system

Abstract

The system according to this embodiment aims to enable users to easily decipher ancient documents and obtain modern Japanese translations. [Solution] The system according to the embodiment comprises a reception unit, a decoding unit, and a provision unit. The reception unit receives voice input from the user. The decoding unit decodes the ancient document based on the voice input received by the reception unit and provides a modern Japanese translation. The provision unit provides local historical information based on the content of the ancient document decoded by the decoding unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that deciphering ancient documents and modern language translation require specialized knowledge and are difficult for ordinary history enthusiasts.

[0005] The system according to the embodiment aims to enable a user to easily decipher an ancient document and obtain a modern language translation.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a deciphering unit, and a providing unit. The reception unit receives voice input from a user. The deciphering unit deciphers an ancient document and performs modern language translation based on the voice input received by the reception unit. The providing unit provides local historical information based on the content of the ancient document deciphered by the deciphering unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily decipher ancient documents and obtain modern Japanese translations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The Classic AI Agent System, according to an embodiment of the present invention, is an innovative tool for historians, students, museum curators, and general history enthusiasts to decipher ancient documents, understand their historical context, and efficiently search for related materials. This Classic AI Agent System allows users to decipher ancient documents through a voice interface, with a generating AI accurately deciphering the documents and presenting modern Japanese translations and contextual information. Furthermore, it uses AR technology to acquire and provide real-time historical information of the location. This mechanism enables users to efficiently decipher ancient documents and understand their historical context. For example, when a user deciphers an ancient document, the generating AI translates its content into modern Japanese and provides relevant historical background information. Additionally, when visiting a location, AR technology is used to acquire and provide real-time historical information of that location. This allows users to gain a deeper understanding and experience when visiting historical sites. The Classic AI Agent System aims to promote the evolution and dissemination of historical research and deepen cultural understanding. It is a tool for realizing a society where more people participate in historical exploration and share new knowledge. This will allow the classic AI agent system to streamline the deciphering of ancient documents for historians and enthusiasts, enabling a deeper understanding of historical context and easier searching of related materials.

[0029] The classic AI agent system according to this embodiment comprises a reception unit, a decoding unit, and a provision unit. The reception unit receives voice input from the user. The reception unit can receive voice input using, for example, a microphone. The reception unit can also convert voice input into text data using speech recognition technology. For example, the reception unit can analyze the voice using speech recognition software and save it as text data. The decoding unit decodes the ancient document based on the voice input received by the reception unit and provides a modern Japanese translation. The decoding unit decodes the ancient document with high accuracy using a generative AI. For example, the decoding unit inputs the text data of the ancient document into the generative AI and generates a modern Japanese translation. The decoding unit can also provide contextual information of the ancient document using the generative AI. For example, the decoding unit understands the context of the ancient document using the generative AI and provides relevant information. The provision unit provides local historical information based on the content of the ancient document decoded by the decoding unit. The provision unit acquires local historical information in real time using AR technology and provides it to the user. For example, the service provider acquires the user's location information and provides historical information related to that location. The service provider can also use AR technology to visually display historical information about the place the user has visited. For example, the service provider can use AR technology to display information about historical events and people in the place the user has visited. In this way, the classic AI agent system according to the embodiment enhances the historical exploration experience by deciphering ancient documents based on the user's voice input and providing local historical information.

[0030] The reception unit receives voice input from the user. For example, the reception unit can receive voice input using a microphone. Specifically, when a user speaks into the microphone, their voice is converted into a digital signal and transmitted to the system. The reception unit can also convert voice input into text data using speech recognition technology. Speech recognition technology analyzes the voice signal and converts the voice into text using a language model. For example, the reception unit can analyze the voice using speech recognition software and save it as text data. The speech recognition software extracts the features of the voice, decomposes them into phonemes and words, and analyzes them. This ensures that the user's voice input is accurately converted into text data, which is then used for further processing. Furthermore, the reception unit can use noise cancellation technology to remove ambient noise and improve the accuracy of voice input. This allows users to perform accurate voice input even in noisy environments. The reception unit is equipped with trigger words and gesture recognition functions to detect the start and end of voice input, allowing users to perform voice input in a natural way. For example, voice input begins when the user says "start" and ends when they say "stop". Furthermore, the reception unit supports multiple languages, allowing it to accurately recognize voice input from users in different languages. This enables the reception unit to efficiently and accurately receive user voice input, improving the overall system performance.

[0031] The decoding unit decodes ancient documents based on voice input received by the reception unit and provides a modern Japanese translation. The decoding unit uses generative AI to decode ancient documents with high accuracy. Specifically, the decoding unit inputs the text data of the ancient document into the generative AI and generates a modern Japanese translation. The generative AI has learned from a large amount of ancient document data and modern Japanese translation data, and can understand the grammar, vocabulary, and context of ancient documents. For example, the decoding unit inputs the text data of the ancient document into the generative AI, and the generative AI analyzes the context and generates a modern Japanese translation. The generative AI uses natural language processing technology to understand the context of the ancient document and provide an appropriate modern Japanese translation. This allows the decoding unit to accurately translate the content of ancient documents into modern Japanese and provide it to the user. Furthermore, the decoding unit can also provide contextual information about the ancient document using the generative AI. For example, the decoding unit uses the generative AI to understand the context of the ancient document and provide relevant information. The generative AI can analyze the content of the ancient document and provide information such as its background, related historical events, and people. This allows users to understand not only the content of ancient documents but also their background and related information. The decoding unit can improve its decoding accuracy by regularly updating the training data of the generating AI and adding new ancient documents and modern Japanese translation data. As a result, the decoding unit can always perform highly accurate decodings based on the latest information and provide them to users.

[0032] The information provider will provide local historical information based on the content of ancient documents deciphered by the deciphering unit. The information provider will acquire local historical information in real time using AR technology and provide it to the user. Specifically, the information provider will acquire the user's location information and provide historical information related to that location. For example, the information provider will use GPS functionality to identify the user's current location and provide information about historical events and people related to that location. The information provider can also use AR technology to visually display historical information about places the user has visited. For example, the information provider will use AR technology to display information about historical events and people at places the user has visited. AR technology can overlay historical information onto the real-world landscape through the user's smartphone or tablet camera. This allows the user to intuitively understand historical information related to a place while viewing the local landscape. Furthermore, the information provider can provide customized historical information according to the user's interests. For example, if the user is interested in a particular era or person, that information will be provided preferentially. The information provider can collect user feedback and continuously improve the accuracy and content of the information it provides. This allows the service provider to offer users high-quality historical information and enhance their historical exploration experience.

[0033] The decoding unit can provide contextual information about ancient documents using generative AI. For example, the decoding unit uses generative AI to understand the context of ancient documents and provide relevant information. For instance, the decoding unit inputs text data from ancient documents into the generative AI and generates contextual information. The decoding unit can also provide background information about ancient documents using generative AI. For example, the decoding unit uses generative AI to understand the historical background of ancient documents and provide relevant information. By providing contextual information about ancient documents using generative AI, the accuracy of decoding is improved.

[0034] The service provider can acquire local historical information in real time and provide it to users. For example, the service provider can acquire local historical information in real time using AR technology. For example, the service provider can acquire the user's location information and provide historical information related to that location. The service provider can also use AR technology to visually display historical information of places the user has visited. For example, the service provider can use AR technology to display information about historical events and people in places the user has visited. By acquiring local historical information in real time and providing it to users, the historical exploration experience is enhanced.

[0035] The deciphering unit can personalize the user's historical exploration experience. For example, it can use generative AI to customize the deciphering of ancient documents based on the user's preferences and history. For instance, it can refer to the user's past exploration history to provide the optimal deciphering method. It can also use generative AI to adjust the deciphering content based on the user's interests. For example, it can prioritize providing relevant information based on the user's interests. This personalizes the user's historical exploration experience, providing an optimal experience for each individual user.

[0036] The reception unit can analyze voice input from users and extract information necessary for deciphering ancient documents. For example, the reception unit can analyze voice input using speech recognition technology and extract the necessary information. For instance, the reception unit can analyze speech using speech recognition software and extract keywords and phrases necessary for deciphering ancient documents. The reception unit can also analyze voice input using generative AI and extract the necessary information. For example, the reception unit can input voice data into generative AI and extract the necessary information. By analyzing voice input from users and extracting the information necessary for deciphering ancient documents, the accuracy of the decipherment is improved.

[0037] The service provider can provide optimal historical information based on the user's location. For example, it can acquire the user's location and provide historical information related to that location. For instance, it can use GPS data to pinpoint the user's location and provide historical information related to that location. Furthermore, the service provider can use augmented reality (AR) technology to visually display historical information about places the user has visited. For example, it can use AR technology to display information about historical events and people in places the user has visited. This enhances the user's historical exploration experience by providing optimal historical information based on their location.

[0038] The reception unit can analyze the user's past voice input history and select the optimal speech recognition algorithm. For example, the reception unit can analyze the user's past voice input history and select the optimal speech recognition algorithm. For example, the reception unit can analyze the patterns of voice input the user has used in the past and select the optimal speech recognition algorithm. The reception unit can also learn specific accents and pronunciation characteristics from the user's past voice input history and apply the optimal algorithm. For example, the reception unit can select a speech recognition algorithm appropriate for a specific time of day or situation based on the user's past voice input history. In this way, the optimal speech recognition algorithm can be selected by analyzing the user's past voice input history.

[0039] The reception unit can filter out the user's current ambient noise when receiving voice input, thereby removing noise. For example, if the user is in a noisy environment, the reception unit can filter out ambient noise to make the voice input clearer. Also, if the user is in a quiet environment, the reception unit can remove even subtle noises, improving the accuracy of the voice input. For example, if the user is making a voice input while on the move, the reception unit can filter out wind noise and traffic noise to remove noise. In this way, the accuracy of the voice input is improved by filtering out ambient noise when receiving voice input.

[0040] The reception unit can prioritize analyzing highly relevant voice input based on the user's geographical location information when receiving voice input. For example, when receiving voice input, the reception unit prioritizes analyzing highly relevant voice input considering the user's geographical location information. For example, if the reception unit is in a specific region, it will prioritize analyzing voice input related to that region. Also, if the reception unit is traveling, it can prioritize analyzing voice input related to the user's current location. For example, if the reception unit is participating in a specific event, it will prioritize analyzing voice input related to that event. This improves the accuracy of voice input by prioritizing the analysis of highly relevant voice input based on the user's geographical location information.

[0041] The reception unit can analyze the user's social media activity when receiving voice input and extract relevant voice input. For example, the reception unit can extract relevant voice input based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and prioritize the analysis of relevant voice input. For example, the reception unit can extract relevant voice input by referring to the activities of the user's social media followers and friends. In this way, relevant voice input can be extracted by analyzing the user's social media activity.

[0042] The deciphering unit can adjust the level of detail in the deciphering process based on the importance of the ancient document. For example, in the case of an important ancient document, the generating AI performs a detailed deciphering to accurately decipher all the information. Alternatively, in the case of a general ancient document, the generating AI can perform a standard deciphering to proceed efficiently. For example, in the case of an ancient document of low importance, the generating AI performs a simplified deciphering to complete the deciphering quickly. This improves the efficiency of deciphering by adjusting the level of detail in the deciphering process based on the importance of the ancient document.

[0043] The decoding unit can apply different decoding algorithms depending on the category of the ancient document during the decoding process. For example, if it is a historical document, the generating AI can apply a decoding algorithm specifically for historical documents. Similarly, if it is a religious document, the generating AI can apply a decoding algorithm specifically for religious documents. For example, if it is a scientific document, the generating AI can apply a decoding algorithm specifically for scientific documents. By applying different decoding algorithms depending on the category of the ancient document, the accuracy of the decoding is improved.

[0044] The decryption unit can determine the priority of decryption based on when the ancient documents were submitted. For example, if an ancient document was submitted a long time ago, the generation AI will prioritize its decryption. Conversely, if an ancient document was submitted a long time ago, the generation AI can also prioritize its decryption. For example, if an ancient document was submitted a long time ago, the generation AI will prioritize its decryption. This improves the efficiency of decryption by determining the priority of decryption based on when the ancient documents were submitted.

[0045] The decoding unit can improve the accuracy of its decoding by referring to related literature for the ancient document during the decoding process. For example, the decoding unit can refer to related literature for the ancient document, and the generating AI can improve the accuracy of the decoding. For example, the decoding unit can adjust the level of detail of the decoding based on related literature for the ancient document. The decoding unit can also refer to related literature for the ancient document, and the generating AI can correct decoding errors. For example, the decoding unit can refer to related literature for the ancient document, and the generating AI can improve the accuracy of the decoding. In this way, the accuracy of decoding is improved by referring to related literature for the ancient document.

[0046] The service provider can optimize current information by referencing historical data. For example, the service provider can use AR technology to optimize current information based on historical data. For instance, the service provider can refer to historical data, and AR technology can provide relevant information. Furthermore, the service provider can use AR technology to improve the accuracy of information based on historical data. For example, the service provider can refer to historical data, and AR technology can adjust the level of detail of the information. This improves the accuracy of current information by referencing historical data.

[0047] The service provider can apply different display methods to each category of historical information at the time of delivery. For example, in the case of historical information about war, the service provider can use AR technology to display a war simulation. Also, in the case of historical information about culture, the service provider can use AR technology to display cultural art and music. For example, in the case of historical information about politics, the service provider can use AR technology to display a recreation of a political event. By applying different display methods to each category of historical information, the visibility of the information is improved.

[0048] The service provider can provide optimal historical information based on the user's location information at the time of delivery. For example, the service provider can acquire the user's location information and provide historical information related to that location. For example, the service provider can identify the user's location using GPS data and provide historical information related to that location. The service provider can also use AR technology to visually display historical information about places the user has visited. For example, the service provider can use AR technology to display information about historical events and people in places the user has visited. This improves the visibility of the information by providing optimal historical information based on the user's location information.

[0049] The provider can improve the accuracy of the information by referencing relevant historical events at the time of provision. For example, the provider can reference relevant historical events, and AR technology can improve the accuracy of the information. For example, the provider can use relevant historical events as a basis for adjusting the level of detail of the information using AR technology. The provider can also reference relevant historical events, and AR technology can correct errors in the information. For example, the provider can reference relevant historical events, and AR technology can improve the accuracy of the information. As a result, the accuracy of the information is improved by referencing relevant historical events.

[0050] The context information provider can select the most appropriate information based on the content of the ancient document when providing context information. For example, if the content of the ancient document is related to war, the generating AI will provide context information on the war. Also, if the content of the ancient document is related to culture, the generating AI can provide context information on the culture. For example, if the content of the ancient document is related to politics, the generating AI will provide context information on the politics. This improves the accuracy of the information by selecting the most appropriate information based on the content of the ancient document.

[0051] The context information provider can improve the accuracy of the information by referring to relevant historical background when providing context information. For example, the context information provider can refer to relevant historical background, and the generating AI can improve the accuracy of the context information. For example, the context information provider can adjust the level of detail of the context information based on the relevant historical background. The context information provider can also refer to relevant historical background, and the generating AI can correct errors in the context information. For example, the context information provider can refer to relevant historical background, and the generating AI can improve the accuracy of the context information. As a result, the accuracy of the information is improved by referring to relevant historical background.

[0052] The personalization unit can provide the optimal experience by referring to the user's past exploration history during the personalization process. For example, the personalization unit's generative AI provides the optimal experience based on the user's past exploration history. For example, the personalization unit provides an experience based on specific interests from the user's past exploration history. The personalization unit can also analyze the user's past exploration history and provide the most efficient experience. For example, the personalization unit refers to the user's past exploration history and its generative AI provides the optimal experience. In this way, the personalization unit can provide the optimal experience by referring to the user's past exploration history.

[0053] The personalization component can provide the optimal experience based on the user's interests during the personalization process. For example, if a user's interests are related to war, the generative AI will provide a war-related experience. For example, if a user's interests are related to culture, the generative AI will provide a culture-related experience. Furthermore, if a user's interests are related to politics, the generative AI can provide a politics-related experience. In short, the personalization component provides the optimal experience based on the user's interests. This improves the quality of the experience by providing the optimal experience based on the user's interests.

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

[0055] The reception unit can select the optimal speech recognition algorithm by referring to the user's past voice input history when receiving user voice input. For example, the reception unit can analyze the patterns of voice input the user has used in the past and select the optimal speech recognition algorithm. Furthermore, the reception unit can learn specific accents and pronunciation characteristics from the user's past voice input history and apply the optimal algorithm. This allows for the selection of the optimal speech recognition algorithm by analyzing the user's past voice input history.

[0056] The decoding unit can provide contextual information about ancient documents using generative AI. For example, the decoding unit uses generative AI to understand the context of an ancient document and provide relevant information. The decoding unit can also input text data from an ancient document into the generative AI to generate contextual information. This improves the accuracy of decoding by providing contextual information about ancient documents through generative AI.

[0057] The service provider can acquire local historical information in real time and provide it to users. For example, the service provider can acquire local historical information in real time using AR technology. Furthermore, the service provider can acquire the user's location information and provide historical information related to that location. This enhances the historical exploration experience by providing users with real-time acquisition of local historical information.

[0058] The deciphering unit can personalize the user's historical exploration experience. For example, it uses generative AI to customize the deciphering of ancient documents based on the user's preferences and history. It can also refer to the user's past exploration history to provide the optimal deciphering method. This personalizes the user's historical exploration experience, providing an optimal experience for each individual user.

[0059] The reception unit can analyze voice input from users and extract information necessary for deciphering ancient documents. For example, the reception unit can analyze voice input using speech recognition technology and extract the necessary information. It can also analyze voice input using generative AI and extract the necessary information. This improves the accuracy of deciphering by analyzing voice input from users and extracting the information necessary for deciphering ancient documents.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception unit receives voice input from the user. The reception unit can receive voice input using, for example, a microphone. Alternatively, the reception unit can convert voice input into text data using speech recognition technology. For example, the reception unit can analyze the voice using speech recognition software and save it as text data. Step 2: The decoding unit decodes the ancient document based on the voice input received by the reception unit and provides a modern Japanese translation. The decoding unit uses generative AI to decode the ancient document with high accuracy. For example, the decoding unit inputs the text data of the ancient document into the generative AI and generates a modern Japanese translation. The decoding unit can also use the generative AI to provide contextual information about the ancient document. For example, the decoding unit uses the generative AI to understand the context of the ancient document and provides relevant information. Step 3: The provider unit provides local historical information based on the content of the ancient documents deciphered by the deciphering unit. The provider unit uses AR technology to acquire local historical information in real time and provide it to the user. For example, the provider unit acquires the user's location information and provides historical information related to that location. The provider unit can also use AR technology to visually display historical information about the places the user has visited. For example, the provider unit uses AR technology to display information about historical events and people in the places the user has visited.

[0062] (Example of form 2) The Classic AI Agent System, according to an embodiment of the present invention, is an innovative tool for historians, students, museum curators, and general history enthusiasts to decipher ancient documents, understand their historical context, and efficiently search for related materials. This Classic AI Agent System allows users to decipher ancient documents through a voice interface, with a generating AI accurately deciphering the documents and presenting modern Japanese translations and contextual information. Furthermore, it uses AR technology to acquire and provide real-time historical information of the location. This mechanism enables users to efficiently decipher ancient documents and understand their historical context. For example, when a user deciphers an ancient document, the generating AI translates its content into modern Japanese and provides relevant historical background information. Additionally, when visiting a location, AR technology is used to acquire and provide real-time historical information of that location. This allows users to gain a deeper understanding and experience when visiting historical sites. The Classic AI Agent System aims to promote the evolution and dissemination of historical research and deepen cultural understanding. It is a tool for realizing a society where more people participate in historical exploration and share new knowledge. This will allow the classic AI agent system to streamline the deciphering of ancient documents for historians and enthusiasts, enabling a deeper understanding of historical context and easier searching of related materials.

[0063] The classic AI agent system according to this embodiment comprises a reception unit, a decoding unit, and a provision unit. The reception unit receives voice input from the user. The reception unit can receive voice input using, for example, a microphone. The reception unit can also convert voice input into text data using speech recognition technology. For example, the reception unit can analyze the voice using speech recognition software and save it as text data. The decoding unit decodes the ancient document based on the voice input received by the reception unit and provides a modern Japanese translation. The decoding unit decodes the ancient document with high accuracy using a generative AI. For example, the decoding unit inputs the text data of the ancient document into the generative AI and generates a modern Japanese translation. The decoding unit can also provide contextual information of the ancient document using the generative AI. For example, the decoding unit understands the context of the ancient document using the generative AI and provides relevant information. The provision unit provides local historical information based on the content of the ancient document decoded by the decoding unit. The provision unit acquires local historical information in real time using AR technology and provides it to the user. For example, the service provider acquires the user's location information and provides historical information related to that location. The service provider can also use AR technology to visually display historical information about the place the user has visited. For example, the service provider can use AR technology to display information about historical events and people in the place the user has visited. In this way, the classic AI agent system according to the embodiment enhances the historical exploration experience by deciphering ancient documents based on the user's voice input and providing local historical information.

[0064] The reception unit receives voice input from the user. For example, the reception unit can receive voice input using a microphone. Specifically, when a user speaks into the microphone, their voice is converted into a digital signal and transmitted to the system. The reception unit can also convert voice input into text data using speech recognition technology. Speech recognition technology analyzes the voice signal and converts the voice into text using a language model. For example, the reception unit can analyze the voice using speech recognition software and save it as text data. The speech recognition software extracts the features of the voice, decomposes them into phonemes and words, and analyzes them. This ensures that the user's voice input is accurately converted into text data, which is then used for further processing. Furthermore, the reception unit can use noise cancellation technology to remove ambient noise and improve the accuracy of voice input. This allows users to perform accurate voice input even in noisy environments. The reception unit is equipped with trigger words and gesture recognition functions to detect the start and end of voice input, allowing users to perform voice input in a natural way. For example, voice input begins when the user says "start" and ends when they say "stop". Furthermore, the reception unit supports multiple languages, allowing it to accurately recognize voice input from users in different languages. This enables the reception unit to efficiently and accurately receive user voice input, improving the overall system performance.

[0065] The decoding unit decodes ancient documents based on voice input received by the reception unit and provides a modern Japanese translation. The decoding unit uses generative AI to decode ancient documents with high accuracy. Specifically, the decoding unit inputs the text data of the ancient document into the generative AI and generates a modern Japanese translation. The generative AI has learned from a large amount of ancient document data and modern Japanese translation data, and can understand the grammar, vocabulary, and context of ancient documents. For example, the decoding unit inputs the text data of the ancient document into the generative AI, and the generative AI analyzes the context and generates a modern Japanese translation. The generative AI uses natural language processing technology to understand the context of the ancient document and provide an appropriate modern Japanese translation. This allows the decoding unit to accurately translate the content of ancient documents into modern Japanese and provide it to the user. Furthermore, the decoding unit can also provide contextual information about the ancient document using the generative AI. For example, the decoding unit uses the generative AI to understand the context of the ancient document and provide relevant information. The generative AI can analyze the content of the ancient document and provide information such as its background, related historical events, and people. This allows users to understand not only the content of ancient documents but also their background and related information. The decoding unit can improve its decoding accuracy by regularly updating the training data of the generating AI and adding new ancient documents and modern Japanese translation data. As a result, the decoding unit can always perform highly accurate decodings based on the latest information and provide them to users.

[0066] The information provider will provide local historical information based on the content of ancient documents deciphered by the deciphering unit. The information provider will acquire local historical information in real time using AR technology and provide it to the user. Specifically, the information provider will acquire the user's location information and provide historical information related to that location. For example, the information provider will use GPS functionality to identify the user's current location and provide information about historical events and people related to that location. The information provider can also use AR technology to visually display historical information about places the user has visited. For example, the information provider will use AR technology to display information about historical events and people at places the user has visited. AR technology can overlay historical information onto the real-world landscape through the user's smartphone or tablet camera. This allows the user to intuitively understand historical information related to a place while viewing the local landscape. Furthermore, the information provider can provide customized historical information according to the user's interests. For example, if the user is interested in a particular era or person, that information will be provided preferentially. The information provider can collect user feedback and continuously improve the accuracy and content of the information it provides. This allows the service provider to offer users high-quality historical information and enhance their historical exploration experience.

[0067] The decoding unit can provide contextual information about ancient documents using generative AI. For example, the decoding unit uses generative AI to understand the context of ancient documents and provide relevant information. For instance, the decoding unit inputs text data from ancient documents into the generative AI and generates contextual information. The decoding unit can also provide background information about ancient documents using generative AI. For example, the decoding unit uses generative AI to understand the historical background of ancient documents and provide relevant information. By providing contextual information about ancient documents using generative AI, the accuracy of decoding is improved.

[0068] The service provider can acquire local historical information in real time and provide it to users. For example, the service provider can acquire local historical information in real time using AR technology. For example, the service provider can acquire the user's location information and provide historical information related to that location. The service provider can also use AR technology to visually display historical information of places the user has visited. For example, the service provider can use AR technology to display information about historical events and people in places the user has visited. By acquiring local historical information in real time and providing it to users, the historical exploration experience is enhanced.

[0069] The deciphering unit can personalize the user's historical exploration experience. For example, it can use generative AI to customize the deciphering of ancient documents based on the user's preferences and history. For instance, it can refer to the user's past exploration history to provide the optimal deciphering method. It can also use generative AI to adjust the deciphering content based on the user's interests. For example, it can prioritize providing relevant information based on the user's interests. This personalizes the user's historical exploration experience, providing an optimal experience for each individual user.

[0070] The reception unit can analyze voice input from users and extract information necessary for deciphering ancient documents. For example, the reception unit can analyze voice input using speech recognition technology and extract the necessary information. For instance, the reception unit can analyze speech using speech recognition software and extract keywords and phrases necessary for deciphering ancient documents. The reception unit can also analyze voice input using generative AI and extract the necessary information. For example, the reception unit can input voice data into generative AI and extract the necessary information. By analyzing voice input from users and extracting the information necessary for deciphering ancient documents, the accuracy of the decipherment is improved.

[0071] The service provider can provide optimal historical information based on the user's location. For example, it can acquire the user's location and provide historical information related to that location. For instance, it can use GPS data to pinpoint the user's location and provide historical information related to that location. Furthermore, the service provider can use augmented reality (AR) technology to visually display historical information about places the user has visited. For example, it can use AR technology to display information about historical events and people in places the user has visited. This enhances the user's historical exploration experience by providing optimal historical information based on their location.

[0072] The reception unit can estimate the user's emotions and adjust the voice input analysis method based on the estimated emotions. For example, the reception unit might estimate the user's emotions using an emotion estimation algorithm. For example, it might estimate the user's emotions using voice tone analysis or facial recognition technology. Furthermore, the reception unit can adjust the voice input analysis method based on the estimated emotions. For example, if the user is nervous, the reception unit might analyze the voice input slowly to provide the user with a sense of security. Conversely, if the user is relaxed, the reception unit might analyze the voice input quickly to provide smooth operation. This improves the accuracy of voice input by adjusting the voice input analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The reception unit can analyze the user's past voice input history and select the optimal speech recognition algorithm. For example, the reception unit can analyze the user's past voice input history and select the optimal speech recognition algorithm. For example, the reception unit can analyze the patterns of voice input the user has used in the past and select the optimal speech recognition algorithm. The reception unit can also learn specific accents and pronunciation characteristics from the user's past voice input history and apply the optimal algorithm. For example, the reception unit can select a speech recognition algorithm appropriate for a specific time of day or situation based on the user's past voice input history. In this way, the optimal speech recognition algorithm can be selected by analyzing the user's past voice input history.

[0074] The reception unit can filter out the user's current ambient noise when receiving voice input, thereby removing noise. For example, if the user is in a noisy environment, the reception unit can filter out ambient noise to make the voice input clearer. Also, if the user is in a quiet environment, the reception unit can remove even subtle noises, improving the accuracy of the voice input. For example, if the user is making a voice input while on the move, the reception unit can filter out wind noise and traffic noise to remove noise. In this way, the accuracy of the voice input is improved by filtering out ambient noise when receiving voice input.

[0075] The reception unit can estimate the user's emotions and determine the priority of voice input based on the estimated emotions. For example, the reception unit might estimate the user's emotions using an emotion estimation algorithm. For instance, it might use voice tone analysis or facial recognition technology to estimate the user's emotions. Furthermore, the reception unit can also determine the priority of voice input based on the estimated emotions. For example, if the user is tense, the reception unit might set a higher priority for voice input to perform a rapid analysis. Conversely, if the user is relaxed, the reception unit might set the priority of voice input to a normal level for a smooth analysis. This streamlines the analysis of voice input by determining the priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The reception unit can prioritize analyzing highly relevant voice input based on the user's geographical location information when receiving voice input. For example, when receiving voice input, the reception unit prioritizes analyzing highly relevant voice input considering the user's geographical location information. For example, if the reception unit is in a specific region, it will prioritize analyzing voice input related to that region. Also, if the reception unit is traveling, it can prioritize analyzing voice input related to the user's current location. For example, if the reception unit is participating in a specific event, it will prioritize analyzing voice input related to that event. This improves the accuracy of voice input by prioritizing the analysis of highly relevant voice input based on the user's geographical location information.

[0077] The reception unit can analyze the user's social media activity when receiving voice input and extract relevant voice input. For example, the reception unit can extract relevant voice input based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and prioritize the analysis of relevant voice input. For example, the reception unit can extract relevant voice input by referring to the activities of the user's social media followers and friends. In this way, relevant voice input can be extracted by analyzing the user's social media activity.

[0078] The decoding unit can estimate the user's emotions and adjust the accuracy of decoding ancient documents based on the estimated emotions. For example, the decoding unit estimates the user's emotions using an emotion estimation algorithm. For example, it may use voice tone analysis or facial recognition technology to estimate the user's emotions. Furthermore, the decoding unit can adjust the accuracy of decoding ancient documents based on the estimated emotions. For example, if the user is tense, the generating AI can increase the decoding accuracy to prevent misinterpretation. Conversely, if the user is relaxed, the generating AI can set the decoding accuracy to normal, allowing for smooth decoding. This improves the decoding accuracy by adjusting the decoding accuracy of ancient documents based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generating AI. Generating AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The deciphering unit can adjust the level of detail in the deciphering process based on the importance of the ancient document. For example, in the case of an important ancient document, the generating AI performs a detailed deciphering to accurately decipher all the information. Alternatively, in the case of a general ancient document, the generating AI can perform a standard deciphering to proceed efficiently. For example, in the case of an ancient document of low importance, the generating AI performs a simplified deciphering to complete the deciphering quickly. This improves the efficiency of deciphering by adjusting the level of detail in the deciphering process based on the importance of the ancient document.

[0080] The decoding unit can apply different decoding algorithms depending on the category of the ancient document during the decoding process. For example, if it is a historical document, the generating AI can apply a decoding algorithm specifically for historical documents. Similarly, if it is a religious document, the generating AI can apply a decoding algorithm specifically for religious documents. For example, if it is a scientific document, the generating AI can apply a decoding algorithm specifically for scientific documents. By applying different decoding algorithms depending on the category of the ancient document, the accuracy of the decoding is improved.

[0081] The decoding unit can estimate the user's emotions and adjust the display method of the decoding results based on the estimated user emotions. For example, the decoding unit estimates the user's emotions using an emotion estimation algorithm. For example, the decoding unit estimates the user's emotions using voice tone analysis or facial expression recognition technology. The decoding unit can also adjust the display method of the decoding results based on the estimated user emotions. For example, if the user is tense, the decoding unit can have the generating AI provide a simple and highly visible display method. If the user is relaxed, the decoding unit can have the generating AI provide a display method that includes detailed information. This improves the readability of the decoding results by adjusting the display method of the decoding results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The decryption unit can determine the priority of decryption based on when the ancient documents were submitted. For example, if an ancient document was submitted a long time ago, the generation AI will prioritize its decryption. Conversely, if an ancient document was submitted a long time ago, the generation AI can also prioritize its decryption. For example, if an ancient document was submitted a long time ago, the generation AI will prioritize its decryption. This improves the efficiency of decryption by determining the priority of decryption based on when the ancient documents were submitted.

[0083] The decoding unit can improve the accuracy of its decoding by referring to related literature for the ancient document during the decoding process. For example, the decoding unit can refer to related literature for the ancient document, and the generating AI can improve the accuracy of the decoding. For example, the decoding unit can adjust the level of detail of the decoding based on related literature for the ancient document. The decoding unit can also refer to related literature for the ancient document, and the generating AI can correct decoding errors. For example, the decoding unit can refer to related literature for the ancient document, and the generating AI can improve the accuracy of the decoding. In this way, the accuracy of decoding is improved by referring to related literature for the ancient document.

[0084] The service provider can estimate the user's emotions and adjust the display method of historical information based on the estimated user emotions. For example, the service provider can estimate the user's emotions using an emotion estimation algorithm. For example, the service provider can estimate the user's emotions using voice tone analysis or facial recognition technology. The service provider can also adjust the display method of historical information based on the estimated user emotions. For example, if the user is tense, the service provider can use AR technology to provide a simple and highly visible display method. Also, if the user is relaxed, the service provider can use AR technology to provide a display method that includes detailed information. This improves the visibility of information by adjusting the display method of historical information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The service provider can optimize current information by referencing historical data. For example, the service provider can use AR technology to optimize current information based on historical data. For instance, the service provider can refer to historical data, and AR technology can provide relevant information. Furthermore, the service provider can use AR technology to improve the accuracy of information based on historical data. For example, the service provider can refer to historical data, and AR technology can adjust the level of detail of the information. This improves the accuracy of current information by referencing historical data.

[0086] The service provider can apply different display methods to each category of historical information at the time of delivery. For example, in the case of historical information about war, the service provider can use AR technology to display a war simulation. Also, in the case of historical information about culture, the service provider can use AR technology to display cultural art and music. For example, in the case of historical information about politics, the service provider can use AR technology to display a recreation of a political event. By applying different display methods to each category of historical information, the visibility of the information is improved.

[0087] The service provider can estimate the user's emotions and adjust the importance of historical information based on the estimated emotions. For example, the service provider can estimate the user's emotions using an emotion estimation algorithm. For example, the service provider can estimate the user's emotions using voice tone analysis or facial recognition technology. Furthermore, the service provider can adjust the importance of historical information based on the estimated emotions. For example, if the user is tense, the service provider can use AR technology to prioritize displaying information of high importance. Conversely, if the user is relaxed, the service provider can use AR technology to display information at normal importance. This improves the visibility of information by adjusting the importance of historical information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The service provider can provide optimal historical information based on the user's location information at the time of delivery. For example, the service provider can acquire the user's location information and provide historical information related to that location. For example, the service provider can identify the user's location using GPS data and provide historical information related to that location. The service provider can also use AR technology to visually display historical information about places the user has visited. For example, the service provider can use AR technology to display information about historical events and people in places the user has visited. This improves the visibility of the information by providing optimal historical information based on the user's location information.

[0089] The provider can improve the accuracy of the information by referencing relevant historical events at the time of provision. For example, the provider can reference relevant historical events, and AR technology can improve the accuracy of the information. For example, the provider can use relevant historical events as a basis for adjusting the level of detail of the information using AR technology. The provider can also reference relevant historical events, and AR technology can correct errors in the information. For example, the provider can reference relevant historical events, and AR technology can improve the accuracy of the information. As a result, the accuracy of the information is improved by referencing relevant historical events.

[0090] The context information provider can estimate the user's emotions and adjust the display method of context information based on the estimated user emotions. For example, the context information provider can estimate the user's emotions using an emotion estimation algorithm. For example, it can estimate the user's emotions using voice tone analysis or facial expression recognition technology. Furthermore, the context information provider can also adjust the display method of context information based on the estimated user emotions. For example, if the user is tense, the generating AI can provide a simple and highly visible display method. Conversely, if the user is relaxed, the generating AI can provide a display method that includes detailed information. This improves the visibility of information by adjusting the display method of context information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The context information provider can select the most appropriate information based on the content of the ancient document when providing context information. For example, if the content of the ancient document is related to war, the generating AI will provide context information on the war. Also, if the content of the ancient document is related to culture, the generating AI can provide context information on the culture. For example, if the content of the ancient document is related to politics, the generating AI will provide context information on the politics. This improves the accuracy of the information by selecting the most appropriate information based on the content of the ancient document.

[0092] The context information provider can estimate the user's emotions and determine the priority of context information based on the estimated emotions. For example, the context information provider can estimate the user's emotions using an emotion estimation algorithm. For example, it can estimate the user's emotions using voice tone analysis or facial expression recognition technology. Furthermore, the context information provider can also determine the priority of context information based on the estimated emotions. For example, if the user is tense, the generating AI will prioritize displaying high-importance context information. Conversely, if the user is relaxed, the generating AI can display context information with normal priority. This improves the visibility of information by prioritizing context information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The context information provider can improve the accuracy of the information by referring to relevant historical background when providing context information. For example, the context information provider can refer to relevant historical background, and the generating AI can improve the accuracy of the context information. For example, the context information provider can adjust the level of detail of the context information based on the relevant historical background. The context information provider can also refer to relevant historical background, and the generating AI can correct errors in the context information. For example, the context information provider can refer to relevant historical background, and the generating AI can improve the accuracy of the context information. As a result, the accuracy of the information is improved by referring to relevant historical background.

[0094] The personalization unit can estimate the user's emotions and adjust the personalization method of the history exploration experience based on the estimated user emotions. For example, the personalization unit estimates the user's emotions using emotion estimation algorithms. For example, the personalization unit estimates the user's emotions using voice tone analysis or facial recognition technology. The personalization unit can also adjust the personalization method of the history exploration experience based on the estimated user emotions. For example, if the user is tense, the personalization unit can use generative AI to provide a simple and visually clear personalization method. Alternatively, if the user is relaxed, the personalization unit can use generative AI to provide a personalization method that includes more detailed information. This improves the quality of the experience by adjusting the personalization method of the history exploration experience based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The personalization unit can provide the optimal experience by referring to the user's past exploration history during the personalization process. For example, the personalization unit's generative AI provides the optimal experience based on the user's past exploration history. For example, the personalization unit provides an experience based on specific interests from the user's past exploration history. The personalization unit can also analyze the user's past exploration history and provide the most efficient experience. For example, the personalization unit refers to the user's past exploration history and its generative AI provides the optimal experience. In this way, the personalization unit can provide the optimal experience by referring to the user's past exploration history.

[0096] The personalization unit can estimate the user's emotions and determine the priority of personalization based on the estimated emotions. For example, the personalization unit might estimate the user's emotions using an emotion estimation algorithm. For example, it might estimate the user's emotions using voice tone analysis or facial recognition technology. The personalization unit can also determine the priority of personalization based on the estimated emotions. For example, if the user is stressed, the personalization unit might have the generative AI prioritize providing high-priority personalization. Conversely, if the user is relaxed, the personalization unit might have the generative AI provide personalization with normal priority. This improves the quality of the experience by prioritizing personalization based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The personalization component can provide the optimal experience based on the user's interests during the personalization process. For example, if a user's interests are related to war, the generative AI will provide a war-related experience. For example, if a user's interests are related to culture, the generative AI will provide a culture-related experience. Furthermore, if a user's interests are related to politics, the generative AI can provide a politics-related experience. In short, the personalization component provides the optimal experience based on the user's interests. This improves the quality of the experience by providing the optimal experience based on the user's interests.

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

[0099] The reception unit can select the optimal speech recognition algorithm by referring to the user's past voice input history when receiving user voice input. For example, the reception unit can analyze the patterns of voice input the user has used in the past and select the optimal speech recognition algorithm. Furthermore, the reception unit can learn specific accents and pronunciation characteristics from the user's past voice input history and apply the optimal algorithm. This allows for the selection of the optimal speech recognition algorithm by analyzing the user's past voice input history.

[0100] The decoding unit can provide contextual information about ancient documents using generative AI. For example, the decoding unit uses generative AI to understand the context of an ancient document and provide relevant information. The decoding unit can also input text data from an ancient document into the generative AI to generate contextual information. This improves the accuracy of decoding by providing contextual information about ancient documents through generative AI.

[0101] The service provider can acquire local historical information in real time and provide it to users. For example, the service provider can acquire local historical information in real time using AR technology. Furthermore, the service provider can acquire the user's location information and provide historical information related to that location. This enhances the historical exploration experience by providing users with real-time acquisition of local historical information.

[0102] The deciphering unit can personalize the user's historical exploration experience. For example, it uses generative AI to customize the deciphering of ancient documents based on the user's preferences and history. It can also refer to the user's past exploration history to provide the optimal deciphering method. This personalizes the user's historical exploration experience, providing an optimal experience for each individual user.

[0103] The reception unit can analyze voice input from users and extract information necessary for deciphering ancient documents. For example, the reception unit can analyze voice input using speech recognition technology and extract the necessary information. It can also analyze voice input using generative AI and extract the necessary information. This improves the accuracy of deciphering by analyzing voice input from users and extracting the information necessary for deciphering ancient documents.

[0104] The reception unit can estimate the user's emotions and adjust the voice input analysis method based on the estimated emotions. For example, the reception unit estimates the user's emotions using an emotion estimation algorithm. Furthermore, the reception unit can adjust the voice input analysis method based on the estimated emotions. This improves the accuracy of voice input by adjusting the analysis method based on the user's emotions.

[0105] The decoding unit can estimate the user's emotions and adjust the accuracy of the ancient document decoding based on the estimated emotions. For example, the decoding unit estimates the user's emotions using an emotion estimation algorithm. Furthermore, the decoding unit can adjust the accuracy of the ancient document decoding based on the estimated emotions. This improves the decoding accuracy by adjusting the decoding accuracy based on the user's emotions.

[0106] The service provider can estimate the user's emotions and adjust the display method of historical information based on the estimated user emotions. For example, the service provider can estimate the user's emotions using an emotion estimation algorithm. Furthermore, the service provider can adjust the display method of historical information based on the estimated user emotions. This improves the visibility of the information by adjusting the display method of historical information based on the user's emotions.

[0107] The context information provider can estimate the user's emotions and adjust the display method of the context information based on the estimated emotions. For example, the context information provider can estimate the user's emotions using an emotion estimation algorithm. Furthermore, the context information provider can adjust the display method of the context information based on the estimated emotions. This improves the visibility of the information by adjusting the display method of the context information based on the user's emotions.

[0108] The personalization unit can estimate the user's emotions and adjust the personalization method of the history exploration experience based on those estimated emotions. For example, the personalization unit estimates the user's emotions using an emotion estimation algorithm. Furthermore, the personalization unit can adjust the personalization method of the history exploration experience based on the estimated user emotions. This improves the quality of the experience by adjusting the personalization method of the history exploration experience based on the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception unit receives voice input from the user. The reception unit can receive voice input using, for example, a microphone. Alternatively, the reception unit can convert voice input into text data using speech recognition technology. For example, the reception unit can analyze the voice using speech recognition software and save it as text data. Step 2: The decoding unit decodes the ancient document based on the voice input received by the reception unit and provides a modern Japanese translation. The decoding unit uses generative AI to decode the ancient document with high accuracy. For example, the decoding unit inputs the text data of the ancient document into the generative AI and generates a modern Japanese translation. The decoding unit can also use the generative AI to provide contextual information about the ancient document. For example, the decoding unit uses the generative AI to understand the context of the ancient document and provides relevant information. Step 3: The provider unit provides local historical information based on the content of the ancient documents deciphered by the deciphering unit. The provider unit uses AR technology to acquire local historical information in real time and provide it to the user. For example, the provider unit acquires the user's location information and provides historical information related to that location. The provider unit can also use AR technology to visually display historical information about the places the user has visited. For example, the provider unit uses AR technology to display information about historical events and people in the places the user has visited.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, decoding unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14 and converts the voice input into text data using speech recognition technology by the control unit 46A. The decoding unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and decodes ancient documents with high accuracy using generation AI and generates a modern Japanese translation. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and acquires local historical information in real time using AR technology and provides it to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, decoding unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214 and converts the voice input into text data using voice recognition technology via the control unit 46A. The decoding unit is implemented in the identification processing unit 290 of the data processing unit 12 and uses generation AI to decode ancient documents with high accuracy and generate a modern Japanese translation. The provision unit is implemented in the control unit 46A of the smart glasses 214 and uses AR technology to acquire local historical information in real time and provide it to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, decoding unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the headset terminal 314 and converts the voice input into text data using speech recognition technology via the control unit 46A. The decoding unit is implemented in the identification processing unit 290 of the data processing unit 12, which decodes ancient documents with high accuracy using generation AI and generates a modern Japanese translation. The provision unit is implemented in the control unit 46A of the headset terminal 314, which acquires local historical information in real time using AR technology and provides it to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, decoding unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414 and converts the voice input into text data using speech recognition technology by the control unit 46A. The decoding unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and decodes ancient documents with high accuracy using generation AI and generates a modern Japanese translation. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and acquires local historical information in real time using AR technology and provides it to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A reception desk that accepts voice input from users, The decoding unit deciphers ancient documents based on voice input received by the aforementioned reception unit and performs a modern Japanese translation. The system includes a provisioning unit that provides local historical information based on the contents of ancient documents deciphered by the aforementioned deciphering unit. A system characterized by the following features. (Note 2) The aforementioned decoding unit, Generative AI provides contextual information for ancient documents. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We acquire local historical information in real time and provide it to users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned decoding unit, Personalize the user's history exploration experience. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system analyzes voice input from users and extracts information necessary for deciphering ancient documents. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provides optimal historical information based on the user's location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the voice input analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal speech recognition algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving voice input, the system filters out the user's current ambient noise to remove unwanted sounds. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving voice input, the system prioritizes analyzing the most relevant voice input based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving voice input, the system analyzes the user's social media activity and extracts relevant voice input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned decoding unit, The system estimates the user's emotions and adjusts the accuracy of deciphering ancient documents based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned decoding unit, During the deciphering process, the level of detail in the deciphering is adjusted based on the importance of the ancient document. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned decoding unit, During deciphering, different deciphering algorithms are applied depending on the category of the ancient document. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned decoding unit, It estimates the user's emotions and adjusts how the decoding results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned decoding unit, When deciphering ancient documents, the priority of deciphering is determined based on when the documents were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned decoding unit, During the deciphering process, we refer to related documents to improve the accuracy of the decipherment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how historical information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Optimizing current information by referencing historical data from the past. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the information, different display methods will be applied depending on the category of historical information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the importance of historical information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, the system will provide the most relevant historical information based on the user's location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we refer to relevant historical events to improve its accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned context information providing unit, It estimates the user's emotions and adjusts how contextual information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned context information providing unit, When providing contextual information, select the most appropriate information based on the content of the historical document. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned context information providing unit, It estimates the user's emotions and prioritizes contextual information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned context information providing unit, When providing contextual information, referencing relevant historical background improves the accuracy of the information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The personalization unit described above is It estimates the user's emotions and adjusts how the history exploration experience is personalized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The personalization unit described above is When personalizing, we refer to the user's past exploration history to provide the optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 31) The personalization unit described above is It estimates the user's emotions and determines personalization priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The personalization unit described above is When personalizing, provide the optimal experience based on the user's interests. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A reception desk that accepts voice input from users, The decoding unit deciphers ancient documents based on voice input received by the aforementioned reception unit and performs a modern Japanese translation. The system includes a provisioning unit that provides local historical information based on the contents of ancient documents deciphered by the aforementioned deciphering unit. A system characterized by the following features.

2. The aforementioned decoding unit, Generative AI provides contextual information for ancient documents. The system according to feature 1.

3. The aforementioned supply unit is, We acquire local historical information in real time and provide it to users. The system according to feature 1.

4. The aforementioned decoding unit, Personalize the user's history exploration experience. The system according to feature 1.

5. The aforementioned reception unit is The system analyzes voice input from users and extracts information necessary for deciphering ancient documents. The system according to feature 1.

6. The aforementioned supply unit is, Provides optimal historical information based on the user's location. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the voice input analysis method based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal speech recognition algorithm. The system according to feature 1.

9. The aforementioned reception unit is When receiving voice input, the system filters out the user's current ambient noise to remove unwanted sounds. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice input based on the estimated emotions. The system according to feature 1.

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