system

Abstract

We provide the system. [Solution] The information processing device provides a means for acquiring user schedule information, A means for analyzing acquired schedule information and detecting leeway within a specific time frame, A means for automatically reserving time slots for learning activities based on detected free time, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern busy lives, it is difficult for users to efficiently carry out learning activities for skill improvement or retraining. Especially when it is difficult to secure a substantial amount of time, there is a problem that planned learning cannot be maintained. In conventional learning management systems, since it is the responsibility of the user side to plan time individually, it is difficult to maintain the motivation for continuous learning, which is a problem.

Means for Solving the Problems

[0005] This invention provides a means for automatically acquiring a user's schedule information using an information processing device and detecting available time within a specific time frame by analyzing the acquired schedule information. Furthermore, it has a function to automatically reserve a time frame for learning activities based on the detected available time. This enables users to make effective use of their time and promotes planned learning. In addition, it provides a notification before the start of the learning activity time frame so that users do not forget to study. Furthermore, by providing a means for suggesting learning content, it provides users with the optimal learning opportunity.

[0006] An "information processing device" is a machine or device, such as a computer system or a smartphone, used for acquiring, analyzing, storing, and transmitting data.

[0007] "User" refers to an individual who uses an information processing device to plan and carry out learning activities.

[0008] "Schedule information" refers to information about the time and content of events and tasks recorded in calendars or scheduling tools.

[0009] "Free time" refers to unplanned, flexible time that can be used freely within a specific time frame in daily activities based on scheduled information.

[0010] "Learning activities" refer to actions such as studying or training aimed at improving specific skills or knowledge.

[0011] A "reservation" is the act of securing a specific time slot for a specific purpose, following predetermined procedures.

[0012] A "notification" is a message or alert displayed by an information processing device to inform a user of a pre-planned event or action.

[0013] "Analysis" is the process of examining acquired data in detail to understand its patterns and characteristics.

[0014] "Means of proposal" refers to methods and algorithms for selecting and presenting the most suitable learning content according to the user's needs and circumstances. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that uses an information processing device to manage users' schedules and efficiently advance learning activities. This system acquires schedule data from the user's device (e.g., a smartphone or personal computer) and sends it to a server. The server analyzes this data to detect free time. It then suggests appropriate learning activities and automatically incorporates them into the user's schedule.

[0037] Specifically, the device obtains permission from the user to access schedule data and retrieves data from Google Calendar and other applications. The retrieved data is sent to the server as the user's schedule information. The server analyzes this schedule data to identify any free time that occurs within a day or a week.

[0038] Based on this, the server generates candidate learning activities and recommends the most suitable learning content for the user. This includes online learning materials and practice problems. The server sets a specific learning schedule and incorporates it into the user's schedule. At this point, the device sends a pre-learning notification to the user a little before the scheduled time. Also, if other activity is detected during the learning time slot, the device displays an alert to the user prompting them to continue learning.

[0039] For example, if a user wants to improve their data analysis skills, their morning commute or afternoon break might be free time. The system can use this time to suggest and automatically schedule tutorials and exercises on specific data analysis topics for the user.

[0040] These automated systems allow users to systematically improve their skills without missing opportunities, even amidst their busy daily lives. This system functions not merely as a time management tool, but as a crucial tool to support user development.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device displays a notification requesting permission from the user to access their schedule data. If the user grants permission, the device retrieves data from Google Calendar and other scheduling tools.

[0044] Step 2:

[0045] The terminal sends the acquired schedule data to the server. This prepares the server to receive the data and begin analysis.

[0046] Step 3:

[0047] The server analyzes the received schedule data and detects available time slots within a specific time frame. The server can also identify periods with no scheduled appointments or short periods of free time.

[0048] Step 4:

[0049] Based on the available time detected by the server, a recommendation algorithm is executed to suggest the most suitable learning activities for the user. In this process, the server incorporates online learning materials and practice problems, providing content tailored to the user's learning needs.

[0050] Step 5:

[0051] The server reserves learning activities as time slots based on available time, and automatically adds events to the user's schedule. This ensures that users do not miss learning opportunities and that their schedules are planned systematically.

[0052] Step 6:

[0053] The device sends a pre-notification to the user before the scheduled learning time. The notification includes the learning content and links to help the user start learning smoothly.

[0054] Step 7:

[0055] If the device is used by other applications or features during a learning session, this feature detects the activity and displays an alert prompting the user to continue learning. This reduces the risk of the user's learning time being interrupted.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] A challenge exists in that users find it difficult to efficiently find time for learning and engage in meaningful learning activities within their daily schedules. Appropriate time management and activity suggestions are needed to enable users to effectively improve their skills amidst their busy daily lives.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, means for proposing learning activities using a generated AI model based on the detected available time and automatically reserving the time frame, means for incorporating learning activities into the user's schedule, and means for displaying a warning if other operations are performed during the learning activities. This enables users to make the most of available time in their daily schedules and systematically improve their skills.

[0061] An "information processing device" is a device that has computing functions for collecting, analyzing, storing, and transferring data.

[0062] "Schedule information" refers to data that shows the date, time, location, and content of activities or appointments that the user will be making in the future.

[0063] "Analysis" is the process of scrutinizing acquired data and extracting useful information from it.

[0064] "Available time" refers to free time or unoccupied time identified within a user's schedule information.

[0065] A "generative AI model" is a model that utilizes AI technology to select the optimal option from various patterns and choices.

[0066] "Learning activities" include activities that involve online learning materials and practice exercises for acquiring knowledge and skills.

[0067] A "time frame" refers to a specific period of time set aside in a schedule for carrying out a particular activity.

[0068] A "warning" is a notification that informs a user of important information or the need for action.

[0069] This invention is a system that uses an information processing device to manage a user's schedule and support efficient learning activities. It mainly consists of three elements: a server, a terminal, and a user.

[0070] The devices include smartphones and personal computers. After obtaining permission from the user to access schedule data, the device retrieves appointment information from Google Calendar or similar scheduling applications. This retrieved data reflects the user's activity plan and is securely transmitted to the server.

[0071] The server analyzes schedule data to identify free time within a day or week. To do this, the server uses data analysis tools such as Python and R, employing complex algorithms. Furthermore, the server leverages generative AI models to automatically select learning activities suitable for the identified free time. This process generates appropriate online learning materials and practice problems based on the user's past learning behavior and current interests.

[0072] The user reviews and performs the suggested learning activities. The learning schedule, pre-programmed by the server, is notified in advance via the device, allowing the user to plan these activities systematically. If other operations are performed during a learning activity, the device displays a warning and prompts the user to continue.

[0073] For example, if a user wants to use their commute time to improve their data analysis skills, this system can detect that commute time from their schedule and automatically incorporate online data analysis learning materials into their schedule. By using prompts such as, "Please suggest how I should schedule my time to efficiently improve my data analysis skills in my busy daily life," the generative AI model will provide appropriate suggestions. This allows users to make the most of their limited time and systematically improve their skills.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The device obtains user permission and collects appointment information from the scheduling application. The input is the user's permission and the name of the application to be used, and the output is the user's schedule data. Specifically, the device accesses the application via an API and retrieves appointments for a specified period.

[0077] Step 2:

[0078] The terminal sends the acquired schedule data to the server. The input is the schedule data obtained in step 1, and the output is a notification that the data transfer to the server is complete. The terminal uses a secure communication protocol to ensure the secure transmission of data.

[0079] Step 3:

[0080] The server analyzes the received schedule data to identify slack time within a specific time frame. The input is the schedule data sent to the server, and the output is the detected slack time data. The server uses a data analysis tool to extract and list the time slots that are not scheduled.

[0081] Step 4:

[0082] The server suggests appropriate learning activities based on the available time detected using a generative AI model. The input consists of available time data, the user's learning objectives, and past activity history, while the output is a list of recommended learning activities. Specifically, the AI ​​model selects learning resources that match the user's interests.

[0083] Step 5:

[0084] The server incorporates the suggested learning activities into the user's schedule. The input is the list of suggested learning activities and the user's schedule data, and the output is the updated schedule. The server uses an automated scheduling algorithm to place the learning activities within appropriate time slots.

[0085] Step 6:

[0086] The device receives the updated schedule from the server and sets up advance notifications for learning activities for the user. The input is the updated schedule, and the output is the notification setting completion status. The device adjusts the system to display the reminder at the specified time.

[0087] Step 7:

[0088] The device displays a warning to the user if other applications are used during learning activities. The input is the user's activity log, and the output is a warning message. The device monitors activity in real time and immediately notifies the user if any behavior that disrupts learning activities is detected.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] One challenge for users of electronic payments is that it is difficult to efficiently manage their budgets and reduce unnecessary spending. In particular, the busy nature of daily life makes it difficult to obtain information that can help save money at the right time, which makes it difficult to consciously manage one's finances.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for acquiring transaction history information, means for analyzing the transaction history information to detect surplus funds within the budget limit, and means for automatically creating a savings plan. This enables users to efficiently manage their budget and reduce unnecessary spending.

[0094] An "information processing device" is an electronic device that collects and analyzes data from users and provides necessary information.

[0095] "Transaction history information" refers to records of payments and purchases made by a user, including data such as the date and time, amount, and place of purchase.

[0096] "Analysis" is the act of extracting useful information by processing acquired data statistically or logically.

[0097] "Detecting surplus funds within a specific budget limit" refers to finding available surplus funds within the budget set by the user.

[0098] A "savings plan" is a method or schedule proposed to promote rational and efficient financial management based on the user's income and expenses.

[0099] "Automatically generated" refers to a process in which the system autonomously generates information without user intervention.

[0100] Embodiments of this invention mainly consist of an information processing device, a server, and a user's terminal.

[0101] The server retrieves transaction history information related to electronic payments from the user's terminal. The retrieved transaction history information is analyzed by software on the server to detect the user's available funds within their budget limit in real time. Existing data analysis algorithms are used for the analysis to extract specific patterns from the transaction history and estimate the amount that can be saved.

[0102] Next, the server automatically creates a savings plan based on available funds. The savings plan includes savings suggestions that take into account the user's past spending habits, and provides specific saving actions and points to note when making purchases. These suggestions are generated using a generative AI model, so they are tailored to the user's characteristics and interests.

[0103] These savings plans are notified to the user's device, and alerts are also displayed at pre-set times. Notifications are sent at the optimal time based on the user's schedule, helping users effectively manage their budget.

[0104] As a concrete example, for users whose monthly grocery expenses are rising, the app will notify them of special sales at nearby supermarkets on specific days, encouraging them to plan their shopping. Furthermore, this information will be linked to the user's calendar app and automatically incorporated into their schedule.

[0105] An example of a prompt message would be, "Based on the user's transaction history, please suggest appropriate saving methods and explain how to incorporate them into the schedule." This demonstrates how the AI ​​model can be used to generate such prompts.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server receives transaction history information from the user's terminal. This information includes the date, time, amount, and trading partner for each transaction based on electronic payment. The server stores this data in a database and formats it into a parseable format.

[0109] Step 2:

[0110] The server analyzes the acquired transaction history to calculate the surplus funds within the monthly budget limit. The calculation uses an algorithm that analyzes past transaction patterns to identify consistent trends and fluctuations in monthly spending. The server then outputs the surplus funds amount based on this analysis.

[0111] Step 3:

[0112] The server generates a savings plan based on the analysis results. Using a generation AI model, it proposes optimal savings strategies tailored to the user's past spending habits and budget settings. Specifically, it creates a plan that includes the timing of product purchases expected to yield savings and suggestions for reducing spending on non-essential items.

[0113] Step 4:

[0114] The server notifies the user's device of the generated savings plan. The notification includes suggestions for the next time to make a purchase and important savings actions to incorporate into the schedule. This allows users to review their budget management and become more mindful of planned spending.

[0115] Step 5:

[0116] The user's device integrates the received savings plan into the user's personal schedule. The device also works with the user's calendar app to set reminders so that savings actions can be taken at the optimal time.

[0117] Step 6:

[0118] Users follow notifications from their devices and implement planned saving behaviors. For example, by shopping on recommended dates, they can actually reduce spending in their daily lives.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention is a system that uses an information processing device to efficiently manage users' schedules and conduct learning activities based on their emotional state. This system has the function of acquiring schedule data through the user's terminal and analyzing it on a server. Furthermore, by incorporating an emotion engine, it recognizes the user's emotional state and adjusts the learning content accordingly.

[0121] Specifically, the device obtains permission from the user to access schedule data and retrieves data from schedule management tools such as Google Calendar. This schedule data is sent to a server, where it is analyzed. Through data analysis, the server understands the user's schedule information and detects any available time within a specific time frame.

[0122] Using this detected free time, the server recommends learning activities that take into account the output of the emotion engine. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone to evaluate the user's current emotional state. Based on this evaluation, the server selects learning content and proposes the optimal learning plan for the user. For example, if the user is judged to be calm, tasks requiring concentration will be included in the schedule; if the user is highly stressed, content with a relaxing effect will be suggested.

[0123] As the time slot for a learning activity approaches, the device sends a notification to the user to help them start learning smoothly. Furthermore, if other operations are detected during the learning time, the device displays an alert and prompts the user to continue learning. In addition, after completion, feedback from the emotion engine is used to reflect on the learning experience and help improve the content and schedule of the next learning session.

[0124] This system, which utilizes an emotion engine, not only manages schedules but also enables flexible suggestions of learning content based on the user's emotional state, providing a more effective learning experience.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The device displays a notification requesting permission from the user to access their schedule data. If the user grants permission, the device retrieves data from Google Calendar and other scheduling tools.

[0128] Step 2:

[0129] The terminal sends the acquired schedule data to the server. This prepares the server to receive the data and begin analysis.

[0130] Step 3:

[0131] The server analyzes schedule data and identifies periods without scheduled appointments, thereby detecting the user's free time within a day or week.

[0132] Step 4:

[0133] The device collects emotional data using its camera and microphone to capture the user's facial expressions and voice data. The device then invokes an emotion engine and sends this data to a server for emotion recognition.

[0134] Step 5:

[0135] The server's emotion engine analyzes the user's emotional state and evaluates it (e.g., stress level and attention level). This evaluation is then used to determine the optimal learning content and format for the user.

[0136] Step 6:

[0137] Based on the available time detected by the server and the output of the emotion engine, the system automatically selects learning content and incorporates it into the schedule. It generates events that include specific learning activities and necessary resources (such as links to textbooks and online learning materials).

[0138] Step 7:

[0139] After a learning activity is scheduled, the device sends a notification to the user a short time before the scheduled learning session. This notification includes links to the learning content and resources.

[0140] Step 8:

[0141] Once learning begins, the device monitors the user's activity during the learning session and displays an alert prompting them to return to learning if another application is being used.

[0142] Step 9:

[0143] After a learning activity is completed, the system provides feedback to the user using data acquired through the emotion engine. This allows the user to incorporate the feedback into future learning plans, thereby improving the quality of their learning.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] Conventional scheduling management systems simply allocate time for learning activities based on the user's schedule, and have the challenge of not being able to propose flexible learning plans that take into account the user's emotional state. As a result, the efficiency of learning, which is dependent on emotions, does not improve, and it is not possible to provide an optimal learning experience.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, and means for evaluating the user's emotional state. This makes it possible to select learning content and reserve time slots according to the user's emotional state.

[0149] An "information processing device" refers to any device that performs various processes based on data input by users or acquired information, and provides the results.

[0150] "Schedule information" refers to data that shows the time and content related to the user's actions and tasks.

[0151] "Analysis" refers to a method of examining acquired data in detail and deriving useful patterns and results.

[0152] "Free time" refers to any period of time available for free use between scheduled events or tasks.

[0153] A "time slot" refers to a range of time allocated for a specific event or activity.

[0154] "Automatic booking" refers to the system selecting an appropriate time and setting an appointment without user intervention.

[0155] "Emotional analysis" refers to the process of understanding a user's current emotional state from their facial expressions, voice, and other factors.

[0156] "Learning content" refers to the specific educational and training programs and activities that users are expected to engage in.

[0157] This invention provides a system that uses an information processing device to efficiently manage users' schedules and conduct learning activities based on their emotional state. This system collects schedule data via the user's terminal and analyzes it on a server. Furthermore, by using an emotion analysis engine, it proposes learning content tailored to the user's emotional state.

[0158] The device obtains permission from the user to access the schedule management tool and retrieves the schedule information. Specifically, the server retrieves data from schedule management tools such as Google Calendar and uses this information to detect available time within a specific time frame. This analysis process is performed on the server and includes data calculations such as data formatting and statistical evaluation.

[0159] Furthermore, the device uses input devices such as a camera and microphone to evaluate the user's emotional state. The emotion analysis engine accurately grasps the user's emotions through facial recognition and voice analysis. Based on these analysis results, the server selects learning content appropriate to the user's emotional state and proposes a learning plan. The proposed plan is notified to the user's device, supporting a smooth start to learning.

[0160] For example, if a user is enjoying relaxing content at 3:00 PM, a typical afternoon free time, and the emotion analysis engine detects a decrease in stress, the server will suggest a learning activity requiring concentration during the next free time. This activity will be communicated to the user via a notification from their device.

[0161] Examples of prompt statements that utilize a generative AI model are as follows:

[0162] "Based on the user's schedule and emotional data, please develop learning activities to suggest for their afternoon leisure time. Specifically, please present different learning plans for when the user is calm and when they are experiencing high stress levels."

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The device obtains permission from the user to access the schedule management tool.

[0166] In terms of the specific operation, the user visually confirms an access permission confirmation dialog on the device and grants permission. This permission grants the device API access rights to retrieve schedule data. The input is the user's permission information, and the output is the device obtaining API access rights.

[0167] Step 2:

[0168] The device retrieves schedule information through the schedule management tool's API.

[0169] The device uses acquired API access rights to download event information from Google Calendar and other sources. Input consists of API access rights and data from the scheduling tool; this data is formatted as a list of events. Output is an event list including the event start time, end time, and title.

[0170] Step 3:

[0171] The device sends the schedule information to the server.

[0172] In terms of specific operation, the terminal sends a formatted schedule list to a dedicated endpoint on the server via an HTTP request. The input is the formatted schedule list, and the output is a confirmation of transmission to the server.

[0173] Step 4:

[0174] The server analyzes the received schedule data to detect buffer time.

[0175] The server executes a program to analyze the scheduled data and calculate the time difference between events. The input is the scheduled data, and the output is a list of buffer times detected through data analysis.

[0176] Step 5:

[0177] The device collects user emotion data.

[0178] The device uses a camera and microphone to record facial expressions and voice for emotion analysis. Input is the user's image and voice data, and output is a state where this data is ready to be sent to the server.

[0179] Step 6:

[0180] The server uses an emotion analysis engine to evaluate the emotional state.

[0181] The server passes the received data to the emotion analysis engine to determine the user's emotional state. The input consists of image and audio data, and the output is the result of the emotional state determination (e.g., relaxed state, stressed state).

[0182] Step 7:

[0183] The server selects learning content based on available time and emotional state.

[0184] The server generates an optimal learning plan and selects learning content based on the existing list of available time and emotional state. The input is the list of available time and emotional state, and the output is the appropriate learning plan.

[0185] Step 8:

[0186] The device notifies the user of learning activities.

[0187] The device displays a notification on the user's screen based on the learning plan received from the server. The notification includes the start time and the suggested learning content. The input is the learning plan, and the output is the notification displayed on the screen.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] In modern personal information management, while optimizing time allocation based on users' schedules is crucial, providing suggestions and services that consider users' emotional states is insufficient. Furthermore, in shopping experiences such as in stores, there is a need for products and services optimized to individual emotional states. To address these challenges, a system is needed that provides effective suggestions based on users' schedules and emotional states.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes means for an information processing device to acquire the user's schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, means for recognizing and analyzing the user's emotional state, and means for proposing the most suitable product or service based on the analyzed emotional state of the user. This enables optimal proposals that comprehensively consider the user's schedule and emotional state.

[0193] An "information processing device" is a computer system used to acquire and analyze users' schedule information and emotional states.

[0194] "Schedule information" refers to chronological activity information entered into the schedule management tool used by the user.

[0195] "Available time" refers to a flexible time slot within a user's schedule that can be allocated to other activities.

[0196] "Emotional state" refers to data that indicates the user's emotional changes and state at any given time, and is information obtained from facial expressions and voice.

[0197] "Analysis" is the process of performing information processing based on acquired data in order to derive specific objectives or results.

[0198] "Proposing a product or service" refers to the act of selecting and recommending suitable products or services based on the user's needs and circumstances.

[0199] To realize this invention, it is necessary to build a system in which the server, terminal, and user elements work together in coordination. The server analyzes schedule information and emotional state data obtained from the user's terminal. The terminal is required to have a schedule management tool and emotion recognition function in order to obtain this information. Specifically, schedule information is obtained using the Google Calendar API, and emotional state is obtained using the camera and microphone installed in the smartphone or smart glasses. Azure Cognitive Services will be used for analyzing the emotional state.

[0200] On the server, based on the acquired data, the system detects the user's available time within a specific time frame and generates suggestions for learning activities and products / services to be used according to that available time. Based on the analysis results, it determines and proposes what is optimal for the user. The terminal also functions as an interface to notify the user of these suggestions.

[0201] For example, if the system detects that a user has free time on a weekday afternoon, and they appear relaxed, it will suggest discounts on specific items in the store. Conversely, if the system detects that the user is busy, it will suggest pre-selecting relevant items from their purchase history so they can purchase them quickly.

[0202] An example of a prompt message would be, "Please create an optimal algorithm for suggesting products based on the emotional state of a specific customer." This allows you to create instructions to generate optimal suggestions using a generative AI model. This makes it possible to provide users with personalized, high-quality services.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device obtains permission from the user to access schedule data. Based on this permission, it retrieves schedule information using the Google Calendar API and saves it to the device. The input is the user's schedule data, and the output is the event information obtained through the API.

[0206] Step 2:

[0207] The device uses its built-in camera and microphone to record the user's facial expressions and voice, and to detect their emotional state. This data is analyzed using Azure Cognitive Services. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state information.

[0208] Step 3:

[0209] The server receives schedule information and emotional state data sent from the terminal and detects the available time. The available time is determined by analyzing the schedule information. The input is the schedule and emotional data, and the output is the determined available time.

[0210] Step 4:

[0211] The server generates a prompt sentence suitable for the generating AI model based on the detected free time and emotional state. This prompt sentence is input to the AI ​​model to generate a suggestion for the optimal product or service. The input is free time and emotional state, and the output is the suggestion obtained from the AI.

[0212] Step 5:

[0213] The suggestions generated by the server are sent to the terminal and notified to the user. The terminal uses its notification function to present information to the user at the appropriate time and prompt action. The input is the suggestions from the server, and the output is the notification to the user.

[0214] Step 6:

[0215] Users can review the notifications they receive and choose whether to accept the suggested products or services. User feedback is fed back into the system to improve the accuracy of future suggestions. The input is the user's choice, and the output is the feedback to the system.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0232] This invention is a system that uses an information processing device to manage users' schedules and efficiently advance learning activities. This system acquires schedule data from the user's device (e.g., a smartphone or personal computer) and sends it to a server. The server analyzes this data to detect free time. It then suggests appropriate learning activities and automatically incorporates them into the user's schedule.

[0233] Specifically, the device obtains permission from the user to access schedule data and retrieves data from Google Calendar and other applications. The retrieved data is sent to the server as the user's schedule information. The server analyzes this schedule data to identify any free time that occurs during the day or week.

[0234] Based on this, the server generates candidate learning activities and recommends the most suitable learning content for the user. This includes online learning materials and practice problems. The server sets a specific learning schedule and incorporates it into the user's schedule. At this point, the device sends a pre-learning notification to the user a little before the scheduled time. Also, if other activity is detected during the learning time slot, the device displays an alert to the user prompting them to continue learning.

[0235] For example, if a user wants to improve their data analysis skills, their morning commute or afternoon break might be free time. The system can use this time to suggest and automatically schedule tutorials and exercises on specific data analysis topics for the user.

[0236] These automated systems allow users to systematically improve their skills without missing opportunities, even amidst their busy daily lives. This system functions not merely as a time management tool, but as a crucial tool to support user development.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The device displays a notification requesting permission from the user to access their schedule data. If the user grants permission, the device retrieves data from Google Calendar and other scheduling tools.

[0240] Step 2:

[0241] The terminal sends the acquired schedule data to the server. This prepares the server to receive the data and begin analysis.

[0242] Step 3:

[0243] The server analyzes the received schedule data and detects available time slots within a specific time frame. The server can also identify periods with no scheduled appointments or short periods of free time.

[0244] Step 4:

[0245] Based on the available time detected by the server, a recommendation algorithm is executed to suggest the most suitable learning activities for the user. In this process, the server incorporates online learning materials and practice problems, providing content tailored to the user's learning needs.

[0246] Step 5:

[0247] The server reserves learning activities as time slots based on available time, and automatically adds events to the user's schedule. This ensures that users do not miss learning opportunities and that their schedules are planned systematically.

[0248] Step 6:

[0249] The device sends a pre-notification to the user before the scheduled learning time. The notification includes the learning content and links to help the user start learning smoothly.

[0250] Step 7:

[0251] If the device is used by other applications or features during a learning session, this feature detects the activity and displays an alert prompting the user to continue learning. This reduces the risk of the user's learning time being interrupted.

[0252] (Example 1)

[0253] Next, we will describe Example 1. 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."

[0254] A challenge exists in that users find it difficult to efficiently find time for learning and engage in meaningful learning activities within their daily schedules. Appropriate time management and activity suggestions are needed to enable users to effectively improve their skills amidst their busy daily lives.

[0255] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0256] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, means for proposing learning activities using a generated AI model based on the detected available time and automatically reserving the time frame, means for incorporating learning activities into the user's schedule, and means for displaying a warning if other operations are performed during the learning activities. This enables users to make the most of available time in their daily schedules and systematically improve their skills.

[0257] An "information processing device" is a device that has computing functions for collecting, analyzing, storing, and transferring data.

[0258] "Schedule information" refers to data that shows the date, time, location, and content of activities or appointments that the user will be making in the future.

[0259] "Analysis" is the process of scrutinizing acquired data and extracting useful information from it.

[0260] "Available time" refers to free time or unoccupied time identified within a user's schedule information.

[0261] A "generative AI model" is a model that utilizes AI technology to select the optimal option from various patterns and choices.

[0262] "Learning activities" include activities that involve online learning materials and practice exercises for acquiring knowledge and skills.

[0263] A "time frame" refers to a specific period of time set aside in a schedule for carrying out a particular activity.

[0264] A "warning" is a notification that informs a user of important information or the need for action.

[0265] This invention is a system that uses an information processing device to manage a user's schedule and support efficient learning activities. It mainly consists of three elements: a server, a terminal, and a user.

[0266] The devices include smartphones and personal computers. After obtaining permission from the user to access schedule data, the device retrieves appointment information from Google Calendar or similar scheduling applications. This retrieved data reflects the user's activity plan and is securely transmitted to the server.

[0267] The server analyzes schedule data to identify free time within a day or week. To do this, the server uses data analysis tools such as Python and R, employing complex algorithms. Furthermore, the server leverages generative AI models to automatically select learning activities suitable for the identified free time. This process generates appropriate online learning materials and practice problems based on the user's past learning behavior and current interests.

[0268] The user reviews and performs the suggested learning activities. The learning schedule, pre-programmed by the server, is notified in advance via the device, allowing the user to plan these activities systematically. If other operations are performed during a learning activity, the device displays a warning and prompts the user to continue.

[0269] For example, if a user wants to use their commute time to improve their data analysis skills, this system can detect that commute time from their schedule and automatically incorporate online data analysis learning materials into their schedule. By using prompts such as, "Please suggest how I should schedule my time to efficiently improve my data analysis skills in my busy daily life," the generative AI model will provide appropriate suggestions. This allows users to make the most of their limited time and systematically improve their skills.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The device obtains user permission and collects appointment information from the scheduling application. The input is the user's permission and the name of the application to be used, and the output is the user's schedule data. Specifically, the device accesses the application via an API and retrieves appointments for a specified period.

[0273] Step 2:

[0274] The terminal sends the acquired schedule data to the server. The input is the schedule data obtained in step 1, and the output is a notification that the data transfer to the server is complete. The terminal uses a secure communication protocol to ensure the secure transmission of data.

[0275] Step 3:

[0276] The server analyzes the received schedule data to identify slack time within a specific time frame. The input is the schedule data sent to the server, and the output is the detected slack time data. The server uses a data analysis tool to extract and list the time slots that are not scheduled.

[0277] Step 4:

[0278] Based on the spare time detected using the generative AI model, the server proposes appropriate learning activities. The input is the spare time data, the user's learning goals, and past activity history, and the output is a list of recommended learning activities. Specifically, the AI model selects learning resources that match the user's interests.

[0279] Step 5:

[0280] The server incorporates the proposed learning activities into the user's schedule. The input is the list of proposed learning activities and the user's schedule data, and the output is the updated schedule. The server uses an automatic scheduling algorithm to arrange the learning activities within an appropriate time frame.

[0281] Step 6:

[0282] The terminal receives the updated schedule from the server and sets a prior notification of the learning activity for the user. The input is the updated schedule, and the output is the status of the completion of the notification setting. The terminal adjusts the system to display a reminder at the specified time.

[0283] Step 7:

[0284] When other applications are used during the learning activity, the terminal displays a warning to the user. The input is the user's operation log, and the output is a warning message. The terminal monitors the operations in real time and immediately notifies when an action that interferes with the learning activity is detected.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] One challenge for users of electronic payments is that it is difficult to efficiently manage their budgets and reduce unnecessary spending. In particular, the busy nature of daily life makes it difficult to obtain information that can help save money at the right time, which makes it difficult to consciously manage one's finances.

[0288] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for acquiring transaction history information, means for analyzing the transaction history information to detect surplus funds within the budget limit, and means for automatically creating a savings plan. This enables users to efficiently manage their budget and reduce unnecessary spending.

[0290] An "information processing device" is an electronic device that collects and analyzes data from users and provides necessary information.

[0291] "Transaction history information" refers to records of payments and purchases made by a user, including data such as the date and time, amount, and place of purchase.

[0292] "Analysis" is the act of extracting useful information by processing acquired data statistically or logically.

[0293] "Detecting surplus funds within a specific budget limit" refers to finding available surplus funds within the budget set by the user.

[0294] A "savings plan" is a method or schedule proposed to promote rational and efficient financial management based on the user's income and expenses.

[0295] "Automatically generated" refers to a process in which the system autonomously generates information without user intervention.

[0296] Embodiments of this invention mainly consist of an information processing device, a server, and a user's terminal.

[0297] The server retrieves transaction history information related to electronic payments from the user's terminal. The retrieved transaction history information is analyzed by software on the server to detect the user's available funds within their budget limit in real time. Existing data analysis algorithms are used for the analysis to extract specific patterns from the transaction history and estimate the amount that can be saved.

[0298] Next, the server automatically creates a savings plan based on available funds. The savings plan includes savings suggestions that take into account the user's past spending habits, and provides specific saving actions and points to note when making purchases. These suggestions are generated using a generative AI model, so they are tailored to the user's characteristics and interests.

[0299] These savings plans are notified to the user's device, and alerts are also displayed at pre-set times. Notifications are sent at the optimal time based on the user's schedule, helping users effectively manage their budget.

[0300] As a concrete example, for users whose monthly grocery expenses are rising, the app will notify them of special sales at nearby supermarkets on specific days, encouraging them to plan their shopping. Furthermore, this information will be linked to the user's calendar app and automatically incorporated into their schedule.

[0301] An example of a prompt message would be, "Based on the user's transaction history, please suggest appropriate saving methods and explain how to incorporate them into the schedule." This demonstrates how the AI ​​model can be used to generate such prompts.

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The server receives transaction history information from the user's terminal. This information indicates the date and time, amount, and transaction recipient of each transaction based on electronic payment. The server stores this data in a database and formats it into an analyzable form.

[0305] Step 2:

[0306] The server analyzes the acquired transaction history to calculate the surplus funds within the monthly budget limit. An algorithm is used for the calculation, and by analyzing past transaction patterns, the constant trends and fluctuations in monthly expenditures are identified. As a result of this analysis, the surplus fund amount is output.

[0307] Step 3:

[0308] The server generates a savings plan based on the analysis results. Here, a generation AI model is used to propose optimal savings measures according to the user's past spending trends and budget settings. Specifically, a plan document is created that includes the timing of product purchases where savings effects can be expected and proposals for reducing expenditures on non-essential items.

[0309] Step 4:

[0310] The server notifies the user's terminal of the generated savings plan. The notification includes proposals for the upcoming purchase timing and important savings actions to be reflected in the schedule. This enables the user to review budget management and be aware of planned expenditures.

[0311] Step 5:

[0312] The user's terminal incorporates the received savings plan into the user's personal schedule. The terminal is linked to the user's calendar app and sets reminders so that savings actions can be executed at the optimal timing.

[0313] Step 6:

[0314] Users follow notifications from their devices and implement planned saving behaviors. For example, by shopping on recommended dates, they can actually reduce spending in their daily lives.

[0315] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0316] This invention is a system that uses an information processing device to efficiently manage users' schedules and conduct learning activities based on their emotional state. This system has the function of acquiring schedule data through the user's terminal and analyzing it on a server. Furthermore, by incorporating an emotion engine, it recognizes the user's emotional state and adjusts the learning content accordingly.

[0317] Specifically, the device obtains permission from the user to access schedule data and retrieves data from schedule management tools such as Google Calendar. This schedule data is sent to a server, where it is analyzed. Through data analysis, the server understands the user's schedule information and detects any available time within a specific time frame.

[0318] Using this detected free time, the server recommends learning activities that take into account the output of the emotion engine. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone to evaluate the user's current emotional state. Based on this evaluation, the server selects learning content and proposes the optimal learning plan for the user. For example, if the user is judged to be calm, tasks requiring concentration will be included in the schedule; if the user is highly stressed, content with a relaxing effect will be suggested.

[0319] As the time slot for a learning activity approaches, the device sends a notification to the user to help them start learning smoothly. Furthermore, if other operations are detected during the learning time, the device displays an alert and prompts the user to continue learning. In addition, after completion, feedback from the emotion engine is used to reflect on the learning experience and help improve the content and schedule of the next learning session.

[0320] This system, which utilizes an emotion engine, not only manages schedules but also enables flexible suggestions of learning content based on the user's emotional state, providing a more effective learning experience.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The device displays a notification requesting permission from the user to access their schedule data. If the user grants permission, the device retrieves data from Google Calendar and other scheduling tools.

[0324] Step 2:

[0325] The terminal sends the acquired schedule data to the server. This prepares the server to receive the data and begin analysis.

[0326] Step 3:

[0327] The server analyzes schedule data and identifies periods without scheduled appointments, thereby detecting the user's free time within a day or week.

[0328] Step 4:

[0329] The device collects emotional data using its camera and microphone to capture the user's facial expressions and voice data. The device then invokes an emotion engine and sends this data to a server for emotion recognition.

[0330] Step 5:

[0331] The server's emotion engine analyzes the user's emotional state and evaluates it (e.g., stress level and attention level). This evaluation is then used to determine the optimal learning content and format for the user.

[0332] Step 6:

[0333] Based on the available time detected by the server and the output of the emotion engine, the system automatically selects learning content and incorporates it into the schedule. It generates events that include specific learning activities and necessary resources (such as links to textbooks and online learning materials).

[0334] Step 7:

[0335] After a learning activity is scheduled, the device sends a notification to the user a short time before the scheduled learning session. This notification includes links to the learning content and resources.

[0336] Step 8:

[0337] Once learning begins, the device monitors the user's activity during the learning session and displays an alert prompting them to return to learning if another application is being used.

[0338] Step 9:

[0339] After a learning activity is completed, the system provides feedback to the user using data acquired through the emotion engine. This allows the user to incorporate the feedback into future learning plans, thereby improving the quality of their learning.

[0340] (Example 2)

[0341] Next, we will describe Example 2. 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".

[0342] Conventional scheduling management systems simply allocate time for learning activities based on the user's schedule, and have the challenge of not being able to propose flexible learning plans that take into account the user's emotional state. As a result, the efficiency of learning, which is dependent on emotions, does not improve, and it is not possible to provide an optimal learning experience.

[0343] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0344] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, and means for evaluating the user's emotional state. This makes it possible to select learning content and reserve time slots according to the user's emotional state.

[0345] An "information processing device" refers to any device that performs various processes based on data input by users or acquired information, and provides the results.

[0346] "Schedule information" refers to data that shows the time and content related to the user's actions and tasks.

[0347] "Analysis" refers to a method of examining acquired data in detail and deriving useful patterns and results.

[0348] "Free time" refers to any period of time available for free use between scheduled events or tasks.

[0349] A "time slot" refers to a range of time allocated for a specific event or activity.

[0350] "Automatic booking" refers to the system selecting an appropriate time and setting an appointment without user intervention.

[0351] "Emotional analysis" refers to the process of understanding a user's current emotional state from their facial expressions, voice, and other factors.

[0352] "Learning content" refers to the specific educational and training programs and activities that users are expected to engage in.

[0353] This invention provides a system that uses an information processing device to efficiently manage users' schedules and conduct learning activities based on their emotional state. This system collects schedule data via the user's terminal and analyzes it on a server. Furthermore, by using an emotion analysis engine, it proposes learning content tailored to the user's emotional state.

[0354] The device obtains permission from the user to access the schedule management tool and retrieves the schedule information. Specifically, the server retrieves data from schedule management tools such as Google Calendar and uses this information to detect available time within a specific time frame. This analysis process is performed on the server and includes data calculations such as data formatting and statistical evaluation.

[0355] Furthermore, the device uses input devices such as a camera and microphone to evaluate the user's emotional state. The emotion analysis engine accurately grasps the user's emotions through facial recognition and voice analysis. Based on these analysis results, the server selects learning content appropriate to the user's emotional state and proposes a learning plan. The proposed plan is notified to the user's device, supporting a smooth start to learning.

[0356] For example, if a user is enjoying relaxing content at 3:00 PM, a typical afternoon free time, and the emotion analysis engine detects a decrease in stress, the server will suggest a learning activity requiring concentration during the next free time. This activity will be communicated to the user via a notification from their device.

[0357] Examples of prompt statements that utilize a generative AI model are as follows:

[0358] "Based on the user's schedule and emotional data, please develop learning activities to suggest for their afternoon leisure time. Specifically, please present different learning plans for when the user is calm and when they are experiencing high stress levels."

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The device obtains permission from the user to access the schedule management tool.

[0362] In terms of the specific operation, the user visually confirms an access permission confirmation dialog on the device and grants permission. This permission grants the device API access rights to retrieve schedule data. The input is the user's permission information, and the output is the device obtaining API access rights.

[0363] Step 2:

[0364] The device retrieves schedule information through the schedule management tool's API.

[0365] The device uses acquired API access rights to download event information from Google Calendar and other sources. Input consists of API access rights and data from the scheduling tool; this data is formatted as a list of events. Output is an event list including the event start time, end time, and title.

[0366] Step 3:

[0367] The device sends the schedule information to the server.

[0368] In terms of specific operation, the terminal sends a formatted schedule list to a dedicated endpoint on the server via an HTTP request. The input is the formatted schedule list, and the output is a confirmation of transmission to the server.

[0369] Step 4:

[0370] The server analyzes the received schedule data to detect buffer time.

[0371] The server executes a program to analyze the scheduled data and calculate the time difference between events. The input is the scheduled data, and the output is a list of buffer times detected through data analysis.

[0372] Step 5:

[0373] The device collects user emotion data.

[0374] The device uses a camera and microphone to record facial expressions and voice for emotion analysis. Input is the user's image and voice data, and output is a state where this data is ready to be sent to the server.

[0375] Step 6:

[0376] The server uses an emotion analysis engine to evaluate the emotional state.

[0377] The server passes the received data to the emotion analysis engine to determine the user's emotional state. The input consists of image and audio data, and the output is the result of the emotional state determination (e.g., relaxed state, stressed state).

[0378] Step 7:

[0379] The server selects learning content based on available time and emotional state.

[0380] The server generates an optimal learning plan and selects learning content based on the existing list of available time and emotional state. The input is the list of available time and emotional state, and the output is the appropriate learning plan.

[0381] Step 8:

[0382] The device notifies the user of learning activities.

[0383] The device displays a notification on the user's screen based on the learning plan received from the server. The notification includes the start time and the suggested learning content. The input is the learning plan, and the output is the notification displayed on the screen.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0386] In modern personal information management, while optimizing time allocation based on users' schedules is crucial, providing suggestions and services that consider users' emotional states is insufficient. Furthermore, in shopping experiences such as in stores, there is a need for products and services optimized to individual emotional states. To address these challenges, a system is needed that provides effective suggestions based on users' schedules and emotional states.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0388] In this invention, the server includes means for an information processing device to acquire the user's schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, means for recognizing and analyzing the user's emotional state, and means for proposing the most suitable product or service based on the analyzed emotional state of the user. This enables optimal proposals that comprehensively consider the user's schedule and emotional state.

[0389] An "information processing device" is a computer system used to acquire and analyze users' schedule information and emotional states.

[0390] "Schedule information" refers to chronological activity information entered into the schedule management tool used by the user.

[0391] "Available time" refers to a flexible time slot within a user's schedule that can be allocated to other activities.

[0392] "Emotional state" refers to data that indicates the user's emotional changes and state at any given time, and is information obtained from facial expressions and voice.

[0393] "Analysis" is the process of performing information processing based on acquired data in order to derive specific objectives or results.

[0394] "Proposing a product or service" refers to the act of selecting and recommending suitable products or services based on the user's needs and circumstances.

[0395] To realize this invention, it is necessary to build a system in which the server, terminal, and user elements work together in coordination. The server analyzes schedule information and emotional state data obtained from the user's terminal. The terminal is required to have a schedule management tool and emotion recognition function in order to obtain this information. Specifically, schedule information is obtained using the Google Calendar API, and emotional state is obtained using the camera and microphone installed in the smartphone or smart glasses. Azure Cognitive Services is used for analyzing the emotional state.

[0396] On the server, based on the acquired data, the system detects the user's available time within a specific time frame and generates suggestions for learning activities and products / services to be used according to that available time. Based on the analysis results, it determines and proposes what is optimal for the user. The terminal also functions as an interface to notify the user of these suggestions.

[0397] For example, if the system detects that a user has free time on a weekday afternoon, and they appear relaxed, it will suggest discounts on specific items in the store. Conversely, if the system detects that the user is busy, it will suggest pre-selecting relevant items from their purchase history so they can purchase them quickly.

[0398] An example of a prompt message would be, "Please create an optimal algorithm for suggesting products based on the emotional state of a specific customer." This allows you to create instructions to generate optimal suggestions using a generative AI model. This makes it possible to provide users with personalized, high-quality services.

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The device obtains permission from the user to access schedule data. Based on this permission, it retrieves schedule information using the Google Calendar API and saves it to the device. The input is the user's schedule data, and the output is the event information obtained through the API.

[0402] Step 2:

[0403] The device uses its built-in camera and microphone to record the user's facial expressions and voice, and to detect their emotional state. This data is analyzed using Azure Cognitive Services. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state information.

[0404] Step 3:

[0405] The server receives schedule information and emotional state data sent from the terminal and detects the available time. The available time is determined by analyzing the schedule information. The input is the schedule and emotional data, and the output is the determined available time.

[0406] Step 4:

[0407] The server generates a prompt sentence suitable for the generating AI model based on the detected free time and emotional state. This prompt sentence is input to the AI ​​model to generate a suggestion for the optimal product or service. The input is free time and emotional state, and the output is the suggestion obtained from the AI.

[0408] Step 5:

[0409] The suggestions generated by the server are sent to the terminal and notified to the user. The terminal uses its notification function to present information to the user at the appropriate time and prompt action. The input is the suggestions from the server, and the output is the notification to the user.

[0410] Step 6:

[0411] Users can review the notifications they receive and choose whether to accept the suggested products or services. User feedback is fed back into the system to improve the accuracy of future suggestions. The input is the user's choice, and the output is the feedback to the system.

[0412] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0413] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0418] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0420] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0422] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0423] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0424] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0425] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0427] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0428] This invention is a system that uses an information processing device to manage users' schedules and efficiently advance learning activities. This system acquires schedule data from the user's device (e.g., a smartphone or personal computer) and sends it to a server. The server analyzes this data to detect free time. It then suggests appropriate learning activities and automatically incorporates them into the user's schedule.

[0429] Specifically, the device obtains permission from the user to access schedule data and retrieves data from Google Calendar and other applications. The retrieved data is sent to the server as the user's schedule information. The server analyzes this schedule data to identify any free time that occurs during the day or week.

[0430] Based on this, the server generates candidate learning activities and recommends the most suitable learning content for the user. This includes online learning materials and practice problems. The server sets a specific learning schedule and incorporates it into the user's schedule. At this point, the device sends a pre-learning notification to the user a little before the scheduled time. Also, if other activity is detected during the learning time slot, the device displays an alert to the user prompting them to continue learning.

[0431] For example, if a user wants to improve their data analysis skills, their morning commute or afternoon break might be free time. The system can use this time to suggest and automatically schedule tutorials and exercises on specific data analysis topics for the user.

[0432] These automated systems allow users to systematically improve their skills without missing opportunities, even amidst their busy daily lives. This system functions not merely as a time management tool, but as a crucial tool to support user development.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] The device displays a notification requesting permission from the user to access their schedule data. If the user grants permission, the device retrieves data from Google Calendar and other scheduling tools.

[0436] Step 2:

[0437] The terminal sends the acquired schedule data to the server. This prepares the server to receive the data and begin analysis.

[0438] Step 3:

[0439] The server analyzes the received schedule data and detects available time slots within a specific time frame. The server can also identify periods with no scheduled appointments or short periods of free time.

[0440] Step 4:

[0441] Based on the available time detected by the server, a recommendation algorithm is executed to suggest the most suitable learning activities for the user. In this process, the server incorporates online learning materials and practice problems, providing content tailored to the user's learning needs.

[0442] Step 5:

[0443] The server reserves learning activities as time slots based on available time, and automatically adds events to the user's schedule. This ensures that users do not miss learning opportunities and that their schedules are planned systematically.

[0444] Step 6:

[0445] The device sends a pre-notification to the user before the scheduled learning time. The notification includes the learning content and links to help the user start learning smoothly.

[0446] Step 7:

[0447] If the device is used by other applications or features during a learning session, this feature detects the activity and displays an alert prompting the user to continue learning. This reduces the risk of the user's learning time being interrupted.

[0448] (Example 1)

[0449] Next, we will describe Example 1. 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."

[0450] A challenge exists in that users find it difficult to efficiently find time for learning and engage in meaningful learning activities within their daily schedules. Appropriate time management and activity suggestions are needed to enable users to effectively improve their skills amidst their busy daily lives.

[0451] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0452] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, means for proposing learning activities using a generated AI model based on the detected available time and automatically reserving the time frame, means for incorporating learning activities into the user's schedule, and means for displaying a warning if other operations are performed during the learning activities. This enables users to make the most of available time in their daily schedules and systematically improve their skills.

[0453] An "information processing device" is a device that has computing functions for collecting, analyzing, storing, and transferring data.

[0454] "Schedule information" refers to data that shows the date, time, location, and content of activities or appointments that the user will be making in the future.

[0455] "Analysis" is the process of scrutinizing acquired data and extracting useful information from it.

[0456] "Available time" refers to free time or unoccupied time identified within a user's schedule information.

[0457] A "generative AI model" is a model that utilizes AI technology to select the optimal option from various patterns and choices.

[0458] "Learning activities" include activities that involve online learning materials and practice exercises for acquiring knowledge and skills.

[0459] A "time frame" refers to a specific period of time set aside in a schedule for carrying out a particular activity.

[0460] A "warning" is a notification that informs a user of important information or the need for action.

[0461] This invention is a system that uses an information processing device to manage a user's schedule and support efficient learning activities. It mainly consists of three elements: a server, a terminal, and a user.

[0462] The devices include smartphones and personal computers. After obtaining permission from the user to access schedule data, the device retrieves appointment information from Google Calendar or similar scheduling applications. This retrieved data reflects the user's activity plan and is securely transmitted to the server.

[0463] The server analyzes schedule data to identify free time within a day or week. To do this, the server uses data analysis tools such as Python and R, employing complex algorithms. Furthermore, the server leverages generative AI models to automatically select learning activities suitable for the identified free time. This process generates appropriate online learning materials and practice problems based on the user's past learning behavior and current interests.

[0464] The user reviews and performs the suggested learning activities. The learning schedule, pre-programmed by the server, is notified in advance via the device, allowing the user to plan these activities systematically. If other operations are performed during a learning activity, the device displays a warning and prompts the user to continue.

[0465] For example, if a user wants to use their commute time to improve their data analysis skills, this system can detect that commute time from their schedule and automatically incorporate online data analysis learning materials into their schedule. By using prompts such as, "Please suggest how I should schedule my time to efficiently improve my data analysis skills in my busy daily life," the generative AI model will provide appropriate suggestions. This allows users to make the most of their limited time and systematically improve their skills.

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] The device obtains user permission and collects appointment information from the scheduling application. The input is the user's permission and the name of the application to be used, and the output is the user's schedule data. Specifically, the device accesses the application via an API and retrieves appointments for a specified period.

[0469] Step 2:

[0470] The terminal sends the acquired schedule data to the server. The input is the schedule data obtained in step 1, and the output is a notification that the data transfer to the server is complete. The terminal uses a secure communication protocol to ensure the secure transmission of data.

[0471] Step 3:

[0472] The server analyzes the received schedule data to identify slack time within a specific time frame. The input is the schedule data sent to the server, and the output is the detected slack time data. The server uses a data analysis tool to extract and list the time slots that are not scheduled.

[0473] Step 4:

[0474] The server suggests appropriate learning activities based on the available time detected using a generative AI model. The input consists of available time data, the user's learning objectives, and past activity history, while the output is a list of recommended learning activities. Specifically, the AI ​​model selects learning resources that match the user's interests.

[0475] Step 5:

[0476] The server incorporates the suggested learning activities into the user's schedule. The input is the list of suggested learning activities and the user's schedule data, and the output is the updated schedule. The server uses an automated scheduling algorithm to place the learning activities within appropriate time slots.

[0477] Step 6:

[0478] The device receives the updated schedule from the server and sets up advance notifications for learning activities for the user. The input is the updated schedule, and the output is the notification setting completion status. The device adjusts the system to display the reminder at the specified time.

[0479] Step 7:

[0480] The device displays a warning to the user if other applications are used during learning activities. The input is the user's activity log, and the output is a warning message. The device monitors activity in real time and immediately notifies the user if any behavior that disrupts learning activities is detected.

[0481] (Application Example 1)

[0482] Next, we will explain Application Example 1. In the following explanation, 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."

[0483] One challenge for users of electronic payments is that it is difficult to efficiently manage their budgets and reduce unnecessary spending. In particular, the busy nature of daily life makes it difficult to obtain information that can help save money at the right time, which makes it difficult to consciously manage one's finances.

[0484] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0485] In this invention, the server includes means for acquiring transaction history information, means for analyzing the transaction history information to detect surplus funds within the budget limit, and means for automatically creating a savings plan. This enables users to efficiently manage their budget and reduce unnecessary spending.

[0486] An "information processing device" is an electronic device that collects and analyzes data from users and provides necessary information.

[0487] "Transaction history information" refers to records of payments and purchases made by a user, including data such as the date and time, amount, and place of purchase.

[0488] "Analysis" is the act of extracting useful information by processing acquired data statistically or logically.

[0489] "Detecting surplus funds within a specific budget limit" refers to finding available surplus funds within the budget set by the user.

[0490] A "savings plan" is a method or schedule proposed to promote rational and efficient financial management based on the user's income and expenses.

[0491] "Automatically generated" refers to a process in which the system autonomously generates information without user intervention.

[0492] Embodiments of this invention mainly consist of an information processing device, a server, and a user's terminal.

[0493] The server retrieves transaction history information related to electronic payments from the user's terminal. The retrieved transaction history information is analyzed by software on the server to detect the user's available funds within their budget limit in real time. Existing data analysis algorithms are used for the analysis to extract specific patterns from the transaction history and estimate the amount that can be saved.

[0494] Next, the server automatically creates a savings plan based on available funds. The savings plan includes savings suggestions that take into account the user's past spending habits, and provides specific saving actions and points to note when making purchases. These suggestions are generated using a generative AI model, so they are tailored to the user's characteristics and interests.

[0495] These savings plans are notified to the user's device, and alerts are also displayed at pre-set times. Notifications are sent at the optimal time based on the user's schedule, helping users effectively manage their budget.

[0496] As a concrete example, for users whose monthly grocery expenses are rising, the app will notify them of special sales at nearby supermarkets on specific days, encouraging them to plan their shopping. Furthermore, this information will be linked to the user's calendar app and automatically incorporated into their schedule.

[0497] An example of a prompt message would be, "Based on the user's transaction history, please suggest appropriate saving methods and explain how to incorporate them into the schedule." This demonstrates how the AI ​​model can be used to generate such prompts.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The server receives transaction history information from the user's terminal. This information includes the date, time, amount, and trading partner for each transaction based on electronic payment. The server stores this data in a database and formats it into a parseable format.

[0501] Step 2:

[0502] The server analyzes the acquired transaction history to calculate the surplus funds within the monthly budget limit. The calculation uses an algorithm that analyzes past transaction patterns to identify consistent trends and fluctuations in monthly spending. The server then outputs the surplus funds amount based on this analysis.

[0503] Step 3:

[0504] The server generates a savings plan based on the analysis results. Using a generation AI model, it proposes optimal savings strategies tailored to the user's past spending habits and budget settings. Specifically, it creates a plan that includes the timing of product purchases expected to yield savings and suggestions for reducing spending on non-essential items.

[0505] Step 4:

[0506] The server notifies the user's device of the generated savings plan. The notification includes suggestions for the next time to make a purchase and important savings actions to incorporate into the schedule. This allows users to review their budget management and become more mindful of planned spending.

[0507] Step 5:

[0508] The user's device integrates the received savings plan into the user's personal schedule. The device also works with the user's calendar app to set reminders so that savings actions can be taken at the optimal time.

[0509] Step 6:

[0510] Users follow notifications from their devices and implement planned saving behaviors. For example, by shopping on recommended dates, they can actually reduce spending in their daily lives.

[0511] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0512] This invention is a system that uses an information processing device to efficiently manage users' schedules and conduct learning activities based on their emotional state. This system has the function of acquiring schedule data through the user's terminal and analyzing it on a server. Furthermore, by incorporating an emotion engine, it recognizes the user's emotional state and adjusts the learning content accordingly.

[0513] Specifically, the device obtains permission from the user to access schedule data and retrieves data from schedule management tools such as Google Calendar. This schedule data is sent to a server, where it is analyzed. Through data analysis, the server understands the user's schedule information and detects any available time within a specific time frame.

[0514] Using this detected free time, the server recommends learning activities that take into account the output of the emotion engine. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone to evaluate the user's current emotional state. Based on this evaluation, the server selects learning content and proposes the optimal learning plan for the user. For example, if the user is judged to be calm, tasks requiring concentration will be included in the schedule; if the user is highly stressed, content with a relaxing effect will be suggested.

[0515] As the time slot for a learning activity approaches, the device sends a notification to the user to help them start learning smoothly. Furthermore, if other operations are detected during the learning time, the device displays an alert and prompts the user to continue learning. In addition, after completion, feedback from the emotion engine is used to reflect on the learning experience and help improve the content and schedule of the next learning session.

[0516] This system, which utilizes an emotion engine, not only manages schedules but also enables flexible suggestions of learning content based on the user's emotional state, providing a more effective learning experience.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The device displays a notification requesting permission from the user to access their schedule data. If the user grants permission, the device retrieves data from Google Calendar and other scheduling tools.

[0520] Step 2:

[0521] The terminal sends the acquired schedule data to the server. This prepares the server to receive the data and begin analysis.

[0522] Step 3:

[0523] The server analyzes schedule data and identifies periods without scheduled appointments, thereby detecting the user's free time within a day or week.

[0524] Step 4:

[0525] The device collects emotional data using its camera and microphone to capture the user's facial expressions and voice data. The device then invokes an emotion engine and sends this data to a server for emotion recognition.

[0526] Step 5:

[0527] The server's emotion engine analyzes the user's emotional state and evaluates it (e.g., stress level and attention level). This evaluation is then used to determine the optimal learning content and format for the user.

[0528] Step 6:

[0529] Based on the available time detected by the server and the output of the emotion engine, the system automatically selects learning content and incorporates it into the schedule. It generates events that include specific learning activities and necessary resources (such as links to textbooks and online learning materials).

[0530] Step 7:

[0531] After a learning activity is scheduled, the device sends a notification to the user a short time before the scheduled learning session. This notification includes links to the learning content and resources.

[0532] Step 8:

[0533] Once learning begins, the device monitors the user's activity during the learning session and displays an alert prompting them to return to learning if another application is being used.

[0534] Step 9:

[0535] After a learning activity is completed, the system provides feedback to the user using data acquired through the emotion engine. This allows the user to incorporate the feedback into future learning plans, thereby improving the quality of their learning.

[0536] (Example 2)

[0537] Next, we will describe Example 2. 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."

[0538] Conventional scheduling management systems simply allocate time for learning activities based on the user's schedule, and have the challenge of not being able to propose flexible learning plans that take into account the user's emotional state. As a result, the efficiency of learning, which is dependent on emotions, does not improve, and it is not possible to provide an optimal learning experience.

[0539] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0540] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, and means for evaluating the user's emotional state. This makes it possible to select learning content and reserve time slots according to the user's emotional state.

[0541] An "information processing device" refers to any device that performs various processes based on data input by users or acquired information, and provides the results.

[0542] "Schedule information" refers to data that shows the time and content related to the user's actions and tasks.

[0543] "Analysis" refers to a method of examining acquired data in detail and deriving useful patterns and results.

[0544] "Free time" refers to any period of time available for free use between scheduled events or tasks.

[0545] A "time slot" refers to a range of time allocated for a specific event or activity.

[0546] "Automatic booking" refers to the system selecting an appropriate time and setting an appointment without user intervention.

[0547] "Emotional analysis" refers to the process of understanding a user's current emotional state from their facial expressions, voice, and other factors.

[0548] "Learning content" refers to the specific educational and training programs and activities that users are expected to engage in.

[0549] This invention provides a system that uses an information processing device to efficiently manage users' schedules and conduct learning activities based on their emotional state. This system collects schedule data via the user's terminal and analyzes it on a server. Furthermore, by using an emotion analysis engine, it proposes learning content tailored to the user's emotional state.

[0550] The device obtains permission from the user to access the schedule management tool and retrieves the schedule information. Specifically, the server retrieves data from schedule management tools such as Google Calendar and uses this information to detect available time within a specific time frame. This analysis process is performed on the server and includes data calculations such as data formatting and statistical evaluation.

[0551] Furthermore, the device uses input devices such as a camera and microphone to evaluate the user's emotional state. The emotion analysis engine accurately grasps the user's emotions through facial recognition and voice analysis. Based on these analysis results, the server selects learning content appropriate to the user's emotional state and proposes a learning plan. The proposed plan is notified to the user's device, supporting a smooth start to learning.

[0552] For example, if a user is enjoying relaxing content at 3:00 PM, a typical afternoon free time, and the emotion analysis engine detects a decrease in stress, the server will suggest a learning activity requiring concentration during the next free time. This activity will be communicated to the user via a notification from their device.

[0553] Examples of prompt statements that utilize a generative AI model are as follows:

[0554] "Based on the user's schedule and emotional data, please develop learning activities to suggest for their afternoon leisure time. Specifically, please present different learning plans for when the user is calm and when they are experiencing high stress levels."

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] The device obtains permission from the user to access the schedule management tool.

[0558] In terms of the specific operation, the user visually confirms an access permission confirmation dialog on the device and grants permission. This permission grants the device API access rights to retrieve schedule data. The input is the user's permission information, and the output is the device obtaining API access rights.

[0559] Step 2:

[0560] The device retrieves schedule information through the schedule management tool's API.

[0561] The device uses acquired API access rights to download event information from Google Calendar and other sources. Input consists of API access rights and data from the scheduling tool; this data is formatted as a list of events. Output is an event list including the event start time, end time, and title.

[0562] Step 3:

[0563] The device sends the schedule information to the server.

[0564] In terms of specific operation, the terminal sends a formatted schedule list to a dedicated endpoint on the server via an HTTP request. The input is the formatted schedule list, and the output is a confirmation of transmission to the server.

[0565] Step 4:

[0566] The server analyzes the received schedule data to detect buffer time.

[0567] The server executes a program to analyze the scheduled data and calculate the time difference between events. The input is the scheduled data, and the output is a list of buffer times detected through data analysis.

[0568] Step 5:

[0569] The device collects user emotion data.

[0570] The device uses a camera and microphone to record facial expressions and voice for emotion analysis. Input is the user's image and voice data, and output is a state where this data is ready to be sent to the server.

[0571] Step 6:

[0572] The server uses an emotion analysis engine to evaluate the emotional state.

[0573] The server passes the received data to the emotion analysis engine to determine the user's emotional state. The input consists of image and audio data, and the output is the result of the emotional state determination (e.g., relaxed state, stressed state).

[0574] Step 7:

[0575] The server selects learning content based on available time and emotional state.

[0576] The server generates an optimal learning plan and selects learning content based on the existing list of available time and emotional state. The input is the list of available time and emotional state, and the output is the appropriate learning plan.

[0577] Step 8:

[0578] The device notifies the user of learning activities.

[0579] The device displays a notification on the user's screen based on the learning plan received from the server. The notification includes the start time and the suggested learning content. The input is the learning plan, and the output is the notification displayed on the screen.

[0580] (Application Example 2)

[0581] Next, we will explain application example 2. In the following explanation, 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."

[0582] In modern personal information management, while optimizing time allocation based on users' schedules is crucial, providing suggestions and services that consider users' emotional states is insufficient. Furthermore, in shopping experiences such as in stores, there is a need for products and services optimized to individual emotional states. To address these challenges, a system is needed that provides effective suggestions based on users' schedules and emotional states.

[0583] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0584] In this invention, the server includes means for an information processing device to acquire the user's schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, means for recognizing and analyzing the user's emotional state, and means for proposing the most suitable product or service based on the analyzed emotional state of the user. This enables optimal proposals that comprehensively consider the user's schedule and emotional state.

[0585] An "information processing device" is a computer system used to acquire and analyze users' schedule information and emotional states.

[0586] "Schedule information" refers to chronological activity information entered into the schedule management tool used by the user.

[0587] "Available time" refers to a flexible time slot within a user's schedule that can be allocated to other activities.

[0588] "Emotional state" refers to data that indicates the user's emotional changes and state at any given time, and is information obtained from facial expressions and voice.

[0589] "Analysis" is the process of performing information processing based on acquired data in order to derive specific objectives or results.

[0590] "Proposing a product or service" refers to the act of selecting and recommending suitable products or services based on the user's needs and circumstances.

[0591] To realize this invention, it is necessary to build a system in which the server, terminal, and user elements work together in coordination. The server analyzes schedule information and emotional state data obtained from the user's terminal. The terminal is required to have a schedule management tool and emotion recognition function in order to obtain this information. Specifically, schedule information is obtained using the Google Calendar API, and emotional state is obtained using the camera and microphone installed in the smartphone or smart glasses. Azure Cognitive Services is used for analyzing the emotional state.

[0592] On the server, based on the acquired data, the system detects the user's available time within a specific time frame and generates suggestions for learning activities and products / services to be used according to that available time. Based on the analysis results, it determines and proposes what is optimal for the user. The terminal also functions as an interface to notify the user of these suggestions.

[0593] For example, if the system detects that a user has free time on a weekday afternoon, and they appear relaxed, it will suggest discounts on specific items in the store. Conversely, if the system detects that the user is busy, it will suggest pre-selecting relevant items from their purchase history so they can purchase them quickly.

[0594] An example of a prompt message would be, "Please create an optimal algorithm for suggesting products based on the emotional state of a specific customer." This allows you to create instructions to generate optimal suggestions using a generative AI model. This makes it possible to provide users with personalized, high-quality services.

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The device obtains permission from the user to access schedule data. Based on this permission, it retrieves schedule information using the Google Calendar API and saves it to the device. The input is the user's schedule data, and the output is the event information obtained through the API.

[0598] Step 2:

[0599] The device uses its built-in camera and microphone to record the user's facial expressions and voice, and to detect their emotional state. This data is analyzed using Azure Cognitive Services. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state information.

[0600] Step 3:

[0601] The server receives schedule information and emotional state data sent from the terminal and detects the available time. The available time is determined by analyzing the schedule information. The input is the schedule and emotional data, and the output is the determined available time.

[0602] Step 4:

[0603] The server generates a prompt sentence suitable for the generating AI model based on the detected free time and emotional state. This prompt sentence is input to the AI ​​model to generate a suggestion for the optimal product or service. The input is free time and emotional state, and the output is the suggestion obtained from the AI.

[0604] Step 5:

[0605] The suggestions generated by the server are sent to the terminal and notified to the user. The terminal uses its notification function to present information to the user at the appropriate time and prompt action. The input is the suggestions from the server, and the output is the notification to the user.

[0606] Step 6:

[0607] Users can review the notifications they receive and choose whether to accept the suggested products or services. User feedback is fed back into the system to improve the accuracy of future suggestions. The input is the user's choice, and the output is the feedback to the system.

[0608] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0610] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0614] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0615] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0616] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0617] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0618] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0619] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0620] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0621] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0622] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0623] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0624] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] This invention is a system that uses an information processing device to manage users' schedules and efficiently advance learning activities. This system acquires schedule data from the user's device (e.g., a smartphone or personal computer) and sends it to a server. The server analyzes this data to detect free time. It then suggests appropriate learning activities and automatically incorporates them into the user's schedule.

[0626] Specifically, the device obtains permission from the user to access schedule data and retrieves data from Google Calendar and other applications. The retrieved data is sent to the server as the user's schedule information. The server analyzes this schedule data to identify any free time that occurs during the day or week.

[0627] Based on this, the server generates candidate learning activities and recommends the most suitable learning content for the user. This includes online learning materials and practice problems. The server sets a specific learning schedule and incorporates it into the user's schedule. At this point, the device sends a pre-learning notification to the user a little before the scheduled time. Also, if other activity is detected during the learning time slot, the device displays an alert to the user prompting them to continue learning.

[0628] For example, if a user wants to improve their data analysis skills, their morning commute or afternoon break might be free time. The system can use this time to suggest and automatically schedule tutorials and exercises on specific data analysis topics for the user.

[0629] These automated systems allow users to systematically improve their skills without missing opportunities, even amidst their busy daily lives. This system functions not merely as a time management tool, but as a crucial tool to support user development.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The device displays a notification requesting permission from the user to access their schedule data. If the user grants permission, the device retrieves data from Google Calendar and other scheduling tools.

[0633] Step 2:

[0634] The terminal sends the acquired schedule data to the server. This prepares the server to receive the data and begin analysis.

[0635] Step 3:

[0636] The server analyzes the received schedule data and detects available time slots within a specific time frame. The server can also identify periods with no scheduled appointments or short periods of free time.

[0637] Step 4:

[0638] Based on the available time detected by the server, a recommendation algorithm is executed to suggest the most suitable learning activities for the user. In this process, the server incorporates online learning materials and practice problems, providing content tailored to the user's learning needs.

[0639] Step 5:

[0640] The server reserves learning activities as time slots based on available time, and automatically adds events to the user's schedule. This ensures that users do not miss learning opportunities and that their schedules are planned systematically.

[0641] Step 6:

[0642] The device sends a pre-notification to the user before the scheduled learning time. The notification includes the learning content and links to help the user start learning smoothly.

[0643] Step 7:

[0644] If the device is used by other applications or features during a learning session, this feature detects the activity and displays an alert prompting the user to continue learning. This reduces the risk of the user's learning time being interrupted.

[0645] (Example 1)

[0646] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0647] A challenge exists in that users find it difficult to efficiently find time for learning and engage in meaningful learning activities within their daily schedules. Appropriate time management and activity suggestions are needed to enable users to effectively improve their skills amidst their busy daily lives.

[0648] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0649] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, means for proposing learning activities using a generated AI model based on the detected available time and automatically reserving the time frame, means for incorporating learning activities into the user's schedule, and means for displaying a warning if other operations are performed during the learning activities. This enables users to make the most of available time in their daily schedules and systematically improve their skills.

[0650] An "information processing device" is a device that has computing functions for collecting, analyzing, storing, and transferring data.

[0651] "Schedule information" refers to data that shows the date, time, location, and content of activities or appointments that the user will be making in the future.

[0652] "Analysis" is the process of scrutinizing acquired data and extracting useful information from it.

[0653] "Available time" refers to free time or unoccupied time identified within a user's schedule information.

[0654] A "generative AI model" is a model that utilizes AI technology to select the optimal option from various patterns and choices.

[0655] "Learning activities" include activities that involve online learning materials and practice exercises for acquiring knowledge and skills.

[0656] A "time frame" refers to a specific period of time set aside in a schedule for carrying out a particular activity.

[0657] A "warning" is a notification that informs a user of important information or the need for action.

[0658] This invention is a system that uses an information processing device to manage a user's schedule and support efficient learning activities. It mainly consists of three elements: a server, a terminal, and a user.

[0659] The devices include smartphones and personal computers. After obtaining permission from the user to access schedule data, the device retrieves appointment information from Google Calendar or similar scheduling applications. This retrieved data reflects the user's activity plan and is securely transmitted to the server.

[0660] The server analyzes schedule data to identify free time within a day or week. To do this, the server uses data analysis tools such as Python and R, employing complex algorithms. Furthermore, the server leverages generative AI models to automatically select learning activities suitable for the identified free time. This process generates appropriate online learning materials and practice problems based on the user's past learning behavior and current interests.

[0661] The user reviews and performs the suggested learning activities. The learning schedule, pre-programmed by the server, is notified in advance via the device, allowing the user to plan these activities systematically. If other operations are performed during a learning activity, the device displays a warning and prompts the user to continue.

[0662] For example, if a user wants to use their commute time to improve their data analysis skills, this system can detect that commute time from their schedule and automatically incorporate online data analysis learning materials into their schedule. By using prompts such as, "Please suggest how I should schedule my time to efficiently improve my data analysis skills in my busy daily life," the generative AI model will provide appropriate suggestions. This allows users to make the most of their limited time and systematically improve their skills.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The device obtains user permission and collects appointment information from the scheduling application. The input is the user's permission and the name of the application to be used, and the output is the user's schedule data. Specifically, the device accesses the application via an API and retrieves appointments for a specified period.

[0666] Step 2:

[0667] The terminal sends the acquired schedule data to the server. The input is the schedule data obtained in step 1, and the output is a notification that the data transfer to the server is complete. The terminal uses a secure communication protocol to ensure the secure transmission of data.

[0668] Step 3:

[0669] The server analyzes the received schedule data to identify slack time within a specific time frame. The input is the schedule data sent to the server, and the output is the detected slack time data. The server uses a data analysis tool to extract and list the time slots that are not scheduled.

[0670] Step 4:

[0671] The server suggests appropriate learning activities based on the available time detected using a generative AI model. The input consists of available time data, the user's learning objectives, and past activity history, while the output is a list of recommended learning activities. Specifically, the AI ​​model selects learning resources that match the user's interests.

[0672] Step 5:

[0673] The server incorporates the suggested learning activities into the user's schedule. The input is the list of suggested learning activities and the user's schedule data, and the output is the updated schedule. The server uses an automated scheduling algorithm to place the learning activities within appropriate time slots.

[0674] Step 6:

[0675] The device receives the updated schedule from the server and sets up advance notifications for learning activities for the user. The input is the updated schedule, and the output is the notification setting completion status. The device adjusts the system to display the reminder at the specified time.

[0676] Step 7:

[0677] The device displays a warning to the user if other applications are used during learning activities. The input is the user's activity log, and the output is a warning message. The device monitors activity in real time and immediately notifies the user if any behavior that disrupts learning activities is detected.

[0678] (Application Example 1)

[0679] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] One challenge for users of electronic payments is that it is difficult to efficiently manage their budgets and reduce unnecessary spending. In particular, the busy nature of daily life makes it difficult to obtain information that can help save money at the right time, which makes it difficult to consciously manage one's finances.

[0681] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0682] In this invention, the server includes means for acquiring transaction history information, means for analyzing the transaction history information to detect surplus funds within the budget limit, and means for automatically creating a savings plan. This enables users to efficiently manage their budget and reduce unnecessary spending.

[0683] An "information processing device" is an electronic device that collects and analyzes data from users and provides necessary information.

[0684] "Transaction history information" refers to records of payments and purchases made by a user, including data such as the date and time, amount, and place of purchase.

[0685] "Analysis" is the act of extracting useful information by processing acquired data statistically or logically.

[0686] "Detecting surplus funds within a specific budget limit" refers to finding available surplus funds within the budget set by the user.

[0687] A "savings plan" is a method or schedule proposed to promote rational and efficient financial management based on the user's income and expenses.

[0688] "Automatically generated" refers to a process in which the system autonomously generates information without user intervention.

[0689] Embodiments of this invention mainly consist of an information processing device, a server, and a user's terminal.

[0690] The server retrieves transaction history information related to electronic payments from the user's terminal. The retrieved transaction history information is analyzed by software on the server to detect the user's available funds within their budget limit in real time. Existing data analysis algorithms are used for the analysis to extract specific patterns from the transaction history and estimate the amount that can be saved.

[0691] Next, the server automatically creates a savings plan based on available funds. The savings plan includes savings suggestions that take into account the user's past spending habits, and provides specific saving actions and points to note when making purchases. These suggestions are generated using a generative AI model, so they are tailored to the user's characteristics and interests.

[0692] These savings plans are notified to the user's device, and alerts are also displayed at pre-set times. Notifications are sent at the optimal time based on the user's schedule, helping users effectively manage their budget.

[0693] As a concrete example, for users whose monthly grocery expenses are rising, the app will notify them of special sales at nearby supermarkets on specific days, encouraging them to plan their shopping. Furthermore, this information will be linked to the user's calendar app and automatically incorporated into their schedule.

[0694] An example of a prompt message would be, "Based on the user's transaction history, please suggest appropriate saving methods and explain how to incorporate them into the schedule." This demonstrates how the AI ​​model can be used to generate such prompts.

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The server receives transaction history information from the user's terminal. This information includes the date, time, amount, and trading partner for each transaction based on electronic payment. The server stores this data in a database and formats it into a parseable format.

[0698] Step 2:

[0699] The server analyzes the acquired transaction history to calculate the surplus funds within the monthly budget limit. The calculation uses an algorithm that analyzes past transaction patterns to identify consistent trends and fluctuations in monthly spending. The server then outputs the surplus funds amount based on this analysis.

[0700] Step 3:

[0701] The server generates a savings plan based on the analysis results. Using a generation AI model, it proposes optimal savings strategies tailored to the user's past spending habits and budget settings. Specifically, it creates a plan that includes the timing of product purchases expected to yield savings and suggestions for reducing spending on non-essential items.

[0702] Step 4:

[0703] The server notifies the user's device of the generated savings plan. The notification includes suggestions for the next time to make a purchase and important savings actions to incorporate into the schedule. This allows users to review their budget management and become more mindful of planned spending.

[0704] Step 5:

[0705] The user's device integrates the received savings plan into the user's personal schedule. The device also works with the user's calendar app to set reminders so that savings actions can be taken at the optimal time.

[0706] Step 6:

[0707] Users follow notifications from their devices and implement planned saving behaviors. For example, by shopping on recommended dates, they can actually reduce spending in their daily lives.

[0708] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0709] This invention is a system that uses an information processing device to efficiently manage users' schedules and conduct learning activities based on their emotional state. This system has the function of acquiring schedule data through the user's terminal and analyzing it on a server. Furthermore, by incorporating an emotion engine, it recognizes the user's emotional state and adjusts the learning content accordingly.

[0710] Specifically, the device obtains permission from the user to access schedule data and retrieves data from schedule management tools such as Google Calendar. This schedule data is sent to a server, where it is analyzed. Through data analysis, the server understands the user's schedule information and detects any available time within a specific time frame.

[0711] Using this detected free time, the server recommends learning activities that take into account the output of the emotion engine. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone to evaluate the user's current emotional state. Based on this evaluation, the server selects learning content and proposes the optimal learning plan for the user. For example, if the user is judged to be calm, tasks requiring concentration will be included in the schedule; if the user is highly stressed, content with a relaxing effect will be suggested.

[0712] As the time slot for a learning activity approaches, the device sends a notification to the user to help them start learning smoothly. Furthermore, if other operations are detected during the learning time, the device displays an alert and prompts the user to continue learning. In addition, after completion, feedback from the emotion engine is used to reflect on the learning experience and help improve the content and schedule of the next learning session.

[0713] This system, which utilizes an emotion engine, not only manages schedules but also enables flexible suggestions of learning content based on the user's emotional state, providing a more effective learning experience.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The device displays a notification requesting permission from the user to access their schedule data. If the user grants permission, the device retrieves data from Google Calendar and other scheduling tools.

[0717] Step 2:

[0718] The terminal sends the acquired schedule data to the server. This prepares the server to receive the data and begin analysis.

[0719] Step 3:

[0720] The server analyzes schedule data and identifies periods without scheduled appointments, thereby detecting the user's free time within a day or week.

[0721] Step 4:

[0722] The device collects emotional data using its camera and microphone to capture the user's facial expressions and voice data. The device then invokes an emotion engine and sends this data to a server for emotion recognition.

[0723] Step 5:

[0724] The server's emotion engine analyzes the user's emotional state and evaluates it (e.g., stress level and attention level). This evaluation is then used to determine the optimal learning content and format for the user.

[0725] Step 6:

[0726] Based on the available time detected by the server and the output of the emotion engine, the system automatically selects learning content and incorporates it into the schedule. It generates events that include specific learning activities and necessary resources (such as links to textbooks and online learning materials).

[0727] Step 7:

[0728] After a learning activity is scheduled, the device sends a notification to the user a short time before the scheduled learning session. This notification includes links to the learning content and resources.

[0729] Step 8:

[0730] Once learning begins, the device monitors the user's activity during the learning session and displays an alert prompting them to return to learning if another application is being used.

[0731] Step 9:

[0732] After a learning activity is completed, the system provides feedback to the user using data acquired through the emotion engine. This allows the user to incorporate the feedback into future learning plans, thereby improving the quality of their learning.

[0733] (Example 2)

[0734] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0735] Conventional scheduling management systems simply allocate time for learning activities based on the user's schedule, and have the challenge of not being able to propose flexible learning plans that take into account the user's emotional state. As a result, the efficiency of learning, which is dependent on emotions, does not improve, and it is not possible to provide an optimal learning experience.

[0736] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0737] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, and means for evaluating the user's emotional state. This makes it possible to select learning content and reserve time slots according to the user's emotional state.

[0738] An "information processing device" refers to any device that performs various processes based on data input by users or acquired information, and provides the results.

[0739] "Schedule information" refers to data that shows the time and content related to the user's actions and tasks.

[0740] "Analysis" refers to a method of examining acquired data in detail and deriving useful patterns and results.

[0741] "Free time" refers to any period of time available for free use between scheduled events or tasks.

[0742] A "time slot" refers to a range of time allocated for a specific event or activity.

[0743] "Automatic booking" refers to the system selecting an appropriate time and setting an appointment without user intervention.

[0744] "Emotional analysis" refers to the process of understanding a user's current emotional state from their facial expressions, voice, and other factors.

[0745] "Learning content" refers to the specific educational and training programs and activities that users are expected to engage in.

[0746] This invention provides a system that uses an information processing device to efficiently manage users' schedules and conduct learning activities based on their emotional state. This system collects schedule data via the user's terminal and analyzes it on a server. Furthermore, by using an emotion analysis engine, it proposes learning content tailored to the user's emotional state.

[0747] The device obtains permission from the user to access the schedule management tool and retrieves the schedule information. Specifically, the server retrieves data from schedule management tools such as Google Calendar and uses this information to detect available time within a specific time frame. This analysis process is performed on the server and includes data calculations such as data formatting and statistical evaluation.

[0748] Furthermore, the device uses input devices such as a camera and microphone to evaluate the user's emotional state. The emotion analysis engine accurately grasps the user's emotions through facial recognition and voice analysis. Based on these analysis results, the server selects learning content appropriate to the user's emotional state and proposes a learning plan. The proposed plan is notified to the user's device, supporting a smooth start to learning.

[0749] For example, if a user is enjoying relaxing content at 3:00 PM, a typical afternoon free time, and the emotion analysis engine detects a decrease in stress, the server will suggest a learning activity requiring concentration during the next free time. This activity will be communicated to the user via a notification from their device.

[0750] Examples of prompt statements that utilize a generative AI model are as follows:

[0751] "Based on the user's schedule and emotional data, please develop learning activities to suggest for their afternoon leisure time. Specifically, please present different learning plans for when the user is calm and when they are experiencing high stress levels."

[0752] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0753] Step 1:

[0754] The device obtains permission from the user to access the schedule management tool.

[0755] In terms of the specific operation, the user visually confirms an access permission confirmation dialog on the device and grants permission. This permission grants the device API access rights to retrieve schedule data. The input is the user's permission information, and the output is the device obtaining API access rights.

[0756] Step 2:

[0757] The device retrieves schedule information through the schedule management tool's API.

[0758] The device uses acquired API access rights to download event information from Google Calendar and other sources. Input consists of API access rights and data from the scheduling tool; this data is formatted as a list of events. Output is an event list including the event start time, end time, and title.

[0759] Step 3:

[0760] The device sends the schedule information to the server.

[0761] In terms of specific operation, the terminal sends a formatted schedule list to a dedicated endpoint on the server via an HTTP request. The input is the formatted schedule list, and the output is a confirmation of transmission to the server.

[0762] Step 4:

[0763] The server analyzes the received schedule data to detect buffer time.

[0764] The server executes a program to analyze the scheduled data and calculate the time difference between events. The input is the scheduled data, and the output is a list of buffer times detected through data analysis.

[0765] Step 5:

[0766] The device collects user emotion data.

[0767] The device uses a camera and microphone to record facial expressions and voice for emotion analysis. Input is the user's image and voice data, and output is a state where this data is ready to be sent to the server.

[0768] Step 6:

[0769] The server uses an emotion analysis engine to evaluate the emotional state.

[0770] The server passes the received data to the emotion analysis engine to determine the user's emotional state. The input consists of image and audio data, and the output is the result of the emotional state determination (e.g., relaxed state, stressed state).

[0771] Step 7:

[0772] The server selects learning content based on available time and emotional state.

[0773] The server generates an optimal learning plan and selects learning content based on the existing list of available time and emotional state. The input is the list of available time and emotional state, and the output is the appropriate learning plan.

[0774] Step 8:

[0775] The device notifies the user of learning activities.

[0776] The device displays a notification on the user's screen based on the learning plan received from the server. The notification includes the start time and the suggested learning content. The input is the learning plan, and the output is the notification displayed on the screen.

[0777] (Application Example 2)

[0778] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0779] In modern personal information management, while optimizing time allocation based on users' schedules is crucial, providing suggestions and services that consider users' emotional states is insufficient. Furthermore, in shopping experiences such as in stores, there is a need for products and services optimized to individual emotional states. To address these challenges, a system is needed that provides effective suggestions based on users' schedules and emotional states.

[0780] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0781] In this invention, the server includes means for an information processing device to acquire the user's schedule information, means for analyzing the acquired schedule information and detecting available time within a specific time frame, means for recognizing and analyzing the user's emotional state, and means for proposing the most suitable product or service based on the analyzed emotional state of the user. This enables optimal proposals that comprehensively consider the user's schedule and emotional state.

[0782] An "information processing device" is a computer system used to acquire and analyze users' schedule information and emotional states.

[0783] "Schedule information" refers to chronological activity information entered into the schedule management tool used by the user.

[0784] "Available time" refers to a flexible time slot within a user's schedule that can be allocated to other activities.

[0785] "Emotional state" refers to data that indicates the user's emotional changes and state at any given time, and is information obtained from facial expressions and voice.

[0786] "Analysis" is the process of performing information processing based on acquired data in order to derive specific objectives or results.

[0787] "Proposing a product or service" refers to the act of selecting and recommending suitable products or services based on the user's needs and circumstances.

[0788] To realize this invention, it is necessary to build a system in which the server, terminal, and user elements work together in coordination. The server analyzes schedule information and emotional state data obtained from the user's terminal. The terminal is required to have a schedule management tool and emotion recognition function in order to obtain this information. Specifically, schedule information is obtained using the Google Calendar API, and emotional state is obtained using the camera and microphone installed in the smartphone or smart glasses. Azure Cognitive Services is used for analyzing the emotional state.

[0789] On the server, based on the acquired data, the system detects the user's available time within a specific time frame and generates suggestions for learning activities and products / services to be used according to that available time. Based on the analysis results, it determines and proposes what is optimal for the user. The terminal also functions as an interface to notify the user of these suggestions.

[0790] For example, if the system detects that a user has free time on a weekday afternoon, and they appear relaxed, it will suggest discounts on specific items in the store. Conversely, if the system detects that the user is busy, it will suggest pre-selecting relevant items from their purchase history so they can purchase them quickly.

[0791] An example of a prompt message would be, "Please create an optimal algorithm for suggesting products based on the emotional state of a specific customer." This allows you to create instructions to generate optimal suggestions using a generative AI model. This makes it possible to provide users with personalized, high-quality services.

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] The device obtains permission from the user to access schedule data. Based on this permission, it retrieves schedule information using the Google Calendar API and saves it to the device. The input is the user's schedule data, and the output is the event information obtained through the API.

[0795] Step 2:

[0796] The device uses its built-in camera and microphone to record the user's facial expressions and voice, and to detect their emotional state. This data is analyzed using Azure Cognitive Services. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state information.

[0797] Step 3:

[0798] The server receives schedule information and emotional state data sent from the terminal and detects the available time. The available time is determined by analyzing the schedule information. The input is the schedule and emotional data, and the output is the determined available time.

[0799] Step 4:

[0800] The server generates a prompt sentence suitable for the generating AI model based on the detected free time and emotional state. This prompt sentence is input to the AI ​​model to generate a suggestion for the optimal product or service. The input is free time and emotional state, and the output is the suggestion obtained from the AI.

[0801] Step 5:

[0802] The suggestions generated by the server are sent to the terminal and notified to the user. The terminal uses its notification function to present information to the user at the appropriate time and prompt action. The input is the suggestions from the server, and the output is the notification to the user.

[0803] Step 6:

[0804] Users can review the notifications they receive and choose whether to accept the suggested products or services. User feedback is fed back into the system to improve the accuracy of future suggestions. The input is the user's choice, and the output is the feedback to the system.

[0805] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0806] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0807] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0808] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0809] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0810] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0811] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0812] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0813] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0814] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0815] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0816] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0817] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0818] 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.

[0819] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0820] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0821] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0822] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0823] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0824] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0825] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] The information processing device provides a means for acquiring user schedule information,

[0829] A means for analyzing acquired schedule information and detecting leeway within a specific time frame,

[0830] A means for automatically reserving time slots for learning activities based on detected free time,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for providing notification before the start of a learning activity time slot.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for suggesting learning content based on detected free time.

[0836] "Example 1"

[0837] (Claim 1)

[0838] The information processing device provides a means for acquiring user schedule information,

[0839] A means for analyzing acquired schedule information and detecting leeway within a specific time frame,

[0840] A means of proposing learning activities using a generative AI model based on detected free time, and automatically reserving time slots,

[0841] A means of incorporating learning activities into the user's schedule,

[0842] A means of displaying a warning if other operations are performed during a learning activity,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for providing notification before the start of a learning activity time slot.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for selecting and proposing learning content based on the user's needs.

[0848] "Application Example 1"

[0849] (Claim 1)

[0850] The information processing device includes means for acquiring user transaction history information,

[0851] A means of analyzing acquired transaction history information to detect surplus funds within a specific budget limit,

[0852] A means to automatically create a savings plan based on the detected surplus funds,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising means for notifying a savings plan.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for proposing goods and services based on detected surplus funds.

[0858] "Example 2 of combining an emotion engine"

[0859] (Claim 1)

[0860] The information processing device provides a means for acquiring user schedule information,

[0861] A means for analyzing acquired schedule information and detecting leeway within a specific time frame,

[0862] A means for automatically reserving time slots for learning activities based on detected free time,

[0863] A means of analyzing the emotional state of a user,

[0864] A means for selecting learning content based on the evaluation of emotion analysis methods,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, further comprising means for providing notification before the start of a learning activity time slot.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising means for suggesting learning content based on the detected free time and the results of sentiment analysis.

[0870] "Application example 2 when combining with an emotional engine"

[0871] (Claim 1)

[0872] The information processing device provides a means for acquiring user schedule information,

[0873] A means for analyzing acquired schedule information and detecting leeway within a specific time frame,

[0874] A means for automatically reserving time slots for learning activities based on detected free time,

[0875] A means of recognizing and analyzing the emotional state of users,

[0876] A means of suggesting the most suitable product or service based on the analyzed emotional state of the user,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for providing notification before the start of a learning activity time slot.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for suggesting learning content based on detected free time, and means for optimizing the suggestion based on the user's emotional state. [Explanation of Symbols]

[0882] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The information processing device provides a means for acquiring user schedule information, A means for analyzing acquired schedule information and detecting leeway within a specific time frame, A means for automatically reserving time slots for learning activities based on detected free time, A system that includes this.

2. The system according to claim 1, further comprising means for providing notification before the start of a learning activity time slot.

3. The system according to claim 1, further comprising means for suggesting learning content based on detected free time.

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