System

Abstract

An object of the system according to the embodiment is to provide an interactive experience by dynamically generating exhibition contents in accordance with the interest and behavior of a visitor.SOLUTION: A system includes an exhibition content generation unit, an interactive guide unit, and an interactive experience unit. The exhibition content generating unit dynamically generates an exhibition content using the generation AI. The interactive guide portion generates answers to the visitor's questions. The interactive experience part changes exhibition contents according to the movement and gesture of the visitor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have the problem that the exhibit content is fixed and dynamic exhibits that respond to the interests and behavior of visitors are not provided.

[0005] The system according to the embodiment aims to provide an interactive experience by dynamically generating exhibition content according to the interests and behavior of visitors. [Means for solving the problem]

[0006] The system according to the embodiment includes an exhibition content generation unit, an interactive guide unit, and an interactive experience unit. The exhibition content generation unit dynamically generates exhibition content using generation AI. The interactive guide unit generates answers to visitors' questions. The interactive experience unit changes the exhibition content in response to the visitors' movements and gestures. [Effects of the Invention]

[0007] The system according to the embodiment can dynamically generate exhibition content according to the interests and behavior of visitors, providing an interactive experience. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 exhibition system according to an embodiment of the present invention utilizes generative AI to provide visitors with more interactive and engaging exhibits and experiences, providing customized information tailored to the visitor's interests and enabling them to obtain detailed information through an interactive guide system.

[0029] An exhibition system according to an embodiment includes an exhibition content generation unit, an interactive guide unit, and an interactive experience unit. The exhibition content generation unit dynamically generates exhibition content using a generation AI. For example, the generation AI customizes the exhibition content based on the visitor's interests. The generation AI can also analyze the visitor's past visit history and browsing history to generate exhibition content optimized for each individual visitor. The generation AI can also analyze the visitor's facial expressions and tone of voice in real time and dynamically adjust the exhibition content according to their emotions. The interactive guide unit generates answers to visitors' questions. For example, when a visitor asks, "Tell me more about this exhibit," the generation AI generates an appropriate answer and provides it in audio or text. The generation AI can also analyze the visitor's question history and prepare for anticipated questions the next time the visitor visits. The generation AI can also use an emotion estimation function to analyze the visitor's emotional response to their questions and generate more empathetic answers. The interactive experience unit changes the exhibition content in response to the visitor's movements and gestures. For example, the generation AI can analyze a visitor's movements and automatically display information related to a particular exhibit when the visitor approaches that exhibit. The generation AI can also automatically generate and provide games and quizzes related to the exhibit to attract the visitor's interest. Furthermore, the generation AI can use its emotion estimation function to provide an interactive experience that corresponds to the visitor's emotions and elicit positive emotions. This allows the exhibition system according to the embodiment to provide visitors with more interactive and engaging exhibits and experiences. For example, visitors can obtain information that matches their interests and enjoy interacting with the exhibits. Furthermore, the enhanced educational program also improves the learning effect of visitors.

[0030] The exhibition content generation unit can retrieve a visitor's past visit history or browsing history from a database and generate exhibition content optimized for each individual visitor. For example, the exhibition content generation unit uses a generation AI to retrieve a visitor's past visit history from a database and generate new exhibition content based on exhibits or themes in which the visitor previously showed interest. For example, if a visitor was previously interested in exhibits related to the Warring States period, the generation AI can generate new exhibition content related to the Warring States period based on that information. The generation AI can also analyze a visitor's browsing history and generate optimal exhibition content based on the pages the visitor previously viewed and the viewing time. This makes it possible to provide optimal exhibition content based on the visitor's past interests and concerns.

[0031] The exhibition content generation unit generates exhibition content that corresponds to different languages ​​or cultural backgrounds, making it possible to accommodate international visitors. For example, the generation AI automatically translates the exhibition content based on the visitor's language setting and provides it in a different language. For example, it generates exhibition content that corresponds to multiple languages, such as English, French, and Chinese. The generation AI can also take into account the visitor's cultural background and generate exhibition content that is appropriate for that culture. For example, if a visitor is interested in Japanese culture, it can provide exhibition content related to Japanese traditions and customs. This makes it possible to provide exhibition content that can accommodate international visitors.

[0032] When generating the exhibit content, the exhibit content generation unit can customize it according to the visitor's age or learning level. For example, the generation AI customizes the exhibit content based on the visitor's age. For example, it may use simple explanations and illustrations for elementary school students, and provide more detailed information and opportunities for discussion for junior high school students. The generation AI can also customize the exhibit content based on the visitor's learning level. For example, it may provide basic information for beginners and specialized information for advanced learners. This makes it possible to provide customized exhibit content according to the visitor's age and learning level.

[0033] The interactive guide unit can automatically generate and provide video or audio content related to visitors' questions. For example, the interactive guide unit uses a generation AI to analyze visitors' questions and automatically generate and provide video content related to the question. For example, if a visitor asks, "Please tell me the process of creating this exhibit," the generation AI generates a video showing the creation process. The generation AI can also automatically generate and provide audio content related to visitors' questions. For example, if a visitor asks, "Please tell me about the background of this exhibit," the generation AI generates audio content that explains the background. This makes it possible to provide video or audio content related to visitors' questions.

[0034] The interactive guide unit can analyze the visitor's question history and prepare for questions that are predicted to come their next time they visit. In the interactive guide unit, for example, the generation AI retrieves the visitor's past question history from a database and prepares answers to questions that are predicted to come their next time they visit. For example, if the visitor has asked many questions about a particular topic in the past, new information related to that topic can be prepared. The generation AI can also analyze the visitor's question history and optimize answers to questions that are predicted to come their next time they visit. This makes it possible to prepare for questions that are predicted to come their next time they visit.

[0035] The interactive guide unit can generate answers to visitors' questions by taking into account the opinions or feedback of other visitors. In the interactive guide unit, for example, the generation AI retrieves the opinions and feedback of other visitors from a database and generates answers to the visitors' questions. For example, the generation AI can provide answers based on feedback from visitors who have asked the same question in the past. The generation AI can also analyze the opinions of other visitors and provide answers that take those opinions into account. For example, if a visitor asks, "What do other people think about this exhibit?", the generation AI can provide an answer based on the opinions of other visitors. This makes it possible to provide answers that take into account the opinions and feedback of other visitors.

[0036] The interactive guide unit can provide links to related exhibits or materials in response to visitors' questions, promoting in-depth learning. In the interactive guide unit, for example, the generation AI analyzes the visitor's question and provides links to exhibits or materials related to the question. For example, if a visitor asks, "Tell me more about this exhibit," a link to materials related to that exhibit is provided. The generation AI can also provide links to related exhibits in response to visitors' questions. For example, if a visitor asks, "Are there any other exhibits related to this exhibit?" a link to the related exhibit is provided. In this way, links can be provided to promote in-depth learning by visitors.

[0037] The interactive experience section can analyze the movements or gestures of visitors and change the exhibit content in real time. For example, in the interactive experience section, the generation AI uses a camera to analyze the movements of visitors, and when a visitor approaches a particular exhibit, it automatically displays information related to that exhibit. For example, when a visitor approaches a dinosaur exhibit, detailed information about that dinosaur is displayed. The generation AI can also analyze the visitor's gestures and change the exhibit content in response to those gestures. For example, when a visitor waves their hand, the exhibit content changes in response to that action. This makes it possible to change the exhibit content in real time in response to the visitor's movements and gestures.

[0038] The interactive experience unit can automatically generate and provide games or quizzes related to the exhibits to attract the interest of visitors. In the interactive experience unit, for example, the generation AI automatically generates quizzes related to the exhibits based on the visitor's interests and provides them to the visitors. For example, a quiz related to a historical exhibit can be generated, and visitors can answer it to gain a deeper understanding of the exhibit content. The generation AI can also automatically generate games related to the exhibits and provide them to the visitors. For example, a simulation game related to a science exhibit can be generated, and visitors can participate in it to learn about the exhibit content experientially. In this way, games and quizzes related to the exhibits can be provided to attract the interest of visitors.

[0039] The interactive experience section can analyze the movements or gestures of visitors and enhance the exhibit content with different visual or sound effects. For example, in the interactive experience section, the generation AI analyzes the movements of visitors and adds visual effects related to a particular exhibit when the visitor approaches that exhibit. For example, when a visitor approaches a dinosaur exhibit, the generation AI provides a visual effect of the dinosaur moving. The generation AI can also analyze the gestures of visitors and add sound effects according to those gestures. For example, when a visitor waves their hand, a sound effect is played in response to that action. This allows the exhibit content to be enhanced with visual and sound effects according to the visitor's movements and gestures.

[0040] The interactive experience unit can provide a customized exhibition tour based on the visitor's interests, personalizing the visitor's experience. For example, the interactive experience unit uses a generation AI to automatically generate a customized exhibition tour based on the visitor's interests and provide it to the visitor. For example, if the visitor is interested in history, the tour will be centered around exhibits related to history. The generation AI can also analyze the visitor's interests in real time and provide an exhibition tour based on those interests. For example, if the visitor is interested in science, the tour will be centered around science exhibits. This makes it possible to provide a customized exhibition tour based on the visitor's interests.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The exhibition system may further include a health monitoring unit that monitors the health condition of the visitor. For example, the health monitoring unit may use sensors to measure the visitor's heart rate and body temperature and provide exhibit content appropriate to the visitor's health condition. For example, if the visitor is tired, the health monitoring unit may provide relaxing exhibit content. The health monitoring unit may also collect the visitor's health data and provide the most appropriate exhibit content for the visitor's next visit. This allows the visitor to have a customized exhibition experience tailored to their health condition.

[0043] The exhibition system may further include a preference learning unit that learns the preferences of visitors. For example, the preference learning unit may learn the exhibition content selected by the visitor in the past and themes that the visitor has shown interest in, and provide the most suitable exhibition content for the visitor's next visit. If the visitor is interested in art, art-related exhibition content may be provided preferentially. The preference learning unit may also analyze the visitor's preferences in real time and provide exhibition content that matches those preferences. This allows the visitor to have a customized exhibition experience that matches their preferences.

[0044] The exhibition system may further include a behavior prediction unit that predicts visitor behavior. For example, the behavior prediction unit may predict the next exhibit a visitor will visit based on the visitor's past behavior data and prepare information related to that exhibit in advance. If a visitor is interested in a particular exhibit, information related to that exhibit may be provided preferentially. The behavior prediction unit may also analyze visitor behavior in real time and provide exhibit content according to that behavior. This allows for a customized exhibition experience tailored to the visitor's behavior.

[0045] The exhibition system may further include a learning progress tracking unit that tracks the learning progress of the visitor. For example, the learning progress tracking unit may track the content and understanding level of the visitor in the past, and provide exhibition content according to that progress. If the visitor wants to learn more about a particular topic, detailed information related to that topic may be provided. The learning progress tracking unit may also analyze the visitor's learning progress in real time, and provide exhibition content according to that progress. This makes it possible to provide a customized exhibition experience according to the visitor's learning progress.

[0046] The exhibition system can automatically generate and provide artwork related to the exhibit to further attract the interest of visitors. For example, the generation AI can automatically generate artwork related to the exhibit based on the visitor's interests and provide it to the visitor. If the visitor is interested in history, it can generate artwork related to history. The generation AI can also automatically generate artwork related to the exhibit and provide it to the visitor. This makes it possible to provide artwork related to the exhibit to attract the interest of visitors.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The exhibit content generation unit dynamically generates exhibit content using generation AI. The generation AI customizes the exhibit content based on the visitor's interests and generates exhibit content optimized for each individual visitor by analyzing their past visit history and browsing history. It also analyzes the visitor's facial expressions and tone of voice in real time and dynamically adjusts the exhibit content according to their emotions. Step 2: The interactive guide generates answers to the visitor's questions. The generation AI generates appropriate answers to the visitor's questions and provides them in voice or text. It also analyzes the visitor's question history and prepares for questions that are predicted for the next visit. Furthermore, it uses an emotion estimation function to analyze the visitor's emotional response to the question and generate a more empathetic answer. Step 3: The interactive experience section changes the exhibit content in response to the visitor's movements and gestures. The generation AI analyzes the visitor's movements and automatically displays information related to a particular exhibit when the visitor approaches that exhibit. It can also automatically generate and provide games and quizzes related to the exhibit. Furthermore, it uses an emotion estimation function to provide an interactive experience that responds to the visitor's emotions, eliciting positive feelings.

[0049] (Example 2) An exhibition system according to an embodiment of the present invention utilizes generative AI to provide visitors with more interactive and engaging exhibits and experiences, providing customized information tailored to the visitor's interests and enabling them to obtain detailed information through an interactive guide system.

[0050] An exhibition system according to an embodiment includes an exhibition content generation unit, an interactive guide unit, and an interactive experience unit. The exhibition content generation unit dynamically generates exhibition content using a generation AI. For example, the generation AI customizes the exhibition content based on the visitor's interests. The generation AI can also analyze the visitor's past visit history and browsing history to generate exhibition content optimized for each individual visitor. The generation AI can also analyze the visitor's facial expressions and tone of voice in real time and dynamically adjust the exhibition content according to their emotions. The interactive guide unit generates answers to visitors' questions. For example, when a visitor asks, "Tell me more about this exhibit," the generation AI generates an appropriate answer and provides it in audio or text. The generation AI can also analyze the visitor's question history and prepare for anticipated questions the next time the visitor visits. The generation AI can also use an emotion estimation function to analyze the visitor's emotional response to their questions and generate more empathetic answers. The interactive experience unit changes the exhibition content in response to the visitor's movements and gestures. For example, the generation AI can analyze a visitor's movements and automatically display information related to a particular exhibit when the visitor approaches that exhibit. The generation AI can also automatically generate and provide games and quizzes related to the exhibit to attract the visitor's interest. Furthermore, the generation AI can use its emotion estimation function to provide an interactive experience that corresponds to the visitor's emotions and elicit positive emotions. This allows the exhibition system according to the embodiment to provide visitors with more interactive and engaging exhibits and experiences. For example, visitors can obtain information that matches their interests and enjoy interacting with the exhibits. Furthermore, the enhanced educational program also improves the learning effect of visitors.

[0051] The exhibition content generation unit can retrieve a visitor's past visit history or browsing history from a database and generate exhibition content optimized for each individual visitor. For example, the exhibition content generation unit uses a generation AI to retrieve a visitor's past visit history from a database and generate new exhibition content based on exhibits or themes in which the visitor previously showed interest. For example, if a visitor was previously interested in exhibits related to the Warring States period, the generation AI can generate new exhibition content related to the Warring States period based on that information. The generation AI can also analyze a visitor's browsing history and generate optimal exhibition content based on the pages the visitor previously viewed and the viewing time. This makes it possible to provide optimal exhibition content based on the visitor's past interests and concerns.

[0052] The exhibition content generation unit can analyze visitors' facial expressions in real time using a camera and their tone of voice using a microphone, and dynamically adjust the exhibition content according to their emotions. In the exhibition content generation unit, for example, the generation AI analyzes the visitors' facial expressions using a camera to determine whether they are interested. For example, if the visitor smiles, the generation AI can provide more in-depth exhibition content related to that theme. The generation AI can also analyze the visitor's tone of voice using a microphone to determine whether the visitor is interested. For example, if the visitor's tone of voice becomes higher, the generation AI can provide more detailed exhibition content related to that theme. This makes it possible to dynamically adjust the exhibition content according to the visitor's emotions.

[0053] The exhibition content generation unit can use the emotion estimation function to estimate the emotions of visitors and generate exhibition content that elicits positive emotions. For example, the exhibition content generation unit generates exhibition content by selecting a theme that is likely to interest visitors so that the generation AI can estimate the emotions of visitors and elicit positive emotions. For example, if a visitor is interested in history, it will exhibit positive episodes related to history. The generation AI can also analyze the emotions of visitors in real time, and if the visitor expresses positive emotions, it can provide exhibition content that further elicits those emotions. This makes it possible to provide exhibition content that elicits positive emotions in visitors.

[0054] The exhibition content generation unit generates exhibition content that corresponds to different languages ​​or cultural backgrounds, making it possible to accommodate international visitors. For example, the generation AI automatically translates the exhibition content based on the visitor's language setting and provides it in a different language. For example, it generates exhibition content that corresponds to multiple languages, such as English, French, and Chinese. The generation AI can also take into account the visitor's cultural background and generate exhibition content that is appropriate for that culture. For example, if a visitor is interested in Japanese culture, it can provide exhibition content related to Japanese traditions and customs. This makes it possible to provide exhibition content that can accommodate international visitors.

[0055] When generating the exhibit content, the exhibit content generation unit can customize it according to the visitor's age or learning level. For example, the generation AI customizes the exhibit content based on the visitor's age. For example, it may use simple explanations and illustrations for elementary school students, and provide more detailed information and opportunities for discussion for junior high school students. The generation AI can also customize the exhibit content based on the visitor's learning level. For example, it may provide basic information for beginners and specialized information for advanced learners. This makes it possible to provide customized exhibit content according to the visitor's age and learning level.

[0056] The exhibit content generation unit can use the emotion estimation function to collect feedback on exhibit content based on visitor emotions and reflect this in the next exhibit content. For example, the exhibit content generation unit uses generation AI to analyze visitor emotions in real time and collect feedback on exhibit content based on that data. For example, exhibit content that made visitors smile can be reflected in the next exhibit. The generation AI can also analyze visitor emotion data and optimize the next exhibit content. For example, it can generate new exhibit content based on themes that visitors are interested in. This allows feedback based on visitor emotions to be reflected in the next exhibit content.

[0057] The interactive guide unit can automatically generate and provide video or audio content related to visitors' questions. For example, the interactive guide unit uses a generation AI to analyze visitors' questions and automatically generate and provide video content related to the question. For example, if a visitor asks, "Please tell me the process of creating this exhibit," the generation AI generates a video showing the creation process. The generation AI can also automatically generate and provide audio content related to visitors' questions. For example, if a visitor asks, "Please tell me about the background of this exhibit," the generation AI generates audio content that explains the background. This makes it possible to provide video or audio content related to visitors' questions.

[0058] The interactive guide unit can analyze the visitor's question history and prepare for questions that are predicted to come their next time they visit. In the interactive guide unit, for example, the generation AI retrieves the visitor's past question history from a database and prepares answers to questions that are predicted to come their next time they visit. For example, if the visitor has asked many questions about a particular topic in the past, new information related to that topic can be prepared. The generation AI can also analyze the visitor's question history and optimize answers to questions that are predicted to come their next time they visit. This makes it possible to prepare for questions that are predicted to come their next time they visit.

[0059] The interactive guide unit uses the emotion estimation function to analyze the emotional reactions of visitors to their questions and generate more empathetic answers. For example, the interactive guide unit uses a generation AI to analyze the emotional reactions of visitors to their questions and generate empathetic answers based on that data. For example, if the visitor is emotionally moved, the generation AI can provide an answer that empathizes with that emotion. The generation AI can also analyze the visitor's emotions in real time and provide an answer that is in tune with those emotions. For example, if the visitor is feeling anxious, the generation AI can provide an answer that eases that anxiety. This makes it possible to provide an empathetic answer that is in tune with the visitor's emotions.

[0060] The interactive guide unit can generate answers to visitors' questions by taking into account the opinions or feedback of other visitors. In the interactive guide unit, for example, the generation AI retrieves the opinions and feedback of other visitors from a database and generates answers to the visitors' questions. For example, the generation AI can provide answers based on feedback from visitors who have asked the same question in the past. The generation AI can also analyze the opinions of other visitors and provide answers that take those opinions into account. For example, if a visitor asks, "What do other people think about this exhibit?", the generation AI can provide an answer based on the opinions of other visitors. This makes it possible to provide answers that take into account the opinions and feedback of other visitors.

[0061] The interactive guide unit can provide links to related exhibits or materials in response to visitors' questions, promoting in-depth learning. In the interactive guide unit, for example, the generation AI analyzes the visitor's question and provides links to exhibits or materials related to the question. For example, if a visitor asks, "Tell me more about this exhibit," a link to materials related to that exhibit is provided. The generation AI can also provide links to related exhibits in response to visitors' questions. For example, if a visitor asks, "Are there any other exhibits related to this exhibit?" a link to the related exhibit is provided. In this way, links can be provided to promote in-depth learning by visitors.

[0062] The interactive guide unit can use the emotion estimation function to collect emotional responses to visitors' questions and use the data to improve the guide system. For example, in the interactive guide unit, the generation AI collects emotional responses to visitors' questions in real time and identifies areas for improvement in the guide system based on that data. For example, if a visitor expresses dissatisfaction, the generation AI can analyze the cause and implement measures to improve the system. The generation AI can also analyze visitors' emotional data and improve the guide system's algorithm. For example, if a visitor expresses dissatisfaction with a particular question, the response to that question can be improved. In this way, the emotional responses of visitors can be collected and used to improve the guide system.

[0063] The interactive experience section can analyze the movements or gestures of visitors and change the exhibit content in real time. For example, in the interactive experience section, the generation AI uses a camera to analyze the movements of visitors, and when a visitor approaches a particular exhibit, it automatically displays information related to that exhibit. For example, when a visitor approaches a dinosaur exhibit, detailed information about that dinosaur is displayed. The generation AI can also analyze the visitor's gestures and change the exhibit content in response to those gestures. For example, when a visitor waves their hand, the exhibit content changes in response to that action. This makes it possible to change the exhibit content in real time in response to the visitor's movements and gestures.

[0064] The interactive experience unit can automatically generate and provide games or quizzes related to the exhibits to attract the interest of visitors. In the interactive experience unit, for example, the generation AI automatically generates quizzes related to the exhibits based on the visitor's interests and provides them to the visitors. For example, a quiz related to a historical exhibit can be generated, and visitors can answer it to gain a deeper understanding of the exhibit content. The generation AI can also automatically generate games related to the exhibits and provide them to the visitors. For example, a simulation game related to a science exhibit can be generated, and visitors can participate in it to learn about the exhibit content experientially. In this way, games and quizzes related to the exhibits can be provided to attract the interest of visitors.

[0065] The interactive experience unit uses the emotion estimation function to provide an interactive experience that corresponds to the visitor's emotions, thereby eliciting positive emotions. For example, the interactive experience unit uses a generation AI to analyze the visitor's emotions in real time and provide an interactive experience based on that data. For example, if a visitor smiles, it will provide a fun experience that corresponds to that smile. The generation AI can also analyze the visitor's emotions and provide an experience that corresponds to that emotion. For example, if the visitor is excited, it will provide an experience that further increases that excitement. This makes it possible to provide an interactive experience that elicits positive emotions from visitors.

[0066] The interactive experience section can analyze the movements or gestures of visitors and enhance the exhibit content with different visual or sound effects. For example, in the interactive experience section, the generation AI analyzes the movements of visitors and adds visual effects related to a particular exhibit when the visitor approaches that exhibit. For example, when a visitor approaches a dinosaur exhibit, the generation AI provides a visual effect of the dinosaur moving. The generation AI can also analyze the gestures of visitors and add sound effects according to those gestures. For example, when a visitor waves their hand, a sound effect is played in response to that action. This allows the exhibit content to be enhanced with visual and sound effects according to the visitor's movements and gestures.

[0067] The interactive experience unit can provide a customized exhibition tour based on the visitor's interests, personalizing the visitor's experience. For example, the interactive experience unit uses a generation AI to automatically generate a customized exhibition tour based on the visitor's interests and provide it to the visitor. For example, if the visitor is interested in history, the tour will be centered around exhibits related to history. The generation AI can also analyze the visitor's interests in real time and provide an exhibition tour based on those interests. For example, if the visitor is interested in science, the tour will be centered around science exhibits. This makes it possible to provide a customized exhibition tour based on the visitor's interests.

[0068] The interactive experience section can use the emotion estimation function to collect feedback on the interactive experience based on the visitor's emotions and reflect this in the next exhibit. In the interactive experience section, for example, the generation AI analyzes the visitor's emotions in real time and collects feedback on the interactive experience based on that data. For example, an experience that made the visitor smile can be reflected in the next exhibit. The generation AI can also analyze the visitor's emotion data and optimize the content of the next exhibit. For example, it can generate new exhibit content based on themes that the visitor showed interest in. This allows feedback based on the visitor's emotions to be reflected in the next exhibit.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The exhibition system may further include a health monitoring unit that monitors the health condition of the visitor. For example, the health monitoring unit may use sensors to measure the visitor's heart rate and body temperature and provide exhibit content appropriate to the visitor's health condition. For example, if the visitor is tired, the health monitoring unit may provide relaxing exhibit content. The health monitoring unit may also collect the visitor's health data and provide the most appropriate exhibit content for the visitor's next visit. This allows the visitor to have a customized exhibition experience tailored to their health condition.

[0071] The exhibition system may further include a preference learning unit that learns the preferences of visitors. For example, the preference learning unit may learn the exhibition content selected by the visitor in the past and themes that the visitor has shown interest in, and provide the most suitable exhibition content for the visitor's next visit. If the visitor is interested in art, art-related exhibition content may be provided preferentially. The preference learning unit may also analyze the visitor's preferences in real time and provide exhibition content that matches those preferences. This allows the visitor to have a customized exhibition experience that matches their preferences.

[0072] The exhibition system may further include a behavior prediction unit that predicts visitor behavior. For example, the behavior prediction unit may predict the next exhibit a visitor will visit based on the visitor's past behavior data and prepare information related to that exhibit in advance. If a visitor is interested in a particular exhibit, information related to that exhibit may be provided preferentially. The behavior prediction unit may also analyze visitor behavior in real time and provide exhibit content according to that behavior. This allows for a customized exhibition experience tailored to the visitor's behavior.

[0073] The exhibition system may further include a learning progress tracking unit that tracks the learning progress of the visitor. For example, the learning progress tracking unit may track the content and understanding level of the visitor in the past, and provide exhibition content according to that progress. If the visitor wants to learn more about a particular topic, detailed information related to that topic may be provided. The learning progress tracking unit may also analyze the visitor's learning progress in real time, and provide exhibition content according to that progress. This makes it possible to provide a customized exhibition experience according to the visitor's learning progress.

[0074] The exhibition system may further include an emotion adjustment unit that estimates the visitor's emotion and adjusts the exhibit content based on that emotion. For example, the emotion adjustment unit may analyze the visitor's emotion in real time and provide exhibit content that corresponds to that emotion. If the visitor is excited, the emotion adjustment unit may provide exhibit content that further enhances that excitement. The emotion adjustment unit may also collect visitor emotion data and provide optimal exhibit content for the visitor's next visit. This allows for a customized exhibition experience that corresponds to the visitor's emotion.

[0075] The exhibition system may further include an emotion feedback unit that estimates the emotions of visitors and provides feedback on the exhibition content based on those emotions. For example, the emotion feedback unit may analyze the emotions of visitors in real time and collect feedback on the exhibition content based on that data. The exhibition content that made the visitor smile is reflected in the next exhibition. The emotion feedback unit may also analyze the emotion data of visitors and optimize the next exhibition content. This allows feedback based on the visitor's emotions to be reflected in the next exhibition content.

[0076] The exhibition system can also be equipped with an emotion evaluation unit that estimates the emotions of visitors and evaluates the exhibit content based on those emotions. For example, the emotion evaluation unit can analyze the emotions of visitors in real time and evaluate the exhibit content based on that data. Exhibit content in which visitors express positive emotions is given a high rating and reflected in the next exhibit. The emotion evaluation unit can also analyze the emotion data of visitors and identify areas in the exhibit content that need improvement. This allows the evaluation based on the visitors' emotions to be reflected in the next exhibit content.

[0077] The exhibition system may further include an emotion optimization unit that estimates the emotions of visitors and optimizes the exhibition content based on those emotions. For example, the emotion optimization unit may analyze the emotions of visitors in real time and optimize the exhibition content based on that data. New exhibition content may be generated based on themes that the visitor has shown interest in. The emotion optimization unit may also collect emotion data of visitors and provide optimal exhibition content for the next visit. This makes it possible to provide an optimal exhibition experience based on the visitor's emotions.

[0078] The exhibition system may further include an emotion customization unit that estimates the visitor's emotions and customizes the exhibition content based on those emotions. For example, the emotion customization unit may analyze the visitor's emotions in real time and customize the exhibition content based on that data. New exhibition content may be generated based on themes for which the visitor expressed positive emotions. The emotion customization unit may also collect visitor's emotion data and provide optimal exhibition content for the visitor's next visit. This makes it possible to provide a customized exhibition experience based on the visitor's emotions.

[0079] The exhibition system can automatically generate and provide artwork related to the exhibit to further attract the interest of visitors. For example, the generation AI can automatically generate artwork related to the exhibit based on the visitor's interests and provide it to the visitor. If the visitor is interested in history, it can generate artwork related to history. The generation AI can also automatically generate artwork related to the exhibit and provide it to the visitor. This makes it possible to provide artwork related to the exhibit to attract the interest of visitors.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The exhibit content generation unit dynamically generates exhibit content using generation AI. The generation AI customizes the exhibit content based on the visitor's interests and generates exhibit content optimized for each individual visitor by analyzing their past visit history and browsing history. It also analyzes the visitor's facial expressions and tone of voice in real time and dynamically adjusts the exhibit content according to their emotions. Step 2: The interactive guide generates answers to the visitor's questions. The generation AI generates appropriate answers to the visitor's questions and provides them in voice or text. It also analyzes the visitor's question history and prepares for questions that are predicted for the next visit. Furthermore, it uses an emotion estimation function to analyze the visitor's emotional response to the question and generate a more empathetic answer. Step 3: The interactive experience section changes the exhibit content in response to the visitor's movements and gestures. The generation AI analyzes the visitor's movements and automatically displays information related to a particular exhibit when the visitor approaches that exhibit. It can also automatically generate and provide games and quizzes related to the exhibit. Furthermore, it uses an emotion estimation function to provide an interactive experience that responds to the visitor's emotions, eliciting positive feelings.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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, in order to avoid confusion and to 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.

[0148] 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. [Explanation of symbols]

[0149] 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. an exhibition content generation unit that dynamically generates exhibition content using generation AI; an interactive guide unit that generates answers to visitor questions; and an interactive experience section that changes the exhibit content in response to visitors' movements and gestures. A system characterized by:

2. The exhibition content generation unit Retrieving the visitor's past visit history or browsing history from the database to generate display content optimized for each individual visitor 2. The system of claim 1.

3. The interactive guide unit includes: Automatically generate and serve relevant video or audio content in response to visitor questions 2. The system of claim 1.

4. The interactive experience unit includes: Analyze visitors' movements or gestures and change exhibit content in real time 2. The system of claim 1.

5. The exhibition content generation unit The camera analyzes the visitor's facial expressions in real time, and the microphone analyzes the tone of their voice, dynamically adjusting the exhibit content according to their emotions.

2. The system of claim 1.

6. The interactive guide unit includes: Use sentiment estimation to analyze visitors' emotional responses to questions and generate more empathetic answers 2. The system of claim 1.

7. The interactive experience unit includes: Use emotion estimation to provide an interactive experience that responds to visitors' emotions and elicits positive emotions.

2. The system of claim 1.

8. The interactive experience unit includes: Use emotion estimation to collect feedback on interactive experiences based on visitors' emotions and incorporate it into future exhibits.

2. The system of claim 1.

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