System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring schedule information of a user; means for analyzing a spare time from the schedule information; means for generating learning content related to a set learning theme based on the analyzed spare time; means for notifying the user of the generated learning content; and means for recording a learning result of the user.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] Currently, self-learning and the use of online AI assistants are attracting attention, but these methods rely on the user's initiative, making it difficult for many users to take the first step. It is also difficult to find time to study efficiently in busy daily lives. To solve this problem, a system is needed that can effectively utilize users' spare time and automatically provide the necessary learning materials. [Means for solving the problem]

[0005] The present invention provides a system that acquires a user's schedule information and analyzes their free time from it. This system can generate learning content related to a set learning topic based on the analyzed free time. Furthermore, the system notifies the user of the generated learning content and records the user's learning results. In this way, the user can study efficiently. It also includes a function to suggest related books, further deepening the user's learning. By utilizing generation AI, this system can provide the optimal learning content for the user.

[0006] "User" refers to the person who uses the system and performs the operations and inputs.

[0007] "Schedule information" is data that records a user's schedule and events, and is information that is managed by a calendar.

[0008] "Spare time" refers to free time that exists between scheduled events or tasks in the user's schedule information.

[0009] "Analysis" refers to the process of identifying free time from schedule information and extracting the necessary data.

[0010] A "learning topic" refers to the content of a specific knowledge or skill that a user wishes to acquire.

[0011] "Learning content" refers to learning materials that contain information and assignments related to a designated learning topic.

[0012] "Generation" refers to the process of automatically creating learning content, especially when done with the help of generative AI.

[0013] "Generative AI" refers to software that uses artificial intelligence techniques to automatically generate new data and information.

[0014] "Notification" refers to a means of informing a user of specific information or actions.

[0015] "Study results" refers to the results and grades achieved when a user engages in learning content, and includes recorded data.

[0016] "Recording" refers to the process of saving learning results so that they can be referenced later.

[0017] "Related books" refers to books and materials related to the learning topic, and are additional learning resources that can deepen the user's understanding. [Brief explanation of the drawings]

[0018] [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. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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, a 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), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] 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), Bluetooth (registered trademark), etc.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The system of the present invention is designed to effectively utilize users' spare time and support efficient learning. This system acquires the user's calendar information and generates, notifies, and records learning content based on the analyzed spare time. The program and processing of this system are explained in natural language below.

[0040] Get a user's calendar information

[0041] The device periodically obtains the user's calendar information. If the user uses Google (registered trademark) Calendar, for example, the device obtains schedule information using the Google Calendar API and sends it to the server. The server then stores the received calendar information in a database.

[0042] Analysis of spare time

[0043] The server analyzes the received calendar information and finds available time slots. The server calculates the time difference between calendar events and identifies the most suitable available time slots. The analyzed available time slots are then listed.

[0044] Registering and managing study topics

[0045] Users register the topic they want to learn through the application. For example, they can enter topics such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[0046] Generating learning content

[0047] The server uses generative AI to generate learning content based on the registered learning themes. For example, it uses the OpenAI (registered trademark) API to generate basic Python programming explanations and exercises. This allows for the creation of customized learning materials tailored to the user's learning level.

[0048] Learning content notifications

[0049] The server then sends the generated learning content to the device in the next available time slot. The device then notifies the user and encourages them to study. The notification includes a link to the learning content and a brief introduction.

[0050] Recording learning results

[0051] The user works through the provided learning content and inputs the results. For example, this could be quiz answers or study time. The device sends the learning results to the server, which stores them in a database. This allows the user's learning history to be accumulated and progress to be checked.

[0052] Related book suggestions

[0053] The server suggests related books based on the user's study topic and history. For example, it uses the Amazon API to obtain a list of books related to the user's study topic. The server sends this information to the device, and the device notifies the user.

[0054] Specific examples

[0055] Suppose User B has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User B and prompts him / her to access the learning content. When User B finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User B via the device.

[0056] In this way, the system of the present invention makes effective use of the user's spare time and improves learning efficiency, thereby enabling the user to actively engage in learning.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The device retrieves the user's calendar information. If the user uses Google Calendar, the device uses the Google Calendar API to retrieve the user's schedule information. This information is converted into JSON format and then sent to the server as a POST request.

[0060] Step 2:

[0061] The server receives the calendar information, parses it, and extracts the start and end times of each event. The extracted information is then stored in a database.

[0062] Step 3:

[0063] The server analyzes the calendar information stored in the database to identify gaps in time. Specifically, it calculates the difference between the end time of each event and the start time of the next event, and lists the gaps.

[0064] Step 4:

[0065] The server ranks the listed spare time slots by priority, based on the length of the spare time slots and the user's past learning history.

[0066] Step 5:

[0067] Users register the topic they want to learn through the learning application. They enter topics such as "Python programming" or "data science" into an input form on the application, and the device sends this information to the server.

[0068] Step 6:

[0069] The server uses generative AI to generate relevant learning content based on the learning topic information stored in the database. For example, it uses a specific API to generate textbooks and quizzes on basic concepts of Python programming.

[0070] Step 7:

[0071] The server transmits the generated study content to the device in time for the next free time. After the device receives the study content, it sends a notification to the user when the next free time approaches.

[0072] Step 8:

[0073] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0074] Step 9:

[0075] The user engages with the provided learning content, for example, by reading learning resources or answering quizzes. When the user has finished learning, they enter the results into the device.

[0076] Step 10:

[0077] The device sends the user's learning results (e.g., number of correct answers in quizzes, study time) to the server. The server receives this information and stores it in a database to update the user's learning history.

[0078] Step 11:

[0079] The server suggests related books based on the user's study topic and study history. It uses a specific API to obtain a list of related books and sends it to the device.

[0080] Step 12:

[0081] The device notifies the user of book suggestions, including a synopsis and a link to purchase, allowing the user to further their learning.

[0082] Through these steps, the system allows users to effectively utilize their spare time and automatically obtain the most suitable learning content for studying.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] In today's busy lifestyles, it is difficult for individuals to find time to study effectively. In particular, there is a lack of support for effectively utilizing spare time to advance their studies. Furthermore, existing learning support systems have difficulty responding flexibly to users' schedules and providing individually customized content. Therefore, there is a need for a system that can effectively utilize users' spare time and support their studies efficiently and effectively.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for acquiring a user's schedule information, means for analyzing spare time from the schedule information, means for generating study content related to a set study topic based on the analyzed spare time, means for notifying the user of the generated study content, means for recording the user's study results, and means for using a generative AI model to generate the study content, thereby enabling analysis of the user's spare time and providing individually customized study content.

[0088] "User's schedule information" is data containing the user's schedule, obtained from a calendar application or schedule management system.

[0089] "Split time" refers to a short period of unused time between a user's scheduled appointments.

[0090] A "study topic" is a particular field or topic that a user wishes to study.

[0091] "Study content" refers to information such as teaching materials and exercises that are provided to users when they study.

[0092] A "generative AI model" is a program or system that uses artificial intelligence to generate learning content for users.

[0093] A "prompt" is a text instruction that instructs a generative AI model to generate specific learning content.

[0094] The "means for acquiring" refers to a function or device that performs the processing required to acquire the user's schedule information.

[0095] The "analyzing means" refers to a function or device for identifying and analyzing free time from the acquired schedule information.

[0096] The "means for generating" refers to a function or device that creates learning content based on the analyzed spare time.

[0097] The "notification means" refers to a function or device for notifying the user of the generated learning content.

[0098] "Recording means" refers to a function or device for saving and managing the user's learning results.

[0099] "Means for using a generative AI model" refers to a function or device for generating learning content using a generative AI model.

[0100] "Related materials" refers to information such as external books and literature that is necessary or useful for the user's study.

[0101] The present invention provides a system for effectively utilizing a user's spare time and supporting efficient learning. This system acquires the user's schedule information and generates, notifies, and records learning content based on the analyzed spare time. An embodiment of this system is described below in detail.

[0102] Get user calendar information

[0103] The device periodically retrieves the user's calendar information. If the user uses a schedule management system such as Google Calendar, the device uses the Google Calendar API to obtain the user's authentication information and retrieves the user's schedule information. This information includes the start time, end time, and event title of each event set by the user. The retrieved calendar information is sent to the server via a secure connection. The server stores the received calendar information in a database.

[0104] Analysis of spare time

[0105] The server analyzes the calendar information stored in the database. The server calculates the time difference between calendar events and identifies the user's free time. Free time is set based on certain criteria (e.g., 15 minutes or more of unused time). This analysis results in a list of the user's available study times during the day.

[0106] Registering and managing study topics

[0107] Users input the topic they want to learn through the application. For example, they can select "Python programming" or "data science." This input allows for customization according to the user's learning needs. Once the topic input is complete, the device sends this information to the server, which then stores the topic information in a database.

[0108] Generating learning content

[0109] The server generates learning content using a generative AI model based on the learning themes registered by the user. The generative AI uses, for example, the OpenAI API. By inputting appropriate prompts to the generative AI, learning materials appropriate for the user are generated.

[0110] Prompt Sentence Examples

[0111] The topic users want to learn is "Python Programming Basics." Please create simple learning materials for beginners. Please provide concise learning materials that include:

[0112] Python Basic Syntax

[0113] Simple code example

[0114] Exercises

[0115] Learning content notifications

[0116] The server sends the generated learning content to the device in time for the next available time slot. For example, the learning content is sent to the device in time for the available time slot between 9:00 and 9:30 the next day. The device receives this content and notifies the user. The notification includes a link to the learning content and a brief introduction.

[0117] Recording learning results

[0118] The user works through the provided learning content and enters the results into the application. This includes, for example, the results of quiz answers and the time spent studying. These results are sent to the server via the device. The server stores the received learning results in a database and manages them as the user's learning history.

[0119] Suggestions for related materials

[0120] The server suggests relevant materials based on the user's learning topic and learning history. For example, it uses the Amazon API to retrieve a list of books on "data science." Information about the suggested books is then sent to the user via their device.

[0121] Example operation

[0122] Let's say User B registers "Data Science" as a study topic in the application and wishes to study during their morning commute. First, the device sends the user's Google Calendar information to the server in the morning. The server finds a gap in time between 9:00 and 9:30. Next, the server sends a prompt to the generation AI to generate "Basic Data Science Teaching Materials," and the materials are generated. Then, as 9:00 approaches, the server sends the learning content to the device, and the device notifies User B. When User B finishes studying and enters the results, the device sends them to the server, which saves them. The server then retrieves a list of related books through the Amazon API and notifies User B.

[0123] In this way, the system of the present invention provides an efficient and customizable learning environment that allows users to make effective use of their spare time.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1:

[0126] The device retrieves the user's calendar information. As input, the user's authentication information is required. If using the Google Calendar API, the device retrieves the calendar information using the user's authentication information. As output, the retrieved calendar information is returned. The calendar information includes the start time, end time, and title of the event. This information is then sent to the server over a secure connection.

[0127] Step 2:

[0128] The server receives the calendar information sent from the device and stores it in a database. As input, it takes the calendar information sent from the device. It analyzes the calendar information and identifies available time slots by calculating the time difference between events. As output, it obtains a list of available time slots stored in the database. This list contains the available time slots for the user throughout the day.

[0129] Step 3:

[0130] The user registers a learning topic using the application. As input, the user enters a learning topic such as "Python programming" or "data science." Once the topic has been entered, the device sends this information to the server. As output, the topic information is stored in a database on the server side.

[0131] Step 4:

[0132] The server uses a generative AI model to generate learning content based on the learning topic registered by the user. The learning topic and a prompt are used as input. For example, the prompt "The topic the user wants to learn is 'Python programming basics'. Please create simple learning materials for beginners." is input to the generative AI model. The generated learning content is obtained as output. This content includes learning materials and exercises.

[0133] Step 5:

[0134] The server sends the generated learning content to the device in time for the next available time. The generated learning content and the identified available time are used as input. The output is the learning content sent to the device. The device receives this content and notifies the user using push notifications or alarms.

[0135] Step 6:

[0136] The user works on the provided learning content. As input, access to the learning content is required. The user proceeds with the learning and enters the results into the application. As output, quiz answers, study time, etc. are obtained. The device sends these results to the server.

[0137] Step 7:

[0138] The server stores the received learning results in a database. The learning results sent by the user are used as input. The learning results are stored in a database as output and managed as the user's learning history.

[0139] Step 8:

[0140] The server suggests related materials based on the user's learning topic and history. The user's learning topic and history are used as input. The server obtains a list of related books, for example, using the Amazon API. The output is a list of suggested related materials. The device receives this information and notifies the user.

[0141] In this way, the system effectively utilizes users' spare time and supports efficient learning by providing individually customized learning content.

[0142] (Application example 1)

[0143] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0144] In modern society, there are many systems that allow users to effectively utilize their "spare time," but these systems have several problems. For example, if the spare time is too short, learning and information intake may not be effective, or the appropriate content may not be provided to the user. Furthermore, existing systems do not fully utilize generative AI models, making it difficult to provide customized learning materials suited to each user's individual learning topic and level. Therefore, a new system that effectively utilizes users' spare time and provides individually optimized learning content is needed.

[0145] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0146] In this invention, the server includes a means for acquiring the user's calendar information, a means for analyzing spare time from the calendar information, a means for providing study content only if the analyzed spare time is equal to or greater than a certain level, and a means for using a generative AI model to generate study content according to the study topic. This makes it possible to effectively utilize spare time and provide optimal study content according to the user's study topic.

[0147] "User calendar information" is data obtained from applications and systems that users use to record their daily schedules and appointments.

[0148] "Spare time" refers to free time between scheduled events in the user's calendar information.

[0149] A "study topic" is a particular subject or area that a user wishes to learn.

[0150] "Learning content" refers to information such as teaching materials, exercises, and explanatory texts that are provided based on a learning theme set by the user.

[0151] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to generate appropriate outputs for specific inputs.

[0152] The "scheduling means" is a means for periodically updating calendar information and carrying out planned processing for analyzing free time.

[0153] "Study history" refers to data that records what a user has learned and their progress to date.

[0154] "Related books" are books and reference materials related to the study theme set by the user.

[0155] This invention is a system that analyzes a user's calendar information and provides optimal learning content for spare time. Users can access this system using their smartphones to register and manage their calendar information.

[0156] First, the device (smartphone) periodically collects the user's calendar information using the Google Calendar API, etc. The collected calendar information is sent to a server, which then stores the information in a database.

[0157] Next, the server analyzes the calendar information and calculates the gap time between each event. This analysis involves first calculating the time difference between calendar events, and then listing gap times that are greater than a certain range (for example, 10 minutes or more).

[0158] Users register their study topics through a smartphone application. For example, they can set themes such as "Python programming" or "data science." The study topics registered by the user are sent to the server and stored in a database.

[0159] The server uses a generative AI model to generate learning content based on the learning themes registered by the user. For example, it uses the OpenAI API to generate basic explanations and exercises for "Python programming." The generated learning content is customized to the user's learning level.

[0160] The generated learning content is then sent to the device at the optimal time for the user to study. The device then notifies the user of the learning content and encourages them to study. The notification includes a link to the learning content and a brief introduction.

[0161] The user works on the learning content and inputs the results. For example, this includes quiz answers and study time. The device sends the learning results to the server, which stores them in a database. This allows the user's learning history to be accumulated and progress to be checked.

[0162] Furthermore, the server suggests related books based on the user's study topic and history, using the Amazon API or similar to obtain a list of books related to the study topic, and notifies the user via the device.

[0163] As a concrete example, suppose User B has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User B and prompts him / her to access the learning content. When User B finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User B via the device.

[0164] An example prompt is:

[0165] "Topic: Python Programming\nGenerate a textbook that provides basic explanations and exercises for Python."

[0166] By using the above means, the present invention can efficiently utilize the users' spare time and provide content that meets their individual learning needs.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] The device periodically collects the user's calendar information using the Google Calendar API, etc. At this time, the device acquires the calendar information and sends it to the server. The input is the event data acquired from the Google Calendar API, and the output is the calendar information sent to the server.

[0170] Step 2:

[0171] The server saves the received calendar information in a database, which stores the user's schedule. The input is the calendar information sent from the terminal, and the output is the calendar information stored in the database.

[0172] Step 3:

[0173] The server analyzes the stored calendar information and calculates the gap time between each event. The analysis involves calculating the time difference between calendar events and listing gap times greater than a certain amount (for example, 10 minutes or more). The input is the calendar information obtained from the database, and the output is the listed gap time.

[0174] Step 4:

[0175] The user registers a learning topic using the terminal. For example, they set a topic such as "Python programming" or "data science." The input is the learning topic that the user enters into the terminal, and the output is the learning topic information sent to the server.

[0176] Step 5:

[0177] The server stores the learning topic information in a database and manages the user's learning topics. The input is the learning topic information sent from the terminal, and the output is the learning topic information stored in the database.

[0178] Step 6:

[0179] The server generates learning content using a generative AI model based on the learning topic registered by the user. At this time, a prompt is sent using the generative AI (e.g., OpenAI API) to obtain the generated content. The input is the learning topic and prompt registered by the user, and the output is the generated learning content. An example of a prompt is: "Topic: Python programming\nPlease generate a text that provides basic explanations and practice questions for Python."

[0180] Step 7:

[0181] The server sends the generated learning content to the device according to the listed spare time. The notification includes a link to the learning content and a brief introduction. The input is the generated learning content and spare time information, and the output is the learning content notified to the device.

[0182] Step 8:

[0183] The user works on the learning content received from the device and inputs the results, such as quiz answer results and study time. The input is the learning results that the user enters into the device, and the output is the learning results sent to the server.

[0184] Step 9:

[0185] The server stores the user's learning results in a database and accumulates the user's learning history, allowing the user to check their progress. The input is the learning results sent from the device, and the output is the learning history stored in the database.

[0186] Step 10:

[0187] The server suggests related books based on the user's study topic and history. It obtains a list of related books using the Amazon API or similar and notifies the device. The input is the user's study topic and history, and the output is the obtained list of related books.

[0188] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0189] The system of the present invention is designed to effectively utilize users' spare time and support efficient learning. The system acquires the user's calendar information and generates, notifies, and records learning content based on the analyzed spare time. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotions and further improves the learning experience.

[0190] Get a user's calendar information

[0191] The device periodically retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves the user's schedule information using the Google Calendar API. This information is converted into JSON format and sent to the server as a POST request. The server stores the received calendar information in a database.

[0192] Analysis of spare time

[0193] The server analyzes the calendar information stored in the database to identify gaps in time. It calculates the difference between the end time of each event and the start time of the next event to create a list of optimal gaps. From the list of gaps, it ranks the most appropriate times based on priority. This ranking is based on the length of the gaps and past learning history.

[0194] Registering and managing study topics

[0195] Users register the topic they want to learn through the learning application. For example, they can input a topic such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[0196] Generating learning content

[0197] The server uses generative AI to generate learning content based on the registered learning topic. For example, it generates text and quizzes on basic concepts of Python programming. The generated learning content is then sent to the device in the next available time slot.

[0198] Introducing the Emotion Engine

[0199] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. The emotion engine's analysis results are sent from the device to the server.

[0200] Emotion-based learning content adjustment

[0201] The server dynamically adjusts learning content based on the emotional data obtained from the emotion engine. For example, if a user is nervous, it can provide relaxing content or encouraging messages.

[0202] Learning content notifications

[0203] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0204] Recording learning results

[0205] The user works through the provided learning content and inputs the results, such as quiz answers and study time. The device sends the learning results to the server, which then stores them in a database and updates the user's learning history.

[0206] Related book suggestions

[0207] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions. The notification includes a summary of the book and a link to purchase it.

[0208] Specific examples

[0209] Consider the case where User A has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User A and encourages him / her to access the learning content. The device further analyzes the user's emotions using an emotion engine and provides appropriate relaxation techniques and encouraging messages. When User A finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User A via the device.

[0210] In this way, the system of the present invention is a system that effectively utilizes the user's spare time to efficiently advance learning, and further improves the user's learning experience by using an emotion engine.

[0211] The processing flow will be explained below.

[0212] Step 1:

[0213] The device retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves schedule information using the Google Calendar API. This information is converted into JSON format and sent to the server.

[0214] Step 2:

[0215] The server receives the calendar information and stores it in a database. The start and end times of each event in the calendar are extracted and stored.

[0216] Step 3:

[0217] The server analyzes the calendar information stored in the database to identify gaps in time, calculates the difference between the end time of each event and the start time of the next event, and lists them.

[0218] Step 4:

[0219] The server ranks the listed spare time slots by priority, based on the length of the spare time slots and the user's past learning history.

[0220] Step 5:

[0221] Users register the topic they want to learn through the learning application. They enter topics such as "Python programming" or "data science" into the application's input form. After entering the information, the device sends the topic information to the server.

[0222] Step 6:

[0223] Based on the learning topics stored in the database, the server uses generative AI to generate relevant learning content, such as textbooks and quizzes on basic Python programming concepts.

[0224] Step 7:

[0225] The device uses an emotion engine to support user learning. When a user uses an application, the device uses a camera and microphone to capture the user's facial expressions and voice tone, which are then analyzed by the emotion engine.

[0226] Step 8:

[0227] The server adjusts the learning content based on the generated learning content and the analysis results of the emotion engine. For example, if the user is nervous, it adds content to help them relax or an encouraging message.

[0228] Step 9:

[0229] The server transmits the adjusted learning content to the terminal in time for the next free time. The terminal receives the learning content and transmits a notification to the user when the next free time approaches.

[0230] Step 10:

[0231] The device notifies the user at the designated time and displays a link to the learning content. When the user clicks the link, the application displays the learning content.

[0232] Step 11:

[0233] The user engages with the provided learning content, for example, by reading learning resources or answering quizzes. When the user has finished learning, they enter the results into the device.

[0234] Step 12:

[0235] The device sends the user's learning results (e.g., number of correct answers in quizzes, study time) to the server. The server receives this information and stores it in a database to update the user's learning history.

[0236] Step 13:

[0237] The server suggests related books based on the user's study topic and study history. It uses a specific API to obtain a list of related books and sends it to the device.

[0238] Step 14:

[0239] The device notifies the user of book suggestions, which include a synopsis of the book and a link to purchase it for easy access by the user.

[0240] Through these steps, users can effectively utilize their free time and study. The introduction of the emotion engine provides appropriate learning support according to the user's psychological state, improving the learning experience.

[0241] Example 2

[0242] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0243] Conventional learning systems have had difficulty efficiently utilizing users' spare time. Furthermore, they have not provided learning content that takes into account the user's emotional state, which can lead to reduced learning efficiency. Furthermore, the suggestion of related books must be done manually, creating a need for an automated system.

[0244] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0245] In this invention, the server includes a means for acquiring a user's schedule information, a means for analyzing spare time, a means for generating study content related to a study topic based on the analyzed spare time, a means for notifying the user of the generated study content, a means for recording the user's study results, and a means for dynamically adjusting the study content based on the emotion data, incorporating an emotion engine for recognizing the user's emotions. This allows for effective use of the user's spare time and provision of study content that takes into account their emotional state. Furthermore, related book suggestions are also automated, providing users with optimal study resources.

[0246] "Calendar information" is data that includes details of a user's schedule and events.

[0247] "Spare time" is free time between consecutive events in a user's schedule information.

[0248] A "study topic" is a specific topic or area that a user wishes to study.

[0249] "Study content" refers to information such as learning materials, quizzes, and texts that users use to study.

[0250] "Generative AI" is an artificial intelligence technology that automatically generates text and content.

[0251] "Notification" is the process of communicating information from the system to the user.

[0252] The "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to determine their emotional state.

[0253] "Emotion data" is information that indicates the user's emotional state analyzed by the emotion engine.

[0254] "Study results" refers to data such as the results and answers obtained when a user engages with study content.

[0255] "Related books" are books related to the study topic and are suggested to complement the user's studies.

[0256] MODE FOR CARRYING OUT THE INVENTION

[0257] The system of the present invention is designed to effectively utilize a user's spare time and support efficient learning. The system acquires the user's schedule information and generates, notifies, and records learning content based on the analyzed spare time. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotions and further improves the learning experience. Specific embodiments are described below.

[0258] Get user calendar information

[0259] The device periodically obtains the user's calendar information. For example, if the user uses Google Calendar, the device obtains the user's schedule information using the Google Calendar API. This schedule information is converted into JSON format and then sent to the server as a POST request. The server then stores the received calendar information in a database.

[0260] Hardware used: Device (smartphone or tablet)

[0261] Software used: Google Calendar API, server software (e.g., Node.js server)

[0262] Example: A device sends a request to the "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events" endpoint to retrieve the user's calendar information.

[0263] Analysis of spare time

[0264] The server analyzes the schedule information stored in the database to identify gap times. It calculates the difference between the end time of each event and the start time of the next event to list the optimal gap times. From this list, it ranks the most appropriate gap times based on priority. This ranking is based on the length of the gap time and past learning history.

[0265] Hardware used: Server

[0266] Software used: Database (MySQL (registered trademark) or PostgreSQL), backend script (Python or JavaScript (registered trademark))

[0267] Example: If the events in a schedule are from 9:00 to 10:00 and from 12:00 to 13:00, the two hours from 10:00 to 12:00 are recognized as gap time.

[0268] Registering and managing study topics

[0269] Users register the topic they want to learn through the learning application. For example, they can enter topics such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[0270] Hardware used: Terminal

[0271] Software used: Front-end application (ANDROID (registered trademark) / iOS app, web app)

[0272] Example: A user enters "Python programming" into an application form and submits the information via a POST request to "https: / / example.com / api / register_theme."

[0273] Generating learning content

[0274] The server uses AI to generate learning content based on the registered learning topic. For example, it generates text and quizzes on basic concepts of "Python programming." The generated content is then sent to the device in the next available time slot.

[0275] Hardware used: Server

[0276] Software used: Generative AI model (e.g., OpenAI GPT-3®)

[0277] Example: The prompt "Please create a lesson for beginners on Python programming" is input into a generative AI model to generate learning content.

[0278] Introducing the Emotion Engine

[0279] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. The emotion engine's analysis results are sent from the device to the server.

[0280] Hardware used: Device (camera, microphone)

[0281] Software used: Emotion recognition engine (e.g., Microsoft® Azure® Emotion API)

[0282] Example: The device captures the user's facial expression with the camera and sends it to "https: / / centralus.api.cognitive.microsoft.com / face / v1.0 / detect" for analysis.

[0283] Emotion-based learning content adjustment

[0284] The server dynamically adjusts learning content based on the emotion data sent from the emotion engine. For example, if the user is nervous, it provides content to help them relax or messages of encouragement.

[0285] Hardware used: Server

[0286] Software used: Backend script (Python / Node.js)

[0287] Example: A server receives data that says "The user is nervous" and generates a message that says "Try some deep breathing exercises to relax."

[0288] Learning content notifications

[0289] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0290] Hardware used: Terminal

[0291] Software used: Push notification service (e.g., Firebase Cloud Messaging)

[0292] Example: Your device receives a notification saying, "Learn basic Python concepts between 9:00-9:30."

[0293] Recording learning results

[0294] The user works through the provided learning content and inputs the results, such as quiz answers and study time. This information is sent from the device to the server, which stores it in a database and updates the user's learning history.

[0295] Hardware used: Terminal

[0296] Software used: Front-end application, back-end API

[0297] Example: A user submits quiz results to "https: / / example.com / api / submit_results". The server saves the results to a database.

[0298] Related book suggestions

[0299] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions.

[0300] Hardware used: Server

[0301] Software used: Book suggestion API (e.g., Amazon API)

[0302] Example: A server requests "books related to Python programming" from the Amazon API and notifies the user of the results.

[0303] In this way, the system is designed to make effective use of users' spare time and allow them to study efficiently.

[0304] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0305] Step 1:

[0306] The device retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves the schedule information using the Google Calendar API. This information is converted to JSON format and sent to the server as a POST request.

[0307] Input: Google Calendar API endpoint "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events"

[0308] Data processing: Convert schedule information into JSON format

[0309] Output: Sends JSON formatted calendar information to the server.

[0310] Specific behavior: The device accesses the API endpoint to retrieve the user's calendar information, then converts the information into JSON format and sends a POST request to "https: / / example.com / api / calendar_data".

[0311] Step 2:

[0312] The server stores the received calendar information in a database, which is used for later analysis.

[0313] Input: Calendar information in JSON format sent from the device

[0314] Data processing: Insert the received JSON data into the database

[0315] Output: Calendar information stored in a database

[0316] Specific operation: The server receives the POST request, parses the JSON data, and saves it to the database.

[0317] Step 3:

[0318] The server analyzes the schedule information stored in the database to identify gap times, calculates the difference between the end time of each event and the start time of the next event, and lists the gap times.

[0319] Input: Calendar information stored in a database

[0320] Data processing: Calculate gap time by comparing the end time of each event with the start time of the next event

[0321] Output: List of gap times

[0322] Specific operation: The server retrieves the schedule information from the database, calculates the time difference between events, and lists the gap time.

[0323] Step 4:

[0324] Users register the topic they want to learn through the learning application, for example, entering a topic such as "Python programming."

[0325] Input: The learning topic that the user enters into the input form

[0326] Data processing: Convert the input theme information into JSON format

[0327] Output: Study topic information sent to the server

[0328] Specific behavior: The user enters "Python programming" into the app's input form and sends a POST request to "https: / / example.com / api / register_theme".

[0329] Step 5:

[0330] The server uses generative AI to generate learning content based on registered learning themes, such as text and quizzes on "Python programming."

[0331] Input: The learning topic registered by the user (e.g., Python programming)

[0332] Data processing: Enter a prompt into the generation AI and obtain the generated content.

[0333] Output: Generated learning content

[0334] Specific operation: The server inputs the prompt "Please create a lesson for beginners in Python programming" into the generation AI and obtains the result.

[0335] Step 6:

[0336] The device recognizes the user's emotions using an emotion engine, which analyzes the user's facial expressions and vocal tone to determine their current emotional state.

[0337] Input: User's facial expression data and voice tone

[0338] Data processing: Analysis using emotion engine

[0339] Output: Parsed emotion data

[0340] Specific operation: The device captures the user's facial expression with the camera, sends it to "https: / / centralus.api.cognitive.microsoft.com / face / v1.0 / detect" for analysis, and then sends the results to the server.

[0341] Step 7:

[0342] The server dynamically adjusts learning content based on the emotional data obtained from the emotion engine. For example, if the user is nervous, it provides content to help them relax or messages of encouragement.

[0343] Input: Emotion data sent from the emotion engine

[0344] Data manipulation: adjusting content based on sentiment data

[0345] Output: Tailored learning content

[0346] Specific behavior: The server receives data that "the user is nervous" and generates a message that says "Try some deep breathing exercises to relax."

[0347] Step 8:

[0348] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0349] Input: Learning content and time information sent from the server

[0350] Data processing: Converting learning content information into notification format

[0351] Output: Learning content link notified to user

[0352] What happens: Your device displays a push notification saying, "Learn basic Python concepts between 9:00 and 9:30."

[0353] Step 9:

[0354] The user works through the provided learning content and inputs the results, such as quiz answers and study time. This information is sent from the device to the server, which stores it in a database and updates the user's learning history.

[0355] Input: User-entered learning results

[0356] Data processing: Converting learning result data into JSON format

[0357] Output: Training results sent to the server

[0358] Specific behavior: The user submits the quiz results to "https: / / example.com / api / submit_results". The server saves the results in the database.

[0359] Step 10:

[0360] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions.

[0361] Input: User's study topic and history

[0362] Data processing: Use related book API to obtain book information

[0363] Output: A list of suggested related books

[0364] Specific operation: The server requests "books related to Python programming" from the Amazon API and notifies the user of the results.

[0365] (Application example 2)

[0366] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0367] Today's busy brick-and-mortar stores face challenges in effectively utilizing their free time and efficiently acquiring the necessary skills and knowledge. Furthermore, learning content is not tailored to individual emotions and situations, and uniform training can lower employee motivation. Furthermore, learning results are not properly recorded, and related books are scarce, limiting the ability to provide continuous learning support for employees.

[0368] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's schedule information, means for analyzing spare time from the schedule information, means for generating study content related to a study topic set based on the analyzed spare time, means for analyzing the user's emotions and means for adjusting the study content based on the emotions, means for notifying the user of the generated study content, and means for recording the user's study results. This allows store employees to efficiently utilize their spare time to study, and in the process, content tailored to their individual emotional state is provided, thereby improving motivation and continuously improving their skills.

[0369] The "means for acquiring user's schedule information" is a function for periodically acquiring schedule information from the schedule system used by the user.

[0370] The "means for analyzing free time from schedule information" is a function for analyzing the acquired schedule information and identifying free time (free time) that exists between schedules.

[0371] The "means for generating learning content related to a learning topic" is a function for automatically creating content to be used in education or training based on a specific learning topic.

[0372] The "means for analyzing the user's emotions" is a function for analyzing the user's current emotional state using data such as the user's facial expressions, voice, and behavior.

[0373] The "means for adjusting learning content based on emotions" is a function that changes the difficulty level, content, etc. according to the analyzed user emotions, thereby optimizing the user's learning experience.

[0374] The "means for notifying the user of the generated study content" is a function for notifying the user of the generated study content as appropriate and encouraging them to start studying.

[0375] "Means for recording user learning results" refers to a function that allows users to record the results and progress of the learning content they have worked on, so that they can be referenced and evaluated later.

[0376] This invention provides a system that effectively utilizes a user's spare time and promotes efficient learning. This system acquires the user's schedule information, analyzes spare time, and generates, notifies, and records learning content based on that spare time.

[0377] System configuration

[0378] Hardware and Software Requirements

[0379] Device: A mobile device such as a smartphone or tablet used by a user.

[0380] Calendar API: An API for retrieving a user's calendar information, such as the Google Calendar API.

[0381] Server: A server for managing users' schedule information, spare time, study themes, study results, etc.

[0382] Generative AI models: Generative AI models for generating learning content, such as OpenAI's GPT-3.

[0383] Emotion engine: An engine that analyzes the user's facial expressions and voice to determine their emotional state.

[0384] Notification system: A system for notifying users of learning content.

[0385] Database: A database for storing users' calendar information, study topics, and study results.

[0386] Processing flow

[0387] 1. Get the user's calendar information:

[0388] The device periodically retrieves the user's calendar information using a calendar API such as Google Calendar. This information is converted into JSON format and sent to the server as a POST request. The server then stores the received calendar information in a database.

[0389] 2. Analysis of gap time:

[0390] The server analyzes the schedule information stored in the database to identify gaps in time. It calculates the difference between the end time of each event and the start time of the next event to list the optimal gaps in time. It then ranks the most suitable gaps from the list based on priority. This ranking is based on the length of the gaps and past learning history.

[0391] 3. Register and manage study topics:

[0392] The user registers the topic they want to learn through the learning application. For example, they can input topics such as "improving customer service skills" or "product knowledge." The device sends this information to the server, which then stores the topic information in a database.

[0393] 4. Learning content generation:

[0394] The server generates learning content using a generative AI model based on the registered learning theme. The generated learning content is provided in the form of text and quizzes. For example, text and quizzes on basic customer service etiquette are generated based on the theme "improving customer service skills." The content is generated based on the prompt, "Please generate learning content on improving customer service skills. The content should include basic customer service etiquette, communication techniques with customers, and methods for increasing customer satisfaction."

[0395] 5. Introducing the Emotion Engine:

[0396] The device is equipped with an emotion engine for analyzing the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice using the camera and microphone installed in the smartphone or tablet. The emotion engine's analysis results are sent from the device to a server.

[0397] 6. Emotion-based learning content adjustment:

[0398] The server dynamically adjusts learning content based on emotional data obtained from the emotion engine. For example, if the user is tired, it can provide relaxing content or encouraging messages.

[0399] 7. Learning Content Notification:

[0400] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0401] 8. Recording learning results:

[0402] The user works through the provided learning content and inputs the results, such as quiz answers and study time. The device sends the learning results to the server, which then stores them in a database and updates the user's learning history.

[0403] This system allows store employees to effectively utilize their free time to study efficiently, and by optimizing the learning experience based on emotional data, it is possible to increase motivation and continuously improve skills.

[0404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0405] Step 1:

[0406] Get calendar information

[0407] The device periodically retrieves the user's calendar information using the Google Calendar API. This information is converted to JSON format and sent to the server as a POST request. The input is the user's schedule retrieved from the Google Calendar API, and the output is the calendar information in JSON format. The server stores this information in a database.

[0408] Step 2:

[0409] Analysis of spare time

[0410] The server analyzes the schedule information stored in the database and identifies gap times by calculating the difference between the end time of each event and the start time of the next event. The input is the schedule information stored in the database, and the output is a list of gap times. The server ranks the optimal gap times based on priority.

[0411] Step 3:

[0412] Registering study themes

[0413] The user inputs a learning topic using a smartphone or tablet. For example, a topic such as "improving customer service skills" or "product knowledge" is registered in the application. The input is the learning topic entered by the user, and the output is the topic information sent to the server. The server stores this information in a database.

[0414] Step 4:

[0415] Generating learning content

[0416] The server uses a generative AI model based on the registered learning theme to generate learning content. The prompt statement is, "Please generate learning content related to improving customer service skills. The content should include basic customer service etiquette, communication techniques with customers, and ways to increase customer satisfaction." The input is the specified learning theme and prompt statement, and the output is the generated learning content.

[0417] Step 5:

[0418] Emotion analysis using an emotion engine

[0419] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice via a camera and microphone to determine their emotional state. The input is facial expression data and voice data obtained from the device, and the output is analyzed emotional state data. The device then sends this data to a server.

[0420] Step 6:

[0421] Emotion-based content adjustment

[0422] The server adjusts the learning content appropriately based on the data obtained from the emotion engine. For example, if the user is tired, it provides content to help them relax. The input is the analyzed emotion data and the generated learning content, and the output is the adjusted learning content.

[0423] Step 7:

[0424] Learning content notifications

[0425] The device notifies the user at the specified spare time and provides a link to the learning content. The input is the learning content adjusted for the spare time, and the output is the notification to the user. When the user clicks the link, the learning content is displayed.

[0426] Step 8:

[0427] Recording learning results

[0428] The user performs the learning content and inputs the results. For example, they record the results of quizzes and the time spent studying. The input is the learning results entered by the user, and the output is the learning result data sent to the server. The server stores this in a database and updates the user's learning history.

[0429] This allows the system to enable store employees to efficiently utilize their spare time and provide learning content tailored to their individual emotional state, thereby improving motivation and enabling continuous skill development.

[0430] 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.

[0431] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0432] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0433] [Second embodiment]

[0434] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0435] 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.

[0436] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0437] 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.

[0438] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0439] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0440] 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.

[0441] 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.

[0442] 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 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.

[0443] 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.

[0444] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0445] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0446] The system of the present invention is designed to effectively utilize users' spare time and support efficient learning. This system acquires the user's calendar information and generates, notifies, and records learning content based on the analyzed spare time. The program and processing of this system are explained in natural language below.

[0447] Get a user's calendar information

[0448] The device periodically obtains the user's calendar information. For example, if the user uses Google Calendar, the device obtains schedule information using the Google Calendar API and sends it to the server. The server then stores the received calendar information in a database.

[0449] Analysis of spare time

[0450] The server analyzes the received calendar information and finds available time slots. The server calculates the time difference between calendar events and identifies the most suitable available time slots. The analyzed available time slots are then listed.

[0451] Registering and managing study topics

[0452] Users register the topic they want to learn through the application. For example, they can enter topics such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[0453] Generating learning content

[0454] The server uses generative AI to generate learning content based on registered learning themes. For example, it uses the OpenAI API to generate basic Python programming explanations and exercises. This allows for the creation of customized learning materials tailored to the user's learning level.

[0455] Learning content notifications

[0456] The server then sends the generated learning content to the device in the next available time slot. The device then notifies the user and encourages them to study. The notification includes a link to the learning content and a brief introduction.

[0457] Recording learning results

[0458] The user works through the provided learning content and inputs the results. For example, this could be quiz answers or study time. The device sends the learning results to the server, which stores them in a database. This allows the user's learning history to be accumulated and progress to be checked.

[0459] Related book suggestions

[0460] The server suggests related books based on the user's study topic and history. For example, it uses the Amazon API to obtain a list of books related to the user's study topic. The server sends this information to the device, and the device notifies the user.

[0461] Specific examples

[0462] Suppose User B has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User B and prompts him / her to access the learning content. When User B finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User B via the device.

[0463] In this way, the system of the present invention makes effective use of the user's spare time and improves learning efficiency, thereby enabling the user to actively engage in learning.

[0464] The processing flow will be explained below.

[0465] Step 1:

[0466] The device retrieves the user's calendar information. If the user uses Google Calendar, the device uses the Google Calendar API to retrieve the user's schedule information. This information is converted into JSON format and then sent to the server as a POST request.

[0467] Step 2:

[0468] The server receives the calendar information, parses it, and extracts the start and end times of each event. The extracted information is then stored in a database.

[0469] Step 3:

[0470] The server analyzes the calendar information stored in the database to identify gaps in time. Specifically, it calculates the difference between the end time of each event and the start time of the next event, and lists the gaps.

[0471] Step 4:

[0472] The server ranks the listed spare time slots by priority, based on the length of the spare time slots and the user's past learning history.

[0473] Step 5:

[0474] Users register the topic they want to learn through the learning application. They enter topics such as "Python programming" or "data science" into an input form on the application, and the device sends this information to the server.

[0475] Step 6:

[0476] The server uses generative AI to generate relevant learning content based on the learning topic information stored in the database. For example, it uses a specific API to generate textbooks and quizzes on basic concepts of Python programming.

[0477] Step 7:

[0478] The server transmits the generated study content to the device in time for the next free time. After the device receives the study content, it sends a notification to the user when the next free time approaches.

[0479] Step 8:

[0480] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0481] Step 9:

[0482] The user engages with the provided learning content, for example, by reading learning resources or answering quizzes. When the user has finished learning, they enter the results into the device.

[0483] Step 10:

[0484] The device sends the user's learning results (e.g., number of correct answers in quizzes, study time) to the server. The server receives this information and stores it in a database to update the user's learning history.

[0485] Step 11:

[0486] The server suggests related books based on the user's study topic and study history. It uses a specific API to obtain a list of related books and sends it to the device.

[0487] Step 12:

[0488] The device notifies the user of book suggestions, including a synopsis and a link to purchase, allowing the user to further their learning.

[0489] Through these steps, the system allows users to effectively utilize their spare time and automatically obtain the most suitable learning content for studying.

[0490] Example 1

[0491] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0492] In today's busy lifestyles, it is difficult for individuals to find time to study effectively. In particular, there is a lack of support for effectively utilizing spare time to advance their studies. Furthermore, existing learning support systems have difficulty responding flexibly to users' schedules and providing individually customized content. Therefore, there is a need for a system that can effectively utilize users' spare time and support their studies efficiently and effectively.

[0493] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0494] In this invention, the server includes means for acquiring a user's schedule information, means for analyzing spare time from the schedule information, means for generating study content related to a set study topic based on the analyzed spare time, means for notifying the user of the generated study content, means for recording the user's study results, and means for using a generative AI model to generate the study content, thereby enabling analysis of the user's spare time and providing individually customized study content.

[0495] "User's schedule information" is data containing the user's schedule, obtained from a calendar application or schedule management system.

[0496] "Split time" refers to a short period of unused time between a user's scheduled appointments.

[0497] A "study topic" is a particular field or topic that a user wishes to study.

[0498] "Study content" refers to information such as teaching materials and exercises that are provided to users when they study.

[0499] A "generative AI model" is a program or system that uses artificial intelligence to generate learning content for users.

[0500] A "prompt" is a text instruction that instructs a generative AI model to generate specific learning content.

[0501] The "means for acquiring" refers to a function or device that performs the processing required to acquire the user's schedule information.

[0502] The "analyzing means" refers to a function or device for identifying and analyzing free time from the acquired schedule information.

[0503] The "means for generating" refers to a function or device that creates learning content based on the analyzed spare time.

[0504] The "notification means" refers to a function or device for notifying the user of the generated learning content.

[0505] "Recording means" refers to a function or device for saving and managing the user's learning results.

[0506] "Means for using a generative AI model" refers to a function or device for generating learning content using a generative AI model.

[0507] "Related materials" refers to information such as external books and literature that is necessary or useful for the user's study.

[0508] The present invention provides a system for effectively utilizing a user's spare time and supporting efficient learning. This system acquires the user's schedule information and generates, notifies, and records learning content based on the analyzed spare time. An embodiment of this system is described below in detail.

[0509] Get user calendar information

[0510] The device periodically retrieves the user's calendar information. If the user uses a schedule management system such as Google Calendar, the device uses the Google Calendar API to obtain the user's authentication information and retrieves the user's schedule information. This information includes the start time, end time, and event title of each event set by the user. The retrieved calendar information is sent to the server via a secure connection. The server stores the received calendar information in a database.

[0511] Analysis of spare time

[0512] The server analyzes the calendar information stored in the database. The server calculates the time difference between calendar events and identifies the user's free time. Free time is set based on certain criteria (e.g., 15 minutes or more of unused time). This analysis results in a list of the user's available study times during the day.

[0513] Registering and managing study topics

[0514] Users input the topic they want to learn through the application. For example, they can select "Python programming" or "data science." This input allows for customization according to the user's learning needs. Once the topic input is complete, the device sends this information to the server, which then stores the topic information in a database.

[0515] Generating learning content

[0516] The server generates learning content using a generative AI model based on the learning themes registered by the user. The generative AI uses, for example, the OpenAI API. By inputting appropriate prompts to the generative AI, learning materials appropriate for the user are generated.

[0517] Prompt Sentence Examples

[0518] The topic users want to learn is "Python Programming Basics." Please create simple learning materials for beginners. Please provide concise learning materials that include:

[0519] Python Basic Syntax

[0520] Simple code example

[0521] Exercises

[0522] Learning content notifications

[0523] The server sends the generated learning content to the device in time for the next available time slot. For example, the learning content is sent to the device in time for the available time slot between 9:00 and 9:30 the next day. The device receives this content and notifies the user. The notification includes a link to the learning content and a brief introduction.

[0524] Recording learning results

[0525] The user works through the provided learning content and enters the results into the application. This includes, for example, the results of quiz answers and the time spent studying. These results are sent to the server via the device. The server stores the received learning results in a database and manages them as the user's learning history.

[0526] Suggestions for related materials

[0527] The server suggests relevant materials based on the user's learning topic and learning history. For example, it uses the Amazon API to retrieve a list of books on "data science." Information about the suggested books is then sent to the user via their device.

[0528] Example operation

[0529] Let's say User B registers "Data Science" as a study topic in the application and wishes to study during their morning commute. First, the device sends the user's Google Calendar information to the server in the morning. The server finds a gap in time between 9:00 and 9:30. Next, the server sends a prompt to the generation AI to generate "Basic Data Science Teaching Materials," and the materials are generated. Then, as 9:00 approaches, the server sends the learning content to the device, and the device notifies User B. When User B finishes studying and enters the results, the device sends them to the server, which saves them. The server then retrieves a list of related books through the Amazon API and notifies User B.

[0530] In this way, the system of the present invention provides an efficient and customizable learning environment that allows users to make effective use of their spare time.

[0531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0532] Step 1:

[0533] The device retrieves the user's calendar information. As input, the user's authentication information is required. If using the Google Calendar API, the device retrieves the calendar information using the user's authentication information. As output, the retrieved calendar information is returned. The calendar information includes the start time, end time, and title of the event. This information is then sent to the server over a secure connection.

[0534] Step 2:

[0535] The server receives the calendar information sent from the device and stores it in a database. As input, it takes the calendar information sent from the device. It analyzes the calendar information and identifies available time slots by calculating the time difference between events. As output, it obtains a list of available time slots stored in the database. This list contains the available time slots for the user throughout the day.

[0536] Step 3:

[0537] The user registers a learning topic using the application. As input, the user enters a learning topic such as "Python programming" or "data science." Once the topic has been entered, the device sends this information to the server. As output, the topic information is stored in a database on the server side.

[0538] Step 4:

[0539] The server uses a generative AI model to generate learning content based on the learning topic registered by the user. The learning topic and a prompt are used as input. For example, the prompt "The topic the user wants to learn is 'Python programming basics'. Please create simple learning materials for beginners." is input to the generative AI model. The generated learning content is obtained as output. This content includes learning materials and exercises.

[0540] Step 5:

[0541] The server sends the generated learning content to the device in time for the next available time. The generated learning content and the identified available time are used as input. The output is the learning content sent to the device. The device receives this content and notifies the user using push notifications or alarms.

[0542] Step 6:

[0543] The user works on the provided learning content. As input, access to the learning content is required. The user proceeds with the learning and enters the results into the application. As output, quiz answers, study time, etc. are obtained. The device sends these results to the server.

[0544] Step 7:

[0545] The server stores the received learning results in a database. The learning results sent by the user are used as input. The learning results are stored in a database as output and managed as the user's learning history.

[0546] Step 8:

[0547] The server suggests related materials based on the user's learning topic and history. The user's learning topic and history are used as input. The server obtains a list of related books, for example, using the Amazon API. The output is a list of suggested related materials. The device receives this information and notifies the user.

[0548] In this way, the system effectively utilizes users' spare time and supports efficient learning by providing individually customized learning content.

[0549] (Application example 1)

[0550] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0551] In modern society, there are many systems that allow users to effectively utilize their "spare time," but these systems have several problems. For example, if the spare time is too short, learning and information intake may not be effective, or the appropriate content may not be provided to the user. Furthermore, existing systems do not fully utilize generative AI models, making it difficult to provide customized learning materials suited to each user's individual learning topic and level. Therefore, a new system that effectively utilizes users' spare time and provides individually optimized learning content is needed.

[0552] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0553] In this invention, the server includes a means for acquiring the user's calendar information, a means for analyzing spare time from the calendar information, a means for providing study content only if the analyzed spare time is equal to or greater than a certain level, and a means for using a generative AI model to generate study content according to the study topic. This makes it possible to effectively utilize spare time and provide optimal study content according to the user's study topic.

[0554] "User calendar information" is data obtained from applications and systems that users use to record their daily schedules and appointments.

[0555] "Spare time" refers to free time between scheduled events in the user's calendar information.

[0556] A "study topic" is a particular subject or area that a user wishes to learn.

[0557] "Learning content" refers to information such as teaching materials, exercises, and explanatory texts that are provided based on a learning theme set by the user.

[0558] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to generate appropriate outputs for specific inputs.

[0559] The "scheduling means" is a means for periodically updating calendar information and carrying out planned processing for analyzing free time.

[0560] "Study history" refers to data that records what a user has learned and their progress to date.

[0561] "Related books" are books and reference materials related to the study theme set by the user.

[0562] This invention is a system that analyzes a user's calendar information and provides optimal learning content for spare time. Users can access this system using their smartphones to register and manage their calendar information.

[0563] First, the device (smartphone) periodically collects the user's calendar information using the Google Calendar API, etc. The collected calendar information is sent to a server, which then stores the information in a database.

[0564] Next, the server analyzes the calendar information and calculates the gap time between each event. This analysis involves first calculating the time difference between calendar events, and then listing gap times that are greater than a certain range (for example, 10 minutes or more).

[0565] Users register their study topics through a smartphone application. For example, they can set themes such as "Python programming" or "data science." The study topics registered by the user are sent to the server and stored in a database.

[0566] The server uses a generative AI model to generate learning content based on the learning themes registered by the user. For example, it uses the OpenAI API to generate basic explanations and exercises for "Python programming." The generated learning content is customized to the user's learning level.

[0567] The generated learning content is then sent to the device at the optimal time for the user to study. The device then notifies the user of the learning content and encourages them to study. The notification includes a link to the learning content and a brief introduction.

[0568] The user works on the learning content and inputs the results. For example, this includes quiz answers and study time. The device sends the learning results to the server, which stores them in a database. This allows the user's learning history to be accumulated and progress to be checked.

[0569] Furthermore, the server suggests related books based on the user's study topic and history, using the Amazon API or similar to obtain a list of books related to the study topic, and notifies the user via the device.

[0570] As a concrete example, suppose User B has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User B and prompts him / her to access the learning content. When User B finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User B via the device.

[0571] An example prompt is:

[0572] "Topic: Python Programming\nGenerate a textbook that provides basic explanations and exercises for Python."

[0573] By using the above means, the present invention can efficiently utilize the users' spare time and provide content that meets their individual learning needs.

[0574] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0575] Step 1:

[0576] The device periodically collects the user's calendar information using the Google Calendar API, etc. At this time, the device acquires the calendar information and sends it to the server. The input is the event data acquired from the Google Calendar API, and the output is the calendar information sent to the server.

[0577] Step 2:

[0578] The server saves the received calendar information in a database, which stores the user's schedule. The input is the calendar information sent from the terminal, and the output is the calendar information stored in the database.

[0579] Step 3:

[0580] The server analyzes the stored calendar information and calculates the gap time between each event. The analysis involves calculating the time difference between calendar events and listing gap times greater than a certain amount (for example, 10 minutes or more). The input is the calendar information obtained from the database, and the output is the listed gap time.

[0581] Step 4:

[0582] The user registers a learning topic using the terminal. For example, they set a topic such as "Python programming" or "data science." The input is the learning topic that the user enters into the terminal, and the output is the learning topic information sent to the server.

[0583] Step 5:

[0584] The server stores the learning topic information in a database and manages the user's learning topics. The input is the learning topic information sent from the terminal, and the output is the learning topic information stored in the database.

[0585] Step 6:

[0586] The server generates learning content using a generative AI model based on the learning topic registered by the user. At this time, a prompt is sent using the generative AI (e.g., OpenAI API) to obtain the generated content. The input is the learning topic and prompt registered by the user, and the output is the generated learning content. An example of a prompt is: "Topic: Python programming\nPlease generate a text that provides basic explanations and practice questions for Python."

[0587] Step 7:

[0588] The server sends the generated learning content to the device according to the listed spare time. The notification includes a link to the learning content and a brief introduction. The input is the generated learning content and spare time information, and the output is the learning content notified to the device.

[0589] Step 8:

[0590] The user works on the learning content received from the device and inputs the results, such as quiz answer results and study time. The input is the learning results that the user enters into the device, and the output is the learning results sent to the server.

[0591] Step 9:

[0592] The server stores the user's learning results in a database and accumulates the user's learning history, allowing the user to check their progress. The input is the learning results sent from the device, and the output is the learning history stored in the database.

[0593] Step 10:

[0594] The server suggests related books based on the user's study topic and history. It obtains a list of related books using the Amazon API or similar and notifies the device. The input is the user's study topic and history, and the output is the obtained list of related books.

[0595] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0596] The system of the present invention is designed to effectively utilize users' spare time and support efficient learning. The system acquires the user's calendar information and generates, notifies, and records learning content based on the analyzed spare time. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotions and further improves the learning experience.

[0597] Get a user's calendar information

[0598] The device periodically retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves the user's schedule information using the Google Calendar API. This information is converted into JSON format and sent to the server as a POST request. The server stores the received calendar information in a database.

[0599] Analysis of spare time

[0600] The server analyzes the calendar information stored in the database to identify gaps in time. It calculates the difference between the end time of each event and the start time of the next event to create a list of optimal gaps. From the list of gaps, it ranks the most appropriate times based on priority. This ranking is based on the length of the gaps and past learning history.

[0601] Registering and managing study topics

[0602] Users register the topic they want to learn through the learning application. For example, they can input a topic such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[0603] Generating learning content

[0604] The server uses generative AI to generate learning content based on the registered learning topic. For example, it generates text and quizzes on basic concepts of Python programming. The generated learning content is then sent to the device in the next available time slot.

[0605] Introducing the Emotion Engine

[0606] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. The emotion engine's analysis results are sent from the device to the server.

[0607] Emotion-based learning content adjustment

[0608] The server dynamically adjusts learning content based on the emotional data obtained from the emotion engine. For example, if a user is nervous, it can provide relaxing content or encouraging messages.

[0609] Learning content notifications

[0610] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0611] Recording learning results

[0612] The user works through the provided learning content and inputs the results, such as quiz answers and study time. The device sends the learning results to the server, which then stores them in a database and updates the user's learning history.

[0613] Related book suggestions

[0614] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions. The notification includes a summary of the book and a link to purchase it.

[0615] Specific examples

[0616] Consider the case where User A has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User A and encourages him / her to access the learning content. The device further analyzes the user's emotions using an emotion engine and provides appropriate relaxation techniques and encouraging messages. When User A finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User A via the device.

[0617] In this way, the system of the present invention is a system that effectively utilizes the user's spare time to efficiently advance learning, and further improves the user's learning experience by using an emotion engine.

[0618] The processing flow will be explained below.

[0619] Step 1:

[0620] The device retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves schedule information using the Google Calendar API. This information is converted into JSON format and sent to the server.

[0621] Step 2:

[0622] The server receives the calendar information and stores it in a database. The start and end times of each event in the calendar are extracted and stored.

[0623] Step 3:

[0624] The server analyzes the calendar information stored in the database to identify gaps in time, calculates the difference between the end time of each event and the start time of the next event, and lists them.

[0625] Step 4:

[0626] The server ranks the listed spare time slots by priority, based on the length of the spare time slots and the user's past learning history.

[0627] Step 5:

[0628] Users register the topic they want to learn through the learning application. They enter topics such as "Python programming" or "data science" into the application's input form. After entering the information, the device sends the topic information to the server.

[0629] Step 6:

[0630] Based on the learning topics stored in the database, the server uses generative AI to generate relevant learning content, such as textbooks and quizzes on basic Python programming concepts.

[0631] Step 7:

[0632] The device uses an emotion engine to support user learning. When a user uses an application, the device uses a camera and microphone to capture the user's facial expressions and voice tone, which are then analyzed by the emotion engine.

[0633] Step 8:

[0634] The server adjusts the learning content based on the generated learning content and the analysis results of the emotion engine. For example, if the user is nervous, it adds content to help them relax or an encouraging message.

[0635] Step 9:

[0636] The server transmits the adjusted learning content to the terminal in time for the next free time. The terminal receives the learning content and transmits a notification to the user when the next free time approaches.

[0637] Step 10:

[0638] The device notifies the user at the designated time and displays a link to the learning content. When the user clicks the link, the application displays the learning content.

[0639] Step 11:

[0640] The user engages with the provided learning content, for example, by reading learning resources or answering quizzes. When the user has finished learning, they enter the results into the device.

[0641] Step 12:

[0642] The device sends the user's learning results (e.g., number of correct answers in quizzes, study time) to the server. The server receives this information and stores it in a database to update the user's learning history.

[0643] Step 13:

[0644] The server suggests related books based on the user's study topic and study history. It uses a specific API to obtain a list of related books and sends it to the device.

[0645] Step 14:

[0646] The device notifies the user of book suggestions, which include a synopsis of the book and a link to purchase it for easy access by the user.

[0647] Through these steps, users can effectively utilize their free time and study. The introduction of the emotion engine provides appropriate learning support according to the user's psychological state, improving the learning experience.

[0648] Example 2

[0649] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0650] Conventional learning systems have had difficulty efficiently utilizing users' spare time. Furthermore, they have not provided learning content that takes into account the user's emotional state, which can lead to reduced learning efficiency. Furthermore, the suggestion of related books must be done manually, creating a need for an automated system.

[0651] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0652] In this invention, the server includes a means for acquiring a user's schedule information, a means for analyzing spare time, a means for generating study content related to a study topic based on the analyzed spare time, a means for notifying the user of the generated study content, a means for recording the user's study results, and a means for dynamically adjusting the study content based on the emotion data, incorporating an emotion engine for recognizing the user's emotions. This allows for effective use of the user's spare time and provision of study content that takes into account their emotional state. Furthermore, related book suggestions are also automated, providing users with optimal study resources.

[0653] "Calendar information" is data that includes details of a user's schedule and events.

[0654] "Spare time" is free time between consecutive events in a user's schedule information.

[0655] A "study topic" is a specific topic or area that a user wishes to study.

[0656] "Study content" refers to information such as learning materials, quizzes, and texts that users use to study.

[0657] "Generative AI" is an artificial intelligence technology that automatically generates text and content.

[0658] "Notification" is the process of communicating information from the system to the user.

[0659] The "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to determine their emotional state.

[0660] "Emotion data" is information that indicates the user's emotional state analyzed by the emotion engine.

[0661] "Study results" refers to data such as the results and answers obtained when a user engages with study content.

[0662] "Related books" are books related to the study topic and are suggested to complement the user's studies.

[0663] MODE FOR CARRYING OUT THE INVENTION

[0664] The system of the present invention is designed to effectively utilize a user's spare time and support efficient learning. The system acquires the user's schedule information and generates, notifies, and records learning content based on the analyzed spare time. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotions and further improves the learning experience. Specific embodiments are described below.

[0665] Get user calendar information

[0666] The device periodically obtains the user's calendar information. For example, if the user uses Google Calendar, the device obtains the user's schedule information using the Google Calendar API. This schedule information is converted into JSON format and then sent to the server as a POST request. The server then stores the received calendar information in a database.

[0667] Hardware used: Device (smartphone or tablet)

[0668] Software used: Google Calendar API, server software (e.g., Node.js server)

[0669] Example: A device sends a request to the "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events" endpoint to retrieve the user's calendar information.

[0670] Analysis of spare time

[0671] The server analyzes the schedule information stored in the database to identify gap times. It calculates the difference between the end time of each event and the start time of the next event to list the optimal gap times. From this list, it ranks the most appropriate gap times based on priority. This ranking is based on the length of the gap time and past learning history.

[0672] Hardware used: Server

[0673] Software used: Database (MySQL or PostgreSQL), backend script (Python or JavaScript)

[0674] Example: If the events in a schedule are from 9:00 to 10:00 and from 12:00 to 13:00, the two hours from 10:00 to 12:00 are recognized as gap time.

[0675] Registering and managing study topics

[0676] Users register the topic they want to learn through the learning application. For example, they can enter topics such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[0677] Hardware used: Terminal

[0678] Software used: Front-end application (Android / iOS app, web app)

[0679] Example: A user enters "Python programming" into an application form and submits the information via a POST request to "https: / / example.com / api / register_theme."

[0680] Generating learning content

[0681] The server uses AI to generate learning content based on the registered learning topic. For example, it generates text and quizzes on basic concepts of "Python programming." The generated content is then sent to the device in the next available time slot.

[0682] Hardware used: Server

[0683] Software used: Generative AI models (e.g., OpenAI GPT-3)

[0684] Example: The prompt "Please create a lesson for beginners on Python programming" is input into a generative AI model to generate learning content.

[0685] Introducing the Emotion Engine

[0686] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. The emotion engine's analysis results are sent from the device to the server.

[0687] Hardware used: Device (camera, microphone)

[0688] Software used: Emotion recognition engine (e.g., Microsoft Azure Emotion API)

[0689] Example: The device captures the user's facial expression with the camera and sends it to "https: / / centralus.api.cognitive.microsoft.com / face / v1.0 / detect" for analysis.

[0690] Emotion-based learning content adjustment

[0691] The server dynamically adjusts learning content based on the emotion data sent from the emotion engine. For example, if the user is nervous, it provides content to help them relax or messages of encouragement.

[0692] Hardware used: Server

[0693] Software used: Backend script (Python / Node.js)

[0694] Example: A server receives data that says "The user is nervous" and generates a message that says "Try some deep breathing exercises to relax."

[0695] Learning content notifications

[0696] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0697] Hardware used: Terminal

[0698] Software used: Push notification service (e.g., Firebase Cloud Messaging)

[0699] Example: Your device receives a notification saying, "Learn basic Python concepts between 9:00-9:30."

[0700] Recording learning results

[0701] The user works through the provided learning content and inputs the results, such as quiz answers and study time. This information is sent from the device to the server, which stores it in a database and updates the user's learning history.

[0702] Hardware used: Terminal

[0703] Software used: Front-end application, back-end API

[0704] Example: A user submits quiz results to "https: / / example.com / api / submit_results". The server saves the results to a database.

[0705] Related book suggestions

[0706] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions.

[0707] Hardware used: Server

[0708] Software used: Book suggestion API (e.g., Amazon API)

[0709] Example: A server requests "books related to Python programming" from the Amazon API and notifies the user of the results.

[0710] In this way, the system is designed to make effective use of users' spare time and allow them to study efficiently.

[0711] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0712] Step 1:

[0713] The device retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves the schedule information using the Google Calendar API. This information is converted to JSON format and sent to the server as a POST request.

[0714] Input: Google Calendar API endpoint "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events"

[0715] Data processing: Convert schedule information into JSON format

[0716] Output: Sends JSON formatted calendar information to the server.

[0717] Specific behavior: The device accesses the API endpoint to retrieve the user's calendar information, then converts the information into JSON format and sends a POST request to "https: / / example.com / api / calendar_data".

[0718] Step 2:

[0719] The server stores the received calendar information in a database, which is used for later analysis.

[0720] Input: Calendar information in JSON format sent from the device

[0721] Data processing: Insert the received JSON data into the database

[0722] Output: Calendar information stored in a database

[0723] Specific operation: The server receives the POST request, parses the JSON data, and saves it to the database.

[0724] Step 3:

[0725] The server analyzes the schedule information stored in the database to identify gap times, calculates the difference between the end time of each event and the start time of the next event, and lists the gap times.

[0726] Input: Calendar information stored in a database

[0727] Data processing: Calculate gap time by comparing the end time of each event with the start time of the next event

[0728] Output: List of gap times

[0729] Specific operation: The server retrieves the schedule information from the database, calculates the time difference between events, and lists the gap time.

[0730] Step 4:

[0731] Users register the topic they want to learn through the learning application, for example, entering a topic such as "Python programming."

[0732] Input: The learning topic that the user enters into the input form

[0733] Data processing: Convert the input theme information into JSON format

[0734] Output: Study topic information sent to the server

[0735] Specific behavior: The user enters "Python programming" into the app's input form and sends a POST request to "https: / / example.com / api / register_theme".

[0736] Step 5:

[0737] The server uses generative AI to generate learning content based on registered learning themes, such as text and quizzes on "Python programming."

[0738] Input: The learning topic registered by the user (e.g., Python programming)

[0739] Data processing: Enter a prompt into the generation AI and obtain the generated content.

[0740] Output: Generated learning content

[0741] Specific operation: The server inputs the prompt "Please create a lesson for beginners in Python programming" into the generation AI and obtains the result.

[0742] Step 6:

[0743] The device recognizes the user's emotions using an emotion engine, which analyzes the user's facial expressions and vocal tone to determine their current emotional state.

[0744] Input: User's facial expression data and voice tone

[0745] Data processing: Analysis using emotion engine

[0746] Output: Parsed emotion data

[0747] Specific operation: The device captures the user's facial expression with the camera, sends it to "https: / / centralus.api.cognitive.microsoft.com / face / v1.0 / detect" for analysis, and then sends the results to the server.

[0748] Step 7:

[0749] The server dynamically adjusts learning content based on the emotional data obtained from the emotion engine. For example, if the user is nervous, it provides content to help them relax or messages of encouragement.

[0750] Input: Emotion data sent from the emotion engine

[0751] Data manipulation: adjusting content based on sentiment data

[0752] Output: Tailored learning content

[0753] Specific behavior: The server receives data that "the user is nervous" and generates a message that says "Try some deep breathing exercises to relax."

[0754] Step 8:

[0755] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0756] Input: Learning content and time information sent from the server

[0757] Data processing: Converting learning content information into notification format

[0758] Output: Learning content link notified to user

[0759] What happens: Your device displays a push notification saying, "Learn basic Python concepts between 9:00 and 9:30."

[0760] Step 9:

[0761] The user works through the provided learning content and inputs the results, such as quiz answers and study time. This information is sent from the device to the server, which stores it in a database and updates the user's learning history.

[0762] Input: User-entered learning results

[0763] Data processing: Converting learning result data into JSON format

[0764] Output: Training results sent to the server

[0765] Specific behavior: The user submits the quiz results to "https: / / example.com / api / submit_results". The server saves the results in the database.

[0766] Step 10:

[0767] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions.

[0768] Input: User's study topic and history

[0769] Data processing: Use related book API to obtain book information

[0770] Output: A list of suggested related books

[0771] Specific operation: The server requests "books related to Python programming" from the Amazon API and notifies the user of the results.

[0772] (Application example 2)

[0773] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0774] Today's busy brick-and-mortar stores face challenges in effectively utilizing their free time and efficiently acquiring the necessary skills and knowledge. Furthermore, learning content is not tailored to individual emotions and situations, and uniform training can lower employee motivation. Furthermore, learning results are not properly recorded, and related books are scarce, limiting the ability to provide continuous learning support for employees.

[0775] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's schedule information, means for analyzing spare time from the schedule information, means for generating study content related to a study topic set based on the analyzed spare time, means for analyzing the user's emotions and means for adjusting the study content based on the emotions, means for notifying the user of the generated study content, and means for recording the user's study results. This allows store employees to efficiently utilize their spare time to study, and in the process, content tailored to their individual emotional state is provided, thereby improving motivation and continuously improving their skills.

[0776] The "means for acquiring user's schedule information" is a function for periodically acquiring schedule information from the schedule system used by the user.

[0777] The "means for analyzing free time from schedule information" is a function for analyzing the acquired schedule information and identifying free time (free time) that exists between schedules.

[0778] The "means for generating learning content related to a learning topic" is a function for automatically creating content to be used in education or training based on a specific learning topic.

[0779] The "means for analyzing the user's emotions" is a function for analyzing the user's current emotional state using data such as the user's facial expressions, voice, and behavior.

[0780] The "means for adjusting learning content based on emotions" is a function that changes the difficulty level, content, etc. according to the analyzed user emotions, thereby optimizing the user's learning experience.

[0781] The "means for notifying the user of the generated study content" is a function for notifying the user of the generated study content as appropriate and encouraging them to start studying.

[0782] "Means for recording user learning results" refers to a function that allows users to record the results and progress of the learning content they have worked on, so that they can be referenced and evaluated later.

[0783] This invention provides a system that effectively utilizes a user's spare time and promotes efficient learning. This system acquires the user's schedule information, analyzes spare time, and generates, notifies, and records learning content based on that spare time.

[0784] System configuration

[0785] Hardware and Software Requirements

[0786] Device: A mobile device such as a smartphone or tablet used by a user.

[0787] Calendar API: An API for retrieving a user's calendar information, such as the Google Calendar API.

[0788] Server: A server for managing users' schedule information, spare time, study themes, study results, etc.

[0789] Generative AI models: Generative AI models for generating learning content, such as OpenAI's GPT-3.

[0790] Emotion engine: An engine that analyzes the user's facial expressions and voice to determine their emotional state.

[0791] Notification system: A system for notifying users of learning content.

[0792] Database: A database for storing users' calendar information, study topics, and study results.

[0793] Processing flow

[0794] 1. Get the user's calendar information:

[0795] The device periodically retrieves the user's calendar information using a calendar API such as Google Calendar. This information is converted into JSON format and sent to the server as a POST request. The server then stores the received calendar information in a database.

[0796] 2. Analysis of gap time:

[0797] The server analyzes the schedule information stored in the database to identify gaps in time. It calculates the difference between the end time of each event and the start time of the next event to list the optimal gaps in time. It then ranks the most suitable gaps from the list based on priority. This ranking is based on the length of the gaps and past learning history.

[0798] 3. Register and manage study topics:

[0799] The user registers the topic they want to learn through the learning application. For example, they can input topics such as "improving customer service skills" or "product knowledge." The device sends this information to the server, which then stores the topic information in a database.

[0800] 4. Learning content generation:

[0801] The server generates learning content using a generative AI model based on the registered learning theme. The generated learning content is provided in the form of text and quizzes. For example, text and quizzes on basic customer service etiquette are generated based on the theme "improving customer service skills." The content is generated based on the prompt, "Please generate learning content on improving customer service skills. The content should include basic customer service etiquette, communication techniques with customers, and methods for increasing customer satisfaction."

[0802] 5. Introducing the Emotion Engine:

[0803] The device is equipped with an emotion engine for analyzing the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice using the camera and microphone installed in the smartphone or tablet. The emotion engine's analysis results are sent from the device to a server.

[0804] 6. Emotion-based learning content adjustment:

[0805] The server dynamically adjusts learning content based on emotional data obtained from the emotion engine. For example, if the user is tired, it can provide relaxing content or encouraging messages.

[0806] 7. Learning Content Notification:

[0807] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0808] 8. Recording learning results:

[0809] The user works through the provided learning content and inputs the results, such as quiz answers and study time. The device sends the learning results to the server, which then stores them in a database and updates the user's learning history.

[0810] This system allows store employees to effectively utilize their free time to study efficiently, and by optimizing the learning experience based on emotional data, it is possible to increase motivation and continuously improve skills.

[0811] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0812] Step 1:

[0813] Get calendar information

[0814] The device periodically retrieves the user's calendar information using the Google Calendar API. This information is converted to JSON format and sent to the server as a POST request. The input is the user's schedule retrieved from the Google Calendar API, and the output is the calendar information in JSON format. The server stores this information in a database.

[0815] Step 2:

[0816] Analysis of spare time

[0817] The server analyzes the schedule information stored in the database and identifies gap times by calculating the difference between the end time of each event and the start time of the next event. The input is the schedule information stored in the database, and the output is a list of gap times. The server ranks the optimal gap times based on priority.

[0818] Step 3:

[0819] Registering study themes

[0820] The user inputs a learning topic using a smartphone or tablet. For example, a topic such as "improving customer service skills" or "product knowledge" is registered in the application. The input is the learning topic entered by the user, and the output is the topic information sent to the server. The server stores this information in a database.

[0821] Step 4:

[0822] Generating learning content

[0823] The server uses a generative AI model based on the registered learning theme to generate learning content. The prompt statement is, "Please generate learning content related to improving customer service skills. The content should include basic customer service etiquette, communication techniques with customers, and ways to increase customer satisfaction." The input is the specified learning theme and prompt statement, and the output is the generated learning content.

[0824] Step 5:

[0825] Emotion analysis using an emotion engine

[0826] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice via a camera and microphone to determine their emotional state. The input is facial expression data and voice data obtained from the device, and the output is analyzed emotional state data. The device then sends this data to a server.

[0827] Step 6:

[0828] Emotion-based content adjustment

[0829] The server adjusts the learning content appropriately based on the data obtained from the emotion engine. For example, if the user is tired, it provides content to help them relax. The input is the analyzed emotion data and the generated learning content, and the output is the adjusted learning content.

[0830] Step 7:

[0831] Learning content notifications

[0832] The device notifies the user at the specified spare time and provides a link to the learning content. The input is the learning content adjusted for the spare time, and the output is the notification to the user. When the user clicks the link, the learning content is displayed.

[0833] Step 8:

[0834] Recording learning results

[0835] The user performs the learning content and inputs the results. For example, they record the results of quizzes and the time spent studying. The input is the learning results entered by the user, and the output is the learning result data sent to the server. The server stores this in a database and updates the user's learning history.

[0836] This allows the system to enable store employees to efficiently utilize their spare time and provide learning content tailored to their individual emotional state, thereby improving motivation and enabling continuous skill development.

[0837] 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.

[0838] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0839] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0840] [Third embodiment]

[0841] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0842] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0843] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0844] 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.

[0845] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0846] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0847] 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.

[0848] 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.

[0849] 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 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.

[0850] 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.

[0851] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0852] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0853] The system of the present invention is designed to effectively utilize users' spare time and support efficient learning. This system acquires the user's calendar information and generates, notifies, and records learning content based on the analyzed spare time. The program and processing of this system are explained in natural language below.

[0854] Get a user's calendar information

[0855] The device periodically obtains the user's calendar information. For example, if the user uses Google Calendar, the device obtains schedule information using the Google Calendar API and sends it to the server. The server then stores the received calendar information in a database.

[0856] Analysis of spare time

[0857] The server analyzes the received calendar information and finds available time slots. The server calculates the time difference between calendar events and identifies the most suitable available time slots. The analyzed available time slots are then listed.

[0858] Registering and managing study topics

[0859] Users register the topic they want to learn through the application. For example, they can enter topics such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[0860] Generating learning content

[0861] The server uses generative AI to generate learning content based on registered learning themes. For example, it uses the OpenAI API to generate basic Python programming explanations and exercises. This allows for the creation of customized learning materials tailored to the user's learning level.

[0862] Learning content notifications

[0863] The server then sends the generated learning content to the device in the next available time slot. The device then notifies the user and encourages them to study. The notification includes a link to the learning content and a brief introduction.

[0864] Recording learning results

[0865] The user works through the provided learning content and inputs the results. For example, this could be quiz answers or study time. The device sends the learning results to the server, which stores them in a database. This allows the user's learning history to be accumulated and progress to be checked.

[0866] Related book suggestions

[0867] The server suggests related books based on the user's study topic and history. For example, it uses the Amazon API to obtain a list of books related to the user's study topic. The server sends this information to the device, and the device notifies the user.

[0868] Specific examples

[0869] Suppose User B has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User B and prompts him / her to access the learning content. When User B finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User B via the device.

[0870] In this way, the system of the present invention makes effective use of the user's spare time and improves learning efficiency, thereby enabling the user to actively engage in learning.

[0871] The processing flow will be explained below.

[0872] Step 1:

[0873] The device retrieves the user's calendar information. If the user uses Google Calendar, the device uses the Google Calendar API to retrieve the user's schedule information. This information is converted into JSON format and then sent to the server as a POST request.

[0874] Step 2:

[0875] The server receives the calendar information, parses it, and extracts the start and end times of each event. The extracted information is then stored in a database.

[0876] Step 3:

[0877] The server analyzes the calendar information stored in the database to identify gaps in time. Specifically, it calculates the difference between the end time of each event and the start time of the next event, and lists the gaps.

[0878] Step 4:

[0879] The server ranks the listed spare time slots by priority, based on the length of the spare time slots and the user's past learning history.

[0880] Step 5:

[0881] Users register the topic they want to learn through the learning application. They enter topics such as "Python programming" or "data science" into an input form on the application, and the device sends this information to the server.

[0882] Step 6:

[0883] The server uses generative AI to generate relevant learning content based on the learning topic information stored in the database. For example, it uses a specific API to generate textbooks and quizzes on basic concepts of Python programming.

[0884] Step 7:

[0885] The server transmits the generated study content to the device in time for the next free time. After the device receives the study content, it sends a notification to the user when the next free time approaches.

[0886] Step 8:

[0887] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[0888] Step 9:

[0889] The user engages with the provided learning content, for example, by reading learning resources or answering quizzes. When the user has finished learning, they enter the results into the device.

[0890] Step 10:

[0891] The device sends the user's learning results (e.g., number of correct answers in quizzes, study time) to the server. The server receives this information and stores it in a database to update the user's learning history.

[0892] Step 11:

[0893] The server suggests related books based on the user's study topic and study history. It uses a specific API to obtain a list of related books and sends it to the device.

[0894] Step 12:

[0895] The device notifies the user of book suggestions, including a synopsis and a link to purchase, allowing the user to further their learning.

[0896] Through these steps, the system allows users to effectively utilize their spare time and automatically obtain the most suitable learning content for studying.

[0897] Example 1

[0898] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0899] In today's busy lifestyles, it is difficult for individuals to find time to study effectively. In particular, there is a lack of support for effectively utilizing spare time to advance their studies. Furthermore, existing learning support systems have difficulty responding flexibly to users' schedules and providing individually customized content. Therefore, there is a need for a system that can effectively utilize users' spare time and support their studies efficiently and effectively.

[0900] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0901] In this invention, the server includes means for acquiring a user's schedule information, means for analyzing spare time from the schedule information, means for generating study content related to a set study topic based on the analyzed spare time, means for notifying the user of the generated study content, means for recording the user's study results, and means for using a generative AI model to generate the study content, thereby enabling analysis of the user's spare time and providing individually customized study content.

[0902] "User's schedule information" is data containing the user's schedule, obtained from a calendar application or schedule management system.

[0903] "Split time" refers to a short period of unused time between a user's scheduled appointments.

[0904] A "study topic" is a particular field or topic that a user wishes to study.

[0905] "Study content" refers to information such as teaching materials and exercises that are provided to users when they study.

[0906] A "generative AI model" is a program or system that uses artificial intelligence to generate learning content for users.

[0907] A "prompt" is a text instruction that instructs a generative AI model to generate specific learning content.

[0908] The "means for acquiring" refers to a function or device that performs the processing required to acquire the user's schedule information.

[0909] The "analyzing means" refers to a function or device for identifying and analyzing free time from the acquired schedule information.

[0910] The "means for generating" refers to a function or device that creates learning content based on the analyzed spare time.

[0911] The "notification means" refers to a function or device for notifying the user of the generated learning content.

[0912] "Recording means" refers to a function or device for saving and managing the user's learning results.

[0913] "Means for using a generative AI model" refers to a function or device for generating learning content using a generative AI model.

[0914] "Related materials" refers to information such as external books and literature that is necessary or useful for the user's study.

[0915] The present invention provides a system for effectively utilizing a user's spare time and supporting efficient learning. This system acquires the user's schedule information and generates, notifies, and records learning content based on the analyzed spare time. An embodiment of this system is described below in detail.

[0916] Get user calendar information

[0917] The device periodically retrieves the user's calendar information. If the user uses a schedule management system such as Google Calendar, the device uses the Google Calendar API to obtain the user's authentication information and retrieves the user's schedule information. This information includes the start time, end time, and event title of each event set by the user. The retrieved calendar information is sent to the server via a secure connection. The server stores the received calendar information in a database.

[0918] Analysis of spare time

[0919] The server analyzes the calendar information stored in the database. The server calculates the time difference between calendar events and identifies the user's free time. Free time is set based on certain criteria (e.g., 15 minutes or more of unused time). This analysis results in a list of the user's available study times during the day.

[0920] Registering and managing study topics

[0921] Users input the topic they want to learn through the application. For example, they can select "Python programming" or "data science." This input allows for customization according to the user's learning needs. Once the topic input is complete, the device sends this information to the server, which then stores the topic information in a database.

[0922] Generating learning content

[0923] The server generates learning content using a generative AI model based on the learning themes registered by the user. The generative AI uses, for example, the OpenAI API. By inputting appropriate prompts to the generative AI, learning materials appropriate for the user are generated.

[0924] Prompt Sentence Examples

[0925] The topic users want to learn is "Python Programming Basics." Please create simple learning materials for beginners. Please provide concise learning materials that include:

[0926] Python Basic Syntax

[0927] Simple code example

[0928] Exercises

[0929] Learning content notifications

[0930] The server sends the generated learning content to the device in time for the next available time slot. For example, the learning content is sent to the device in time for the available time slot between 9:00 and 9:30 the next day. The device receives this content and notifies the user. The notification includes a link to the learning content and a brief introduction.

[0931] Recording learning results

[0932] The user works through the provided learning content and enters the results into the application. This includes, for example, the results of quiz answers and the time spent studying. These results are sent to the server via the device. The server stores the received learning results in a database and manages them as the user's learning history.

[0933] Suggestions for related materials

[0934] The server suggests relevant materials based on the user's learning topic and learning history. For example, it uses the Amazon API to retrieve a list of books on "data science." Information about the suggested books is then sent to the user via their device.

[0935] Example operation

[0936] Let's say User B registers "Data Science" as a study topic in the application and wishes to study during their morning commute. First, the device sends the user's Google Calendar information to the server in the morning. The server finds a gap in time between 9:00 and 9:30. Next, the server sends a prompt to the generation AI to generate "Basic Data Science Teaching Materials," and the materials are generated. Then, as 9:00 approaches, the server sends the learning content to the device, and the device notifies User B. When User B finishes studying and enters the results, the device sends them to the server, which saves them. The server then retrieves a list of related books through the Amazon API and notifies User B.

[0937] In this way, the system of the present invention provides an efficient and customizable learning environment that allows users to make effective use of their spare time.

[0938] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0939] Step 1:

[0940] The device retrieves the user's calendar information. As input, the user's authentication information is required. If using the Google Calendar API, the device retrieves the calendar information using the user's authentication information. As output, the retrieved calendar information is returned. The calendar information includes the start time, end time, and title of the event. This information is then sent to the server over a secure connection.

[0941] Step 2:

[0942] The server receives the calendar information sent from the device and stores it in a database. As input, it takes the calendar information sent from the device. It analyzes the calendar information and identifies available time slots by calculating the time difference between events. As output, it obtains a list of available time slots stored in the database. This list contains the available time slots for the user throughout the day.

[0943] Step 3:

[0944] The user registers a learning topic using the application. As input, the user enters a learning topic such as "Python programming" or "data science." Once the topic has been entered, the device sends this information to the server. As output, the topic information is stored in a database on the server side.

[0945] Step 4:

[0946] The server uses a generative AI model to generate learning content based on the learning topic registered by the user. The learning topic and a prompt are used as input. For example, the prompt "The topic the user wants to learn is 'Python programming basics'. Please create simple learning materials for beginners." is input to the generative AI model. The generated learning content is obtained as output. This content includes learning materials and exercises.

[0947] Step 5:

[0948] The server sends the generated learning content to the device in time for the next available time. The generated learning content and the identified available time are used as input. The output is the learning content sent to the device. The device receives this content and notifies the user using push notifications or alarms.

[0949] Step 6:

[0950] The user works on the provided learning content. As input, access to the learning content is required. The user proceeds with the learning and enters the results into the application. As output, quiz answers, study time, etc. are obtained. The device sends these results to the server.

[0951] Step 7:

[0952] The server stores the received learning results in a database. The learning results sent by the user are used as input. The learning results are stored in a database as output and managed as the user's learning history.

[0953] Step 8:

[0954] The server suggests related materials based on the user's learning topic and history. The user's learning topic and history are used as input. The server obtains a list of related books, for example, using the Amazon API. The output is a list of suggested related materials. The device receives this information and notifies the user.

[0955] In this way, the system effectively utilizes users' spare time and supports efficient learning by providing individually customized learning content.

[0956] (Application example 1)

[0957] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0958] In modern society, there are many systems that allow users to effectively utilize their "spare time," but these systems have several problems. For example, if the spare time is too short, learning and information intake may not be effective, or the appropriate content may not be provided to the user. Furthermore, existing systems do not fully utilize generative AI models, making it difficult to provide customized learning materials suited to each user's individual learning topic and level. Therefore, a new system that effectively utilizes users' spare time and provides individually optimized learning content is needed.

[0959] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0960] In this invention, the server includes a means for acquiring the user's calendar information, a means for analyzing spare time from the calendar information, a means for providing study content only if the analyzed spare time is equal to or greater than a certain level, and a means for using a generative AI model to generate study content according to the study topic. This makes it possible to effectively utilize spare time and provide optimal study content according to the user's study topic.

[0961] "User calendar information" is data obtained from applications and systems that users use to record their daily schedules and appointments.

[0962] "Spare time" refers to free time between scheduled events in the user's calendar information.

[0963] A "study topic" is a particular subject or area that a user wishes to learn.

[0964] "Learning content" refers to information such as teaching materials, exercises, and explanatory texts that are provided based on a learning theme set by the user.

[0965] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to generate appropriate outputs for specific inputs.

[0966] The "scheduling means" is a means for periodically updating calendar information and carrying out planned processing for analyzing free time.

[0967] "Study history" refers to data that records what a user has learned and their progress to date.

[0968] "Related books" are books and reference materials related to the study theme set by the user.

[0969] This invention is a system that analyzes a user's calendar information and provides optimal learning content for spare time. Users can access this system using their smartphones to register and manage their calendar information.

[0970] First, the device (smartphone) periodically collects the user's calendar information using the Google Calendar API, etc. The collected calendar information is sent to a server, which then stores the information in a database.

[0971] Next, the server analyzes the calendar information and calculates the gap time between each event. This analysis involves first calculating the time difference between calendar events, and then listing gap times that are greater than a certain range (for example, 10 minutes or more).

[0972] Users register their study topics through a smartphone application. For example, they can set themes such as "Python programming" or "data science." The study topics registered by the user are sent to the server and stored in a database.

[0973] The server uses a generative AI model to generate learning content based on the learning themes registered by the user. For example, it uses the OpenAI API to generate basic explanations and exercises for "Python programming." The generated learning content is customized to the user's learning level.

[0974] The generated learning content is then sent to the device at the optimal time for the user to study. The device then notifies the user of the learning content and encourages them to study. The notification includes a link to the learning content and a brief introduction.

[0975] The user works on the learning content and inputs the results. For example, this includes quiz answers and study time. The device sends the learning results to the server, which stores them in a database. This allows the user's learning history to be accumulated and progress to be checked.

[0976] Furthermore, the server suggests related books based on the user's study topic and history, using the Amazon API or similar to obtain a list of books related to the study topic, and notifies the user via the device.

[0977] As a concrete example, suppose User B has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User B and prompts him / her to access the learning content. When User B finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User B via the device.

[0978] An example prompt is:

[0979] "Topic: Python Programming\nGenerate a textbook that provides basic explanations and exercises for Python."

[0980] By using the above means, the present invention can efficiently utilize the users' spare time and provide content that meets their individual learning needs.

[0981] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0982] Step 1:

[0983] The device periodically collects the user's calendar information using the Google Calendar API, etc. At this time, the device acquires the calendar information and sends it to the server. The input is the event data acquired from the Google Calendar API, and the output is the calendar information sent to the server.

[0984] Step 2:

[0985] The server saves the received calendar information in a database, which stores the user's schedule. The input is the calendar information sent from the terminal, and the output is the calendar information stored in the database.

[0986] Step 3:

[0987] The server analyzes the stored calendar information and calculates the gap time between each event. The analysis involves calculating the time difference between calendar events and listing gap times greater than a certain amount (for example, 10 minutes or more). The input is the calendar information obtained from the database, and the output is the listed gap time.

[0988] Step 4:

[0989] The user registers a learning topic using the terminal. For example, they set a topic such as "Python programming" or "data science." The input is the learning topic that the user enters into the terminal, and the output is the learning topic information sent to the server.

[0990] Step 5:

[0991] The server stores the learning topic information in a database and manages the user's learning topics. The input is the learning topic information sent from the terminal, and the output is the learning topic information stored in the database.

[0992] Step 6:

[0993] The server generates learning content using a generative AI model based on the learning topic registered by the user. At this time, a prompt is sent using the generative AI (e.g., OpenAI API) to obtain the generated content. The input is the learning topic and prompt registered by the user, and the output is the generated learning content. An example of a prompt is: "Topic: Python programming\nPlease generate a text that provides basic explanations and practice questions for Python."

[0994] Step 7:

[0995] The server sends the generated learning content to the device according to the listed spare time. The notification includes a link to the learning content and a brief introduction. The input is the generated learning content and spare time information, and the output is the learning content notified to the device.

[0996] Step 8:

[0997] The user works on the learning content received from the device and inputs the results, such as quiz answer results and study time. The input is the learning results that the user enters into the device, and the output is the learning results sent to the server.

[0998] Step 9:

[0999] The server stores the user's learning results in a database and accumulates the user's learning history, allowing the user to check their progress. The input is the learning results sent from the device, and the output is the learning history stored in the database.

[1000] Step 10:

[1001] The server suggests related books based on the user's study topic and history. It obtains a list of related books using the Amazon API or similar and notifies the device. The input is the user's study topic and history, and the output is the obtained list of related books.

[1002] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1003] The system of the present invention is designed to effectively utilize users' spare time and support efficient learning. The system acquires the user's calendar information and generates, notifies, and records learning content based on the analyzed spare time. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotions and further improves the learning experience.

[1004] Get a user's calendar information

[1005] The device periodically retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves the user's schedule information using the Google Calendar API. This information is converted into JSON format and sent to the server as a POST request. The server stores the received calendar information in a database.

[1006] Analysis of spare time

[1007] The server analyzes the calendar information stored in the database to identify gaps in time. It calculates the difference between the end time of each event and the start time of the next event to create a list of optimal gaps. From the list of gaps, it ranks the most appropriate times based on priority. This ranking is based on the length of the gaps and past learning history.

[1008] Registering and managing study topics

[1009] Users register the topic they want to learn through the learning application. For example, they can input a topic such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[1010] Generating learning content

[1011] The server uses generative AI to generate learning content based on the registered learning topic. For example, it generates text and quizzes on basic concepts of Python programming. The generated learning content is then sent to the device in the next available time slot.

[1012] Introducing the Emotion Engine

[1013] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. The emotion engine's analysis results are sent from the device to the server.

[1014] Emotion-based learning content adjustment

[1015] The server dynamically adjusts learning content based on the emotional data obtained from the emotion engine. For example, if a user is nervous, it can provide relaxing content or encouraging messages.

[1016] Learning content notifications

[1017] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1018] Recording learning results

[1019] The user works through the provided learning content and inputs the results, such as quiz answers and study time. The device sends the learning results to the server, which then stores them in a database and updates the user's learning history.

[1020] Related book suggestions

[1021] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions. The notification includes a summary of the book and a link to purchase it.

[1022] Specific examples

[1023] Consider the case where User A has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User A and encourages him / her to access the learning content. The device further analyzes the user's emotions using an emotion engine and provides appropriate relaxation techniques and encouraging messages. When User A finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User A via the device.

[1024] In this way, the system of the present invention is a system that effectively utilizes the user's spare time to efficiently advance learning, and further improves the user's learning experience by using an emotion engine.

[1025] The processing flow will be explained below.

[1026] Step 1:

[1027] The device retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves schedule information using the Google Calendar API. This information is converted into JSON format and sent to the server.

[1028] Step 2:

[1029] The server receives the calendar information and stores it in a database. The start and end times of each event in the calendar are extracted and stored.

[1030] Step 3:

[1031] The server analyzes the calendar information stored in the database to identify gaps in time, calculates the difference between the end time of each event and the start time of the next event, and lists them.

[1032] Step 4:

[1033] The server ranks the listed spare time slots by priority, based on the length of the spare time slots and the user's past learning history.

[1034] Step 5:

[1035] Users register the topic they want to learn through the learning application. They enter topics such as "Python programming" or "data science" into the application's input form. After entering the information, the device sends the topic information to the server.

[1036] Step 6:

[1037] Based on the learning topics stored in the database, the server uses generative AI to generate relevant learning content, such as textbooks and quizzes on basic Python programming concepts.

[1038] Step 7:

[1039] The device uses an emotion engine to support user learning. When a user uses an application, the device uses a camera and microphone to capture the user's facial expressions and voice tone, which are then analyzed by the emotion engine.

[1040] Step 8:

[1041] The server adjusts the learning content based on the generated learning content and the analysis results of the emotion engine. For example, if the user is nervous, it adds content to help them relax or an encouraging message.

[1042] Step 9:

[1043] The server transmits the adjusted learning content to the terminal in time for the next free time. The terminal receives the learning content and transmits a notification to the user when the next free time approaches.

[1044] Step 10:

[1045] The device notifies the user at the designated time and displays a link to the learning content. When the user clicks the link, the application displays the learning content.

[1046] Step 11:

[1047] The user engages with the provided learning content, for example, by reading learning resources or answering quizzes. When the user has finished learning, they enter the results into the device.

[1048] Step 12:

[1049] The device sends the user's learning results (e.g., number of correct answers in quizzes, study time) to the server. The server receives this information and stores it in a database to update the user's learning history.

[1050] Step 13:

[1051] The server suggests related books based on the user's study topic and study history. It uses a specific API to obtain a list of related books and sends it to the device.

[1052] Step 14:

[1053] The device notifies the user of book suggestions, which include a synopsis of the book and a link to purchase it for easy access by the user.

[1054] Through these steps, users can effectively utilize their free time and study. The introduction of the emotion engine provides appropriate learning support according to the user's psychological state, improving the learning experience.

[1055] Example 2

[1056] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1057] Conventional learning systems have had difficulty efficiently utilizing users' spare time. Furthermore, they have not provided learning content that takes into account the user's emotional state, which can lead to reduced learning efficiency. Furthermore, the suggestion of related books must be done manually, creating a need for an automated system.

[1058] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1059] In this invention, the server includes a means for acquiring a user's schedule information, a means for analyzing spare time, a means for generating study content related to a study topic based on the analyzed spare time, a means for notifying the user of the generated study content, a means for recording the user's study results, and a means for dynamically adjusting the study content based on the emotion data, incorporating an emotion engine for recognizing the user's emotions. This allows for effective use of the user's spare time and provision of study content that takes into account their emotional state. Furthermore, related book suggestions are also automated, providing users with optimal study resources.

[1060] "Calendar information" is data that includes details of a user's schedule and events.

[1061] "Spare time" is free time between consecutive events in a user's schedule information.

[1062] A "study topic" is a specific topic or area that a user wishes to study.

[1063] "Study content" refers to information such as learning materials, quizzes, and texts that users use to study.

[1064] "Generative AI" is an artificial intelligence technology that automatically generates text and content.

[1065] "Notification" is the process of communicating information from the system to the user.

[1066] The "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to determine their emotional state.

[1067] "Emotion data" is information that indicates the user's emotional state analyzed by the emotion engine.

[1068] "Study results" refers to data such as the results and answers obtained when a user engages with study content.

[1069] "Related books" are books related to the study topic and are suggested to complement the user's studies.

[1070] MODE FOR CARRYING OUT THE INVENTION

[1071] The system of the present invention is designed to effectively utilize a user's spare time and support efficient learning. The system acquires the user's schedule information and generates, notifies, and records learning content based on the analyzed spare time. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotions and further improves the learning experience. Specific embodiments are described below.

[1072] Get user calendar information

[1073] The device periodically obtains the user's calendar information. For example, if the user uses Google Calendar, the device obtains the user's schedule information using the Google Calendar API. This schedule information is converted into JSON format and then sent to the server as a POST request. The server then stores the received calendar information in a database.

[1074] Hardware used: Device (smartphone or tablet)

[1075] Software used: Google Calendar API, server software (e.g., Node.js server)

[1076] Example: A device sends a request to the "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events" endpoint to retrieve the user's calendar information.

[1077] Analysis of spare time

[1078] The server analyzes the schedule information stored in the database to identify gap times. It calculates the difference between the end time of each event and the start time of the next event to list the optimal gap times. From this list, it ranks the most appropriate gap times based on priority. This ranking is based on the length of the gap time and past learning history.

[1079] Hardware used: Server

[1080] Software used: Database (MySQL or PostgreSQL), backend script (Python or JavaScript)

[1081] Example: If the events in a schedule are from 9:00 to 10:00 and from 12:00 to 13:00, the two hours from 10:00 to 12:00 are recognized as gap time.

[1082] Registering and managing study topics

[1083] Users register the topic they want to learn through the learning application. For example, they can enter topics such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[1084] Hardware used: Terminal

[1085] Software used: Front-end application (Android / iOS app, web app)

[1086] Example: A user enters "Python programming" into an application form and submits the information via a POST request to "https: / / example.com / api / register_theme."

[1087] Generating learning content

[1088] The server uses AI to generate learning content based on the registered learning topic. For example, it generates text and quizzes on basic concepts of "Python programming." The generated content is then sent to the device in the next available time slot.

[1089] Hardware used: Server

[1090] Software used: Generative AI models (e.g., OpenAI GPT-3)

[1091] Example: The prompt "Please create a lesson for beginners on Python programming" is input into a generative AI model to generate learning content.

[1092] Introducing the Emotion Engine

[1093] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. The emotion engine's analysis results are sent from the device to the server.

[1094] Hardware used: Device (camera, microphone)

[1095] Software used: Emotion recognition engine (e.g., Microsoft Azure Emotion API)

[1096] Example: The device captures the user's facial expression with the camera and sends it to "https: / / centralus.api.cognitive.microsoft.com / face / v1.0 / detect" for analysis.

[1097] Emotion-based learning content adjustment

[1098] The server dynamically adjusts learning content based on the emotion data sent from the emotion engine. For example, if the user is nervous, it provides content to help them relax or messages of encouragement.

[1099] Hardware used: Server

[1100] Software used: Backend script (Python / Node.js)

[1101] Example: A server receives data that says "The user is nervous" and generates a message that says "Try some deep breathing exercises to relax."

[1102] Learning content notifications

[1103] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1104] Hardware used: Terminal

[1105] Software used: Push notification service (e.g., Firebase Cloud Messaging)

[1106] Example: Your device receives a notification saying, "Learn basic Python concepts between 9:00-9:30."

[1107] Recording learning results

[1108] The user works through the provided learning content and inputs the results, such as quiz answers and study time. This information is sent from the device to the server, which stores it in a database and updates the user's learning history.

[1109] Hardware used: Terminal

[1110] Software used: Front-end application, back-end API

[1111] Example: A user submits quiz results to "https: / / example.com / api / submit_results". The server saves the results to a database.

[1112] Related book suggestions

[1113] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions.

[1114] Hardware used: Server

[1115] Software used: Book suggestion API (e.g., Amazon API)

[1116] Example: A server requests "books related to Python programming" from the Amazon API and notifies the user of the results.

[1117] In this way, the system is designed to make effective use of users' spare time and allow them to study efficiently.

[1118] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1119] Step 1:

[1120] The device retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves the schedule information using the Google Calendar API. This information is converted to JSON format and sent to the server as a POST request.

[1121] Input: Google Calendar API endpoint "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events"

[1122] Data processing: Convert schedule information into JSON format

[1123] Output: Sends JSON formatted calendar information to the server.

[1124] Specific behavior: The device accesses the API endpoint to retrieve the user's calendar information, then converts the information into JSON format and sends a POST request to "https: / / example.com / api / calendar_data".

[1125] Step 2:

[1126] The server stores the received calendar information in a database, which is used for later analysis.

[1127] Input: Calendar information in JSON format sent from the device

[1128] Data processing: Insert the received JSON data into the database

[1129] Output: Calendar information stored in a database

[1130] Specific operation: The server receives the POST request, parses the JSON data, and saves it to the database.

[1131] Step 3:

[1132] The server analyzes the schedule information stored in the database to identify gap times, calculates the difference between the end time of each event and the start time of the next event, and lists the gap times.

[1133] Input: Calendar information stored in a database

[1134] Data processing: Calculate gap time by comparing the end time of each event with the start time of the next event

[1135] Output: List of gap times

[1136] Specific operation: The server retrieves the schedule information from the database, calculates the time difference between events, and lists the gap time.

[1137] Step 4:

[1138] Users register the topic they want to learn through the learning application, for example, entering a topic such as "Python programming."

[1139] Input: The learning topic that the user enters into the input form

[1140] Data processing: Convert the input theme information into JSON format

[1141] Output: Study topic information sent to the server

[1142] Specific behavior: The user enters "Python programming" into the app's input form and sends a POST request to "https: / / example.com / api / register_theme".

[1143] Step 5:

[1144] The server uses generative AI to generate learning content based on registered learning themes, such as text and quizzes on "Python programming."

[1145] Input: The learning topic registered by the user (e.g., Python programming)

[1146] Data processing: Enter a prompt into the generation AI and obtain the generated content.

[1147] Output: Generated learning content

[1148] Specific operation: The server inputs the prompt "Please create a lesson for beginners in Python programming" into the generation AI and obtains the result.

[1149] Step 6:

[1150] The device recognizes the user's emotions using an emotion engine, which analyzes the user's facial expressions and vocal tone to determine their current emotional state.

[1151] Input: User's facial expression data and voice tone

[1152] Data processing: Analysis using emotion engine

[1153] Output: Parsed emotion data

[1154] Specific operation: The device captures the user's facial expression with the camera, sends it to "https: / / centralus.api.cognitive.microsoft.com / face / v1.0 / detect" for analysis, and then sends the results to the server.

[1155] Step 7:

[1156] The server dynamically adjusts learning content based on the emotional data obtained from the emotion engine. For example, if the user is nervous, it provides content to help them relax or messages of encouragement.

[1157] Input: Emotion data sent from the emotion engine

[1158] Data manipulation: adjusting content based on sentiment data

[1159] Output: Tailored learning content

[1160] Specific behavior: The server receives data that "the user is nervous" and generates a message that says "Try some deep breathing exercises to relax."

[1161] Step 8:

[1162] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1163] Input: Learning content and time information sent from the server

[1164] Data processing: Converting learning content information into notification format

[1165] Output: Learning content link notified to user

[1166] What happens: Your device displays a push notification saying, "Learn basic Python concepts between 9:00 and 9:30."

[1167] Step 9:

[1168] The user works through the provided learning content and inputs the results, such as quiz answers and study time. This information is sent from the device to the server, which stores it in a database and updates the user's learning history.

[1169] Input: User-entered learning results

[1170] Data processing: Converting learning result data into JSON format

[1171] Output: Training results sent to the server

[1172] Specific behavior: The user submits the quiz results to "https: / / example.com / api / submit_results". The server saves the results in the database.

[1173] Step 10:

[1174] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions.

[1175] Input: User's study topic and history

[1176] Data processing: Use related book API to obtain book information

[1177] Output: A list of suggested related books

[1178] Specific operation: The server requests "books related to Python programming" from the Amazon API and notifies the user of the results.

[1179] (Application example 2)

[1180] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1181] Today's busy brick-and-mortar stores face challenges in effectively utilizing their free time and efficiently acquiring the necessary skills and knowledge. Furthermore, learning content is not tailored to individual emotions and situations, and uniform training can lower employee motivation. Furthermore, learning results are not properly recorded, and related books are scarce, limiting the ability to provide continuous learning support for employees.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's schedule information, means for analyzing spare time from the schedule information, means for generating study content related to a study topic set based on the analyzed spare time, means for analyzing the user's emotions and means for adjusting the study content based on the emotions, means for notifying the user of the generated study content, and means for recording the user's study results. This allows store employees to efficiently utilize their spare time to study, and in the process, content tailored to their individual emotional state is provided, thereby improving motivation and continuously improving their skills.

[1183] The "means for acquiring user's schedule information" is a function for periodically acquiring schedule information from the schedule system used by the user.

[1184] The "means for analyzing free time from schedule information" is a function for analyzing the acquired schedule information and identifying free time (free time) that exists between schedules.

[1185] The "means for generating learning content related to a learning topic" is a function for automatically creating content to be used in education or training based on a specific learning topic.

[1186] The "means for analyzing the user's emotions" is a function for analyzing the user's current emotional state using data such as the user's facial expressions, voice, and behavior.

[1187] The "means for adjusting learning content based on emotions" is a function that changes the difficulty level, content, etc. according to the analyzed user emotions, thereby optimizing the user's learning experience.

[1188] The "means for notifying the user of the generated study content" is a function for notifying the user of the generated study content as appropriate and encouraging them to start studying.

[1189] "Means for recording user learning results" refers to a function that allows users to record the results and progress of the learning content they have worked on, so that they can be referenced and evaluated later.

[1190] This invention provides a system that effectively utilizes a user's spare time and promotes efficient learning. This system acquires the user's schedule information, analyzes spare time, and generates, notifies, and records learning content based on that spare time.

[1191] System configuration

[1192] Hardware and Software Requirements

[1193] Device: A mobile device such as a smartphone or tablet used by a user.

[1194] Calendar API: An API for retrieving a user's calendar information, such as the Google Calendar API.

[1195] Server: A server for managing users' schedule information, spare time, study themes, study results, etc.

[1196] Generative AI models: Generative AI models for generating learning content, such as OpenAI's GPT-3.

[1197] Emotion engine: An engine that analyzes the user's facial expressions and voice to determine their emotional state.

[1198] Notification system: A system for notifying users of learning content.

[1199] Database: A database for storing users' calendar information, study topics, and study results.

[1200] Processing flow

[1201] 1. Get the user's calendar information:

[1202] The device periodically retrieves the user's calendar information using a calendar API such as Google Calendar. This information is converted into JSON format and sent to the server as a POST request. The server then stores the received calendar information in a database.

[1203] 2. Analysis of gap time:

[1204] The server analyzes the schedule information stored in the database to identify gaps in time. It calculates the difference between the end time of each event and the start time of the next event to list the optimal gaps in time. It then ranks the most suitable gaps from the list based on priority. This ranking is based on the length of the gaps and past learning history.

[1205] 3. Register and manage study topics:

[1206] The user registers the topic they want to learn through the learning application. For example, they can input topics such as "improving customer service skills" or "product knowledge." The device sends this information to the server, which then stores the topic information in a database.

[1207] 4. Learning content generation:

[1208] The server generates learning content using a generative AI model based on the registered learning theme. The generated learning content is provided in the form of text and quizzes. For example, text and quizzes on basic customer service etiquette are generated based on the theme "improving customer service skills." The content is generated based on the prompt, "Please generate learning content on improving customer service skills. The content should include basic customer service etiquette, communication techniques with customers, and methods for increasing customer satisfaction."

[1209] 5. Introducing the Emotion Engine:

[1210] The device is equipped with an emotion engine for analyzing the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice using the camera and microphone installed in the smartphone or tablet. The emotion engine's analysis results are sent from the device to a server.

[1211] 6. Emotion-based learning content adjustment:

[1212] The server dynamically adjusts learning content based on emotional data obtained from the emotion engine. For example, if the user is tired, it can provide relaxing content or encouraging messages.

[1213] 7. Learning Content Notification:

[1214] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1215] 8. Recording learning results:

[1216] The user works through the provided learning content and inputs the results, such as quiz answers and study time. The device sends the learning results to the server, which then stores them in a database and updates the user's learning history.

[1217] This system allows store employees to effectively utilize their free time to study efficiently, and by optimizing the learning experience based on emotional data, it is possible to increase motivation and continuously improve skills.

[1218] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1219] Step 1:

[1220] Get calendar information

[1221] The device periodically retrieves the user's calendar information using the Google Calendar API. This information is converted to JSON format and sent to the server as a POST request. The input is the user's schedule retrieved from the Google Calendar API, and the output is the calendar information in JSON format. The server stores this information in a database.

[1222] Step 2:

[1223] Analysis of spare time

[1224] The server analyzes the schedule information stored in the database and identifies gap times by calculating the difference between the end time of each event and the start time of the next event. The input is the schedule information stored in the database, and the output is a list of gap times. The server ranks the optimal gap times based on priority.

[1225] Step 3:

[1226] Registering study themes

[1227] The user inputs a learning topic using a smartphone or tablet. For example, a topic such as "improving customer service skills" or "product knowledge" is registered in the application. The input is the learning topic entered by the user, and the output is the topic information sent to the server. The server stores this information in a database.

[1228] Step 4:

[1229] Generating learning content

[1230] The server uses a generative AI model based on the registered learning theme to generate learning content. The prompt statement is, "Please generate learning content related to improving customer service skills. The content should include basic customer service etiquette, communication techniques with customers, and ways to increase customer satisfaction." The input is the specified learning theme and prompt statement, and the output is the generated learning content.

[1231] Step 5:

[1232] Emotion analysis using an emotion engine

[1233] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice via a camera and microphone to determine their emotional state. The input is facial expression data and voice data obtained from the device, and the output is analyzed emotional state data. The device then sends this data to a server.

[1234] Step 6:

[1235] Emotion-based content adjustment

[1236] The server adjusts the learning content appropriately based on the data obtained from the emotion engine. For example, if the user is tired, it provides content to help them relax. The input is the analyzed emotion data and the generated learning content, and the output is the adjusted learning content.

[1237] Step 7:

[1238] Learning content notifications

[1239] The device notifies the user at the specified spare time and provides a link to the learning content. The input is the learning content adjusted for the spare time, and the output is the notification to the user. When the user clicks the link, the learning content is displayed.

[1240] Step 8:

[1241] Recording learning results

[1242] The user performs the learning content and inputs the results. For example, they record the results of quizzes and the time spent studying. The input is the learning results entered by the user, and the output is the learning result data sent to the server. The server stores this in a database and updates the user's learning history.

[1243] This allows the system to enable store employees to efficiently utilize their spare time and provide learning content tailored to their individual emotional state, thereby improving motivation and enabling continuous skill development.

[1244] 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.

[1245] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1246] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1247] [Fourth embodiment]

[1248] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1249] 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.

[1250] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1251] 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.

[1252] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1253] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1254] 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.

[1255] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1256] 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.

[1257] 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 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.

[1258] 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.

[1259] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1260] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1261] The system of the present invention is designed to effectively utilize users' spare time and support efficient learning. This system acquires the user's calendar information and generates, notifies, and records learning content based on the analyzed spare time. The program and processing of this system are explained in natural language below.

[1262] Get a user's calendar information

[1263] The device periodically obtains the user's calendar information. For example, if the user uses Google Calendar, the device obtains schedule information using the Google Calendar API and sends it to the server. The server then stores the received calendar information in a database.

[1264] Analysis of spare time

[1265] The server analyzes the received calendar information and finds available time slots. The server calculates the time difference between calendar events and identifies the most suitable available time slots. The analyzed available time slots are then listed.

[1266] Registering and managing study topics

[1267] Users register the topic they want to learn through the application. For example, they can enter topics such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[1268] Generating learning content

[1269] The server uses generative AI to generate learning content based on registered learning themes. For example, it uses the OpenAI API to generate basic Python programming explanations and exercises. This allows for the creation of customized learning materials tailored to the user's learning level.

[1270] Learning content notifications

[1271] The server then sends the generated learning content to the device in the next available time slot. The device then notifies the user and encourages them to study. The notification includes a link to the learning content and a brief introduction.

[1272] Recording learning results

[1273] The user works through the provided learning content and inputs the results. For example, this could be quiz answers or study time. The device sends the learning results to the server, which stores them in a database. This allows the user's learning history to be accumulated and progress to be checked.

[1274] Related book suggestions

[1275] The server suggests related books based on the user's study topic and history. For example, it uses the Amazon API to obtain a list of books related to the user's study topic. The server sends this information to the device, and the device notifies the user.

[1276] Specific examples

[1277] Suppose User B has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User B and prompts him / her to access the learning content. When User B finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User B via the device.

[1278] In this way, the system of the present invention makes effective use of the user's spare time and improves learning efficiency, thereby enabling the user to actively engage in learning.

[1279] The processing flow will be explained below.

[1280] Step 1:

[1281] The device retrieves the user's calendar information. If the user uses Google Calendar, the device uses the Google Calendar API to retrieve the user's schedule information. This information is converted into JSON format and then sent to the server as a POST request.

[1282] Step 2:

[1283] The server receives the calendar information, parses it, and extracts the start and end times of each event. The extracted information is then stored in a database.

[1284] Step 3:

[1285] The server analyzes the calendar information stored in the database to identify gaps in time. Specifically, it calculates the difference between the end time of each event and the start time of the next event, and lists the gaps.

[1286] Step 4:

[1287] The server ranks the listed spare time slots by priority, based on the length of the spare time slots and the user's past learning history.

[1288] Step 5:

[1289] Users register the topic they want to learn through the learning application. They enter topics such as "Python programming" or "data science" into an input form on the application, and the device sends this information to the server.

[1290] Step 6:

[1291] The server uses generative AI to generate relevant learning content based on the learning topic information stored in the database. For example, it uses a specific API to generate textbooks and quizzes on basic concepts of Python programming.

[1292] Step 7:

[1293] The server transmits the generated study content to the device in time for the next free time. After the device receives the study content, it sends a notification to the user when the next free time approaches.

[1294] Step 8:

[1295] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1296] Step 9:

[1297] The user engages with the provided learning content, for example, by reading learning resources or answering quizzes. When the user has finished learning, they enter the results into the device.

[1298] Step 10:

[1299] The device sends the user's learning results (e.g., number of correct answers in quizzes, study time) to the server. The server receives this information and stores it in a database to update the user's learning history.

[1300] Step 11:

[1301] The server suggests related books based on the user's study topic and study history. It uses a specific API to obtain a list of related books and sends it to the device.

[1302] Step 12:

[1303] The device notifies the user of book suggestions, including a synopsis and a link to purchase, allowing the user to further their learning.

[1304] Through these steps, the system allows users to effectively utilize their spare time and automatically obtain the most suitable learning content for studying.

[1305] Example 1

[1306] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1307] In today's busy lifestyles, it is difficult for individuals to find time to study effectively. In particular, there is a lack of support for effectively utilizing spare time to advance their studies. Furthermore, existing learning support systems have difficulty responding flexibly to users' schedules and providing individually customized content. Therefore, there is a need for a system that can effectively utilize users' spare time and support their studies efficiently and effectively.

[1308] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1309] In this invention, the server includes means for acquiring a user's schedule information, means for analyzing spare time from the schedule information, means for generating study content related to a set study topic based on the analyzed spare time, means for notifying the user of the generated study content, means for recording the user's study results, and means for using a generative AI model to generate the study content, thereby enabling analysis of the user's spare time and providing individually customized study content.

[1310] "User's schedule information" is data containing the user's schedule, obtained from a calendar application or schedule management system.

[1311] "Split time" refers to a short period of unused time between a user's scheduled appointments.

[1312] A "study topic" is a particular field or topic that a user wishes to study.

[1313] "Study content" refers to information such as teaching materials and exercises that are provided to users when they study.

[1314] A "generative AI model" is a program or system that uses artificial intelligence to generate learning content for users.

[1315] A "prompt" is a text instruction that instructs a generative AI model to generate specific learning content.

[1316] The "means for acquiring" refers to a function or device that performs the processing required to acquire the user's schedule information.

[1317] The "analyzing means" refers to a function or device for identifying and analyzing free time from the acquired schedule information.

[1318] The "means for generating" refers to a function or device that creates learning content based on the analyzed spare time.

[1319] The "notification means" refers to a function or device for notifying the user of the generated learning content.

[1320] "Recording means" refers to a function or device for saving and managing the user's learning results.

[1321] "Means for using a generative AI model" refers to a function or device for generating learning content using a generative AI model.

[1322] "Related materials" refers to information such as external books and literature that is necessary or useful for the user's study.

[1323] The present invention provides a system for effectively utilizing a user's spare time and supporting efficient learning. This system acquires the user's schedule information and generates, notifies, and records learning content based on the analyzed spare time. An embodiment of this system is described below in detail.

[1324] Get user calendar information

[1325] The device periodically retrieves the user's calendar information. If the user uses a schedule management system such as Google Calendar, the device uses the Google Calendar API to obtain the user's authentication information and retrieves the user's schedule information. This information includes the start time, end time, and event title of each event set by the user. The retrieved calendar information is sent to the server via a secure connection. The server stores the received calendar information in a database.

[1326] Analysis of spare time

[1327] The server analyzes the calendar information stored in the database. The server calculates the time difference between calendar events and identifies the user's free time. Free time is set based on certain criteria (e.g., 15 minutes or more of unused time). This analysis results in a list of the user's available study times during the day.

[1328] Registering and managing study topics

[1329] Users input the topic they want to learn through the application. For example, they can select "Python programming" or "data science." This input allows for customization according to the user's learning needs. Once the topic is entered, the device sends this information to the server, which stores the topic information in a database.

[1330] Generating learning content

[1331] The server generates learning content using a generative AI model based on the learning themes registered by the user. The generative AI uses, for example, the OpenAI API. By inputting appropriate prompts to the generative AI, learning materials appropriate for the user are generated.

[1332] Prompt Sentence Examples

[1333] The topic users want to learn is "Python Programming Basics." Please create simple learning materials for beginners. Please provide concise learning materials that include:

[1334] Python Basic Syntax

[1335] Simple code example

[1336] Exercises

[1337] Learning content notifications

[1338] The server sends the generated learning content to the device in time for the next available time slot. For example, the learning content is sent to the device in time for the available time slot between 9:00 and 9:30 the next day. The device receives this content and notifies the user. The notification includes a link to the learning content and a brief introduction.

[1339] Recording learning results

[1340] The user works through the provided learning content and enters the results into the application. This includes, for example, the results of quiz answers and the time spent studying. These results are sent to the server via the device. The server stores the received learning results in a database and manages them as the user's learning history.

[1341] Suggestions for related materials

[1342] The server suggests relevant materials based on the user's learning topic and learning history. For example, it uses the Amazon API to retrieve a list of books on "data science." Information about the suggested books is then sent to the user via their device.

[1343] Example operation

[1344] Let's say User B registers "Data Science" as a study topic in the application and wishes to study during their morning commute. First, the device sends the user's Google Calendar information to the server in the morning. The server finds a gap in time between 9:00 and 9:30. Next, the server sends a prompt to the generation AI to generate "Basic Data Science Teaching Materials," and the materials are generated. Then, as 9:00 approaches, the server sends the learning content to the device, and the device notifies User B. When User B finishes studying and enters the results, the device sends them to the server, which saves them. The server then retrieves a list of related books through the Amazon API and notifies User B.

[1345] In this way, the system of the present invention provides an efficient and customizable learning environment that allows users to make effective use of their spare time.

[1346] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1347] Step 1:

[1348] The device retrieves the user's calendar information. As input, the user's authentication information is required. If using the Google Calendar API, the device retrieves the calendar information using the user's authentication information. As output, the retrieved calendar information is returned. The calendar information includes the start time, end time, and title of the event. This information is then sent to the server over a secure connection.

[1349] Step 2:

[1350] The server receives the calendar information sent from the device and stores it in a database. As input, it takes the calendar information sent from the device. It analyzes the calendar information and identifies available time slots by calculating the time difference between events. As output, it obtains a list of available time slots stored in the database. This list contains the available time slots for the user throughout the day.

[1351] Step 3:

[1352] The user registers a learning topic using the application. As input, the user enters a learning topic such as "Python programming" or "data science." Once the topic has been entered, the device sends this information to the server. As output, the topic information is stored in a database on the server side.

[1353] Step 4:

[1354] The server uses a generative AI model to generate learning content based on the learning topic registered by the user. The learning topic and a prompt are used as input. For example, the prompt "The topic the user wants to learn is 'Python programming basics'. Please create simple learning materials for beginners." is input to the generative AI model. The generated learning content is obtained as output. This content includes learning materials and exercises.

[1355] Step 5:

[1356] The server sends the generated learning content to the device in time for the next available time. The generated learning content and the identified available time are used as input. The output is the learning content sent to the device. The device receives this content and notifies the user using push notifications or alarms.

[1357] Step 6:

[1358] The user works on the provided learning content. As input, access to the learning content is required. The user proceeds with the learning and enters the results into the application. As output, quiz answers, study time, etc. are obtained. The device sends these results to the server.

[1359] Step 7:

[1360] The server stores the received learning results in a database. The learning results sent by the user are used as input. The learning results are stored in a database as output and managed as the user's learning history.

[1361] Step 8:

[1362] The server suggests related materials based on the user's learning topic and history. The user's learning topic and history are used as input. The server obtains a list of related books, for example, using the Amazon API. The output is a list of suggested related materials. The device receives this information and notifies the user.

[1363] In this way, the system effectively utilizes users' spare time and supports efficient learning by providing individually customized learning content.

[1364] (Application example 1)

[1365] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1366] In modern society, there are many systems that allow users to effectively utilize their "spare time," but these systems have several problems. For example, if the spare time is too short, learning and information intake may not be effective, or the appropriate content may not be provided to the user. Furthermore, existing systems do not fully utilize generative AI models, making it difficult to provide customized learning materials suited to each user's individual learning topic and level. Therefore, a new system that effectively utilizes users' spare time and provides individually optimized learning content is needed.

[1367] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1368] In this invention, the server includes a means for acquiring the user's calendar information, a means for analyzing spare time from the calendar information, a means for providing study content only if the analyzed spare time is equal to or greater than a certain level, and a means for using a generative AI model to generate study content according to the study topic. This makes it possible to effectively utilize spare time and provide optimal study content according to the user's study topic.

[1369] "User calendar information" is data obtained from applications and systems that users use to record their daily schedules and appointments.

[1370] "Spare time" refers to free time between scheduled events in the user's calendar information.

[1371] A "study topic" is a particular subject or area that a user wishes to learn.

[1372] "Learning content" refers to information such as teaching materials, exercises, and explanatory texts that are provided based on a learning theme set by the user.

[1373] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to generate appropriate outputs for specific inputs.

[1374] The "scheduling means" is a means for periodically updating calendar information and carrying out planned processing for analyzing free time.

[1375] "Study history" refers to data that records what a user has learned and their progress to date.

[1376] "Related books" are books and reference materials related to the study theme set by the user.

[1377] This invention is a system that analyzes a user's calendar information and provides optimal learning content for spare time. Users can access this system using their smartphones to register and manage their calendar information.

[1378] First, the device (smartphone) periodically collects the user's calendar information using the Google Calendar API, etc. The collected calendar information is sent to a server, which then stores the information in a database.

[1379] Next, the server analyzes the calendar information and calculates the gap time between each event. This analysis involves first calculating the time difference between calendar events, and then listing gap times that are greater than a certain range (for example, 10 minutes or more).

[1380] Users register their study topics through a smartphone application. For example, they can set themes such as "Python programming" or "data science." The study topics registered by the user are sent to the server and stored in a database.

[1381] The server uses a generative AI model to generate learning content based on the learning themes registered by the user. For example, it uses the OpenAI API to generate basic explanations and exercises for "Python programming." The generated learning content is customized to the user's learning level.

[1382] The generated learning content is then sent to the device at the optimal time for the user to study. The device then notifies the user of the learning content and encourages them to study. The notification includes a link to the learning content and a brief introduction.

[1383] The user works on the learning content and inputs the results. For example, this includes quiz answers and study time. The device sends the learning results to the server, which stores them in a database. This allows the user's learning history to be accumulated and progress to be checked.

[1384] Furthermore, the server suggests related books based on the user's study topic and history, using the Amazon API or similar to obtain a list of books related to the study topic, and notifies the user via the device.

[1385] As a concrete example, suppose User B has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User B and prompts him / her to access the learning content. When User B finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User B via the device.

[1386] An example prompt is:

[1387] "Topic: Python Programming\nGenerate a textbook that provides basic explanations and exercises for Python."

[1388] By using the above means, the present invention can efficiently utilize the users' spare time and provide content that meets their individual learning needs.

[1389] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1390] Step 1:

[1391] The device periodically collects the user's calendar information using the Google Calendar API, etc. At this time, the device acquires the calendar information and sends it to the server. The input is the event data acquired from the Google Calendar API, and the output is the calendar information sent to the server.

[1392] Step 2:

[1393] The server saves the received calendar information in a database, which stores the user's schedule. The input is the calendar information sent from the terminal, and the output is the calendar information stored in the database.

[1394] Step 3:

[1395] The server analyzes the stored calendar information and calculates the gap time between each event. The analysis involves calculating the time difference between calendar events and listing gap times greater than a certain amount (for example, 10 minutes or more). The input is the calendar information obtained from the database, and the output is the listed gap time.

[1396] Step 4:

[1397] The user registers a learning topic using the terminal. For example, they set a topic such as "Python programming" or "data science." The input is the learning topic that the user enters into the terminal, and the output is the learning topic information sent to the server.

[1398] Step 5:

[1399] The server stores the learning topic information in a database and manages the user's learning topics. The input is the learning topic information sent from the terminal, and the output is the learning topic information stored in the database.

[1400] Step 6:

[1401] The server generates learning content using a generative AI model based on the learning topic registered by the user. At this time, a prompt is sent using the generative AI (e.g., OpenAI API) to obtain the generated content. The input is the learning topic and prompt registered by the user, and the output is the generated learning content. An example of a prompt is: "Topic: Python programming\nPlease generate a text that provides basic explanations and practice questions for Python."

[1402] Step 7:

[1403] The server sends the generated learning content to the device according to the listed spare time. The notification includes a link to the learning content and a brief introduction. The input is the generated learning content and spare time information, and the output is the learning content notified to the device.

[1404] Step 8:

[1405] The user works on the learning content received from the device and inputs the results, such as quiz answer results and study time. The input is the learning results that the user enters into the device, and the output is the learning results sent to the server.

[1406] Step 9:

[1407] The server stores the user's learning results in a database and accumulates the user's learning history, allowing the user to check their progress. The input is the learning results sent from the device, and the output is the learning history stored in the database.

[1408] Step 10:

[1409] The server suggests related books based on the user's study topic and history. It obtains a list of related books using the Amazon API or similar and notifies the device. The input is the user's study topic and history, and the output is the obtained list of related books.

[1410] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1411] The system of the present invention is designed to effectively utilize users' spare time and support efficient learning. The system acquires the user's calendar information and generates, notifies, and records learning content based on the analyzed spare time. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotions and further improves the learning experience.

[1412] Get a user's calendar information

[1413] The device periodically retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves the user's schedule information using the Google Calendar API. This information is converted into JSON format and sent to the server as a POST request. The server stores the received calendar information in a database.

[1414] Analysis of spare time

[1415] The server analyzes the calendar information stored in the database to identify gaps in time. It calculates the difference between the end time of each event and the start time of the next event to create a list of optimal gaps. From the list of gaps, it ranks the most appropriate times based on priority. This ranking is based on the length of the gaps and past learning history.

[1416] Registering and managing study topics

[1417] Users register the topic they want to learn through the learning application. For example, they can input a topic such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[1418] Generating learning content

[1419] The server uses generative AI to generate learning content based on the registered learning topic. For example, it generates text and quizzes on basic concepts of Python programming. The generated learning content is then sent to the device in the next available time slot.

[1420] Introducing the Emotion Engine

[1421] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. The emotion engine's analysis results are sent from the device to the server.

[1422] Emotion-based learning content adjustment

[1423] The server dynamically adjusts learning content based on the emotional data obtained from the emotion engine. For example, if a user is nervous, it can provide relaxing content or encouraging messages.

[1424] Learning content notifications

[1425] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1426] Recording learning results

[1427] The user works through the provided learning content and inputs the results, such as quiz answers and study time. The device sends the learning results to the server, which then stores them in a database and updates the user's learning history.

[1428] Related book suggestions

[1429] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions. The notification includes a summary of the book and a link to purchase it.

[1430] Specific examples

[1431] Consider the case where User A has registered "Data Science" as a study topic and wishes to study during his / her morning commute. The device sends the user's calendar information to the server early in the morning, and the server finds a gap in time during his / her commute (for example, 9:00-9:30). The server uses generative AI to generate basic learning materials and quizzes related to data science. When it is time to commute, the device notifies User A and encourages him / her to access the learning content. The device further analyzes the user's emotions using an emotion engine and provides appropriate relaxation techniques and encouraging messages. When User A finishes studying, the results are sent to the server via the device and stored. The server then suggests books related to data science and notifies User A via the device.

[1432] In this way, the system of the present invention is a system that effectively utilizes the user's spare time to efficiently advance learning, and further improves the user's learning experience by using an emotion engine.

[1433] The processing flow will be explained below.

[1434] Step 1:

[1435] The device retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves schedule information using the Google Calendar API. This information is converted into JSON format and sent to the server.

[1436] Step 2:

[1437] The server receives the calendar information and stores it in a database. The start and end times of each event in the calendar are extracted and stored.

[1438] Step 3:

[1439] The server analyzes the calendar information stored in the database to identify gaps in time, calculates the difference between the end time of each event and the start time of the next event, and lists them.

[1440] Step 4:

[1441] The server ranks the listed spare time slots by priority, based on the length of the spare time slots and the user's past learning history.

[1442] Step 5:

[1443] Users register the topic they want to learn through the learning application. They enter topics such as "Python programming" or "data science" into the application's input form. After entering the information, the device sends the topic information to the server.

[1444] Step 6:

[1445] Based on the learning topics stored in the database, the server uses generative AI to generate relevant learning content, such as textbooks and quizzes on basic Python programming concepts.

[1446] Step 7:

[1447] The device uses an emotion engine to support user learning. When a user uses an application, the device uses a camera and microphone to capture the user's facial expressions and voice tone, which are then analyzed by the emotion engine.

[1448] Step 8:

[1449] The server adjusts the learning content based on the generated learning content and the analysis results of the emotion engine. For example, if the user is nervous, it adds content to help them relax or an encouraging message.

[1450] Step 9:

[1451] The server transmits the adjusted learning content to the terminal in time for the next free time. The terminal receives the learning content and transmits a notification to the user when the next free time approaches.

[1452] Step 10:

[1453] The device notifies the user at the designated time and displays a link to the learning content. When the user clicks the link, the application displays the learning content.

[1454] Step 11:

[1455] The user engages with the provided learning content, for example, by reading learning resources or answering quizzes. When the user has finished learning, they enter the results into the device.

[1456] Step 12:

[1457] The device sends the user's learning results (e.g., number of correct answers in quizzes, study time) to the server. The server receives this information and stores it in a database to update the user's learning history.

[1458] Step 13:

[1459] The server suggests related books based on the user's study topic and study history. It uses a specific API to obtain a list of related books and sends it to the device.

[1460] Step 14:

[1461] The device notifies the user of book suggestions, which include a synopsis of the book and a link to purchase it for easy access by the user.

[1462] Through these steps, users can effectively utilize their free time and study. The introduction of the emotion engine provides appropriate learning support according to the user's psychological state, improving the learning experience.

[1463] Example 2

[1464] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1465] Conventional learning systems have had difficulty efficiently utilizing users' spare time. Furthermore, they have not provided learning content that takes into account the user's emotional state, which can lead to reduced learning efficiency. Furthermore, the suggestion of related books must be done manually, creating a need for an automated system.

[1466] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1467] In this invention, the server includes a means for acquiring a user's schedule information, a means for analyzing spare time, a means for generating study content related to a study topic based on the analyzed spare time, a means for notifying the user of the generated study content, a means for recording the user's study results, and a means for dynamically adjusting the study content based on the emotion data, incorporating an emotion engine for recognizing the user's emotions. This allows for effective use of the user's spare time and provision of study content that takes into account their emotional state. Furthermore, related book suggestions are also automated, providing users with optimal study resources.

[1468] "Calendar information" is data that includes details of a user's schedule and events.

[1469] "Spare time" is free time between consecutive events in a user's schedule information.

[1470] A "study topic" is a specific topic or area that a user wishes to study.

[1471] "Study content" refers to information such as learning materials, quizzes, and texts that users use to study.

[1472] "Generative AI" is an artificial intelligence technology that automatically generates text and content.

[1473] "Notification" is the process of communicating information from the system to the user.

[1474] The "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to determine their emotional state.

[1475] "Emotion data" is information that indicates the user's emotional state analyzed by the emotion engine.

[1476] "Study results" refers to data such as the results and answers obtained when a user engages with study content.

[1477] "Related books" are books related to the study topic and are suggested to complement the user's studies.

[1478] MODE FOR CARRYING OUT THE INVENTION

[1479] The system of the present invention is designed to effectively utilize a user's spare time and support efficient learning. The system acquires the user's schedule information and generates, notifies, and records learning content based on the analyzed spare time. Furthermore, by combining it with an emotion engine, the system recognizes the user's emotions and further improves the learning experience. Specific embodiments are described below.

[1480] Get user calendar information

[1481] The device periodically obtains the user's calendar information. For example, if the user uses Google Calendar, the device obtains the user's schedule information using the Google Calendar API. This schedule information is converted into JSON format and then sent to the server as a POST request. The server then stores the received calendar information in a database.

[1482] Hardware used: Device (smartphone or tablet)

[1483] Software used: Google Calendar API, server software (e.g., Node.js server)

[1484] Example: A device sends a request to the "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events" endpoint to retrieve the user's calendar information.

[1485] Analysis of spare time

[1486] The server analyzes the schedule information stored in the database to identify gap times. It calculates the difference between the end time of each event and the start time of the next event to list the optimal gap times. From this list, it ranks the most appropriate gap times based on priority. This ranking is based on the length of the gap time and past learning history.

[1487] Hardware used: Server

[1488] Software used: Database (MySQL or PostgreSQL), backend script (Python or JavaScript)

[1489] Example: If the events in a schedule are from 9:00 to 10:00 and from 12:00 to 13:00, the two hours from 10:00 to 12:00 are recognized as gap time.

[1490] Registering and managing study topics

[1491] Users register the topic they want to learn through the learning application. For example, they can enter topics such as "Python programming" or "data science." The device sends this information to the server, which then stores the topic information in a database.

[1492] Hardware used: Terminal

[1493] Software used: Front-end application (Android / iOS app, web app)

[1494] Example: A user enters "Python programming" into an application form and submits the information via a POST request to "https: / / example.com / api / register_theme."

[1495] Generating learning content

[1496] The server uses AI to generate learning content based on the registered learning topic. For example, it generates text and quizzes on basic concepts of "Python programming." The generated content is then sent to the device in the next available time slot.

[1497] Hardware used: Server

[1498] Software used: Generative AI models (e.g., OpenAI GPT-3)

[1499] Example: The prompt "Please create a lesson for beginners on Python programming" is input into a generative AI model to generate learning content.

[1500] Introducing the Emotion Engine

[1501] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. The emotion engine's analysis results are sent from the device to the server.

[1502] Hardware used: Device (camera, microphone)

[1503] Software used: Emotion recognition engine (e.g., Microsoft Azure Emotion API)

[1504] Example: The device captures the user's facial expression with the camera and sends it to "https: / / centralus.api.cognitive.microsoft.com / face / v1.0 / detect" for analysis.

[1505] Emotion-based learning content adjustment

[1506] The server dynamically adjusts learning content based on the emotion data sent from the emotion engine. For example, if the user is nervous, it provides content to help them relax or messages of encouragement.

[1507] Hardware used: Server

[1508] Software used: Backend script (Python / Node.js)

[1509] Example: A server receives data that says "The user is nervous" and generates a message that says "Try some deep breathing exercises to relax."

[1510] Learning content notifications

[1511] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1512] Hardware used: Terminal

[1513] Software used: Push notification service (e.g., Firebase Cloud Messaging)

[1514] Example: Your device receives a notification saying, "Learn basic Python concepts between 9:00-9:30."

[1515] Recording learning results

[1516] The user works through the provided learning content and inputs the results, such as quiz answers and study time. This information is sent from the device to the server, which stores it in a database and updates the user's learning history.

[1517] Hardware used: Terminal

[1518] Software used: Front-end application, back-end API

[1519] Example: A user submits quiz results to "https: / / example.com / api / submit_results". The server saves the results to a database.

[1520] Related book suggestions

[1521] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions.

[1522] Hardware used: Server

[1523] Software used: Book suggestion API (e.g., Amazon API)

[1524] Example: A server requests "books related to Python programming" from the Amazon API and notifies the user of the results.

[1525] In this way, the system is designed to make effective use of users' spare time and allow them to study efficiently.

[1526] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1527] Step 1:

[1528] The device retrieves the user's calendar information. If the user uses Google Calendar, the device retrieves the schedule information using the Google Calendar API. This information is converted to JSON format and sent to the server as a POST request.

[1529] Input: Google Calendar API endpoint "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events"

[1530] Data processing: Convert schedule information into JSON format

[1531] Output: Sends JSON formatted calendar information to the server.

[1532] Specific behavior: The device accesses the API endpoint to retrieve the user's calendar information, then converts the information into JSON format and sends a POST request to "https: / / example.com / api / calendar_data".

[1533] Step 2:

[1534] The server stores the received calendar information in a database, which is used for later analysis.

[1535] Input: Calendar information in JSON format sent from the device

[1536] Data processing: Insert the received JSON data into the database

[1537] Output: Calendar information stored in a database

[1538] Specific operation: The server receives the POST request, parses the JSON data, and saves it to the database.

[1539] Step 3:

[1540] The server analyzes the schedule information stored in the database to identify gap times, calculates the difference between the end time of each event and the start time of the next event, and lists the gap times.

[1541] Input: Calendar information stored in a database

[1542] Data processing: Calculate gap time by comparing the end time of each event with the start time of the next event

[1543] Output: List of gap times

[1544] Specific operation: The server retrieves the schedule information from the database, calculates the time difference between events, and lists the gap time.

[1545] Step 4:

[1546] Users register the topic they want to learn through the learning application, for example, entering a topic such as "Python programming."

[1547] Input: The learning topic that the user enters into the input form

[1548] Data processing: Convert the input theme information into JSON format

[1549] Output: Study topic information sent to the server

[1550] Specific behavior: The user enters "Python programming" into the app's input form and sends a POST request to "https: / / example.com / api / register_theme".

[1551] Step 5:

[1552] The server uses generative AI to generate learning content based on registered learning themes, such as text and quizzes on "Python programming."

[1553] Input: The learning topic registered by the user (e.g., Python programming)

[1554] Data processing: Enter a prompt into the generation AI and obtain the generated content.

[1555] Output: Generated learning content

[1556] Specific operation: The server inputs the prompt "Please create a lesson for beginners in Python programming" into the generation AI and obtains the result.

[1557] Step 6:

[1558] The device recognizes the user's emotions using an emotion engine, which analyzes the user's facial expressions and vocal tone to determine their current emotional state.

[1559] Input: User's facial expression data and voice tone

[1560] Data processing: Analysis using emotion engine

[1561] Output: Parsed emotion data

[1562] Specific operation: The device captures the user's facial expression with the camera, sends it to "https: / / centralus.api.cognitive.microsoft.com / face / v1.0 / detect" for analysis, and then sends the results to the server.

[1563] Step 7:

[1564] The server dynamically adjusts learning content based on the emotional data obtained from the emotion engine. For example, if the user is nervous, it provides content to help them relax or messages of encouragement.

[1565] Input: Emotion data sent from the emotion engine

[1566] Data manipulation: adjusting content based on sentiment data

[1567] Output: Tailored learning content

[1568] Specific behavior: The server receives data that "the user is nervous" and generates a message that says "Try some deep breathing exercises to relax."

[1569] Step 8:

[1570] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1571] Input: Learning content and time information sent from the server

[1572] Data processing: Converting learning content information into notification format

[1573] Output: Learning content link notified to user

[1574] What happens: Your device displays a push notification saying, "Learn basic Python concepts between 9:00 and 9:30."

[1575] Step 9:

[1576] The user works through the provided learning content and inputs the results, such as quiz answers and study time. This information is sent from the device to the server, which stores it in a database and updates the user's learning history.

[1577] Input: User-entered learning results

[1578] Data processing: Converting learning result data into JSON format

[1579] Output: Training results sent to the server

[1580] Specific behavior: The user submits the quiz results to "https: / / example.com / api / submit_results". The server saves the results in the database.

[1581] Step 10:

[1582] The server suggests related books based on the user's learning topic and history. It uses a specific API to obtain a list of related books and sends it to the device. The device then notifies the user of the book suggestions.

[1583] Input: User's study topic and history

[1584] Data processing: Use related book API to obtain book information

[1585] Output: A list of suggested related books

[1586] Specific operation: The server requests "books related to Python programming" from the Amazon API and notifies the user of the results.

[1587] (Application example 2)

[1588] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1589] Today's busy brick-and-mortar stores face challenges in effectively utilizing their free time and efficiently acquiring the necessary skills and knowledge. Furthermore, learning content is not tailored to individual emotions and situations, and uniform training can lower employee motivation. Furthermore, learning results are not properly recorded, and related books are scarce, limiting the ability to provide continuous learning support for employees.

[1590] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's schedule information, means for analyzing spare time from the schedule information, means for generating study content related to a study topic set based on the analyzed spare time, means for analyzing the user's emotions and means for adjusting the study content based on the emotions, means for notifying the user of the generated study content, and means for recording the user's study results. This allows store employees to efficiently utilize their spare time to study, and in the process, content tailored to their individual emotional state is provided, thereby improving motivation and continuously improving their skills.

[1591] The "means for acquiring user's schedule information" is a function for periodically acquiring schedule information from the schedule system used by the user.

[1592] The "means for analyzing free time from schedule information" is a function for analyzing the acquired schedule information and identifying free time (free time) that exists between schedules.

[1593] The "means for generating learning content related to a learning topic" is a function for automatically creating content to be used in education or training based on a specific learning topic.

[1594] The "means for analyzing the user's emotions" is a function for analyzing the user's current emotional state using data such as the user's facial expressions, voice, and behavior.

[1595] The "means for adjusting learning content based on emotions" is a function that changes the difficulty level, content, etc. according to the analyzed user emotions, thereby optimizing the user's learning experience.

[1596] The "means for notifying the user of the generated study content" is a function for notifying the user of the generated study content as appropriate and encouraging them to start studying.

[1597] "Means for recording user learning results" refers to a function that allows users to record the results and progress of the learning content they have worked on, so that they can be referenced and evaluated later.

[1598] This invention provides a system that effectively utilizes a user's spare time and promotes efficient learning. This system acquires the user's schedule information, analyzes spare time, and generates, notifies, and records learning content based on that spare time.

[1599] System configuration

[1600] Hardware and Software Requirements

[1601] Device: A mobile device such as a smartphone or tablet used by a user.

[1602] Calendar API: An API for retrieving a user's calendar information, such as the Google Calendar API.

[1603] Server: A server for managing users' schedule information, spare time, study themes, study results, etc.

[1604] Generative AI models: Generative AI models for generating learning content, such as OpenAI's GPT-3.

[1605] Emotion engine: An engine that analyzes the user's facial expressions and voice to determine their emotional state.

[1606] Notification system: A system for notifying users of learning content.

[1607] Database: A database for storing users' calendar information, study topics, and study results.

[1608] Processing flow

[1609] 1. Get the user's calendar information:

[1610] The device periodically retrieves the user's calendar information using a calendar API such as Google Calendar. This information is converted into JSON format and sent to the server as a POST request. The server then stores the received calendar information in a database.

[1611] 2. Analysis of gap time:

[1612] The server analyzes the schedule information stored in the database to identify gaps in time. It calculates the difference between the end time of each event and the start time of the next event to list the optimal gaps in time. It then ranks the most suitable gaps from the list based on priority. This ranking is based on the length of the gaps and past learning history.

[1613] 3. Register and manage study topics:

[1614] The user registers the topic they want to learn through the learning application. For example, they can input topics such as "improving customer service skills" or "product knowledge." The device sends this information to the server, which then stores the topic information in a database.

[1615] 4. Learning content generation:

[1616] The server generates learning content using a generative AI model based on the registered learning theme. The generated learning content is provided in the form of text and quizzes. For example, text and quizzes on basic customer service etiquette are generated based on the theme "improving customer service skills." The content is generated based on the prompt, "Please generate learning content on improving customer service skills. The content should include basic customer service etiquette, communication techniques with customers, and methods for increasing customer satisfaction."

[1617] 5. Introducing the Emotion Engine:

[1618] The device is equipped with an emotion engine for analyzing the user's emotions. This emotion engine analyzes the user's facial expressions and tone of voice using the camera and microphone installed in the smartphone or tablet. The emotion engine's analysis results are sent from the device to a server.

[1619] 6. Emotion-based learning content adjustment:

[1620] The server dynamically adjusts learning content based on emotional data obtained from the emotion engine. For example, if the user is tired, it can provide relaxing content or encouraging messages.

[1621] 7. Learning Content Notification:

[1622] The device will send a notification to the user at the designated time slot and display a link to the learning content. When the user clicks the link, the application will display the learning content.

[1623] 8. Recording learning results:

[1624] The user works through the provided learning content and inputs the results, such as quiz answers and study time. The device sends the learning results to the server, which then stores them in a database and updates the user's learning history.

[1625] This system allows store employees to effectively utilize their free time to study efficiently, and by optimizing the learning experience based on emotional data, it is possible to increase motivation and continuously improve skills.

[1626] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1627] Step 1:

[1628] Get calendar information

[1629] The device periodically retrieves the user's calendar information using the Google Calendar API. This information is converted to JSON format and sent to the server as a POST request. The input is the user's schedule retrieved from the Google Calendar API, and the output is the calendar information in JSON format. The server stores this information in a database.

[1630] Step 2:

[1631] Analysis of spare time

[1632] The server analyzes the schedule information stored in the database and identifies gap times by calculating the difference between the end time of each event and the start time of the next event. The input is the schedule information stored in the database, and the output is a list of gap times. The server ranks the optimal gap times based on priority.

[1633] Step 3:

[1634] Registering study themes

[1635] The user inputs a learning topic using a smartphone or tablet. For example, a topic such as "improving customer service skills" or "product knowledge" is registered in the application. The input is the learning topic entered by the user, and the output is the topic information sent to the server. The server stores this information in a database.

[1636] Step 4:

[1637] Generating learning content

[1638] The server uses a generative AI model based on the registered learning theme to generate learning content. The prompt statement is, "Please generate learning content related to improving customer service skills. The content should include basic customer service etiquette, communication techniques with customers, and ways to increase customer satisfaction." The input is the specified learning theme and prompt statement, and the output is the generated learning content.

[1639] Step 5:

[1640] Emotion analysis using an emotion engine

[1641] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice via a camera and microphone to determine their emotional state. The input is facial expression data and voice data obtained from the device, and the output is analyzed emotional state data. The device then sends this data to a server.

[1642] Step 6:

[1643] Emotion-based content adjustment

[1644] The server adjusts the learning content appropriately based on the data obtained from the emotion engine. For example, if the user is tired, it provides content to help them relax. The input is the analyzed emotion data and the generated learning content, and the output is the adjusted learning content.

[1645] Step 7:

[1646] Learning content notifications

[1647] The device notifies the user at the specified spare time and provides a link to the learning content. The input is the learning content adjusted for the spare time, and the output is the notification to the user. When the user clicks the link, the learning content is displayed.

[1648] Step 8:

[1649] Recording learning results

[1650] The user performs the learning content and inputs the results. For example, they record the results of quizzes and the time spent studying. The input is the learning results entered by the user, and the output is the learning result data sent to the server. The server stores this in a database and updates the user's learning history.

[1651] This allows the system to enable store employees to efficiently utilize their spare time and provide learning content tailored to their individual emotional state, thereby improving motivation and enabling continuous skill development.

[1652] 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.

[1653] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1654] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1655] 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.

[1656] 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 includes both affect 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.

[1657] 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.

[1658] 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).

[1659] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1660] 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."

[1661] 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.

[1662] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1663] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1668] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] The following is further disclosed regarding the above embodiment.

[1674] (Claim 1)

[1675] means for obtaining a user's calendar information;

[1676] means for analyzing spare time from the schedule information;

[1677] A means for generating learning content related to a set learning theme based on the analyzed spare time;

[1678] means for notifying a user of the generated learning content;

[1679] a means for recording the user's learning results;

[1680] A system including:

[1681] (Claim 2)

[1682] The system of claim 1 further comprising means for suggesting related books related to the study topic.

[1683] (Claim 3)

[1684] The system of claim 1 , wherein the learning content is generated using generative AI.

[1685] "Example 1"

[1686] (Claim 1)

[1687] means for obtaining a user's calendar information;

[1688] means for analyzing spare time from the schedule information;

[1689] A means for generating learning content related to a set learning theme based on the analyzed spare time;

[1690] means for notifying a user of the generated learning content;

[1691] a means for recording the user's learning results;

[1692] means for using a generative AI model to generate the learning content;

[1693] A system including:

[1694] (Claim 2)

[1695] The system of claim 1 further comprising means for suggesting related materials related to the learning topic.

[1696] (Claim 3)

[1697] The system of claim 1 further comprising means for inputting a prompt sentence to the generative AI model to generate learning content.

[1698] "Application Example 1"

[1699] (Claim 1)

[1700] means for obtaining a user's calendar information;

[1701] means for analyzing spare time from the calendar information;

[1702] A means for generating learning content related to a set learning theme based on the analyzed spare time;

[1703] means for notifying a user of the generated learning content;

[1704] a means for recording the user's learning results;

[1705] A means to provide learning content only when there is a certain amount of spare time;

[1706] means for using a generative AI model to generate the learning content;

[1707] A system including:

[1708] (Claim 2)

[1709] 10. The system of claim 1, further comprising a scheduling means for periodically updating said calendar information.

[1710] (Claim 3)

[1711] 10. The system according to claim 1, further comprising means for suggesting related books based on the user's learning history.

[1712] "Example 2: Combining Emotion Engines"

[1713] (Claim 1)

[1714] means for obtaining a user's calendar information;

[1715] means for analyzing spare time from the schedule information;

[1716] A means for generating learning content related to a set learning theme based on the analyzed spare time;

[1717] means for notifying a user of the generated learning content;

[1718] a means for recording the user's learning results;

[1719] a means for dynamically adjusting learning content based on emotion data obtained from an emotion engine that recognizes the user's emotions;

[1720] A system including:

[1721] (Claim 2)

[1722] The system of claim 1 further comprising means for suggesting related books related to the study topic.

[1723] (Claim 3)

[1724] The system of claim 1 , wherein the learning content is generated using a generative artificial intelligence model.

[1725] "Application example 2 when combining emotion engines"

[1726] (Claim 1)

[1727] means for obtaining a user's calendar information;

[1728] means for analyzing spare time from the schedule information;

[1729] A means for generating learning content related to a set learning theme based on the analyzed spare time;

[1730] means for analyzing a user's emotions and means for adjusting learning content based on the emotions;

[1731] means for notifying a user of the generated learning content;

[1732] a means for recording the user's learning results;

[1733] A system including:

[1734] (Claim 2)

[1735] The system of claim 1 further comprising means for suggesting related books related to the study topic.

[1736] (Claim 3)

[1737] The system of claim 1 , wherein the learning content is generated using generative AI. [Explanation of symbols]

[1738] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for obtaining a user's calendar information; means for analyzing spare time from the schedule information; A means for generating learning content related to a set learning theme based on the analyzed spare time; means for notifying a user of the generated learning content; a means for recording the user's learning results; A system including:

2. The system of claim 1 further comprising means for suggesting related books on the study topic.

3. The system of claim 1 , wherein the learning content is generated using a generative AI.

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