system

Abstract

The system according to this embodiment aims to efficiently collect book data held by the National Diet Library and to construct a generative AI that possesses unique Japanese information. [Solution] The system according to the embodiment comprises a collection unit, a reading unit, and an analysis unit. The collection unit collects book data held by the National Diet Library. The reading unit feeds the book data collected by the collection unit into a generating AI. The analysis unit analyzes the data read by the reading unit and holds information unique to Japan.

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, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently collect book data stored in the National Diet Library and load it into a generative AI.

[0005] The system according to the embodiment aims to efficiently collect book data stored in the National Diet Library and construct a generative AI that holds Japanese-specific information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a reading unit, and an analysis unit. The collection unit collects book data held by the National Diet Library. The reading unit feeds the book data collected by the collection unit into a generating AI. The analysis unit analyzes the data read by the reading unit and holds information unique to Japan. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect book data held by the National Diet Library and construct a generative AI that possesses unique Japanese information. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 generative AI system according to an embodiment of the present invention is a system that constructs a uniquely Japanese generative AI containing uniquely Japanese data and data not available on the internet by manually feeding all the book data held in the National Diet Library into the generative AI. The generative AI system manually collects book data held in the National Diet Library and feeds the collected data into the generative AI. In this process, the generative AI analyzes uniquely Japanese data and can possess information not available on the internet. This constructs a uniquely Japanese generative AI. This generative AI enables the construction of a unique and high-quality generative AI that cannot be imitated by other countries or companies. For example, it can provide detailed information on Japanese history and culture. It can also be used as a generative AI with specialized knowledge in a particular field. This makes it possible to provide value that cannot be obtained elsewhere. As a result, the generative AI system can possess uniquely Japanese data and provide value that cannot be obtained elsewhere.

[0029] The generation AI system according to this embodiment comprises a collection unit, a reading unit, and an analysis unit. The collection unit collects book data held by the National Diet Library. The collection unit can, for example, collect book data manually. The collection unit can manually input book data and convert it into a digital format. The collection unit can also scan book data and convert it into text data using OCR technology. For example, the collection unit scans book data with a high-resolution scanner and converts it into text information using OCR technology. The collection unit can also directly collect book data in digital format. The reading unit feeds the book data collected by the collection unit into the generation AI. For example, the reading unit inputs the collected book data into the generation AI and converts it into a format that the generation AI can analyze. The reading unit can convert the data format and provide the data in a format suitable for the generation AI. For example, the reading unit converts the collected book data into text format and inputs it into the generation AI. The reading unit can adjust the data reading speed to process the data efficiently. The analysis unit analyzes the data read by the reading unit and holds information unique to Japan. The analysis unit, for example, uses generative AI to analyze data and extract information not available on the internet. The analysis unit can provide detailed information about Japanese history and culture. The analysis unit can be utilized as a generative AI with specialized knowledge in specific fields. For example, the analysis unit can provide detailed information about Japanese history using generative AI. Furthermore, the analysis unit can also provide specialized knowledge about Japanese culture using generative AI. As a result, the generative AI system according to this embodiment can possess unique Japanese data and provide value that cannot be obtained elsewhere.

[0030] The Collection Department collects book data held by the National Diet Library. For example, the Collection Department can collect book data manually. Specifically, a member of the Collection Department visits the National Diet Library, checks each book in its collection, and manually inputs the necessary data. This manual input includes basic information such as the book's title, author's name, publication year, and summary of its contents. The Collection Department can also scan book data and convert it into text data using OCR technology. For example, the Collection Department uses a high-resolution scanner to scan book pages and extracts text information from the scanned images using OCR software. In this process, it is important to adjust scanner settings to optimize the resolution and quality of the scanned images and to use training data to improve the accuracy of the OCR software. Furthermore, the Collection Department can directly collect book data in digital format. For example, it collects digital book data provided by publishers and authors and incorporates this data into the system. In this case, the Collection Department verifies the data format and metadata integrity, and cleans and normalizes the data as needed. This allows the Collection Department to collect and incorporate book data into the system in a variety of ways.

[0031] The reading unit feeds the book data collected by the collection unit into the generating AI. Specifically, it converts the collected book data into a format that the generating AI can analyze. For example, if the collected book data is in image format, the reading unit uses OCR technology to convert it into text format and inputs it into the generating AI. The data converted into text format undergoes tokenization and normalization as preprocessing for natural language processing by the generating AI. The reading unit can convert data formats and provide data in a format suitable for the generating AI. For example, if the collected book data is in PDF format, the reading unit extracts text from the PDF and converts it into a format that is easy for the generating AI to analyze. The reading unit can also adjust the data reading speed to process data efficiently. For example, instead of reading a large amount of data at once, it performs batch processing so that the generating AI can analyze the data efficiently. Furthermore, the reading unit can perform data integrity checks and error checks to ensure data quality. As a result, the reading unit can properly feed the collected book data into the generating AI and process the data efficiently and accurately.

[0032] The analysis unit analyzes the data read by the reading unit and possesses unique Japanese information. Specifically, it uses generative AI to analyze data and extract information not available on the internet. For example, the generative AI uses natural language processing technology to analyze book data and extract detailed information about Japanese history and culture. The generative AI extracts important keywords and phrases from the book data and evaluates the relevance of the information based on them. Furthermore, the generative AI can generate detailed reports on specific themes and topics based on the extracted information. For example, it can analyze book data on Japanese history and provide detailed information on specific periods and events. The generative AI can also provide expert knowledge on Japanese culture. For example, it can analyze book data on traditional Japanese arts and crafts and provide detailed information on them. The analysis unit can utilize the generative AI as a generative AI with expert knowledge in specific fields. This allows the analysis unit to possess unique Japanese data and provide value that cannot be obtained elsewhere. Furthermore, the analysis unit can continuously add collected book data as training data for the generative AI, improving its accuracy and knowledge. This allows the analysis unit to perform highly accurate analyses based on the latest information at all times, providing valuable information to users.

[0033] The collection unit can manually collect book data held by the National Diet Library. For example, the collection unit can manually input book data and convert it into a digital format. The collection unit can also scan book data and convert it into text data using OCR technology. For example, the collection unit can scan book data with a high-resolution scanner and convert it into text information using OCR technology. The collection unit can also directly collect book data in digital format. This allows for accurate data collection by manually collecting book data. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can manually input book data and input it into a generating AI.

[0034] The reading unit can feed the collected book data into the generating AI. For example, the reading unit can input the collected book data into the generating AI and convert it into a format that the generating AI can analyze. The reading unit can convert the data format and provide the data in a format suitable for the generating AI. For example, the reading unit can convert the collected book data into text format and input it into the generating AI. The reading unit can adjust the data reading speed and process the data efficiently. This makes it possible to analyze the data by feeding the collected book data into the generating AI. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the collected book data into the generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze unique Japanese data using generative AI and possess information not available on the internet. For example, the analysis unit can use generative AI to analyze data and extract information not available on the internet. The analysis unit can provide detailed information about Japanese history and culture. The analysis unit can be utilized as a generative AI with specialized knowledge in specific fields. For example, the analysis unit can use generative AI to provide detailed information about Japanese history. Furthermore, the analysis unit can also use generative AI to provide specialized knowledge about Japanese culture. This allows the analysis unit to provide unique value by analyzing unique Japanese data using generative AI and possessing information not available on the internet. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have generative AI perform data analysis and extract information not available on the internet.

[0036] The analysis unit can provide detailed information about Japanese history and culture. For example, the analysis unit can provide detailed information about Japanese history using generative AI. The analysis unit can also provide expert knowledge about Japanese culture using generative AI. For example, the analysis unit analyzes data using generative AI to provide detailed information about Japanese history. The analysis unit can also analyze data using generative AI to provide expert knowledge about Japanese culture. In this way, by providing detailed information about Japanese history and culture, it can be utilized as a generative AI with expert knowledge. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generative AI perform data analysis to provide detailed information about Japanese history and culture.

[0037] The analysis unit can be used as a generative AI with specialized knowledge in a specific field. For example, the analysis unit can provide specialized knowledge in a specific field using generative AI. The analysis unit can analyze data related to a specific field using generative AI and provide specialized knowledge. For example, the analysis unit can analyze data related to a specific field using generative AI and provide specialized knowledge. The analysis unit can also analyze data related to a specific field using generative AI and provide specialized knowledge. By utilizing it as a generative AI with specialized knowledge in a specific field, it can provide value that cannot be obtained elsewhere. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have generative AI perform data analysis and provide specialized knowledge in a specific field.

[0038] The data collection unit can determine collection priorities based on the genre and publication year of the books when collecting book data. For example, the data collection unit can prioritize the collection of books published in the most recent year to ensure up-to-date information. The data collection unit can also prioritize the collection of books in a specific genre (e.g., history or culture) to build a specialized database. The data collection unit can also prioritize the collection of classic books to preserve historical data. This enables efficient data collection by determining collection priorities based on the genre and publication year of the books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the genre and publication year of the book data into a generating AI and have the generating AI determine the collection priorities.

[0039] The data collection unit can select the optimal collection method when collecting book data, taking into account the physical condition of the book. For example, the data collection unit will digitize books that are in a state of deterioration while handling them carefully. The data collection unit can quickly scan and collect data from new books. The data collection unit can also collect books in special formats using a dedicated scanner. This allows for the preservation of books and efficient data collection by selecting the optimal collection method considering the physical condition of the book. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the physical condition of the book into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can prioritize the collection of highly relevant books by considering the author information of the books when collecting book data. For example, the data collection unit can collect books by the same author together and build a database for each author. The data collection unit can prioritize the collection of books by well-known authors, thereby ensuring reliable data. The data collection unit can also prioritize the collection of books by authors who are influential in a particular field. This enables efficient data collection by prioritizing the collection of highly relevant books by considering the author information of the books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the author information of books into a generating AI and have the generating AI perform the priority collection of highly relevant books.

[0041] The data collection unit can analyze book lending history when collecting book data and prioritize the collection of popular books. For example, the data collection unit can prioritize the collection of books with a high number of lending sessions to secure data with high demand. The data collection unit can collect books that were popular during a specific period to reflect trends. The data collection unit can also prioritize the collection of books of a specific genre or theme from lending history. This ensures that data with high demand can be secured by analyzing book lending history and prioritizing the collection of popular books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input book lending history into a generating AI and have the generating AI perform the priority collection of popular books.

[0042] The reading unit can adjust the level of detail of the book data based on its content. For example, it can read books containing important information in detail, books with general content at a standard level of detail, and books with simple content at a simplified level. By adjusting the level of detail based on the book's content, efficient data processing becomes possible. Some or all of the above-described processes in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the book's content into a generating AI and have the generating AI adjust the level of detail of the reading.

[0043] The reading unit can apply different reading algorithms depending on the book format when reading book data. For example, the reading unit can apply a text analysis algorithm to text-formatted books, an image analysis algorithm to image-formatted books, and an audio analysis algorithm to audio-formatted books. This allows for efficient data processing by applying different reading algorithms depending on the book format. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the book format into a generating AI and have the generating AI apply an appropriate reading algorithm.

[0044] The reading unit can select the optimal reading method when reading book data, taking into account the language information of the book. For example, the reading unit can apply a Japanese language analysis algorithm to Japanese books, an English language analysis algorithm to English books, and a language analysis algorithm corresponding to each language to multilingual books. This enables efficient data processing by selecting the optimal reading method while considering the language information of the book. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the language information of the book into a generating AI and have the generating AI select the optimal reading method.

[0045] The reading unit can improve the accuracy of reading book data by referring to related literature. For example, the reading unit can supplement the content of the book by referring to related literature. The reading unit can verify the content of the book based on the information in the related literature. The reading unit can also integrate the data from related literature to enhance the content of the book. This enables efficient data processing by improving the accuracy of reading by referring to related literature. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the data from related literature into a generating AI and have the generating AI perform the task of improving the accuracy of reading.

[0046] The analysis unit can apply different analysis methods to each genre of book when analyzing book data. For example, the analysis unit can apply an analysis method that takes historical background into account to history books. For science books, it can apply an analysis method based on scientific data. For literary books, it can also apply an analysis method that takes literary expression into account. This allows for efficient data analysis by applying different analysis methods to each genre of book. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input book genre information into a generating AI and have the generating AI execute the application of an appropriate analysis method.

[0047] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between the contents of books when analyzing book data. For example, the analysis unit can analyze citation relationships between books to enhance the relevance of the content. The analysis unit can compare and analyze books on the same theme to clarify commonalities and differences. The analysis unit can also cross-reference the contents of books and extract mutually complementary information. By improving the accuracy of the analysis by considering the interrelationships between the contents of books, more accurate data analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships between the contents of books into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0048] The analysis unit can adjust the level of detail in its analysis of book data based on the publication year of the book. For example, the analysis unit will analyze books published recently in detail. For classic books, the analysis unit can focus on summarizing the content. For books with a specific historical context, the analysis unit can also take that historical context into consideration during the analysis. By adjusting the level of detail based on the publication year of the book, efficient data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the publication year information of the book into a generating AI and have the generating AI perform the adjustment of the level of detail in the analysis.

[0049] The analysis unit can supplement its analysis results by referring to relevant market data for books when analyzing book data. For example, the analysis unit can supplement the content of books based on relevant market data. The analysis unit can update the analysis results to reflect trends in market data. The analysis unit can also integrate market data to enhance the content of books. This allows for more accurate data analysis by supplementing the analysis results by referring to relevant market data for books. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the supplementation of the analysis results.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can determine collection priorities based on the genre and publication year of the books when collecting book data. For example, it can prioritize the collection of books published in the most recent year to ensure up-to-date information. It can also prioritize the collection of books in a specific genre (e.g., history or culture) to build a specialized database. It can also prioritize the collection of classic books to preserve historical data. By determining collection priorities based on the genre and publication year of the books, efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the genre and publication year of the book data into a generating AI and have the generating AI determine the collection priorities.

[0052] The data collection unit can select the optimal collection method when collecting book data, taking into account the physical condition of the books. For example, books that are deteriorating will be digitized while being handled carefully. New books can be quickly scanned and data collected. Books in special formats can also be collected using a dedicated scanner. This allows for the preservation of books and efficient data collection by selecting the optimal collection method considering the physical condition of the books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the physical condition of the books into a generating AI and have the generating AI select the optimal collection method.

[0053] The data collection unit can prioritize the collection of highly relevant books by considering the author information of the books when collecting book data. For example, it can collect books by the same author together and build a database for each author. By prioritizing the collection of books by well-known authors, it can ensure reliable data. It can also prioritize the collection of books by authors who are influential in a particular field. This enables efficient data collection by prioritizing the collection of highly relevant books by considering the author information of the books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the author information of the books into a generating AI and have the generating AI perform the priority collection of highly relevant books.

[0054] The data collection unit can analyze book lending history when collecting book data and prioritize the collection of popular books. For example, it can prioritize the collection of books with a high number of lendings to secure data with high demand. It can also collect books that were popular during a specific period to reflect trends. It can also prioritize the collection of books of a specific genre or theme based on lending history. In this way, by analyzing book lending history and prioritizing the collection of popular books, data with high demand can be secured. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input book lending history into a generating AI and have the generating AI perform the priority collection of popular books.

[0055] The reading unit can adjust the level of detail of the reading based on the content of the book when reading book data. For example, books containing important information will be read in detail. Books with general content can be read at a standard level of detail. Books with simple content can be read in a simplified manner. By adjusting the level of detail of the reading based on the content of the book, efficient data processing becomes possible. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the content of the book into a generating AI and have the generating AI perform the adjustment of the level of detail of the reading.

[0056] The reading unit can apply different reading algorithms depending on the book format when reading book data. For example, a text analysis algorithm can be applied to text-based books, an image analysis algorithm to image-based books, and an audio analysis algorithm to audio-based books. This allows for efficient data processing by applying different reading algorithms depending on the book format. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the book format into a generating AI and have the generating AI apply an appropriate reading algorithm.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects book data held by the National Diet Library. The collection unit can collect book data manually, inputting it by hand and converting it to digital format. The collection unit can also scan book data and convert it to text data using OCR technology. For example, it can scan with a high-resolution scanner and convert it to text information using OCR technology. Furthermore, the collection unit can also directly collect book data in digital format. Step 2: The reading unit loads the book data collected by the collection unit into the generating AI. The reading unit inputs the collected book data into the generating AI and converts it into a format that the generating AI can analyze. For example, the collected book data can be converted into text format and input into the generating AI. The reading unit can convert the data format and provide the data in a format suitable for the generating AI. It can also adjust the data loading speed to process the data efficiently. Step 3: The analysis unit analyzes the data read by the reading unit and acquires unique Japanese information. The analysis unit uses generative AI to analyze the data and extract information not available on the internet. For example, it can provide detailed information about Japanese history and culture. The analysis unit can be used as a generative AI with specialized knowledge in a specific field. As a result, the generative AI system can acquire unique Japanese data and provide value that cannot be obtained elsewhere.

[0059] (Example of form 2) The generative AI system according to an embodiment of the present invention is a system that constructs a uniquely Japanese generative AI containing uniquely Japanese data and data not available on the internet by manually feeding all the book data held in the National Diet Library into the generative AI. The generative AI system manually collects book data held in the National Diet Library and feeds the collected data into the generative AI. In this process, the generative AI analyzes uniquely Japanese data and can possess information not available on the internet. This constructs a uniquely Japanese generative AI. This generative AI enables the construction of a unique and high-quality generative AI that cannot be imitated by other countries or companies. For example, it can provide detailed information on Japanese history and culture. It can also be used as a generative AI with specialized knowledge in a particular field. This makes it possible to provide value that cannot be obtained elsewhere. As a result, the generative AI system can possess uniquely Japanese data and provide value that cannot be obtained elsewhere.

[0060] The generation AI system according to this embodiment comprises a collection unit, a reading unit, and an analysis unit. The collection unit collects book data held by the National Diet Library. The collection unit can, for example, collect book data manually. The collection unit can manually input book data and convert it into a digital format. The collection unit can also scan book data and convert it into text data using OCR technology. For example, the collection unit scans book data with a high-resolution scanner and converts it into text information using OCR technology. The collection unit can also directly collect book data in digital format. The reading unit feeds the book data collected by the collection unit into the generation AI. For example, the reading unit inputs the collected book data into the generation AI and converts it into a format that the generation AI can analyze. The reading unit can convert the data format and provide the data in a format suitable for the generation AI. For example, the reading unit converts the collected book data into text format and inputs it into the generation AI. The reading unit can adjust the data reading speed to process the data efficiently. The analysis unit analyzes the data read by the reading unit and holds information unique to Japan. The analysis unit, for example, uses generative AI to analyze data and extract information not available on the internet. The analysis unit can provide detailed information about Japanese history and culture. The analysis unit can be utilized as a generative AI with specialized knowledge in specific fields. For example, the analysis unit can provide detailed information about Japanese history using generative AI. Furthermore, the analysis unit can also provide specialized knowledge about Japanese culture using generative AI. As a result, the generative AI system according to this embodiment can possess unique Japanese data and provide value that cannot be obtained elsewhere.

[0061] The Collection Department collects book data held by the National Diet Library. For example, the Collection Department can collect book data manually. Specifically, a member of the Collection Department visits the National Diet Library, checks each book in its collection, and manually inputs the necessary data. This manual input includes basic information such as the book's title, author's name, publication year, and summary of its contents. The Collection Department can also scan book data and convert it into text data using OCR technology. For example, the Collection Department uses a high-resolution scanner to scan book pages and extracts text information from the scanned images using OCR software. In this process, it is important to adjust scanner settings to optimize the resolution and quality of the scanned images and to use training data to improve the accuracy of the OCR software. Furthermore, the Collection Department can directly collect book data in digital format. For example, it collects digital book data provided by publishers and authors and incorporates this data into the system. In this case, the Collection Department verifies the data format and metadata integrity, and cleans and normalizes the data as needed. This allows the Collection Department to collect and incorporate book data into the system in a variety of ways.

[0062] The reading unit feeds the book data collected by the collection unit into the generating AI. Specifically, it converts the collected book data into a format that the generating AI can analyze. For example, if the collected book data is in image format, the reading unit uses OCR technology to convert it into text format and inputs it into the generating AI. The data converted into text format undergoes tokenization and normalization as preprocessing for natural language processing by the generating AI. The reading unit can convert data formats and provide data in a format suitable for the generating AI. For example, if the collected book data is in PDF format, the reading unit extracts text from the PDF and converts it into a format that is easy for the generating AI to analyze. The reading unit can also adjust the data reading speed to process data efficiently. For example, instead of reading a large amount of data at once, it performs batch processing so that the generating AI can analyze the data efficiently. Furthermore, the reading unit can perform data integrity checks and error checks to ensure data quality. As a result, the reading unit can properly feed the collected book data into the generating AI and process the data efficiently and accurately.

[0063] The analysis unit analyzes the data read by the reading unit and possesses unique Japanese information. Specifically, it uses generative AI to analyze data and extract information not available on the internet. For example, the generative AI uses natural language processing technology to analyze book data and extract detailed information about Japanese history and culture. The generative AI extracts important keywords and phrases from the book data and evaluates the relevance of the information based on them. Furthermore, the generative AI can generate detailed reports on specific themes and topics based on the extracted information. For example, it can analyze book data on Japanese history and provide detailed information on specific periods and events. The generative AI can also provide expert knowledge on Japanese culture. For example, it can analyze book data on traditional Japanese arts and crafts and provide detailed information on them. The analysis unit can utilize the generative AI as a generative AI with expert knowledge in specific fields. This allows the analysis unit to possess unique Japanese data and provide value that cannot be obtained elsewhere. Furthermore, the analysis unit can continuously add collected book data as training data for the generative AI, improving its accuracy and knowledge. This allows the analysis unit to perform highly accurate analyses based on the latest information at all times, providing valuable information to users.

[0064] The collection unit can manually collect book data held by the National Diet Library. For example, the collection unit can manually input book data and convert it into a digital format. The collection unit can also scan book data and convert it into text data using OCR technology. For example, the collection unit can scan book data with a high-resolution scanner and convert it into text information using OCR technology. The collection unit can also directly collect book data in digital format. This allows for accurate data collection by manually collecting book data. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can manually input book data and input it into a generating AI.

[0065] The reading unit can feed the collected book data into the generating AI. For example, the reading unit can input the collected book data into the generating AI and convert it into a format that the generating AI can analyze. The reading unit can convert the data format and provide the data in a format suitable for the generating AI. For example, the reading unit can convert the collected book data into text format and input it into the generating AI. The reading unit can adjust the data reading speed and process the data efficiently. This makes it possible to analyze the data by feeding the collected book data into the generating AI. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the collected book data into the generating AI and have the generating AI perform data analysis.

[0066] The analysis unit can analyze unique Japanese data using generative AI and possess information not available on the internet. For example, the analysis unit can use generative AI to analyze data and extract information not available on the internet. The analysis unit can provide detailed information about Japanese history and culture. The analysis unit can be utilized as a generative AI with specialized knowledge in specific fields. For example, the analysis unit can use generative AI to provide detailed information about Japanese history. Furthermore, the analysis unit can also use generative AI to provide specialized knowledge about Japanese culture. This allows the analysis unit to provide unique value by analyzing unique Japanese data using generative AI and possessing information not available on the internet. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have generative AI perform data analysis and extract information not available on the internet.

[0067] The analysis unit can provide detailed information about Japanese history and culture. For example, the analysis unit can provide detailed information about Japanese history using generative AI. The analysis unit can also provide expert knowledge about Japanese culture using generative AI. For example, the analysis unit analyzes data using generative AI to provide detailed information about Japanese history. The analysis unit can also analyze data using generative AI to provide expert knowledge about Japanese culture. In this way, by providing detailed information about Japanese history and culture, it can be utilized as a generative AI with expert knowledge. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generative AI perform data analysis to provide detailed information about Japanese history and culture.

[0068] The analysis unit can be used as a generative AI with specialized knowledge in a specific field. For example, the analysis unit can provide specialized knowledge in a specific field using generative AI. The analysis unit can analyze data related to a specific field using generative AI and provide specialized knowledge. For example, the analysis unit can analyze data related to a specific field using generative AI and provide specialized knowledge. The analysis unit can also analyze data related to a specific field using generative AI and provide specialized knowledge. By utilizing it as a generative AI with specialized knowledge in a specific field, it can provide value that cannot be obtained elsewhere. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have generative AI perform data analysis and provide specialized knowledge in a specific field.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of book data collection based on the estimated emotions. For example, if the user is focused, the data collection unit can set the collection timing to be frequent to efficiently collect data. If the user is tired, the data collection unit can also delay the collection timing and collect data while taking breaks. If the user is relaxed, the data collection unit can flexibly set the collection timing and collect data at the user's pace. This allows for efficient data collection by adjusting the collection timing 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. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0070] The data collection unit can determine collection priorities based on the genre and publication year of the books when collecting book data. For example, the data collection unit can prioritize the collection of books published in the most recent year to ensure up-to-date information. The data collection unit can also prioritize the collection of books in a specific genre (e.g., history or culture) to build a specialized database. The data collection unit can also prioritize the collection of classic books to preserve historical data. This enables efficient data collection by determining collection priorities based on the genre and publication year of the books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the genre and publication year of the book data into a generating AI and have the generating AI determine the collection priorities.

[0071] The data collection unit can select the optimal collection method when collecting book data, taking into account the physical condition of the book. For example, the data collection unit will digitize books that are in a state of deterioration while handling them carefully. The data collection unit can quickly scan and collect data from new books. The data collection unit can also collect books in special formats using a dedicated scanner. This allows for the preservation of books and efficient data collection by selecting the optimal collection method considering the physical condition of the book. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the physical condition of the book into a generating AI and have the generating AI select the optimal collection method.

[0072] The data collection unit can estimate the user's emotions and determine the priority of book data to collect based on the estimated emotions. For example, the data collection unit may prioritize collecting books in genres that the user is interested in. If the user is in a hurry, the data collection unit may prioritize collecting important books. If the user is relaxed, the data collection unit may also collect books from a wide range of genres. This enables efficient data collection by prioritizing book data 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of book data to collect.

[0073] The data collection unit can prioritize the collection of highly relevant books by considering the author information of the books when collecting book data. For example, the data collection unit can collect books by the same author together and build a database for each author. The data collection unit can prioritize the collection of books by well-known authors, thereby ensuring reliable data. The data collection unit can also prioritize the collection of books by authors who are influential in a particular field. This enables efficient data collection by prioritizing the collection of highly relevant books by considering the author information of the books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the author information of books into a generating AI and have the generating AI perform the priority collection of highly relevant books.

[0074] The data collection unit can analyze book lending history when collecting book data and prioritize the collection of popular books. For example, the data collection unit can prioritize the collection of books with a high number of lending sessions to secure data with high demand. The data collection unit can collect books that were popular during a specific period to reflect trends. The data collection unit can also prioritize the collection of books of a specific genre or theme from lending history. This ensures that data with high demand can be secured by analyzing book lending history and prioritizing the collection of popular books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input book lending history into a generating AI and have the generating AI perform the priority collection of popular books.

[0075] The reading unit can estimate the user's emotions and adjust the reading speed of the book data based on the estimated emotions. For example, if the user is in a hurry, the reading unit can speed up the reading to process the data quickly. If the user is relaxed, the reading unit can slow down the reading to improve the accuracy of the data. If the user is focused, the reading unit can read the data at an optimal speed. This allows for efficient data processing by adjusting the reading speed 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. Some or all of the above processing in the reading unit may be performed using AI or not using AI. For example, the reading unit can input user emotion data into the generative AI and have the generative AI adjust the reading speed.

[0076] The reading unit can adjust the level of detail of the book data based on its content. For example, it can read books containing important information in detail, books with general content at a standard level of detail, and books with simple content at a simplified level. By adjusting the level of detail based on the book's content, efficient data processing becomes possible. Some or all of the above-described processes in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the book's content into a generating AI and have the generating AI adjust the level of detail of the reading.

[0077] The reading unit can apply different reading algorithms depending on the book format when reading book data. For example, the reading unit can apply a text analysis algorithm to text-formatted books, an image analysis algorithm to image-formatted books, and an audio analysis algorithm to audio-formatted books. This allows for efficient data processing by applying different reading algorithms depending on the book format. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the book format into a generating AI and have the generating AI apply an appropriate reading algorithm.

[0078] The reading unit can estimate the user's emotions and adjust the order of book data to be read based on the estimated emotions. For example, the reading unit may prioritize reading books in genres that the user is interested in. If the user is in a hurry, the reading unit may prioritize reading important books. If the user is relaxed, the reading unit may also read books from a wide range of genres. This allows for efficient data processing by adjusting the order of book data to be read 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not using AI. For example, the reading unit can input user emotion data into a generative AI and have the generative AI adjust the order of book data to be read.

[0079] The reading unit can select the optimal reading method when reading book data, taking into account the language information of the book. For example, the reading unit can apply a Japanese language analysis algorithm to Japanese books, an English language analysis algorithm to English books, and a language analysis algorithm corresponding to each language to multilingual books. This enables efficient data processing by selecting the optimal reading method while considering the language information of the book. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the language information of the book into a generating AI and have the generating AI select the optimal reading method.

[0080] The reading unit can improve the accuracy of reading book data by referring to related literature. For example, the reading unit can supplement the content of the book by referring to related literature. The reading unit can verify the content of the book based on the information in the related literature. The reading unit can also integrate the data from related literature to enhance the content of the book. This enables efficient data processing by improving the accuracy of reading by referring to related literature. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the data from related literature into a generating AI and have the generating AI perform the task of improving the accuracy of reading.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows for efficient information delivery by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0082] The analysis unit can apply different analysis methods to each genre of book when analyzing book data. For example, the analysis unit can apply an analysis method that takes historical background into account to history books. For science books, it can apply an analysis method based on scientific data. For literary books, it can also apply an analysis method that takes literary expression into account. This allows for efficient data analysis by applying different analysis methods to each genre of book. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input book genre information into a generating AI and have the generating AI execute the application of an appropriate analysis method.

[0083] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between the contents of books when analyzing book data. For example, the analysis unit can analyze citation relationships between books to enhance the relevance of the content. The analysis unit can compare and analyze books on the same theme to clarify commonalities and differences. The analysis unit can also cross-reference the contents of books and extract mutually complementary information. By improving the accuracy of the analysis by considering the interrelationships between the contents of books, more accurate data analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships between the contents of books into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit can prioritize displaying analysis results on topics the user is interested in. If the user is in a hurry, the analysis unit can prioritize displaying important analysis results. If the user is relaxed, the analysis unit can also display a wide range of analysis results. This enables efficient information provision by prioritizing analysis results 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0085] The analysis unit can adjust the level of detail in its analysis of book data based on the publication year of the book. For example, the analysis unit will analyze books published recently in detail. For classic books, the analysis unit can focus on summarizing the content. For books with a specific historical context, the analysis unit can also take that historical context into consideration during the analysis. By adjusting the level of detail based on the publication year of the book, efficient data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the publication year information of the book into a generating AI and have the generating AI perform the adjustment of the level of detail in the analysis.

[0086] The analysis unit can supplement its analysis results by referring to relevant market data for books when analyzing book data. For example, the analysis unit can supplement the content of books based on relevant market data. The analysis unit can update the analysis results to reflect trends in market data. The analysis unit can also integrate market data to enhance the content of books. This allows for more accurate data analysis by supplementing the analysis results by referring to relevant market data for books. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the supplementation of the analysis results.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of book data collection based on the estimated emotions. For example, if the user is focused, the data collection timing can be set to be frequent to efficiently collect data. If the user is tired, the data collection timing can be delayed, allowing for breaks during data collection. If the user is relaxed, the data collection timing can be set flexibly to match the user's pace. This allows for efficient data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0089] The data collection unit can determine collection priorities based on the genre and publication year of the books when collecting book data. For example, it can prioritize the collection of books published in the most recent year to ensure up-to-date information. It can also prioritize the collection of books in a specific genre (e.g., history or culture) to build a specialized database. It can also prioritize the collection of classic books to preserve historical data. By determining collection priorities based on the genre and publication year of the books, efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the genre and publication year of the book data into a generating AI and have the generating AI determine the collection priorities.

[0090] The data collection unit can select the optimal collection method when collecting book data, taking into account the physical condition of the books. For example, books that are deteriorating will be digitized while being handled carefully. New books can be quickly scanned and data collected. Books in special formats can also be collected using a dedicated scanner. This allows for the preservation of books and efficient data collection by selecting the optimal collection method considering the physical condition of the books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the physical condition of the books into a generating AI and have the generating AI select the optimal collection method.

[0091] The data collection unit can estimate the user's emotions and determine the priority of book data to collect based on the estimated emotions. For example, it can prioritize collecting books in genres the user is interested in. If the user is in a hurry, it can prioritize collecting important books. If the user is relaxed, it can collect books from a wide range of genres. This enables efficient data collection by prioritizing book data 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of book data to collect.

[0092] The data collection unit can prioritize the collection of highly relevant books by considering the author information of the books when collecting book data. For example, it can collect books by the same author together and build a database for each author. By prioritizing the collection of books by well-known authors, it can ensure reliable data. It can also prioritize the collection of books by authors who are influential in a particular field. This enables efficient data collection by prioritizing the collection of highly relevant books by considering the author information of the books. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the author information of the books into a generating AI and have the generating AI perform the priority collection of highly relevant books.

[0093] The data collection unit can analyze book lending history when collecting book data and prioritize the collection of popular books. For example, it can prioritize the collection of books with a high number of lendings to secure data with high demand. It can also collect books that were popular during a specific period to reflect trends. It can also prioritize the collection of books of a specific genre or theme based on lending history. In this way, by analyzing book lending history and prioritizing the collection of popular books, data with high demand can be secured. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input book lending history into a generating AI and have the generating AI perform the priority collection of popular books.

[0094] The reading unit can estimate the user's emotions and adjust the reading speed of the book data based on the estimated emotions. For example, if the user is in a hurry, the reading speed can be increased to process the data quickly. If the user is relaxed, the reading speed can be slowed down to improve data accuracy. If the user is focused, the data can be read at an optimal speed. This allows for efficient data processing by adjusting the reading speed 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input user emotion data into a generative AI and have the generative AI adjust the reading speed.

[0095] The reading unit can adjust the level of detail of the reading based on the content of the book when reading book data. For example, books containing important information will be read in detail. Books with general content can be read at a standard level of detail. Books with simple content can be read in a simplified manner. By adjusting the level of detail of the reading based on the content of the book, efficient data processing becomes possible. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the content of the book into a generating AI and have the generating AI perform the adjustment of the level of detail of the reading.

[0096] The reading unit can apply different reading algorithms depending on the book format when reading book data. For example, a text analysis algorithm can be applied to text-based books, an image analysis algorithm to image-based books, and an audio analysis algorithm to audio-based books. This allows for efficient data processing by applying different reading algorithms depending on the book format. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the book format into a generating AI and have the generating AI apply an appropriate reading algorithm.

[0097] The reading unit can estimate the user's emotions and adjust the order in which it reads book data based on the estimated emotions. For example, it can prioritize reading books in genres the user is interested in. If the user is in a hurry, it can prioritize reading important books. If the user is relaxed, it can read books from a wide range of genres. This allows for efficient data processing by adjusting the order in which it reads book data 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input user emotion data into a generative AI and have the generative AI adjust the order in which it reads book data.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The collection unit collects book data held by the National Diet Library. The collection unit can collect book data manually, inputting it by hand and converting it to digital format. The collection unit can also scan book data and convert it to text data using OCR technology. For example, it can scan with a high-resolution scanner and convert it to text information using OCR technology. Furthermore, the collection unit can also directly collect book data in digital format. Step 2: The reading unit loads the book data collected by the collection unit into the generating AI. The reading unit inputs the collected book data into the generating AI and converts it into a format that the generating AI can analyze. For example, the collected book data can be converted into text format and input into the generating AI. The reading unit can convert the data format and provide the data in a format suitable for the generating AI. It can also adjust the data loading speed to process the data efficiently. Step 3: The analysis unit analyzes the data read by the reading unit and acquires unique Japanese information. The analysis unit uses generative AI to analyze the data and extract information not available on the internet. For example, it can provide detailed information about Japanese history and culture. The analysis unit can be used as a generative AI with specialized knowledge in a specific field. As a result, the generative AI system can acquire unique Japanese data and provide value that cannot be obtained elsewhere.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the collection unit, reading unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit scans book data using the camera 42 and receiving device 38 of the smart device 14 and converts it into text data using OCR technology. The reading unit is implemented in the specific processing unit 290 of the data processing unit 12 and converts the collected book data into a format suitable for the generating AI. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes Japan-specific information using the generating AI and extracts information not available on the internet. 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.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, reading unit, and analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit scans book data using the camera 42 and microphone 238 of the smart glasses 214 and converts it into text data using OCR technology. The reading unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and converts the collected book data into a format suitable for the generating AI. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes Japan-specific information using the generating AI and extracts information not available on the internet. 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.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, reading unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit scans book data using the camera 42 and microphone 238 of the headset terminal 314 and converts it into text data using OCR technology. The reading unit is implemented in the specific processing unit 290 of the data processing unit 12 and converts the collected book data into a format suitable for the generating AI. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes Japan-specific information using the generating AI and extracts information not available on the internet. 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.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, reading unit, and analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit scans book data using the camera 42 and microphone 238 of the robot 414 and converts it into text data using OCR technology. The reading unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and converts the collected book data into a format suitable for the generating AI. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes Japan-specific information using the generating AI and extracts information not available on the internet. 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) The National Diet Library has a collection department that collects book data, and A reading unit that loads the book data collected by the aforementioned collection unit into a generating AI, The system includes an analysis unit that analyzes the data read by the aforementioned reading unit and holds information unique to Japan. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collecting book data held by the National Diet Library manually. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reading unit, The collected book data is fed into the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, By using generation AI to analyze unique Japanese data, we possess information not available on the internet. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Provides detailed information about Japanese history and culture. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It can be used as a generative AI with specialized knowledge in a specific field. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of book data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting book data, prioritize collection based on the book's genre and publication year. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting book data, the optimal collection method is selected considering the physical condition of the books. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the user's emotions and determine the priority of book data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting book data, the system prioritizes the collection of highly relevant books, taking into account the author's information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting book data, the lending history of books is analyzed, and popular books are prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reading unit, It estimates the user's emotions and adjusts the book data loading speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reading unit, When loading book data, adjust the level of detail based on the book's content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reading unit, When reading book data, different reading algorithms are applied depending on the book's format. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reading unit, It estimates the user's emotions and adjusts the order in which book data is loaded based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reading unit, When loading book data, the optimal loading method is selected by considering the book's language information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reading unit, When loading book data, the system improves loading accuracy by referencing related literature within the book. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing book data, different analysis methods are applied depending on the genre of the book. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When analyzing book data, consider the interrelationships between the contents of the books to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, When analyzing book data, adjust the level of detail of the analysis based on the book's publication year. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, When analyzing book data, supplement the analysis results by referring to related market data for books. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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. The National Diet Library has a collection department that collects book data, and A reading unit that loads the book data collected by the aforementioned collection unit into a generating AI, The system includes an analysis unit that analyzes the data read by the aforementioned reading unit and holds information unique to Japan. A system characterized by the following features.

2. The aforementioned collection unit is Collecting book data held by the National Diet Library manually. The system according to feature 1.

3. The aforementioned reading unit, The collected book data is fed into the generating AI. The system according to feature 1.

4. The aforementioned analysis unit, By using generational AI to analyze unique Japanese data, we possess information not available on the internet. The system according to feature 1.

5. The aforementioned analysis unit, Provides detailed information about Japanese history and culture. The system according to feature 1.

6. The aforementioned analysis unit, It can be used as a generative AI with specialized knowledge in a specific field. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of book data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting book data, prioritize collection based on the book's genre and publication year. The system according to feature 1.

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