System

Abstract

A system is provided.SOLUTION: This system includes a means for learning the action history of a user, a means for automatically preparing a periodical schedule based on the learned action history, a means for collecting relevant event information based on the interest registered by the user, and a means for notifying the user based on the collected event information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional calendar apps require users to manually enter appointments, often leading to forgetting appointments in the midst of a busy life. They also lack the functionality to automatically retrieve and update event information based on users' interests, which can lead to users missing important events or information of interest. Furthermore, they lack the functionality to utilize the user's location information to provide nearby event information. There is a need to solve these issues and streamline schedule management for users. [Means for solving the problem]

[0005] The present invention provides a means for learning a user's behavioral history and automatically creating regular schedules based on the learned behavioral history. It also includes a means for collecting related event information based on the user's registered interests and notifying the user based on the collected event information. Furthermore, by adding a means for acquiring the user's location information, collecting nearby event information based on the acquired location information, and notifying the user based on the collected event information, a system is provided that streamlines the user's schedule management and prevents them from missing important schedules or information of interest. The system also includes a function for analyzing the user's past calendar events, inferring behavioral patterns, automatically creating future schedules based on the inferred behavioral patterns, and notifying the user of detailed information, achieving even greater convenience.

[0006] "Behavioral history" is a record of a series of activities or events that a user has performed in the past.

[0007] A "recurring event" is an appointment or event that occurs repeatedly at regular intervals.

[0008] "Interests" are topics or themes that a user feels particularly interested in and wants to be involved in.

[0009] "Related event information" refers to an event with a specific date, time, and content that is related to the user's interests and past behavioral history.

[0010] "Location information" is data relating to a geographic location where a user is currently or has been located in the past.

[0011] "Analysis" is the process of examining data in detail to clarify its meaning and role.

[0012] A "behavioral pattern" is a habit or tendency of a user to repeatedly perform a behavior at a certain cycle or under specific conditions.

[0013] "Future plans" are activities or events that the user plans to perform in the future.

[0014] A "notification" is an alert or message that notifies the user of specific information.

[0015] A "Calendar Event" is an activity or event scheduled to occur at a specific date and time listed on a calendar.

[0016] A "system" is a collection of software and hardware that provides a series of processes and functions in the present invention. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] Overall system configuration

[0039] The system of the present invention is implemented as a calendar application consisting of a user terminal and a server. The user terminal is a device such as a smartphone or tablet, and the server is located on the cloud and stores and analyzes user information and event data.

[0040] User registration and initial settings

[0041] User: To use the system, the user first installs the calendar app and registers an account when the system is first launched. When registering, the user enters their interests (e.g., hair salons, live concerts, movies, etc.).

[0042] Device: Once you have completed your account registration and entered your interests, that information will be sent to the server.

[0043] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[0044] Analysis of behavioral history

[0045] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[0046] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if it detects that the user goes to the hair salon on the third Saturday of every month, it automatically generates future regular schedules based on this information.

[0047] Creating an Automatic Schedule

[0048] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[0049] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[0050] On the device: The user will be notified via push notification that the reservation has been completed.

[0051] Event information collection and notification

[0052] Server: Based on the user's interests, collects relevant event information (for example, information about live performances by favorite artists or new movie releases), and prepares to notify the user at the optimal time based on the collected event information.

[0053] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[0054] Use of location information

[0055] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[0056] Server: Collects information about nearby events (such as newly opened cafes or special sales at supermarkets) based on location information. Sends the collected event information to the user's device.

[0057] Device: Sends push notifications to users based on nearby event information received.

[0058] Specific examples

[0059] Hair salon reservation example

[0060] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[0061] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[0062] Concert ticket purchase example

[0063] User: Register a favorite artist (e.g., a particular musician) as a topic.

[0064] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[0065] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[0066] Example of supermarket sale information

[0067] Device: Sends the user's location information to the server.

[0068] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[0069] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[0070] This concludes the "Mode for Carrying Out the Invention" of the system of the present invention. This system utilizes the user's behavioral history, interests, and location information to realize efficient schedule management and support the user's daily life.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] User: Installs the calendar app on their smartphone and launches it for the first time.

[0074] Step 2:

[0075] On device: The app prompts the user to sign up for an account and asks them to enter topics or keywords of interest.

[0076] Step 3:

[0077] Users: Enter topics of interest such as hair salons, live concerts, movies, etc., and submit them along with their account information.

[0078] Step 4:

[0079] Terminal: Stores the entered information in local storage and prepares it for transmission to the server.

[0080] Step 5:

[0081] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[0082] Step 6:

[0083] Device: Collects the user's past calendar events and behavioral history data and sends them to the server.

[0084] Step 7:

[0085] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns.

[0086] Step 8:

[0087] Server: Automatically generates regular schedules for users based on the learning results. For example, predict the date of a hair salon appointment and call the reservation API to make the appointment.

[0088] Step 9:

[0089] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[0090] Step 10:

[0091] On the device: The user will be notified via push notification that the reservation has been completed.

[0092] Step 11:

[0093] Server: Collects and analyzes relevant event information based on user interests.

[0094] Step 12:

[0095] Server: Based on the analysis results, selects the event information to notify the user at the optimal time.

[0096] Step 13:

[0097] Server: Sends selected event information to the device.

[0098] Step 14:

[0099] On the device: Sends push notifications to users based on the received event information and automatically adds events to their calendars.

[0100] Step 15:

[0101] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[0102] Step 16:

[0103] Server: Collects and analyzes information about nearby events based on location information.

[0104] Step 17:

[0105] Server: Based on the collected and analyzed results, it creates event information to notify the user and sends it to the terminal.

[0106] Step 18:

[0107] Device: Sends push notifications to users based on nearby event information received.

[0108] Specific examples

[0109] Hair salon reservation example

[0110] Step 1:

[0111] Server: Analyzes the user's past hair salon reservation history and learns that the user visits the hair salon on the third Saturday of every month.

[0112] Step 2:

[0113] Server: When the next third Saturday approaches, make a reservation through the hair salon booking API.

[0114] Step 3:

[0115] Server: After the reservation is completed, create a notification message and send it to the device.

[0116] Step 4:

[0117] Device: A push notification will be sent to the user upon receiving the reservation completion notification.

[0118] Concert ticket purchase example

[0119] Step 1:

[0120] User: Register a favorite artist (e.g., a particular musician) as a topic.

[0121] Step 2:

[0122] Server: Collects concert information for the artist in question and checks when tickets go on sale.

[0123] Step 3:

[0124] Server: Prepare information to notify users as the sale date approaches.

[0125] Step 4:

[0126] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[0127] Example of supermarket sale information

[0128] Step 1:

[0129] Device: Sends the user's location information to the server.

[0130] Step 2:

[0131] Server: Collects information about special offers at nearby supermarkets based on location information.

[0132] Step 3:

[0133] Server: Analyzes sale information and creates notification information.

[0134] Step 4:

[0135] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[0136] Example 1

[0137] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0138] In modern society, users need to manage their busy schedules. However, conventional schedule management systems lacked the ability to respond individually to users' behavioral patterns and interests, requiring a great deal of time and effort for manual input and management. Furthermore, they lacked mechanisms for efficiently collecting and notifying users of nearby event information and service reservation information. This often led to users missing important information, reducing the efficiency and convenience of schedule management.

[0139] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0140] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting relevant event information based on the user's registered interests, means for notifying the user based on the collected event information, means for collecting event information of interest based on location information transmitted from the user terminal, means for notifying the user at an optimal timing based on the acquired location information and interest information, means for automatically making a reservation by calling a service reservation API for a beauty salon or the like, means for confirming the success of the reservation and, if successful, sending a reservation completion notification to the user terminal, means for analyzing past calendar events and estimating behavioral patterns, means for automatically creating future schedules based on the estimated behavioral patterns, and means for notifying the user of detailed schedule information. This allows users to manage their schedules individually and efficiently live their daily lives without missing important information.

[0141] "User" refers to a person who uses the system and receives services through a terminal.

[0142] "Behavioral history" refers to a record of actions and events that a user has taken in the past, including information entered into a schedule or calendar.

[0143] A "recurring appointment" refers to a regular schedule or reservation that is automatically generated based on a user's behavioral patterns.

[0144] "Interests" refers to the types of things or events that a user is interested in, such as hair salons, live concerts, movies, etc.

[0145] "Event information" refers to detailed information about an event related to the user's interests, including the date, time, location, and content.

[0146] "Location Information" means geographic location data obtained by a User's Device, such as data obtained using GPS or other technologies.

[0147] "Booking API" means an application programming interface for making reservations for certain services over the Internet.

[0148] "Push notification" refers to a communication method that transmits information from a server to a user's device in real time, allowing the user to be notified immediately.

[0149] "Behavioral patterns" refer to certain behavioral tendencies and habits that are estimated based on a user's past behavioral history.

[0150] "Machine learning algorithms" refer to computer algorithms that analyze data, learn, and automatically recognize patterns.

[0151] The system of the present invention is implemented as a calendar application using a user terminal such as a smartphone or tablet, and a server located on the cloud, which stores and analyzes user information and event data.

[0152] User registration and initial settings

[0153] To use the system, users first install the calendar app on their device and register an account when they first launch the system. When registering, users input their interests (e.g., beauty salons, live concerts, movies, etc.).

[0154] The terminal transmits the registration information and interest data to the server.

[0155] The server stores the received data in a database for future analysis.

[0156] Analysis of behavioral history

[0157] The device periodically collects events and behavioral history data previously entered in the calendar and sends it to the server.

[0158] The server then uses a machine learning algorithm to analyze the received behavioral history data. For example, it can detect a pattern where a user visits a beauty salon on the third Saturday of every month. The learning results are stored in a database and used for future predictions.

[0159] Creating an Automatic Schedule

[0160] The server predicts the next appointment date for the hair salon based on the learning results, calls the salon's reservation API according to the predicted date, checks whether the reservation process was successful, and creates a reservation completion notification if it was successful.

[0161] The server sends a reservation completion notification to the user terminal.

[0162] The terminal receives the reservation completion notification and notifies the user via push notification.

[0163] Event information collection and notification

[0164] The server collects relevant event information based on the user's interests (for example, information on live performances by favorite artists or new movie releases) through web scraping or APIs, and stores the collected event information in a database.

[0165] The server prepares to notify the user of the collected event information at the optimal timing, which is set based on the user's behavioral patterns and past interests.

[0166] The server transmits the event information that is ready for notification to the user terminal.

[0167] The device receives the event information notification, notifies the user via push notification, and automatically adds the event to the calendar.

[0168] Use of location information

[0169] The device acquires location information when the user launches the app or periodically, and sends the acquired location information to the server.

[0170] The server uses the received location information to collect information about nearby events (such as newly opened cafes or special sales at supermarkets) using web scraping and APIs.

[0171] The server transmits the collected event information to the user terminal.

[0172] The device will then send a push notification to the user based on the received information about nearby events.

[0173] Prompt Sentence Examples

[0174] An example of a prompt sentence to input to the generative AI model is as follows:

[0175] "Imagine a system that learns how often a user visits the hair salon on the third Saturday of every month and automatically schedules the next appointment. Include a function that calls the salon's booking API and notifies the user on success."

[0176] The system of the present invention effectively utilizes a user's behavioral history, interests, and location information to provide efficient schedule management and event information. This system allows users to live their daily lives efficiently without missing important information.

[0177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0178] Step 1:

[0179] User: Install the calendar app on the device and register an account the first time you launch it.

[0180] Specific Actions: The user enters their name, email address, password, and interests (e.g., hair salons, live concerts, movies, etc.).

[0181] Input: User information and interest data.

[0182] Output: The registration information is saved on the device.

[0183] Step 2:

[0184] Device: Sends registration information and interest data to a server.

[0185] Specific operation: The terminal sends data to the server via the Internet.

[0186] Input: User information and interest data.

[0187] Output: Received by the server.

[0188] Step 3:

[0189] Server: Stores the received data in a database.

[0190] Specific operation: The server adds user information and interest data to the database.

[0191] Input: User information and interest data.

[0192] Output: The database is updated.

[0193] Step 4:

[0194] Device: Past events entered in the calendar and activity history data are periodically collected and sent to the server.

[0195] Specific operation: The terminal reads past event data and sends it to the server at the set interval.

[0196] Input: Past events and behavioral history data.

[0197] Output: Received by the server.

[0198] Step 5:

[0199] Server: Analyzes the received behavioral history data using machine learning algorithms.

[0200] Specific operation: The server uses analytical software (e.g., Python and scikit-learn) to analyze the data and extract behavioral patterns.

[0201] Input: Behavioral history data.

[0202] Output: A pattern of behavior as a result of learning.

[0203] Step 6:

[0204] Server: Predicts the next hair salon appointment date based on the learning results and calls the hair salon's reservation API according to the predicted date.

[0205] Specific operation: The server analyzes behavioral patterns, determines an appropriate reservation date and time, and calls the reservation API.

[0206] Input: behavioral patterns, reservation API information.

[0207] Output: Booking request.

[0208] Step 7:

[0209] Server: Checks whether the reservation process was successful and creates a reservation completion notification if successful.

[0210] Specific operation: The server checks the response from the reservation API and generates a notification message if successful.

[0211] Input: Booking API response.

[0212] Output: Reservation completion notification.

[0213] Step 8:

[0214] Server: Sends a reservation completion notification to the user's terminal.

[0215] Specific operation: The server sends the generated notification message to the user terminal.

[0216] Input: Reservation completion notification.

[0217] Output: Received by the user terminal.

[0218] Step 9:

[0219] Device: Receives a notification that the reservation is complete and notifies the user via push notification.

[0220] Specific operation: The device uses the push notification function to display a message to the user.

[0221] Input: Reservation completion notification.

[0222] Output: The user is notified.

[0223] Step 10:

[0224] Server: Collects relevant event information based on user interests through web scraping or APIs.

[0225] Specific Operation: The server searches the Internet and collects event information related to your interests.

[0226] Input: Interest data.

[0227] Output: Collected event information.

[0228] Step 11:

[0229] Server: Stores the collected event information in a database.

[0230] Specific operation: The server adds the event information to the database.

[0231] Input: Event information.

[0232] Output: The database is updated.

[0233] Step 12:

[0234] Server: Prepares to notify the user of the collected event information at the optimal time.

[0235] Specific operation: The server calculates the notification timing and generates a notification message for the user.

[0236] Input: Event information, notification timing.

[0237] Output: Informational message.

[0238] Step 13:

[0239] Server: Sends event information that is ready for notification to the user device.

[0240] Specific operation: The server sends the generated notification message to the user terminal.

[0241] Input: Notification message.

[0242] Output: Received by the user terminal.

[0243] Step 14:

[0244] Device: Receives event information notifications and notifies the user via push notifications.

[0245] Specific operation: The device uses the push notification function to display a message to the user.

[0246] Input: Notification message.

[0247] Output: The user is notified.

[0248] Step 15:

[0249] Device: Location information is obtained when the user launches the app and periodically.

[0250] What happens: Your device uses GPS and other location services to get its current location.

[0251] Enter: Location Services.

[0252] Output: The location information obtained.

[0253] Step 16:

[0254] Device: Sends the acquired location information to the server.

[0255] Specific operation: The device sends the acquired location information to the server.

[0256] Input: The acquired location information.

[0257] Output: Received by the server.

[0258] Step 17:

[0259] Server: Collects nearby event information based on the received location information.

[0260] Specific operation: The server searches the Internet and collects nearby event information based on location information.

[0261] Input: Location.

[0262] Output: Collected event information.

[0263] Step 18:

[0264] Server: Sends collected event information to the user's device.

[0265] Specific operation: The server transmits the collected event information to the user terminal.

[0266] Input: Event information.

[0267] Output: Received by the user terminal.

[0268] Step 19:

[0269] Device: Sends push notifications to users based on nearby event information received.

[0270] Specific operation: The device uses the push notification function to display a message to the user.

[0271] Input: Event information.

[0272] Output: The user is notified.

[0273] (Application example 1)

[0274] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0275] Existing calendar applications optimize schedules by utilizing user behavioral history, interests, and location information, but there is also room for application to machine operation management and maintenance management in factory environments. However, current systems lack the functionality to comprehensively manage the operation history and location information of machines in a factory and automatically generate efficient schedules based on that data. Therefore, a solution to improve factory management efficiency is desired.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0277] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting related event information based on interests registered by the user, means for notifying the user based on the collected event information, means for learning the operation history of machines in a factory, and means for automatically creating periodic maintenance schedules based on the learned operation history, thereby enabling efficient management and optimization of operation schedules and maintenance schedules for machines in a factory.

[0278] "Means for learning user behavior history" refers to algorithms and tools that analyze users' past behavioral data and identify trends.

[0279] A "means for automatically creating periodic schedules" is a program or system for automatically generating future schedules for a user or machine based on learned behavioral patterns.

[0280] "Means for collecting related event information based on the interests registered by the user" refers to technology or services that collect event information related to the interests and concerns registered by the user from the Internet.

[0281] The "means for notifying the user based on the collected event information" is a function for providing the collected information to the user as a push notification or an alert.

[0282] "Means for learning the operating history of machines in a factory" refers to algorithms and tools that analyze past operating data of machines in a factory and understand their patterns.

[0283] The "means for automatically creating periodic maintenance schedules" refers to a program or system for automatically generating periodic maintenance schedules based on the learned operating patterns of the machine.

[0284] "Means for acquiring user location information" refers to technologies and devices for measuring a user's current location and movement history.

[0285] The "means for collecting nearby event information based on acquired location information" refers to technology or services for collecting information about events held in the vicinity of the user's current location from the Internet.

[0286] "Means for acquiring location information of machines in a factory" refers to technologies and devices for measuring the current locations of machines and equipment in a factory.

[0287] "Means for collecting event information in a specific area based on acquired location information" refers to technologies and services for collecting event information in that area from the Internet based on acquired machine location information.

[0288] The "means for analyzing a user's past calendar events and inferring behavioral patterns" refers to algorithms or tools for analyzing a user's calendar history and inferring future behavioral patterns.

[0289] "Means for analyzing operation data of machines in a factory and estimating operation patterns" refers to algorithms and tools for analyzing past operation data of machines in a factory and estimating future operation patterns.

[0290] The system of the present invention is composed of a user terminal and a server, and is designed to optimize the operation history and maintenance schedule of machines in a factory.

[0291] Overall system configuration

[0292] The system for implementing the invention consists of a factory robot, a smartphone or tablet as a user terminal, and a cloud server. The user terminal collects machine operation data and location information, and the server stores and analyzes this data to generate an optimal schedule.

[0293] User registration and initial settings

[0294] The factory manager first installs the calendar application and registers an account. Next, he or she registers the role, operation pattern, and regular maintenance information of each machine in the factory on the cloud server. This secures basic management information.

[0295] Analysis of behavioral history

[0296] The terminal collects the operation history data of the machines in the factory and sends it to the server. The server analyzes the received operation history data and learns the operation patterns of each machine. For example, there are machines that perform assembly work for eight hours every day, and machines that require regular maintenance once a month.

[0297] Creating an Automatic Schedule

[0298] Based on the analysis results, the server automatically generates future operation and maintenance schedules for each machine and notifies factory managers and maintenance staff, thereby optimizing and streamlining work.

[0299] Event information collection and notification

[0300] The server collects information about special events and new work instructions within the factory and sends push notifications to the user's device, enabling a prompt response.

[0301] Use of location information

[0302] The terminal periodically acquires the machine's location information and sends it to the server, which then collects event information for a specific area based on the acquired location information, supporting effective work allocation and anomaly detection.

[0303] Specific examples

[0304] Maintenance Schedule Example

[0305] The server studies the operating history of machines in the factory and detects patterns that indicate monthly maintenance is required. It predicts the timing of the next maintenance and notifies the maintenance staff. When the scheduled maintenance date approaches, a push notification is sent stating, "The next maintenance is scheduled for XX month."

[0306] Example of anomaly detection in a specific area

[0307] The terminal sends the machine's location information to the server and detects abnormal events in a specific area (e.g., abnormal machine shutdown) in real time. The server collects the abnormality information and notifies the maintenance staff that "an abnormality has been detected in a specific area."

[0308] Hardware and software used

[0309] Hardware:

[0310] Factory robots

[0311] Wi-Fi network in the factory

[0312] Smartphones and tablets

[0313] software:

[0314] Cloud servers (e.g. AWS, Google Cloud)

[0315] REST API

[0316] Push notification system for smart devices (e.g., Firebase Cloud Messaging)

[0317] Prompt Sentence Examples

[0318] The factory robot "R123" performs assembly work for 8 hours every day and requires maintenance once a month. Based on past operation data, generate an optimal maintenance schedule and create notification messages for maintenance staff.

[0319] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0320] Step 1:

[0321] The user installs a calendar application and registers an account. The input information is the user's basic information and the roles and operating patterns of machines in the factory. This information is sent to a cloud server, and the basic data of the user and machine is stored in a database. The output is the registered user account and machine data.

[0322] Step 2:

[0323] The terminal collects the operation history data of machines in the factory in real time. The input information is the operation data of each machine (operation start time, end time, stop time, etc.). This data is sent to a cloud server, which stores the operation history data in a database. The output is the stored operation history data.

[0324] Step 3:

[0325] The server analyzes the collected operation history data and learns the operation patterns of each machine. The input is the saved operation history data. An analytical algorithm is used to extract the machine's operation pattern (e.g., 8 hours of operation every day, maintenance once a month). The output is the learned operation pattern information.

[0326] Step 4:

[0327] The server automatically creates future operation and maintenance schedules based on the learned operation patterns. The input is the learned operation pattern information. A scheduling algorithm is used to predict the next maintenance timing and generate the schedule. The output is the generated future schedule.

[0328] Step 5:

[0329] The server notifies the generated future schedule to the factory manager or maintenance staff. The input is the generated future schedule. A push notification system is used to send detailed information about the schedule to smartphones or tablets. The output is the notified schedule information.

[0330] Step 6:

[0331] The terminal periodically acquires the machine's location information and sends it to the server. The input is the machine's location data. The location information is sent to the cloud server, which stores the information in a database. The output is the stored location information.

[0332] Step 7:

[0333] The server collects event information for a specific area based on the acquired location information. The input is the saved location information. Event information (e.g., abnormal shutdowns, emergencies, etc.) for the specific area is collected using web scraping or APIs. The output is the collected event information.

[0334] Step 8:

[0335] The server notifies the maintenance staff of the collected event information. The input is the collected event information. A push notification system is used to notify the smart device of abnormal information in real time. The output is the notified event information.

[0336] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0337] Overall system configuration

[0338] The system of the present invention is implemented as a calendar application consisting of a user terminal, a server, and an emotion engine. The user terminal is a device such as a smartphone or tablet, and the server is located on the cloud and stores and analyzes user information and event data. The emotion engine recognizes the user's emotions and provides recommended information and schedule adjustments based on the user's emotional state.

[0339] User registration and initial settings

[0340] User: To use the system, the user first installs the calendar app and registers an account when the system is first launched. When registering, the user enters their interests (e.g., hair salons, live concerts, movies, etc.).

[0341] Device: Once you have completed your account registration and entered your interests, that information will be sent to the server.

[0342] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[0343] Analysis of behavioral history

[0344] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[0345] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if it detects that the user goes to the hair salon on the third Saturday of every month, it automatically generates future regular schedules based on this information.

[0346] Creating an Automatic Schedule

[0347] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[0348] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[0349] On the device: The user will be notified via push notification that the reservation has been completed.

[0350] Event information collection and notification

[0351] Server: Based on the user's interests, collects relevant event information (for example, information about live performances by favorite artists or new movie releases), and prepares to notify the user at the optimal time based on the collected event information.

[0352] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[0353] Use of location information

[0354] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[0355] Server: Collects information about nearby events (such as newly opened cafes or special sales at supermarkets) based on location information. Sends the collected event information to the user's device.

[0356] Device: Sends push notifications to users based on nearby event information received.

[0357] Implementing the Emotion Engine

[0358] Terminal: Collects data to analyze the user's emotions through daily events and input data, and sends it to the emotion engine.

[0359] Emotion engine: Analyzes the user's emotional data and estimates their emotional state in real time, using, for example, text analysis, voice analysis, and facial expression analysis.

[0360] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it can suggest relaxation events when the user is under high stress.

[0361] Specific examples

[0362] Hair salon reservation example

[0363] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[0364] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[0365] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[0366] Concert ticket purchase example

[0367] User: Register a favorite artist (e.g., a particular musician) as a topic.

[0368] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[0369] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[0370] Emotion Engine: If a user has a strong emotional connection to this artist, they can receive special reminders or early notifications of ticket sales.

[0371] Example of supermarket sale information

[0372] Device: Sends the user's location information to the server.

[0373] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[0374] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[0375] Emotion engine: If a user responds positively to a sale, the system will prioritize notifications of similar sales.

[0376] This concludes the "Mode for Carrying Out the Invention" of the system of the present invention. This system not only utilizes the user's behavioral history, interests, and location information, but also combines it with an emotion engine to achieve more personalized schedule management and support the user's daily life.

[0377] The processing flow will be explained below.

[0378] Basic processing steps of the system

[0379] Step 1:

[0380] User: Installs the calendar app on their smartphone and launches it for the first time.

[0381] Step 2:

[0382] On device: The app prompts the user to sign up for an account and asks them to enter topics or keywords of interest.

[0383] Step 3:

[0384] Users: Enter topics of interest such as hair salons, live concerts, movies, etc., and submit them along with their account information.

[0385] Step 4:

[0386] Terminal: Stores the entered information in local storage and prepares it for transmission to the server.

[0387] Step 5:

[0388] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[0389] Analysis of behavioral history

[0390] Step 6:

[0391] Device: Collects the user's past calendar events and behavioral history data and sends them to the server.

[0392] Step 7:

[0393] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns.

[0394] Creating an Automatic Schedule

[0395] Step 8:

[0396] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[0397] Step 9:

[0398] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[0399] Step 10:

[0400] On the device: The user will be notified via push notification that the reservation has been completed.

[0401] Event information collection and notification

[0402] Step 11:

[0403] Server: Collects relevant event information based on the user's interests (for example, information about live performances by favorite artists or new movie releases).

[0404] Step 12:

[0405] Server: Based on the collected event information, selects the information to notify the user at the optimal time.

[0406] Step 13:

[0407] Server: Sends selected event information to the device.

[0408] Step 14:

[0409] On the device: Sends push notifications to users based on the received event information and automatically adds events to their calendars.

[0410] Acquisition and use of location information

[0411] Step 15:

[0412] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[0413] Step 16:

[0414] Server: Collects and analyzes information about nearby events based on location information.

[0415] Step 17:

[0416] Server: Selects the collected nearby event information and creates notification information.

[0417] Step 18:

[0418] Device: Sends push notifications to users based on nearby event information received.

[0419] Emotion Recognition and Applications

[0420] Step 19:

[0421] Terminal: Collects data to analyze the user's emotions through daily events and data entered by the user (emails, chat logs, etc.) and sends it to the server.

[0422] Step 20:

[0423] Server: Sends the received emotion data to the emotion engine for analysis.

[0424] Step 21:

[0425] Emotion Engine: Analyzes the user's emotional state and classifies it as positive, negative, or stressful.

[0426] Step 22:

[0427] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it suggests relaxation events when stress levels are high.

[0428] Specific examples

[0429] Hair salon reservation example

[0430] Step 1:

[0431] Server: From the user's behavioral history, it learns that the user visits the beauty salon on the third Saturday of every month.

[0432] Step 2:

[0433] Server: When the next third Saturday approaches, make a reservation through the hair salon booking API.

[0434] Step 3:

[0435] Server: After the reservation is completed, create a notification message and send it to the device.

[0436] Step 4:

[0437] Device: A push notification will be sent to the user upon receiving the reservation completion notification.

[0438] Step 5:

[0439] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[0440] Concert ticket purchase example

[0441] Step 1:

[0442] User: Register a favorite artist (e.g., a particular musician) as a topic.

[0443] Step 2:

[0444] Server: Collects concert information for the artist in question and checks when tickets go on sale.

[0445] Step 3:

[0446] Server: Prepare information to notify users as the sale date approaches.

[0447] Step 4:

[0448] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[0449] Step 5:

[0450] Emotion Engine: If a user has a strong emotional connection to this artist, they will receive special reminders or early notifications of ticket sales.

[0451] Example of supermarket sale information

[0452] Step 1:

[0453] Device: Sends the user's location information to the server.

[0454] Step 2:

[0455] Server: Collects information about special offers at nearby supermarkets based on location information.

[0456] Step 3:

[0457] Server: Creates notification information based on the results of collection and analysis.

[0458] Step 4:

[0459] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[0460] Step 5:

[0461] Emotion engine: If a user responds positively to a sale, the engine will prioritize notifications of similar sales.

[0462] The above is the specific processing flow of the system of the present invention. By combining it with an emotion engine, personalized schedule management based on the user's emotional state becomes possible, making the user's daily life more fulfilling.

[0463] Example 2

[0464] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0465] In recent years, schedule management applications using mobile devices such as smartphones and tablets have become widespread. However, conventional calendar and scheduling applications do not adequately provide personalized schedule management that takes into account a user's behavioral history, interests, location information, and even emotional state. This results in a problem in that truly useful schedule suggestions and notifications cannot be realized. Furthermore, because schedules are not adjusted according to the user's emotional state, suggestions for stress management and relaxation are insufficient. The present invention aims to solve these problems.

[0466] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0467] In this invention, the server includes a means for learning a user's behavioral history, a means for automatically creating a recurring schedule based on the learned behavioral history, and a means for collecting related event information based on the user's registered interests. This allows the server to learn the user's behavioral patterns and automatically generate a recurring schedule based on them. Furthermore, event information based on the user's interests can be collected and notified, enabling appropriate event suggestions to be made to the user. Furthermore, by analyzing the user's emotional data and adjusting the schedule and notification content based on the user's emotional state, relaxation events tailored to the user's emotional state can be suggested and flexible schedule management can be realized.

[0468] "User" refers to a person who uses this system, and is a person who uses a mobile device such as a smartphone or tablet.

[0469] "Action history" refers to records of events such as calendar entries and event participation made by a user in the past.

[0470] "Means of learning" refers to algorithms or programs that analyze collected user behavior history data and identify certain patterns and trends.

[0471] The "means for automatically creating periodic schedules" is a program that has the function of automatically generating future schedules for the user based on learned behavioral patterns.

[0472] "Interests" refers to topics or areas of interest registered by a user (e.g., hair salons, live music, movies, etc.).

[0473] The "means for collecting relevant event information" is a program for collecting event data from the Internet and other sources based on the user's interests.

[0474] The "means for notifying the user" is a program that has the function of notifying the user of collected event information and generated schedules via push notifications or other methods on the user's device.

[0475] "Location information" refers to data related to a user's current location and movement history.

[0476] The means for collecting "neighborhood event information" is a program for acquiring information about events and facilities held in the vicinity based on the user's current location.

[0477] "Emotional data" refers to data that indicates the user's emotional state (e.g., stress level, happiness level, etc.), and is obtained through text analysis, voice analysis, facial expression analysis, etc.

[0478] "Means for adjusting schedules and notification content based on emotional state" refers to algorithms or programs for dynamically changing a user's schedule and notification content based on emotional data.

[0479] Overall system configuration

[0480] The system of the present invention is implemented as a calendar management application consisting of a user terminal, a server, and an emotion engine. The user terminal includes mobile devices such as smartphones and tablets. The server is located on the cloud and stores and analyzes user information and event data. The emotion engine recognizes the user's emotions and makes recommended information and schedule adjustments based on the user's emotional state.

[0481] User registration and initial settings

[0482] User: To use the system, after installing the calendar app, users must register an account the first time they start the app by entering their username, email address, password, and interests (e.g., hair salons, live concerts, movies, etc.).

[0483] Device: Once you have registered and entered your interests, this information is sent to our server.

[0484] Server: The server stores the received user information and interest data in a database and prepares it for analysis.

[0485] Analysis of behavioral history

[0486] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[0487] Server: The server analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if the server detects that the user visits the beauty salon on the third Saturday of every month, it will automatically generate future regular schedules based on this information.

[0488] Creating an Automatic Schedule

[0489] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[0490] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[0491] On the device: The user will be notified via push notification that the reservation has been completed.

[0492] Event information collection and notification

[0493] Server: Collects relevant event information based on the user's interests (e.g., information about live performances by favorite artists or new movie releases). Prepares notifications based on the collected event information.

[0494] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[0495] Use of location information

[0496] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[0497] Server: Based on location information, collects information about nearby events (e.g., information about newly opened cafes or special sales at supermarkets) and sends the collected event information to the user's device.

[0498] Device: Sends push notifications to users based on nearby event information received.

[0499] Implementing the Emotion Engine

[0500] Terminal: Collects data to analyze the user's emotions through daily events and input data, and sends it to the emotion engine.

[0501] Emotion engine: The emotion engine analyzes the user's emotional data and estimates their emotional state in real time, for example, by using text analysis, voice analysis, and facial expression analysis.

[0502] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it can suggest relaxation events when the user is under high stress.

[0503] Specific examples

[0504] Hair salon reservation example

[0505] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[0506] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[0507] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[0508] Concert ticket purchase example

[0509] User: Register a favorite artist (e.g., a specific musician) as a topic.

[0510] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[0511] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[0512] Emotion Engine: If a user has a strong emotional connection to this artist, they can receive special reminders or early notifications of ticket sales.

[0513] Example of supermarket sale information

[0514] Device: Sends the user's location information to the server.

[0515] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[0516] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[0517] Emotion engine: If a user responds positively to a sale, the system will prioritize notifications of similar sales.

[0518] Example prompts for generative AI models

[0519] 1. "Please explain the algorithm of a hair salon reservation system."

[0520] 2. "Please give a specific example of how the emotion engine detects a user's stress level."

[0521] 3. "Please explain the structure of a system that uses location information to notify users of nearby events."

[0522] This concludes the description of the embodiment of the present invention. By utilizing the user's behavioral history, interests, and location information, and by combining it with an emotion engine, it is possible to achieve more personalized schedule management and support the user's daily life.

[0523] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0524] Step 1:

[0525] The user installs the Calendar app and launches it for the first time.

[0526] Input: A user installs a calendar app on their smartphone or tablet.

[0527] Output: The app launches for the first time and the account registration screen appears.

[0528] What happens: A user downloads an app from the app store, installs it, and then launches it.

[0529] Step 2:

[0530] The user registers an account.

[0531] Input: The user enters their account information (username, email address, password).

[0532] Output: Account information is registered in the app.

[0533] Specific behavior: The user fills in the required information in the provided form and clicks the "Register" button.

[0534] Step 3:

[0535] The user enters their interests.

[0536] Input: User selects or inputs an interest (e.g., hair salon, live concert, movie, etc.).

[0537] Output: Interest data is stored on the device.

[0538] What happens: The user selects an interest from a list and taps the "Save" button on the screen.

[0539] Step 4:

[0540] The terminal transmits the user information to the server.

[0541] Input: Registered account information and interest data.

[0542] Output: User information is saved on the server.

[0543] Specific operation: The device sends data to the server via the Internet.

[0544] Step 5:

[0545] The server stores the user information in a database.

[0546] Input: User information and interest data sent from your device.

[0547] Output: User information stored in the database.

[0548] What happens: The server parses the received data and stores it in the appropriate fields in the database.

[0549] Step 6:

[0550] The device collects past calendar event data and sends it to the server.

[0551] Input: Past calendar event data stored on your device.

[0552] Output: Behavioral history data sent to the server.

[0553] Specific operation: The device reads past event information from the local calendar database and sends it to the server.

[0554] Step 7:

[0555] The server analyzes behavioral history data and learns behavioral patterns.

[0556] Input: Past calendar event data.

[0557] Output: Learned behavioral patterns.

[0558] How it works: The server uses an AI model to analyze behavioral data and learn certain behavioral patterns (e.g., going to the hairdresser on the third Saturday of every month).

[0559] Step 8:

[0560] The server automatically generates a recurring schedule based on the learning results.

[0561] Input: Learned behavioral patterns.

[0562] Output: Recurring appointment data.

[0563] Specific operation: Based on the behavioral patterns learned by the AI ​​model, the system generates the user's next appointment and confirms it via the salon's booking API.

[0564] Step 9:

[0565] The server creates a reservation completion notice and sends it to the user terminal.

[0566] Input: Reservation confirmation information.

[0567] Output: Reservation completion notification.

[0568] Specific operation: The server generates a reservation completion message based on the reservation information and sends it to the user's specified terminal.

[0569] Step 10:

[0570] The device will send a push notification of reservation completion to the user.

[0571] Input: Reservation completion notification information.

[0572] Output: Push notification to the user.

[0573] Specific operation: The device displays the received notification information as a push notification to notify the user.

[0574] Step 11:

[0575] The server collects relevant event information and prepares notifications.

[0576] Input: User interest data.

[0577] Output: Related event information.

[0578] Specific operation: The server filters relevant event information collected from the Internet and selects information appropriate for the user.

[0579] Step 12:

[0580] The device receives the notification information and pushes it to the user.

[0581] Input: Relevant event notification information.

[0582] Output: Push notification to the user.

[0583] Specific operation: The device displays the received notification information as a push notification to notify the user.

[0584] Step 13:

[0585] The device periodically obtains location information or when the app is launched and sends it to the server.

[0586] Input: Location data.

[0587] Output: The location sent to the server.

[0588] Specific operation: The device uses the GPS function to obtain its current location and sends it to a server via the Internet.

[0589] Step 14:

[0590] The server collects nearby event information based on location information.

[0591] Input: Location data.

[0592] Output: Nearby event information.

[0593] Specific operation: The server collects event information for the specified area from the Internet based on the location information.

[0594] Step 15:

[0595] The server transmits the collected event information to the user terminal.

[0596] Input: Nearby event information.

[0597] Output: Event information notification.

[0598] Specific operation: The server sends filtered nearby event information to the user's device via the Internet.

[0599] Step 16:

[0600] The device sends push notifications to users based on nearby event information received.

[0601] Input: Nearby event information.

[0602] Output: Push notification to the user.

[0603] Specific operation: The device notifies the user of the event information received via a push notification.

[0604] Step 17:

[0605] The terminal collects the user's emotion data and sends it to the emotion engine.

[0606] Input: User text, voice, and facial expression data.

[0607] Output: Emotion data.

[0608] Specific operation: The device analyzes the user's input data (e.g., diary, voice memo) and sends it to the emotion engine.

[0609] Step 18:

[0610] The emotion engine analyzes the emotion data and estimates the emotional state.

[0611] Input: User emotion data.

[0612] Output: Estimated emotional state.

[0613] What it does: The emotion engine performs text analysis, speech analysis, and facial expression analysis to estimate emotional states (e.g., stress, joy).

[0614] Step 19:

[0615] The server adjusts schedules and notification content based on emotional state data.

[0616] Input: Estimated emotional state.

[0617] Output: Adjusted schedule and notification content.

[0618] Specific operation: Based on the data from the emotion engine, the server suggests relaxation events and changes task priorities, and notifies the user.

[0619] (Application example 2)

[0620] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0621] Conventional calendar applications have limited ability to manage personalized schedules based on user behavior and interests, and they also lack the ability to flexibly adjust schedules based on the user's emotional state or location information. Therefore, there is a need for an effective means to enrich users' daily lives and in-store experiences.

[0622] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0623] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting related event information based on the interests registered by the user, means for notifying the user based on the collected event information, emotion analysis means for analyzing the user's emotions, and means for providing suggested information based on the emotion analysis results. This makes it possible to provide optimal schedules and suggested information in real time based on the user's behavioral history and interests as well as their emotional state and location information.

[0624] "Behavioral history" refers to a record of a user's past activities and events.

[0625] A "regular appointment" refers to a schedule that occurs periodically based on the user's behavioral patterns.

[0626] "Interests" refer to the themes, categories, and activities in which a user is particularly interested.

[0627] "Event information" refers to detailed information about events and activities related to the user's interests.

[0628] "Notification" refers to an alert or message that conveys information to a user.

[0629] "Emotion analysis" refers to the process for analyzing and inferring a user's emotional state.

[0630] "Suggested information" refers to recommendations and actions provided to users based on sentiment analysis, behavioral history, and interests.

[0631] "Location information" refers to geographic data that indicates the user's current location.

[0632] This invention provides personalized schedules and recommended information based on a user's behavioral history, interests, emotional state, and location information. The main components include a user terminal, a cloud server, and an emotion analysis engine. Specifically, this system is implemented as follows:

[0633] User terminal

[0634] User devices are primarily smartphones and smart glasses, which collect user behavioral history and location information. These devices are operated by users downloading apps and registering their interests through the initial setup. For example, apps installed on smartphones have the ability to routinely collect user location information and send it to a server.

[0635] Cloud Server

[0636] The cloud server stores and analyzes users' behavioral history, interests, and location information. The generated data is used to learn each user's patterns and automatically create recurring schedules. The cloud server also collects external event information and generates notifications at optimal times based on the user's interests.

[0637] Sentiment Analysis Engine

[0638] The emotion analysis engine analyzes the user's emotional state in real time. Using technologies such as text analysis, voice analysis, and facial expression analysis, it estimates whether the user is stressed or relaxed. Based on this emotional state, the cloud server generates suggested information appropriate for the user and notifies the device. For example, if the emotion analysis engine detects that the user is stressed, it will prioritize notifying the user of information about nearby relaxation facilities.

[0639] Specific examples

[0640] 1. Hair salon reservation example

[0641] The server learns from the user's behavioral history that they visit the hair salon on the third Saturday of every month, and makes the same reservation the next time. It calls the hair salon's reservation API and automatically completes the reservation. The device sends a reservation completion notification to the user, informing them that "your next hair salon reservation has been completed."

[0642] 2. Example of store navigation based on emotional state

[0643] If the emotion analysis engine detects that the user's stress level is high, the cloud server collects information about nearby relaxation facilities and discounts, and notifies the user via push notification, saying, "There is a relaxation facility nearby."

[0644] Prompt Sentence Examples

[0645] The following prompt sentence is used as input to the generative AI model:

[0646] User ID: 12345

[0647] Emotional state: Stress

[0648] Location: Latitude 35.6895, Longitude 139.6917

[0649] Interests: [Fashion, Beauty, Music]

[0650] Q: Can you recommend an event or service that would be suitable for this user?

[0651] This invention makes it possible to provide optimal schedules and suggested information in real time based on a user's behavioral history and interests as well as emotional state and location information.

[0652] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0653] Step 1: The user installs the calendar app and sets it up.

[0654] Input: User installs of the app, interest registrations.

[0655] How it works: A user installs a calendar app on their smartphone or smart glasses and registers their interests (e.g., fashion, beauty, music).

[0656] Output: Registered user information and interest data are sent to the cloud server.

[0657] Step 2: The user device collects behavioral history data and sends it to the server.

[0658] Inputs: Events and past actions that users enter into their calendar.

[0659] How it works: The user's device periodically collects event data entered in the calendar and sends it to a cloud server.

[0660] Output: The collected behavioral history data is stored on a cloud server.

[0661] Step 3: The cloud server learns behavioral patterns and automatically creates recurring schedules.

[0662] Input: Behavioral history data stored on the server.

[0663] How it works: The server uses machine learning algorithms to analyze the user's behavioral patterns and automatically create future recurring appointments (e.g., a hair salon appointment on the third Saturday of every month).

[0664] Output: The automatically generated recurring events are reflected in the calendar.

[0665] Step 4: The emotion analysis engine analyzes the user's emotional state in real time.

[0666] Input: Data required for emotion analysis, such as text data, audio data, and facial expression data.

[0667] How it works: The emotion analysis engine analyzes this data and estimates the user's emotional state (e.g., high stress level, relaxed, etc.).

[0668] Output: The estimated emotional state is sent to the cloud server.

[0669] Step 5: The cloud server collects appropriate event information based on the emotional state and location information.

[0670] Input: User's emotional state data, current location.

[0671] How it works: The cloud server accesses an external event information API and collects event information related to the user's interests. If the emotional state is stressed, it will prioritize collecting relaxation events.

[0672] Output: The collected event information is stored on the server in an appropriate format.

[0673] Step 6: The cloud server generates recommendation information and notifies the user device.

[0674] Input: Emotional state data, location information, collected event information.

[0675] How it works: The server uses a recommendation algorithm to select the most suitable event information for the user and generate a notification message, such as "There are currently relaxation facilities nearby."

[0676] Output: A notification message is sent to the user's terminal.

[0677] Step 7: The user terminal receives the notification and displays the notification to the user.

[0678] Input: The notification message sent by the server.

[0679] How it works: Notifications are pushed to the user's smartphone or smart glasses and appear as pop-ups or alerts.

[0680] Output: The user can confirm the notification.

[0681] This series of processing steps allows users to receive personalized schedule and event recommendations based on their behavioral history, interests, emotional state, and location.

[0682] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0683] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0684] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0685] [Second embodiment]

[0686] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0687] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0688] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0689] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0690] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0691] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0692] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0693] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0694] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0695] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0696] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0697] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0698] Overall system configuration

[0699] The system of the present invention is implemented as a calendar application consisting of a user terminal and a server. The user terminal is a device such as a smartphone or tablet, and the server is located on the cloud and stores and analyzes user information and event data.

[0700] User registration and initial settings

[0701] User: To use the system, the user first installs the calendar app and registers an account when the system is first launched. When registering, the user enters their interests (e.g., hair salons, live concerts, movies, etc.).

[0702] Device: Once you have completed your account registration and entered your interests, that information will be sent to the server.

[0703] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[0704] Analysis of behavioral history

[0705] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[0706] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if it detects that the user goes to the hair salon on the third Saturday of every month, it automatically generates future regular schedules based on this information.

[0707] Creating an Automatic Schedule

[0708] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[0709] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[0710] On the device: The user will be notified via push notification that the reservation has been completed.

[0711] Event information collection and notification

[0712] Server: Based on the user's interests, collects relevant event information (for example, information about live performances by favorite artists or new movie releases), and prepares to notify the user at the optimal time based on the collected event information.

[0713] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[0714] Use of location information

[0715] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[0716] Server: Collects information about nearby events (such as newly opened cafes or special sales at supermarkets) based on location information. Sends the collected event information to the user's device.

[0717] Device: Sends push notifications to users based on nearby event information received.

[0718] Specific examples

[0719] Hair salon reservation example

[0720] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[0721] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[0722] Concert ticket purchase example

[0723] User: Register a favorite artist (e.g., a particular musician) as a topic.

[0724] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[0725] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[0726] Example of supermarket sale information

[0727] Device: Sends the user's location information to the server.

[0728] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[0729] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[0730] This concludes the "Mode for Carrying Out the Invention" of the system of the present invention. This system utilizes the user's behavioral history, interests, and location information to realize efficient schedule management and support the user's daily life.

[0731] The processing flow will be explained below.

[0732] Step 1:

[0733] User: Installs the calendar app on their smartphone and launches it for the first time.

[0734] Step 2:

[0735] On device: The app prompts the user to sign up for an account and asks them to enter topics or keywords of interest.

[0736] Step 3:

[0737] Users: Enter topics of interest such as hair salons, live concerts, movies, etc., and submit them along with their account information.

[0738] Step 4:

[0739] Terminal: Stores the entered information in local storage and prepares it for transmission to the server.

[0740] Step 5:

[0741] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[0742] Step 6:

[0743] Device: Collects the user's past calendar events and behavioral history data and sends them to the server.

[0744] Step 7:

[0745] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns.

[0746] Step 8:

[0747] Server: Automatically generates regular schedules for users based on the learning results. For example, predict the date of a hair salon appointment and call the reservation API to make the appointment.

[0748] Step 9:

[0749] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[0750] Step 10:

[0751] On the device: The user will be notified via push notification that the reservation has been completed.

[0752] Step 11:

[0753] Server: Collects and analyzes relevant event information based on user interests.

[0754] Step 12:

[0755] Server: Based on the analysis results, selects the event information to notify the user at the optimal time.

[0756] Step 13:

[0757] Server: Sends selected event information to the device.

[0758] Step 14:

[0759] On the device: Sends push notifications to users based on the received event information and automatically adds events to their calendars.

[0760] Step 15:

[0761] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[0762] Step 16:

[0763] Server: Collects and analyzes information about nearby events based on location information.

[0764] Step 17:

[0765] Server: Based on the collected and analyzed results, it creates event information to notify the user and sends it to the terminal.

[0766] Step 18:

[0767] Device: Sends push notifications to users based on nearby event information received.

[0768] Specific examples

[0769] Hair salon reservation example

[0770] Step 1:

[0771] Server: Analyzes the user's past hair salon reservation history and learns that the user visits the hair salon on the third Saturday of every month.

[0772] Step 2:

[0773] Server: When the next third Saturday approaches, make a reservation through the hair salon booking API.

[0774] Step 3:

[0775] Server: After the reservation is completed, create a notification message and send it to the device.

[0776] Step 4:

[0777] Device: A push notification will be sent to the user upon receiving the reservation completion notification.

[0778] Concert ticket purchase example

[0779] Step 1:

[0780] User: Register a favorite artist (e.g., a particular musician) as a topic.

[0781] Step 2:

[0782] Server: Collects concert information for the artist in question and checks when tickets go on sale.

[0783] Step 3:

[0784] Server: Prepare information to notify users as the sale date approaches.

[0785] Step 4:

[0786] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[0787] Example of supermarket sale information

[0788] Step 1:

[0789] Device: Sends the user's location information to the server.

[0790] Step 2:

[0791] Server: Collects information about special offers at nearby supermarkets based on location information.

[0792] Step 3:

[0793] Server: Analyzes sale information and creates notification information.

[0794] Step 4:

[0795] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[0796] Example 1

[0797] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0798] In modern society, users need to manage their busy schedules. However, conventional schedule management systems lacked the ability to respond individually to users' behavioral patterns and interests, requiring a great deal of time and effort for manual input and management. Furthermore, they lacked mechanisms for efficiently collecting and notifying users of nearby event information and service reservation information. This often led to users missing important information, reducing the efficiency and convenience of schedule management.

[0799] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0800] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting relevant event information based on the user's registered interests, means for notifying the user based on the collected event information, means for collecting event information of interest based on location information transmitted from the user terminal, means for notifying the user at an optimal timing based on the acquired location information and interest information, means for automatically making a reservation by calling a service reservation API for a beauty salon or the like, means for confirming the success of the reservation and, if successful, sending a reservation completion notification to the user terminal, means for analyzing past calendar events and estimating behavioral patterns, means for automatically creating future schedules based on the estimated behavioral patterns, and means for notifying the user of detailed schedule information. This allows users to manage their schedules individually and efficiently live their daily lives without missing important information.

[0801] "User" refers to a person who uses the system and receives services through a terminal.

[0802] "Behavioral history" refers to a record of actions and events that a user has taken in the past, including information entered into a schedule or calendar.

[0803] A "recurring appointment" refers to a regular schedule or reservation that is automatically generated based on a user's behavioral patterns.

[0804] "Interests" refers to the types of things or events that a user is interested in, such as hair salons, live concerts, movies, etc.

[0805] "Event information" refers to detailed information about an event related to the user's interests, including the date, time, location, and content.

[0806] "Location Information" means geographic location data obtained by a User's Device, such as data obtained using GPS or other technologies.

[0807] "Booking API" means an application programming interface for making reservations for certain services over the Internet.

[0808] "Push notification" refers to a communication method that transmits information from a server to a user's device in real time, allowing the user to be notified immediately.

[0809] "Behavioral patterns" refer to certain behavioral tendencies and habits that are estimated based on a user's past behavioral history.

[0810] "Machine learning algorithms" refer to computer algorithms that analyze data, learn, and automatically recognize patterns.

[0811] The system of the present invention is implemented as a calendar application using a user terminal such as a smartphone or tablet, and a server located on the cloud, which stores and analyzes user information and event data.

[0812] User registration and initial settings

[0813] To use the system, users first install the calendar app on their device and register an account when they first launch the system. When registering, users input their interests (e.g., beauty salons, live concerts, movies, etc.).

[0814] The terminal transmits the registration information and interest data to the server.

[0815] The server stores the received data in a database for future analysis.

[0816] Analysis of behavioral history

[0817] The device periodically collects events and behavioral history data previously entered in the calendar and sends it to the server.

[0818] The server then uses a machine learning algorithm to analyze the received behavioral history data. For example, it can detect a pattern where a user visits a beauty salon on the third Saturday of every month. The learning results are stored in a database and used for future predictions.

[0819] Creating an Automatic Schedule

[0820] The server predicts the next appointment date for the hair salon based on the learning results, calls the salon's reservation API according to the predicted date, checks whether the reservation process was successful, and creates a reservation completion notification if it was successful.

[0821] The server sends a reservation completion notification to the user terminal.

[0822] The terminal receives the reservation completion notification and notifies the user via push notification.

[0823] Event information collection and notification

[0824] The server collects relevant event information based on the user's interests (for example, information on live performances by favorite artists or new movie releases) through web scraping or APIs, and stores the collected event information in a database.

[0825] The server prepares to notify the user of the collected event information at the optimal timing, which is set based on the user's behavioral patterns and past interests.

[0826] The server transmits the event information that is ready for notification to the user terminal.

[0827] The device receives the event information notification, notifies the user via push notification, and automatically adds the event to the calendar.

[0828] Use of location information

[0829] The device acquires location information when the user launches the app or periodically, and sends the acquired location information to the server.

[0830] The server uses the received location information to collect information about nearby events (such as newly opened cafes or special sales at supermarkets) using web scraping and APIs.

[0831] The server transmits the collected event information to the user terminal.

[0832] The device will then send a push notification to the user based on the received information about nearby events.

[0833] Prompt Sentence Examples

[0834] An example of a prompt sentence to input to the generative AI model is as follows:

[0835] "Imagine a system that learns how often a user visits the hair salon on the third Saturday of every month and automatically schedules the next appointment. Include a function that calls the salon's booking API and notifies the user on success."

[0836] The system of the present invention effectively utilizes a user's behavioral history, interests, and location information to provide efficient schedule management and event information. This system allows users to live their daily lives efficiently without missing important information.

[0837] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0838] Step 1:

[0839] User: Install the calendar app on the device and register an account the first time you launch it.

[0840] Specific Actions: The user enters their name, email address, password, and interests (e.g., hair salons, live concerts, movies, etc.).

[0841] Input: User information and interest data.

[0842] Output: The registration information is saved on the device.

[0843] Step 2:

[0844] Device: Sends registration information and interest data to a server.

[0845] Specific operation: The terminal sends data to the server via the Internet.

[0846] Input: User information and interest data.

[0847] Output: Received by the server.

[0848] Step 3:

[0849] Server: Stores the received data in a database.

[0850] Specific operation: The server adds user information and interest data to the database.

[0851] Input: User information and interest data.

[0852] Output: The database is updated.

[0853] Step 4:

[0854] Device: Past events entered in the calendar and activity history data are periodically collected and sent to the server.

[0855] Specific operation: The terminal reads past event data and sends it to the server at the set interval.

[0856] Input: Past events and behavioral history data.

[0857] Output: Received by the server.

[0858] Step 5:

[0859] Server: Analyzes the received behavioral history data using machine learning algorithms.

[0860] Specific operation: The server uses analytical software (e.g., Python and scikit-learn) to analyze the data and extract behavioral patterns.

[0861] Input: Behavioral history data.

[0862] Output: A pattern of behavior as a result of learning.

[0863] Step 6:

[0864] Server: Predicts the next hair salon appointment date based on the learning results and calls the hair salon's reservation API according to the predicted date.

[0865] Specific operation: The server analyzes behavioral patterns, determines an appropriate reservation date and time, and calls the reservation API.

[0866] Input: behavioral patterns, reservation API information.

[0867] Output: Booking request.

[0868] Step 7:

[0869] Server: Checks whether the reservation process was successful and creates a reservation completion notification if successful.

[0870] Specific operation: The server checks the response from the reservation API and generates a notification message if successful.

[0871] Input: Booking API response.

[0872] Output: Reservation completion notification.

[0873] Step 8:

[0874] Server: Sends a reservation completion notification to the user's terminal.

[0875] Specific operation: The server sends the generated notification message to the user terminal.

[0876] Input: Reservation completion notification.

[0877] Output: Received by the user terminal.

[0878] Step 9:

[0879] Device: Receives a notification that the reservation is complete and notifies the user via push notification.

[0880] Specific operation: The device uses the push notification function to display a message to the user.

[0881] Input: Reservation completion notification.

[0882] Output: The user is notified.

[0883] Step 10:

[0884] Server: Collects relevant event information based on user interests through web scraping or APIs.

[0885] Specific Operation: The server searches the Internet and collects event information related to your interests.

[0886] Input: Interest data.

[0887] Output: Collected event information.

[0888] Step 11:

[0889] Server: Stores the collected event information in a database.

[0890] Specific operation: The server adds the event information to the database.

[0891] Input: Event information.

[0892] Output: The database is updated.

[0893] Step 12:

[0894] Server: Prepares to notify the user of the collected event information at the optimal time.

[0895] Specific operation: The server calculates the notification timing and generates a notification message for the user.

[0896] Input: Event information, notification timing.

[0897] Output: Informational message.

[0898] Step 13:

[0899] Server: Sends event information that is ready for notification to the user device.

[0900] Specific operation: The server sends the generated notification message to the user terminal.

[0901] Input: Notification message.

[0902] Output: Received by the user terminal.

[0903] Step 14:

[0904] Device: Receives event information notifications and notifies the user via push notifications.

[0905] Specific operation: The device uses the push notification function to display a message to the user.

[0906] Input: Notification message.

[0907] Output: The user is notified.

[0908] Step 15:

[0909] Device: Location information is obtained when the user launches the app and periodically.

[0910] What happens: Your device uses GPS and other location services to get its current location.

[0911] Enter: Location Services.

[0912] Output: The location information obtained.

[0913] Step 16:

[0914] Device: Sends the acquired location information to the server.

[0915] Specific operation: The device sends the acquired location information to the server.

[0916] Input: The acquired location information.

[0917] Output: Received by the server.

[0918] Step 17:

[0919] Server: Collects nearby event information based on the received location information.

[0920] Specific operation: The server searches the Internet and collects nearby event information based on location information.

[0921] Input: Location.

[0922] Output: Collected event information.

[0923] Step 18:

[0924] Server: Sends collected event information to the user's device.

[0925] Specific operation: The server transmits the collected event information to the user terminal.

[0926] Input: Event information.

[0927] Output: Received by the user terminal.

[0928] Step 19:

[0929] Device: Sends push notifications to users based on nearby event information received.

[0930] Specific operation: The device uses the push notification function to display a message to the user.

[0931] Input: Event information.

[0932] Output: The user is notified.

[0933] (Application example 1)

[0934] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0935] Existing calendar applications optimize schedules by utilizing user behavioral history, interests, and location information, but there is also room for application to machine operation management and maintenance management in factory environments. However, current systems lack the functionality to comprehensively manage the operation history and location information of machines in a factory and automatically generate efficient schedules based on that data. Therefore, a solution to improve factory management efficiency is desired.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0937] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting related event information based on interests registered by the user, means for notifying the user based on the collected event information, means for learning the operation history of machines in a factory, and means for automatically creating periodic maintenance schedules based on the learned operation history, thereby enabling efficient management and optimization of operation schedules and maintenance schedules for machines in a factory.

[0938] "Means for learning user behavior history" refers to algorithms and tools that analyze users' past behavioral data and identify trends.

[0939] A "means for automatically creating periodic schedules" is a program or system for automatically generating future schedules for a user or machine based on learned behavioral patterns.

[0940] "Means for collecting related event information based on the interests registered by the user" refers to technology or services that collect event information related to the interests and concerns registered by the user from the Internet.

[0941] The "means for notifying the user based on the collected event information" is a function for providing the collected information to the user as a push notification or an alert.

[0942] "Means for learning the operating history of machines in a factory" refers to algorithms and tools that analyze past operating data of machines in a factory and understand their patterns.

[0943] The "means for automatically creating periodic maintenance schedules" refers to a program or system for automatically generating periodic maintenance schedules based on the learned operating patterns of the machine.

[0944] "Means for acquiring user location information" refers to technologies and devices for measuring a user's current location and movement history.

[0945] The "means for collecting nearby event information based on acquired location information" refers to technology or services for collecting information about events held in the vicinity of the user's current location from the Internet.

[0946] "Means for acquiring location information of machines in a factory" refers to technologies and devices for measuring the current locations of machines and equipment in a factory.

[0947] "Means for collecting event information in a specific area based on acquired location information" refers to technologies and services for collecting event information in that area from the Internet based on acquired machine location information.

[0948] The "means for analyzing a user's past calendar events and inferring behavioral patterns" refers to algorithms or tools for analyzing a user's calendar history and inferring future behavioral patterns.

[0949] "Means for analyzing operation data of machines in a factory and estimating operation patterns" refers to algorithms and tools for analyzing past operation data of machines in a factory and estimating future operation patterns.

[0950] The system of the present invention is composed of a user terminal and a server, and is designed to optimize the operation history and maintenance schedule of machines in a factory.

[0951] Overall system configuration

[0952] The system for implementing the invention consists of a factory robot, a smartphone or tablet as a user terminal, and a cloud server. The user terminal collects machine operation data and location information, and the server stores and analyzes this data to generate an optimal schedule.

[0953] User registration and initial settings

[0954] The factory manager first installs the calendar application and registers an account. Next, he or she registers the role, operation pattern, and regular maintenance information of each machine in the factory on the cloud server. This secures basic management information.

[0955] Analysis of behavioral history

[0956] The terminal collects the operation history data of the machines in the factory and sends it to the server. The server analyzes the received operation history data and learns the operation patterns of each machine. For example, there are machines that perform assembly work for eight hours every day, and machines that require regular maintenance once a month.

[0957] Creating an Automatic Schedule

[0958] Based on the analysis results, the server automatically generates future operation and maintenance schedules for each machine and notifies factory managers and maintenance staff, thereby optimizing and streamlining work.

[0959] Event information collection and notification

[0960] The server collects information about special events and new work instructions within the factory and sends push notifications to the user's device, enabling a prompt response.

[0961] Use of location information

[0962] The terminal periodically acquires the machine's location information and sends it to the server, which then collects event information for a specific area based on the acquired location information, supporting effective work allocation and anomaly detection.

[0963] Specific examples

[0964] Maintenance Schedule Example

[0965] The server studies the operating history of machines in the factory and detects patterns that indicate monthly maintenance is required. It predicts the timing of the next maintenance and notifies the maintenance staff. When the scheduled maintenance date approaches, a push notification is sent stating, "The next maintenance is scheduled for XX month."

[0966] Example of anomaly detection in a specific area

[0967] The terminal sends the machine's location information to the server and detects abnormal events in a specific area (e.g., abnormal machine shutdown) in real time. The server collects the abnormality information and notifies the maintenance staff that "an abnormality has been detected in a specific area."

[0968] Hardware and software used

[0969] Hardware:

[0970] Factory robots

[0971] Wi-Fi network in the factory

[0972] Smartphones and tablets

[0973] software:

[0974] Cloud servers (e.g. AWS, Google Cloud)

[0975] REST API

[0976] Push notification system for smart devices (e.g., Firebase Cloud Messaging)

[0977] Prompt Sentence Examples

[0978] The factory robot "R123" performs assembly work for 8 hours every day and requires maintenance once a month. Based on past operation data, generate an optimal maintenance schedule and create notification messages for maintenance staff.

[0979] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0980] Step 1:

[0981] The user installs a calendar application and registers an account. The input information is the user's basic information and the roles and operating patterns of machines in the factory. This information is sent to a cloud server, and the basic data of the user and machine is stored in a database. The output is the registered user account and machine data.

[0982] Step 2:

[0983] The terminal collects the operation history data of machines in the factory in real time. The input information is the operation data of each machine (operation start time, end time, stop time, etc.). This data is sent to a cloud server, which stores the operation history data in a database. The output is the stored operation history data.

[0984] Step 3:

[0985] The server analyzes the collected operation history data and learns the operation patterns of each machine. The input is the saved operation history data. An analytical algorithm is used to extract the machine's operation pattern (e.g., 8 hours of operation every day, maintenance once a month). The output is the learned operation pattern information.

[0986] Step 4:

[0987] The server automatically creates future operation and maintenance schedules based on the learned operation patterns. The input is the learned operation pattern information. A scheduling algorithm is used to predict the next maintenance timing and generate the schedule. The output is the generated future schedule.

[0988] Step 5:

[0989] The server notifies the generated future schedule to the factory manager or maintenance staff. The input is the generated future schedule. A push notification system is used to send detailed information about the schedule to smartphones or tablets. The output is the notified schedule information.

[0990] Step 6:

[0991] The terminal periodically acquires the machine's location information and sends it to the server. The input is the machine's location data. The location information is sent to the cloud server, which stores the information in a database. The output is the stored location information.

[0992] Step 7:

[0993] The server collects event information for a specific area based on the acquired location information. The input is the saved location information. Event information (e.g., abnormal shutdowns, emergencies, etc.) for the specific area is collected using web scraping or APIs. The output is the collected event information.

[0994] Step 8:

[0995] The server notifies the maintenance staff of the collected event information. The input is the collected event information. A push notification system is used to notify the smart device of abnormal information in real time. The output is the notified event information.

[0996] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0997] Overall system configuration

[0998] The system of the present invention is implemented as a calendar application consisting of a user terminal, a server, and an emotion engine. The user terminal is a device such as a smartphone or tablet, and the server is located on the cloud and stores and analyzes user information and event data. The emotion engine recognizes the user's emotions and provides recommended information and schedule adjustments based on the user's emotional state.

[0999] User registration and initial settings

[1000] User: To use the system, the user first installs the calendar app and registers an account when the system is first launched. When registering, the user enters their interests (e.g., hair salons, live concerts, movies, etc.).

[1001] Device: Once you have completed your account registration and entered your interests, that information will be sent to the server.

[1002] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[1003] Analysis of behavioral history

[1004] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[1005] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if it detects that the user goes to the hair salon on the third Saturday of every month, it automatically generates future regular schedules based on this information.

[1006] Creating an Automatic Schedule

[1007] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[1008] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[1009] On the device: The user will be notified via push notification that the reservation has been completed.

[1010] Event information collection and notification

[1011] Server: Based on the user's interests, collects relevant event information (for example, information about live performances by favorite artists or new movie releases), and prepares to notify the user at the optimal time based on the collected event information.

[1012] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[1013] Use of location information

[1014] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[1015] Server: Collects information about nearby events (such as newly opened cafes or special sales at supermarkets) based on location information. Sends the collected event information to the user's device.

[1016] Device: Sends push notifications to users based on nearby event information received.

[1017] Implementing the Emotion Engine

[1018] Terminal: Collects data to analyze the user's emotions through daily events and input data, and sends it to the emotion engine.

[1019] Emotion engine: Analyzes the user's emotional data and estimates their emotional state in real time, using, for example, text analysis, voice analysis, and facial expression analysis.

[1020] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it can suggest relaxation events when the user is under high stress.

[1021] Specific examples

[1022] Hair salon reservation example

[1023] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[1024] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[1025] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[1026] Concert ticket purchase example

[1027] User: Register a favorite artist (e.g., a particular musician) as a topic.

[1028] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[1029] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[1030] Emotion Engine: If a user has a strong emotional connection to this artist, they can receive special reminders or early notifications of ticket sales.

[1031] Example of supermarket sale information

[1032] Device: Sends the user's location information to the server.

[1033] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[1034] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[1035] Emotion engine: If a user responds positively to a sale, the system will prioritize notifications of similar sales.

[1036] This concludes the "Mode for Carrying Out the Invention" of the system of the present invention. This system not only utilizes the user's behavioral history, interests, and location information, but also combines it with an emotion engine to achieve more personalized schedule management and support the user's daily life.

[1037] The processing flow will be explained below.

[1038] Basic processing steps of the system

[1039] Step 1:

[1040] User: Installs the calendar app on their smartphone and launches it for the first time.

[1041] Step 2:

[1042] On device: The app prompts the user to sign up for an account and asks them to enter topics or keywords of interest.

[1043] Step 3:

[1044] Users: Enter topics of interest such as hair salons, live concerts, movies, etc., and submit them along with their account information.

[1045] Step 4:

[1046] Terminal: Stores the entered information in local storage and prepares it for transmission to the server.

[1047] Step 5:

[1048] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[1049] Analysis of behavioral history

[1050] Step 6:

[1051] Device: Collects the user's past calendar events and behavioral history data and sends them to the server.

[1052] Step 7:

[1053] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns.

[1054] Creating an Automatic Schedule

[1055] Step 8:

[1056] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[1057] Step 9:

[1058] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[1059] Step 10:

[1060] On the device: The user will be notified via push notification that the reservation has been completed.

[1061] Event information collection and notification

[1062] Step 11:

[1063] Server: Collects relevant event information based on the user's interests (for example, information about live performances by favorite artists or new movie releases).

[1064] Step 12:

[1065] Server: Based on the collected event information, selects the information to notify the user at the optimal time.

[1066] Step 13:

[1067] Server: Sends selected event information to the device.

[1068] Step 14:

[1069] On the device: Sends push notifications to users based on the received event information and automatically adds events to their calendars.

[1070] Acquisition and use of location information

[1071] Step 15:

[1072] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[1073] Step 16:

[1074] Server: Collects and analyzes information about nearby events based on location information.

[1075] Step 17:

[1076] Server: Selects the collected nearby event information and creates notification information.

[1077] Step 18:

[1078] Device: Sends push notifications to users based on nearby event information received.

[1079] Emotion Recognition and Applications

[1080] Step 19:

[1081] Terminal: Collects data to analyze the user's emotions through daily events and data entered by the user (emails, chat logs, etc.) and sends it to the server.

[1082] Step 20:

[1083] Server: Sends the received emotion data to the emotion engine for analysis.

[1084] Step 21:

[1085] Emotion Engine: Analyzes the user's emotional state and classifies it as positive, negative, or stressful.

[1086] Step 22:

[1087] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it suggests relaxation events when stress levels are high.

[1088] Specific examples

[1089] Hair salon reservation example

[1090] Step 1:

[1091] Server: From the user's behavioral history, it learns that the user visits the beauty salon on the third Saturday of every month.

[1092] Step 2:

[1093] Server: When the next third Saturday approaches, make a reservation through the hair salon booking API.

[1094] Step 3:

[1095] Server: After the reservation is completed, create a notification message and send it to the device.

[1096] Step 4:

[1097] Device: A push notification will be sent to the user upon receiving the reservation completion notification.

[1098] Step 5:

[1099] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[1100] Concert ticket purchase example

[1101] Step 1:

[1102] User: Register a favorite artist (e.g., a particular musician) as a topic.

[1103] Step 2:

[1104] Server: Collects concert information for the artist in question and checks when tickets go on sale.

[1105] Step 3:

[1106] Server: Prepare information to notify users as the sale date approaches.

[1107] Step 4:

[1108] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[1109] Step 5:

[1110] Emotion Engine: If a user has a strong emotional connection to this artist, they will receive special reminders or early notifications of ticket sales.

[1111] Example of supermarket sale information

[1112] Step 1:

[1113] Device: Sends the user's location information to the server.

[1114] Step 2:

[1115] Server: Collects information about special offers at nearby supermarkets based on location information.

[1116] Step 3:

[1117] Server: Creates notification information based on the results of collection and analysis.

[1118] Step 4:

[1119] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[1120] Step 5:

[1121] Emotion engine: If a user responds positively to a sale, the engine will prioritize notifications of similar sales.

[1122] The above is the specific processing flow of the system of the present invention. By combining it with an emotion engine, personalized schedule management based on the user's emotional state becomes possible, making the user's daily life more fulfilling.

[1123] Example 2

[1124] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1125] In recent years, schedule management applications using mobile devices such as smartphones and tablets have become widespread. However, conventional calendar and scheduling applications do not adequately provide personalized schedule management that takes into account a user's behavioral history, interests, location information, and even emotional state. This results in a problem in that truly useful schedule suggestions and notifications cannot be realized. Furthermore, because schedules are not adjusted according to the user's emotional state, suggestions for stress management and relaxation are insufficient. The present invention aims to solve these problems.

[1126] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1127] In this invention, the server includes a means for learning a user's behavioral history, a means for automatically creating a recurring schedule based on the learned behavioral history, and a means for collecting related event information based on the user's registered interests. This allows the server to learn the user's behavioral patterns and automatically generate a recurring schedule based on them. Furthermore, event information based on the user's interests can be collected and notified, enabling appropriate event suggestions to be made to the user. Furthermore, by analyzing the user's emotional data and adjusting the schedule and notification content based on the user's emotional state, relaxation events tailored to the user's emotional state can be suggested and flexible schedule management can be realized.

[1128] "User" refers to a person who uses this system, and is a person who uses a mobile device such as a smartphone or tablet.

[1129] "Action history" refers to records of events such as calendar entries and event participation made by a user in the past.

[1130] "Means of learning" refers to algorithms or programs that analyze collected user behavior history data and identify certain patterns and trends.

[1131] The "means for automatically creating periodic schedules" is a program that has the function of automatically generating future schedules for the user based on learned behavioral patterns.

[1132] "Interests" refers to topics or areas of interest registered by a user (e.g., hair salons, live music, movies, etc.).

[1133] The "means for collecting relevant event information" is a program for collecting event data from the Internet and other sources based on the user's interests.

[1134] The "means for notifying the user" is a program that has the function of notifying the user of collected event information and generated schedules via push notifications or other methods on the user's device.

[1135] "Location information" refers to data related to a user's current location and movement history.

[1136] The means for collecting "neighborhood event information" is a program for acquiring information about events and facilities held in the vicinity based on the user's current location.

[1137] "Emotional data" refers to data that indicates the user's emotional state (e.g., stress level, happiness level, etc.), and is obtained through text analysis, voice analysis, facial expression analysis, etc.

[1138] "Means for adjusting schedules and notification content based on emotional state" refers to algorithms or programs for dynamically changing a user's schedule and notification content based on emotional data.

[1139] Overall system configuration

[1140] The system of the present invention is implemented as a calendar management application consisting of a user terminal, a server, and an emotion engine. The user terminal includes mobile devices such as smartphones and tablets. The server is located on the cloud and stores and analyzes user information and event data. The emotion engine recognizes the user's emotions and makes recommended information and schedule adjustments based on the user's emotional state.

[1141] User registration and initial settings

[1142] User: To use the system, after installing the calendar app, users must register an account the first time they start the app by entering their username, email address, password, and interests (e.g., hair salons, live concerts, movies, etc.).

[1143] Device: Once you have registered and entered your interests, this information is sent to our server.

[1144] Server: The server stores the received user information and interest data in a database and prepares it for analysis.

[1145] Analysis of behavioral history

[1146] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[1147] Server: The server analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if the server detects that the user visits the beauty salon on the third Saturday of every month, it will automatically generate future regular schedules based on this information.

[1148] Creating an Automatic Schedule

[1149] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[1150] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[1151] On the device: The user will be notified via push notification that the reservation has been completed.

[1152] Event information collection and notification

[1153] Server: Collects relevant event information based on the user's interests (e.g., information about live performances by favorite artists or new movie releases). Prepares notifications based on the collected event information.

[1154] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[1155] Use of location information

[1156] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[1157] Server: Based on location information, collects information about nearby events (e.g., information about newly opened cafes or special sales at supermarkets) and sends the collected event information to the user's device.

[1158] Device: Sends push notifications to users based on nearby event information received.

[1159] Implementing the Emotion Engine

[1160] Terminal: Collects data to analyze the user's emotions through daily events and input data, and sends it to the emotion engine.

[1161] Emotion engine: The emotion engine analyzes the user's emotional data and estimates their emotional state in real time, for example, by using text analysis, voice analysis, and facial expression analysis.

[1162] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it can suggest relaxation events when the user is under high stress.

[1163] Specific examples

[1164] Hair salon reservation example

[1165] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[1166] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[1167] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[1168] Concert ticket purchase example

[1169] User: Register a favorite artist (e.g., a specific musician) as a topic.

[1170] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[1171] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[1172] Emotion Engine: If a user has a strong emotional connection to this artist, they can receive special reminders or early notifications of ticket sales.

[1173] Example of supermarket sale information

[1174] Device: Sends the user's location information to the server.

[1175] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[1176] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[1177] Emotion engine: If a user responds positively to a sale, the system will prioritize notifications of similar sales.

[1178] Example prompts for generative AI models

[1179] 1. "Please explain the algorithm of a hair salon reservation system."

[1180] 2. "Please give a specific example of how the emotion engine detects a user's stress level."

[1181] 3. "Please explain the structure of a system that uses location information to notify users of nearby events."

[1182] This concludes the description of the embodiment of the present invention. By utilizing the user's behavioral history, interests, and location information, and by combining it with an emotion engine, it is possible to achieve more personalized schedule management and support the user's daily life.

[1183] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1184] Step 1:

[1185] The user installs the Calendar app and launches it for the first time.

[1186] Input: A user installs a calendar app on their smartphone or tablet.

[1187] Output: The app launches for the first time and the account registration screen appears.

[1188] What happens: A user downloads an app from the app store, installs it, and then launches it.

[1189] Step 2:

[1190] The user registers an account.

[1191] Input: The user enters their account information (username, email address, password).

[1192] Output: Account information is registered in the app.

[1193] Specific behavior: The user fills in the required information in the provided form and clicks the "Register" button.

[1194] Step 3:

[1195] The user enters their interests.

[1196] Input: User selects or inputs an interest (e.g., hair salon, live concert, movie, etc.).

[1197] Output: Interest data is stored on the device.

[1198] What happens: The user selects an interest from a list and taps the "Save" button on the screen.

[1199] Step 4:

[1200] The terminal transmits the user information to the server.

[1201] Input: Registered account information and interest data.

[1202] Output: User information is saved on the server.

[1203] Specific operation: The device sends data to the server via the Internet.

[1204] Step 5:

[1205] The server stores the user information in a database.

[1206] Input: User information and interest data sent from your device.

[1207] Output: User information stored in the database.

[1208] What happens: The server parses the received data and stores it in the appropriate fields in the database.

[1209] Step 6:

[1210] The device collects past calendar event data and sends it to the server.

[1211] Input: Past calendar event data stored on your device.

[1212] Output: Behavioral history data sent to the server.

[1213] Specific operation: The device reads past event information from the local calendar database and sends it to the server.

[1214] Step 7:

[1215] The server analyzes behavioral history data and learns behavioral patterns.

[1216] Input: Past calendar event data.

[1217] Output: Learned behavioral patterns.

[1218] How it works: The server uses an AI model to analyze behavioral data and learn certain behavioral patterns (e.g., going to the hairdresser on the third Saturday of every month).

[1219] Step 8:

[1220] The server automatically generates a recurring schedule based on the learning results.

[1221] Input: Learned behavioral patterns.

[1222] Output: Recurring appointment data.

[1223] Specific operation: Based on the behavioral patterns learned by the AI ​​model, the system generates the user's next appointment and confirms it via the salon's booking API.

[1224] Step 9:

[1225] The server creates a reservation completion notice and sends it to the user terminal.

[1226] Input: Reservation confirmation information.

[1227] Output: Reservation completion notification.

[1228] Specific operation: The server generates a reservation completion message based on the reservation information and sends it to the user's specified terminal.

[1229] Step 10:

[1230] The device will send a push notification of reservation completion to the user.

[1231] Input: Reservation completion notification information.

[1232] Output: Push notification to the user.

[1233] Specific operation: The device displays the received notification information as a push notification to notify the user.

[1234] Step 11:

[1235] The server collects relevant event information and prepares notifications.

[1236] Input: User interest data.

[1237] Output: Related event information.

[1238] Specific operation: The server filters relevant event information collected from the Internet and selects information appropriate for the user.

[1239] Step 12:

[1240] The device receives the notification information and pushes it to the user.

[1241] Input: Relevant event notification information.

[1242] Output: Push notification to the user.

[1243] Specific operation: The device displays the received notification information as a push notification to notify the user.

[1244] Step 13:

[1245] The device periodically obtains location information or when the app is launched and sends it to the server.

[1246] Input: Location data.

[1247] Output: The location sent to the server.

[1248] Specific operation: The device uses the GPS function to obtain its current location and sends it to a server via the Internet.

[1249] Step 14:

[1250] The server collects nearby event information based on location information.

[1251] Input: Location data.

[1252] Output: Nearby event information.

[1253] Specific operation: The server collects event information for the specified area from the Internet based on the location information.

[1254] Step 15:

[1255] The server transmits the collected event information to the user terminal.

[1256] Input: Nearby event information.

[1257] Output: Event information notification.

[1258] Specific operation: The server sends filtered nearby event information to the user's device via the Internet.

[1259] Step 16:

[1260] The device sends push notifications to users based on nearby event information received.

[1261] Input: Nearby event information.

[1262] Output: Push notification to the user.

[1263] Specific operation: The device notifies the user of the event information received via a push notification.

[1264] Step 17:

[1265] The terminal collects the user's emotion data and sends it to the emotion engine.

[1266] Input: User text, voice, and facial expression data.

[1267] Output: Emotion data.

[1268] Specific operation: The device analyzes the user's input data (e.g., diary, voice memo) and sends it to the emotion engine.

[1269] Step 18:

[1270] The emotion engine analyzes the emotion data and estimates the emotional state.

[1271] Input: User emotion data.

[1272] Output: Estimated emotional state.

[1273] What it does: The emotion engine performs text analysis, speech analysis, and facial expression analysis to estimate emotional states (e.g., stress, joy).

[1274] Step 19:

[1275] The server adjusts schedules and notification content based on emotional state data.

[1276] Input: Estimated emotional state.

[1277] Output: Adjusted schedule and notification content.

[1278] Specific operation: Based on the data from the emotion engine, the server suggests relaxation events and changes task priorities, and notifies the user.

[1279] (Application example 2)

[1280] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1281] Conventional calendar applications have limited ability to manage personalized schedules based on user behavior and interests, and they also lack the ability to flexibly adjust schedules based on the user's emotional state or location information. Therefore, there is a need for an effective means to enrich users' daily lives and in-store experiences.

[1282] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1283] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting related event information based on the interests registered by the user, means for notifying the user based on the collected event information, emotion analysis means for analyzing the user's emotions, and means for providing suggested information based on the emotion analysis results. This makes it possible to provide optimal schedules and suggested information in real time based on the user's behavioral history and interests as well as their emotional state and location information.

[1284] "Behavioral history" refers to a record of a user's past activities and events.

[1285] A "regular appointment" refers to a schedule that occurs periodically based on the user's behavioral patterns.

[1286] "Interests" refer to the themes, categories, and activities in which a user is particularly interested.

[1287] "Event information" refers to detailed information about events and activities related to the user's interests.

[1288] "Notification" refers to an alert or message that conveys information to a user.

[1289] "Emotion analysis" refers to the process for analyzing and inferring a user's emotional state.

[1290] "Suggested information" refers to recommendations and actions provided to users based on sentiment analysis, behavioral history, and interests.

[1291] "Location information" refers to geographic data that indicates the user's current location.

[1292] This invention provides personalized schedules and recommended information based on a user's behavioral history, interests, emotional state, and location information. The main components include a user terminal, a cloud server, and an emotion analysis engine. Specifically, this system is implemented as follows:

[1293] User terminal

[1294] User devices are primarily smartphones and smart glasses, which collect user behavioral history and location information. These devices are operated by users downloading apps and registering their interests through the initial setup. For example, apps installed on smartphones have the ability to routinely collect user location information and send it to a server.

[1295] Cloud Server

[1296] The cloud server stores and analyzes users' behavioral history, interests, and location information. The generated data is used to learn each user's patterns and automatically create recurring schedules. The cloud server also collects external event information and generates notifications at optimal times based on the user's interests.

[1297] Sentiment Analysis Engine

[1298] The emotion analysis engine analyzes the user's emotional state in real time. Using technologies such as text analysis, voice analysis, and facial expression analysis, it estimates whether the user is stressed or relaxed. Based on this emotional state, the cloud server generates suggested information appropriate for the user and notifies the device. For example, if the emotion analysis engine detects that the user is stressed, it will prioritize notifying the user of information about nearby relaxation facilities.

[1299] Specific examples

[1300] 1. Hair salon reservation example

[1301] The server learns from the user's behavioral history that they visit the hair salon on the third Saturday of every month, and makes the same reservation the next time. It calls the hair salon's reservation API and automatically completes the reservation. The device sends a reservation completion notification to the user, informing them that "your next hair salon reservation has been completed."

[1302] 2. Example of store navigation based on emotional state

[1303] If the emotion analysis engine detects that the user's stress level is high, the cloud server collects information about nearby relaxation facilities and discounts, and notifies the user via push notification, saying, "There is a relaxation facility nearby."

[1304] Prompt Sentence Examples

[1305] The following prompt sentence is used as input to the generative AI model:

[1306] User ID: 12345

[1307] Emotional state: Stress

[1308] Location: Latitude 35.6895, Longitude 139.6917

[1309] Interests: [Fashion, Beauty, Music]

[1310] Q: Can you recommend an event or service that would be suitable for this user?

[1311] This invention makes it possible to provide optimal schedules and suggested information in real time based on a user's behavioral history and interests as well as emotional state and location information.

[1312] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1313] Step 1: The user installs the calendar app and sets it up.

[1314] Input: User installs of the app, interest registrations.

[1315] How it works: A user installs a calendar app on their smartphone or smart glasses and registers their interests (e.g., fashion, beauty, music).

[1316] Output: Registered user information and interest data are sent to the cloud server.

[1317] Step 2: The user device collects behavioral history data and sends it to the server.

[1318] Inputs: Events and past actions that users enter into their calendar.

[1319] How it works: The user's device periodically collects event data entered in the calendar and sends it to a cloud server.

[1320] Output: The collected behavioral history data is stored on a cloud server.

[1321] Step 3: The cloud server learns behavioral patterns and automatically creates recurring schedules.

[1322] Input: Behavioral history data stored on the server.

[1323] How it works: The server uses machine learning algorithms to analyze the user's behavioral patterns and automatically create future recurring appointments (e.g., a hair salon appointment on the third Saturday of every month).

[1324] Output: The automatically generated recurring events are reflected in the calendar.

[1325] Step 4: The emotion analysis engine analyzes the user's emotional state in real time.

[1326] Input: Data required for emotion analysis, such as text data, audio data, and facial expression data.

[1327] How it works: The emotion analysis engine analyzes this data and estimates the user's emotional state (e.g., high stress level, relaxed, etc.).

[1328] Output: The estimated emotional state is sent to the cloud server.

[1329] Step 5: The cloud server collects appropriate event information based on the emotional state and location information.

[1330] Input: User's emotional state data, current location.

[1331] How it works: The cloud server accesses an external event information API and collects event information related to the user's interests. If the emotional state is stressed, it will prioritize collecting relaxation events.

[1332] Output: The collected event information is stored on the server in an appropriate format.

[1333] Step 6: The cloud server generates recommendation information and notifies the user device.

[1334] Input: Emotional state data, location information, collected event information.

[1335] How it works: The server uses a recommendation algorithm to select the most suitable event information for the user and generate a notification message, such as "There are currently relaxation facilities nearby."

[1336] Output: A notification message is sent to the user's terminal.

[1337] Step 7: The user terminal receives the notification and displays the notification to the user.

[1338] Input: The notification message sent by the server.

[1339] How it works: Notifications are pushed to the user's smartphone or smart glasses and appear as pop-ups or alerts.

[1340] Output: The user can confirm the notification.

[1341] This series of processing steps allows users to receive personalized schedule and event recommendations based on their behavioral history, interests, emotional state, and location.

[1342] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1343] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1344] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1345] [Third embodiment]

[1346] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1347] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1348] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1349] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1350] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1351] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1352] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1353] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1354] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1355] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1356] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1357] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1358] Overall system configuration

[1359] The system of the present invention is implemented as a calendar application consisting of a user terminal and a server. The user terminal is a device such as a smartphone or tablet, and the server is located on the cloud and stores and analyzes user information and event data.

[1360] User registration and initial settings

[1361] User: To use the system, the user first installs the calendar app and registers an account when the system is first launched. When registering, the user enters their interests (e.g., hair salons, live concerts, movies, etc.).

[1362] Device: Once you have completed your account registration and entered your interests, that information will be sent to the server.

[1363] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[1364] Analysis of behavioral history

[1365] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[1366] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if it detects that the user goes to the hair salon on the third Saturday of every month, it automatically generates future regular schedules based on this information.

[1367] Creating an Automatic Schedule

[1368] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[1369] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[1370] On the device: The user will be notified via push notification that the reservation has been completed.

[1371] Event information collection and notification

[1372] Server: Based on the user's interests, collects relevant event information (for example, information about live performances by favorite artists or new movie releases), and prepares to notify the user at the optimal time based on the collected event information.

[1373] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[1374] Use of location information

[1375] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[1376] Server: Collects information about nearby events (such as newly opened cafes or special sales at supermarkets) based on location information. Sends the collected event information to the user's device.

[1377] Device: Sends push notifications to users based on nearby event information received.

[1378] Specific examples

[1379] Hair salon reservation example

[1380] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[1381] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[1382] Concert ticket purchase example

[1383] User: Register a favorite artist (e.g., a particular musician) as a topic.

[1384] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[1385] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[1386] Example of supermarket sale information

[1387] Device: Sends the user's location information to the server.

[1388] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[1389] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[1390] This concludes the "Mode for Carrying Out the Invention" of the system of the present invention. This system utilizes the user's behavioral history, interests, and location information to realize efficient schedule management and support the user's daily life.

[1391] The processing flow will be explained below.

[1392] Step 1:

[1393] User: Installs the calendar app on their smartphone and launches it for the first time.

[1394] Step 2:

[1395] On device: The app prompts the user to sign up for an account and asks them to enter topics or keywords of interest.

[1396] Step 3:

[1397] Users: Enter topics of interest such as hair salons, live concerts, movies, etc., and submit them along with their account information.

[1398] Step 4:

[1399] Terminal: Stores the entered information in local storage and prepares it for transmission to the server.

[1400] Step 5:

[1401] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[1402] Step 6:

[1403] Device: Collects the user's past calendar events and behavioral history data and sends them to the server.

[1404] Step 7:

[1405] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns.

[1406] Step 8:

[1407] Server: Automatically generates regular schedules for users based on the learning results. For example, predict the date of a hair salon appointment and call the reservation API to make the appointment.

[1408] Step 9:

[1409] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[1410] Step 10:

[1411] On the device: The user will be notified via push notification that the reservation has been completed.

[1412] Step 11:

[1413] Server: Collects and analyzes relevant event information based on user interests.

[1414] Step 12:

[1415] Server: Based on the analysis results, selects the event information to notify the user at the optimal time.

[1416] Step 13:

[1417] Server: Sends selected event information to the device.

[1418] Step 14:

[1419] On the device: Sends push notifications to users based on the received event information and automatically adds events to their calendars.

[1420] Step 15:

[1421] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[1422] Step 16:

[1423] Server: Collects and analyzes information about nearby events based on location information.

[1424] Step 17:

[1425] Server: Based on the collected and analyzed results, it creates event information to notify the user and sends it to the terminal.

[1426] Step 18:

[1427] Device: Sends push notifications to users based on nearby event information received.

[1428] Specific examples

[1429] Hair salon reservation example

[1430] Step 1:

[1431] Server: Analyzes the user's past hair salon reservation history and learns that the user visits the hair salon on the third Saturday of every month.

[1432] Step 2:

[1433] Server: When the next third Saturday approaches, make a reservation through the hair salon booking API.

[1434] Step 3:

[1435] Server: After the reservation is completed, create a notification message and send it to the device.

[1436] Step 4:

[1437] Device: A push notification will be sent to the user upon receiving the reservation completion notification.

[1438] Concert ticket purchase example

[1439] Step 1:

[1440] User: Register a favorite artist (e.g., a particular musician) as a topic.

[1441] Step 2:

[1442] Server: Collects concert information for the artist in question and checks when tickets go on sale.

[1443] Step 3:

[1444] Server: Prepare information to notify users as the sale date approaches.

[1445] Step 4:

[1446] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[1447] Example of supermarket sale information

[1448] Step 1:

[1449] Device: Sends the user's location information to the server.

[1450] Step 2:

[1451] Server: Collects information about special offers at nearby supermarkets based on location information.

[1452] Step 3:

[1453] Server: Analyzes sale information and creates notification information.

[1454] Step 4:

[1455] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[1456] Example 1

[1457] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1458] In modern society, users need to manage their busy schedules. However, conventional schedule management systems lacked the ability to respond individually to users' behavioral patterns and interests, requiring a great deal of time and effort for manual input and management. Furthermore, they lacked mechanisms for efficiently collecting and notifying users of nearby event information and service reservation information. This often led to users missing important information, reducing the efficiency and convenience of schedule management.

[1459] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1460] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting relevant event information based on the user's registered interests, means for notifying the user based on the collected event information, means for collecting event information of interest based on location information transmitted from the user terminal, means for notifying the user at an optimal timing based on the acquired location information and interest information, means for automatically making a reservation by calling a service reservation API for a beauty salon or the like, means for confirming the success of the reservation and, if successful, sending a reservation completion notification to the user terminal, means for analyzing past calendar events and estimating behavioral patterns, means for automatically creating future schedules based on the estimated behavioral patterns, and means for notifying the user of detailed schedule information. This allows users to manage their schedules individually and efficiently live their daily lives without missing important information.

[1461] "User" refers to a person who uses the system and receives services through a terminal.

[1462] "Behavioral history" refers to a record of actions and events that a user has taken in the past, including information entered into a schedule or calendar.

[1463] A "recurring appointment" refers to a regular schedule or reservation that is automatically generated based on a user's behavioral patterns.

[1464] "Interests" refers to the types of things or events that a user is interested in, such as hair salons, live concerts, movies, etc.

[1465] "Event information" refers to detailed information about an event related to the user's interests, including the date, time, location, and content.

[1466] "Location Information" means geographic location data obtained by a User's Device, such as data obtained using GPS or other technologies.

[1467] "Booking API" means an application programming interface for making reservations for certain services over the Internet.

[1468] "Push notification" refers to a communication method that transmits information from a server to a user's device in real time, allowing the user to be notified immediately.

[1469] "Behavioral patterns" refer to certain behavioral tendencies and habits that are estimated based on a user's past behavioral history.

[1470] "Machine learning algorithms" refer to computer algorithms that analyze data, learn, and automatically recognize patterns.

[1471] The system of the present invention is implemented as a calendar application using a user terminal such as a smartphone or tablet, and a server located on the cloud, which stores and analyzes user information and event data.

[1472] User registration and initial settings

[1473] To use the system, users first install the calendar app on their device and register an account when they first launch the system. When registering, users input their interests (e.g., beauty salons, live concerts, movies, etc.).

[1474] The terminal transmits the registration information and interest data to the server.

[1475] The server stores the received data in a database for future analysis.

[1476] Analysis of behavioral history

[1477] The device periodically collects events and behavioral history data previously entered in the calendar and sends it to the server.

[1478] The server then uses a machine learning algorithm to analyze the received behavioral history data. For example, it can detect a pattern where a user visits a beauty salon on the third Saturday of every month. The learning results are stored in a database and used for future predictions.

[1479] Creating an Automatic Schedule

[1480] The server predicts the next appointment date for the hair salon based on the learning results, calls the salon's reservation API according to the predicted date, checks whether the reservation process was successful, and creates a reservation completion notification if it was successful.

[1481] The server sends a reservation completion notification to the user terminal.

[1482] The terminal receives the reservation completion notification and notifies the user via push notification.

[1483] Event information collection and notification

[1484] The server collects relevant event information based on the user's interests (for example, information on live performances by favorite artists or new movie releases) through web scraping or APIs, and stores the collected event information in a database.

[1485] The server prepares to notify the user of the collected event information at the optimal timing, which is set based on the user's behavioral patterns and past interests.

[1486] The server transmits the event information that is ready for notification to the user terminal.

[1487] The device receives the event information notification, notifies the user via push notification, and automatically adds the event to the calendar.

[1488] Use of location information

[1489] The device acquires location information when the user launches the app or periodically, and sends the acquired location information to the server.

[1490] The server uses the received location information to collect information about nearby events (such as newly opened cafes or special sales at supermarkets) using web scraping and APIs.

[1491] The server transmits the collected event information to the user terminal.

[1492] The device will then send a push notification to the user based on the received information about nearby events.

[1493] Prompt Sentence Examples

[1494] An example of a prompt sentence to input to the generative AI model is as follows:

[1495] "Imagine a system that learns how often a user visits the hair salon on the third Saturday of every month and automatically schedules the next appointment. Include a function that calls the salon's booking API and notifies the user on success."

[1496] The system of the present invention effectively utilizes a user's behavioral history, interests, and location information to provide efficient schedule management and event information. This system allows users to live their daily lives efficiently without missing important information.

[1497] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1498] Step 1:

[1499] User: Install the calendar app on the device and register an account the first time you launch it.

[1500] Specific Actions: The user enters their name, email address, password, and interests (e.g., hair salons, live concerts, movies, etc.).

[1501] Input: User information and interest data.

[1502] Output: The registration information is saved on the device.

[1503] Step 2:

[1504] Device: Sends registration information and interest data to a server.

[1505] Specific operation: The terminal sends data to the server via the Internet.

[1506] Input: User information and interest data.

[1507] Output: Received by the server.

[1508] Step 3:

[1509] Server: Stores the received data in a database.

[1510] Specific operation: The server adds user information and interest data to the database.

[1511] Input: User information and interest data.

[1512] Output: The database is updated.

[1513] Step 4:

[1514] Device: Past events entered in the calendar and activity history data are periodically collected and sent to the server.

[1515] Specific operation: The terminal reads past event data and sends it to the server at the set interval.

[1516] Input: Past events and behavioral history data.

[1517] Output: Received by the server.

[1518] Step 5:

[1519] Server: Analyzes the received behavioral history data using machine learning algorithms.

[1520] Specific operation: The server uses analytical software (e.g., Python and scikit-learn) to analyze the data and extract behavioral patterns.

[1521] Input: Behavioral history data.

[1522] Output: A pattern of behavior as a result of learning.

[1523] Step 6:

[1524] Server: Predicts the next hair salon appointment date based on the learning results and calls the hair salon's reservation API according to the predicted date.

[1525] Specific operation: The server analyzes behavioral patterns, determines an appropriate reservation date and time, and calls the reservation API.

[1526] Input: behavioral patterns, reservation API information.

[1527] Output: Booking request.

[1528] Step 7:

[1529] Server: Checks whether the reservation process was successful and creates a reservation completion notification if successful.

[1530] Specific operation: The server checks the response from the reservation API and generates a notification message if successful.

[1531] Input: Booking API response.

[1532] Output: Reservation completion notification.

[1533] Step 8:

[1534] Server: Sends a reservation completion notification to the user's terminal.

[1535] Specific operation: The server sends the generated notification message to the user terminal.

[1536] Input: Reservation completion notification.

[1537] Output: Received by the user terminal.

[1538] Step 9:

[1539] Device: Receives a notification that the reservation is complete and notifies the user via push notification.

[1540] Specific operation: The device uses the push notification function to display a message to the user.

[1541] Input: Reservation completion notification.

[1542] Output: The user is notified.

[1543] Step 10:

[1544] Server: Collects relevant event information based on user interests through web scraping or APIs.

[1545] Specific Operation: The server searches the Internet and collects event information related to your interests.

[1546] Input: Interest data.

[1547] Output: Collected event information.

[1548] Step 11:

[1549] Server: Stores the collected event information in a database.

[1550] Specific operation: The server adds the event information to the database.

[1551] Input: Event information.

[1552] Output: The database is updated.

[1553] Step 12:

[1554] Server: Prepares to notify the user of the collected event information at the optimal time.

[1555] Specific operation: The server calculates the notification timing and generates a notification message for the user.

[1556] Input: Event information, notification timing.

[1557] Output: Informational message.

[1558] Step 13:

[1559] Server: Sends event information that is ready for notification to the user device.

[1560] Specific operation: The server sends the generated notification message to the user terminal.

[1561] Input: Notification message.

[1562] Output: Received by the user terminal.

[1563] Step 14:

[1564] Device: Receives event information notifications and notifies the user via push notifications.

[1565] Specific operation: The device uses the push notification function to display a message to the user.

[1566] Input: Notification message.

[1567] Output: The user is notified.

[1568] Step 15:

[1569] Device: Location information is obtained when the user launches the app and periodically.

[1570] What happens: Your device uses GPS and other location services to get its current location.

[1571] Enter: Location Services.

[1572] Output: The location information obtained.

[1573] Step 16:

[1574] Device: Sends the acquired location information to the server.

[1575] Specific operation: The device sends the acquired location information to the server.

[1576] Input: The acquired location information.

[1577] Output: Received by the server.

[1578] Step 17:

[1579] Server: Collects nearby event information based on the received location information.

[1580] Specific operation: The server searches the Internet and collects nearby event information based on location information.

[1581] Input: Location.

[1582] Output: Collected event information.

[1583] Step 18:

[1584] Server: Sends collected event information to the user's device.

[1585] Specific operation: The server transmits the collected event information to the user terminal.

[1586] Input: Event information.

[1587] Output: Received by the user terminal.

[1588] Step 19:

[1589] Device: Sends push notifications to users based on nearby event information received.

[1590] Specific operation: The device uses the push notification function to display a message to the user.

[1591] Input: Event information.

[1592] Output: The user is notified.

[1593] (Application example 1)

[1594] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1595] Existing calendar applications optimize schedules by utilizing user behavioral history, interests, and location information, but there is also room for application to machine operation management and maintenance management in factory environments. However, current systems lack the functionality to comprehensively manage the operation history and location information of machines in a factory and automatically generate efficient schedules based on that data. Therefore, a solution to improve factory management efficiency is desired.

[1596] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1597] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting related event information based on interests registered by the user, means for notifying the user based on the collected event information, means for learning the operation history of machines in a factory, and means for automatically creating periodic maintenance schedules based on the learned operation history, thereby enabling efficient management and optimization of operation schedules and maintenance schedules for machines in a factory.

[1598] "Means for learning user behavior history" refers to algorithms and tools that analyze users' past behavioral data and identify trends.

[1599] A "means for automatically creating periodic schedules" is a program or system for automatically generating future schedules for a user or machine based on learned behavioral patterns.

[1600] "Means for collecting related event information based on the interests registered by the user" refers to technology or services that collect event information related to the interests and concerns registered by the user from the Internet.

[1601] The "means for notifying the user based on the collected event information" is a function for providing the collected information to the user as a push notification or an alert.

[1602] "Means for learning the operating history of machines in a factory" refers to algorithms and tools that analyze past operating data of machines in a factory and understand their patterns.

[1603] The "means for automatically creating periodic maintenance schedules" refers to a program or system for automatically generating periodic maintenance schedules based on the learned operating patterns of the machine.

[1604] "Means for acquiring user location information" refers to technologies and devices for measuring a user's current location and movement history.

[1605] The "means for collecting nearby event information based on acquired location information" refers to technology or services for collecting information about events held in the vicinity of the user's current location from the Internet.

[1606] "Means for acquiring location information of machines in a factory" refers to technologies and devices for measuring the current locations of machines and equipment in a factory.

[1607] "Means for collecting event information in a specific area based on acquired location information" refers to technologies and services for collecting event information in that area from the Internet based on acquired machine location information.

[1608] The "means for analyzing a user's past calendar events and inferring behavioral patterns" refers to algorithms or tools for analyzing a user's calendar history and inferring future behavioral patterns.

[1609] "Means for analyzing operation data of machines in a factory and estimating operation patterns" refers to algorithms and tools for analyzing past operation data of machines in a factory and estimating future operation patterns.

[1610] The system of the present invention is composed of a user terminal and a server, and is designed to optimize the operation history and maintenance schedule of machines in a factory.

[1611] Overall system configuration

[1612] The system for implementing the invention consists of a factory robot, a smartphone or tablet as a user terminal, and a cloud server. The user terminal collects machine operation data and location information, and the server stores and analyzes this data to generate an optimal schedule.

[1613] User registration and initial settings

[1614] The factory manager first installs the calendar application and registers an account. Next, he or she registers the role, operation pattern, and regular maintenance information of each machine in the factory on the cloud server. This secures basic management information.

[1615] Analysis of behavioral history

[1616] The terminal collects the operation history data of the machines in the factory and sends it to the server. The server analyzes the received operation history data and learns the operation patterns of each machine. For example, there are machines that perform assembly work for eight hours every day, and machines that require regular maintenance once a month.

[1617] Creating an Automatic Schedule

[1618] Based on the analysis results, the server automatically generates future operation and maintenance schedules for each machine and notifies factory managers and maintenance staff, thereby optimizing and streamlining work.

[1619] Event information collection and notification

[1620] The server collects information about special events and new work instructions within the factory and sends push notifications to the user's device, enabling a prompt response.

[1621] Use of location information

[1622] The terminal periodically acquires the machine's location information and sends it to the server, which then collects event information for a specific area based on the acquired location information, supporting effective work allocation and anomaly detection.

[1623] Specific examples

[1624] Maintenance Schedule Example

[1625] The server studies the operating history of machines in the factory and detects patterns that indicate monthly maintenance is required. It predicts the timing of the next maintenance and notifies the maintenance staff. When the scheduled maintenance date approaches, a push notification is sent stating, "The next maintenance is scheduled for XX month."

[1626] Example of anomaly detection in a specific area

[1627] The terminal sends the machine's location information to the server and detects abnormal events in a specific area (e.g., abnormal machine shutdown) in real time. The server collects the abnormality information and notifies the maintenance staff that "an abnormality has been detected in a specific area."

[1628] Hardware and software used

[1629] Hardware:

[1630] Factory robots

[1631] Wi-Fi network in the factory

[1632] Smartphones and tablets

[1633] software:

[1634] Cloud servers (e.g. AWS, Google Cloud)

[1635] REST API

[1636] Push notification system for smart devices (e.g., Firebase Cloud Messaging)

[1637] Prompt Sentence Examples

[1638] The factory robot "R123" performs assembly work for 8 hours every day and requires maintenance once a month. Based on past operation data, generate an optimal maintenance schedule and create notification messages for maintenance staff.

[1639] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1640] Step 1:

[1641] The user installs a calendar application and registers an account. The input information is the user's basic information and the roles and operating patterns of machines in the factory. This information is sent to a cloud server, and the basic data of the user and machine is stored in a database. The output is the registered user account and machine data.

[1642] Step 2:

[1643] The terminal collects the operation history data of machines in the factory in real time. The input information is the operation data of each machine (operation start time, end time, stop time, etc.). This data is sent to a cloud server, which stores the operation history data in a database. The output is the stored operation history data.

[1644] Step 3:

[1645] The server analyzes the collected operation history data and learns the operation patterns of each machine. The input is the saved operation history data. An analytical algorithm is used to extract the machine's operation pattern (e.g., 8 hours of operation every day, maintenance once a month). The output is the learned operation pattern information.

[1646] Step 4:

[1647] The server automatically creates future operation and maintenance schedules based on the learned operation patterns. The input is the learned operation pattern information. A scheduling algorithm is used to predict the next maintenance timing and generate the schedule. The output is the generated future schedule.

[1648] Step 5:

[1649] The server notifies the generated future schedule to the factory manager or maintenance staff. The input is the generated future schedule. A push notification system is used to send detailed information about the schedule to smartphones or tablets. The output is the notified schedule information.

[1650] Step 6:

[1651] The terminal periodically acquires the machine's location information and sends it to the server. The input is the machine's location data. The location information is sent to the cloud server, which stores the information in a database. The output is the stored location information.

[1652] Step 7:

[1653] The server collects event information for a specific area based on the acquired location information. The input is the saved location information. Event information (e.g., abnormal shutdowns, emergencies, etc.) for the specific area is collected using web scraping or APIs. The output is the collected event information.

[1654] Step 8:

[1655] The server notifies the maintenance staff of the collected event information. The input is the collected event information. A push notification system is used to notify the smart device of abnormal information in real time. The output is the notified event information.

[1656] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1657] Overall system configuration

[1658] The system of the present invention is implemented as a calendar application consisting of a user terminal, a server, and an emotion engine. The user terminal is a device such as a smartphone or tablet, and the server is located on the cloud and stores and analyzes user information and event data. The emotion engine recognizes the user's emotions and provides recommended information and schedule adjustments based on the user's emotional state.

[1659] User registration and initial settings

[1660] User: To use the system, the user first installs the calendar app and registers an account when the system is first launched. When registering, the user enters their interests (e.g., hair salons, live concerts, movies, etc.).

[1661] Device: Once you have completed your account registration and entered your interests, that information will be sent to the server.

[1662] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[1663] Analysis of behavioral history

[1664] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[1665] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if it detects that the user goes to the hair salon on the third Saturday of every month, it automatically generates future regular schedules based on this information.

[1666] Creating an Automatic Schedule

[1667] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[1668] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[1669] On the device: The user will be notified via push notification that the reservation has been completed.

[1670] Event information collection and notification

[1671] Server: Based on the user's interests, collects relevant event information (for example, information about live performances by favorite artists or new movie releases), and prepares to notify the user at the optimal time based on the collected event information.

[1672] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[1673] Use of location information

[1674] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[1675] Server: Collects information about nearby events (such as newly opened cafes or special sales at supermarkets) based on location information. Sends the collected event information to the user's device.

[1676] Device: Sends push notifications to users based on nearby event information received.

[1677] Implementing the Emotion Engine

[1678] Terminal: Collects data to analyze the user's emotions through daily events and input data, and sends it to the emotion engine.

[1679] Emotion engine: Analyzes the user's emotional data and estimates their emotional state in real time, using, for example, text analysis, voice analysis, and facial expression analysis.

[1680] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it can suggest relaxation events when the user is under high stress.

[1681] Specific examples

[1682] Hair salon reservation example

[1683] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[1684] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[1685] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[1686] Concert ticket purchase example

[1687] User: Register a favorite artist (e.g., a particular musician) as a topic.

[1688] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[1689] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[1690] Emotion Engine: If a user has a strong emotional connection to this artist, they can receive special reminders or early notifications of ticket sales.

[1691] Example of supermarket sale information

[1692] Device: Sends the user's location information to the server.

[1693] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[1694] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[1695] Emotion engine: If a user responds positively to a sale, the system will prioritize notifications of similar sales.

[1696] This concludes the "Mode for Carrying Out the Invention" of the system of the present invention. This system not only utilizes the user's behavioral history, interests, and location information, but also combines it with an emotion engine to achieve more personalized schedule management and support the user's daily life.

[1697] The processing flow will be explained below.

[1698] Basic processing steps of the system

[1699] Step 1:

[1700] User: Installs the calendar app on their smartphone and launches it for the first time.

[1701] Step 2:

[1702] On device: The app prompts the user to sign up for an account and asks them to enter topics or keywords of interest.

[1703] Step 3:

[1704] Users: Enter topics of interest such as hair salons, live concerts, movies, etc., and submit them along with their account information.

[1705] Step 4:

[1706] Terminal: Stores the entered information in local storage and prepares it for transmission to the server.

[1707] Step 5:

[1708] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[1709] Analysis of behavioral history

[1710] Step 6:

[1711] Device: Collects the user's past calendar events and behavioral history data and sends them to the server.

[1712] Step 7:

[1713] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns.

[1714] Creating an Automatic Schedule

[1715] Step 8:

[1716] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[1717] Step 9:

[1718] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[1719] Step 10:

[1720] On the device: The user will be notified via push notification that the reservation has been completed.

[1721] Event information collection and notification

[1722] Step 11:

[1723] Server: Collects relevant event information based on the user's interests (for example, information about live performances by favorite artists or new movie releases).

[1724] Step 12:

[1725] Server: Based on the collected event information, selects the information to notify the user at the optimal time.

[1726] Step 13:

[1727] Server: Sends selected event information to the device.

[1728] Step 14:

[1729] On the device: Sends push notifications to users based on the received event information and automatically adds events to their calendars.

[1730] Acquisition and use of location information

[1731] Step 15:

[1732] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[1733] Step 16:

[1734] Server: Collects and analyzes information about nearby events based on location information.

[1735] Step 17:

[1736] Server: Selects the collected nearby event information and creates notification information.

[1737] Step 18:

[1738] Device: Sends push notifications to users based on nearby event information received.

[1739] Emotion Recognition and Applications

[1740] Step 19:

[1741] Terminal: Collects data to analyze the user's emotions through daily events and data entered by the user (emails, chat logs, etc.) and sends it to the server.

[1742] Step 20:

[1743] Server: Sends the received emotion data to the emotion engine for analysis.

[1744] Step 21:

[1745] Emotion Engine: Analyzes the user's emotional state and classifies it as positive, negative, or stressful.

[1746] Step 22:

[1747] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it suggests relaxation events when stress levels are high.

[1748] Specific examples

[1749] Hair salon reservation example

[1750] Step 1:

[1751] Server: From the user's behavioral history, it learns that the user visits the beauty salon on the third Saturday of every month.

[1752] Step 2:

[1753] Server: When the next third Saturday approaches, make a reservation through the hair salon booking API.

[1754] Step 3:

[1755] Server: After the reservation is completed, create a notification message and send it to the device.

[1756] Step 4:

[1757] Device: A push notification will be sent to the user upon receiving the reservation completion notification.

[1758] Step 5:

[1759] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[1760] Concert ticket purchase example

[1761] Step 1:

[1762] User: Register a favorite artist (e.g., a particular musician) as a topic.

[1763] Step 2:

[1764] Server: Collects concert information for the artist in question and checks when tickets go on sale.

[1765] Step 3:

[1766] Server: Prepare information to notify users as the sale date approaches.

[1767] Step 4:

[1768] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[1769] Step 5:

[1770] Emotion Engine: If a user has a strong emotional connection to this artist, they will receive special reminders or early notifications of ticket sales.

[1771] Example of supermarket sale information

[1772] Step 1:

[1773] Device: Sends the user's location information to the server.

[1774] Step 2:

[1775] Server: Collects information about special offers at nearby supermarkets based on location information.

[1776] Step 3:

[1777] Server: Creates notification information based on the results of collection and analysis.

[1778] Step 4:

[1779] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[1780] Step 5:

[1781] Emotion engine: If a user responds positively to a sale, the engine will prioritize notifications of similar sales.

[1782] The above is the specific processing flow of the system of the present invention. By combining it with an emotion engine, personalized schedule management based on the user's emotional state becomes possible, making the user's daily life more fulfilling.

[1783] Example 2

[1784] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1785] In recent years, schedule management applications using mobile devices such as smartphones and tablets have become widespread. However, conventional calendar and scheduling applications do not adequately provide personalized schedule management that takes into account a user's behavioral history, interests, location information, and even emotional state. This results in a problem in that truly useful schedule suggestions and notifications cannot be realized. Furthermore, because schedules are not adjusted according to the user's emotional state, suggestions for stress management and relaxation are insufficient. The present invention aims to solve these problems.

[1786] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1787] In this invention, the server includes a means for learning a user's behavioral history, a means for automatically creating a recurring schedule based on the learned behavioral history, and a means for collecting related event information based on the user's registered interests. This allows the server to learn the user's behavioral patterns and automatically generate a recurring schedule based on them. Furthermore, event information based on the user's interests can be collected and notified, enabling appropriate event suggestions to be made to the user. Furthermore, by analyzing the user's emotional data and adjusting the schedule and notification content based on the user's emotional state, relaxation events tailored to the user's emotional state can be suggested and flexible schedule management can be realized.

[1788] "User" refers to a person who uses this system, and is a person who uses a mobile device such as a smartphone or tablet.

[1789] "Action history" refers to records of events such as calendar entries and event participation made by a user in the past.

[1790] "Means of learning" refers to algorithms or programs that analyze collected user behavior history data and identify certain patterns and trends.

[1791] The "means for automatically creating periodic schedules" is a program that has the function of automatically generating future schedules for the user based on learned behavioral patterns.

[1792] "Interests" refers to topics or areas of interest registered by a user (e.g., hair salons, live music, movies, etc.).

[1793] The "means for collecting relevant event information" is a program for collecting event data from the Internet and other sources based on the user's interests.

[1794] The "means for notifying the user" is a program that has the function of notifying the user of collected event information and generated schedules via push notifications or other methods on the user's device.

[1795] "Location information" refers to data related to a user's current location and movement history.

[1796] The means for collecting "neighborhood event information" is a program for acquiring information about events and facilities held in the vicinity based on the user's current location.

[1797] "Emotional data" refers to data that indicates the user's emotional state (e.g., stress level, happiness level, etc.), and is obtained through text analysis, voice analysis, facial expression analysis, etc.

[1798] "Means for adjusting schedules and notification content based on emotional state" refers to algorithms or programs for dynamically changing a user's schedule and notification content based on emotional data.

[1799] Overall system configuration

[1800] The system of the present invention is implemented as a calendar management application consisting of a user terminal, a server, and an emotion engine. The user terminal includes mobile devices such as smartphones and tablets. The server is located on the cloud and stores and analyzes user information and event data. The emotion engine recognizes the user's emotions and makes recommended information and schedule adjustments based on the user's emotional state.

[1801] User registration and initial settings

[1802] User: To use the system, after installing the calendar app, users must register an account the first time they start the app by entering their username, email address, password, and interests (e.g., hair salons, live concerts, movies, etc.).

[1803] Device: Once you have registered and entered your interests, this information is sent to our server.

[1804] Server: The server stores the received user information and interest data in a database and prepares it for analysis.

[1805] Analysis of behavioral history

[1806] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[1807] Server: The server analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if the server detects that the user visits the beauty salon on the third Saturday of every month, it will automatically generate future regular schedules based on this information.

[1808] Creating an Automatic Schedule

[1809] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[1810] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[1811] On the device: The user will be notified via push notification that the reservation has been completed.

[1812] Event information collection and notification

[1813] Server: Collects relevant event information based on the user's interests (e.g., information about live performances by favorite artists or new movie releases). Prepares notifications based on the collected event information.

[1814] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[1815] Use of location information

[1816] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[1817] Server: Based on location information, collects information about nearby events (e.g., information about newly opened cafes or special sales at supermarkets) and sends the collected event information to the user's device.

[1818] Device: Sends push notifications to users based on nearby event information received.

[1819] Implementing the Emotion Engine

[1820] Terminal: Collects data to analyze the user's emotions through daily events and input data, and sends it to the emotion engine.

[1821] Emotion engine: The emotion engine analyzes the user's emotional data and estimates their emotional state in real time, for example, by using text analysis, voice analysis, and facial expression analysis.

[1822] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it can suggest relaxation events when the user is under high stress.

[1823] Specific examples

[1824] Hair salon reservation example

[1825] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[1826] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[1827] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[1828] Concert ticket purchase example

[1829] User: Register a favorite artist (e.g., a specific musician) as a topic.

[1830] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[1831] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[1832] Emotion Engine: If a user has a strong emotional connection to this artist, they can receive special reminders or early notifications of ticket sales.

[1833] Example of supermarket sale information

[1834] Device: Sends the user's location information to the server.

[1835] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[1836] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[1837] Emotion engine: If a user responds positively to a sale, the system will prioritize notifications of similar sales.

[1838] Example prompts for generative AI models

[1839] 1. "Please explain the algorithm of a hair salon reservation system."

[1840] 2. "Please give a specific example of how the emotion engine detects a user's stress level."

[1841] 3. "Please explain the structure of a system that uses location information to notify users of nearby events."

[1842] This concludes the description of the embodiment of the present invention. By utilizing the user's behavioral history, interests, and location information, and by combining it with an emotion engine, it is possible to achieve more personalized schedule management and support the user's daily life.

[1843] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1844] Step 1:

[1845] The user installs the Calendar app and launches it for the first time.

[1846] Input: A user installs a calendar app on their smartphone or tablet.

[1847] Output: The app launches for the first time and the account registration screen appears.

[1848] What happens: A user downloads an app from the app store, installs it, and then launches it.

[1849] Step 2:

[1850] The user registers an account.

[1851] Input: The user enters their account information (username, email address, password).

[1852] Output: Account information is registered in the app.

[1853] Specific behavior: The user fills in the required information in the provided form and clicks the "Register" button.

[1854] Step 3:

[1855] The user enters their interests.

[1856] Input: User selects or inputs an interest (e.g., hair salon, live concert, movie, etc.).

[1857] Output: Interest data is stored on the device.

[1858] What happens: The user selects an interest from a list and taps the "Save" button on the screen.

[1859] Step 4:

[1860] The terminal transmits the user information to the server.

[1861] Input: Registered account information and interest data.

[1862] Output: User information is saved on the server.

[1863] Specific operation: The device sends data to the server via the Internet.

[1864] Step 5:

[1865] The server stores the user information in a database.

[1866] Input: User information and interest data sent from your device.

[1867] Output: User information stored in the database.

[1868] What happens: The server parses the received data and stores it in the appropriate fields in the database.

[1869] Step 6:

[1870] The device collects past calendar event data and sends it to the server.

[1871] Input: Past calendar event data stored on your device.

[1872] Output: Behavioral history data sent to the server.

[1873] Specific operation: The device reads past event information from the local calendar database and sends it to the server.

[1874] Step 7:

[1875] The server analyzes behavioral history data and learns behavioral patterns.

[1876] Input: Past calendar event data.

[1877] Output: Learned behavioral patterns.

[1878] How it works: The server uses an AI model to analyze behavioral data and learn certain behavioral patterns (e.g., going to the hairdresser on the third Saturday of every month).

[1879] Step 8:

[1880] The server automatically generates a recurring schedule based on the learning results.

[1881] Input: Learned behavioral patterns.

[1882] Output: Recurring appointment data.

[1883] Specific operation: Based on the behavioral patterns learned by the AI ​​model, the system generates the user's next appointment and confirms it via the salon's booking API.

[1884] Step 9:

[1885] The server creates a reservation completion notice and sends it to the user terminal.

[1886] Input: Reservation confirmation information.

[1887] Output: Reservation completion notification.

[1888] Specific operation: The server generates a reservation completion message based on the reservation information and sends it to the user's specified terminal.

[1889] Step 10:

[1890] The device will send a push notification of reservation completion to the user.

[1891] Input: Reservation completion notification information.

[1892] Output: Push notification to the user.

[1893] Specific operation: The device displays the received notification information as a push notification to notify the user.

[1894] Step 11:

[1895] The server collects relevant event information and prepares notifications.

[1896] Input: User interest data.

[1897] Output: Related event information.

[1898] Specific operation: The server filters relevant event information collected from the Internet and selects information appropriate for the user.

[1899] Step 12:

[1900] The device receives the notification information and pushes it to the user.

[1901] Input: Relevant event notification information.

[1902] Output: Push notification to the user.

[1903] Specific operation: The device displays the received notification information as a push notification to notify the user.

[1904] Step 13:

[1905] The device periodically obtains location information or when the app is launched and sends it to the server.

[1906] Input: Location data.

[1907] Output: The location sent to the server.

[1908] Specific operation: The device uses the GPS function to obtain its current location and sends it to a server via the Internet.

[1909] Step 14:

[1910] The server collects nearby event information based on location information.

[1911] Input: Location data.

[1912] Output: Nearby event information.

[1913] Specific operation: The server collects event information for the specified area from the Internet based on the location information.

[1914] Step 15:

[1915] The server transmits the collected event information to the user terminal.

[1916] Input: Nearby event information.

[1917] Output: Event information notification.

[1918] Specific operation: The server sends filtered nearby event information to the user's device via the Internet.

[1919] Step 16:

[1920] The device sends push notifications to users based on nearby event information received.

[1921] Input: Nearby event information.

[1922] Output: Push notification to the user.

[1923] Specific operation: The device notifies the user of the event information received via a push notification.

[1924] Step 17:

[1925] The terminal collects the user's emotion data and sends it to the emotion engine.

[1926] Input: User text, voice, and facial expression data.

[1927] Output: Emotion data.

[1928] Specific operation: The device analyzes the user's input data (e.g., diary, voice memo) and sends it to the emotion engine.

[1929] Step 18:

[1930] The emotion engine analyzes the emotion data and estimates the emotional state.

[1931] Input: User emotion data.

[1932] Output: Estimated emotional state.

[1933] What it does: The emotion engine performs text analysis, speech analysis, and facial expression analysis to estimate emotional states (e.g., stress, joy).

[1934] Step 19:

[1935] The server adjusts schedules and notification content based on emotional state data.

[1936] Input: Estimated emotional state.

[1937] Output: Adjusted schedule and notification content.

[1938] Specific operation: Based on the data from the emotion engine, the server suggests relaxation events and changes task priorities, and notifies the user.

[1939] (Application example 2)

[1940] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1941] Conventional calendar applications have limited ability to manage personalized schedules based on user behavior and interests, and they also lack the ability to flexibly adjust schedules based on the user's emotional state or location information. Therefore, there is a need for an effective means to enrich users' daily lives and in-store experiences.

[1942] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1943] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting related event information based on the interests registered by the user, means for notifying the user based on the collected event information, emotion analysis means for analyzing the user's emotions, and means for providing suggested information based on the emotion analysis results. This makes it possible to provide optimal schedules and suggested information in real time based on the user's behavioral history and interests as well as their emotional state and location information.

[1944] "Behavioral history" refers to a record of a user's past activities and events.

[1945] A "regular appointment" refers to a schedule that occurs periodically based on the user's behavioral patterns.

[1946] "Interests" refer to the themes, categories, and activities in which a user is particularly interested.

[1947] "Event information" refers to detailed information about events and activities related to the user's interests.

[1948] "Notification" refers to an alert or message that conveys information to a user.

[1949] "Emotion analysis" refers to the process for analyzing and inferring a user's emotional state.

[1950] "Suggested information" refers to recommendations and actions provided to users based on sentiment analysis, behavioral history, and interests.

[1951] "Location information" refers to geographic data that indicates the user's current location.

[1952] This invention provides personalized schedules and recommended information based on a user's behavioral history, interests, emotional state, and location information. The main components include a user terminal, a cloud server, and an emotion analysis engine. Specifically, this system is implemented as follows:

[1953] User terminal

[1954] User devices are primarily smartphones and smart glasses, which collect user behavioral history and location information. These devices are operated by users downloading apps and registering their interests through the initial setup. For example, apps installed on smartphones have the ability to routinely collect user location information and send it to a server.

[1955] Cloud Server

[1956] The cloud server stores and analyzes users' behavioral history, interests, and location information. The generated data is used to learn each user's patterns and automatically create recurring schedules. The cloud server also collects external event information and generates notifications at optimal times based on the user's interests.

[1957] Sentiment Analysis Engine

[1958] The emotion analysis engine analyzes the user's emotional state in real time. Using technologies such as text analysis, voice analysis, and facial expression analysis, it estimates whether the user is stressed or relaxed. Based on this emotional state, the cloud server generates suggested information appropriate for the user and notifies the device. For example, if the emotion analysis engine detects that the user is stressed, it will prioritize notifying the user of information about nearby relaxation facilities.

[1959] Specific examples

[1960] 1. Hair salon reservation example

[1961] The server learns from the user's behavioral history that they visit the hair salon on the third Saturday of every month, and makes the same reservation the next time. It calls the hair salon's reservation API and automatically completes the reservation. The device sends a reservation completion notification to the user, informing them that "your next hair salon reservation has been completed."

[1962] 2. Example of store navigation based on emotional state

[1963] If the emotion analysis engine detects that the user's stress level is high, the cloud server collects information about nearby relaxation facilities and discounts, and notifies the user via push notification, saying, "There is a relaxation facility nearby."

[1964] Prompt Sentence Examples

[1965] The following prompt sentence is used as input to the generative AI model:

[1966] User ID: 12345

[1967] Emotional state: Stress

[1968] Location: Latitude 35.6895, Longitude 139.6917

[1969] Interests: [Fashion, Beauty, Music]

[1970] Q: Can you recommend an event or service that would be suitable for this user?

[1971] This invention makes it possible to provide optimal schedules and suggested information in real time based on a user's behavioral history and interests as well as emotional state and location information.

[1972] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1973] Step 1: The user installs the calendar app and sets it up.

[1974] Input: User installs of the app, interest registrations.

[1975] How it works: A user installs a calendar app on their smartphone or smart glasses and registers their interests (e.g., fashion, beauty, music).

[1976] Output: Registered user information and interest data are sent to the cloud server.

[1977] Step 2: The user device collects behavioral history data and sends it to the server.

[1978] Inputs: Events and past actions that users enter into their calendar.

[1979] How it works: The user's device periodically collects event data entered in the calendar and sends it to a cloud server.

[1980] Output: The collected behavioral history data is stored on a cloud server.

[1981] Step 3: The cloud server learns behavioral patterns and automatically creates recurring schedules.

[1982] Input: Behavioral history data stored on the server.

[1983] How it works: The server uses machine learning algorithms to analyze the user's behavioral patterns and automatically create future recurring appointments (e.g., a hair salon appointment on the third Saturday of every month).

[1984] Output: The automatically generated recurring events are reflected in the calendar.

[1985] Step 4: The emotion analysis engine analyzes the user's emotional state in real time.

[1986] Input: Data required for emotion analysis, such as text data, audio data, and facial expression data.

[1987] How it works: The emotion analysis engine analyzes this data and estimates the user's emotional state (e.g., high stress level, relaxed, etc.).

[1988] Output: The estimated emotional state is sent to the cloud server.

[1989] Step 5: The cloud server collects appropriate event information based on the emotional state and location information.

[1990] Input: User's emotional state data, current location.

[1991] How it works: The cloud server accesses an external event information API and collects event information related to the user's interests. If the emotional state is stressed, it will prioritize collecting relaxation events.

[1992] Output: The collected event information is stored on the server in an appropriate format.

[1993] Step 6: The cloud server generates recommendation information and notifies the user device.

[1994] Input: Emotional state data, location information, collected event information.

[1995] How it works: The server uses a recommendation algorithm to select the most suitable event information for the user and generate a notification message, such as "There are currently relaxation facilities nearby."

[1996] Output: A notification message is sent to the user's terminal.

[1997] Step 7: The user terminal receives the notification and displays the notification to the user.

[1998] Input: The notification message sent by the server.

[1999] How it works: Notifications are pushed to the user's smartphone or smart glasses and appear as pop-ups or alerts.

[2000] Output: The user can confirm the notification.

[2001] This series of processing steps allows users to receive personalized schedule and event recommendations based on their behavioral history, interests, emotional state, and location.

[2002] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2003] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2004] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2005] [Fourth embodiment]

[2006] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2007] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2008] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2009] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2010] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2011] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2012] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2013] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2014] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2015] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2016] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2017] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2018] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2019] Overall system configuration

[2020] The system of the present invention is implemented as a calendar application consisting of a user terminal and a server. The user terminal is a device such as a smartphone or tablet, and the server is located on the cloud and stores and analyzes user information and event data.

[2021] User registration and initial settings

[2022] User: To use the system, the user first installs the calendar app and registers an account when the system is first launched. When registering, the user enters their interests (e.g., hair salons, live concerts, movies, etc.).

[2023] Device: Once you have completed your account registration and entered your interests, that information will be sent to the server.

[2024] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[2025] Analysis of behavioral history

[2026] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[2027] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if it detects that the user goes to the hair salon on the third Saturday of every month, it automatically generates future regular schedules based on this information.

[2028] Creating an Automatic Schedule

[2029] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[2030] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[2031] On the device: The user will be notified via push notification that the reservation has been completed.

[2032] Event information collection and notification

[2033] Server: Based on the user's interests, collects relevant event information (for example, information about live performances by favorite artists or new movie releases), and prepares to notify the user at the optimal time based on the collected event information.

[2034] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[2035] Use of location information

[2036] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[2037] Server: Collects information about nearby events (such as newly opened cafes or special sales at supermarkets) based on location information. Sends the collected event information to the user's device.

[2038] Device: Sends push notifications to users based on nearby event information received.

[2039] Specific examples

[2040] Hair salon reservation example

[2041] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[2042] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[2043] Concert ticket purchase example

[2044] User: Register a favorite artist (e.g., a particular musician) as a topic.

[2045] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[2046] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[2047] Example of supermarket sale information

[2048] Device: Sends the user's location information to the server.

[2049] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[2050] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[2051] This concludes the "Mode for Carrying Out the Invention" of the system of the present invention. This system utilizes the user's behavioral history, interests, and location information to realize efficient schedule management and support the user's daily life.

[2052] The processing flow will be explained below.

[2053] Step 1:

[2054] User: Installs the calendar app on their smartphone and launches it for the first time.

[2055] Step 2:

[2056] On device: The app prompts the user to sign up for an account and asks them to enter topics or keywords of interest.

[2057] Step 3:

[2058] Users: Enter topics of interest such as hair salons, live concerts, movies, etc., and submit them along with their account information.

[2059] Step 4:

[2060] Terminal: Stores the entered information in local storage and prepares it for transmission to the server.

[2061] Step 5:

[2062] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[2063] Step 6:

[2064] Device: Collects the user's past calendar events and behavioral history data and sends them to the server.

[2065] Step 7:

[2066] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns.

[2067] Step 8:

[2068] Server: Automatically generates regular schedules for users based on the learning results. For example, predict the date of a hair salon appointment and call the reservation API to make the appointment.

[2069] Step 9:

[2070] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[2071] Step 10:

[2072] On the device: The user will be notified via push notification that the reservation has been completed.

[2073] Step 11:

[2074] Server: Collects and analyzes relevant event information based on user interests.

[2075] Step 12:

[2076] Server: Based on the analysis results, selects the event information to notify the user at the optimal time.

[2077] Step 13:

[2078] Server: Sends selected event information to the device.

[2079] Step 14:

[2080] On the device: Sends push notifications to users based on the received event information and automatically adds events to their calendars.

[2081] Step 15:

[2082] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[2083] Step 16:

[2084] Server: Collects and analyzes information about nearby events based on location information.

[2085] Step 17:

[2086] Server: Based on the collected and analyzed results, it creates event information to notify the user and sends it to the terminal.

[2087] Step 18:

[2088] Device: Sends push notifications to users based on nearby event information received.

[2089] Specific examples

[2090] Hair salon reservation example

[2091] Step 1:

[2092] Server: Analyzes the user's past hair salon reservation history and learns that the user visits the hair salon on the third Saturday of every month.

[2093] Step 2:

[2094] Server: When the next third Saturday approaches, make a reservation through the hair salon booking API.

[2095] Step 3:

[2096] Server: After the reservation is completed, create a notification message and send it to the device.

[2097] Step 4:

[2098] Device: A push notification will be sent to the user upon receiving the reservation completion notification.

[2099] Concert ticket purchase example

[2100] Step 1:

[2101] User: Register a favorite artist (e.g., a particular musician) as a topic.

[2102] Step 2:

[2103] Server: Collects concert information for the artist in question and checks when tickets go on sale.

[2104] Step 3:

[2105] Server: Prepare information to notify users as the sale date approaches.

[2106] Step 4:

[2107] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[2108] Example of supermarket sale information

[2109] Step 1:

[2110] Device: Sends the user's location information to the server.

[2111] Step 2:

[2112] Server: Collects information about special offers at nearby supermarkets based on location information.

[2113] Step 3:

[2114] Server: Analyzes sale information and creates notification information.

[2115] Step 4:

[2116] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[2117] Example 1

[2118] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2119] In modern society, users need to manage their busy schedules. However, conventional schedule management systems lacked the ability to respond individually to users' behavioral patterns and interests, requiring a great deal of time and effort for manual input and management. Furthermore, they lacked mechanisms for efficiently collecting and notifying users of nearby event information and service reservation information. This often led to users missing important information, reducing the efficiency and convenience of schedule management.

[2120] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2121] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting relevant event information based on the user's registered interests, means for notifying the user based on the collected event information, means for collecting event information of interest based on location information transmitted from the user terminal, means for notifying the user at an optimal timing based on the acquired location information and interest information, means for automatically making a reservation by calling a service reservation API for a beauty salon or the like, means for confirming the success of the reservation and, if successful, sending a reservation completion notification to the user terminal, means for analyzing past calendar events and estimating behavioral patterns, means for automatically creating future schedules based on the estimated behavioral patterns, and means for notifying the user of detailed schedule information. This allows users to manage their schedules individually and efficiently live their daily lives without missing important information.

[2122] "User" refers to a person who uses the system and receives services through a terminal.

[2123] "Behavioral history" refers to a record of actions and events that a user has taken in the past, including information entered into a schedule or calendar.

[2124] A "recurring appointment" refers to a regular schedule or reservation that is automatically generated based on a user's behavioral patterns.

[2125] "Interests" refers to the types of things or events that a user is interested in, such as hair salons, live concerts, movies, etc.

[2126] "Event information" refers to detailed information about an event related to the user's interests, including the date, time, location, and content.

[2127] "Location Information" means geographic location data obtained by a User's Device, such as data obtained using GPS or other technologies.

[2128] "Booking API" means an application programming interface for making reservations for certain services over the Internet.

[2129] "Push notification" refers to a communication method that transmits information from a server to a user's device in real time, allowing the user to be notified immediately.

[2130] "Behavioral patterns" refer to certain behavioral tendencies and habits that are estimated based on a user's past behavioral history.

[2131] "Machine learning algorithms" refer to computer algorithms that analyze data, learn, and automatically recognize patterns.

[2132] The system of the present invention is implemented as a calendar application using a user terminal such as a smartphone or tablet, and a server located on the cloud, which stores and analyzes user information and event data.

[2133] User registration and initial settings

[2134] To use the system, users first install the calendar app on their device and register an account when they first launch the system. When registering, users input their interests (e.g., beauty salons, live concerts, movies, etc.).

[2135] The terminal transmits the registration information and interest data to the server.

[2136] The server stores the received data in a database for future analysis.

[2137] Analysis of behavioral history

[2138] The device periodically collects events and behavioral history data previously entered in the calendar and sends it to the server.

[2139] The server then uses a machine learning algorithm to analyze the received behavioral history data. For example, it can detect a pattern where a user visits a beauty salon on the third Saturday of every month. The learning results are stored in a database and used for future predictions.

[2140] Creating an Automatic Schedule

[2141] The server predicts the next appointment date for the hair salon based on the learning results, calls the salon's reservation API according to the predicted date, checks whether the reservation process was successful, and creates a reservation completion notification if it was successful.

[2142] The server sends a reservation completion notification to the user terminal.

[2143] The terminal receives the reservation completion notification and notifies the user via push notification.

[2144] Event information collection and notification

[2145] The server collects relevant event information based on the user's interests (for example, information on live performances by favorite artists or new movie releases) through web scraping or APIs, and stores the collected event information in a database.

[2146] The server prepares to notify the user of the collected event information at the optimal timing, which is set based on the user's behavioral patterns and past interests.

[2147] The server transmits the event information that is ready for notification to the user terminal.

[2148] The device receives the event information notification, notifies the user via push notification, and automatically adds the event to the calendar.

[2149] Use of location information

[2150] The device acquires location information when the user launches the app or periodically, and sends the acquired location information to the server.

[2151] The server uses the received location information to collect information about nearby events (such as newly opened cafes or special sales at supermarkets) using web scraping and APIs.

[2152] The server transmits the collected event information to the user terminal.

[2153] The device will then send a push notification to the user based on the received information about nearby events.

[2154] Prompt Sentence Examples

[2155] An example of a prompt sentence to input to the generative AI model is as follows:

[2156] "Imagine a system that learns how often a user visits the hair salon on the third Saturday of every month and automatically schedules the next appointment. Include a function that calls the salon's booking API and notifies the user on success."

[2157] The system of the present invention effectively utilizes a user's behavioral history, interests, and location information to provide efficient schedule management and event information. This system allows users to live their daily lives efficiently without missing important information.

[2158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2159] Step 1:

[2160] User: Install the calendar app on the device and register an account the first time you launch it.

[2161] Specific Actions: The user enters their name, email address, password, and interests (e.g., hair salons, live concerts, movies, etc.).

[2162] Input: User information and interest data.

[2163] Output: The registration information is saved on the device.

[2164] Step 2:

[2165] Device: Sends registration information and interest data to a server.

[2166] Specific operation: The terminal sends data to the server via the Internet.

[2167] Input: User information and interest data.

[2168] Output: Received by the server.

[2169] Step 3:

[2170] Server: Stores the received data in a database.

[2171] Specific operation: The server adds user information and interest data to the database.

[2172] Input: User information and interest data.

[2173] Output: The database is updated.

[2174] Step 4:

[2175] Device: Past events entered in the calendar and activity history data are periodically collected and sent to the server.

[2176] Specific operation: The terminal reads past event data and sends it to the server at the set interval.

[2177] Input: Past events and behavioral history data.

[2178] Output: Received by the server.

[2179] Step 5:

[2180] Server: Analyzes the received behavioral history data using machine learning algorithms.

[2181] Specific operation: The server uses analytical software (e.g., Python and scikit-learn) to analyze the data and extract behavioral patterns.

[2182] Input: Behavioral history data.

[2183] Output: A pattern of behavior as a result of learning.

[2184] Step 6:

[2185] Server: Predicts the next hair salon appointment date based on the learning results and calls the hair salon's reservation API according to the predicted date.

[2186] Specific operation: The server analyzes behavioral patterns, determines an appropriate reservation date and time, and calls the reservation API.

[2187] Input: behavioral patterns, reservation API information.

[2188] Output: Booking request.

[2189] Step 7:

[2190] Server: Checks whether the reservation process was successful and creates a reservation completion notification if successful.

[2191] Specific operation: The server checks the response from the reservation API and generates a notification message if successful.

[2192] Input: Booking API response.

[2193] Output: Reservation completion notification.

[2194] Step 8:

[2195] Server: Sends a reservation completion notification to the user's terminal.

[2196] Specific operation: The server sends the generated notification message to the user terminal.

[2197] Input: Reservation completion notification.

[2198] Output: Received by the user terminal.

[2199] Step 9:

[2200] Device: Receives a notification that the reservation is complete and notifies the user via push notification.

[2201] Specific operation: The device uses the push notification function to display a message to the user.

[2202] Input: Reservation completion notification.

[2203] Output: The user is notified.

[2204] Step 10:

[2205] Server: Collects relevant event information based on user interests through web scraping or APIs.

[2206] Specific Operation: The server searches the Internet and collects event information related to your interests.

[2207] Input: Interest data.

[2208] Output: Collected event information.

[2209] Step 11:

[2210] Server: Stores the collected event information in a database.

[2211] Specific operation: The server adds the event information to the database.

[2212] Input: Event information.

[2213] Output: The database is updated.

[2214] Step 12:

[2215] Server: Prepares to notify the user of the collected event information at the optimal time.

[2216] Specific operation: The server calculates the notification timing and generates a notification message for the user.

[2217] Input: Event information, notification timing.

[2218] Output: Informational message.

[2219] Step 13:

[2220] Server: Sends event information that is ready for notification to the user device.

[2221] Specific operation: The server sends the generated notification message to the user terminal.

[2222] Input: Notification message.

[2223] Output: Received by the user terminal.

[2224] Step 14:

[2225] Device: Receives event information notifications and notifies the user via push notifications.

[2226] Specific operation: The device uses the push notification function to display a message to the user.

[2227] Input: Notification message.

[2228] Output: The user is notified.

[2229] Step 15:

[2230] Device: Location information is obtained when the user launches the app and periodically.

[2231] What happens: Your device uses GPS and other location services to get its current location.

[2232] Enter: Location Services.

[2233] Output: The location information obtained.

[2234] Step 16:

[2235] Device: Sends the acquired location information to the server.

[2236] Specific operation: The device sends the acquired location information to the server.

[2237] Input: The acquired location information.

[2238] Output: Received by the server.

[2239] Step 17:

[2240] Server: Collects nearby event information based on the received location information.

[2241] Specific operation: The server searches the Internet and collects nearby event information based on location information.

[2242] Input: Location.

[2243] Output: Collected event information.

[2244] Step 18:

[2245] Server: Sends collected event information to the user's device.

[2246] Specific operation: The server transmits the collected event information to the user terminal.

[2247] Input: Event information.

[2248] Output: Received by the user terminal.

[2249] Step 19:

[2250] Device: Sends push notifications to users based on nearby event information received.

[2251] Specific operation: The device uses the push notification function to display a message to the user.

[2252] Input: Event information.

[2253] Output: The user is notified.

[2254] (Application example 1)

[2255] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2256] Existing calendar applications optimize schedules by utilizing user behavioral history, interests, and location information, but there is also room for application to machine operation management and maintenance management in factory environments. However, current systems lack the functionality to comprehensively manage the operation history and location information of machines in a factory and automatically generate efficient schedules based on that data. Therefore, a solution to improve factory management efficiency is desired.

[2257] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2258] In this invention, the server includes means for learning a user's behavioral history, means for automatically creating periodic schedules based on the learned behavioral history, means for collecting related event information based on interests registered by the user, means for notifying the user based on the collected event information, means for learning the operation history of machines in a factory, and means for automatically creating periodic maintenance schedules based on the learned operation history, thereby enabling efficient management and optimization of operation schedules and maintenance schedules for machines in a factory.

[2259] "Means for learning user behavior history" refers to algorithms and tools that analyze users' past behavioral data and identify trends.

[2260] A "means for automatically creating periodic schedules" is a program or system for automatically generating future schedules for a user or machine based on learned behavioral patterns.

[2261] "Means for collecting related event information based on the interests registered by the user" refers to technology or services that collect event information related to the interests and concerns registered by the user from the Internet.

[2262] The "means for notifying the user based on the collected event information" is a function for providing the collected information to the user as a push notification or an alert.

[2263] "Means for learning the operating history of machines in a factory" refers to algorithms and tools that analyze past operating data of machines in a factory and understand their patterns.

[2264] The "means for automatically creating periodic maintenance schedules" refers to a program or system for automatically generating periodic maintenance schedules based on the learned operating patterns of the machine.

[2265] "Means for acquiring user location information" refers to technologies and devices for measuring a user's current location and movement history.

[2266] The "means for collecting nearby event information based on acquired location information" refers to technology or services for collecting information about events held in the vicinity of the user's current location from the Internet.

[2267] "Means for acquiring location information of machines in a factory" refers to technologies and devices for measuring the current locations of machines and equipment in a factory.

[2268] "Means for collecting event information in a specific area based on acquired location information" refers to technologies and services for collecting event information in that area from the Internet based on acquired machine location information.

[2269] The "means for analyzing a user's past calendar events and inferring behavioral patterns" refers to algorithms or tools for analyzing a user's calendar history and inferring future behavioral patterns.

[2270] "Means for analyzing operation data of machines in a factory and estimating operation patterns" refers to algorithms and tools for analyzing past operation data of machines in a factory and estimating future operation patterns.

[2271] The system of the present invention is composed of a user terminal and a server, and is designed to optimize the operation history and maintenance schedule of machines in a factory.

[2272] Overall system configuration

[2273] The system for implementing the invention consists of a factory robot, a smartphone or tablet as a user terminal, and a cloud server. The user terminal collects machine operation data and location information, and the server stores and analyzes this data to generate an optimal schedule.

[2274] User registration and initial settings

[2275] The factory manager first installs the calendar application and registers an account. Next, he or she registers the role, operation pattern, and regular maintenance information of each machine in the factory on the cloud server. This secures basic management information.

[2276] Analysis of behavioral history

[2277] The terminal collects the operation history data of the machines in the factory and sends it to the server. The server analyzes the received operation history data and learns the operation patterns of each machine. For example, there are machines that perform assembly work for eight hours every day, and machines that require regular maintenance once a month.

[2278] Creating an Automatic Schedule

[2279] Based on the analysis results, the server automatically generates future operation and maintenance schedules for each machine and notifies factory managers and maintenance staff, thereby optimizing and streamlining work.

[2280] Event information collection and notification

[2281] The server collects information about special events and new work instructions within the factory and sends push notifications to the user's device, enabling a prompt response.

[2282] Use of location information

[2283] The terminal periodically acquires the machine's location information and sends it to the server, which then collects event information for a specific area based on the acquired location information, supporting effective work allocation and anomaly detection.

[2284] Specific examples

[2285] Maintenance Schedule Example

[2286] The server studies the operating history of machines in the factory and detects patterns that indicate monthly maintenance is required. It predicts the timing of the next maintenance and notifies the maintenance staff. When the scheduled maintenance date approaches, a push notification is sent stating, "The next maintenance is scheduled for XX month."

[2287] Example of anomaly detection in a specific area

[2288] The terminal sends the machine's location information to the server and detects abnormal events in a specific area (e.g., abnormal machine shutdown) in real time. The server collects the abnormality information and notifies the maintenance staff that "an abnormality has been detected in a specific area."

[2289] Hardware and software used

[2290] Hardware:

[2291] Factory robots

[2292] Wi-Fi network in the factory

[2293] Smartphones and tablets

[2294] software:

[2295] Cloud servers (e.g. AWS, Google Cloud)

[2296] REST API

[2297] Push notification system for smart devices (e.g., Firebase Cloud Messaging)

[2298] Prompt Sentence Examples

[2299] The factory robot "R123" performs assembly work for 8 hours every day and requires maintenance once a month. Based on past operation data, generate an optimal maintenance schedule and create notification messages for maintenance staff.

[2300] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2301] Step 1:

[2302] The user installs a calendar application and registers an account. The input information is the user's basic information and the roles and operating patterns of machines in the factory. This information is sent to a cloud server, and the basic data of the user and machine is stored in a database. The output is the registered user account and machine data.

[2303] Step 2:

[2304] The terminal collects the operation history data of machines in the factory in real time. The input information is the operation data of each machine (operation start time, end time, stop time, etc.). This data is sent to a cloud server, which stores the operation history data in a database. The output is the stored operation history data.

[2305] Step 3:

[2306] The server analyzes the collected operation history data and learns the operation patterns of each machine. The input is the saved operation history data. An analytical algorithm is used to extract the machine's operation pattern (e.g., 8 hours of operation every day, maintenance once a month). The output is the learned operation pattern information.

[2307] Step 4:

[2308] The server automatically creates future operation and maintenance schedules based on the learned operation patterns. The input is the learned operation pattern information. A scheduling algorithm is used to predict the next maintenance timing and generate the schedule. The output is the generated future schedule.

[2309] Step 5:

[2310] The server notifies the generated future schedule to the factory manager or maintenance staff. The input is the generated future schedule. A push notification system is used to send detailed information about the schedule to smartphones or tablets. The output is the notified schedule information.

[2311] Step 6:

[2312] The terminal periodically acquires the machine's location information and sends it to the server. The input is the machine's location data. The location information is sent to the cloud server, which stores the information in a database. The output is the stored location information.

[2313] Step 7:

[2314] The server collects event information for a specific area based on the acquired location information. The input is the saved location information. Event information (e.g., abnormal shutdowns, emergencies, etc.) for the specific area is collected using web scraping or APIs. The output is the collected event information.

[2315] Step 8:

[2316] The server notifies the maintenance staff of the collected event information. The input is the collected event information. A push notification system is used to notify the smart device of abnormal information in real time. The output is the notified event information.

[2317] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2318] Overall system configuration

[2319] The system of the present invention is implemented as a calendar application consisting of a user terminal, a server, and an emotion engine. The user terminal is a device such as a smartphone or tablet, and the server is located on the cloud and stores and analyzes user information and event data. The emotion engine recognizes the user's emotions and provides recommended information and schedule adjustments based on the user's emotional state.

[2320] User registration and initial settings

[2321] User: To use the system, the user first installs the calendar app and registers an account when the system is first launched. When registering, the user enters their interests (e.g., hair salons, live concerts, movies, etc.).

[2322] Device: Once you have completed your account registration and entered your interests, that information will be sent to the server.

[2323] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[2324] Analysis of behavioral history

[2325] Device: Collects events entered in the calendar in the past and behavioral history data and sends it to the server.

[2326] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns. For example, if it detects that the user goes to the hair salon on the third Saturday of every month, it automatically generates future regular schedules based on this information.

[2327] Creating an Automatic Schedule

[2328] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[2329] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[2330] On the device: The user will be notified via push notification that the reservation has been completed.

[2331] Event information collection and notification

[2332] Server: Based on the user's interests, collects relevant event information (for example, information about live performances by favorite artists or new movie releases), and prepares to notify the user at the optimal time based on the collected event information.

[2333] Device: Sends push notifications to users based on the received notification information and automatically adds events to their calendars.

[2334] Use of location information

[2335] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[2336] Server: Collects information about nearby events (such as newly opened cafes or special sales at supermarkets) based on location information. Sends the collected event information to the user's device.

[2337] Device: Sends push notifications to users based on nearby event information received.

[2338] Implementing the Emotion Engine

[2339] Terminal: Collects data to analyze the user's emotions through daily events and input data, and sends it to the emotion engine.

[2340] Emotion engine: Analyzes the user's emotional data and estimates their emotional state in real time, using, for example, text analysis, voice analysis, and facial expression analysis.

[2341] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it can suggest relaxation events when the user is under high stress.

[2342] Specific examples

[2343] Hair salon reservation example

[2344] Server: Based on the user's behavioral history, the server learns that the user visits the hair salon on the third Saturday of every month, and prepares to make a reservation at the same time next time. It automatically calls the hair salon's reservation API and completes the reservation.

[2345] Terminal: Sends a reservation completion notification to the user, informing them that "your next appointment at the hair salon has been completed."

[2346] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[2347] Concert ticket purchase example

[2348] User: Register a favorite artist (e.g., a particular musician) as a topic.

[2349] Server: Collects concert information for the artist in question, checks the ticket sales start date, and prepares information to notify users as the ticket sales date approaches.

[2350] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[2351] Emotion Engine: If a user has a strong emotional connection to this artist, they can receive special reminders or early notifications of ticket sales.

[2352] Example of supermarket sale information

[2353] Device: Sends the user's location information to the server.

[2354] Server: Collects sales information from nearby supermarkets based on location information and sends the collected sales information to the user's device.

[2355] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[2356] Emotion engine: If a user responds positively to a sale, the system will prioritize notifications of similar sales.

[2357] This concludes the "Mode for Carrying Out the Invention" of the system of the present invention. This system not only utilizes the user's behavioral history, interests, and location information, but also combines it with an emotion engine to achieve more personalized schedule management and support the user's daily life.

[2358] The processing flow will be explained below.

[2359] Basic processing steps of the system

[2360] Step 1:

[2361] User: Installs the calendar app on their smartphone and launches it for the first time.

[2362] Step 2:

[2363] On device: The app prompts the user to sign up for an account and asks them to enter topics or keywords of interest.

[2364] Step 3:

[2365] Users: Enter topics of interest such as hair salons, live concerts, movies, etc., and submit them along with their account information.

[2366] Step 4:

[2367] Terminal: Stores the entered information in local storage and prepares it for transmission to the server.

[2368] Step 5:

[2369] Server: Stores the received user information and interest data in a database and prepares it for analysis.

[2370] Analysis of behavioral history

[2371] Step 6:

[2372] Device: Collects the user's past calendar events and behavioral history data and sends them to the server.

[2373] Step 7:

[2374] Server: Analyzes the received behavioral history data and learns the user's behavioral patterns.

[2375] Creating an Automatic Schedule

[2376] Step 8:

[2377] Server: Automatically generates regular schedules for the user based on the learning results. For example, it predicts the next date of a hair salon appointment and automatically calls the salon's booking API to make the appointment.

[2378] Step 9:

[2379] Server: If the reservation is successful, create a reservation completion notification and send it to the user's terminal.

[2380] Step 10:

[2381] On the device: The user will be notified via push notification that the reservation has been completed.

[2382] Event information collection and notification

[2383] Step 11:

[2384] Server: Collects relevant event information based on the user's interests (for example, information about live performances by favorite artists or new movie releases).

[2385] Step 12:

[2386] Server: Based on the collected event information, selects the information to notify the user at the optimal time.

[2387] Step 13:

[2388] Server: Sends selected event information to the device.

[2389] Step 14:

[2390] On the device: Sends push notifications to users based on the received event information and automatically adds events to their calendars.

[2391] Acquisition and use of location information

[2392] Step 15:

[2393] Device: The location information is acquired periodically or when the user launches the app, and sent to the server.

[2394] Step 16:

[2395] Server: Collects and analyzes information about nearby events based on location information.

[2396] Step 17:

[2397] Server: Selects the collected nearby event information and creates notification information.

[2398] Step 18:

[2399] Device: Sends push notifications to users based on nearby event information received.

[2400] Emotion Recognition and Applications

[2401] Step 19:

[2402] Terminal: Collects data to analyze the user's emotions through daily events and data entered by the user (emails, chat logs, etc.) and sends it to the server.

[2403] Step 20:

[2404] Server: Sends the received emotion data to the emotion engine for analysis.

[2405] Step 21:

[2406] Emotion Engine: Analyzes the user's emotional state and classifies it as positive, negative, or stressful.

[2407] Step 22:

[2408] Server: Adjusts the user's schedule and notifications based on the emotional state data received from the emotion engine. For example, it suggests relaxation events when stress levels are high.

[2409] Specific examples

[2410] Hair salon reservation example

[2411] Step 1:

[2412] Server: From the user's behavioral history, it learns that the user visits the beauty salon on the third Saturday of every month.

[2413] Step 2:

[2414] Server: When the next third Saturday approaches, make a reservation through the hair salon booking API.

[2415] Step 3:

[2416] Server: After the reservation is completed, create a notification message and send it to the device.

[2417] Step 4:

[2418] Device: A push notification will be sent to the user upon receiving the reservation completion notification.

[2419] Step 5:

[2420] Emotion Engine: If it detects that the user's stress level is high, it suggests additional relaxation events (e.g., booking a massage).

[2421] Concert ticket purchase example

[2422] Step 1:

[2423] User: Register a favorite artist (e.g., a particular musician) as a topic.

[2424] Step 2:

[2425] Server: Collects concert information for the artist in question and checks when tickets go on sale.

[2426] Step 3:

[2427] Server: Prepare information to notify users as the sale date approaches.

[2428] Step 4:

[2429] Device: The day before the sale, a push notification will be sent saying, "Concert tickets for a specific musician will go on sale tomorrow."

[2430] Step 5:

[2431] Emotion Engine: If a user has a strong emotional connection to this artist, they will receive special reminders or early notifications of ticket sales.

[2432] Example of supermarket sale information

[2433] Step 1:

[2434] Device: Sends the user's location information to the server.

[2435] Step 2:

[2436] Server: Collects information about special offers at nearby supermarkets based on location information.

[2437] Step 3:

[2438] Server: Creates notification information based on the results of collection and analysis.

[2439] Step 4:

[2440] On device: A push notification notifies the user that "your local supermarket is having a sale this weekend."

[2441] Step 5:

[2442] Emotion engine: If a user responds positively to a sale, the engine will prioritize notifications of similar sales.

[2443] The above is the specific processing flow of the system of the present invention. By combining it with an emotion engine, personalized schedule management based on the user's emotional state becomes possible, making the user's daily life more fulfilling.

[2444] Example 2

[2445] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2446] In recent years, schedule management applications using mobile devices such as smartphones and tablets have become widespread. However, conventional calendar and scheduling applications do not adequately provide personalized schedule management that takes into account a user's behavioral history, interests, location information, and even emotional state. This results in a problem in that truly useful schedule suggestions and notifications cannot be realized. Furthermore, because schedules are not adjusted according to the user's emotional state, suggestions for stress management and relaxation are insufficient. The present invention aims to solve these problems.

[2447] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2448] In this invention, the server includes a means for learning a user's behavioral history, a means for automatically creating a recurring schedule based on the learned behavioral history, and a means for collecting related event information based on the user's registered interests. This allows the server to learn the user's behavioral patterns and automatically generate a recurring schedule based on them. Furthermore, event information based on the user's interests can be collected and notified, enabling appropriate event suggestions to be made to the user. Furthermore, by analyzing the user's emotional data and adjusting the schedule and notification content based on the user's emotional state, relaxation events tailored to the user's emotional state can be suggested and flexible schedule management can be realized.

[2449] "User" refers to a person who uses this system, and is a person who uses a mobile device such as a smartphone or tablet.

[2450] "Action history" refers to records of events such as calendar entries and event participation made by a user in the past.

[2451] "Means of learning" refers to algorithms or programs that analyze collected user behavior history data and identify certain patterns and trends.

[2452] The "means for automatically creating periodic schedules" is a program that has the function of automatically generating future schedules for the user based on learned behavioral patterns.

[2453] ...

Claims

1. A means for learning a user's behavior history; A means for automatically creating recurring appointments based on the learned behavioral history; a means for collecting relevant event information based on the user's registered interests; a means for notifying a user based on the collected event information; A system including:

2. A means for acquiring user location information; A means for collecting nearby event information based on the acquired location information; a means for notifying a user based on the collected event information; The system of claim 1 further comprising:

3. means for analyzing a user's past calendar events and inferring behavioral patterns; A means for automatically creating a future schedule based on the estimated behavioral pattern; a means for notifying a user of detailed information about a schedule; The system of claim 1 further comprising:

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