system

Abstract

The system according to the embodiment aims to enable a user to understand the contents of a book in a short time. [Solution] A system according to an embodiment includes an acquisition unit, a generation unit, and a customization unit. The acquisition unit acquires text data by scanning a barcode of a book. The generation unit analyzes the text data acquired by the acquisition unit and generates a summary. The customization unit customizes the summary generated by the generation unit according to user preferences.

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, it is difficult to grasp the contents of a book in a short time, and there is a demand for efficient knowledge acquisition.

[0005] The system according to the embodiment aims to enable a user to understand the contents of a book in a short time. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a generation unit, and a customization unit. The acquisition unit scans the barcode of a book to acquire text data. The generation unit analyzes the text data acquired by the acquisition unit and generates a summary. The customization unit customizes the summary generated by the generation unit according to the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can enable the contents of a book to be understood in a short time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A book summarization system according to an embodiment of the present invention analyzes the text data of a purchased book and generates a summary. When a user purchases a book, this book summarization system scans the book's barcode to acquire text data. Next, AI analyzes the acquired text data, extracts key points and important information, and generates a summary. Furthermore, the summary can be customized according to the user's preferences. This service allows users to quickly grasp the contents of a book and efficiently acquire knowledge. For example, by concisely summarizing important concepts, theories, and examples in specialized books, users can quickly acquire the necessary knowledge. Furthermore, summaries can be customized to focus on specific themes or chapters, allowing information to be provided according to the user's needs. This allows the book summarization system to quickly grasp the contents of a book and efficiently acquire knowledge.

[0029] A book summarization system according to an embodiment includes an acquisition unit, a generation unit, and a customization unit. The acquisition unit acquires text data by scanning a barcode of a book. For example, the acquisition unit may acquire text data by reading the barcode of a book with a scanner. The acquisition unit may also acquire text data by scanning a barcode with a smartphone camera. The acquisition unit may also acquire text data directly from an electronic version of a book. The generation unit analyzes the text data acquired by the acquisition unit using AI, extracts key points and important information, and generates a summary. For example, the generation unit may analyze the text data using natural language processing technology and extract important information. The generation unit may also generate a summary from the text data using a machine learning algorithm. The generation unit may also analyze the text data and generate a summary using deep learning technology. The customization unit customizes the summary generated by the generation unit according to user preferences. For example, the customization unit collects user preferences and customizes the summary based on them. The customization unit may also customize the summary based on the user's past usage history. Furthermore, the customization unit can customize the summary based on the user's current field of interest. This allows the book summarization system according to the embodiment to allow the user to quickly grasp the contents of the book and efficiently acquire knowledge. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input character data acquired by the acquisition unit into the generation AI and cause the generation AI to generate a summary. Some or all of the above-described processing in the customization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the customization unit may input the summary generated by the generation unit into the generation AI and cause the generation AI to customize the summary according to the user's preferences.

[0030] The acquisition unit can acquire character data by scanning the barcode of a book. For example, the acquisition unit can read the barcode of a book with a scanner and acquire the character data. The acquisition unit can also acquire character data by scanning the barcode using a smartphone camera. Furthermore, the acquisition unit can acquire character data directly from the electronic version of the book. This makes it possible to acquire character data by scanning the barcode of a book. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the acquisition unit can input the character data acquired by scanning the barcode of a book into the generation AI and cause the generation AI to acquire the character data.

[0031] The generation unit can analyze the acquired text data using AI, extract key points and important information, and generate a summary. The generation unit can analyze the text data using, for example, natural language processing technology, and extract important information. The generation unit can also generate a summary from the text data using a machine learning algorithm. Furthermore, the generation unit can analyze the text data and generate a summary using deep learning technology. This makes it possible to extract key points and important information and generate a summary. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the acquired text data to a generation AI and have the generation AI generate a summary.

[0032] The customization unit can customize the summary according to the user's preferences. For example, the customization unit collects the user's preferences and customizes the summary based on them. The customization unit can also customize the summary based on the user's past usage history. Furthermore, the customization unit can customize the summary based on the user's current areas of interest. This makes it possible to provide a summary that meets the user's preferences. Some or all of the above-mentioned processing in the customization unit may be performed using, or without, a generation AI. For example, the customization unit can input the summary generated by the generation unit into the generation AI and cause the generation AI to customize the summary according to the user's preferences.

[0033] The generation unit can summarize important concepts, theories, cases, etc. in specialized books. For example, the generation unit concisely summarizes important concepts, theories, cases, etc. in specialized books. The generation unit can also extract key points of technical terms and theories and generate a summary based on them. Furthermore, the generation unit can analyze the contents of specialized books and extract important data and cases to generate a summary. This allows for concise summarization of important concepts, theories, and cases in specialized books. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input text data from specialized books into the generation AI and have the generation AI generate a summary.

[0034] The customization unit can provide a summary focused on a specific theme or chapter. The customization unit can provide a summary focused on a specific theme or chapter based on, for example, the user's field of interest. The customization unit can also provide a summary focused on a specific theme or chapter based on the user's past usage history. Furthermore, the customization unit can provide a summary focused on a specific theme or chapter based on the user's current learning status. This makes it possible to provide a summary focused on a specific theme or chapter. Some or all of the above-described processing in the customization unit can be performed using, or without, a generation AI, for example. For example, the customization unit can input the summary generated by the generation unit into the generation AI and cause the generation AI to perform customization focused on a specific theme or chapter.

[0035] When scanning the barcode of a book, the acquisition unit can select an appropriate acquisition method by referring to the user's past purchase history. The acquisition unit can customize the scanning procedure, for example, based on the genre of books the user has purchased in the past. The acquisition unit can also preferentially suggest scanning methods (manual, voice instructions, etc.) that the user has used in the past. Furthermore, the acquisition unit can also preferentially scan books by a specific publisher or author based on the user's past purchase history. This makes it possible to select the optimal acquisition method based on the user's past purchase history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's past purchase history data into the generation AI and have the generation AI select the optimal acquisition method.

[0036] When scanning the barcode of a book, the acquisition unit can perform filtering based on the user's current area of ​​interest. For example, the acquisition unit can prioritize scanning books related to a topic that the user is currently interested in. The acquisition unit can also filter and scan related books based on the user's recent search history. Furthermore, the acquisition unit can scan related books based on topics in online communities in which the user participates. This makes it possible to filter books based on the user's current area of ​​interest. Some or all of the above-described processing in the acquisition unit can be performed using, or without, the generation AI. For example, the acquisition unit can input data on the user's current area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0037] When scanning the barcode of a book, the acquisition unit can prioritize acquiring highly relevant books based on the user's geographical location information. For example, if the user is in a specific area, the acquisition unit can prioritize acquiring books related to that area. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring guidebooks and tourist information related to the travel destination. Furthermore, if the user is participating in a specific event, the acquisition unit can prioritize acquiring books related to the event. This makes it possible to prioritize acquiring highly relevant books based on the user's geographical location information. Some or all of the above-mentioned processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant books.

[0038] When scanning the barcode of a book, the acquisition unit can analyze the user's social media activity and acquire related books. For example, the acquisition unit can prioritize acquiring books by authors the user follows on social media. The acquisition unit can also acquire books related to articles and posts the user has shared on social media. Furthermore, the acquisition unit can also acquire books related to topics in online groups and communities in which the user participates. This makes it possible to acquire related books based on the user's social media activity. Some or all of the above-mentioned processing in the acquisition unit can be performed using, or without, a generation AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to select related books.

[0039] When generating a summary, the generation unit can adjust the level of detail of the summary based on the importance of the book. For example, in the case of an important book, the generation unit generates a detailed summary. In addition, in the case of a general book, the generation unit can also generate a concise summary. Furthermore, in the case of a specialized book, the generation unit can also generate a detailed summary including technical terms and concepts. This makes it possible to adjust the level of detail of the summary based on the importance of the book. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input book importance data into the generation AI and have the generation AI adjust the level of detail of the summary.

[0040] When generating a summary, the generation unit can apply different summarization algorithms depending on the book category. For example, in the case of a novel, the generation unit generates a summary that emphasizes the flow of the story. In addition, in the case of an academic book, the generation unit can generate a summary that emphasizes major theories and research results. Furthermore, in the case of a business book, the generation unit can generate a summary that emphasizes practical advice and case studies. This allows different summarization algorithms to be applied depending on the book category. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input book category data into the generation AI and have the generation AI apply a summarization algorithm.

[0041] When generating summaries, the generation unit can determine the priority of summaries based on the publication date of the book. For example, the generation unit can prioritize generating summaries for the latest books. The generation unit can also postpone generating summaries for classic books. Furthermore, the generation unit can prioritize generating summaries for books related to specific events or trends. This makes it possible to determine the priority of summaries based on the publication date of the book. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input book publication date data into the generation AI and have the generation AI determine the priority of summaries.

[0042] When generating summaries, the generation unit can adjust the order of summaries based on the relevance of the books. For example, the generation unit prioritizes summarizing books related to a topic in which the user is interested. The generation unit can also prioritize summarizing highly relevant books based on the user's past search history. Furthermore, the generation unit can prioritize summarizing books related to topics in online communities in which the user participates. This makes it possible to adjust the order of summaries based on the relevance of the books. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input book relevance data into the generation AI and cause the generation AI to adjust the order of summaries.

[0043] During customization, the customization unit can select the optimal customization method by referring to the user's past summarization usage history. For example, the customization unit can preferentially suggest summarization styles that the user has previously preferred. The customization unit can also suggest customizations that focus on specific themes or chapters based on the user's past summarization usage history. Furthermore, the customization unit can select the optimal customization method based on the length and level of detail of summaries used by the user in the past. This makes it possible to select the optimal customization method based on the user's past summarization usage history. Some or all of the above-described processing in the customization unit can be performed using, or without, the generation AI, for example. For example, the customization unit can input the user's past summarization usage history data into the generation AI and have the generation AI select the optimal customization method.

[0044] During customization, the customization unit can customize the summary based on the user's current learning situation. For example, the customization unit provides a summary related to the topic the user is currently studying. The customization unit can also adjust the level of detail of the summary according to the user's learning progress. Furthermore, the customization unit can also provide an optimal summary based on the tasks and goals the user is currently studying. This makes it possible to customize the summary based on the user's current learning situation. Some or all of the above-mentioned processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input data on the user's current learning situation into the generation AI and cause the generation AI to customize the summary.

[0045] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, if the user is in a specific area, the customization unit can prioritize customizing summaries of books related to that area. Furthermore, if the user is traveling, the customization unit can prioritize customizing summaries of guidebooks and tourist information related to the user's travel destination. Furthermore, if the user is participating in a specific event, the customization unit can prioritize customizing summaries of books related to the event. This allows the optimal customization method to be selected based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, or without, a generation AI. For example, the customization unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal customization method.

[0046] During customization, the customization unit can analyze the user's social media activity to customize the summary. For example, the customization unit prioritizes customizing summaries of books by authors the user follows on social media. The customization unit can also customize book summaries related to articles or posts the user has shared on social media. Furthermore, the customization unit can customize book summaries related to topics in online groups or communities in which the user participates. This allows summaries to be customized based on the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, or without, a generation AI. For example, the customization unit can input the user's social media activity data into the generation AI and have the generation AI customize the summary.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When scanning the barcode of a book, the acquisition unit can recommend related books by referring to the user's past reading history. For example, similar books can be recommended based on the genre or theme of books the user has read in the past. The acquisition unit can also recommend similar books based on books that the user has given high ratings to in the past. Furthermore, the acquisition unit can preferentially recommend new books by a specific author or series based on the user's reading history. This makes it possible to recommend related books based on the user's past reading history.

[0049] When generating a summary, the generator can add visual elements based on the book's content. For example, graphs and charts can be added to highlight important points and concepts. The generator can also add timelines and flowcharts to visually illustrate the flow of a story. Furthermore, the generator can add infographics to explain technical terms and complex theories. This visually complements the book's content and aids comprehension.

[0050] The customization unit can adjust the summary format according to the user's learning style. For example, a summary that makes extensive use of charts and illustrations can be provided to visual learners. An audio summary can also be provided to auditory learners. Furthermore, an interactive summary can be provided to tactile learners. This makes it possible to provide an optimal summary according to the user's learning style.

[0051] When scanning the barcode of a book, the acquisition unit can prioritize acquiring related books based on the user's current learning goals. For example, if the user is studying for a specific exam, the acquisition unit can prioritize acquiring books related to that exam. Also, if the user is trying to master a specific skill, the acquisition unit can prioritize acquiring books related to that skill. Furthermore, if the user is working on a specific project, the acquisition unit can prioritize acquiring books related to that project. This makes it possible to acquire the most suitable books based on the user's current learning goals.

[0052] The generator can add interactive elements based on the content of the book when generating the summary. For example, it can add quizzes or questions on key points or concepts. The generator can also provide options as the user reads the summary, allowing them to progress through the summary in different routes. Furthermore, the generator can provide feedback to check the user's understanding after the user has finished reading the summary. This makes the summary interactive and deepens the user's understanding.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The acquisition unit scans the barcode of the book to acquire the text data. For example, the acquisition unit reads the barcode of the book with a scanner to acquire the text data. The acquisition unit can also scan the barcode using a smartphone camera to acquire the text data. Furthermore, the acquisition unit can also acquire the text data directly from the electronic version of the book. Step 2: The generation unit uses AI to analyze the text data acquired by the acquisition unit, extracting key points and important information to generate a summary. The generation unit may analyze the text data using natural language processing technology, for example, and extract important information. The generation unit may also use machine learning algorithms to generate summaries from the text data. Furthermore, the generation unit may also use deep learning technology to analyze the text data and generate summaries. Step 3: The customization unit customizes the summary generated by the generation unit according to the user's preferences. For example, the customization unit collects the user's preferences and customizes the summary based on them. The customization unit can also customize the summary based on the user's past usage history. Furthermore, the customization unit can customize the summary based on the user's current areas of interest.

[0055] (Example 2) A book summarization system according to an embodiment of the present invention analyzes the text data of a purchased book and generates a summary. When a user purchases a book, this book summarization system scans the book's barcode to acquire text data. Next, AI analyzes the acquired text data, extracts key points and important information, and generates a summary. Furthermore, the summary can be customized according to the user's preferences. This service allows users to quickly grasp the contents of a book and efficiently acquire knowledge. For example, by concisely summarizing important concepts, theories, and examples in specialized books, users can quickly acquire the necessary knowledge. Furthermore, summaries can be customized to focus on specific themes or chapters, allowing information to be provided according to the user's needs. This allows the book summarization system to quickly grasp the contents of a book and efficiently acquire knowledge.

[0056] A book summarization system according to an embodiment includes an acquisition unit, a generation unit, and a customization unit. The acquisition unit acquires text data by scanning a barcode of a book. For example, the acquisition unit may acquire text data by reading the barcode of a book with a scanner. The acquisition unit may also acquire text data by scanning a barcode with a smartphone camera. The acquisition unit may also acquire text data directly from an electronic version of a book. The generation unit analyzes the text data acquired by the acquisition unit using AI, extracts key points and important information, and generates a summary. For example, the generation unit may analyze the text data using natural language processing technology and extract important information. The generation unit may also generate a summary from the text data using a machine learning algorithm. The generation unit may also analyze the text data and generate a summary using deep learning technology. The customization unit customizes the summary generated by the generation unit according to user preferences. For example, the customization unit collects user preferences and customizes the summary based on them. The customization unit may also customize the summary based on the user's past usage history. Furthermore, the customization unit can customize the summary based on the user's current field of interest. This allows the book summarization system according to the embodiment to allow the user to quickly grasp the contents of the book and efficiently acquire knowledge. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input character data acquired by the acquisition unit into the generation AI and cause the generation AI to generate a summary. Some or all of the above-described processing in the customization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the customization unit may input the summary generated by the generation unit into the generation AI and cause the generation AI to customize the summary according to the user's preferences.

[0057] The acquisition unit can acquire character data by scanning the barcode of a book. For example, the acquisition unit can read the barcode of a book with a scanner and acquire the character data. The acquisition unit can also acquire character data by scanning the barcode using a smartphone camera. Furthermore, the acquisition unit can acquire character data directly from the electronic version of the book. This makes it possible to acquire character data by scanning the barcode of a book. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the acquisition unit can input the character data acquired by scanning the barcode of a book into the generation AI and cause the generation AI to acquire the character data.

[0058] The generation unit can analyze the acquired text data using AI, extract key points and important information, and generate a summary. The generation unit can analyze the text data using, for example, natural language processing technology, and extract important information. The generation unit can also generate a summary from the text data using a machine learning algorithm. Furthermore, the generation unit can analyze the text data and generate a summary using deep learning technology. This makes it possible to extract key points and important information and generate a summary. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the acquired text data to a generation AI and have the generation AI generate a summary.

[0059] The customization unit can customize the summary according to the user's preferences. For example, the customization unit collects the user's preferences and customizes the summary based on them. The customization unit can also customize the summary based on the user's past usage history. Furthermore, the customization unit can customize the summary based on the user's current areas of interest. This makes it possible to provide a summary that meets the user's preferences. Some or all of the above-mentioned processing in the customization unit may be performed using, or without, a generation AI. For example, the customization unit can input the summary generated by the generation unit into the generation AI and cause the generation AI to customize the summary according to the user's preferences.

[0060] The generation unit can summarize important concepts, theories, cases, etc. in specialized books. For example, the generation unit concisely summarizes important concepts, theories, cases, etc. in specialized books. The generation unit can also extract key points of technical terms and theories and generate a summary based on them. Furthermore, the generation unit can analyze the contents of specialized books and extract important data and cases to generate a summary. This allows for concise summarization of important concepts, theories, and cases in specialized books. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input text data from specialized books into the generation AI and have the generation AI generate a summary.

[0061] The customization unit can provide a summary focused on a specific theme or chapter. The customization unit can provide a summary focused on a specific theme or chapter based on, for example, the user's field of interest. The customization unit can also provide a summary focused on a specific theme or chapter based on the user's past usage history. Furthermore, the customization unit can provide a summary focused on a specific theme or chapter based on the user's current learning status. This makes it possible to provide a summary focused on a specific theme or chapter. Some or all of the above-described processing in the customization unit can be performed using, or without, a generation AI, for example. For example, the customization unit can input the summary generated by the generation unit into the generation AI and cause the generation AI to perform customization focused on a specific theme or chapter.

[0062] The acquisition unit can estimate the user's emotions and adjust the timing of scanning the barcode of a book based on the estimated user's emotions. For example, if the user is excited, the acquisition unit displays an interface that prompts the user to scan the barcode immediately. Furthermore, if the user is relaxed, the acquisition unit can adjust the timing of scanning to match the user's pace. Furthermore, if the user is stressed, the acquisition unit can simplify the scanning procedure and complete the scan quickly. This allows the timing of scanning the barcode of a book to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, the generation AI, or without the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0063] When scanning the barcode of a book, the acquisition unit can select an appropriate acquisition method by referring to the user's past purchase history. The acquisition unit can customize the scanning procedure, for example, based on the genre of books the user has purchased in the past. The acquisition unit can also preferentially suggest scanning methods (manual, voice instructions, etc.) that the user has used in the past. Furthermore, the acquisition unit can also preferentially scan books by a specific publisher or author based on the user's past purchase history. This makes it possible to select the optimal acquisition method based on the user's past purchase history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's past purchase history data into the generation AI and have the generation AI select the optimal acquisition method.

[0064] When scanning the barcode of a book, the acquisition unit can perform filtering based on the user's current area of ​​interest. For example, the acquisition unit can prioritize scanning books related to a topic that the user is currently interested in. The acquisition unit can also filter and scan related books based on the user's recent search history. Furthermore, the acquisition unit can scan related books based on topics in online communities in which the user participates. This makes it possible to filter books based on the user's current area of ​​interest. Some or all of the above-described processing in the acquisition unit can be performed using, or without, the generation AI. For example, the acquisition unit can input data on the user's current area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0065] The acquisition unit can estimate the user's emotions and determine the priority of books to be acquired based on the estimated user emotions. For example, if the user is excited, the acquisition unit can prioritize acquiring the latest best-selling books. Furthermore, if the user is relaxed, the acquisition unit can prioritize acquiring books with relaxing content. Furthermore, if the user is stressed, the acquisition unit can prioritize acquiring books that are useful for relieving stress. This makes it possible to determine the priority of books to be acquired according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0066] When scanning the barcode of a book, the acquisition unit can prioritize acquiring highly relevant books based on the user's geographical location information. For example, if the user is in a specific area, the acquisition unit can prioritize acquiring books related to that area. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring guidebooks and tourist information related to the travel destination. Furthermore, if the user is participating in a specific event, the acquisition unit can prioritize acquiring books related to the event. This makes it possible to prioritize acquiring highly relevant books based on the user's geographical location information. Some or all of the above-mentioned processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant books.

[0067] When scanning the barcode of a book, the acquisition unit can analyze the user's social media activity and acquire related books. For example, the acquisition unit can prioritize acquiring books by authors the user follows on social media. The acquisition unit can also acquire books related to articles and posts the user has shared on social media. Furthermore, the acquisition unit can also acquire books related to topics in online groups and communities in which the user participates. This makes it possible to acquire related books based on the user's social media activity. Some or all of the above-mentioned processing in the acquisition unit can be performed using, or without, a generation AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to select related books.

[0068] The generation unit can estimate the user's emotions and adjust the presentation style of the summary based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a summary using soft expressions. If the user is in a hurry, the generation unit can also generate a concise summary that focuses on the main points. Furthermore, if the user is excited, the generation unit can also generate a visually stimulating summary. This allows the presentation style of the summary to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation style of the summary.

[0069] When generating a summary, the generation unit can adjust the level of detail of the summary based on the importance of the book. For example, in the case of an important book, the generation unit generates a detailed summary. In addition, in the case of a general book, the generation unit can also generate a concise summary. Furthermore, in the case of a specialized book, the generation unit can also generate a detailed summary including technical terms and concepts. This makes it possible to adjust the level of detail of the summary based on the importance of the book. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input book importance data into the generation AI and have the generation AI adjust the level of detail of the summary.

[0070] When generating a summary, the generation unit can apply different summarization algorithms depending on the book category. For example, in the case of a novel, the generation unit generates a summary that emphasizes the flow of the story. In addition, in the case of an academic book, the generation unit can generate a summary that emphasizes major theories and research results. Furthermore, in the case of a business book, the generation unit can generate a summary that emphasizes practical advice and case studies. This allows different summarization algorithms to be applied depending on the book category. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input book category data into the generation AI and have the generation AI apply a summarization algorithm.

[0071] The generation unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise summary. If the user is relaxed, the generation unit can also generate a longer summary with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate a summary with visually stimulating effects. This allows the length of the summary to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the summary.

[0072] When generating summaries, the generation unit can determine the priority of summaries based on the publication date of the book. For example, the generation unit can prioritize generating summaries for the latest books. The generation unit can also postpone generating summaries for classic books. Furthermore, the generation unit can prioritize generating summaries for books related to specific events or trends. This makes it possible to determine the priority of summaries based on the publication date of the book. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input book publication date data into the generation AI and have the generation AI determine the priority of summaries.

[0073] When generating summaries, the generation unit can adjust the order of summaries based on the relevance of the books. For example, the generation unit prioritizes summarizing books related to a topic in which the user is interested. The generation unit can also prioritize summarizing highly relevant books based on the user's past search history. Furthermore, the generation unit can prioritize summarizing books related to topics in online communities in which the user participates. This makes it possible to adjust the order of summaries based on the relevance of the books. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input book relevance data into the generation AI and cause the generation AI to adjust the order of summaries.

[0074] The customization unit can estimate the user's emotions and adjust the summary customization method based on the estimated user's emotions. For example, if the user is relaxed, the customization unit customizes the summary with soft expressions. If the user is in a hurry, the customization unit can also customize a summary that is concise and to the point. If the user is excited, the customization unit can also customize a summary that is visually stimulating. This allows the summary customization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the customization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the customization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the summary customization method.

[0075] During customization, the customization unit can select the optimal customization method by referring to the user's past summarization usage history. For example, the customization unit can preferentially suggest summarization styles that the user has previously preferred. The customization unit can also suggest customizations that focus on specific themes or chapters based on the user's past summarization usage history. Furthermore, the customization unit can select the optimal customization method based on the length and level of detail of summaries used by the user in the past. This makes it possible to select the optimal customization method based on the user's past summarization usage history. Some or all of the above-described processing in the customization unit can be performed using, or without, the generation AI, for example. For example, the customization unit can input the user's past summarization usage history data into the generation AI and have the generation AI select the optimal customization method.

[0076] During customization, the customization unit can customize the summary based on the user's current learning situation. For example, the customization unit provides a summary related to the topic the user is currently studying. The customization unit can also adjust the level of detail of the summary according to the user's learning progress. Furthermore, the customization unit can also provide an optimal summary based on the tasks and goals the user is currently studying. This makes it possible to customize the summary based on the user's current learning situation. Some or all of the above-mentioned processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input data on the user's current learning situation into the generation AI and cause the generation AI to customize the summary.

[0077] The customization unit can estimate the user's emotions and determine the priority of summary customization based on the estimated user emotions. For example, if the user is excited, the customization unit can prioritize customizing summaries of the latest best-selling books. Furthermore, if the user is relaxed, the customization unit can prioritize customizing summaries of books with relaxing content. Furthermore, if the user is stressed, the customization unit can prioritize customizing summaries of books that are helpful for stress relief. This allows the priority of summary customization to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the customization unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of summary customization.

[0078] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, if the user is in a specific area, the customization unit can prioritize customizing summaries of books related to that area. Furthermore, if the user is traveling, the customization unit can prioritize customizing summaries of guidebooks and tourist information related to the user's travel destination. Furthermore, if the user is participating in a specific event, the customization unit can prioritize customizing summaries of books related to the event. This allows the optimal customization method to be selected based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, or without, a generation AI. For example, the customization unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal customization method.

[0079] During customization, the customization unit can analyze the user's social media activity to customize the summary. For example, the customization unit prioritizes customizing summaries of books by authors the user follows on social media. The customization unit can also customize book summaries related to articles or posts the user has shared on social media. Furthermore, the customization unit can customize book summaries related to topics in online groups or communities in which the user participates. This allows summaries to be customized based on the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, or without, a generation AI. For example, the customization unit can input the user's social media activity data into the generation AI and have the generation AI customize the summary. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, generation unit, and customization unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit scans a barcode of a book using the camera 42 of the smart device 14 to acquire text data. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the acquired text data using AI, and generates a summary. The customization unit is realized by the control unit 46A of the smart device 14, and customizes the generated summary according to the user's preferences. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, generation unit, and customization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit scans a barcode of a book using the camera 42 of the smart glasses 214 to acquire text data. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the acquired text data using AI, and generates a summary. The customization unit is realized by the control unit 46A of the smart glasses 214, and customizes the generated summary according to the user's preferences. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, generation unit, and customization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit scans a barcode of a book using the camera 42 of the headset type terminal 314 to acquire text data. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the acquired text data using AI to generate a summary. The customization unit is realized by the control unit 46A of the headset type terminal 314, and customizes the generated summary according to the user's preferences. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, generation unit, and customization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit scans a barcode of a book using the camera 42 of the robot 414 to acquire text data. The generation unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the acquired text data using AI, and generates a summary. The customization unit is realized by the control unit 46A of the robot 414, and customizes the generated summary according to the user's preferences.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When scanning the barcode of a book, the acquisition unit can recommend related books by referring to the user's past reading history. For example, similar books can be recommended based on the genre or theme of books the user has read in the past. The acquisition unit can also recommend similar books based on books that the user has given high ratings to in the past. Furthermore, the acquisition unit can preferentially recommend new books by a specific author or series based on the user's reading history. This makes it possible to recommend related books based on the user's past reading history.

[0082] When generating a summary, the generator can add visual elements based on the book's content. For example, graphs and charts can be added to highlight important points and concepts. The generator can also add timelines and flowcharts to visually illustrate the flow of a story. Furthermore, the generator can add infographics to explain technical terms and complex theories. This visually complements the book's content and aids comprehension.

[0083] The customization unit can adjust the summary format according to the user's learning style. For example, a summary that makes extensive use of charts and illustrations can be provided to visual learners. An audio summary can also be provided to auditory learners. Furthermore, an interactive summary can be provided to tactile learners. This makes it possible to provide an optimal summary according to the user's learning style.

[0084] When scanning the barcode of a book, the acquisition unit can prioritize acquiring related books based on the user's current learning goals. For example, if the user is studying for a specific exam, the acquisition unit can prioritize acquiring books related to that exam. Also, if the user is trying to master a specific skill, the acquisition unit can prioritize acquiring books related to that skill. Furthermore, if the user is working on a specific project, the acquisition unit can prioritize acquiring books related to that project. This makes it possible to acquire the most suitable books based on the user's current learning goals.

[0085] The generator can add interactive elements based on the content of the book when generating the summary. For example, it can add quizzes or questions on key points or concepts. The generator can also provide options as the user reads the summary, allowing them to progress through the summary in different routes. Furthermore, the generator can provide feedback to check the user's understanding after the user has finished reading the summary. This makes the summary interactive and deepens the user's understanding.

[0086] The generation unit can estimate the user's emotion and adjust the tone of the summary based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a summary in a calm tone. If the user is excited, the generation unit can also generate a summary in an energetic tone. Furthermore, if the user is stressed, the generation unit can also generate a summary in a calm tone. This makes it possible to provide a summary in an optimal tone according to the user's emotion.

[0087] The customization unit can estimate the user's emotion and adjust the content of the summary based on the estimated user's emotion. For example, if the user is relaxed, the customization unit can provide a summary with relaxing content. If the user is excited, the customization unit can also provide a summary with exciting content. Furthermore, if the user is stressed, the customization unit can also provide a summary with content that is useful for relieving stress. In this way, a summary with optimal content according to the user's emotion can be provided.

[0088] The acquisition unit can estimate the user's emotions and adjust the interface for scanning the book barcode based on the estimated user's emotions. For example, if the user is excited, a bright and colorful interface can be displayed. If the user is relaxed, the acquisition unit can also display a calm interface. Furthermore, if the user is stressed, the acquisition unit can also display a simple and intuitive interface. This makes it possible to provide an optimal interface according to the user's emotions.

[0089] The generation unit can estimate the user's emotion and adjust the visual design of the summary based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a summary with soft colors and a simple design. If the user is excited, the generation unit can also generate a summary with vivid colors and a dynamic design. Furthermore, if the user is stressed, the generation unit can also generate a summary with calm colors and a simple design. This makes it possible to provide a summary with an optimal visual design according to the user's emotion.

[0090] The customization unit can estimate the user's emotions and adjust the summary presentation method based on the estimated user's emotions. For example, if the user is relaxed, the summary can be presented at a slow pace. If the user is in a hurry, the customization unit can also provide a summary that covers the main points in a short amount of time. Furthermore, if the user is excited, the customization unit can also provide a visually stimulating summary. In this way, the summary can be presented in an optimal presentation method according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The acquisition unit scans the barcode of the book to acquire the text data. For example, the acquisition unit reads the barcode of the book with a scanner to acquire the text data. The acquisition unit can also scan the barcode using a smartphone camera to acquire the text data. Furthermore, the acquisition unit can also acquire the text data directly from the electronic version of the book. Step 2: The generation unit uses AI to analyze the text data acquired by the acquisition unit, extracting key points and important information to generate a summary. The generation unit may analyze the text data using natural language processing technology, for example, and extract important information. The generation unit may also use machine learning algorithms to generate summaries from the text data. Furthermore, the generation unit may also use deep learning technology to analyze the text data and generate summaries. Step 3: The customization unit customizes the summary generated by the generation unit according to the user's preferences. For example, the customization unit collects the user's preferences and customizes the summary based on them. The customization unit can also customize the summary based on the user's past usage history. Furthermore, the customization unit can customize the summary based on the user's current areas of interest.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that scans the barcode of a book to acquire character data; a generation unit that analyzes the character data acquired by the acquisition unit and generates a summary; a customization unit that customizes the summary generated by the generation unit according to user preferences. A system characterized by:

2. The acquisition unit Scan the barcode of a book to get text data 2. The system of claim 1.

3. The generation unit The acquired text data is analyzed using AI, and a summary is generated by extracting key points and important information.

2. The system of claim 1.

4. The customization unit Customize the summary according to user preferences 2. The system of claim 1.

5. The generation unit Summarize important concepts, theories, and examples from specialized books 2. The system of claim 1.

6. The customization unit Provide summaries that focus on specific themes or chapters 2. The system of claim 1.

7. The acquisition unit Estimate the user's emotions and adjust the timing of barcode scanning of books based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit When scanning a book's barcode, the system selects the appropriate acquisition method by referring to the user's past purchase history.

2. The system of claim 1.

9. The acquisition unit When scanning a book's barcode, filtering is performed based on the user's current interests.

2. The system of claim 1.

10. The acquisition unit Estimate the user's sentiment and prioritize the books to be retrieved based on the estimated sentiment.

2. The system of claim 1.

Images