multiple languages, such as English and Chinese.The translation unit can also provide highly accurate translations using translations by experts, allowing the interactive experience system according to the embodiment to respond to visitors' questions in real time and provide information about animals.

[0030] The interactive experience system includes an interface for visitors to input questions. Examples of the interface include, but are not limited to, a web interface, a mobile app, and a voice input interface. For example, the interface may provide a web interface for visitors to input questions in text format into a device. The interface may also provide a mobile app for visitors to input questions using a smartphone or tablet. The interface may also provide a voice input interface for visitors to input questions by voice. This allows visitors to easily input questions.

[0031] The interactive experience system includes a display that displays the generated answer. Examples of the display include, but are not limited to, a liquid crystal display, an LED display, and a touch screen. For example, the display may provide a liquid crystal display for visually displaying the generated answer. Alternatively, the display may provide an LED display for displaying the generated answer in high brightness. Alternatively, the display may provide a touch screen that allows visitors to check the answer by touch operation. This allows visitors to visually check the generated answer.

[0032] The collection unit can collect information on the ecology and behavior of animals and update the database. Information on ecology and behavior includes, but is not limited to, observation data, sensor data, literature information, and the like. For example, the collection unit collects observation data on the ecology and behavior of animals and adds it to the database. The collection unit can also monitor the health status of animals using sensor data and update the database. The collection unit can also collect the latest literature information on animals and add it to the database. This allows answers to be generated based on the latest information at all times.

[0033] The translation unit can translate the generated answer into multiple languages. Examples of translation into multiple languages ​​include, but are not limited to, machine translation, expert translation, and a list of supported languages. For example, the translation unit can use machine translation to translate the generated answer into multiple languages, such as English and Chinese. The translation unit can also provide highly accurate translations using expert translation. The translation unit can also translate the answer to match the visitor's language based on the list of supported languages. This allows for international visitors to be accommodated.

[0034] The providing unit may provide an interface for displaying the generated answer to the visitor. Examples of the interface include, but are not limited to, a web interface, a mobile app, and an audio output interface. For example, the providing unit may provide a web interface for displaying the generated answer on a device display. The providing unit may also provide a mobile app for displaying the generated answer on a smartphone or tablet. The providing unit may also provide an audio output interface for providing the generated answer by audio. This allows the visitor to easily check the generated answer.

[0035] The reception unit can analyze the visitor's past question history and select the optimal reception method. For example, the reception unit automatically suggests related questions based on the content of questions frequently asked by the visitor in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the visitor has used in the past. The reception unit can also predict and suggest questions to be used in a specific time period based on the visitor's past question history. This makes it possible to provide the optimal reception method based on the visitor's past question history. The past question history includes, for example, the content of the question, the frequency of the question, question trends, etc., but is not limited to these examples. For example, the reception unit can analyze the content of questions asked by the visitor in the past and suggest related questions. The reception unit can also suggest the optimal input method based on the input methods frequently used by the visitor in the past. The reception unit can also predict and suggest questions to be used in a specific time period based on the visitor's past question history.

[0036] When receiving a question, the reception unit can filter the questions based on the visitor's current areas of interest. For example, the reception unit can prioritize receiving questions about animals in which the visitor is currently interested. The reception unit can also suggest related questions based on information recently searched by the visitor. The reception unit can also prioritize receiving questions about animals in which the visitor has spent a long time in the exhibition area. This makes it possible to prioritize receiving questions that match the visitor's interests. Areas of interest include, but are not limited to, survey results, past questions, and browsing history. For example, the reception unit filters questions based on the areas of interest answered by the visitor in a survey. The reception unit can also suggest related questions based on the visitor's past questions. The reception unit can also suggest questions related to the visitor's current areas of interest based on the visitor's browsing history.

[0037] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the visitor's input method. For example, if the visitor inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, if the visitor inputs a question by text, the acceptance unit can also accept the question using text analysis technology. Furthermore, if the visitor inputs a question using an image, the acceptance unit can also accept the question using image recognition technology. This makes it possible to provide the optimal acceptance means depending on the visitor's input method. Input methods include, but are not limited to, text input, voice input, and image input. For example, if the visitor inputs a question in text format, the acceptance unit accepts the question using text analysis technology. Furthermore, if the visitor inputs a question in voice format, the acceptance unit can also accept the question using voice recognition technology. Furthermore, if the visitor inputs a question in image format, the acceptance unit can also accept the question using image recognition technology.

[0038] When accepting questions, the reception unit can prioritize relevant questions by taking into account the visitor's geographical location information. For example, if the visitor is in a specific animal exhibit area, the reception unit can prioritize questions about that animal. Furthermore, if the visitor is at the entrance to the exhibit area, the reception unit can prioritize questions about general directions. Furthermore, if the visitor is near a specific animal, the reception unit can prioritize questions about the specific animal. This allows for prioritized acceptance of relevant questions based on the visitor's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, the reception unit can prioritize questions related to the visitor's current location based on the visitor's GPS data. Furthermore, the reception unit can also suggest related questions based on the visitor's IP address. Furthermore, the reception unit can identify the visitor's current location using location information services and suggest related questions.

[0039] When accepting a question, the reception unit can analyze the visitor's social media activity and accept related questions. For example, the reception unit can prioritize receiving questions related to places where the visitor has checked in on social media. The reception unit can also analyze the content of the visitor's posts on social media and suggest related questions. The reception unit can also refer to the activities of the visitor's friends on social media to suggest related questions. This makes it possible to accept related questions based on the visitor's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the reception unit can prioritize receiving questions related to places where the visitor has checked in on social media. The reception unit can also analyze the content of the visitor's posts on social media and suggest related questions. The reception unit can also refer to the activities of the visitor's friends on social media to suggest related questions.

[0040] The reception unit can customize the reception method by reflecting the visitor's past feedback when receiving a question. For example, the reception unit can suggest an optimal question reception method based on feedback provided by the visitor in the past. The reception unit can also preferentially suggest a specific question format based on the visitor's past feedback. The reception unit can also analyze the visitor's past feedback and provide an optimal question reception interface. This makes it possible to provide an optimal reception method based on the visitor's past feedback. Past feedback includes, but is not limited to, evaluation comments, survey results, and usage history. For example, the reception unit can suggest an optimal question reception method based on feedback provided by the visitor in the past. The reception unit can also preferentially suggest a specific question format based on the visitor's past feedback. The reception unit can also analyze the visitor's past feedback and provide an optimal question reception interface.

[0041] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit can perform a detailed analysis on a question of high importance to provide a comprehensive answer. The analysis unit can also perform a concise analysis on a question of low importance to provide a quick answer. The analysis unit can also perform an analysis with an appropriate level of detail on a question of medium importance to provide a balanced answer. This allows the analysis to be performed with an appropriate level of detail depending on the importance of the question. The importance of a question includes, for example, the urgency of the question, the frequency of the question, and the impact of the question, but is not limited to these examples. For example, the analysis unit can adjust the level of detail of the analysis based on the urgency of the question. The analysis unit can also adjust the level of detail of the analysis based on the frequency of the question. The analysis unit can also adjust the level of detail of the analysis based on the impact of the question.

[0042] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit can apply an analysis algorithm specialized for diet to a question about an animal's diet. The analysis unit can also apply an analysis algorithm specialized for habitat to a question about an animal's habitat. The analysis unit can also apply an analysis algorithm specialized for animal behavior to a question about animal behavior. This makes it possible to apply an optimal analysis algorithm depending on the category of the question. Question categories include, for example, by topic, by difficulty, by subject, etc., but are not limited to these examples. For example, the analysis unit can apply an analysis algorithm specialized for diet to a question about an animal's diet. The analysis unit can also apply an analysis algorithm specialized for habitat to a question about an animal's habitat. The analysis unit can also apply an analysis algorithm specialized for behavior to a question about animal behavior.

[0043] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the visitor's past question results. The analysis unit can improve the analysis accuracy of the current question, for example, based on questions the visitor has asked in the past and their answers. The analysis unit can also analyze the visitor's past question results to perform more accurate analysis of similar questions. The analysis unit can also select the optimal analysis method for the current question by referring to the visitor's past question history. This can improve the accuracy of the analysis based on the visitor's past question results. The past question results include, for example, answer content, answer accuracy, and answer evaluation, but are not limited to these examples. For example, the analysis unit improves the analysis accuracy of the current question based on the visitor's past question results and their answers. The analysis unit can also analyze the visitor's past question results to perform more accurate analysis of similar questions. The analysis unit can also select the optimal analysis method for the current question by referring to the visitor's past question history.

[0044] When analyzing a question, the analysis unit can determine the priority of the analysis based on the time when the question was submitted. For example, the analysis unit can set a high priority during a time period when many questions are submitted and perform a quick analysis. The analysis unit can also set a low priority during a time period when few questions are submitted and perform a detailed analysis. The analysis unit can also dynamically adjust the priority of the analysis according to the time when the question was submitted. This allows analysis to be performed with an appropriate priority according to the time when the question was submitted. The submission time includes, but is not limited to, a timestamp, a submission date and time, and a submission order. For example, the analysis unit can set a high priority during a time period when many questions are submitted and perform a quick analysis. The analysis unit can also set a low priority during a time period when few questions are submitted and perform a detailed analysis. The analysis unit can also dynamically adjust the priority of the analysis according to the time when the question was submitted.

[0045] When analyzing a question, the analysis unit can adjust the order of analysis based on the relevance of the question. For example, if the question is highly relevant, the analysis unit can prioritize analysis and provide an answer quickly. Furthermore, if the question is less relevant, the analysis unit can postpone analysis. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the question. This allows analysis to be performed in an appropriate order based on the relevance of the question. Relevance includes, but is not limited to, for example, topic consistency, keyword co-occurrence, past relevance evaluation, etc. For example, if the question is highly relevant, the analysis unit can prioritize analysis and provide an answer quickly. Furthermore, if the question is less relevant, the analysis unit can postpone analysis. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the question.

[0046] When analyzing a question, the analysis unit can adjust the use of technical terms in the analysis according to the visitor's level of expertise. For example, if the visitor has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the visitor does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the visitor's level of expertise. This allows analysis results to be provided using appropriate technical terms according to the visitor's level of expertise. Examples of expertise levels include, but are not limited to, qualification information, previous question content, and self-reporting. For example, if the visitor has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the visitor does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the visitor's level of expertise.

[0047] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also generate an answer with an appropriate level of detail for a question of medium importance. This makes it possible to generate an answer with an appropriate level of detail according to the importance of the question. The importance of a question includes, for example, the urgency of the question, the frequency of the question, and the impact of the question, but is not limited to these examples. For example, the generation unit adjusts the level of detail of the answer based on the urgency of the question. The generation unit can also adjust the level of detail of the answer based on the frequency of the question. The generation unit can also adjust the level of detail of the answer based on the impact of the question.

[0048] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit can apply a generation algorithm specialized for diet to a question about an animal's diet. Furthermore, the generation unit can also apply a generation algorithm specialized for habitat to a question about an animal's habitat. Furthermore, the generation unit can also apply a generation algorithm specialized for animal behavior to a question about an animal's behavior. This makes it possible to apply an optimal generation algorithm depending on the category of the question. Question categories include, for example, by topic, by difficulty, by subject, etc., but are not limited to these examples. For example, the generation unit can apply a generation algorithm specialized for diet to a question about an animal's diet. Furthermore, the generation unit can also apply a generation algorithm specialized for habitat to a question about an animal's habitat. Furthermore, the generation unit can also apply a generation algorithm specialized for behavior to a question about an animal's behavior.

[0049] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the visitor's past question results. The generation unit can improve the accuracy of the answer to the current question, for example, based on questions the visitor has previously asked and the answers to those questions. The generation unit can also analyze the visitor's past question results to generate more accurate answers to similar questions. The generation unit can also generate an optimal answer to the current question by referring to the visitor's past question history. This makes it possible to improve the accuracy of the answer based on the visitor's past question results. The past question results include, for example, answer content, answer accuracy, and answer evaluation, but are not limited to these examples. For example, the generation unit can improve the accuracy of the answer to the current question based on the visitor's past question results and the answers to those questions. The generation unit can also analyze the visitor's past question results to generate more accurate answers to similar questions. The generation unit can also generate an optimal answer to the current question by referring to the visitor's past question history.

[0050] When generating an answer, the generation unit can determine the priority of the answer based on the time when the question was submitted. For example, the generation unit can set a high priority during a time period when many questions are submitted and generate an answer quickly. The generation unit can also set a low priority during a time period when few questions are submitted and generate a detailed answer. The generation unit can also dynamically adjust the priority of the answer depending on the time when the question was submitted. This allows an answer to be generated with an appropriate priority depending on the time when the question was submitted. The submission time includes, but is not limited to, a timestamp, a submission date and time, and a submission order. For example, the generation unit can set a high priority during a time period when many questions are submitted and generate an answer quickly. The generation unit can also set a low priority during a time period when few questions are submitted and generate a detailed answer. The generation unit can also dynamically adjust the priority of the answer depending on the time when the question was submitted.

[0051] When generating answers, the generation unit can adjust the order of answers based on the relevance of the question. For example, if the question is highly relevant, the generation unit may generate an answer preferentially. Furthermore, if the question is less relevant, the generation unit may generate an answer later. Furthermore, the generation unit can dynamically adjust the order of answers according to the relevance of the question. This allows answers to be generated in an appropriate order according to the relevance of the question. Examples of relevance include, but are not limited to, topic agreement, keyword co-occurrence, and past relevance evaluation. For example, if the question is highly relevant, the generation unit may generate an answer preferentially. Furthermore, if the question is less relevant, the generation unit may generate an answer later. Furthermore, the generation unit can dynamically adjust the order of answers according to the relevance of the question.

[0052] When generating an answer, the generation unit can adjust the use of technical terms in the answer depending on the visitor's level of expertise. For example, if the visitor has technical expertise, the generation unit can generate an answer that uses a lot of technical terms. Furthermore, if the visitor does not have technical expertise, the generation unit can generate an answer that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the answer depending on the visitor's level of expertise. This makes it possible to provide an answer that uses appropriate technical terms depending on the visitor's level of expertise. Examples of expertise levels include, but are not limited to, qualification information, previous question content, and self-declaration. For example, if the visitor has technical expertise, the generation unit can generate an answer that uses a lot of technical terms. Furthermore, if the visitor does not have technical expertise, the generation unit can generate an answer that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the answer depending on the visitor's level of expertise.

[0053] When providing an answer, the providing unit can select an optimal providing method by referring to the visitor's past operation history. For example, the providing unit selects the optimal providing method based on a display format used by the visitor in the past. The providing unit can also preferentially suggest a specific display format based on the visitor's past operation history. The providing unit can also analyze the visitor's past operation history and provide an optimal providing interface. This makes it possible to provide an optimal providing method based on the visitor's past operation history. The past operation history includes, for example, a click history, a browsing history, and an operation time, but is not limited to these examples. For example, the providing unit selects the optimal providing method based on a display format used by the visitor in the past. The providing unit can also preferentially suggest a specific display format based on the visitor's past operation history. The providing unit can also analyze the visitor's past operation history and provide an optimal providing interface.

[0054] When providing an answer, the providing unit can customize the content to be provided based on the visitor's current areas of interest. For example, the providing unit can prioritize providing information about animals in which the visitor is currently interested. The providing unit can also provide related information based on information recently searched by the visitor. The providing unit can also prioritize providing information about animals in which the visitor has spent a long time in the exhibition area. This makes it possible to provide an answer with appropriate content according to the visitor's current areas of interest. Areas of interest include, but are not limited to, survey results, past questions, browsing history, etc. For example, the providing unit can provide information based on the areas of interest answered by the visitor in a survey. The providing unit can also provide related information based on the visitor's past questions. The providing unit can also provide information related to the visitor's current areas of interest based on the visitor's browsing history.

[0055] The providing unit can improve the providing method by reflecting the visitor's feedback when providing an answer. For example, the providing unit can suggest an optimal providing method based on feedback previously provided by the visitor. The providing unit can also preferentially suggest a specific providing format based on the visitor's past feedback. The providing unit can also analyze the visitor's past feedback and provide an optimal providing interface. This makes it possible to provide an optimal providing method based on the visitor's feedback. Feedback includes, for example, evaluation comments, survey results, usage history, etc., but is not limited to these examples. For example, the providing unit can suggest an optimal providing method based on feedback previously provided by the visitor. The providing unit can also preferentially suggest a specific providing format based on the visitor's past feedback. The providing unit can also analyze the visitor's past feedback and provide an optimal providing interface.

[0056] When providing an answer, the providing unit can select the optimal providing method by taking into consideration the visitor's device information. For example, if the visitor is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the visitor is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the visitor is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal providing method based on the visitor's device information. Device information includes, but is not limited to, the device type, OS, browser information, etc. For example, if the visitor is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the visitor is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the visitor is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible.

[0057] When providing an answer, the providing unit can make the provided content multilingual based on the visitor's language setting. The providing unit, for example, automatically sets the provided content based on the language setting of the visitor's device. The providing unit can also provide a language switching function when the visitor uses multiple languages. The providing unit can also display the provided content in a specific language when the visitor selects that language. This allows the answer to be provided in an appropriate language based on the visitor's language setting. Language settings include, for example, browser language settings, user-selected languages, etc., but are not limited to such examples. For example, the providing unit can automatically set the provided content based on the language setting of the visitor's device. The providing unit can also provide a language switching function when the visitor uses multiple languages. The providing unit can also display the provided content in a specific language when the visitor selects that language.

[0058] When providing an answer, the providing unit can customize the delivery method by reflecting the visitor's past feedback. For example, the providing unit can suggest an optimal delivery method based on feedback provided by the visitor in the past. The providing unit can also preferentially suggest a specific delivery format based on the visitor's past feedback. The providing unit can also analyze the visitor's past feedback and provide an optimal delivery interface. This makes it possible to provide an optimal delivery method based on the visitor's past feedback. Feedback includes, for example, evaluation comments, survey results, usage history, etc., but is not limited to these examples. For example, the providing unit can suggest an optimal delivery method based on feedback provided by the visitor in the past. The providing unit can also preferentially suggest a specific delivery format based on the visitor's past feedback. The providing unit can also analyze the visitor's past feedback and provide an optimal delivery interface.

[0059] The collection unit can analyze the behavioral patterns of animals during information collection to improve the accuracy of the collection. For example, the collection unit can analyze the behavioral patterns of animals in real time to collect optimal information. The collection unit can also compare the behavioral patterns of animals with past data to improve the accuracy of the collection. The collection unit can also predict the behavioral patterns of animals and use the predicted data to help collect future information. This can improve the accuracy of the collection based on the behavioral patterns of animals. Examples of behavioral patterns include, but are not limited to, observation data, sensor data, and behavior logs. For example, the collection unit can analyze the behavioral patterns of animals in real time to collect optimal information. The collection unit can also compare the behavioral patterns of animals with past data to improve the accuracy of the collection. The collection unit can also predict the behavioral patterns of animals and use the predicted data to help collect future information.

[0060] The collection unit can incorporate a means for updating the animal's biological information in real time when collecting information. The collection unit, for example, collects the animal's biological information in real time and updates the database. The collection unit can also collect the animal's biological information using a sensor and update it in real time. The collection unit can also periodically collect the animal's biological information and provide the latest information. By updating the animal's biological information in real time, the latest information can always be provided. Examples of real-time updating include, but are not limited to, streaming data and real-time processing of sensor data. For example, the collection unit collects the animal's biological information in real time and updates the database. The collection unit can also collect the animal's biological information using a sensor and update it in real time. The collection unit can also periodically collect the animal's biological information and provide the latest information.

[0061] The collection unit can monitor the health status of the animal to supplement the collected data when collecting information. For example, the collection unit can monitor the health status of the animal using a sensor to supplement the collected data. The collection unit can also periodically check the health status of the animal and update the collected data. The collection unit can also have an expert evaluate the health status of the animal to supplement the collected data. In this way, by monitoring the health status of the animal, the accuracy of the collected data can be improved. Examples of health status include, but are not limited to, vital sign measurements, behavioral observations, and health check results. For example, the collection unit can monitor the health status of the animal using a sensor to supplement the collected data. The collection unit can also periodically check the health status of the animal and update the collected data. The collection unit can also have an expert evaluate the health status of the animal to supplement the collected data.

[0062] The collection unit can take the geographical distribution of animals into consideration when collecting information. For example, the collection unit can analyze the geographical distribution of animals in real time and collect optimal information. The collection unit can also compare the geographical distribution of animals with past data to improve the accuracy of collection. The collection unit can also predict the geographical distribution of animals and use the predicted information for future information collection. This allows optimal information to be collected based on the geographical distribution of animals. Examples of geographical distribution include, but are not limited to, GPS data, map information, and observation data. For example, the collection unit can analyze the geographical distribution of animals in real time and collect optimal information. The collection unit can also compare the geographical distribution of animals with past data to improve the accuracy of collection. The collection unit can also predict the geographical distribution of animals and use the predicted information for future information collection.

[0063] The collection unit can improve the accuracy of the information collection by referring to animal-related literature when collecting information. For example, the collection unit can refer to animal-related literature in real time to collect optimal information. The collection unit can also compare animal-related literature with past data to improve the accuracy of the collection. The collection unit can also predict animal-related literature to help with future information collection. This can improve the accuracy of the collection based on animal-related literature. Relevant literature includes, but is not limited to, academic papers, books, online databases, etc. For example, the collection unit can refer to animal-related literature in real time to collect optimal information. The collection unit can also compare animal-related literature with past data to improve the accuracy of the collection. The collection unit can also predict animal-related literature to help with future information collection.

[0064] The collection unit can take the market value of animals into consideration when collecting information. For example, the collection unit can analyze the market value of animals in real time and collect optimal information. The collection unit can also compare the market value of animals with past data to improve the accuracy of collection. The collection unit can also predict the market value of animals and use the predicted value to help collect future information. This allows optimal information to be collected based on the market value of animals. Market value includes, but is not limited to, market research data, price information, and demand forecasts. For example, the collection unit can analyze the market value of animals in real time and collect optimal information. The collection unit can also compare the market value of animals with past data to improve the accuracy of collection. The collection unit can also predict the market value of animals and use the predicted value to help collect future information.

[0065] During translation, the translation unit can adjust the level of detail of the translation based on the importance of the question. For example, the translation unit provides a detailed translation for a question of high importance. The translation unit can also provide a concise translation for a question of low importance. The translation unit can also provide a translation with an appropriate level of detail for a question of medium importance. This makes it possible to provide a translation with an appropriate level of detail according to the importance of the question. The importance of a question includes, for example, the urgency of the question, the frequency of the question, and the impact of the question, but is not limited to these examples. For example, the translation unit adjusts the level of detail of the translation based on the urgency of the question. The translation unit can also adjust the level of detail of the translation based on the frequency of the question. The translation unit can also adjust the level of detail of the translation based on the impact of the question.

[0066] The translation unit can apply different translation algorithms depending on the category of the question during translation. For example, the translation unit can apply a translation algorithm specialized for diet to a question about an animal's diet. The translation unit can also apply a translation algorithm specialized for habitat to a question about an animal's habitat. The translation unit can also apply a translation algorithm specialized for animal behavior to a question about animal behavior. This allows the application of an optimal translation algorithm depending on the category of the question. Question categories include, but are not limited to, categories by topic, difficulty level, and target. For example, the translation unit can apply a translation algorithm specialized for diet to a question about an animal's diet. The translation unit can also apply a translation algorithm specialized for habitat to a question about an animal's habitat. The translation unit can also apply a translation algorithm specialized for behavior to a question about animal behavior.

[0067] The translation unit can improve the accuracy of the translation by referring to the visitor's past question results during translation. The translation unit can improve the accuracy of the translation of the current question, for example, based on the visitor's past questions and their answers. The translation unit can also analyze the visitor's past question results and provide a more accurate translation for similar questions. The translation unit can also refer to the visitor's past question history to provide an optimal translation for the current question. This can improve the accuracy of the translation based on the visitor's past question results. The past question results include, for example, but are not limited to, the content of the answer, the accuracy of the answer, and an evaluation of the answer. For example, the translation unit can improve the accuracy of the translation of the current question based on the visitor's past questions and their answers. The translation unit can also analyze the visitor's past question results and provide a more accurate translation for similar questions. The translation unit can also refer to the visitor's past question history to provide an optimal translation for the current question.

[0068] The translation unit can determine the priority of the translation during translation based on the time when the question was submitted. For example, the translation unit can set a high priority during a time period when many questions are submitted and provide a quick translation. The translation unit can also set a low priority during a time period when few questions are submitted and provide a detailed translation. The translation unit can also dynamically adjust the priority of the translation depending on the time when the question was submitted. This allows the translation to be provided with an appropriate priority depending on the time when the question was submitted. The submission time includes, but is not limited to, a timestamp, a submission date and time, and a submission order. For example, the translation unit can set a high priority during a time period when many questions are submitted and provide a quick translation. The translation unit can also set a low priority during a time period when few questions are submitted and provide a detailed translation. The translation unit can also dynamically adjust the priority of the translation depending on the time when the question was submitted.

[0069] During translation, the translation unit can adjust the order of translations based on the relevance of the question. For example, if the question is highly relevant, the translation unit may provide the translation first. Alternatively, if the question is less relevant, the translation unit may provide the translation later. Alternatively, the translation unit can dynamically adjust the order of translations according to the relevance of the question. This allows translations to be provided in an appropriate order according to the relevance of the question. Examples of relevance include, but are not limited to, topic agreement, keyword co-occurrence, and past relevance evaluations. For example, if the question is highly relevant, the translation unit may provide the translation first. Alternatively, if the question is less relevant, the translation unit may provide the translation later. Alternatively, the translation unit can dynamically adjust the order of translations according to the relevance of the question.

[0070] The translation unit can adjust the use of technical terms in the translation depending on the visitor's level of expertise during translation. For example, if the visitor has technical expertise, the translation unit can provide a translation that uses a lot of technical terms. Alternatively, if the visitor does not have technical expertise, the translation unit can provide a translation that avoids technical terms. The translation unit can also dynamically adjust the use of technical terms in the translation depending on the visitor's level of expertise. This allows the translation to be provided using appropriate technical terms depending on the visitor's level of expertise. Examples of expertise levels include, but are not limited to, qualification information, previous question content, and self-declaration. For example, if the visitor has technical expertise, the translation unit can provide a translation that uses a lot of technical terms. Alternatively, if the visitor does not have technical expertise, the translation unit can provide a translation that avoids technical terms. The translation unit can also dynamically adjust the use of technical terms in the translation depending on the visitor's level of expertise.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When generating an answer to a visitor's question, the interactive experience system can improve the accuracy of the answer by referring to the visitor's past question history. For example, the interactive experience system can generate the optimal answer to the current question based on the visitor's past questions and their answers. The interactive experience system can also analyze the visitor's past question history to provide more accurate answers to similar questions. Furthermore, the interactive experience system can adjust the level of detail of the answer to the current question by referring to the visitor's past question results. This makes it possible to provide a more accurate answer based on the visitor's past question history.

[0073] The interactive experience system can prioritize providing information about related animals based on the visitor's current location information. For example, if the visitor is in a specific animal exhibition area, information about that animal can be prioritized. Also, if the visitor is at the entrance to the exhibition area, general guidance information can be provided. Furthermore, if the visitor is near a specific animal, detailed information about that animal can be provided. This makes it possible to provide highly relevant information based on the visitor's current location information.

[0074] The interactive experience system can analyze the visitor's social media activity and provide relevant information. For example, it can prioritize providing information about places where the visitor has checked in on social media. It can also analyze the visitor's social media posts and suggest relevant information. It can also suggest relevant information based on the visitor's social media friends' activities. This makes it possible to provide highly relevant information based on the visitor's social media activity.

[0075] The interactive experience system can customize the method for accepting questions by reflecting the visitor's past feedback. For example, the system can suggest the optimal method for accepting questions based on the visitor's past feedback. It can also preferentially suggest specific question formats based on the visitor's past feedback. Furthermore, it can analyze the visitor's past feedback and provide the optimal question acceptance interface. This makes it possible to provide the optimal method for accepting questions based on the visitor's past feedback.

[0076] The interactive experience system can adjust the use of technical terms in answers according to the visitor's level of expertise. For example, if the visitor has technical expertise, the system can generate answers that use a lot of technical terms. On the other hand, if the visitor does not have technical expertise, the system can generate answers that avoid technical terms. Furthermore, the system can dynamically adjust the use of technical terms in answers according to the visitor's level of expertise. This makes it possible to provide answers using appropriate technical terms according to the visitor's level of expertise.

[0077] The interactive experience system can select the optimal answer delivery method by referring to the visitor's past operation history. For example, the optimal delivery method can be selected based on the display format used by the visitor in the past. It can also preferentially suggest a specific display format based on the visitor's past operation history. Furthermore, it can analyze the visitor's past operation history and provide the optimal delivery interface. This makes it possible to provide the optimal delivery method based on the visitor's past operation history.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The reception unit accepts a visitor's question. The visitor's question may be in text format, voice format, or on a specific topic. For example, the reception unit accepts a text format question entered by the visitor into a device or a voice format question using a voice input interface. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis is performed using natural language processing technology or machine learning algorithms. For example, the content of the question is analyzed and information for generating an appropriate answer is extracted. The intent of the question can also be analyzed using machine learning algorithms. Step 3: The generation unit generates answers based on the information analyzed by the analysis unit. Generation is performed using text generation AI (e.g., LLM) or multimodal generation AI. For example, text generation AI can be used to generate answers to visitors' questions, and multimodal generation AI can be used to generate answers that include not only text but also images and audio. Step 4: The providing unit provides the answer generated by the generating unit to the visitor. The providing unit can provide the answer to the visitor by using an interface that displays the answer on the device display or by audio using an audio output interface. Step 5: The collection unit collects information about the animals and updates the database. Collection is performed using observation data and sensor data. For example, observation data about the ecology and behavior of animals is collected and added to the database. Sensor data can also be used to monitor the health of animals and update the database. Step 6: The translation unit translates the generated answers into multiple languages. This can be done using machine translation or expert translation. For example, machine translation can be used to translate the generated answers into multiple languages, such as English and Chinese. Expert translation can also be used to provide highly accurate translations.

[0080] (Example 2) An interactive experience system according to an embodiment of the present invention provides visitors with an interactive experience with animals through a device installed in an animal exhibition area. In this system, visitors input questions into the device, and a generation AI analyzes the questions and generates answers based on information about the animals. For example, if a visitor asks, "What does this animal eat?", the generation AI generates an answer based on information about the animal's diet and provides it to the visitor. Furthermore, this system supports multiple languages, making it accessible to international visitors. For example, if a visitor inputs a question in English, the generation AI generates an answer in English and provides it to the visitor. This allows visitors to deepen their understanding of the ecology and behavior of animals. This interactive experience system allows visitors to enjoy real-time, two-way dialogue with animals, deepening their understanding of the ecology and behavior of animals. Furthermore, the system supports multiple languages, making it accessible to international visitors.

[0081] An interactive experience system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, a collection unit, and a translation unit. The reception unit receives a visitor's question. The visitor's question may be, for example, in text format, audio format, or on a specific topic, but is not limited to these examples. The reception unit may receive, for example, a text-format question entered by the visitor into a device. The reception unit may also receive audio-format questions from the visitor using a voice input interface. The analysis unit analyzes the question received by the reception unit. The analysis may be performed using, for example, a natural language processing technique or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may analyze the content of the question using a natural language processing technique and extract information for generating an appropriate answer. The analysis unit may also analyze the intent of the question using a machine learning algorithm. The generation unit generates an answer based on the information analyzed by the analysis unit. The generation may be performed using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit may generate an answer to the visitor's question using a text generation AI. The generation unit can also use multimodal generation AI to generate answers that include not only text but also images and audio. The provision unit provides the answer generated by the generation unit to the visitor. For example, provision can be achieved by, but not limited to, providing an interface that displays the answer on a device display. For example, the provision unit can display the generated answer on a device display. The provision unit can also provide the generated answer by audio using an audio output interface. The collection unit collects information about the animals and updates the database. For example, collection can be achieved using observation data or sensor data, for example, but not limited to. For example, the collection unit can collect observation data on the ecology and behavior of animals and add it to the database. The collection unit can also monitor the health of animals using sensor data and update the database. The translation unit translates the generated answer into multiple languages. For example, translation can be achieved using machine translation or expert translation, for example, but not limited to. For example, the translation unit can use machine translation to translate the generated answer into multiple languages, such as English and Chinese.The translation unit can also provide highly accurate translations using translations by experts, allowing the interactive experience system according to the embodiment to respond to visitors' questions in real time and provide information about animals.

[0082] The interactive experience system includes an interface for visitors to input questions. Examples of the interface include, but are not limited to, a web interface, a mobile app, and a voice input interface. For example, the interface may provide a web interface for visitors to input questions in text format into a device. The interface may also provide a mobile app for visitors to input questions using a smartphone or tablet. The interface may also provide a voice input interface for visitors to input questions by voice. This allows visitors to easily input questions.

[0083] The interactive experience system includes a display that displays the generated answer. Examples of the display include, but are not limited to, a liquid crystal display, an LED display, and a touch screen. For example, the display may provide a liquid crystal display for visually displaying the generated answer. Alternatively, the display may provide an LED display for displaying the generated answer in high brightness. Alternatively, the display may provide a touch screen that allows visitors to check the answer by touch operation. This allows visitors to visually check the generated answer.

[0084] The collection unit can collect information on the ecology and behavior of animals and update the database. Information on ecology and behavior includes, but is not limited to, observation data, sensor data, literature information, and the like. For example, the collection unit collects observation data on the ecology and behavior of animals and adds it to the database. The collection unit can also monitor the health status of animals using sensor data and update the database. The collection unit can also collect the latest literature information on animals and add it to the database. This allows answers to be generated based on the latest information at all times.

[0085] The translation unit can translate the generated answer into multiple languages. Examples of translation into multiple languages ​​include, but are not limited to, machine translation, expert translation, and a list of supported languages. For example, the translation unit can use machine translation to translate the generated answer into multiple languages, such as English and Chinese. The translation unit can also provide highly accurate translations using expert translation. The translation unit can also translate the answer to match the visitor's language based on the list of supported languages. This allows for international visitors to be accommodated.

[0086] The providing unit may provide an interface for displaying the generated answer to the visitor. Examples of the interface include, but are not limited to, a web interface, a mobile app, and an audio output interface. For example, the providing unit may provide a web interface for displaying the generated answer on a device display. The providing unit may also provide a mobile app for displaying the generated answer on a smartphone or tablet. The providing unit may also provide an audio output interface for providing the generated answer by audio. This allows the visitor to easily check the generated answer.

[0087] The reception unit can estimate the visitor's emotions and adjust the timing of accepting questions based on the estimated visitor's emotions. For example, if the visitor is excited, the reception unit can quickly accept the question and provide an answer immediately. Furthermore, if the visitor is relaxed, the reception unit can slowly accept the question and provide a detailed answer. Furthermore, if the visitor is feeling anxious, the reception unit can politely accept the question and provide an answer that gives the visitor a sense of security. This makes it possible to accept questions at an appropriate time according to the visitor's emotions. Emotions can be estimated using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the reception unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. Furthermore, the reception unit can record the visitor's voice and estimate the emotion using voice analysis technology. Furthermore, the reception unit can analyze the visitor's text input and estimate the emotion using text analysis technology.

[0088] The reception unit can analyze the visitor's past question history and select the optimal reception method. For example, the reception unit automatically suggests related questions based on the content of questions frequently asked by the visitor in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the visitor has used in the past. The reception unit can also predict and suggest questions to be used in a specific time period based on the visitor's past question history. This makes it possible to provide the optimal reception method based on the visitor's past question history. The past question history includes, for example, the content of the question, the frequency of the question, question trends, etc., but is not limited to these examples. For example, the reception unit can analyze the content of questions asked by the visitor in the past and suggest related questions. The reception unit can also suggest the optimal input method based on the input methods frequently used by the visitor in the past. The reception unit can also predict and suggest questions to be used in a specific time period based on the visitor's past question history.

[0089] When receiving a question, the reception unit can filter the questions based on the visitor's current areas of interest. For example, the reception unit can prioritize receiving questions about animals in which the visitor is currently interested. The reception unit can also suggest related questions based on information recently searched by the visitor. The reception unit can also prioritize receiving questions about animals in which the visitor has spent a long time in the exhibition area. This makes it possible to prioritize receiving questions that match the visitor's interests. Areas of interest include, but are not limited to, survey results, past questions, and browsing history. For example, the reception unit filters questions based on the areas of interest answered by the visitor in a survey. The reception unit can also suggest related questions based on the visitor's past questions. The reception unit can also suggest questions related to the visitor's current areas of interest based on the visitor's browsing history.

[0090] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the visitor's input method. For example, if the visitor inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, if the visitor inputs a question by text, the acceptance unit can also accept the question using text analysis technology. Furthermore, if the visitor inputs a question using an image, the acceptance unit can also accept the question using image recognition technology. This makes it possible to provide the optimal acceptance means depending on the visitor's input method. Input methods include, but are not limited to, text input, voice input, and image input. For example, if the visitor inputs a question in text format, the acceptance unit accepts the question using text analysis technology. Furthermore, if the visitor inputs a question in voice format, the acceptance unit can also accept the question using voice recognition technology. Furthermore, if the visitor inputs a question in image format, the acceptance unit can also accept the question using image recognition technology.

[0091] The reception unit can estimate the visitor's emotions and determine the priority of questions to be received based on the estimated visitor's emotions. For example, if the visitor is excited, the reception unit can set the priority of the question to a high level and provide a quick answer. Furthermore, if the visitor is relaxed, the reception unit can set the priority of the question to a low level and provide a detailed answer. Furthermore, if the visitor is anxious, the reception unit can set the priority of the question to a medium level and provide an answer that gives the visitor a sense of security. This allows the priority of questions to be determined according to the visitor's emotions. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the reception unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the visitor's voice and estimate the emotion using voice analysis technology. The reception unit can also analyze the visitor's text input and estimate the emotion using text analysis technology.

[0092] When accepting questions, the reception unit can prioritize relevant questions by taking into account the visitor's geographical location information. For example, if the visitor is in a specific animal exhibit area, the reception unit can prioritize questions about that animal. Furthermore, if the visitor is at the entrance to the exhibit area, the reception unit can prioritize questions about general directions. Furthermore, if the visitor is near a specific animal, the reception unit can prioritize questions about the specific animal. This allows for prioritized acceptance of relevant questions based on the visitor's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, the reception unit can prioritize questions related to the visitor's current location based on the visitor's GPS data. Furthermore, the reception unit can also suggest related questions based on the visitor's IP address. Furthermore, the reception unit can identify the visitor's current location using location information services and suggest related questions.

[0093] When accepting a question, the reception unit can analyze the visitor's social media activity and accept related questions. For example, the reception unit can prioritize receiving questions related to places where the visitor has checked in on social media. The reception unit can also analyze the content of the visitor's posts on social media and suggest related questions. The reception unit can also refer to the activities of the visitor's friends on social media to suggest related questions. This makes it possible to accept related questions based on the visitor's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the reception unit can prioritize receiving questions related to places where the visitor has checked in on social media. The reception unit can also analyze the content of the visitor's posts on social media and suggest related questions. The reception unit can also refer to the activities of the visitor's friends on social media to suggest related questions.

[0094] The reception unit can customize the reception method by reflecting the visitor's past feedback when receiving a question. For example, the reception unit can suggest an optimal question reception method based on feedback provided by the visitor in the past. The reception unit can also preferentially suggest a specific question format based on the visitor's past feedback. The reception unit can also analyze the visitor's past feedback and provide an optimal question reception interface. This makes it possible to provide an optimal reception method based on the visitor's past feedback. Past feedback includes, but is not limited to, evaluation comments, survey results, and usage history. For example, the reception unit can suggest an optimal question reception method based on feedback provided by the visitor in the past. The reception unit can also preferentially suggest a specific question format based on the visitor's past feedback. The reception unit can also analyze the visitor's past feedback and provide an optimal question reception interface.

[0095] The analysis unit can estimate the visitor's emotions and adjust the way the analysis is presented based on the estimated visitor's emotions. For example, if the visitor is excited, the analysis unit can provide the analysis results in a visually stimulating format. Furthermore, if the visitor is relaxed, the analysis unit can provide the analysis results in detail and carefully. Furthermore, if the visitor is feeling anxious, the analysis unit can provide the analysis results in a concise and reassuring format. This makes it possible to provide the analysis results in an appropriate presentation method according to the visitor's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. Furthermore, the analysis unit can record the visitor's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can analyze the visitor's text input and estimate the emotion using text analysis technology.

[0096] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit can perform a detailed analysis on a question of high importance to provide a comprehensive answer. The analysis unit can also perform a concise analysis on a question of low importance to provide a quick answer. The analysis unit can also perform an analysis with an appropriate level of detail on a question of medium importance to provide a balanced answer. This allows the analysis to be performed with an appropriate level of detail depending on the importance of the question. The importance of a question includes, for example, the urgency of the question, the frequency of the question, and the impact of the question, but is not limited to these examples. For example, the analysis unit can adjust the level of detail of the analysis based on the urgency of the question. The analysis unit can also adjust the level of detail of the analysis based on the frequency of the question. The analysis unit can also adjust the level of detail of the analysis based on the impact of the question.

[0097] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit can apply an analysis algorithm specialized for diet to a question about an animal's diet. The analysis unit can also apply an analysis algorithm specialized for habitat to a question about an animal's habitat. The analysis unit can also apply an analysis algorithm specialized for animal behavior to a question about animal behavior. This makes it possible to apply an optimal analysis algorithm depending on the category of the question. Question categories include, for example, by topic, by difficulty, by subject, etc., but are not limited to these examples. For example, the analysis unit can apply an analysis algorithm specialized for diet to a question about an animal's diet. The analysis unit can also apply an analysis algorithm specialized for habitat to a question about an animal's habitat. The analysis unit can also apply an analysis algorithm specialized for behavior to a question about animal behavior.

[0098] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the visitor's past question results. The analysis unit can improve the analysis accuracy of the current question, for example, based on questions the visitor has asked in the past and their answers. The analysis unit can also analyze the visitor's past question results to perform more accurate analysis of similar questions. The analysis unit can also select the optimal analysis method for the current question by referring to the visitor's past question history. This can improve the accuracy of the analysis based on the visitor's past question results. The past question results include, for example, answer content, answer accuracy, and answer evaluation, but are not limited to these examples. For example, the analysis unit improves the analysis accuracy of the current question based on the visitor's past question results and their answers. The analysis unit can also analyze the visitor's past question results to perform more accurate analysis of similar questions. The analysis unit can also select the optimal analysis method for the current question by referring to the visitor's past question history.

[0099] The analysis unit can estimate the visitor's emotions and adjust the length of the analysis based on the estimated visitor's emotions. For example, if the visitor is in a hurry, the analysis unit can provide the analysis results in a short, to-the-point format. If the visitor is relaxed, the analysis unit can provide the analysis results in a detailed and thorough format. If the visitor is excited, the analysis unit can provide the analysis results in a visually stimulating format. This allows the analysis results to be provided at an appropriate length depending on the visitor's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can capture the visitor's facial expressions with a camera and estimate the emotion using facial expression recognition technology. The analysis unit can also record the visitor's voice and estimate the emotion using voice analysis technology. The analysis unit can also analyze the visitor's text input and estimate the emotion using text analysis technology.

[0100] When analyzing a question, the analysis unit can determine the priority of the analysis based on the time when the question was submitted. For example, the analysis unit can set a high priority during a time period when many questions are submitted and perform a quick analysis. The analysis unit can also set a low priority during a time period when few questions are submitted and perform a detailed analysis. The analysis unit can also dynamically adjust the priority of the analysis according to the time when the question was submitted. This allows analysis to be performed with an appropriate priority according to the time when the question was submitted. The submission time includes, but is not limited to, a timestamp, a submission date and time, and a submission order. For example, the analysis unit can set a high priority during a time period when many questions are submitted and perform a quick analysis. The analysis unit can also set a low priority during a time period when few questions are submitted and perform a detailed analysis. The analysis unit can also dynamically adjust the priority of the analysis according to the time when the question was submitted.

[0101] When analyzing a question, the analysis unit can adjust the order of analysis based on the relevance of the question. For example, if the question is highly relevant, the analysis unit can prioritize analysis and provide an answer quickly. Furthermore, if the question is less relevant, the analysis unit can postpone analysis. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the question. This allows analysis to be performed in an appropriate order based on the relevance of the question. Relevance includes, but is not limited to, for example, topic consistency, keyword co-occurrence, past relevance evaluation, etc. For example, if the question is highly relevant, the analysis unit can prioritize analysis and provide an answer quickly. Furthermore, if the question is less relevant, the analysis unit can postpone analysis. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the question.

[0102] When analyzing a question, the analysis unit can adjust the use of technical terms in the analysis according to the visitor's level of expertise. For example, if the visitor has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the visitor does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the visitor's level of expertise. This allows analysis results to be provided using appropriate technical terms according to the visitor's level of expertise. Examples of expertise levels include, but are not limited to, qualification information, previous question content, and self-reporting. For example, if the visitor has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the visitor does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the visitor's level of expertise.

[0103] The generation unit can estimate the visitor's emotions and adjust the answer generation method based on the estimated visitor's emotions. For example, if the visitor is relaxed, the generation unit can generate an answer that progresses at a leisurely pace. If the visitor is in a hurry, the generation unit can generate an answer that emphasizes the shortest route. If the visitor is excited, the generation unit can generate an answer that adds visually stimulating effects. This makes it possible to generate an answer in an appropriate manner according to the visitor's emotions. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. The generation unit can also record the visitor's voice and estimate the emotion using voice analysis technology. The generation unit can also analyze the visitor's text input and estimate the emotion using text analysis technology.

[0104] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also generate an answer with an appropriate level of detail for a question of medium importance. This makes it possible to generate an answer with an appropriate level of detail according to the importance of the question. The importance of a question includes, for example, the urgency of the question, the frequency of the question, and the impact of the question, but is not limited to these examples. For example, the generation unit adjusts the level of detail of the answer based on the urgency of the question. The generation unit can also adjust the level of detail of the answer based on the frequency of the question. The generation unit can also adjust the level of detail of the answer based on the impact of the question.

[0105] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit can apply a generation algorithm specialized for diet to a question about an animal's diet. Furthermore, the generation unit can also apply a generation algorithm specialized for habitat to a question about an animal's habitat. Furthermore, the generation unit can also apply a generation algorithm specialized for animal behavior to a question about an animal's behavior. This makes it possible to apply an optimal generation algorithm depending on the category of the question. Question categories include, for example, by topic, by difficulty, by subject, etc., but are not limited to these examples. For example, the generation unit can apply a generation algorithm specialized for diet to a question about an animal's diet. Furthermore, the generation unit can also apply a generation algorithm specialized for habitat to a question about an animal's habitat. Furthermore, the generation unit can also apply a generation algorithm specialized for behavior to a question about an animal's behavior.

[0106] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the visitor's past question results. The generation unit can improve the accuracy of the answer to the current question, for example, based on questions the visitor has previously asked and the answers to those questions. The generation unit can also analyze the visitor's past question results to generate more accurate answers to similar questions. The generation unit can also generate an optimal answer to the current question by referring to the visitor's past question history. This makes it possible to improve the accuracy of the answer based on the visitor's past question results. The past question results include, for example, answer content, answer accuracy, and answer evaluation, but are not limited to these examples. For example, the generation unit can improve the accuracy of the answer to the current question based on the visitor's past question results and the answers to those questions. The generation unit can also analyze the visitor's past question results to generate more accurate answers to similar questions. The generation unit can also generate an optimal answer to the current question by referring to the visitor's past question history.

[0107] The generation unit can estimate the visitor's emotions and adjust the length of the answer based on the estimated visitor's emotions. For example, if the visitor is in a hurry, the generation unit can generate a short, to-the-point answer. If the visitor is relaxed, the generation unit can generate a longer answer with detailed explanations. If the visitor is excited, the generation unit can generate an answer with visually stimulating effects. This allows answers to be generated with an appropriate length depending on the visitor's emotions. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. The generation unit can also record the visitor's voice and estimate the emotion using voice analysis technology. The generation unit can also analyze the visitor's text input and estimate the emotion using text analysis technology.

[0108] When generating an answer, the generation unit can determine the priority of the answer based on the time when the question was submitted. For example, the generation unit can set a high priority during a time period when many questions are submitted and generate an answer quickly. The generation unit can also set a low priority during a time period when few questions are submitted and generate a detailed answer. The generation unit can also dynamically adjust the priority of the answer depending on the time when the question was submitted. This allows an answer to be generated with an appropriate priority depending on the time when the question was submitted. The submission time includes, but is not limited to, a timestamp, a submission date and time, and a submission order. For example, the generation unit can set a high priority during a time period when many questions are submitted and generate an answer quickly. The generation unit can also set a low priority during a time period when few questions are submitted and generate a detailed answer. The generation unit can also dynamically adjust the priority of the answer depending on the time when the question was submitted.

[0109] When generating answers, the generation unit can adjust the order of answers based on the relevance of the question. For example, if the question is highly relevant, the generation unit may generate an answer preferentially. Furthermore, if the question is less relevant, the generation unit may generate an answer later. Furthermore, the generation unit can dynamically adjust the order of answers according to the relevance of the question. This allows answers to be generated in an appropriate order according to the relevance of the question. Examples of relevance include, but are not limited to, topic agreement, keyword co-occurrence, and past relevance evaluation. For example, if the question is highly relevant, the generation unit may generate an answer preferentially. Furthermore, if the question is less relevant, the generation unit may generate an answer later. Furthermore, the generation unit can dynamically adjust the order of answers according to the relevance of the question.

[0110] When generating an answer, the generation unit can adjust the use of technical terms in the answer depending on the visitor's level of expertise. For example, if the visitor has technical expertise, the generation unit can generate an answer that uses a lot of technical terms. Furthermore, if the visitor does not have technical expertise, the generation unit can generate an answer that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the answer depending on the visitor's level of expertise. This makes it possible to provide an answer that uses appropriate technical terms depending on the visitor's level of expertise. Examples of expertise levels include, but are not limited to, qualification information, previous question content, and self-declaration. For example, if the visitor has technical expertise, the generation unit can generate an answer that uses a lot of technical terms. Furthermore, if the visitor does not have technical expertise, the generation unit can generate an answer that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the answer depending on the visitor's level of expertise.

[0111] The providing unit can estimate the visitor's emotions and adjust the method of providing an answer based on the estimated visitor's emotions. For example, if the visitor is excited, the providing unit can provide an answer in a visually stimulating format. Furthermore, if the visitor is relaxed, the providing unit can provide an answer in a detailed and polite format. Furthermore, if the visitor is anxious, the providing unit can provide an answer in a concise and reassuring format. This makes it possible to provide an answer in an appropriate manner according to the visitor's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. Furthermore, the providing unit can record the visitor's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can analyze the visitor's text input and estimate the emotion using text analysis technology.

[0112] When providing an answer, the providing unit can select an optimal providing method by referring to the visitor's past operation history. For example, the providing unit selects the optimal providing method based on a display format used by the visitor in the past. The providing unit can also preferentially suggest a specific display format based on the visitor's past operation history. The providing unit can also analyze the visitor's past operation history and provide an optimal providing interface. This makes it possible to provide an optimal providing method based on the visitor's past operation history. The past operation history includes, for example, a click history, a browsing history, and an operation time, but is not limited to these examples. For example, the providing unit selects the optimal providing method based on a display format used by the visitor in the past. The providing unit can also preferentially suggest a specific display format based on the visitor's past operation history. The providing unit can also analyze the visitor's past operation history and provide an optimal providing interface.

[0113] When providing an answer, the providing unit can customize the content to be provided based on the visitor's current areas of interest. For example, the providing unit can prioritize providing information about animals in which the visitor is currently interested. The providing unit can also provide related information based on information recently searched by the visitor. The providing unit can also prioritize providing information about animals in which the visitor has spent a long time in the exhibition area. This makes it possible to provide an answer with appropriate content according to the visitor's current areas of interest. Areas of interest include, but are not limited to, survey results, past questions, browsing history, etc. For example, the providing unit can provide information based on the areas of interest answered by the visitor in a survey. The providing unit can also provide related information based on the visitor's past questions. The providing unit can also provide information related to the visitor's current areas of interest based on the visitor's browsing history.

[0114] The providing unit can improve the providing method by reflecting the visitor's feedback when providing an answer. For example, the providing unit can suggest an optimal providing method based on feedback previously provided by the visitor. The providing unit can also preferentially suggest a specific providing format based on the visitor's past feedback. The providing unit can also analyze the visitor's past feedback and provide an optimal providing interface. This makes it possible to provide an optimal providing method based on the visitor's feedback. Feedback includes, for example, evaluation comments, survey results, usage history, etc., but is not limited to these examples. For example, the providing unit can suggest an optimal providing method based on feedback previously provided by the visitor. The providing unit can also preferentially suggest a specific providing format based on the visitor's past feedback. The providing unit can also analyze the visitor's past feedback and provide an optimal providing interface.

[0115] The providing unit can estimate the visitor's emotions and adjust the order in which answers are provided based on the estimated visitor's emotions. For example, if the visitor is excited, the providing unit can prioritize providing important information. Furthermore, if the visitor is relaxed, the providing unit can prioritize providing detailed information. Furthermore, if the visitor is feeling anxious, the providing unit can prioritize providing information that gives a sense of security. This makes it possible to provide answers in an appropriate order according to the visitor's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. Furthermore, the providing unit can record the visitor's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can analyze the visitor's text input and estimate the emotion using text analysis technology.

[0116] When providing an answer, the providing unit can select the optimal providing method by taking into consideration the visitor's device information. For example, if the visitor is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the visitor is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the visitor is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal providing method based on the visitor's device information. Device information includes, but is not limited to, the device type, OS, browser information, etc. For example, if the visitor is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the visitor is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the visitor is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible.

[0117] When providing an answer, the providing unit can make the provided content multilingual based on the visitor's language setting. The providing unit, for example, automatically sets the provided content based on the language setting of the visitor's device. The providing unit can also provide a language switching function when the visitor uses multiple languages. The providing unit can also display the provided content in a specific language when the visitor selects that language. This allows the answer to be provided in an appropriate language based on the visitor's language setting. Language settings include, for example, browser language settings, user-selected languages, etc., but are not limited to such examples. For example, the providing unit can automatically set the provided content based on the language setting of the visitor's device. The providing unit can also provide a language switching function when the visitor uses multiple languages. The providing unit can also display the provided content in a specific language when the visitor selects that language.

[0118] When providing an answer, the providing unit can customize the delivery method by reflecting the visitor's past feedback. For example, the providing unit can suggest an optimal delivery method based on feedback provided by the visitor in the past. The providing unit can also preferentially suggest a specific delivery format based on the visitor's past feedback. The providing unit can also analyze the visitor's past feedback and provide an optimal delivery interface. This makes it possible to provide an optimal delivery method based on the visitor's past feedback. Feedback includes, for example, evaluation comments, survey results, usage history, etc., but is not limited to these examples. For example, the providing unit can suggest an optimal delivery method based on feedback provided by the visitor in the past. The providing unit can also preferentially suggest a specific delivery format based on the visitor's past feedback. The providing unit can also analyze the visitor's past feedback and provide an optimal delivery interface.

[0119] The collection unit can estimate the visitor's emotions and determine the priority of information to be collected based on the estimated visitor's emotions. For example, if the visitor is excited, the collection unit can prioritize collecting the most recent information. Furthermore, if the visitor is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the visitor is feeling anxious, the collection unit can prioritize collecting information that gives the visitor a sense of security. This allows information to be collected in an appropriate priority order according to the visitor's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. Furthermore, the collection unit can record the visitor's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can analyze the visitor's text input and estimate the emotion using text analysis technology.

[0120] The collection unit can analyze the behavioral patterns of animals during information collection to improve the accuracy of the collection. For example, the collection unit can analyze the behavioral patterns of animals in real time to collect optimal information. The collection unit can also compare the behavioral patterns of animals with past data to improve the accuracy of the collection. The collection unit can also predict the behavioral patterns of animals and use the predicted data to help collect future information. This can improve the accuracy of the collection based on the behavioral patterns of animals. Examples of behavioral patterns include, but are not limited to, observation data, sensor data, and behavior logs. For example, the collection unit can analyze the behavioral patterns of animals in real time to collect optimal information. The collection unit can also compare the behavioral patterns of animals with past data to improve the accuracy of the collection. The collection unit can also predict the behavioral patterns of animals and use the predicted data to help collect future information.

[0121] The collection unit can incorporate a means for updating the animal's biological information in real time when collecting information. The collection unit, for example, collects the animal's biological information in real time and updates the database. The collection unit can also collect the animal's biological information using a sensor and update it in real time. The collection unit can also periodically collect the animal's biological information and provide the latest information. By updating the animal's biological information in real time, the latest information can always be provided. Examples of real-time updating include, but are not limited to, streaming data and real-time processing of sensor data. For example, the collection unit collects the animal's biological information in real time and updates the database. The collection unit can also collect the animal's biological information using a sensor and update it in real time. The collection unit can also periodically collect the animal's biological information and provide the latest information.

[0122] The collection unit can monitor the health status of the animal to supplement the collected data when collecting information. For example, the collection unit can monitor the health status of the animal using a sensor to supplement the collected data. The collection unit can also periodically check the health status of the animal and update the collected data. The collection unit can also have an expert evaluate the health status of the animal to supplement the collected data. In this way, by monitoring the health status of the animal, the accuracy of the collected data can be improved. Examples of health status include, but are not limited to, vital sign measurements, behavioral observations, and health check results. For example, the collection unit can monitor the health status of the animal using a sensor to supplement the collected data. The collection unit can also periodically check the health status of the animal and update the collected data. The collection unit can also have an expert evaluate the health status of the animal to supplement the collected data.

[0123] The collection unit can estimate the visitor's emotions and adjust the display method of the collected information based on the estimated visitor's emotions. For example, if the visitor is excited, the collection unit can display information in a visually stimulating format. If the visitor is relaxed, the collection unit can display information in a detailed and polite format. If the visitor is anxious, the collection unit can display information in a concise and reassuring format. This makes it possible to provide information in an appropriate display method depending on the visitor's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit can capture the visitor's facial expressions with a camera and estimate the emotion using facial expression recognition technology. The collection unit can also record the visitor's voice and estimate the emotion using voice analysis technology. The collection unit can also analyze the visitor's text input and estimate the emotion using text analysis technology.

[0124] The collection unit can take the geographical distribution of animals into consideration when collecting information. For example, the collection unit can analyze the geographical distribution of animals in real time and collect optimal information. The collection unit can also compare the geographical distribution of animals with past data to improve the accuracy of collection. The collection unit can also predict the geographical distribution of animals and use the predicted information for future information collection. This allows optimal information to be collected based on the geographical distribution of animals. Examples of geographical distribution include, but are not limited to, GPS data, map information, and observation data. For example, the collection unit can analyze the geographical distribution of animals in real time and collect optimal information. The collection unit can also compare the geographical distribution of animals with past data to improve the accuracy of collection. The collection unit can also predict the geographical distribution of animals and use the predicted information for future information collection.

[0125] The collection unit can improve the accuracy of the information collection by referring to animal-related literature when collecting information. For example, the collection unit can refer to animal-related literature in real time to collect optimal information. The collection unit can also compare animal-related literature with past data to improve the accuracy of the collection. The collection unit can also predict animal-related literature to help with future information collection. This can improve the accuracy of the collection based on animal-related literature. Relevant literature includes, but is not limited to, academic papers, books, online databases, etc. For example, the collection unit can refer to animal-related literature in real time to collect optimal information. The collection unit can also compare animal-related literature with past data to improve the accuracy of the collection. The collection unit can also predict animal-related literature to help with future information collection.

[0126] The collection unit can take the market value of animals into consideration when collecting information. For example, the collection unit can analyze the market value of animals in real time and collect optimal information. The collection unit can also compare the market value of animals with past data to improve the accuracy of collection. The collection unit can also predict the market value of animals and use the predicted value to help collect future information. This allows optimal information to be collected based on the market value of animals. Market value includes, but is not limited to, market research data, price information, and demand forecasts. For example, the collection unit can analyze the market value of animals in real time and collect optimal information. The collection unit can also compare the market value of animals with past data to improve the accuracy of collection. The collection unit can also predict the market value of animals and use the predicted value to help collect future information.

[0127] The translation unit can estimate the visitor's emotions and adjust the way the translation is expressed based on the estimated visitor's emotions. For example, if the visitor is excited, the translation unit can provide the translation in a visually stimulating format. If the visitor is relaxed, the translation unit can provide the translation in a detailed and polite format. If the visitor is anxious, the translation unit can provide the translation in a concise and reassuring format. This makes it possible to provide a translation in an appropriate way according to the visitor's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the translation unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. The translation unit can also record the visitor's voice and estimate the emotion using voice analysis technology. The translation unit can also analyze the visitor's text input and estimate the emotion using text analysis technology.

[0128] During translation, the translation unit can adjust the level of detail of the translation based on the importance of the question. For example, the translation unit provides a detailed translation for a question of high importance. The translation unit can also provide a concise translation for a question of low importance. The translation unit can also provide a translation with an appropriate level of detail for a question of medium importance. This makes it possible to provide a translation with an appropriate level of detail according to the importance of the question. The importance of a question includes, for example, the urgency of the question, the frequency of the question, and the impact of the question, but is not limited to these examples. For example, the translation unit adjusts the level of detail of the translation based on the urgency of the question. The translation unit can also adjust the level of detail of the translation based on the frequency of the question. The translation unit can also adjust the level of detail of the translation based on the impact of the question.

[0129] The translation unit can apply different translation algorithms depending on the category of the question during translation. For example, the translation unit can apply a translation algorithm specialized for diet to a question about an animal's diet. The translation unit can also apply a translation algorithm specialized for habitat to a question about an animal's habitat. The translation unit can also apply a translation algorithm specialized for animal behavior to a question about animal behavior. This allows the application of an optimal translation algorithm depending on the category of the question. Question categories include, but are not limited to, categories by topic, difficulty level, and target. For example, the translation unit can apply a translation algorithm specialized for diet to a question about an animal's diet. The translation unit can also apply a translation algorithm specialized for habitat to a question about an animal's habitat. The translation unit can also apply a translation algorithm specialized for behavior to a question about animal behavior.

[0130] The translation unit can improve the accuracy of the translation by referring to the visitor's past question results during translation. The translation unit can improve the accuracy of the translation of the current question, for example, based on the visitor's past questions and their answers. The translation unit can also analyze the visitor's past question results and provide a more accurate translation for similar questions. The translation unit can also refer to the visitor's past question history to provide an optimal translation for the current question. This can improve the accuracy of the translation based on the visitor's past question results. The past question results include, for example, but are not limited to, the content of the answer, the accuracy of the answer, and an evaluation of the answer. For example, the translation unit can improve the accuracy of the translation of the current question based on the visitor's past questions and their answers. The translation unit can also analyze the visitor's past question results and provide a more accurate translation for similar questions. The translation unit can also refer to the visitor's past question history to provide an optimal translation for the current question.

[0131] The translation unit can estimate the visitor's emotions and adjust the length of the translation based on the estimated visitor's emotions. For example, if the visitor is in a hurry, the translation unit can provide a short, to-the-point translation. If the visitor is relaxed, the translation unit can provide a longer translation with detailed explanations. If the visitor is excited, the translation unit can provide a translation with visually stimulating effects. This allows the translation to be provided at an appropriate length depending on the visitor's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the translation unit can capture the visitor's facial expression with a camera and estimate the emotion using facial expression recognition technology. The translation unit can also record the visitor's voice and estimate the emotion using voice analysis technology. The translation unit can also analyze the visitor's text input and estimate the emotion using text analysis technology.

[0132] The translation unit can determine the priority of the translation during translation based on the time when the question was submitted. For example, the translation unit can set a high priority during a time period when many questions are submitted and provide a quick translation. The translation unit can also set a low priority during a time period when few questions are submitted and provide a detailed translation. The translation unit can also dynamically adjust the priority of the translation depending on the time when the question was submitted. This allows the translation to be provided with an appropriate priority depending on the time when the question was submitted. The submission time includes, but is not limited to, a timestamp, a submission date and time, and a submission order. For example, the translation unit can set a high priority during a time period when many questions are submitted and provide a quick translation. The translation unit can also set a low priority during a time period when few questions are submitted and provide a detailed translation. The translation unit can also dynamically adjust the priority of the translation depending on the time when the question was submitted.

[0133] During translation, the translation unit can adjust the order of translations based on the relevance of the question. For example, if the question is highly relevant, the translation unit may provide the translation first. Alternatively, if the question is less relevant, the translation unit may provide the translation later. Alternatively, the translation unit can dynamically adjust the order of translations according to the relevance of the question. This allows translations to be provided in an appropriate order according to the relevance of the question. Examples of relevance include, but are not limited to, topic agreement, keyword co-occurrence, and past relevance evaluations. For example, if the question is highly relevant, the translation unit may provide the translation first. Alternatively, if the question is less relevant, the translation unit may provide the translation later. Alternatively, the translation unit can dynamically adjust the order of translations according to the relevance of the question.

[0134] The translation unit can adjust the use of technical terms in the translation depending on the visitor's level of expertise during translation. For example, if the visitor has technical expertise, the translation unit can provide a translation that uses a lot of technical terms. Alternatively, if the visitor does not have technical expertise, the translation unit can provide a translation that avoids technical terms. The translation unit can also dynamically adjust the use of technical terms in the translation depending on the visitor's level of expertise. This allows the translation to be provided using appropriate technical terms depending on the visitor's level of expertise. Examples of expertise levels include, but are not limited to, qualification information, previous question content, and self-declaration. For example, if the visitor has technical expertise, the translation unit can provide a translation that uses a lot of technical terms. Alternatively, if the visitor does not have technical expertise, the translation unit can provide a translation that avoids technical terms. The translation unit can also dynamically adjust the use of technical terms in the translation depending on the visitor's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, provision unit, collection unit, and translation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives questions from visitors. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated answers. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects animal information and updates the database 24. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the generated answers into multiple languages. The interface is realized by the reception device 38 of the smart device 14 and provides a web interface or a voice input interface for visitors to input questions. The display is realized by the output device 40 of the smart device 14 and displays the generated answers. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, provision unit, collection unit, and translation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives questions from visitors. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated answers. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects animal information and updates the database 24. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the generated answers into multiple languages. The interface is realized by the microphone 238 of the smart glasses 214 and provides a voice input interface for visitors to input questions. The display is provided by the speaker 240 of the smart glasses 214, which provides the generated answer audibly. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, collection unit, and translation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives questions from visitors. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides the generated answers. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects animal information and updates the database 24. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the generated answers into multiple languages. The interface is realized by the microphone 238 of the headset-type terminal 314 and provides a voice input interface for visitors to input questions. The display is realized by the display 343 of the headset type terminal 314, and displays the generated answer. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, provision unit, collection unit, and translation 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 is realized by the microphone 238 of the robot 414 and receives questions from visitors. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated answers. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects animal information and updates the database 24. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the generated answers into multiple languages. The interface is realized by the microphone 238 of the robot 414 and provides a voice input interface for visitors to input questions. The display is realized by the control object 443 of the robot 414 and displays the generated answers.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] When generating an answer to a visitor's question, the interactive experience system can improve the accuracy of the answer by referring to the visitor's past question history. For example, the interactive experience system can generate the optimal answer to the current question based on the visitor's past questions and their answers. The interactive experience system can also analyze the visitor's past question history to provide more accurate answers to similar questions. Furthermore, the interactive experience system can adjust the level of detail of the answer to the current question by referring to the visitor's past question results. This makes it possible to provide a more accurate answer based on the visitor's past question history.

[0137] The interactive experience system can prioritize providing information about related animals based on the visitor's current location information. For example, if the visitor is in a specific animal exhibition area, information about that animal can be prioritized. Also, if the visitor is at the entrance to the exhibition area, general guidance information can be provided. Furthermore, if the visitor is near a specific animal, detailed information about that animal can be provided. This makes it possible to provide highly relevant information based on the visitor's current location information.

[0138] The interactive experience system can estimate the visitor's emotions and adjust the way it provides answers based on the estimated emotions. For example, if the visitor is excited, it can provide answers in a visually stimulating format. If the visitor is relaxed, it can provide answers in a detailed and polite format. Furthermore, if the visitor is anxious, it can provide answers in a concise and reassuring format. In this way, it is possible to provide answers in an appropriate manner depending on the visitor's emotions.

[0139] The interactive experience system can analyze the visitor's social media activity and provide relevant information. For example, it can prioritize providing information about places where the visitor has checked in on social media. It can also analyze the visitor's social media posts and suggest relevant information. It can also suggest relevant information based on the visitor's social media friends' activities. This makes it possible to provide highly relevant information based on the visitor's social media activity.

[0140] The interactive experience system can estimate the visitor's emotions and adjust the timing of accepting questions based on the estimated emotions. For example, if the visitor is excited, the system can accept questions quickly and provide answers immediately. If the visitor is relaxed, the system can accept questions slowly and provide detailed answers. Furthermore, if the visitor is feeling anxious, the system can accept questions politely and provide answers that give a sense of security. This makes it possible to accept questions at an appropriate time according to the visitor's emotions.

[0141] The interactive experience system can customize the method for accepting questions by reflecting the visitor's past feedback. For example, the system can suggest the optimal method for accepting questions based on the visitor's past feedback. It can also preferentially suggest specific question formats based on the visitor's past feedback. Furthermore, it can analyze the visitor's past feedback and provide the optimal question acceptance interface. This makes it possible to provide the optimal method for accepting questions based on the visitor's past feedback.

[0142] The interactive experience system can estimate the visitor's emotions and adjust the way it generates answers based on the estimated emotions. For example, if the visitor is relaxed, it can generate an answer that proceeds at a leisurely pace. If the visitor is in a hurry, it can generate an answer that emphasizes the shortest route. Furthermore, if the visitor is excited, it can generate an answer that adds visually stimulating effects. In this way, it is possible to generate answers in an appropriate way according to the visitor's emotions.

[0143] The interactive experience system can adjust the use of technical terms in answers according to the visitor's level of expertise. For example, if the visitor has technical expertise, the system can generate answers that use a lot of technical terms. On the other hand, if the visitor does not have technical expertise, the system can generate answers that avoid technical terms. Furthermore, the system can dynamically adjust the use of technical terms in answers according to the visitor's level of expertise. This makes it possible to provide answers using appropriate technical terms according to the visitor's level of expertise.

[0144] The interactive experience system can estimate the visitor's emotions and adjust the length of the answer based on the estimated emotions. For example, if the visitor is in a hurry, it can generate a short, to-the-point answer. If the visitor is relaxed, it can generate a longer answer with detailed explanations. Furthermore, if the visitor is excited, it can generate an answer with visually stimulating effects. This makes it possible to generate answers of appropriate length according to the visitor's emotions.

[0145] The interactive experience system can select the optimal answer delivery method by referring to the visitor's past operation history. For example, the optimal delivery method can be selected based on the display format used by the visitor in the past. It can also preferentially suggest a specific display format based on the visitor's past operation history. Furthermore, it can analyze the visitor's past operation history and provide the optimal delivery interface. This makes it possible to provide the optimal delivery method based on the visitor's past operation history.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The reception unit accepts a visitor's question. The visitor's question may be in text format, voice format, or on a specific topic. For example, the reception unit accepts a text format question entered by the visitor into a device or a voice format question using a voice input interface. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis is performed using natural language processing technology or machine learning algorithms. For example, the content of the question is analyzed and information for generating an appropriate answer is extracted. The intent of the question can also be analyzed using machine learning algorithms. Step 3: The generation unit generates answers based on the information analyzed by the analysis unit. Generation is performed using text generation AI (e.g., LLM) or multimodal generation AI. For example, text generation AI can be used to generate answers to visitors' questions, and multimodal generation AI can be used to generate answers that include not only text but also images and audio. Step 4: The providing unit provides the answer generated by the generating unit to the visitor. The providing unit can provide the answer to the visitor by using an interface that displays the answer on the device display or by audio using an audio output interface. Step 5: The collection unit collects information about the animals and updates the database. Collection is performed using observation data and sensor data. For example, observation data about the ecology and behavior of animals is collected and added to the database. Sensor data can also be used to monitor the health of animals and update the database. Step 6: The translation unit translates the generated answers into multiple languages. This can be done using machine translation or expert translation. For example, machine translation can be used to translate the generated answers into multiple languages, such as English and Chinese. Expert translation can also be used to provide highly accurate translations.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 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 desk for accepting questions from visitors; an analysis unit that analyzes the question received by the reception unit; a generation unit that generates an answer based on the information analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit; a collection department that collects information on animals; Multilingual translation department and Equipped with A system characterized by:

2. Provide an interface for visitors to enter questions 2. The system of claim 1.

3. Equipped with a display that shows the generated answers 2. The system of claim 1.

4. The collecting unit Collect information on animal ecology and behavior and update the database 2. The system of claim 1.

5. The translation unit Translate generated answers into multiple languages 2. The system of claim 1.

6. The providing unit Provides an interface for displaying generated answers to visitors 2. The system of claim 1.

7. The reception unit Estimate the visitor's emotions and adjust the timing of accepting questions based on the estimated emotions of the visitor.

2. The system of claim 1.

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

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

2. The system of claim 1.

10. The reception unit When accepting questions, select the most appropriate method of acceptance depending on the visitor's input method 2. The system of claim 1.

Images