System

Abstract

An object of a system according to an embodiment is to provide appropriate information according to the age of a user and to manage a subscription model.SOLUTION: A system includes a dictionary file storage part, an information retrieval part, a chronological dictionary selection part, and a subscription management part. The dictionary file storage unit stores main dictionary files. An information retrieval part retrieves information from the dictionary file stored in the dictionary file storage part. The age-specific dictionary selection unit selects the information retrieved by the information retrieval unit according to the age of the user. The subscription management unit manages a subscription model for use of the dictionary file.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technologies, there were issues with using dictionary files, such as not providing appropriate information according to the user's age group and not adequately managing the subscription model.

[0005] The system according to the embodiment aims to provide appropriate information according to the age of the user and manage the subscription model. [Means for solving the problem]

[0006] The system according to the embodiment includes a dictionary file storage unit, an information search unit, an age-based dictionary selection unit, and a subscription management unit. The dictionary file storage unit stores main dictionary files. The information search unit searches for information from dictionary files stored in the dictionary file storage unit. The age-based dictionary selection unit selects information searched by the information search unit according to the user's age. The subscription management unit manages a subscription model for the use of dictionary files. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate information according to the age of the user and manage the subscription model. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The information providing system according to an embodiment of the present invention stores a main dictionary file in a storage device, and provides information from the dictionary when a user inputs or speaks what they want to look up through an app. This allows the information providing system to quickly and accurately obtain the information the user needs.

[0029] An information provision system according to an embodiment includes a dictionary file storage unit, an information search unit, a generation-specific dictionary selection unit, and a subscription management unit. The dictionary file storage unit stores major dictionary files. For example, it stores general dictionaries to specialized books, such as Japanese dictionaries, English-Japanese dictionaries, complete collections of laws, and medical dictionaries. The dictionary file storage unit also automates dictionary file updates to ensure that the latest information is always available. The information search unit searches for information from dictionary files stored in the dictionary file storage unit. For example, when a user inputs a query through an app, the information search unit searches for appropriate information from dictionary files in storage and provides an answer. The information search unit can also convert the user's dictation into text using voice input and search for information from dictionary files. The generation-specific dictionary selection unit selects the information searched by the information search unit according to the user's generation. For example, it can provide a dictionary with simple explanations for elementary school students, a more detailed dictionary for junior high school students, and a more specialized dictionary for high school and university students. The subscription management unit manages a subscription model for the use of dictionary files. For example, fees can be divided between using only a general dictionary and using specialized books as well. This allows the information provision system according to the embodiment to quickly and accurately obtain the information a user needs. For example, when a user inputs a question such as "What is the capital of Japan?", the information search unit searches for appropriate information from dictionary files in storage and provides an answer. Similarly, in the case of voice input, the system uses voice recognition technology to analyze the question and provide an appropriate answer. Furthermore, by providing answers from dictionaries appropriate for the user's age group, information can be provided in a format that is easy for the user to understand. The subscription model allows users to select a plan that suits their needs.

[0030] The dictionary file storage unit automates dictionary file updates, allowing the latest information to be provided at all times. To automate dictionary file updates, the dictionary file storage unit, for example, builds a system that periodically retrieves the latest dictionary data from the Internet and stores it in storage. For example, it automatically checks for updates every week and automatically downloads and updates any new data that is found. The dictionary file storage unit also implements a cloud-based update system to ensure that users always have access to the latest dictionary data. For example, it manages dictionary data on the cloud, and the user's app automatically synchronizes the latest data. The dictionary file storage unit also builds a system that uses AI to automatically detect new words and terms and add them to the dictionary data. For example, it analyzes online news articles and social media posts to add new words to the dictionary. This allows the latest information to be provided at all times.

[0031] The dictionary file storage unit stores dictionary files in a distributed storage system, thereby improving access speed and data redundancy. For example, to store dictionary files in a distributed storage system, the dictionary file storage unit distributes and stores data across multiple servers, improving access speed. For example, servers may be allocated by region, allowing users to retrieve data from the nearest server. The dictionary file storage unit also stores the same data on multiple servers to ensure data redundancy, preventing data loss even in the event of a failure. For example, RAID technology is used to ensure data redundancy. The dictionary file storage unit also introduces blockchain technology to improve data reliability and security. For example, dictionary data may be stored on a blockchain to prevent tampering and unauthorized access. This improves access speed and data redundancy.

[0032] The dictionary file storage unit can add multimedia content related to the dictionary file to provide richer information. For example, the dictionary file storage unit can add images related to the dictionary file to provide information that is visually easy to understand. For example, it can display photos or illustrations related to the dictionary entry. The dictionary file storage unit can also add videos related to the dictionary file to provide information using both visual and auditory senses. For example, it can display explanatory videos or tutorials related to the dictionary entry. The dictionary file storage unit can also add audio related to the dictionary file to provide information that is auditorily easy to understand. For example, it can provide pronunciation guides or audio explanations related to the dictionary entry. This makes it possible to provide richer information.

[0033] The dictionary file storage unit can link the dictionary file with other educational tools and applications to provide an integrated learning environment. The dictionary file storage unit, for example, links the dictionary file with other educational tools to provide an integrated learning environment. For example, it links with an online learning platform to incorporate dictionary information into learning content. The dictionary file storage unit also links the dictionary file with other applications to provide an integrated learning environment. For example, it links with a note app or a calendar app to add dictionary information to learning plans and notes. The dictionary file storage unit also links the dictionary file with other educational tools and applications to build a system for centrally managing learning data. For example, it integrates and manages learning history and progress with dictionary data. This makes it possible to provide an integrated learning environment.

[0034] The dictionary file storage unit can introduce blockchain technology into the storage and search of dictionary files, thereby improving the reliability and security of data. The dictionary file storage unit, for example, introduces blockchain technology into the storage of dictionary files to prevent data tampering. For example, dictionary data can be stored on a blockchain to make it impossible to tamper with. The dictionary file storage unit also introduces blockchain technology into the search of dictionary files to improve the reliability of data. For example, the search result history can be recorded on a blockchain to ensure reliability. The dictionary file storage unit also introduces blockchain technology into the storage and search of dictionary files to improve data security. For example, access permissions can be managed on a blockchain to prevent unauthorized access. This improves the reliability and security of data.

[0035] The information search unit can analyze users' search histories and develop algorithms that provide search results optimized for individual users. The information search unit, for example, analyzes users' search histories and develops algorithms that provide search results optimized for individual users. For example, it prioritizes displaying highly relevant information based on past search histories. The information search unit also analyzes users' search histories and develops algorithms using machine learning to provide search results optimized for individual users. For example, it learns users' search patterns and provides predicted search results. The information search unit also analyzes users' search histories and develops personalized search algorithms to provide search results optimized for individual users. For example, it customizes search results based on the user's interests and concerns. This makes it possible to provide search results optimized for individual users.

[0036] The information search unit can continuously train a speech recognition model using a generative AI to improve the accuracy of speech input. The information search unit, for example, continuously trains a speech recognition model using a generative AI to improve the accuracy of speech input. For example, it collects user speech data and improves the accuracy of the model. The information search unit also builds a system that continuously trains a speech recognition model using a generative AI to improve the accuracy of speech input. For example, it improves the model based on user feedback. The information search unit also creates a dataset for continuously training a speech recognition model using a generative AI to improve the accuracy of speech input. For example, it collects speech data including various accents and dialects. This improves the accuracy of speech input.

[0037] The information search unit can enable output in infographic or mind map format to visually display search results. The information search unit, for example, builds a system that enables output in infographic format to visually display search results. For example, the search results are displayed in graphs or charts. The information search unit also builds a system that enables output in mind map format to visually display search results. For example, the search results are displayed with nodes and links to visually show relevance. The information search unit also develops a tool that enables output in infographic or mind map format to visually display search results. For example, a function is provided to visualize search results with drag and drop. This allows the search results to be displayed visually.

[0038] The information search unit can link search results with other applications to promote the use of information. For example, the information search unit links search results with a note application to build a system that promotes the use of information. For example, it provides a function that automatically adds search results to notes. The information search unit also links search results with a calendar application to build a system that promotes the use of information. For example, it provides a function that adds search results as calendar events. The information search unit also develops an API for linking search results with other applications to promote the use of information. For example, it provides a function that sends search results to other applications. This can promote the use of information.

[0039] The age-based dictionary selection unit can develop a system that dynamically adjusts the dictionary contents according to the user's learning progress and level of comprehension. The age-based dictionary selection unit, for example, develops a system that tracks the user's learning progress and dynamically adjusts the dictionary contents accordingly. For example, the difficulty level of the dictionary is automatically changed according to the learning progress. The age-based dictionary selection unit also builds a system that evaluates the user's level of comprehension and dynamically adjusts the dictionary contents based on that. For example, it provides simple explanations when the level of comprehension is low and provides detailed information when the level of comprehension is high. The age-based dictionary selection unit also develops a system that customizes the dictionary contents based on the user's learning progress and level of comprehension. For example, it adds or deletes dictionary entries according to the user's progress. This makes it possible to provide dictionary contents that correspond to the user's learning progress and level of comprehension.

[0040] The era-specific dictionary selection unit can add customization functions to era-specific dictionaries based on the user's interests and concerns. The era-specific dictionary selection unit, for example, builds a system that adds customization functions to era-specific dictionaries based on the user's interests and concerns. For example, dictionary entries related to topics that interest the user are displayed preferentially. The era-specific dictionary selection unit also adds customization functions to era-specific dictionaries based on the user's search history and browsing history. For example, dictionary entries with high relevance are displayed based on past search history. The era-specific dictionary selection unit also develops a system that adds customization functions to era-specific dictionaries based on user feedback. For example, the dictionary contents are adjusted based on user ratings and comments. This makes it possible to customize based on the user's interests and concerns.

[0041] The era-specific dictionary selection unit can add interactive learning content that incorporates game elements to era-specific dictionaries. The era-specific dictionary selection unit, for example, builds a system that adds interactive learning content that incorporates game elements to era-specific dictionaries. For example, it provides a function that allows users to study dictionary contents in quiz format. The era-specific dictionary selection unit also adds interactive learning content that incorporates game elements to era-specific dictionaries to increase learning motivation. For example, it provides a function that allows users to earn points and badges. The era-specific dictionary selection unit also adds interactive learning content that incorporates game elements to era-specific dictionaries to increase the enjoyment of learning. For example, it provides a function that allows users to study dictionary contents in story mode. This makes it possible to provide interactive learning content.

[0042] The era-specific dictionary selection unit can modularize era-specific dictionaries to accommodate different educational curricula and learning styles. The era-specific dictionary selection unit, for example, builds a system that modularizes era-specific dictionaries to accommodate different educational curricula. For example, it provides dictionary modules tailored to the curriculum of each educational institution. The era-specific dictionary selection unit also modularizes era-specific dictionaries to accommodate different learning styles. For example, it provides visual content for visual learners and audio content for auditory learners. The era-specific dictionary selection unit also modularizes era-specific dictionaries to accommodate different educational curricula and learning styles, providing a customizable learning environment. For example, it provides a function that allows users to select dictionary contents to suit their own learning style. This makes it possible to accommodate different educational curricula and learning styles.

[0043] The subscription management unit can develop a system that automatically suggests an optimal subscription plan based on a user's usage status. The subscription management unit, for example, analyzes a user's usage status and develops a system that automatically suggests an optimal subscription plan. For example, a plan is suggested based on usage frequency or usage time. The subscription management unit also develops an algorithm using machine learning to analyze a user's usage status and automatically suggest an optimal subscription plan. For example, the algorithm learns a user's behavioral patterns and suggests an optimal plan. The subscription management unit also develops a dashboard that analyzes a user's usage status and automatically suggests an optimal subscription plan. For example, the dashboard allows a user to check their usage status and select the optimal plan. This makes it possible to automatically suggest an optimal subscription plan.

[0044] The subscription management unit can add a periodic survey function to the subscription model to reflect user feedback. The subscription management unit, for example, builds a system that adds a periodic survey function to the subscription model to reflect user feedback. For example, the subscription management unit periodically sends surveys to users and improves plans based on the results. The subscription management unit also adds a survey function to the subscription model to reflect user feedback, thereby improving user satisfaction. For example, the subscription management unit proposes a new plan based on the survey results. The subscription management unit also adds a survey function to the subscription model to reflect user feedback, thereby providing plans that meet the user's needs. For example, the content of the plan is adjusted based on the user's opinion. This makes it possible to reflect user feedback.

[0045] The subscription management unit can add a shared plan to the subscription model to promote family and group use. The subscription management unit, for example, builds a system that adds a shared plan to the subscription model to promote family and group use. For example, it provides a function that allows the same plan to be shared across multiple accounts. The subscription management unit also adds a shared plan to the subscription model to promote family and group use, improving user convenience. For example, it allows all family members to use the same plan. The subscription management unit also adds a shared plan to the subscription model to promote family and group use, reducing costs. For example, it provides family discounts and group discounts. This can promote family and group use.

[0046] The subscription management unit can add an incentive function to the subscription model that provides rewards and benefits according to the user's learning progress. For example, the subscription management unit builds a system that adds an incentive function to the subscription model that provides rewards and benefits according to the user's learning progress. For example, it provides a function that allows users to earn points and badges according to their learning progress. The subscription management unit also adds an incentive function to the subscription model that provides rewards and benefits according to the user's learning progress, thereby increasing motivation to learn. For example, it provides benefits according to the learning progress. The subscription management unit also adds an incentive function to the subscription model that provides rewards and benefits according to the user's learning progress, thereby encouraging users to continue using the service. For example, it provides special rewards to long-term users. This makes it possible to provide rewards and benefits according to the learning progress.

[0047] The school introduction department can develop a system that monitors usage at schools in real time and makes proposals for curriculum improvement based on usage data. For example, the school introduction department builds a system that monitors usage at schools in real time and makes proposals for curriculum improvement based on usage data. For example, it collects dictionary usage data during lessons and identifies areas for curriculum improvement. The school introduction department also develops a dashboard for monitoring usage at schools in real time and making proposals for curriculum improvement based on usage data. For example, it allows teachers to check usage data and adjust the curriculum. The school introduction department also builds a feedback loop for monitoring usage at schools in real time and making proposals for curriculum improvement based on usage data. For example, it regularly updates the curriculum based on usage data. This makes it possible to make proposals for curriculum improvement.

[0048] The school introduction department can provide dictionary content customized for each school and meet the needs of the educational field. For example, the school introduction department builds a system that provides dictionary content customized for each school. For example, it provides dictionary content that is tailored to the curriculum of each school. The school introduction department also develops a feedback system to provide dictionary content customized for each school and meet the needs of the educational field. For example, it adjusts the dictionary content based on feedback from teachers. The school introduction department also builds a database to provide dictionary content customized for each school and meet the needs of the educational field. For example, it optimizes the dictionary content according to the needs of each school. This makes it possible to meet the needs of the educational field.

[0049] The school onboarding department can provide special plans and discounts for educational institutions to promote use at schools. For example, the school onboarding department builds a system for providing special plans for educational institutions to promote use at schools. For example, it provides a subscription plan for each school. Furthermore, the school onboarding department develops a system for providing discounts for educational institutions to promote use at schools. For example, it applies a discount when a school signs a contract in bulk. Furthermore, the school onboarding department implements a marketing campaign to provide special plans and discounts for educational institutions to promote use at schools. For example, it conducts promotions for schools. As a result, it is possible to promote use at schools by providing special plans and discounts for educational institutions.

[0050] The School Introduction Department analyzes regional and national educational trends based on school usage data, and can use the data to improve educational policy. The School Introduction Department, for example, builds a system that analyzes regional and national educational trends based on school usage data. For example, it aggregates usage data from each school and visualizes educational trends. The School Introduction Department also analyzes regional and national educational trends based on school usage data and creates reports to help improve educational policy. For example, it makes policy recommendations based on educational trends. The School Introduction Department also analyzes regional and national educational trends based on school usage data and builds a database to help improve educational policy. For example, it accumulates educational trend data and uses it in policy planning. This allows educational trends to be analyzed and used to improve educational policy.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The information provision system may further include a personalized information provision unit that provides customized information based on the user's interests. For example, the system may prioritize relevant information based on keywords searched for by the user or pages viewed in the past. The personalized information provision unit may also analyze the user's behavioral patterns and provide information in advance that meets the user's predicted needs. Furthermore, the system may include a function that automatically notifies the user of the latest information related to a specific topic when the user shows interest in that topic. This allows the user to quickly and efficiently obtain information based on their interests.

[0053] The information provision system can further include a learning style adaptation unit that provides information according to the user's learning style. For example, visual learners can be provided with information that makes extensive use of images and videos, while auditory learners can be provided with audio commentary. It can also provide interactive content to tactile learners. Furthermore, the difficulty level of the information can be adjusted according to the user's learning progress, providing information at an appropriate level. This allows users to efficiently obtain information optimized for their learning style.

[0054] The information provision system can also include a news provision unit that automatically provides related news and articles based on the user's search history. For example, if a user shows interest in a particular topic, the latest news and articles related to that topic can be automatically displayed. It can also prioritize the display of highly relevant news based on keywords the user has previously searched for. It can also include a function that periodically notifies the user of news related to topics that interest the user. This allows users to efficiently obtain the latest information.

[0055] The information provision system may further include an advertisement provision unit that displays advertisements optimized for individual users based on the user's search history and browsing history. For example, highly relevant advertisements may be displayed based on keywords the user has previously searched for. Personalized advertisements may also be provided based on the user's interests and concerns. Furthermore, the system may analyze the user's behavioral patterns and display advertisements in advance that correspond to predicted needs. This allows users to efficiently obtain advertisements that are tailored to their interests and concerns.

[0056] The information provision system can further include a learning resource providing unit that provides learning resources optimized for individual users based on the user's search history and browsing history. For example, it can display highly relevant learning resources based on keywords the user has previously searched for. It can also provide personalized learning resources based on the user's interests. It can also provide learning resources at an appropriate level depending on the user's learning progress. This allows users to efficiently obtain resources optimized for their own learning needs.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The dictionary file storage unit stores the main dictionary files. For example, it stores a wide range of dictionaries, from general dictionaries to specialized books, such as Japanese dictionaries, English-Japanese dictionaries, complete collections of laws, and medical dictionaries. The dictionary file storage unit also automates the updating of dictionary files, ensuring that the latest information is always available. Step 2: The information search unit searches for information from the dictionary files stored in the dictionary file storage unit. For example, when a user inputs what they want to know through the app, the information search unit searches for appropriate information from the dictionary files in the storage and provides an answer. The information search unit can also use voice input to convert what the user dictates into text and search for information from the dictionary files. Step 3: The age-specific dictionary selection unit selects the information retrieved by the information search unit according to the user's age. For example, it provides a dictionary with simple explanations for elementary school students, a more detailed dictionary for junior high school students, and a specialized dictionary for high school and university students. Step 4: The subscription management unit manages the subscription model for the use of dictionary files. For example, fees can be divided between using only general dictionaries and using specialized books as well.

[0059] (Example 2) The information providing system according to an embodiment of the present invention stores a main dictionary file in a storage device, and provides information from the dictionary when a user inputs or speaks what they want to look up through an app. This allows the information providing system to quickly and accurately obtain the information the user needs.

[0060] An information provision system according to an embodiment includes a dictionary file storage unit, an information search unit, a generation-specific dictionary selection unit, and a subscription management unit. The dictionary file storage unit stores major dictionary files. For example, it stores general dictionaries to specialized books, such as Japanese dictionaries, English-Japanese dictionaries, complete collections of laws, and medical dictionaries. The dictionary file storage unit also automates dictionary file updates to ensure that the latest information is always available. The information search unit searches for information from dictionary files stored in the dictionary file storage unit. For example, when a user inputs a query through an app, the information search unit searches for appropriate information from dictionary files in storage and provides an answer. The information search unit can also convert the user's dictation into text using voice input and search for information from dictionary files. The generation-specific dictionary selection unit selects the information searched by the information search unit according to the user's generation. For example, it can provide a dictionary with simple explanations for elementary school students, a more detailed dictionary for junior high school students, and a more specialized dictionary for high school and university students. The subscription management unit manages a subscription model for the use of dictionary files. For example, fees can be divided between using only a general dictionary and using specialized books as well. This allows the information provision system according to the embodiment to quickly and accurately obtain the information a user needs. For example, when a user inputs a question such as "What is the capital of Japan?", the information search unit searches for appropriate information from dictionary files in storage and provides an answer. Similarly, in the case of voice input, the system uses voice recognition technology to analyze the question and provide an appropriate answer. Furthermore, by providing answers from dictionaries appropriate for the user's age group, information can be provided in a format that is easy for the user to understand. The subscription model allows users to select a plan that suits their needs.

[0061] The dictionary file storage unit automates dictionary file updates, allowing the latest information to be provided at all times. To automate dictionary file updates, the dictionary file storage unit, for example, builds a system that periodically retrieves the latest dictionary data from the Internet and stores it in storage. For example, it automatically checks for updates every week and automatically downloads and updates any new data that is found. The dictionary file storage unit also implements a cloud-based update system to ensure that users always have access to the latest dictionary data. For example, it manages dictionary data on the cloud, and the user's app automatically synchronizes the latest data. The dictionary file storage unit also builds a system that uses AI to automatically detect new words and terms and add them to the dictionary data. For example, it analyzes online news articles and social media posts to add new words to the dictionary. This allows the latest information to be provided at all times.

[0062] The dictionary file storage unit stores dictionary files in a distributed storage system, thereby improving access speed and data redundancy. For example, to store dictionary files in a distributed storage system, the dictionary file storage unit distributes and stores data across multiple servers, improving access speed. For example, servers may be allocated by region, allowing users to retrieve data from the nearest server. The dictionary file storage unit also stores the same data on multiple servers to ensure data redundancy, preventing data loss even in the event of a failure. For example, RAID technology is used to ensure data redundancy. The dictionary file storage unit also introduces blockchain technology to improve data reliability and security. For example, dictionary data may be stored on a blockchain to prevent tampering and unauthorized access. This improves access speed and data redundancy.

[0063] The dictionary file storage unit uses the emotion estimation function to collect emotion data when a user uses a specific dictionary and can recommend a dictionary according to the user's emotion. The dictionary file storage unit, for example, uses the emotion estimation function to collect emotion data when a user uses a specific dictionary and recommends a dictionary that elicits positive emotions. For example, it preferentially displays dictionaries that allow the user to enjoy learning. The dictionary file storage unit also uses the emotion estimation function to collect emotion data when a user uses a specific dictionary and recommends a dictionary that reduces negative emotions. For example, it displays a simple dictionary that does not cause the user stress. The dictionary file storage unit also uses the emotion estimation function to collect emotion data when a user uses a specific dictionary and customizes the dictionary according to the user's emotion. For example, it adjusts the contents of the dictionary based on the user's interests and concerns. This makes it possible to recommend a dictionary according to the user's emotion.

[0064] The dictionary file storage unit can add multimedia content related to the dictionary file to provide richer information. For example, the dictionary file storage unit can add images related to the dictionary file to provide information that is visually easy to understand. For example, it can display photos or illustrations related to the dictionary entry. The dictionary file storage unit can also add videos related to the dictionary file to provide information using both visual and auditory senses. For example, it can display explanatory videos or tutorials related to the dictionary entry. The dictionary file storage unit can also add audio related to the dictionary file to provide information that is auditorily easy to understand. For example, it can provide pronunciation guides or audio explanations related to the dictionary entry. This makes it possible to provide richer information.

[0065] The dictionary file storage unit can link the dictionary file with other educational tools and applications to provide an integrated learning environment. The dictionary file storage unit, for example, links the dictionary file with other educational tools to provide an integrated learning environment. For example, it links with an online learning platform to incorporate dictionary information into learning content. The dictionary file storage unit also links the dictionary file with other applications to provide an integrated learning environment. For example, it links with a note app or a calendar app to add dictionary information to learning plans and notes. The dictionary file storage unit also links the dictionary file with other educational tools and applications to build a system for centrally managing learning data. For example, it integrates and manages learning history and progress with dictionary data. This makes it possible to provide an integrated learning environment.

[0066] The dictionary file storage unit can introduce blockchain technology into the storage and search of dictionary files, thereby improving the reliability and security of data. The dictionary file storage unit, for example, introduces blockchain technology into the storage of dictionary files to prevent data tampering. For example, dictionary data can be stored on a blockchain to make it impossible to tamper with. The dictionary file storage unit also introduces blockchain technology into the search of dictionary files to improve the reliability of data. For example, the search result history can be recorded on a blockchain to ensure reliability. The dictionary file storage unit also introduces blockchain technology into the storage and search of dictionary files to improve data security. For example, access permissions can be managed on a blockchain to prevent unauthorized access. This improves the reliability and security of data.

[0067] The information search unit can analyze users' search histories and develop algorithms that provide search results optimized for individual users. The information search unit, for example, analyzes users' search histories and develops algorithms that provide search results optimized for individual users. For example, it prioritizes displaying highly relevant information based on past search histories. The information search unit also analyzes users' search histories and develops algorithms using machine learning to provide search results optimized for individual users. For example, it learns users' search patterns and provides predicted search results. The information search unit also analyzes users' search histories and develops personalized search algorithms to provide search results optimized for individual users. For example, it customizes search results based on the user's interests and concerns. This makes it possible to provide search results optimized for individual users.

[0068] The information search unit can continuously train a speech recognition model using a generative AI to improve the accuracy of speech input. The information search unit, for example, continuously trains a speech recognition model using a generative AI to improve the accuracy of speech input. For example, it collects user speech data and improves the accuracy of the model. The information search unit also builds a system that continuously trains a speech recognition model using a generative AI to improve the accuracy of speech input. For example, it improves the model based on user feedback. The information search unit also creates a dataset for continuously training a speech recognition model using a generative AI to improve the accuracy of speech input. For example, it collects speech data including various accents and dialects. This improves the accuracy of speech input.

[0069] The information search unit uses the emotion estimation function to filter search results according to the user's emotion and can preferentially display information that elicits positive emotions. The information search unit, for example, uses the emotion estimation function to filter search results according to the user's emotion and preferentially display information that elicits positive emotions. For example, information that does not cause the user stress is preferentially displayed. The information search unit also uses the emotion estimation function to filter search results according to the user's emotion and preferentially display information that reduces negative emotions. For example, information that helps the user relax is displayed. The information search unit also uses the emotion estimation function to filter search results according to the user's emotion and preferentially display information based on the user's interests and concerns. For example, information that allows the user to learn while having fun is displayed. This makes it possible to preferentially display information that elicits positive emotions.

[0070] The information search unit can enable output in infographic or mind map format to visually display search results. The information search unit, for example, builds a system that enables output in infographic format to visually display search results. For example, the search results are displayed in graphs or charts. The information search unit also builds a system that enables output in mind map format to visually display search results. For example, the search results are displayed with nodes and links to visually show relevance. The information search unit also develops a tool that enables output in infographic or mind map format to visually display search results. For example, a function is provided to visualize search results with drag and drop. This allows the search results to be displayed visually.

[0071] The information search unit can link search results with other applications to promote the use of information. For example, the information search unit links search results with a note application to build a system that promotes the use of information. For example, it provides a function that automatically adds search results to notes. The information search unit also links search results with a calendar application to build a system that promotes the use of information. For example, it provides a function that adds search results as calendar events. The information search unit also develops an API for linking search results with other applications to promote the use of information. For example, it provides a function that sends search results to other applications. This can promote the use of information.

[0072] The information search unit uses the emotion estimation function to monitor the user's emotional response to search results in real time, which can be used to improve the search algorithm. For example, the information search unit uses the emotion estimation function to build a system that monitors the user's emotional response to search results in real time. For example, the information search unit analyzes the user's facial expressions and voice and calculates an emotion score. The information search unit also develops a system that monitors the user's emotional response to search results in real time and improves the search algorithm based on that data. For example, search results with a high number of positive emotional responses are preferentially displayed. The information search unit also uses the emotion estimation function to monitor the user's emotional response to search results in real time, and readjusts the search algorithm if there are a high number of negative emotional responses. For example, the priority of search results is changed based on the user's emotional data. This can be used to improve the search algorithm.

[0073] The age-based dictionary selection unit can develop a system that dynamically adjusts the dictionary contents according to the user's learning progress and level of comprehension. The age-based dictionary selection unit, for example, develops a system that tracks the user's learning progress and dynamically adjusts the dictionary contents accordingly. For example, the difficulty level of the dictionary is automatically changed according to the learning progress. The age-based dictionary selection unit also builds a system that evaluates the user's level of comprehension and dynamically adjusts the dictionary contents based on that. For example, it provides simple explanations when the level of comprehension is low and provides detailed information when the level of comprehension is high. The age-based dictionary selection unit also develops a system that customizes the dictionary contents based on the user's learning progress and level of comprehension. For example, it adds or deletes dictionary entries according to the user's progress. This makes it possible to provide dictionary contents that correspond to the user's learning progress and level of comprehension.

[0074] The era-specific dictionary selection unit can add customization functions to era-specific dictionaries based on the user's interests and concerns. The era-specific dictionary selection unit, for example, builds a system that adds customization functions to era-specific dictionaries based on the user's interests and concerns. For example, dictionary entries related to topics that interest the user are displayed preferentially. The era-specific dictionary selection unit also adds customization functions to era-specific dictionaries based on the user's search history and browsing history. For example, dictionary entries with high relevance are displayed based on past search history. The era-specific dictionary selection unit also develops a system that adds customization functions to era-specific dictionaries based on user feedback. For example, the dictionary contents are adjusted based on user ratings and comments. This makes it possible to customize based on the user's interests and concerns.

[0075] The age-based dictionary selection unit uses the emotion estimation function to select a dictionary according to the user's emotion, thereby increasing motivation to learn. The age-based dictionary selection unit, for example, uses the emotion estimation function to build a system that selects a dictionary according to the user's emotion. For example, dictionaries that the user has positive emotions about are preferentially displayed. The age-based dictionary selection unit also uses the emotion estimation function to select a dictionary according to the user's emotion, thereby developing a system that increases motivation to learn. For example, dictionaries related to topics that interest the user are displayed. The age-based dictionary selection unit also uses the emotion estimation function to build a system that selects a dictionary according to the user's emotion, thereby providing feedback to increase motivation to learn. For example, encouraging messages are displayed based on the user's emotion data. This increases motivation to learn.

[0076] The era-specific dictionary selection unit can add interactive learning content that incorporates game elements to era-specific dictionaries. The era-specific dictionary selection unit, for example, builds a system that adds interactive learning content that incorporates game elements to era-specific dictionaries. For example, it provides a function that allows users to study dictionary contents in quiz format. The era-specific dictionary selection unit also adds interactive learning content that incorporates game elements to era-specific dictionaries to increase learning motivation. For example, it provides a function that allows users to earn points and badges. The era-specific dictionary selection unit also adds interactive learning content that incorporates game elements to era-specific dictionaries to increase the enjoyment of learning. For example, it provides a function that allows users to study dictionary contents in story mode. This makes it possible to provide interactive learning content.

[0077] The era-specific dictionary selection unit can modularize era-specific dictionaries to accommodate different educational curricula and learning styles. The era-specific dictionary selection unit, for example, builds a system that modularizes era-specific dictionaries to accommodate different educational curricula. For example, it provides dictionary modules tailored to the curriculum of each educational institution. The era-specific dictionary selection unit also modularizes era-specific dictionaries to accommodate different learning styles. For example, it provides visual content for visual learners and audio content for auditory learners. The era-specific dictionary selection unit also modularizes era-specific dictionaries to accommodate different educational curricula and learning styles, providing a customizable learning environment. For example, it provides a function that allows users to select dictionary contents to suit their own learning style. This makes it possible to accommodate different educational curricula and learning styles.

[0078] The age-specific dictionary selection unit can use the emotion estimation function to analyze the user's emotion data and continuously improve the content of the age-specific dictionary. For example, the age-specific dictionary selection unit uses the emotion estimation function to analyze the user's emotion data and build a system that continuously improves the content of the age-specific dictionary. For example, it adds or deletes dictionary entries based on the user's emotional reactions. The age-specific dictionary selection unit also uses the emotion estimation function to analyze the user's emotion data and build a feedback loop for continuously improving the content of the age-specific dictionary. For example, it periodically updates the dictionary content based on the user's emotion data. The age-specific dictionary selection unit also uses the emotion estimation function to analyze the user's emotion data and build a database for continuously improving the content of the age-specific dictionary. For example, it accumulates the user's emotion data and optimizes the dictionary content. This allows the content of the age-specific dictionary to be continuously improved.

[0079] The subscription management unit can develop a system that automatically suggests an optimal subscription plan based on a user's usage status. The subscription management unit, for example, analyzes a user's usage status and develops a system that automatically suggests an optimal subscription plan. For example, a plan is suggested based on usage frequency or usage time. The subscription management unit also develops an algorithm using machine learning to analyze a user's usage status and automatically suggest an optimal subscription plan. For example, the algorithm learns a user's behavioral patterns and suggests an optimal plan. The subscription management unit also develops a dashboard that analyzes a user's usage status and automatically suggests an optimal subscription plan. For example, the dashboard allows a user to check their usage status and select the optimal plan. This makes it possible to automatically suggest an optimal subscription plan.

[0080] The subscription management unit can add a periodic survey function to the subscription model to reflect user feedback. The subscription management unit, for example, builds a system that adds a periodic survey function to the subscription model to reflect user feedback. For example, the subscription management unit periodically sends surveys to users and improves plans based on the results. The subscription management unit also adds a survey function to the subscription model to reflect user feedback, thereby improving user satisfaction. For example, the subscription management unit proposes a new plan based on the survey results. The subscription management unit also adds a survey function to the subscription model to reflect user feedback, thereby providing plans that meet the user's needs. For example, the content of the plan is adjusted based on the user's opinion. This makes it possible to reflect user feedback.

[0081] The subscription management unit uses the emotion estimation function to customize plans based on the user's emotions, thereby improving satisfaction. The subscription management unit, for example, uses the emotion estimation function to build a system that customizes plans based on the user's emotions. For example, it preferentially proposes plans for which the user has positive emotions. The subscription management unit also uses the emotion estimation function to develop a system that customizes plans based on the user's emotions and improves satisfaction. For example, it adjusts the content of the plan based on the user's emotion data. The subscription management unit also uses the emotion estimation function to build a system that customizes plans based on the user's emotions and provides plans that meet the user's needs. For example, it optimizes plans based on the user's emotion data. This makes it possible to improve user satisfaction.

[0082] The subscription management unit can add a shared plan to the subscription model to promote family and group use. The subscription management unit, for example, builds a system that adds a shared plan to the subscription model to promote family and group use. For example, it provides a function that allows the same plan to be shared across multiple accounts. The subscription management unit also adds a shared plan to the subscription model to promote family and group use, improving user convenience. For example, it allows all family members to use the same plan. The subscription management unit also adds a shared plan to the subscription model to promote family and group use, reducing costs. For example, it provides family discounts and group discounts. This can promote family and group use.

[0083] The subscription management unit can add an incentive function to the subscription model that provides rewards and benefits according to the user's learning progress. For example, the subscription management unit builds a system that adds an incentive function to the subscription model that provides rewards and benefits according to the user's learning progress. For example, it provides a function that allows users to earn points and badges according to their learning progress. The subscription management unit also adds an incentive function to the subscription model that provides rewards and benefits according to the user's learning progress, thereby increasing motivation to learn. For example, it provides benefits according to the learning progress. The subscription management unit also adds an incentive function to the subscription model that provides rewards and benefits according to the user's learning progress, thereby encouraging users to continue using the service. For example, it provides special rewards to long-term users. This makes it possible to provide rewards and benefits according to the learning progress.

[0084] The subscription management unit can use the emotion estimation function to analyze user emotion data and use the data to improve subscription plans. For example, the subscription management unit uses the emotion estimation function to analyze user emotion data and build a system that helps improve subscription plans. For example, the content of the plan is adjusted based on the user's emotional response. The subscription management unit also uses the emotion estimation function to analyze user emotion data and build a feedback loop that helps improve subscription plans. For example, the plan is periodically updated based on the user emotion data. The subscription management unit also uses the emotion estimation function to analyze user emotion data and build a database that helps improve subscription plans. For example, the subscription management unit accumulates user emotion data and optimizes the content of the plan. This can help improve subscription plans.

[0085] The school introduction department can develop a system that monitors usage at schools in real time and makes proposals for curriculum improvement based on usage data. For example, the school introduction department builds a system that monitors usage at schools in real time and makes proposals for curriculum improvement based on usage data. For example, it collects dictionary usage data during lessons and identifies areas for curriculum improvement. The school introduction department also develops a dashboard for monitoring usage at schools in real time and making proposals for curriculum improvement based on usage data. For example, it allows teachers to check usage data and adjust the curriculum. The school introduction department also builds a feedback loop for monitoring usage at schools in real time and making proposals for curriculum improvement based on usage data. For example, it regularly updates the curriculum based on usage data. This makes it possible to make proposals for curriculum improvement.

[0086] The school introduction department can provide dictionary content customized for each school and meet the needs of the educational field. For example, the school introduction department builds a system that provides dictionary content customized for each school. For example, it provides dictionary content that is tailored to the curriculum of each school. The school introduction department also develops a feedback system to provide dictionary content customized for each school and meet the needs of the educational field. For example, it adjusts the dictionary content based on feedback from teachers. The school introduction department also builds a database to provide dictionary content customized for each school and meet the needs of the educational field. For example, it optimizes the dictionary content according to the needs of each school. This makes it possible to meet the needs of the educational field.

[0087] The school introduction unit can use the emotion estimation function to collect student emotion data and provide feedback to improve educational effectiveness. The school introduction unit, for example, uses the emotion estimation function to collect student emotion data and build a system that provides feedback to improve educational effectiveness. For example, the school introduction unit adjusts the progress of a class based on the student emotion data. The school introduction unit also uses the emotion estimation function to develop a dashboard for collecting student emotion data and providing feedback to improve educational effectiveness. For example, the dashboard enables teachers to check student emotion data and adjust lesson content. The school introduction unit also uses the emotion estimation function to collect student emotion data and build a feedback loop for providing feedback to improve educational effectiveness. For example, the content of a class is updated regularly based on the student emotion data. This makes it possible to provide feedback to improve educational effectiveness.

[0088] The school onboarding department can provide special plans and discounts for educational institutions to promote use at schools. For example, the school onboarding department builds a system for providing special plans for educational institutions to promote use at schools. For example, it provides a subscription plan for each school. Furthermore, the school onboarding department develops a system for providing discounts for educational institutions to promote use at schools. For example, it applies a discount when a school signs a contract in bulk. Furthermore, the school onboarding department implements a marketing campaign to provide special plans and discounts for educational institutions to promote use at schools. For example, it conducts promotions for schools. As a result, it is possible to promote use at schools by providing special plans and discounts for educational institutions.

[0089] The School Introduction Department analyzes regional and national educational trends based on school usage data, and can use the data to improve educational policy. The School Introduction Department, for example, builds a system that analyzes regional and national educational trends based on school usage data. For example, it aggregates usage data from each school and visualizes educational trends. The School Introduction Department also analyzes regional and national educational trends based on school usage data and creates reports to help improve educational policy. For example, it makes policy recommendations based on educational trends. The School Introduction Department also analyzes regional and national educational trends based on school usage data and builds a database to help improve educational policy. For example, it accumulates educational trend data and uses it in policy planning. This allows educational trends to be analyzed and used to improve educational policy.

[0090] The school introduction unit can use the emotion estimation function to analyze students' emotion data and provide support to reduce stress and anxiety in educational settings. The school introduction unit, for example, uses the emotion estimation function to analyze students' emotion data and build a system to provide support to reduce stress and anxiety in educational settings. For example, counseling is provided based on the students' emotion data. The school introduction unit also uses the emotion estimation function to analyze students' emotion data and develop a feedback system to provide support to reduce stress and anxiety in educational settings. For example, it enables teachers to check students' emotion data and provide support. The school introduction unit also uses the emotion estimation function to analyze students' emotion data and build a database to provide support to reduce stress and anxiety in educational settings. For example, it accumulates students' emotion data and improves the quality of support. This makes it possible to provide support to reduce stress and anxiety in educational settings.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The information provision system may further include a personalized information provision unit that provides customized information based on the user's interests. For example, the system may prioritize relevant information based on keywords searched for by the user or pages viewed in the past. The personalized information provision unit may also analyze the user's behavioral patterns and provide information in advance that meets the user's predicted needs. Furthermore, the system may include a function that automatically notifies the user of the latest information related to a specific topic when the user shows interest in that topic. This allows the user to quickly and efficiently obtain information based on their interests.

[0093] The information provision system may further include an emotion-adaptive display unit that estimates the user's emotion and adjusts the way information is displayed based on the estimated emotion. For example, if the user is feeling stressed, the system may provide a simple, easy-to-read interface. Alternatively, if the user is excited, the system may provide a colorful, dynamic interface. Furthermore, the system may change the priority of information according to the user's emotion, preferentially displaying information that evokes positive emotions. This allows the user to receive information that is optimally displayed according to their emotion.

[0094] The information provision system can further include a learning style adaptation unit that provides information according to the user's learning style. For example, visual learners can be provided with information that makes extensive use of images and videos, while auditory learners can be provided with audio commentary. It can also provide interactive content to tactile learners. Furthermore, the difficulty level of the information can be adjusted according to the user's learning progress, providing information at an appropriate level. This allows users to efficiently obtain information optimized for their learning style.

[0095] The information provision system may further include an emotion feedback unit that estimates the user's emotion and provides feedback to motivate the user to learn based on the estimated emotion. For example, if the user has a positive emotion, the system may display an encouraging message. On the other hand, if the user has a negative emotion, the system may provide content that helps the user to relax. Furthermore, the system may evaluate the user's learning progress according to the user's emotion and provide appropriate feedback. This allows the user to have an optimal learning experience that suits their emotion.

[0096] The information provision system can also include a news provision unit that automatically provides related news and articles based on the user's search history. For example, if a user shows interest in a particular topic, the latest news and articles related to that topic can be automatically displayed. It can also prioritize the display of highly relevant news based on keywords the user has previously searched for. It can also include a function that periodically notifies the user of news related to topics that interest the user. This allows users to efficiently obtain the latest information.

[0097] The information provision system can further include an emotion filtering unit that estimates the user's emotion and filters search results based on the estimated emotion. For example, if the user is feeling stressed, the system can prioritize displaying information that will help the user relax. Alternatively, if the user is excited, the system can prioritize displaying stimulating information. Furthermore, the display order of search results can be changed according to the user's emotion, and information that elicits positive emotions can be prioritized. This allows the user to obtain optimal search results that match their emotions.

[0098] The information provision system may further include an advertisement provision unit that displays advertisements optimized for individual users based on the user's search history and browsing history. For example, highly relevant advertisements may be displayed based on keywords the user has previously searched for. Personalized advertisements may also be provided based on the user's interests and concerns. Furthermore, the system may analyze the user's behavioral patterns and display advertisements in advance that correspond to predicted needs. This allows users to efficiently obtain advertisements that are tailored to their interests and concerns.

[0099] The information provision system may further include an emotion-adaptive advertising unit that estimates the user's emotions and adjusts the advertisement display method based on the estimated emotions. For example, if the user is feeling stressed, the system may display a simple, easy-to-read advertisement. Alternatively, if the user is excited, the system may display a colorful, dynamic advertisement. Furthermore, the system may change the priority of advertisements according to the user's emotions and preferentially display advertisements that evoke positive emotions. This allows the user to receive advertisements that are optimally suited to their emotions.

[0100] The information provision system can further include a learning resource providing unit that provides learning resources optimized for individual users based on the user's search history and browsing history. For example, it can display highly relevant learning resources based on keywords the user has previously searched for. It can also provide personalized learning resources based on the user's interests. It can also provide learning resources at an appropriate level depending on the user's learning progress. This allows users to efficiently obtain resources optimized for their own learning needs.

[0101] The information provision system can further include an emotion adaptive learning unit that estimates the user's emotions and adjusts the method of providing learning resources based on the estimated emotions. For example, if the user is feeling stressed, the system can provide learning resources that help the user relax. Alternatively, if the user is excited, the system can provide stimulating learning resources. Furthermore, the system can change the priority of learning resources according to the user's emotions and provide resources that elicit positive emotions preferentially. This allows the user to obtain the optimal learning resources according to their emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The dictionary file storage unit stores the main dictionary files. For example, it stores a wide range of dictionaries, from general dictionaries to specialized books, such as Japanese dictionaries, English-Japanese dictionaries, complete collections of laws, and medical dictionaries. The dictionary file storage unit also automates the updating of dictionary files, ensuring that the latest information is always available. Step 2: The information search unit searches for information from the dictionary files stored in the dictionary file storage unit. For example, when a user inputs what they want to know through the app, the information search unit searches for appropriate information from the dictionary files in the storage and provides an answer. The information search unit can also use voice input to convert what the user dictates into text and search for information from the dictionary files. Step 3: The age-specific dictionary selection unit selects the information retrieved by the information search unit according to the user's age. For example, it provides a dictionary with simple explanations for elementary school students, a more detailed dictionary for junior high school students, and a specialized dictionary for high school and university students. Step 4: The subscription management unit manages the subscription model for the use of dictionary files. For example, fees can be divided between using only general dictionaries and using specialized books as well.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dictionary file storage unit for storing a main dictionary file; an information retrieval unit that retrieves information from the dictionary file stored in the dictionary file storage unit; an age-based dictionary selection unit that selects information retrieved by the information retrieval unit according to the age of the user; a subscription management unit that manages a subscription model for use of the dictionary file; A system characterized by:

2. The dictionary file storage unit Collecting emotional data when the user uses a specific dictionary, and recommending the dictionary according to the user's emotional state 2. The system of claim 1.

3. The information search unit Filtering search results according to the user's emotions, and preferentially displaying information that evokes positive emotions 2. The system of claim 1.

4. The era-specific dictionary selection unit The dictionary is selected in accordance with the user's feelings, and the user's motivation to learn is increased.

2. The system of claim 1.

5. The subscription management unit Customize plans based on the user's emotions to improve satisfaction 2. The system of claim 1.

Images