System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining schedule information from a user; means for transmitting the obtained schedule information to a server; means for analyzing the transmitted schedule information using natural language processing to identify a date, time, event type, and participants; means for integrating information obtained from external data sources to adjust and optimize the schedule; means for interactively prompting the user for confirmation and correction; and means for determining final schedule information and notifying the user's device.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] In today's busy society, users spend a lot of time and effort managing their daily schedules and tasks, which can lead to mistakes and stress. Existing calendar apps and task management tools lack real-time information and automatic optimization functions, making efficient schedule management difficult. The challenge is to solve this problem and enable users to live more efficient and stress-free daily lives. [Means for solving the problem]

[0005] These problems are solved by providing a system that includes a means for acquiring schedule information from a user, a means for transmitting the acquired schedule information to a server, a means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants, a means for integrating information acquired from external data sources and adjusting and optimizing the schedule, a means for interactively prompting the user to confirm and correct the information, and a means for finalizing the schedule information and notifying the user's device.

[0006] "User" means an individual or organization that uses the system to manage their own schedule and tasks.

[0007] "Schedule information" is data about the user's schedule, such as date, time, event type, and participants.

[0008] A "server" is a computer system that receives, analyzes, and optimizes schedule information sent by users and connects with external data sources.

[0009] "Natural language processing" is a technology that allows a computer to understand and analyze human language, and is used in the present invention to analyze schedule information.

[0010] "External data sources" are external information sources that provide additional information needed to optimize schedules, such as calendar APIs or weather APIs.

[0011] The "Calendar API" is an interface that enables integration with external calendar services and performs duplicate checks and synchronization of schedule information.

[0012] The "Weather API" is an interface that enables collaboration with external services that provide weather information and allows for obtaining real-time weather forecasts.

[0013] "Dialogue" refers to a format in which the user and the system communicate in natural language, and is an efficient way of obtaining user confirmation and instructions.

[0014] "Notifications" are messages or alerts that inform users of confirmed schedule information. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is an AI tool for streamlining user schedule and task management. The system acquires the user's schedule information, analyzes it using natural language processing technology, a generative AI model, and optimizes the schedule by integrating information from external data sources. It also prompts the user to confirm and correct the information interactively, and then finalizes and notifies the user of the final schedule information.

[0037] A natural language description of the program's processing

[0038] Retrieving User Information

[0039] A user types into a device (e.g., a smartphone or PC) "Meeting with Bob next Monday at 2 PM." That information is stored on the device.

[0040] Sending information

[0041] The device sends the acquired schedule information to the server. The data sent is in the following format:

[0042] json

[0043] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0044] Performing natural language processing

[0045] The server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information, and the analysis results in detailed information such as:

[0046] json

[0047] {

[0048] "event": "meeting",

[0049] "date": "YYYY-MM-DD",

[0050] "time": "14:00",

[0051] "participant": "Bob"

[0052] }

[0053] Data Integration and Analysis

[0054] The server accesses an external calendar API to check for overlaps with other events. At the same time, it uses a weather API to get the weather forecast for the following Monday. For example, it may predict a cloudy day. This information is used to optimize the schedule.

[0055] Schedule Optimization

[0056] The server then uses this information to optimize the schedule. For example, it can refer to the weather forecast and suggest indoor meetings if an outdoor event is not suitable for the day of the meeting. It also optimizes the time to avoid conflicts with other tasks or appointments.

[0057] Conversational Interaction

[0058] The device interactively asks the user for confirmation: "I'd like to set up a meeting with Bob for Monday at 2 PM. Confirm?" and waits for the user to confirm or amend. If the user answers "yes," it proceeds to the next step.

[0059] Schedule confirmation and notification

[0060] The server confirms the final schedule information and updates the user's calendar. The confirmed schedule information is then sent to the device, informing the user that "A meeting with Bob has been scheduled for next Monday at 2:00 PM."

[0061] Specific examples

[0062] For example, a specific example will be given of a case where a user performs an operation to "schedule an online meeting at 10:00 AM next Tuesday."

[0063] Retrieving User Information

[0064] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[0065] Sending information

[0066] The device sends this information to the server. The data sent is in the following format:

[0067] json

[0068] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[0069] Performing natural language processing

[0070] The server parses this information and converts it into a concrete date and time.

[0071] json

[0072] {

[0073] "event": "online meeting",

[0074] "date": "YYYY-MM-DD",

[0075] "time": "10:00"

[0076] }

[0077] Data Integration and Analysis

[0078] The server uses the calendar API to check for overlaps with other events and also retrieves weather information from the weather API, e.g., a "sunny" forecast.

[0079] Schedule Optimization

[0080] The server determines the optimal schedule based on this information.

[0081] Conversational Interaction

[0082] The device asks the user, "We're setting up an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[0083] Schedule confirmation and notification

[0084] The server confirms the final schedule information and updates the calendar on the user's device. The notification reads, "An online meeting has been scheduled for next Tuesday at 10:00 AM."

[0085] The above is an embodiment of the present invention. This system allows users to manage their schedules efficiently and stress-free.

[0086] The processing flow will be explained below.

[0087] Step 1:

[0088] A user enters "Meeting with Bob next Monday at 2 PM" into the calendar app on their device (e.g., smartphone or PC).

[0089] Step 2:

[0090] The terminal stores the entered schedule information and sends it to the server. The data sent is in the following format:

[0091] json

[0092] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0093] Step 3:

[0094] The server uses a generative AI model (e.g., GPT series) to analyze the received schedule information. The analysis results in detailed information such as:

[0095] json

[0096] {

[0097] "event": "meeting",

[0098] "date": "YYYY-MM-DD",

[0099] "time": "14:00",

[0100] "participant": "Bob"

[0101] }

[0102] Step 4:

[0103] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[0104] Step 5:

[0105] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[0106] Step 6:

[0107] The server takes into account calendar information and weather forecast information to optimize the schedule. For example, if an event is scheduled outdoors, the server considers the weather forecast and suggests an indoor meeting.

[0108] Step 7:

[0109] Based on the device's optimized schedule information, the user is prompted interactively to confirm, "I'm going to schedule a meeting with Bob for Monday at 2 p.m. Would you like to confirm?"

[0110] Step 8:

[0111] The user presses a confirmation button through a dialogue interface or answers "yes" by voice input.

[0112] Step 9:

[0113] The server receives confirmation from the user, finalizes the schedule information, and updates the user's calendar. The updated information looks like this:

[0114] json

[0115] {

[0116] "event": "meeting",

[0117] "date": "YYYY-MM-DD",

[0118] "time": "14:00",

[0119] "participant": "Bob",

[0120] "location": "online"

[0121] }

[0122] Step 10:

[0123] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[0124] The above are the specific operations performed at each step. In this way, the terminal, server, and user work together to achieve efficient and optimal schedule management.

[0125] Example 1

[0126] 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."

[0127] Conventional schedule management systems require users to manually input, check, and correct schedules, which is time-consuming and labor-intensive. Furthermore, they lack the ability to integrate external data, such as weather information and overlap checks with other appointments, making schedule optimization difficult. Furthermore, they are unable to handle natural language input, making user input complex and unintuitive.

[0128] 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.

[0129] In this invention, the server includes means for acquiring schedule information from a user, means for transmitting the acquired schedule information to an information processing device, means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants, means for integrating information acquired from external data sources and adjusting and optimizing the schedule, means for interactively prompting the user for confirmation and correction, means for finalizing the schedule information and notifying the user's terminal, means for analyzing the schedule information using a generative AI model, and means for performing natural language processing using prompt sentences to identify the date and time. This allows the user to efficiently manage their schedule, and realizes optimized schedule management integrated with external data.

[0130] "Schedule information" refers to data about the date, time, type of event, and participants that a user enters as an appointment.

[0131] The term "information processing device" refers to a device or system in general that has the function of receiving, analyzing, and transmitting schedule information.

[0132] "Natural language processing" is a computer technology for analyzing human language and understanding or generating meaning.

[0133] "External Data Sources" refers to external data providers, including calendar APIs and weather APIs, that the server accesses and uses for schedule optimization.

[0134] "Schedule adjustment and optimization" is the process of suggesting optimal times and settings, taking into account the user's existing schedule and external data.

[0135] "Means for interactively prompting confirmation and correction" refers to a function that interactively prompts the user to confirm and correct the schedule contents.

[0136] A "generative AI model" is an artificial intelligence technology that includes large-scale learning models used for natural language processing.

[0137] A "prompt" is text that is input to a generative AI model to instruct it on a specific task.

[0138] "Terminal" refers to the device (such as a smartphone or PC) that a user uses to input and receive schedule information.

[0139] This invention is an advanced scheduling system for streamlining users' schedules and task management. This system is based on a large-scale natural language processing technology called a generative AI model.

[0140] Hardware and software used

[0141] Hardware

[0142] 1. Terminal: A device through which a user inputs schedule information. Examples include smartphones and personal computers (PCs).

[0143] 2. Server: A central management system for analyzing schedule information and integrating external data.

[0144] software

[0145] 1. Natural language processing technology: Generative AI models (e.g., GPT series) are used to analyze the schedule information entered by the user.

[0146] 2. Calendar API: Used to check for overlaps with other events.

[0147] 3. Weather API: Used to obtain weather information required for schedule optimization.

[0148] Data processing and calculation

[0149] 1. Obtaining user information

[0150] A user enters "Meeting next Monday at 2 PM" into the device's calendar app. This information is stored on the device.

[0151] 2. Transmission of information

[0152] The terminal transmits the acquired schedule information to the server.

[0153] For example, data is sent in the following format:

[0154] json

[0155] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0156] 3. Performing Natural Language Processing

[0157] The server analyzes the schedule information using a generative AI model, for example, using the following prompt:

[0158] Example prompt: "Convert 'next Monday at 2 PM' into a specific date and time."

[0159] Examples of what can be obtained as a result of the analysis:

[0160] json

[0161] {

[0162] "event": "meeting",

[0163] "date": "YYYY-MM-DD",

[0164] "time": "14:00",

[0165] "participant": "Bob"

[0166] }

[0167] 4. Data integration and analysis

[0168] The server uses an external calendar API to check for overlaps with other events, and at the same time, retrieves the weather forecast for the day of the meeting using a weather API.

[0169] For example, a "cloudy" forecast is obtained.

[0170] 5. Schedule optimization

[0171] The server optimizes schedules based on weather forecasts and other schedules, for example suggesting indoor meetings if the weather is bad.

[0172] The schedule is updated as a result of the optimization.

[0173] 6. Conversational Interaction

[0174] The device interactively asks the user for confirmation: "I'm going to schedule a meeting for Monday at 2 PM. Would you like to confirm?" and waits for the user to confirm or correct the request.

[0175] 7. Schedule confirmation and notification

[0176] The server determines the final schedule information and notifies the user's device.

[0177] The user is notified of the confirmed schedule information, stating, "A meeting has been scheduled for next Monday at 2:00 p.m."

[0178] Specific examples

[0179] For example, if a user wants to "schedule an online meeting next Tuesday at 10:00 AM":

[0180] The user inputs specific plans into the device.

[0181] The device sends this information to the server.

[0182] The server analyzes it using a generative AI model and converts it into a specific date and time.

[0183] The server uses the calendar and weather APIs to optimize the schedule.

[0184] The device interactively asks the user for confirmation, and the user replies "yes."

[0185] The server confirms the final schedule and reflects it on the user's calendar.

[0186] As described above, the system of the present invention provides users with efficient and stress-free schedule management.

[0187] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0188] Step 1:

[0189] Retrieving User Information

[0190] A user enters "Meeting at 2 PM next Monday" into the calendar app on their device (e.g., smartphone or PC). This entry is saved in the device's database.

[0191] Input: Scheduling information entered by the user (e.g., "Meeting next Monday at 2 PM").

[0192] Output: Schedule information stored in the device database.

[0193] Specific behavior:

[0194] The user launches a calendar app and enters a new event.

[0195] Suppose the input is "Meeting next Monday at 2pm."

[0196] This input is stored in an internal database.

[0197] Step 2:

[0198] Sending information

[0199] The device sends the saved schedule information to the server. The data sent is in JSON format.

[0200] Input: Schedule information stored on the device.

[0201] Output: Schedule information sent to the server in JSON format.

[0202] Specific behavior:

[0203] The device organizes the saved schedule information into JSON format.

[0204] For example, the following JSON is generated:

[0205] json

[0206] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0207] The JSON data is sent over the internet to a server.

[0208] Step 3:

[0209] Performing natural language processing

[0210] The server uses a generative AI model to parse the submitted schedule information, converting dates and times into specific formats using prompts.

[0211] Input: Schedule information in JSON format.

[0212] Output: Schedule information converted into concrete dates and times.

[0213] Specific behavior:

[0214] The server parses the received JSON data.

[0215] Enter the following prompt into the generative AI model: "Convert 'next Monday at 2 PM' into a specific date and time."

[0216] The generative AI model performs the analysis and generates data such as:

[0217] json

[0218] {

[0219] "event": "meeting",

[0220] "date": "YYYY-MM-DD",

[0221] "time": "14:00",

[0222] "participant": "Bob"

[0223] }

[0224] Step 4:

[0225] Data Integration and Analysis

[0226] The server accesses external calendar and weather APIs to check for duplicates and obtain weather information.

[0227] Input: Parsed schedule information.

[0228] Output: Results of overlap check with other events and weather information.

[0229] Specific behavior:

[0230] The server sends a request to the external calendar API to check for conflicts with other events for the user.

[0231] Send a request to the weather API to get weather information for the day the meeting is scheduled.

[0232] For example, a "cloudy" forecast is obtained.

[0233] Step 5:

[0234] Schedule Optimization

[0235] Optimize your schedule based on the information obtained by the server. Suggest the best schedule based on weather forecasts and other schedules.

[0236] Input: Duplicate check results and weather information.

[0237] Output: Optimized schedule information.

[0238] Specific behavior:

[0239] The server calculates the optimal schedule based on weather information and other schedules.

[0240] For example, if the weather is bad, suggest meeting indoors.

[0241] The schedule is updated as a result of the optimization.

[0242] Step 6:

[0243] Conversational Interaction

[0244] The device interactively asks the user for confirmation: "I'd like to schedule a meeting for Monday at 2 PM. Would you like to confirm?"

[0245] Input: Optimized schedule information.

[0246] Output: User confirmation or correction instructions.

[0247] Specific behavior:

[0248] The device displays a notification to the user.

[0249] The user responds with "yes" or "no."

[0250] If the answer is "no", a form is displayed that allows the user to enter a new time.

[0251] Step 7:

[0252] Schedule confirmation and notification

[0253] The server determines the final schedule information and notifies the user's device.

[0254] Input: User confirmed or corrected schedule information.

[0255] Output: Confirmed schedule information and notifications.

[0256] Specific behavior:

[0257] The server saves the confirmed schedule to the user's calendar.

[0258] Send a notification to your device saying "A meeting has been scheduled for next Monday at 2 PM."

[0259] The user's calendar will update to show the new schedule.

[0260] (Application example 1)

[0261] 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."

[0262] Conventional schedule management systems do not provide sufficient support for streamlining user schedule and task management. Especially for high-risk jobs like security guarding, real-time schedule optimization and risk information integration are necessary. This allows for more effective patrol and surveillance, but conventional systems cannot meet these needs. Furthermore, a lack of visual or audio feedback makes it difficult to respond quickly.

[0263] 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.

[0264] In this invention, the server includes means for acquiring schedule information from a user, means for transmitting the acquired schedule information to the server, means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and location, means for integrating information acquired from external data sources to adjust and optimize the schedule, means for interactively prompting the user to confirm and correct the information, means for finalizing the schedule information and notifying the user's device, means for proposing optimal monitoring routes and patrol schedules, means for optimizing patrol routes by integrating risk assessment information, means for managing monitoring points in security areas based on location information, and means for providing visual or audio feedback, thereby enabling effective and efficient schedule management for security guards and optimization of patrol work.

[0265] A "user" is a person or professional who uses the system to manage schedules and tasks.

[0266] "Schedule information" refers to data about appointments and tasks, including dates, times, event types, locations, etc.

[0267] A "server" is a computer system that analyzes, integrates, and optimizes schedule information.

[0268] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0269] "External Data Source" means a source of externally provided data, including a calendar API or weather API.

[0270] "Optimization" means adjusting schedules and tasks efficiently and effectively.

[0271] "Interaction style" refers to the way in which users and systems interact with each other to exchange information.

[0272] A "surveillance route" is the route that security guards follow when patrolling.

[0273] A "patrol schedule" is a plan for security guards to patrol specific locations at specific times.

[0274] "Risk assessment information" refers to data that predicts and provides information about security risks.

[0275] A "patrol route" is a pre-determined route that a security guard follows when patrolling.

[0276] "Location information" is data that indicates the geographical information of the user or the monitored area.

[0277] A "vigilance area" is a location or area that security guards should monitor.

[0278] "Visual feedback" refers to the display of information provided through a display or smart glasses.

[0279] "Audio feedback" refers to information provided through audio output.

[0280] The system for realizing this invention allows users to efficiently manage their schedules, and in particular, optimizes patrol and surveillance operations in security services. The system is implemented by combining the following hardware and software.

[0281] The system uses smart glasses or head-mounted displays (terminals) to obtain the user's schedule information. When the user gives voice commands or inputs to these devices, the information is sent to a cloud server. The software used includes generative AI models (e.g., GPT series) as well as calendar APIs, weather APIs, and risk assessment APIs.

[0282] Processing Description

[0283] Retrieving User Information

[0284] The user commands the smart glasses to "start monitoring the east gate at 2:00 p.m. next Tuesday" by voice, which is then converted into text by the device.

[0285] Sending information

[0286] The device sends the schedule information it has acquired to the cloud server. The data sent will be in the following format:

[0287] json

[0288] {"event": "monitoring", "time": "2pm", "location": "east gate"}

[0289] Performing natural language processing

[0290] The cloud server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information and convert it into specific time and location information. The analysis results are as follows:

[0291] json

[0292] {

[0293] "event": "monitoring",

[0294] "time": "14:00",

[0295] "location": "East Gate"

[0296] }

[0297] Data Integration and Analysis

[0298] The cloud server uses the calendar API and risk assessment API to integrate other schedules with risk information. For example, it can obtain information such as "There is a high possibility that suspicious individuals will be seen around the east gate."

[0299] Schedule optimization

[0300] Based on this information, the cloud server will suggest the most effective surveillance route and time to the user, optimizing areas to be patrolled in addition to the East Gate.

[0301] Conversational Interaction

[0302] The device will ask the user aloud, "We will begin monitoring the East and North Gates at 2:00 p.m. next Tuesday. Would you like to confirm?" If the user responds "Yes," it will proceed to the next step.

[0303] Schedule confirmation and notification

[0304] The cloud server finalizes the monitoring schedule and notifies the user's device, which then provides visual and audio feedback, stating, "Monitoring of the East and North Gates has been set for next Tuesday at 2:00 PM."

[0305] Specific examples

[0306] Here is a specific example of a case where a user commands the smart glasses to "set up a patrol of the south exit at 4:00 p.m. next Wednesday."

[0307] Retrieving User Information

[0308] The user types into the terminal, "Patrol the south exit next Wednesday at 4pm."

[0309] Sending information

[0310] The device sends this information to the cloud server in the following format:

[0311] json

[0312] {"event": "tour", "time": "4pm", "location": "south exit"}

[0313] Performing natural language processing

[0314] The cloud server analyzes the information and converts it into:

[0315] json

[0316] {

[0317] "event": "tour",

[0318] "time": "16:00",

[0319] "location": "South Exit"

[0320] }

[0321] Data Integration and Analysis

[0322] The cloud server uses the risk assessment API to obtain risk information such as "There have been reports of suspicious individuals being seen around the south exit."

[0323] Schedule Optimization

[0324] The cloud server proposes the optimal route and time, and optimization is performed including the surrounding area.

[0325] Conversational Interaction

[0326] The terminal asks, "Patrol of the South Exit and surrounding area will be scheduled for next Wednesday at 4pm. Confirm?"

[0327] Schedule confirmation and notification

[0328] If the user responds "Yes," the cloud server finalizes the patrol schedule and notifies the device, displaying the message, "Patrol of the south exit and surrounding area has been scheduled for next Wednesday at 4 p.m."

[0329] Prompt Sentence Examples

[0330] Based on the command "Start monitoring the East Gate at 2:00 PM next Tuesday," please integrate other schedules and risk information to propose the optimal monitoring schedule.

[0331] This will enable effective and efficient schedule management for security guards and optimization of patrol operations.

[0332] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0333] Step 1:

[0334] The user commands the smart glasses by voice, "Start monitoring the east gate at 2:00 PM next Tuesday." The device converts this voice command into text data and recognizes the content as "schedule information." The input is a voice command, and the output is schedule information in text format. In this step, voice recognition technology is used to convert the voice data into text data.

[0335] Step 2:

[0336] The device sends the acquired schedule information to the cloud server. The data sent is in the following format:

[0337] json

[0338] {"event": "monitoring", "time": "2pm", "location": "east gate"}

[0339] The input is schedule information in text format, and the output is data transmission to a cloud server.

[0340] Step 3:

[0341] The cloud server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information. The analysis results in specific time and location information, which may take the following format:

[0342] json

[0343] {

[0344] "event": "monitoring",

[0345] "time": "14:00",

[0346] "location": "East Gate"

[0347] }

[0348] The input is schedule information in text format, and the output is parsed schedule data. In this step, a generative AI model is used for natural language processing.

[0349] Step 4:

[0350] The cloud server calls the calendar API and risk assessment API, and integrates other schedule and risk information based on the acquired schedule information. For example, it acquires information that "there is a high possibility that a suspicious person will be seen around the east gate." The input is the analyzed schedule data, and the output is the integrated risk assessment data and schedule data. In this step, the API is called to integrate and analyze the data.

[0351] Step 5:

[0352] The cloud server uses this integrated data to propose the most effective surveillance route and time. For example, it may propose additional areas to patrol besides the East Gate. The input is the integrated risk assessment data and schedule data, and the output is an optimized surveillance schedule proposal. This step uses data analysis and optimization algorithms.

[0353] Step 6:

[0354] The terminal asks the user by voice, "Monitoring of the East and North Gates will begin at 2:00 PM next Tuesday. Do you want to confirm?" If the user answers "Yes," it proceeds to the next step. The input is the optimized monitoring schedule proposal, and the output is the user's response. This step uses a conversational interface.

[0355] Step 7:

[0356] After receiving the user's response, the cloud server finalizes the monitoring schedule and notifies the user's device. The device provides visual or audio feedback, such as "Monitoring of the East and North Gates has been set for next Tuesday at 2:00 PM." The input is the user's confirmation response, and the output is notification of the finalized schedule information. This step uses the database update and notification functions.

[0357] 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.

[0358] This invention is a system that streamlines user schedule and task management and, by combining it with an emotion engine, enables flexible responses according to the user's emotional state. This system acquires the user's schedule information, analyzes it using natural language processing technology, and integrates information from external data sources to optimize the schedule. It also recognizes the user's emotions, proposes and adjusts the schedule, and notifies the user of the final schedule information.

[0359] A natural language description of the program's processing

[0360] Retrieving User Information

[0361] A user enters "Meeting with Bob at 2 PM next Monday" into the calendar app on their device (e.g., smartphone or PC). That information is stored on the device.

[0362] Sending information

[0363] The terminal sends the entered schedule information to the server. The data sent is in the following format:

[0364] json

[0365] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0366] Performing natural language processing

[0367] The server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information, and the analysis results in detailed information such as:

[0368] json

[0369] {

[0370] "event": "meeting",

[0371] "date": "YYYY-MM-DD",

[0372] "time": "14:00",

[0373] "participant": "Bob"

[0374] }

[0375] Data Integration and Analysis

[0376] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[0377] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[0378] Running the Emotion Engine

[0379] The device uses facial recognition and voice analysis technologies to recognize the user's emotional state.

[0380] The server uses an emotion engine to adjust the schedule based on the user's perceived emotional state. For example, if the user is feeling stressed, the system will suggest postponing less urgent tasks.

[0381] Schedule Optimization

[0382] The server optimizes the schedule by taking into account calendar information, weather forecast information, and the user's emotional state. For example, it suggests indoor meetings based on the weather forecast and also makes suggestions that match the user's emotional state.

[0383] Conversational Interaction

[0384] Based on the device-optimized schedule information, the user is interactively asked to confirm, "I'm going to schedule a meeting with Bob for Monday at 2 PM. Would you like to confirm?" If the user answers "yes," the process proceeds to the next step.

[0385] Schedule confirmation and notification

[0386] The server determines the final schedule information and updates the user's calendar. The updated information looks like this:

[0387] json

[0388] {

[0389] "event": "meeting",

[0390] "date": "YYYY-MM-DD",

[0391] "time": "14:00",

[0392] "participant": "Bob",

[0393] "location": "online"

[0394] }

[0395] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[0396] Specific examples

[0397] For example, a specific example will be given below in which a user performs an operation to "schedule an online meeting at 10:00 AM next Tuesday."

[0398] Retrieving User Information

[0399] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[0400] Sending information

[0401] The device sends this information to the server. The data sent is in the following format:

[0402] json

[0403] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[0404] Performing natural language processing

[0405] The server parses this information and converts it into a concrete date and time, as well as the type of event and participants:

[0406] json

[0407] {

[0408] "event": "online meeting",

[0409] "date": "YYYY-MM-DD",

[0410] "time": "10:00",

[0411] "participant": "N / A"

[0412] }

[0413] Data Integration and Analysis

[0414] The server uses the calendar API to check for overlapping events and also retrieves weather forecast information from the weather API.

[0415] Running the Emotion Engine

[0416] The device analyzes the user's voice and facial expressions to recognize their emotions. For example, it determines that the user is not feeling stressed.

[0417] Schedule Optimization

[0418] The server determines the optimal schedule based on this information.

[0419] Conversational Interaction

[0420] The device will ask the user for confirmation: "An online meeting will be scheduled for next Tuesday at 10:00 AM. Would you like to confirm?"

[0421] Schedule confirmation and notification

[0422] The server receives the user's confirmation and finalizes the schedule information, which is then reflected in the user's calendar. This schedule information is then notified to the device.

[0423] The above is an embodiment of the present invention. This system allows users to manage their schedules efficiently and stress-free. In particular, by taking into account the user's emotional state, more flexible responses are possible, improving the quality of daily life.

[0424] The processing flow will be explained below.

[0425] Specific steps of the program's processing

[0426] Step 1:

[0427] A user speaks or texts "Meet with Bob next Monday at 2 PM" into the calendar app on their device (e.g., smartphone or PC).

[0428] Step 2:

[0429] The terminal processes the entered schedule information and sends it to the server. The data sent is in the following format:

[0430] json

[0431] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0432] Step 3:

[0433] The server uses a generative AI model (GPT series) to analyze the received schedule information. This converts the input "next Monday" or "2 PM" into a specific date and time, and also identifies the type of event and its attendees. The analysis results are as follows:

[0434] json

[0435] {

[0436] "event": "meeting",

[0437] "date": "YYYY-MM-DD",

[0438] "time": "14:00",

[0439] "participant": "Bob"

[0440] }

[0441] Step 4:

[0442] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[0443] Step 5:

[0444] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[0445] Step 6:

[0446] The device uses an emotion engine (face recognition technology and voice analysis technology) to recognize the user's emotional state. For example, it analyzes the user's facial expressions and voice using a camera or microphone to identify the user's emotional state.

[0447] Step 7:

[0448] The server uses an emotion engine to adjust the schedule based on the user's perceived emotional state. For example, if the user is feeling stressed, the server suggests adjusting meeting times to allow time for relaxation.

[0449] Step 8:

[0450] The server takes into account calendar information, weather forecast information, and emotional state to optimize the schedule. For example, it prioritizes important tasks and suggests appropriate locations depending on the weather.

[0451] Step 9:

[0452] Based on the device-optimized schedule information, the user is interactively asked to confirm, "I'm going to schedule a meeting with Bob next Monday at 2 p.m. Would you like to confirm?"

[0453] Step 10:

[0454] The user confirms by answering "yes" in the dialogue interface.

[0455] Step 11:

[0456] The server receives confirmation from the user, finalizes the schedule information, and updates the user's calendar. The finalized information looks like this:

[0457] json

[0458] {

[0459] "event": "meeting",

[0460] "date": "YYYY-MM-DD",

[0461] "time": "14:00",

[0462] "participant": "Bob",

[0463] "location": "online"

[0464] }

[0465] Step 12:

[0466] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[0467] Specific examples

[0468] For example, a specific example will be given below in which a user performs an operation such as "schedule an online meeting at 10:00 AM next Tuesday."

[0469] Step 1:

[0470] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[0471] Step 2:

[0472] The device sends this information to the server. The data sent is in the following format:

[0473] json

[0474] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[0475] Step 3:

[0476] The server parses this information and converts it into a concrete date and time, as well as the type of event and participants:

[0477] json

[0478] {

[0479] "event": "online meeting",

[0480] "date": "YYYY-MM-DD",

[0481] "time": "10:00",

[0482] "participant": "N / A"

[0483] }

[0484] Step 4:

[0485] The server uses the calendar API to check for overlapping events and also retrieves weather forecast information from the weather API.

[0486] Step 5:

[0487] The device analyzes the user's voice and facial expressions to recognize their emotions, for example, rating the user as "relaxed."

[0488] Step 6:

[0489] The server determines the optimal schedule based on this information, taking into account the user's relaxed emotional state and setting a schedule with ample time.

[0490] Step 7:

[0491] The device prompts the user for confirmation: "I'm setting up an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[0492] Step 8:

[0493] The user checks and corrects as necessary.

[0494] Step 9:

[0495] The server finalizes the schedule information and sends notifications to the user's device.

[0496] The above is a specific embodiment for carrying out the present invention, and this system enables the user to manage their schedule efficiently and stress-free, and to respond flexibly according to their emotional state.

[0497] Example 2

[0498] 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."

[0499] Modern users are required to efficiently manage a variety of tasks and schedules in their busy daily lives. However, traditional schedule management systems do not take into account the user's emotional state, making it difficult to provide appropriate schedule suggestions and adjustments for users who feel stressed or tense. Furthermore, they often lack the functionality to integrate with external data sources and provide optimal schedules. This leads to problems that reduce the user's quality of life.

[0500] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring schedule information from a user; means for transmitting the acquired schedule information to the server; means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants; means for integrating information acquired from external data sources to adjust and optimize the schedule; means for recognizing the user's emotional state; means for adjusting the schedule based on the user's emotional state using an emotion engine; means for interactively prompting the user to confirm and correct the information; and means for finalizing the schedule information and notifying the user's device. This makes it possible to provide an optimal schedule by integrating information from external data sources while taking the user's emotional state into consideration.

[0501] "User" refers to a person who uses the system to manage schedules and tasks.

[0502] "Terminal" means a device that a user uses to access and operate the system, including a smartphone, personal computer, tablet, etc.

[0503] "Server" refers to the central computer system that analyzes schedule information submitted by users and integrates it with external data sources.

[0504] "Schedule information" refers to information about upcoming meetings, events, etc. that a user enters into a terminal.

[0505] "Natural language processing" refers to the technology that allows a computer to understand and analyze strings of characters written in natural language.

[0506] "External data sources" refers to external services or databases that the server accesses to obtain information, including calendar APIs and weather APIs.

[0507] "Schedule adjustment" refers to appropriately changing or optimizing the schedule based on the acquired information.

[0508] "Emotional state" refers to the user's psychological state, including states such as stress, fatigue, and joy.

[0509] An "emotion engine" is a software component that analyzes a user's emotional state and makes schedule suggestions and adjustments based on that.

[0510] "Interaction style" refers to the way in which a system interacts with a user, including speech recognition and text input.

[0511] "Schedule optimization" refers to improving the quality of a user's life by efficiently and rationally organizing and adjusting the user's schedule.

[0512] "Notifications" refers to alerts or messages that inform users of final schedule information.

[0513] This invention is a system that improves the efficiency of a user's schedule and task management and, by combining it with an emotion engine, enables flexible responses according to the user's emotional state. This system is realized using multiple hardware and software components. Specific hardware and software configurations, as well as the data processing and data calculations performed by each, are described below.

[0514] Hardware and software used

[0515] Device:

[0516] Refers to devices that are directly operated by users, such as smartphones, personal computers, and tablets.

[0517] It uses a built-in camera and microphone to enable facial and voice recognition technology.

[0518] server:

[0519] This refers to the central processing unit that processes the schedule information sent by users and analyzes and integrates the data.

[0520] It has the ability to access external data sources such as weather APIs and calendar APIs.

[0521] It has natural language processing capabilities using an emotion engine and generative AI models (e.g., GPT series).

[0522] Data processing and calculation

[0523] Retrieving User Information

[0524] A user enters schedule information into the device's calendar app, for example, "Meeting with Bob next Monday at 2 PM."

[0525] The device receives this information and temporarily stores it.

[0526] Sending information

[0527] The device generates an HTTP request and sends the entered schedule information to the server's API endpoint. The sent data is in JSON format.

[0528] Performing natural language processing

[0529] The server analyzes the submitted schedule information using a generative AI model (e.g., GPT series) to identify specific dates and times, event types, and participants.

[0530] Data Integration and Analysis

[0531] The server obtains the necessary information from the calendar API and weather API, and integrates the weather information and overlaps it with the obtained schedule information.

[0532] Running the Emotion Engine

[0533] The device uses the built-in camera and microphone to recognize the user's emotional state using facial recognition and voice analysis technologies.

[0534] The server uses an emotion engine to adjust the schedule based on the user's emotional state, for example, suggesting more relaxing tasks if the user is feeling stressed.

[0535] Schedule Optimization

[0536] The server integrates all data and automatically generates an appropriate schedule, taking into account the user's emotional state and external data to propose the optimal schedule.

[0537] Conversational Interaction

[0538] The device displays the optimized schedule information and displays an interactive prompt to the user, such as "Do you want to confirm?" If the user responds "yes," the device proceeds to the next step.

[0539] Schedule confirmation and notification

[0540] The server determines the final schedule information and reflects it on the user's calendar.

[0541] The terminal notifies the user of the confirmed schedule information.

[0542] Specific examples

[0543] For example, consider the case where a user requests an online meeting at 10 AM next Tuesday. In this case, the user enters "online meeting at 10 AM next Tuesday" into the device's calendar app. The device receives this input and sends it to the server. The server analyzes it using a generative AI model and obtains the necessary data from the calendar API and weather API. Next, it analyzes the user's emotional state and generates an optimal schedule. The device then asks the user for confirmation, updates the calendar with the final schedule, and notifies the user.

[0544] An example of a prompt might be:

[0545] "We've set up an online meeting for next Tuesday at 10am. Would you like to confirm?"

[0546] In this way, the present invention can provide efficient and flexible schedule management and improve the quality of life of users.

[0547] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0548] Step 1: Get user information

[0549] A user enters "Meeting with Bob next Monday at 2 PM" into their device's calendar app.

[0550] Specific behavior:

[0551] Input: The user enters event information into a calendar app.

[0552] Data processing: The terminal receives the input and temporarily stores this schedule information in an internal database.

[0553] Output: Schedule information stored on the device.

[0554] Step 2: Submit your information

[0555] The terminal transmits the input schedule information to the server.

[0556] Specific behavior:

[0557] Input: Schedule information stored on the device.

[0558] Data processing: The device converts the schedule information into a JSON-formatted HTTP request.

[0559] Output: Schedule information in JSON format is sent to the server.

[0560] json

[0561] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0562] Step 3: Performing Natural Language Processing

[0563] The server analyzes the sent schedule information using a generative AI model (e.g., GPT series).

[0564] Specific behavior:

[0565] Input: JSON formatted schedule information sent from the device to the server.

[0566] Data calculation: The server sends analytical prompts to the generative AI model, which then uses natural language processing to parse this information to identify specific dates, times, and participants.

[0567] Output: Parsed detailed schedule information.

[0568] json

[0569] {

[0570] "event": "meeting",

[0571] "date": "YYYY-MM-DD",

[0572] "time": "14:00",

[0573] "participant": "Bob"

[0574] }

[0575] Step 4: Data integration and analysis

[0576] The server accesses external calendar and weather APIs to retrieve and integrate the required information.

[0577] Specific behavior:

[0578] Input: Parsed detailed schedule information.

[0579] Data processing: The server accesses the calendar API to retrieve existing schedule information and check for duplicates. It also accesses the weather API to retrieve the weather forecast for the relevant day.

[0580] Output: Schedule and weather information with no overlaps. For example, the weather forecast information is: "Cloudy".

[0581] Step 5: Run the Emotion Engine

[0582] The device uses facial recognition and voice analysis technologies to recognize the user's emotional state.

[0583] Specific behavior:

[0584] Input: User's facial expression and voice data.

[0585] Data calculation: Data is collected via the device's built-in camera and microphone and analyzed by a local emotion recognition module.

[0586] Output: Recognized emotional state data.

[0587] The server uses an emotion engine to adjust the schedule based on the user's emotional state.

[0588] Specific behavior:

[0589] Input: Recognized emotional state data and detailed schedule information.

[0590] Data calculation: The server inputs data into the emotion engine and adjusts the schedule based on emotions.

[0591] Output: Adjusted schedule information. For example, if the user is stressed, postpone less urgent tasks.

[0592] Step 6: Schedule optimization

[0593] The server integrates all the data and automatically generates the optimal schedule.

[0594] Specific behavior:

[0595] Input: Adjusted schedule information, duplicate check results, and weather information.

[0596] Data calculation: The server runs the optimization algorithm to integrate the data and generate the optimal schedule.

[0597] Output: Optimized proposed schedule.

[0598] Step 7: Conversational Interaction

[0599] The device displays the optimized schedule information and asks the user for confirmation.

[0600] Specific behavior:

[0601] Input: Optimized proposed schedule.

[0602] Data processing: The device displays a user interface and displays a dialogue prompt such as "I'd like to set up a meeting with Bob for Monday at 2pm. Would you like to confirm?"

[0603] Output: The user's response (e.g., "Yes").

[0604] Step 8: Confirm schedule and notify

[0605] The server determines the final schedule information and reflects it on the user's calendar.

[0606] Specific behavior:

[0607] Input: User's acknowledgment (Yes).

[0608] Data processing: The server updates the schedule information using the calendar API.

[0609] Output: The confirmed schedule information is reflected in the user's calendar.

[0610] The device will notify the user that the schedule has been confirmed.

[0611] Specific behavior:

[0612] Input: Confirmed schedule information.

[0613] Data processing: The terminal generates and displays a notification message.

[0614] Output: The user receives a notification that says "A meeting with Bob has been scheduled for next Monday at 2 PM."

[0615] The above is the specific operation of each processing step and the flow of the data processing and data calculation that accompanies it.

[0616] (Application example 2)

[0617] 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."

[0618] Conventional schedule management systems simply manage a user's schedule information but are unable to consider the user's emotional state. This makes it difficult to flexibly adjust schedules to accommodate changes in stress and emotional state, preventing stress reduction and efficient schedule management for users. Furthermore, they lack the functionality to integrate information from weather forecasts and external data sources to optimize schedules. The present invention aims to solve these problems and improve the quality of life for users by providing a schedule management system that considers the user's emotional state.

[0619] 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.

[0620] In this invention, the server includes means for performing face recognition and voice analysis to recognize the emotional state of the user, means for proposing and adjusting a schedule based on the emotional state using an emotion engine, and means for prompting the user to confirm and correct the schedule in an interactive format, thereby enabling schedule management that flexibly responds to the emotional state of the user.

[0621] "User" means an individual or corporation that uses the system to manage schedules.

[0622] "Schedule information" refers to information such as the date, time, content, location, and participants of a scheduled event entered by the user.

[0623] A "server" is a computer system that processes and stores data over the Internet.

[0624] "Natural language processing" is the technology that enables computers to understand, interpret, and process human language.

[0625] An "external data source" is a service or database that provides data outside of the system, such as a weather API or a calendar API.

[0626] "Facial recognition" is a technology that analyzes a user's facial image to identify an individual.

[0627] "Voice analysis" is a technology that analyzes a user's voice to determine their emotional state and intentions.

[0628] An "emotion engine" is software or a system that analyzes a user's emotional state and determines an appropriate response based on the results.

[0629] "Interactive" refers to the way in which a system and a user exchange information with each other through input and output.

[0630] "Notification" refers to the system providing information to the user regarding schedule confirmation or changes.

[0631] This invention is a system that improves the efficiency of a user's schedule and task management, and by combining it with an emotion engine, enables flexible responses according to the user's emotional state. The detailed configuration and processing for specifically implementing this system are described below.

[0632] System configuration

[0633] User device: Refers to a smartphone, PC, etc., and is the device through which the user inputs schedule information.

[0634] Server: A computer system that processes and stores data over the Internet.

[0635] Facial recognition technology: Technology that analyzes a user's facial image to identify the individual and recognize their emotional state.

[0636] Voice analysis technology: Technology that analyzes the user's voice to determine their emotional state and intentions.

[0637] Emotion engine: Software or a system that determines an appropriate response based on the user's emotional state.

[0638] Calendar API: An external data source used to integrate existing schedule information.

[0639] Weather API: An external data source used to obtain weather forecast information.

[0640] Generative AI model: Software that performs natural language processing, including models used for analysis (e.g., the GPT series).

[0641] A natural language description of the program's processing

[0642] 1. Obtaining user information

[0643] Users enter schedule information into their device's calendar app, such as "Schedule a meeting for next Monday at 2 PM."

[0644] 2. Transmission of information

[0645] The terminal sends this schedule information to the server. The data sent will have the following format, for example:

[0646] json

[0647] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0648] 3. Performing Natural Language Processing

[0649] The server uses a generative AI model to parse the submitted schedule information, converting it into specific dates and times and providing event details.

[0650] json

[0651] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob"}

[0652] 4. Data integration and analysis

[0653] The server uses the calendar API to retrieve existing schedule information and check whether it overlaps with the new schedule information, and at the same time accesses the weather API to retrieve the weather forecast for the following Monday.

[0654] 5. Running the Emotion Engine

[0655] The device uses voice and facial recognition technology to understand the user's emotional state, for example, determining whether the user is feeling stressed.

[0656] 6. Schedule optimization

[0657] The server optimizes the schedule by taking into account the acquired calendar information, weather forecast information, and the user's emotional state. For example, if the weather is bad, the server suggests that the user hold a meeting indoors.

[0658] 7. Conversational Interaction

[0659] The device interactively asks the user to confirm, "I'd like to set up a meeting with Bob on Monday at 2 PM. Would you like to confirm?" If the user answers "yes," it proceeds to the next step.

[0660] 8. Schedule confirmation and notification

[0661] The server determines the final schedule information and reflects it in the user's calendar. The reflected information is as follows:

[0662] json

[0663] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob", "location": "Online"}

[0664] The device will send a notification to the user saying, "A meeting with Bob has been scheduled for next Monday at 2 PM."

[0665] Specific examples

[0666] For example, let us consider the case where a user performs an operation such as "schedule an online meeting at 10:00 AM next Tuesday."

[0667] 1. Obtaining user information: The user enters "Online meeting next Tuesday at 10 AM" into the device's calendar app.

[0668] 2. Sending information: The terminal sends this information to the server. The data sent is in the following format:

[0669] json

[0670] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[0671] 3. Perform natural language processing: The server parses this information and converts it into specific dates and times, as well as identifying the type of event and participants.

[0672] json

[0673] {"event": "Online Meeting", "date": "YYYY-MM-DD", "time": "10:00", "participant": "N / A"}

[0674] 4. Data integration and analysis: The server uses the calendar API to check for overlapping events and retrieves weather forecast information from the weather API.

[0675] 5. Execution of emotion engine: The device analyzes the user's voice and facial expressions to recognize their emotions. For example, it determines that the user is not feeling stressed.

[0676] 6. Schedule optimization: The server determines the optimal schedule based on this information.

[0677] 7. Conversational interaction: The device asks the user for confirmation: "I'm going to schedule an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[0678] 8. Schedule confirmation and notification: The server confirms the schedule information after receiving the user's confirmation, and updates the user's calendar. This schedule information is then notified to the device.

[0679] Example prompts for generative AI models

[0680] A user types, "I want to order dinner at 7 PM." The weather is rainy, and the user is stressed. Suggest the best time and menu to order.

[0681] This system allows users to manage their schedules efficiently and stress-free, and in particular allows for flexible responses that take into account emotional states.

[0682] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0683] Step 1: Get user information

[0684] A user enters an event into the device's calendar app. For example, "Schedule a meeting next Monday at 2 PM." The input information is as follows:

[0685] input:

[0686] json

[0687] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0688] Output: Schedule information stored directly on the user's device.

[0689] Step 2: Submit your information

[0690] The device sends the acquired schedule information to the server. This sending process uses the following HTTP POST request. The input data is the schedule information acquired in step 1:

[0691] input:

[0692] json

[0693] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0694] Output: The schedule information sent to the server.

[0695] Step 3: Performing Natural Language Processing

[0696] The server uses a generative AI model to parse the submitted schedule information, first converting it into formal dates and times and then extracting event details. The input data is the data received in step 2:

[0697] input:

[0698] json

[0699] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0700] The analysis produces the following data:

[0701] output:

[0702] json

[0703] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob"}

[0704] Step 4: Data integration and analysis

[0705] The server accesses external data sources (Calendar API and Weather API) to retrieve existing schedule information and weather forecast information. The input data is the data obtained in step 3 and the new data retrieved from the API:

[0706] Input: Data obtained from the Calendar API and Weather API

[0707] Output: New schedule and weather forecast information without overlaps

[0708] Step 5: Run the Emotion Engine

[0709] The device performs facial recognition and voice analysis to recognize the user's emotions. The input data is the user's facial image and voice data:

[0710] Input: User's facial image and voice data

[0711] Output: Perceived user emotional state (e.g., "stressed")

[0712] Step 6: Schedule optimization

[0713] The server optimizes the schedule by integrating calendar information, weather forecast information, and the user's emotional state. For example, it suggests indoor meetings when the weather forecast is bad, and postpones less important tasks when the user is stressed. The input data are the data obtained in steps 4 and 5:

[0714] Input: All integrated data for optimization

[0715] Output: Optimized schedule proposal (e.g. "Indoor meeting proposal")

[0716] Step 7: Conversational Interaction

[0717] The device interactively asks the user, "I'd like to schedule a meeting with Bob at 2 PM on Monday. Would you like to confirm?" and collects the result. The input data is a schedule optimization proposal:

[0718] Input: Optimized schedule proposal

[0719] Output: User confirmation result (e.g. "Yes")

[0720] Step 8: Confirm schedule and notify

[0721] The server receives the user's confirmation result, finalizes the schedule information, and updates the user's calendar. The terminal then sends a notification to the user. The input data is the user's confirmation result:

[0722] Input: User confirmation result

[0723] Output: Confirmed schedule information and notification message (e.g., "Your meeting with Bob has been scheduled for next Monday at 2 PM.")

[0724] The above is the specific processing content of each step.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] [Second embodiment]

[0729] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0730] 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.

[0731] 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).

[0732] 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.

[0733] 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.

[0734] 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).

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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."

[0741] This invention is an AI tool for streamlining user schedule and task management. The system acquires the user's schedule information, analyzes it using natural language processing technology, a generative AI model, and optimizes the schedule by integrating information from external data sources. It also prompts the user to confirm and correct the information interactively, and then finalizes and notifies the user of the final schedule information.

[0742] A natural language description of the program's processing

[0743] Retrieving User Information

[0744] A user types into a device (e.g., a smartphone or PC) "Meeting with Bob next Monday at 2 PM." That information is stored on the device.

[0745] Sending information

[0746] The device sends the acquired schedule information to the server. The data sent is in the following format:

[0747] json

[0748] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0749] Performing natural language processing

[0750] The server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information, and the analysis results in detailed information such as:

[0751] json

[0752] {

[0753] "event": "meeting",

[0754] "date": "YYYY-MM-DD",

[0755] "time": "14:00",

[0756] "participant": "Bob"

[0757] }

[0758] Data Integration and Analysis

[0759] The server accesses an external calendar API to check for overlaps with other events. At the same time, it uses a weather API to get the weather forecast for the following Monday. For example, it may predict a cloudy day. This information is used to optimize the schedule.

[0760] Schedule Optimization

[0761] The server then uses this information to optimize the schedule. For example, it can refer to the weather forecast and suggest indoor meetings if an outdoor event is not suitable for the day of the meeting. It also optimizes the time to avoid conflicts with other tasks or appointments.

[0762] Conversational Interaction

[0763] The device interactively asks the user for confirmation: "I'd like to set up a meeting with Bob for Monday at 2 PM. Confirm?" and waits for the user to confirm or amend. If the user answers "yes," it proceeds to the next step.

[0764] Schedule confirmation and notification

[0765] The server confirms the final schedule information and updates the user's calendar. The confirmed schedule information is then sent to the device, informing the user that "A meeting with Bob has been scheduled for next Monday at 2:00 PM."

[0766] Specific examples

[0767] For example, a specific example will be given of a case where a user performs an operation to "schedule an online meeting at 10:00 AM next Tuesday."

[0768] Retrieving User Information

[0769] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[0770] Sending information

[0771] The device sends this information to the server. The data sent is in the following format:

[0772] json

[0773] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[0774] Performing natural language processing

[0775] The server parses this information and converts it into a concrete date and time.

[0776] json

[0777] {

[0778] "event": "online meeting",

[0779] "date": "YYYY-MM-DD",

[0780] "time": "10:00"

[0781] }

[0782] Data Integration and Analysis

[0783] The server uses the calendar API to check for overlaps with other events and also retrieves weather information from the weather API, e.g., a "sunny" forecast.

[0784] Schedule Optimization

[0785] The server determines the optimal schedule based on this information.

[0786] Conversational Interaction

[0787] The device asks the user, "We're setting up an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[0788] Schedule confirmation and notification

[0789] The server confirms the final schedule information and updates the calendar on the user's device. The notification reads, "An online meeting has been scheduled for next Tuesday at 10:00 AM."

[0790] The above is an embodiment of the present invention. This system allows users to manage their schedules efficiently and stress-free.

[0791] The processing flow will be explained below.

[0792] Step 1:

[0793] A user enters "Meeting with Bob next Monday at 2 PM" into the calendar app on their device (e.g., smartphone or PC).

[0794] Step 2:

[0795] The terminal stores the entered schedule information and sends it to the server. The data sent is in the following format:

[0796] json

[0797] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0798] Step 3:

[0799] The server uses a generative AI model (e.g., GPT series) to analyze the received schedule information. The analysis results in detailed information such as:

[0800] json

[0801] {

[0802] "event": "meeting",

[0803] "date": "YYYY-MM-DD",

[0804] "time": "14:00",

[0805] "participant": "Bob"

[0806] }

[0807] Step 4:

[0808] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[0809] Step 5:

[0810] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[0811] Step 6:

[0812] The server takes into account calendar information and weather forecast information to optimize the schedule. For example, if an event is scheduled outdoors, the server considers the weather forecast and suggests an indoor meeting.

[0813] Step 7:

[0814] Based on the device's optimized schedule information, the user is prompted interactively to confirm, "I'm going to schedule a meeting with Bob for Monday at 2 p.m. Would you like to confirm?"

[0815] Step 8:

[0816] The user presses a confirmation button through a dialogue interface or answers "yes" by voice input.

[0817] Step 9:

[0818] The server receives confirmation from the user, finalizes the schedule information, and updates the user's calendar. The updated information looks like this:

[0819] json

[0820] {

[0821] "event": "meeting",

[0822] "date": "YYYY-MM-DD",

[0823] "time": "14:00",

[0824] "participant": "Bob",

[0825] "location": "online"

[0826] }

[0827] Step 10:

[0828] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[0829] The above are the specific operations performed at each step. In this way, the terminal, server, and user work together to achieve efficient and optimal schedule management.

[0830] Example 1

[0831] 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."

[0832] Conventional schedule management systems require users to manually input, check, and correct schedules, which is time-consuming and labor-intensive. Furthermore, they lack the ability to integrate external data, such as weather information and overlap checks with other appointments, making schedule optimization difficult. Furthermore, they are unable to handle natural language input, making user input complex and unintuitive.

[0833] 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.

[0834] In this invention, the server includes means for acquiring schedule information from a user, means for transmitting the acquired schedule information to an information processing device, means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants, means for integrating information acquired from external data sources and adjusting and optimizing the schedule, means for interactively prompting the user for confirmation and correction, means for finalizing the schedule information and notifying the user's terminal, means for analyzing the schedule information using a generative AI model, and means for performing natural language processing using prompt sentences to identify the date and time. This allows the user to efficiently manage their schedule, and realizes optimized schedule management integrated with external data.

[0835] "Schedule information" refers to data about the date, time, type of event, and participants that a user enters as an appointment.

[0836] The term "information processing device" refers to a device or system in general that has the function of receiving, analyzing, and transmitting schedule information.

[0837] "Natural language processing" is a computer technology for analyzing human language and understanding or generating meaning.

[0838] "External Data Sources" refers to external data providers, including calendar APIs and weather APIs, that the server accesses and uses for schedule optimization.

[0839] "Schedule adjustment and optimization" is the process of suggesting optimal times and settings, taking into account the user's existing schedule and external data.

[0840] "Means for interactively prompting confirmation and correction" refers to a function that interactively prompts the user to confirm and correct the schedule contents.

[0841] A "generative AI model" is an artificial intelligence technology that includes large-scale learning models used for natural language processing.

[0842] A "prompt" is text that is input to a generative AI model to instruct it on a specific task.

[0843] "Terminal" refers to the device (such as a smartphone or PC) that a user uses to input and receive schedule information.

[0844] This invention is an advanced scheduling system for streamlining users' schedules and task management. This system is based on a large-scale natural language processing technology called a generative AI model.

[0845] Hardware and software used

[0846] Hardware

[0847] 1. Terminal: A device through which a user inputs schedule information. Examples include smartphones and personal computers (PCs).

[0848] 2. Server: A central management system for analyzing schedule information and integrating external data.

[0849] software

[0850] 1. Natural language processing technology: Generative AI models (e.g., GPT series) are used to analyze the schedule information entered by the user.

[0851] 2. Calendar API: Used to check for overlaps with other events.

[0852] 3. Weather API: Used to obtain weather information required for schedule optimization.

[0853] Data processing and calculation

[0854] 1. Obtaining user information

[0855] A user enters "Meeting next Monday at 2 PM" into the device's calendar app. This information is stored on the device.

[0856] 2. Transmission of information

[0857] The terminal transmits the acquired schedule information to the server.

[0858] For example, data is sent in the following format:

[0859] json

[0860] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0861] 3. Performing Natural Language Processing

[0862] The server analyzes the schedule information using a generative AI model, for example, using the following prompt:

[0863] Example prompt: "Convert 'next Monday at 2 PM' into a specific date and time."

[0864] Examples of what can be obtained as a result of the analysis:

[0865] json

[0866] {

[0867] "event": "meeting",

[0868] "date": "YYYY-MM-DD",

[0869] "time": "14:00",

[0870] "participant": "Bob"

[0871] }

[0872] 4. Data integration and analysis

[0873] The server uses an external calendar API to check for overlaps with other events, and at the same time, retrieves the weather forecast for the day of the meeting using a weather API.

[0874] For example, a "cloudy" forecast is obtained.

[0875] 5. Schedule optimization

[0876] The server optimizes schedules based on weather forecasts and other schedules, for example suggesting indoor meetings if the weather is bad.

[0877] The schedule is updated as a result of the optimization.

[0878] 6. Conversational Interaction

[0879] The device interactively asks the user for confirmation: "I'm going to schedule a meeting for Monday at 2 PM. Would you like to confirm?" and waits for the user to confirm or correct the request.

[0880] 7. Schedule confirmation and notification

[0881] The server determines the final schedule information and notifies the user's device.

[0882] The user is notified of the confirmed schedule information, stating, "A meeting has been scheduled for next Monday at 2:00 p.m."

[0883] Specific examples

[0884] For example, if a user wants to "schedule an online meeting next Tuesday at 10:00 AM":

[0885] The user inputs specific plans into the device.

[0886] The device sends this information to the server.

[0887] The server analyzes it using a generative AI model and converts it into a specific date and time.

[0888] The server uses the calendar and weather APIs to optimize the schedule.

[0889] The device interactively asks the user for confirmation, and the user replies "yes."

[0890] The server confirms the final schedule and reflects it on the user's calendar.

[0891] As described above, the system of the present invention provides users with efficient and stress-free schedule management.

[0892] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0893] Step 1:

[0894] Retrieving User Information

[0895] A user enters "Meeting at 2 PM next Monday" into the calendar app on their device (e.g., smartphone or PC). This entry is saved in the device's database.

[0896] Input: Scheduling information entered by the user (e.g., "Meeting next Monday at 2 PM").

[0897] Output: Schedule information stored in the device database.

[0898] Specific behavior:

[0899] The user launches a calendar app and enters a new event.

[0900] Suppose the input is "Meeting next Monday at 2pm."

[0901] This input is stored in an internal database.

[0902] Step 2:

[0903] Sending information

[0904] The device sends the saved schedule information to the server. The data sent is in JSON format.

[0905] Input: Schedule information stored on the device.

[0906] Output: Schedule information sent to the server in JSON format.

[0907] Specific behavior:

[0908] The device organizes the saved schedule information into JSON format.

[0909] For example, the following JSON is generated:

[0910] json

[0911] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[0912] The JSON data is sent over the internet to a server.

[0913] Step 3:

[0914] Performing natural language processing

[0915] The server uses a generative AI model to parse the submitted schedule information, converting dates and times into specific formats using prompts.

[0916] Input: Schedule information in JSON format.

[0917] Output: Schedule information converted into concrete dates and times.

[0918] Specific behavior:

[0919] The server parses the received JSON data.

[0920] Enter the following prompt into the generative AI model: "Convert 'next Monday at 2 PM' into a specific date and time."

[0921] The generative AI model performs the analysis and generates data such as:

[0922] json

[0923] {

[0924] "event": "meeting",

[0925] "date": "YYYY-MM-DD",

[0926] "time": "14:00",

[0927] "participant": "Bob"

[0928] }

[0929] Step 4:

[0930] Data Integration and Analysis

[0931] The server accesses external calendar and weather APIs to check for duplicates and obtain weather information.

[0932] Input: Parsed schedule information.

[0933] Output: Results of overlap check with other events and weather information.

[0934] Specific behavior:

[0935] The server sends a request to the external calendar API to check for conflicts with other events for the user.

[0936] Send a request to the weather API to get weather information for the day the meeting is scheduled.

[0937] For example, a "cloudy" forecast is obtained.

[0938] Step 5:

[0939] Schedule Optimization

[0940] Optimize your schedule based on the information obtained by the server. Suggest the best schedule based on weather forecasts and other schedules.

[0941] Input: Duplicate check results and weather information.

[0942] Output: Optimized schedule information.

[0943] Specific behavior:

[0944] The server calculates the optimal schedule based on weather information and other schedules.

[0945] For example, if the weather is bad, suggest meeting indoors.

[0946] The schedule is updated as a result of the optimization.

[0947] Step 6:

[0948] Conversational Interaction

[0949] The device interactively asks the user for confirmation: "I'd like to schedule a meeting for Monday at 2 PM. Would you like to confirm?"

[0950] Input: Optimized schedule information.

[0951] Output: User confirmation or correction instructions.

[0952] Specific behavior:

[0953] The device displays a notification to the user.

[0954] The user responds with "yes" or "no."

[0955] If the answer is "no", a form is displayed that allows the user to enter a new time.

[0956] Step 7:

[0957] Schedule confirmation and notification

[0958] The server determines the final schedule information and notifies the user's device.

[0959] Input: User confirmed or corrected schedule information.

[0960] Output: Confirmed schedule information and notifications.

[0961] Specific behavior:

[0962] The server saves the confirmed schedule to the user's calendar.

[0963] Send a notification to your device saying "A meeting has been scheduled for next Monday at 2 PM."

[0964] The user's calendar will update to show the new schedule.

[0965] (Application example 1)

[0966] 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."

[0967] Conventional schedule management systems do not provide sufficient support for streamlining user schedule and task management. Especially for high-risk jobs like security guarding, real-time schedule optimization and risk information integration are necessary. This allows for more effective patrol and surveillance, but conventional systems cannot meet these needs. Furthermore, a lack of visual or audio feedback makes it difficult to respond quickly.

[0968] 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.

[0969] In this invention, the server includes means for acquiring schedule information from a user, means for transmitting the acquired schedule information to the server, means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and location, means for integrating information acquired from external data sources to adjust and optimize the schedule, means for interactively prompting the user to confirm and correct the information, means for finalizing the schedule information and notifying the user's device, means for proposing optimal monitoring routes and patrol schedules, means for optimizing patrol routes by integrating risk assessment information, means for managing monitoring points in security areas based on location information, and means for providing visual or audio feedback, thereby enabling effective and efficient schedule management for security guards and optimization of patrol work.

[0970] A "user" is a person or professional who uses the system to manage schedules and tasks.

[0971] "Schedule information" refers to data about appointments and tasks, including dates, times, event types, locations, etc.

[0972] A "server" is a computer system that analyzes, integrates, and optimizes schedule information.

[0973] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0974] "External Data Source" means a source of externally provided data, including a calendar API or weather API.

[0975] "Optimization" means adjusting schedules and tasks efficiently and effectively.

[0976] "Interaction style" refers to the way in which users and systems interact with each other to exchange information.

[0977] A "surveillance route" is the route that security guards follow when patrolling.

[0978] A "patrol schedule" is a plan for security guards to patrol specific locations at specific times.

[0979] "Risk assessment information" refers to data that predicts and provides information about security risks.

[0980] A "patrol route" is a pre-determined route that a security guard follows when patrolling.

[0981] "Location information" is data that indicates the geographical information of the user or the monitored area.

[0982] A "vigilance area" is a location or area that security guards should monitor.

[0983] "Visual feedback" refers to the display of information provided through a display or smart glasses.

[0984] "Audio feedback" refers to information provided through audio output.

[0985] The system for realizing this invention allows users to efficiently manage their schedules, and in particular, optimizes patrol and surveillance operations in security services. The system is implemented by combining the following hardware and software.

[0986] The system uses smart glasses or head-mounted displays (terminals) to obtain the user's schedule information. When the user gives voice commands or inputs to these devices, the information is sent to a cloud server. The software used includes generative AI models (e.g., GPT series) as well as calendar APIs, weather APIs, and risk assessment APIs.

[0987] Processing Description

[0988] Retrieving User Information

[0989] The user commands the smart glasses to "start monitoring the east gate at 2:00 p.m. next Tuesday" by voice, which is then converted into text by the device.

[0990] Sending information

[0991] The device sends the schedule information it has acquired to the cloud server. The data sent will be in the following format:

[0992] json

[0993] {"event": "monitoring", "time": "2pm", "location": "east gate"}

[0994] Performing natural language processing

[0995] The cloud server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information and convert it into specific time and location information. The analysis results are as follows:

[0996] json

[0997] {

[0998] "event": "monitoring",

[0999] "time": "14:00",

[1000] "location": "East Gate"

[1001] }

[1002] Data Integration and Analysis

[1003] The cloud server uses the calendar API and risk assessment API to integrate other schedules with risk information. For example, it can obtain information such as "There is a high possibility that suspicious individuals will be seen around the east gate."

[1004] Schedule optimization

[1005] Based on this information, the cloud server will suggest the most effective surveillance route and time to the user, optimizing areas to be patrolled in addition to the East Gate.

[1006] Conversational Interaction

[1007] The device will ask the user aloud, "We will begin monitoring the East and North Gates at 2:00 p.m. next Tuesday. Would you like to confirm?" If the user responds "Yes," it will proceed to the next step.

[1008] Schedule confirmation and notification

[1009] The cloud server finalizes the monitoring schedule and notifies the user's device, which then provides visual and audio feedback, stating, "Monitoring of the East and North Gates has been set for next Tuesday at 2:00 PM."

[1010] Specific examples

[1011] Here is a specific example of a case where a user commands the smart glasses to "set up a patrol of the south exit at 4:00 p.m. next Wednesday."

[1012] Retrieving User Information

[1013] The user types into the terminal, "Patrol the south exit next Wednesday at 4pm."

[1014] Sending information

[1015] The device sends this information to the cloud server in the following format:

[1016] json

[1017] {"event": "tour", "time": "4pm", "location": "south exit"}

[1018] Performing natural language processing

[1019] The cloud server analyzes the information and converts it into:

[1020] json

[1021] {

[1022] "event": "tour",

[1023] "time": "16:00",

[1024] "location": "South Exit"

[1025] }

[1026] Data Integration and Analysis

[1027] The cloud server uses the risk assessment API to obtain risk information such as "There have been reports of suspicious individuals being seen around the south exit."

[1028] Schedule Optimization

[1029] The cloud server proposes the optimal route and time, and optimization is performed including the surrounding area.

[1030] Conversational Interaction

[1031] The terminal asks, "Patrol of the South Exit and surrounding area will be scheduled for next Wednesday at 4pm. Confirm?"

[1032] Schedule confirmation and notification

[1033] If the user responds "Yes," the cloud server finalizes the patrol schedule and notifies the device, displaying the message, "Patrol of the south exit and surrounding area has been scheduled for next Wednesday at 4 p.m."

[1034] Prompt Sentence Examples

[1035] Based on the command "Start monitoring the East Gate at 2:00 PM next Tuesday," please integrate other schedules and risk information to propose the optimal monitoring schedule.

[1036] This will enable effective and efficient schedule management for security guards and optimization of patrol operations.

[1037] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1038] Step 1:

[1039] The user commands the smart glasses by voice, "Start monitoring the east gate at 2:00 PM next Tuesday." The device converts this voice command into text data and recognizes the content as "schedule information." The input is a voice command, and the output is schedule information in text format. In this step, voice recognition technology is used to convert the voice data into text data.

[1040] Step 2:

[1041] The device sends the acquired schedule information to the cloud server. The data sent is in the following format:

[1042] json

[1043] {"event": "monitoring", "time": "2pm", "location": "east gate"}

[1044] The input is schedule information in text format, and the output is data transmission to a cloud server.

[1045] Step 3:

[1046] The cloud server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information. The analysis results in specific time and location information, which may take the following format:

[1047] json

[1048] {

[1049] "event": "monitoring",

[1050] "time": "14:00",

[1051] "location": "East Gate"

[1052] }

[1053] The input is schedule information in text format, and the output is parsed schedule data. In this step, a generative AI model is used for natural language processing.

[1054] Step 4:

[1055] The cloud server calls the calendar API and risk assessment API, and integrates other schedule and risk information based on the acquired schedule information. For example, it acquires information that "there is a high possibility that a suspicious person will be seen around the east gate." The input is the analyzed schedule data, and the output is the integrated risk assessment data and schedule data. In this step, the API is called to integrate and analyze the data.

[1056] Step 5:

[1057] The cloud server uses this integrated data to propose the most effective surveillance route and time. For example, it may propose additional areas to patrol besides the East Gate. The input is the integrated risk assessment data and schedule data, and the output is an optimized surveillance schedule proposal. This step uses data analysis and optimization algorithms.

[1058] Step 6:

[1059] The terminal asks the user by voice, "Monitoring of the East and North Gates will begin at 2:00 PM next Tuesday. Do you want to confirm?" If the user answers "Yes," it proceeds to the next step. The input is the optimized monitoring schedule proposal, and the output is the user's response. This step uses a conversational interface.

[1060] Step 7:

[1061] After receiving the user's response, the cloud server finalizes the monitoring schedule and notifies the user's device. The device provides visual or audio feedback, such as "Monitoring of the East and North Gates has been set for next Tuesday at 2:00 PM." The input is the user's confirmation response, and the output is notification of the finalized schedule information. This step uses the database update and notification functions.

[1062] 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.

[1063] This invention is a system that streamlines user schedule and task management and, by combining it with an emotion engine, enables flexible responses according to the user's emotional state. This system acquires the user's schedule information, analyzes it using natural language processing technology, and integrates information from external data sources to optimize the schedule. It also recognizes the user's emotions, proposes and adjusts the schedule, and notifies the user of the final schedule information.

[1064] A natural language description of the program's processing

[1065] Retrieving User Information

[1066] A user enters "Meeting with Bob at 2 PM next Monday" into the calendar app on their device (e.g., smartphone or PC). That information is stored on the device.

[1067] Sending information

[1068] The terminal sends the entered schedule information to the server. The data sent is in the following format:

[1069] json

[1070] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1071] Performing natural language processing

[1072] The server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information, and the analysis results in detailed information such as:

[1073] json

[1074] {

[1075] "event": "meeting",

[1076] "date": "YYYY-MM-DD",

[1077] "time": "14:00",

[1078] "participant": "Bob"

[1079] }

[1080] Data Integration and Analysis

[1081] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[1082] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[1083] Running the Emotion Engine

[1084] The device uses facial recognition and voice analysis technologies to recognize the user's emotional state.

[1085] The server uses an emotion engine to adjust the schedule based on the user's perceived emotional state. For example, if the user is feeling stressed, the system will suggest postponing less urgent tasks.

[1086] Schedule Optimization

[1087] The server optimizes the schedule by taking into account calendar information, weather forecast information, and the user's emotional state. For example, it suggests indoor meetings based on the weather forecast and also makes suggestions that match the user's emotional state.

[1088] Conversational Interaction

[1089] Based on the device-optimized schedule information, the user is interactively asked to confirm, "I'm going to schedule a meeting with Bob for Monday at 2 PM. Would you like to confirm?" If the user answers "yes," the process proceeds to the next step.

[1090] Schedule confirmation and notification

[1091] The server determines the final schedule information and updates the user's calendar. The updated information looks like this:

[1092] json

[1093] {

[1094] "event": "meeting",

[1095] "date": "YYYY-MM-DD",

[1096] "time": "14:00",

[1097] "participant": "Bob",

[1098] "location": "online"

[1099] }

[1100] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[1101] Specific examples

[1102] For example, a specific example will be given below in which a user performs an operation to "schedule an online meeting at 10:00 AM next Tuesday."

[1103] Retrieving User Information

[1104] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[1105] Sending information

[1106] The device sends this information to the server. The data sent is in the following format:

[1107] json

[1108] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[1109] Performing natural language processing

[1110] The server parses this information and converts it into a concrete date and time, as well as the type of event and participants:

[1111] json

[1112] {

[1113] "event": "online meeting",

[1114] "date": "YYYY-MM-DD",

[1115] "time": "10:00",

[1116] "participant": "N / A"

[1117] }

[1118] Data Integration and Analysis

[1119] The server uses the calendar API to check for overlapping events and also retrieves weather forecast information from the weather API.

[1120] Running the Emotion Engine

[1121] The device analyzes the user's voice and facial expressions to recognize their emotions. For example, it determines that the user is not feeling stressed.

[1122] Schedule Optimization

[1123] The server determines the optimal schedule based on this information.

[1124] Conversational Interaction

[1125] The device will ask the user for confirmation: "An online meeting will be scheduled for next Tuesday at 10:00 AM. Would you like to confirm?"

[1126] Schedule confirmation and notification

[1127] The server receives the user's confirmation and finalizes the schedule information, which is then reflected in the user's calendar. This schedule information is then notified to the device.

[1128] The above is an embodiment of the present invention. This system allows users to manage their schedules efficiently and stress-free. In particular, by taking into account the user's emotional state, more flexible responses are possible, improving the quality of daily life.

[1129] The processing flow will be explained below.

[1130] Specific steps of the program's processing

[1131] Step 1:

[1132] A user speaks or texts "Meet with Bob next Monday at 2 PM" into the calendar app on their device (e.g., smartphone or PC).

[1133] Step 2:

[1134] The terminal processes the entered schedule information and sends it to the server. The data sent is in the following format:

[1135] json

[1136] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1137] Step 3:

[1138] The server uses a generative AI model (GPT series) to analyze the received schedule information. This converts the input "next Monday" or "2 PM" into a specific date and time, and also identifies the type of event and its attendees. The analysis results are as follows:

[1139] json

[1140] {

[1141] "event": "meeting",

[1142] "date": "YYYY-MM-DD",

[1143] "time": "14:00",

[1144] "participant": "Bob"

[1145] }

[1146] Step 4:

[1147] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[1148] Step 5:

[1149] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[1150] Step 6:

[1151] The device uses an emotion engine (face recognition technology and voice analysis technology) to recognize the user's emotional state. For example, it analyzes the user's facial expressions and voice using a camera or microphone to identify the user's emotional state.

[1152] Step 7:

[1153] The server uses an emotion engine to adjust the schedule based on the user's perceived emotional state. For example, if the user is feeling stressed, the server suggests adjusting meeting times to allow time for relaxation.

[1154] Step 8:

[1155] The server takes into account calendar information, weather forecast information, and emotional state to optimize the schedule. For example, it prioritizes important tasks and suggests appropriate locations depending on the weather.

[1156] Step 9:

[1157] Based on the device-optimized schedule information, the user is interactively asked to confirm, "I'm going to schedule a meeting with Bob next Monday at 2 p.m. Would you like to confirm?"

[1158] Step 10:

[1159] The user confirms by answering "yes" in the dialogue interface.

[1160] Step 11:

[1161] The server receives confirmation from the user, finalizes the schedule information, and updates the user's calendar. The finalized information looks like this:

[1162] json

[1163] {

[1164] "event": "meeting",

[1165] "date": "YYYY-MM-DD",

[1166] "time": "14:00",

[1167] "participant": "Bob",

[1168] "location": "online"

[1169] }

[1170] Step 12:

[1171] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[1172] Specific examples

[1173] For example, a specific example will be given below in which a user performs an operation such as "schedule an online meeting at 10:00 AM next Tuesday."

[1174] Step 1:

[1175] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[1176] Step 2:

[1177] The device sends this information to the server. The data sent is in the following format:

[1178] json

[1179] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[1180] Step 3:

[1181] The server parses this information and converts it into a concrete date and time, as well as the type of event and participants:

[1182] json

[1183] {

[1184] "event": "online meeting",

[1185] "date": "YYYY-MM-DD",

[1186] "time": "10:00",

[1187] "participant": "N / A"

[1188] }

[1189] Step 4:

[1190] The server uses the calendar API to check for overlapping events and also retrieves weather forecast information from the weather API.

[1191] Step 5:

[1192] The device analyzes the user's voice and facial expressions to recognize their emotions, for example, rating the user as "relaxed."

[1193] Step 6:

[1194] The server determines the optimal schedule based on this information, taking into account the user's relaxed emotional state and setting a schedule with ample time.

[1195] Step 7:

[1196] The device prompts the user for confirmation: "I'm setting up an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[1197] Step 8:

[1198] The user checks and corrects as necessary.

[1199] Step 9:

[1200] The server finalizes the schedule information and sends notifications to the user's device.

[1201] The above is a specific embodiment for carrying out the present invention, and this system enables the user to manage their schedule efficiently and stress-free, and to respond flexibly according to their emotional state.

[1202] Example 2

[1203] 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."

[1204] Modern users are required to efficiently manage a variety of tasks and schedules in their busy daily lives. However, traditional schedule management systems do not take into account the user's emotional state, making it difficult to provide appropriate schedule suggestions and adjustments for users who feel stressed or tense. Furthermore, they often lack the functionality to integrate with external data sources and provide optimal schedules. This leads to problems that reduce the user's quality of life.

[1205] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring schedule information from a user; means for transmitting the acquired schedule information to the server; means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants; means for integrating information acquired from external data sources to adjust and optimize the schedule; means for recognizing the user's emotional state; means for adjusting the schedule based on the user's emotional state using an emotion engine; means for interactively prompting the user to confirm and correct the information; and means for finalizing the schedule information and notifying the user's device. This makes it possible to provide an optimal schedule by integrating information from external data sources while taking the user's emotional state into consideration.

[1206] "User" refers to a person who uses the system to manage schedules and tasks.

[1207] "Terminal" means a device that a user uses to access and operate the system, including a smartphone, personal computer, tablet, etc.

[1208] "Server" refers to the central computer system that analyzes schedule information submitted by users and integrates it with external data sources.

[1209] "Schedule information" refers to information about upcoming meetings, events, etc. that a user enters into a terminal.

[1210] "Natural language processing" refers to the technology that allows a computer to understand and analyze strings of characters written in natural language.

[1211] "External data sources" refers to external services or databases that the server accesses to obtain information, including calendar APIs and weather APIs.

[1212] "Schedule adjustment" refers to appropriately changing or optimizing the schedule based on the acquired information.

[1213] "Emotional state" refers to the user's psychological state, including states such as stress, fatigue, and joy.

[1214] An "emotion engine" is a software component that analyzes a user's emotional state and makes schedule suggestions and adjustments based on that.

[1215] "Interaction style" refers to the way in which a system interacts with a user, including speech recognition and text input.

[1216] "Schedule optimization" refers to improving the quality of a user's life by efficiently and rationally organizing and adjusting the user's schedule.

[1217] "Notifications" refers to alerts or messages that inform users of final schedule information.

[1218] This invention is a system that improves the efficiency of a user's schedule and task management and, by combining it with an emotion engine, enables flexible responses according to the user's emotional state. This system is realized using multiple hardware and software components. Specific hardware and software configurations, as well as the data processing and data calculations performed by each, are described below.

[1219] Hardware and software used

[1220] Device:

[1221] Refers to devices that are directly operated by users, such as smartphones, personal computers, and tablets.

[1222] It uses a built-in camera and microphone to enable facial and voice recognition technology.

[1223] server:

[1224] This refers to the central processing unit that processes the schedule information sent by users and analyzes and integrates the data.

[1225] It has the ability to access external data sources such as weather APIs and calendar APIs.

[1226] It has natural language processing capabilities using an emotion engine and generative AI models (e.g., GPT series).

[1227] Data processing and calculation

[1228] Retrieving User Information

[1229] A user enters schedule information into the device's calendar app, for example, "Meeting with Bob next Monday at 2 PM."

[1230] The device receives this information and temporarily stores it.

[1231] Sending information

[1232] The device generates an HTTP request and sends the entered schedule information to the server's API endpoint. The sent data is in JSON format.

[1233] Performing natural language processing

[1234] The server analyzes the submitted schedule information using a generative AI model (e.g., GPT series) to identify specific dates and times, event types, and participants.

[1235] Data Integration and Analysis

[1236] The server obtains the necessary information from the calendar API and weather API, and integrates the weather information and overlaps it with the obtained schedule information.

[1237] Running the Emotion Engine

[1238] The device uses the built-in camera and microphone to recognize the user's emotional state using facial recognition and voice analysis technologies.

[1239] The server uses an emotion engine to adjust the schedule based on the user's emotional state, for example, suggesting more relaxing tasks if the user is feeling stressed.

[1240] Schedule Optimization

[1241] The server integrates all data and automatically generates an appropriate schedule, taking into account the user's emotional state and external data to propose the optimal schedule.

[1242] Conversational Interaction

[1243] The device displays the optimized schedule information and displays an interactive prompt to the user, such as "Do you want to confirm?" If the user responds "yes," the device proceeds to the next step.

[1244] Schedule confirmation and notification

[1245] The server determines the final schedule information and reflects it on the user's calendar.

[1246] The terminal notifies the user of the confirmed schedule information.

[1247] Specific examples

[1248] For example, consider the case where a user requests an online meeting at 10 AM next Tuesday. In this case, the user enters "online meeting at 10 AM next Tuesday" into the device's calendar app. The device receives this input and sends it to the server. The server analyzes it using a generative AI model and obtains the necessary data from the calendar API and weather API. Next, it analyzes the user's emotional state and generates an optimal schedule. The device then asks the user for confirmation, updates the calendar with the final schedule, and notifies the user.

[1249] An example of a prompt might be:

[1250] "We've set up an online meeting for next Tuesday at 10am. Would you like to confirm?"

[1251] In this way, the present invention can provide efficient and flexible schedule management and improve the quality of life of users.

[1252] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1253] Step 1: Get user information

[1254] A user enters "Meeting with Bob next Monday at 2 PM" into their device's calendar app.

[1255] Specific behavior:

[1256] Input: The user enters event information into a calendar app.

[1257] Data processing: The terminal receives the input and temporarily stores this schedule information in an internal database.

[1258] Output: Schedule information stored on the device.

[1259] Step 2: Submit your information

[1260] The terminal transmits the input schedule information to the server.

[1261] Specific behavior:

[1262] Input: Schedule information stored on the device.

[1263] Data processing: The device converts the schedule information into a JSON-formatted HTTP request.

[1264] Output: Schedule information in JSON format is sent to the server.

[1265] json

[1266] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1267] Step 3: Performing Natural Language Processing

[1268] The server analyzes the sent schedule information using a generative AI model (e.g., GPT series).

[1269] Specific behavior:

[1270] Input: JSON formatted schedule information sent from the device to the server.

[1271] Data calculation: The server sends analytical prompts to the generative AI model, which then uses natural language processing to parse this information to identify specific dates, times, and participants.

[1272] Output: Parsed detailed schedule information.

[1273] json

[1274] {

[1275] "event": "meeting",

[1276] "date": "YYYY-MM-DD",

[1277] "time": "14:00",

[1278] "participant": "Bob"

[1279] }

[1280] Step 4: Data integration and analysis

[1281] The server accesses external calendar and weather APIs to retrieve and integrate the required information.

[1282] Specific behavior:

[1283] Input: Parsed detailed schedule information.

[1284] Data processing: The server accesses the calendar API to retrieve existing schedule information and check for duplicates. It also accesses the weather API to retrieve the weather forecast for the relevant day.

[1285] Output: Schedule and weather information with no overlaps. For example, the weather forecast information is: "Cloudy".

[1286] Step 5: Run the Emotion Engine

[1287] The device uses facial recognition and voice analysis technologies to recognize the user's emotional state.

[1288] Specific behavior:

[1289] Input: User's facial expression and voice data.

[1290] Data calculation: Data is collected via the device's built-in camera and microphone and analyzed by a local emotion recognition module.

[1291] Output: Recognized emotional state data.

[1292] The server uses an emotion engine to adjust the schedule based on the user's emotional state.

[1293] Specific behavior:

[1294] Input: Recognized emotional state data and detailed schedule information.

[1295] Data calculation: The server inputs data into the emotion engine and adjusts the schedule based on emotions.

[1296] Output: Adjusted schedule information. For example, if the user is stressed, postpone less urgent tasks.

[1297] Step 6: Schedule optimization

[1298] The server integrates all the data and automatically generates the optimal schedule.

[1299] Specific behavior:

[1300] Input: Adjusted schedule information, duplicate check results, and weather information.

[1301] Data calculation: The server runs the optimization algorithm to integrate the data and generate the optimal schedule.

[1302] Output: Optimized proposed schedule.

[1303] Step 7: Conversational Interaction

[1304] The device displays the optimized schedule information and asks the user for confirmation.

[1305] Specific behavior:

[1306] Input: Optimized proposed schedule.

[1307] Data processing: The device displays a user interface and displays a dialogue prompt such as "I'd like to set up a meeting with Bob for Monday at 2pm. Would you like to confirm?"

[1308] Output: The user's response (e.g., "Yes").

[1309] Step 8: Confirm schedule and notify

[1310] The server determines the final schedule information and reflects it on the user's calendar.

[1311] Specific behavior:

[1312] Input: User's acknowledgment (Yes).

[1313] Data processing: The server updates the schedule information using the calendar API.

[1314] Output: The confirmed schedule information is reflected in the user's calendar.

[1315] The device will notify the user that the schedule has been confirmed.

[1316] Specific behavior:

[1317] Input: Confirmed schedule information.

[1318] Data processing: The terminal generates and displays a notification message.

[1319] Output: The user receives a notification that says "A meeting with Bob has been scheduled for next Monday at 2 PM."

[1320] The above is the specific operation of each processing step and the flow of the data processing and data calculation that accompanies it.

[1321] (Application example 2)

[1322] 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."

[1323] Conventional schedule management systems simply manage a user's schedule information but are unable to consider the user's emotional state. This makes it difficult to flexibly adjust schedules to accommodate changes in stress and emotional state, preventing stress reduction and efficient schedule management for users. Furthermore, they lack the functionality to integrate information from weather forecasts and external data sources to optimize schedules. The present invention aims to solve these problems and improve the quality of life for users by providing a schedule management system that considers the user's emotional state.

[1324] 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.

[1325] In this invention, the server includes means for performing face recognition and voice analysis to recognize the emotional state of the user, means for proposing and adjusting a schedule based on the emotional state using an emotion engine, and means for prompting the user to confirm and correct the schedule in an interactive format, thereby enabling schedule management that flexibly responds to the emotional state of the user.

[1326] "User" means an individual or corporation that uses the system to manage schedules.

[1327] "Schedule information" refers to information such as the date, time, content, location, and participants of a scheduled event entered by the user.

[1328] A "server" is a computer system that processes and stores data over the Internet.

[1329] "Natural language processing" is the technology that enables computers to understand, interpret, and process human language.

[1330] An "external data source" is a service or database that provides data outside of the system, such as a weather API or a calendar API.

[1331] "Facial recognition" is a technology that analyzes a user's facial image to identify an individual.

[1332] "Voice analysis" is a technology that analyzes a user's voice to determine their emotional state and intentions.

[1333] An "emotion engine" is software or a system that analyzes a user's emotional state and determines an appropriate response based on the results.

[1334] "Interactive" refers to the way in which a system and a user exchange information with each other through input and output.

[1335] "Notification" refers to the system providing information to the user regarding schedule confirmation or changes.

[1336] This invention is a system that improves the efficiency of a user's schedule and task management, and by combining it with an emotion engine, enables flexible responses according to the user's emotional state. The detailed configuration and processing for specifically implementing this system are described below.

[1337] System configuration

[1338] User device: Refers to a smartphone, PC, etc., and is the device through which the user inputs schedule information.

[1339] Server: A computer system that processes and stores data over the Internet.

[1340] Facial recognition technology: Technology that analyzes a user's facial image to identify the individual and recognize their emotional state.

[1341] Voice analysis technology: Technology that analyzes the user's voice to determine their emotional state and intentions.

[1342] Emotion engine: Software or a system that determines an appropriate response based on the user's emotional state.

[1343] Calendar API: An external data source used to integrate existing schedule information.

[1344] Weather API: An external data source used to obtain weather forecast information.

[1345] Generative AI model: Software that performs natural language processing, including models used for analysis (e.g., the GPT series).

[1346] A natural language description of the program's processing

[1347] 1. Obtaining user information

[1348] Users enter schedule information into their device's calendar app, such as "Schedule a meeting for next Monday at 2 PM."

[1349] 2. Transmission of information

[1350] The terminal sends this schedule information to the server. The data sent will have the following format, for example:

[1351] json

[1352] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1353] 3. Performing Natural Language Processing

[1354] The server uses a generative AI model to parse the submitted schedule information, converting it into specific dates and times and providing event details.

[1355] json

[1356] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob"}

[1357] 4. Data integration and analysis

[1358] The server uses the calendar API to retrieve existing schedule information and check whether it overlaps with the new schedule information, and at the same time accesses the weather API to retrieve the weather forecast for the following Monday.

[1359] 5. Running the Emotion Engine

[1360] The device uses voice and facial recognition technology to understand the user's emotional state, for example, determining whether the user is feeling stressed.

[1361] 6. Schedule optimization

[1362] The server optimizes the schedule by taking into account the acquired calendar information, weather forecast information, and the user's emotional state. For example, if the weather is bad, the server suggests that the user hold a meeting indoors.

[1363] 7. Conversational Interaction

[1364] The device interactively asks the user to confirm, "I'd like to set up a meeting with Bob on Monday at 2 PM. Would you like to confirm?" If the user answers "yes," it proceeds to the next step.

[1365] 8. Schedule confirmation and notification

[1366] The server determines the final schedule information and reflects it in the user's calendar. The reflected information is as follows:

[1367] json

[1368] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob", "location": "Online"}

[1369] The device will send a notification to the user saying, "A meeting with Bob has been scheduled for next Monday at 2 PM."

[1370] Specific examples

[1371] For example, let us consider the case where a user performs an operation such as "schedule an online meeting at 10:00 AM next Tuesday."

[1372] 1. Obtaining user information: The user enters "Online meeting next Tuesday at 10 AM" into the device's calendar app.

[1373] 2. Sending information: The terminal sends this information to the server. The data sent is in the following format:

[1374] json

[1375] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[1376] 3. Perform natural language processing: The server parses this information and converts it into specific dates and times, as well as identifying the type of event and participants.

[1377] json

[1378] {"event": "Online Meeting", "date": "YYYY-MM-DD", "time": "10:00", "participant": "N / A"}

[1379] 4. Data integration and analysis: The server uses the calendar API to check for overlapping events and retrieves weather forecast information from the weather API.

[1380] 5. Execution of emotion engine: The device analyzes the user's voice and facial expressions to recognize their emotions. For example, it determines that the user is not feeling stressed.

[1381] 6. Schedule optimization: The server determines the optimal schedule based on this information.

[1382] 7. Conversational interaction: The device asks the user for confirmation: "I'm going to schedule an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[1383] 8. Schedule confirmation and notification: The server confirms the schedule information after receiving the user's confirmation, and updates the user's calendar. This schedule information is then notified to the device.

[1384] Example prompts for generative AI models

[1385] A user types, "I want to order dinner at 7 PM." The weather is rainy, and the user is stressed. Suggest the best time and menu to order.

[1386] This system allows users to manage their schedules efficiently and stress-free, and in particular allows for flexible responses that take into account emotional states.

[1387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1388] Step 1: Get user information

[1389] A user enters an event into the device's calendar app. For example, "Schedule a meeting next Monday at 2 PM." The input information is as follows:

[1390] input:

[1391] json

[1392] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1393] Output: Schedule information stored directly on the user's device.

[1394] Step 2: Submit your information

[1395] The device sends the acquired schedule information to the server. This sending process uses the following HTTP POST request. The input data is the schedule information acquired in step 1:

[1396] input:

[1397] json

[1398] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1399] Output: The schedule information sent to the server.

[1400] Step 3: Performing Natural Language Processing

[1401] The server uses a generative AI model to parse the submitted schedule information, first converting it into formal dates and times and then extracting event details. The input data is the data received in step 2:

[1402] input:

[1403] json

[1404] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1405] The analysis produces the following data:

[1406] output:

[1407] json

[1408] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob"}

[1409] Step 4: Data integration and analysis

[1410] The server accesses external data sources (Calendar API and Weather API) to retrieve existing schedule information and weather forecast information. The input data is the data obtained in step 3 and the new data retrieved from the API:

[1411] Input: Data obtained from the Calendar API and Weather API

[1412] Output: New schedule and weather forecast information without overlaps

[1413] Step 5: Run the Emotion Engine

[1414] The device performs facial recognition and voice analysis to recognize the user's emotions. The input data is the user's facial image and voice data:

[1415] Input: User's facial image and voice data

[1416] Output: Perceived user emotional state (e.g., "stressed")

[1417] Step 6: Schedule optimization

[1418] The server optimizes the schedule by integrating calendar information, weather forecast information, and the user's emotional state. For example, it suggests indoor meetings when the weather forecast is bad, and postpones less important tasks when the user is stressed. The input data are the data obtained in steps 4 and 5:

[1419] Input: All integrated data for optimization

[1420] Output: Optimized schedule proposal (e.g. "Indoor meeting proposal")

[1421] Step 7: Conversational Interaction

[1422] The device interactively asks the user, "I'd like to schedule a meeting with Bob at 2 PM on Monday. Would you like to confirm?" and collects the result. The input data is a schedule optimization proposal:

[1423] Input: Optimized schedule proposal

[1424] Output: User confirmation result (e.g. "Yes")

[1425] Step 8: Confirm schedule and notify

[1426] The server receives the user's confirmation result, finalizes the schedule information, and updates the user's calendar. The terminal then sends a notification to the user. The input data is the user's confirmation result:

[1427] Input: User confirmation result

[1428] Output: Confirmed schedule information and notification message (e.g., "Your meeting with Bob has been scheduled for next Monday at 2 PM.")

[1429] The above is the specific processing content of each step.

[1430] 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.

[1431] 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.

[1432] 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.

[1433] [Third embodiment]

[1434] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1435] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1436] 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).

[1437] 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.

[1438] 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.

[1439] 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).

[1440] 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.

[1441] 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.

[1442] 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.

[1443] 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.

[1444] 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.

[1445] 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."

[1446] This invention is an AI tool for streamlining user schedule and task management. The system acquires the user's schedule information, analyzes it using natural language processing technology, a generative AI model, and optimizes the schedule by integrating information from external data sources. It also prompts the user to confirm and correct the information interactively, and then finalizes and notifies the user of the final schedule information.

[1447] A natural language description of the program's processing

[1448] Retrieving User Information

[1449] A user types into a device (e.g., a smartphone or PC) "Meeting with Bob next Monday at 2 PM." That information is stored on the device.

[1450] Sending information

[1451] The device sends the acquired schedule information to the server. The data sent is in the following format:

[1452] json

[1453] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1454] Performing natural language processing

[1455] The server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information, and the analysis results in detailed information such as:

[1456] json

[1457] {

[1458] "event": "meeting",

[1459] "date": "YYYY-MM-DD",

[1460] "time": "14:00",

[1461] "participant": "Bob"

[1462] }

[1463] Data Integration and Analysis

[1464] The server accesses an external calendar API to check for overlaps with other events. At the same time, it uses a weather API to get the weather forecast for the following Monday. For example, it may predict a cloudy day. This information is used to optimize the schedule.

[1465] Schedule Optimization

[1466] The server then uses this information to optimize the schedule. For example, it can refer to the weather forecast and suggest indoor meetings if an outdoor event is not suitable for the day of the meeting. It also optimizes the time to avoid conflicts with other tasks or appointments.

[1467] Conversational Interaction

[1468] The device interactively asks the user for confirmation: "I'd like to set up a meeting with Bob for Monday at 2 PM. Confirm?" and waits for the user to confirm or amend. If the user answers "yes," it proceeds to the next step.

[1469] Schedule confirmation and notification

[1470] The server confirms the final schedule information and updates the user's calendar. The confirmed schedule information is then sent to the device, informing the user that "A meeting with Bob has been scheduled for next Monday at 2:00 PM."

[1471] Specific examples

[1472] For example, a specific example will be given of a case where a user performs an operation to "schedule an online meeting at 10:00 AM next Tuesday."

[1473] Retrieving User Information

[1474] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[1475] Sending information

[1476] The device sends this information to the server. The data sent is in the following format:

[1477] json

[1478] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[1479] Performing natural language processing

[1480] The server parses this information and converts it into a concrete date and time.

[1481] json

[1482] {

[1483] "event": "online meeting",

[1484] "date": "YYYY-MM-DD",

[1485] "time": "10:00"

[1486] }

[1487] Data Integration and Analysis

[1488] The server uses the calendar API to check for overlaps with other events and also retrieves weather information from the weather API, e.g., a "sunny" forecast.

[1489] Schedule Optimization

[1490] The server determines the optimal schedule based on this information.

[1491] Conversational Interaction

[1492] The device asks the user, "We're setting up an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[1493] Schedule confirmation and notification

[1494] The server confirms the final schedule information and updates the calendar on the user's device. The notification reads, "An online meeting has been scheduled for next Tuesday at 10:00 AM."

[1495] The above is an embodiment of the present invention. This system allows users to manage their schedules efficiently and stress-free.

[1496] The processing flow will be explained below.

[1497] Step 1:

[1498] A user enters "Meeting with Bob next Monday at 2 PM" into the calendar app on their device (e.g., smartphone or PC).

[1499] Step 2:

[1500] The terminal stores the entered schedule information and sends it to the server. The data sent is in the following format:

[1501] json

[1502] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1503] Step 3:

[1504] The server uses a generative AI model (e.g., GPT series) to analyze the received schedule information. The analysis results in detailed information such as:

[1505] json

[1506] {

[1507] "event": "meeting",

[1508] "date": "YYYY-MM-DD",

[1509] "time": "14:00",

[1510] "participant": "Bob"

[1511] }

[1512] Step 4:

[1513] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[1514] Step 5:

[1515] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[1516] Step 6:

[1517] The server takes into account calendar information and weather forecast information to optimize the schedule. For example, if an event is scheduled outdoors, the server considers the weather forecast and suggests an indoor meeting.

[1518] Step 7:

[1519] Based on the device's optimized schedule information, the user is prompted interactively to confirm, "I'm going to schedule a meeting with Bob for Monday at 2 p.m. Would you like to confirm?"

[1520] Step 8:

[1521] The user presses a confirmation button through a dialogue interface or answers "yes" by voice input.

[1522] Step 9:

[1523] The server receives confirmation from the user, finalizes the schedule information, and updates the user's calendar. The updated information looks like this:

[1524] json

[1525] {

[1526] "event": "meeting",

[1527] "date": "YYYY-MM-DD",

[1528] "time": "14:00",

[1529] "participant": "Bob",

[1530] "location": "online"

[1531] }

[1532] Step 10:

[1533] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[1534] The above are the specific operations performed at each step. In this way, the terminal, server, and user work together to achieve efficient and optimal schedule management.

[1535] Example 1

[1536] 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."

[1537] Conventional schedule management systems require users to manually input, check, and correct schedules, which is time-consuming and labor-intensive. Furthermore, they lack the ability to integrate external data, such as weather information and overlap checks with other appointments, making schedule optimization difficult. Furthermore, they are unable to handle natural language input, making user input complex and unintuitive.

[1538] 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.

[1539] In this invention, the server includes means for acquiring schedule information from a user, means for transmitting the acquired schedule information to an information processing device, means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants, means for integrating information acquired from external data sources and adjusting and optimizing the schedule, means for interactively prompting the user for confirmation and correction, means for finalizing the schedule information and notifying the user's terminal, means for analyzing the schedule information using a generative AI model, and means for performing natural language processing using prompt sentences to identify the date and time. This allows the user to efficiently manage their schedule, and realizes optimized schedule management integrated with external data.

[1540] "Schedule information" refers to data about the date, time, type of event, and participants that a user enters as an appointment.

[1541] The term "information processing device" refers to a device or system in general that has the function of receiving, analyzing, and transmitting schedule information.

[1542] "Natural language processing" is a computer technology for analyzing human language and understanding or generating meaning.

[1543] "External Data Sources" refers to external data providers, including calendar APIs and weather APIs, that the server accesses and uses for schedule optimization.

[1544] "Schedule adjustment and optimization" is the process of suggesting optimal times and settings, taking into account the user's existing schedule and external data.

[1545] "Means for interactively prompting confirmation and correction" refers to a function that interactively prompts the user to confirm and correct the schedule contents.

[1546] A "generative AI model" is an artificial intelligence technology that includes large-scale learning models used for natural language processing.

[1547] A "prompt" is text that is input to a generative AI model to instruct it on a specific task.

[1548] "Terminal" refers to the device (such as a smartphone or PC) that a user uses to input and receive schedule information.

[1549] This invention is an advanced scheduling system for streamlining users' schedules and task management. This system is based on a large-scale natural language processing technology called a generative AI model.

[1550] Hardware and software used

[1551] Hardware

[1552] 1. Terminal: A device through which a user inputs schedule information. Examples include smartphones and personal computers (PCs).

[1553] 2. Server: A central management system for analyzing schedule information and integrating external data.

[1554] software

[1555] 1. Natural language processing technology: Generative AI models (e.g., GPT series) are used to analyze the schedule information entered by the user.

[1556] 2. Calendar API: Used to check for overlaps with other events.

[1557] 3. Weather API: Used to obtain weather information required for schedule optimization.

[1558] Data processing and calculation

[1559] 1. Obtaining user information

[1560] A user enters "Meeting next Monday at 2 PM" into the device's calendar app. This information is stored on the device.

[1561] 2. Transmission of information

[1562] The terminal transmits the acquired schedule information to the server.

[1563] For example, data is sent in the following format:

[1564] json

[1565] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1566] 3. Performing Natural Language Processing

[1567] The server analyzes the schedule information using a generative AI model, for example, using the following prompt:

[1568] Example prompt: "Convert 'next Monday at 2 PM' into a specific date and time."

[1569] Examples of what can be obtained as a result of the analysis:

[1570] json

[1571] {

[1572] "event": "meeting",

[1573] "date": "YYYY-MM-DD",

[1574] "time": "14:00",

[1575] "participant": "Bob"

[1576] }

[1577] 4. Data integration and analysis

[1578] The server uses an external calendar API to check for overlaps with other events, and at the same time, retrieves the weather forecast for the day of the meeting using a weather API.

[1579] For example, a "cloudy" forecast is obtained.

[1580] 5. Schedule optimization

[1581] The server optimizes schedules based on weather forecasts and other schedules, for example suggesting indoor meetings if the weather is bad.

[1582] The schedule is updated as a result of the optimization.

[1583] 6. Conversational Interaction

[1584] The device interactively asks the user for confirmation: "I'm going to schedule a meeting for Monday at 2 PM. Would you like to confirm?" and waits for the user to confirm or correct the request.

[1585] 7. Schedule confirmation and notification

[1586] The server determines the final schedule information and notifies the user's device.

[1587] The user is notified of the confirmed schedule information, stating, "A meeting has been scheduled for next Monday at 2:00 p.m."

[1588] Specific examples

[1589] For example, if a user wants to "schedule an online meeting next Tuesday at 10:00 AM":

[1590] The user inputs specific plans into the device.

[1591] The device sends this information to the server.

[1592] The server analyzes it using a generative AI model and converts it into a specific date and time.

[1593] The server uses the calendar and weather APIs to optimize the schedule.

[1594] The device interactively asks the user for confirmation, and the user replies "yes."

[1595] The server confirms the final schedule and reflects it on the user's calendar.

[1596] As described above, the system of the present invention provides users with efficient and stress-free schedule management.

[1597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1598] Step 1:

[1599] Retrieving User Information

[1600] A user enters "Meeting at 2 PM next Monday" into the calendar app on their device (e.g., smartphone or PC). This entry is saved in the device's database.

[1601] Input: Scheduling information entered by the user (e.g., "Meeting next Monday at 2 PM").

[1602] Output: Schedule information stored in the device database.

[1603] Specific behavior:

[1604] The user launches a calendar app and enters a new event.

[1605] Suppose the input is "Meeting next Monday at 2pm."

[1606] This input is stored in an internal database.

[1607] Step 2:

[1608] Sending information

[1609] The device sends the saved schedule information to the server. The data sent is in JSON format.

[1610] Input: Schedule information stored on the device.

[1611] Output: Schedule information sent to the server in JSON format.

[1612] Specific behavior:

[1613] The device organizes the saved schedule information into JSON format.

[1614] For example, the following JSON is generated:

[1615] json

[1616] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1617] The JSON data is sent over the internet to a server.

[1618] Step 3:

[1619] Performing natural language processing

[1620] The server uses a generative AI model to parse the submitted schedule information, converting dates and times into specific formats using prompts.

[1621] Input: Schedule information in JSON format.

[1622] Output: Schedule information converted into concrete dates and times.

[1623] Specific behavior:

[1624] The server parses the received JSON data.

[1625] Enter the following prompt into the generative AI model: "Convert 'next Monday at 2 PM' into a specific date and time."

[1626] The generative AI model performs the analysis and generates data such as:

[1627] json

[1628] {

[1629] "event": "meeting",

[1630] "date": "YYYY-MM-DD",

[1631] "time": "14:00",

[1632] "participant": "Bob"

[1633] }

[1634] Step 4:

[1635] Data Integration and Analysis

[1636] The server accesses external calendar and weather APIs to check for duplicates and obtain weather information.

[1637] Input: Parsed schedule information.

[1638] Output: Results of overlap check with other events and weather information.

[1639] Specific behavior:

[1640] The server sends a request to the external calendar API to check for conflicts with other events for the user.

[1641] Send a request to the weather API to get weather information for the day the meeting is scheduled.

[1642] For example, a "cloudy" forecast is obtained.

[1643] Step 5:

[1644] Schedule Optimization

[1645] Optimize your schedule based on the information obtained by the server. Suggest the best schedule based on weather forecasts and other schedules.

[1646] Input: Duplicate check results and weather information.

[1647] Output: Optimized schedule information.

[1648] Specific behavior:

[1649] The server calculates the optimal schedule based on weather information and other schedules.

[1650] For example, if the weather is bad, suggest meeting indoors.

[1651] The schedule is updated as a result of the optimization.

[1652] Step 6:

[1653] Conversational Interaction

[1654] The device interactively asks the user for confirmation: "I'd like to schedule a meeting for Monday at 2 PM. Would you like to confirm?"

[1655] Input: Optimized schedule information.

[1656] Output: User confirmation or correction instructions.

[1657] Specific behavior:

[1658] The device displays a notification to the user.

[1659] The user responds with "yes" or "no."

[1660] If the answer is "no", a form is displayed that allows the user to enter a new time.

[1661] Step 7:

[1662] Schedule confirmation and notification

[1663] The server determines the final schedule information and notifies the user's device.

[1664] Input: User confirmed or corrected schedule information.

[1665] Output: Confirmed schedule information and notifications.

[1666] Specific behavior:

[1667] The server saves the confirmed schedule to the user's calendar.

[1668] Send a notification to your device saying "A meeting has been scheduled for next Monday at 2 PM."

[1669] The user's calendar will update to show the new schedule.

[1670] (Application example 1)

[1671] 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."

[1672] Conventional schedule management systems do not provide sufficient support for streamlining user schedule and task management. Especially for high-risk jobs like security guarding, real-time schedule optimization and risk information integration are necessary. This allows for more effective patrol and surveillance, but conventional systems cannot meet these needs. Furthermore, a lack of visual or audio feedback makes it difficult to respond quickly.

[1673] 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.

[1674] In this invention, the server includes means for acquiring schedule information from a user, means for transmitting the acquired schedule information to the server, means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and location, means for integrating information acquired from external data sources to adjust and optimize the schedule, means for interactively prompting the user to confirm and correct the information, means for finalizing the schedule information and notifying the user's device, means for proposing optimal monitoring routes and patrol schedules, means for optimizing patrol routes by integrating risk assessment information, means for managing monitoring points in security areas based on location information, and means for providing visual or audio feedback, thereby enabling effective and efficient schedule management for security guards and optimization of patrol work.

[1675] A "user" is a person or professional who uses the system to manage schedules and tasks.

[1676] "Schedule information" refers to data about appointments and tasks, including dates, times, event types, locations, etc.

[1677] A "server" is a computer system that analyzes, integrates, and optimizes schedule information.

[1678] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1679] "External Data Source" means a source of externally provided data, including a calendar API or weather API.

[1680] "Optimization" means adjusting schedules and tasks efficiently and effectively.

[1681] "Interaction style" refers to the way in which users and systems interact with each other to exchange information.

[1682] A "surveillance route" is the route that security guards follow when patrolling.

[1683] A "patrol schedule" is a plan for security guards to patrol specific locations at specific times.

[1684] "Risk assessment information" refers to data that predicts and provides information about security risks.

[1685] A "patrol route" is a pre-determined route that a security guard follows when patrolling.

[1686] "Location information" is data that indicates the geographical information of the user or the monitored area.

[1687] A "vigilance area" is a location or area that security guards should monitor.

[1688] "Visual feedback" refers to the display of information provided through a display or smart glasses.

[1689] "Audio feedback" refers to information provided through audio output.

[1690] The system for realizing this invention allows users to efficiently manage their schedules, and in particular, optimizes patrol and surveillance operations in security services. The system is implemented by combining the following hardware and software.

[1691] The system uses smart glasses or head-mounted displays (terminals) to obtain the user's schedule information. When the user gives voice commands or inputs to these devices, the information is sent to a cloud server. The software used includes generative AI models (e.g., GPT series) as well as calendar APIs, weather APIs, and risk assessment APIs.

[1692] Processing Description

[1693] Retrieving User Information

[1694] The user commands the smart glasses to "start monitoring the east gate at 2:00 p.m. next Tuesday" by voice, which is then converted into text by the device.

[1695] Sending information

[1696] The device sends the schedule information it has acquired to the cloud server. The data sent will be in the following format:

[1697] json

[1698] {"event": "monitoring", "time": "2pm", "location": "east gate"}

[1699] Performing natural language processing

[1700] The cloud server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information and convert it into specific time and location information. The analysis results are as follows:

[1701] json

[1702] {

[1703] "event": "monitoring",

[1704] "time": "14:00",

[1705] "location": "East Gate"

[1706] }

[1707] Data Integration and Analysis

[1708] The cloud server uses the calendar API and risk assessment API to integrate other schedules with risk information. For example, it can obtain information such as "There is a high possibility that suspicious individuals will be seen around the east gate."

[1709] Schedule optimization

[1710] Based on this information, the cloud server will suggest the most effective surveillance route and time to the user, optimizing areas to be patrolled in addition to the East Gate.

[1711] Conversational Interaction

[1712] The device will ask the user aloud, "We will begin monitoring the East and North Gates at 2:00 p.m. next Tuesday. Would you like to confirm?" If the user responds "Yes," it will proceed to the next step.

[1713] Schedule confirmation and notification

[1714] The cloud server finalizes the monitoring schedule and notifies the user's device, which then provides visual and audio feedback, stating, "Monitoring of the East and North Gates has been set for next Tuesday at 2:00 PM."

[1715] Specific examples

[1716] Here is a specific example of a case where a user commands the smart glasses to "set up a patrol of the south exit at 4:00 p.m. next Wednesday."

[1717] Retrieving User Information

[1718] The user types into the terminal, "Patrol the south exit next Wednesday at 4pm."

[1719] Sending information

[1720] The device sends this information to the cloud server in the following format:

[1721] json

[1722] {"event": "tour", "time": "4pm", "location": "south exit"}

[1723] Performing natural language processing

[1724] The cloud server analyzes the information and converts it into:

[1725] json

[1726] {

[1727] "event": "tour",

[1728] "time": "16:00",

[1729] "location": "South Exit"

[1730] }

[1731] Data Integration and Analysis

[1732] The cloud server uses the risk assessment API to obtain risk information such as "There have been reports of suspicious individuals being seen around the south exit."

[1733] Schedule Optimization

[1734] The cloud server proposes the optimal route and time, and optimization is performed including the surrounding area.

[1735] Conversational Interaction

[1736] The terminal asks, "Patrol of the South Exit and surrounding area will be scheduled for next Wednesday at 4pm. Confirm?"

[1737] Schedule confirmation and notification

[1738] If the user responds "Yes," the cloud server finalizes the patrol schedule and notifies the device, displaying the message, "Patrol of the south exit and surrounding area has been scheduled for next Wednesday at 4 p.m."

[1739] Prompt Sentence Examples

[1740] Based on the command "Start monitoring the East Gate at 2:00 PM next Tuesday," please integrate other schedules and risk information to propose the optimal monitoring schedule.

[1741] This will enable effective and efficient schedule management for security guards and optimization of patrol operations.

[1742] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1743] Step 1:

[1744] The user commands the smart glasses by voice, "Start monitoring the east gate at 2:00 PM next Tuesday." The device converts this voice command into text data and recognizes the content as "schedule information." The input is a voice command, and the output is schedule information in text format. In this step, voice recognition technology is used to convert the voice data into text data.

[1745] Step 2:

[1746] The device sends the acquired schedule information to the cloud server. The data sent is in the following format:

[1747] json

[1748] {"event": "monitoring", "time": "2pm", "location": "east gate"}

[1749] The input is schedule information in text format, and the output is data transmission to a cloud server.

[1750] Step 3:

[1751] The cloud server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information. The analysis results in specific time and location information, which may take the following format:

[1752] json

[1753] {

[1754] "event": "monitoring",

[1755] "time": "14:00",

[1756] "location": "East Gate"

[1757] }

[1758] The input is schedule information in text format, and the output is parsed schedule data. In this step, a generative AI model is used for natural language processing.

[1759] Step 4:

[1760] The cloud server calls the calendar API and risk assessment API, and integrates other schedule and risk information based on the acquired schedule information. For example, it acquires information that "there is a high possibility that a suspicious person will be seen around the east gate." The input is the analyzed schedule data, and the output is the integrated risk assessment data and schedule data. In this step, the API is called to integrate and analyze the data.

[1761] Step 5:

[1762] The cloud server uses this integrated data to propose the most effective surveillance route and time. For example, it may propose additional areas to patrol besides the East Gate. The input is the integrated risk assessment data and schedule data, and the output is an optimized surveillance schedule proposal. This step uses data analysis and optimization algorithms.

[1763] Step 6:

[1764] The terminal asks the user by voice, "Monitoring of the East and North Gates will begin at 2:00 PM next Tuesday. Do you want to confirm?" If the user answers "Yes," it proceeds to the next step. The input is the optimized monitoring schedule proposal, and the output is the user's response. This step uses a conversational interface.

[1765] Step 7:

[1766] After receiving the user's response, the cloud server finalizes the monitoring schedule and notifies the user's device. The device provides visual or audio feedback, such as "Monitoring of the East and North Gates has been set for next Tuesday at 2:00 PM." The input is the user's confirmation response, and the output is notification of the finalized schedule information. This step uses the database update and notification functions.

[1767] 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.

[1768] This invention is a system that streamlines user schedule and task management and, by combining it with an emotion engine, enables flexible responses according to the user's emotional state. This system acquires the user's schedule information, analyzes it using natural language processing technology, and integrates information from external data sources to optimize the schedule. It also recognizes the user's emotions, proposes and adjusts the schedule, and notifies the user of the final schedule information.

[1769] A natural language description of the program's processing

[1770] Retrieving User Information

[1771] A user enters "Meeting with Bob at 2 PM next Monday" into the calendar app on their device (e.g., smartphone or PC). That information is stored on the device.

[1772] Sending information

[1773] The terminal sends the entered schedule information to the server. The data sent is in the following format:

[1774] json

[1775] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1776] Performing natural language processing

[1777] The server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information, and the analysis results in detailed information such as:

[1778] json

[1779] {

[1780] "event": "meeting",

[1781] "date": "YYYY-MM-DD",

[1782] "time": "14:00",

[1783] "participant": "Bob"

[1784] }

[1785] Data Integration and Analysis

[1786] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[1787] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[1788] Running the Emotion Engine

[1789] The device uses facial recognition and voice analysis technologies to recognize the user's emotional state.

[1790] The server uses an emotion engine to adjust the schedule based on the user's perceived emotional state. For example, if the user is feeling stressed, the system will suggest postponing less urgent tasks.

[1791] Schedule Optimization

[1792] The server optimizes the schedule by taking into account calendar information, weather forecast information, and the user's emotional state. For example, it suggests indoor meetings based on the weather forecast and also makes suggestions that match the user's emotional state.

[1793] Conversational Interaction

[1794] Based on the device-optimized schedule information, the user is interactively asked to confirm, "I'm going to schedule a meeting with Bob for Monday at 2 PM. Would you like to confirm?" If the user answers "yes," the process proceeds to the next step.

[1795] Schedule confirmation and notification

[1796] The server determines the final schedule information and updates the user's calendar. The updated information looks like this:

[1797] json

[1798] {

[1799] "event": "meeting",

[1800] "date": "YYYY-MM-DD",

[1801] "time": "14:00",

[1802] "participant": "Bob",

[1803] "location": "online"

[1804] }

[1805] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[1806] Specific examples

[1807] For example, a specific example will be given below in which a user performs an operation to "schedule an online meeting at 10:00 AM next Tuesday."

[1808] Retrieving User Information

[1809] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[1810] Sending information

[1811] The device sends this information to the server. The data sent is in the following format:

[1812] json

[1813] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[1814] Performing natural language processing

[1815] The server parses this information and converts it into a concrete date and time, as well as the type of event and participants:

[1816] json

[1817] {

[1818] "event": "online meeting",

[1819] "date": "YYYY-MM-DD",

[1820] "time": "10:00",

[1821] "participant": "N / A"

[1822] }

[1823] Data Integration and Analysis

[1824] The server uses the calendar API to check for overlapping events and also retrieves weather forecast information from the weather API.

[1825] Running the Emotion Engine

[1826] The device analyzes the user's voice and facial expressions to recognize their emotions. For example, it determines that the user is not feeling stressed.

[1827] Schedule Optimization

[1828] The server determines the optimal schedule based on this information.

[1829] Conversational Interaction

[1830] The device will ask the user for confirmation: "An online meeting will be scheduled for next Tuesday at 10:00 AM. Would you like to confirm?"

[1831] Schedule confirmation and notification

[1832] The server receives the user's confirmation and finalizes the schedule information, which is then reflected in the user's calendar. This schedule information is then notified to the device.

[1833] The above is an embodiment of the present invention. This system allows users to manage their schedules efficiently and stress-free. In particular, by taking into account the user's emotional state, more flexible responses are possible, improving the quality of daily life.

[1834] The processing flow will be explained below.

[1835] Specific steps of the program's processing

[1836] Step 1:

[1837] A user speaks or texts "Meet with Bob next Monday at 2 PM" into the calendar app on their device (e.g., smartphone or PC).

[1838] Step 2:

[1839] The terminal processes the entered schedule information and sends it to the server. The data sent is in the following format:

[1840] json

[1841] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1842] Step 3:

[1843] The server uses a generative AI model (GPT series) to analyze the received schedule information. This converts the input "next Monday" or "2 PM" into a specific date and time, and also identifies the type of event and its attendees. The analysis results are as follows:

[1844] json

[1845] {

[1846] "event": "meeting",

[1847] "date": "YYYY-MM-DD",

[1848] "time": "14:00",

[1849] "participant": "Bob"

[1850] }

[1851] Step 4:

[1852] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[1853] Step 5:

[1854] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[1855] Step 6:

[1856] The device uses an emotion engine (face recognition technology and voice analysis technology) to recognize the user's emotional state. For example, it analyzes the user's facial expressions and voice using a camera or microphone to identify the user's emotional state.

[1857] Step 7:

[1858] The server uses an emotion engine to adjust the schedule based on the user's perceived emotional state. For example, if the user is feeling stressed, the server suggests adjusting meeting times to allow time for relaxation.

[1859] Step 8:

[1860] The server takes into account calendar information, weather forecast information, and emotional state to optimize the schedule. For example, it prioritizes important tasks and suggests appropriate locations depending on the weather.

[1861] Step 9:

[1862] Based on the device-optimized schedule information, the user is interactively asked to confirm, "I'm going to schedule a meeting with Bob next Monday at 2 p.m. Would you like to confirm?"

[1863] Step 10:

[1864] The user confirms by answering "yes" in the dialogue interface.

[1865] Step 11:

[1866] The server receives confirmation from the user, finalizes the schedule information, and updates the user's calendar. The finalized information looks like this:

[1867] json

[1868] {

[1869] "event": "meeting",

[1870] "date": "YYYY-MM-DD",

[1871] "time": "14:00",

[1872] "participant": "Bob",

[1873] "location": "online"

[1874] }

[1875] Step 12:

[1876] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[1877] Specific examples

[1878] For example, a specific example will be given below in which a user performs an operation such as "schedule an online meeting at 10:00 AM next Tuesday."

[1879] Step 1:

[1880] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[1881] Step 2:

[1882] The device sends this information to the server. The data sent is in the following format:

[1883] json

[1884] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[1885] Step 3:

[1886] The server parses this information and converts it into a concrete date and time, as well as the type of event and participants:

[1887] json

[1888] {

[1889] "event": "online meeting",

[1890] "date": "YYYY-MM-DD",

[1891] "time": "10:00",

[1892] "participant": "N / A"

[1893] }

[1894] Step 4:

[1895] The server uses the calendar API to check for overlapping events and also retrieves weather forecast information from the weather API.

[1896] Step 5:

[1897] The device analyzes the user's voice and facial expressions to recognize their emotions, for example, rating the user as "relaxed."

[1898] Step 6:

[1899] The server determines the optimal schedule based on this information, taking into account the user's relaxed emotional state and setting a schedule with ample time.

[1900] Step 7:

[1901] The device prompts the user for confirmation: "I'm setting up an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[1902] Step 8:

[1903] The user checks and corrects as necessary.

[1904] Step 9:

[1905] The server finalizes the schedule information and sends notifications to the user's device.

[1906] The above is a specific embodiment for carrying out the present invention, and this system enables the user to manage their schedule efficiently and stress-free, and to respond flexibly according to their emotional state.

[1907] Example 2

[1908] 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."

[1909] Modern users are required to efficiently manage a variety of tasks and schedules in their busy daily lives. However, traditional schedule management systems do not take into account the user's emotional state, making it difficult to provide appropriate schedule suggestions and adjustments for users who feel stressed or tense. Furthermore, they often lack the functionality to integrate with external data sources and provide optimal schedules. This leads to problems that reduce the user's quality of life.

[1910] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring schedule information from a user; means for transmitting the acquired schedule information to the server; means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants; means for integrating information acquired from external data sources to adjust and optimize the schedule; means for recognizing the user's emotional state; means for adjusting the schedule based on the user's emotional state using an emotion engine; means for interactively prompting the user to confirm and correct the information; and means for finalizing the schedule information and notifying the user's device. This makes it possible to provide an optimal schedule by integrating information from external data sources while taking the user's emotional state into consideration.

[1911] "User" refers to a person who uses the system to manage schedules and tasks.

[1912] "Terminal" means a device that a user uses to access and operate the system, including a smartphone, personal computer, tablet, etc.

[1913] "Server" refers to the central computer system that analyzes schedule information submitted by users and integrates it with external data sources.

[1914] "Schedule information" refers to information about upcoming meetings, events, etc. that a user enters into a terminal.

[1915] "Natural language processing" refers to the technology that allows a computer to understand and analyze strings of characters written in natural language.

[1916] "External data sources" refers to external services or databases that the server accesses to obtain information, including calendar APIs and weather APIs.

[1917] "Schedule adjustment" refers to appropriately changing or optimizing the schedule based on the acquired information.

[1918] "Emotional state" refers to the user's psychological state, including states such as stress, fatigue, and joy.

[1919] An "emotion engine" is a software component that analyzes a user's emotional state and makes schedule suggestions and adjustments based on that.

[1920] "Interaction style" refers to the way in which a system interacts with a user, including speech recognition and text input.

[1921] "Schedule optimization" refers to improving the quality of a user's life by efficiently and rationally organizing and adjusting the user's schedule.

[1922] "Notifications" refers to alerts or messages that inform users of final schedule information.

[1923] This invention is a system that improves the efficiency of a user's schedule and task management and, by combining it with an emotion engine, enables flexible responses according to the user's emotional state. This system is realized using multiple hardware and software components. Specific hardware and software configurations, as well as the data processing and data calculations performed by each, are described below.

[1924] Hardware and software used

[1925] Device:

[1926] Refers to devices that are directly operated by users, such as smartphones, personal computers, and tablets.

[1927] It uses a built-in camera and microphone to enable facial and voice recognition technology.

[1928] server:

[1929] This refers to the central processing unit that processes the schedule information sent by users and analyzes and integrates the data.

[1930] It has the ability to access external data sources such as weather APIs and calendar APIs.

[1931] It has natural language processing capabilities using an emotion engine and generative AI models (e.g., GPT series).

[1932] Data processing and calculation

[1933] Retrieving User Information

[1934] A user enters schedule information into the device's calendar app, for example, "Meeting with Bob next Monday at 2 PM."

[1935] The device receives this information and temporarily stores it.

[1936] Sending information

[1937] The device generates an HTTP request and sends the entered schedule information to the server's API endpoint. The sent data is in JSON format.

[1938] Performing natural language processing

[1939] The server analyzes the submitted schedule information using a generative AI model (e.g., GPT series) to identify specific dates and times, event types, and participants.

[1940] Data Integration and Analysis

[1941] The server obtains the necessary information from the calendar API and weather API, and integrates the weather information and overlaps it with the obtained schedule information.

[1942] Running the Emotion Engine

[1943] The device uses the built-in camera and microphone to recognize the user's emotional state using facial recognition and voice analysis technologies.

[1944] The server uses an emotion engine to adjust the schedule based on the user's emotional state, for example, suggesting more relaxing tasks if the user is feeling stressed.

[1945] Schedule Optimization

[1946] The server integrates all data and automatically generates an appropriate schedule, taking into account the user's emotional state and external data to propose the optimal schedule.

[1947] Conversational Interaction

[1948] The device displays the optimized schedule information and displays an interactive prompt to the user, such as "Do you want to confirm?" If the user responds "yes," the device proceeds to the next step.

[1949] Schedule confirmation and notification

[1950] The server determines the final schedule information and reflects it on the user's calendar.

[1951] The terminal notifies the user of the confirmed schedule information.

[1952] Specific examples

[1953] For example, consider the case where a user requests an online meeting at 10 AM next Tuesday. In this case, the user enters "online meeting at 10 AM next Tuesday" into the device's calendar app. The device receives this input and sends it to the server. The server analyzes it using a generative AI model and obtains the necessary data from the calendar API and weather API. Next, it analyzes the user's emotional state and generates an optimal schedule. The device then asks the user for confirmation, updates the calendar with the final schedule, and notifies the user.

[1954] An example of a prompt might be:

[1955] "We've set up an online meeting for next Tuesday at 10am. Would you like to confirm?"

[1956] In this way, the present invention can provide efficient and flexible schedule management and improve the quality of life of users.

[1957] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1958] Step 1: Get user information

[1959] A user enters "Meeting with Bob next Monday at 2 PM" into their device's calendar app.

[1960] Specific behavior:

[1961] Input: The user enters event information into a calendar app.

[1962] Data processing: The terminal receives the input and temporarily stores this schedule information in an internal database.

[1963] Output: Schedule information stored on the device.

[1964] Step 2: Submit your information

[1965] The terminal transmits the input schedule information to the server.

[1966] Specific behavior:

[1967] Input: Schedule information stored on the device.

[1968] Data processing: The device converts the schedule information into a JSON-formatted HTTP request.

[1969] Output: Schedule information in JSON format is sent to the server.

[1970] json

[1971] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[1972] Step 3: Performing Natural Language Processing

[1973] The server analyzes the sent schedule information using a generative AI model (e.g., GPT series).

[1974] Specific behavior:

[1975] Input: JSON formatted schedule information sent from the device to the server.

[1976] Data calculation: The server sends analytical prompts to the generative AI model, which then uses natural language processing to parse this information to identify specific dates, times, and participants.

[1977] Output: Parsed detailed schedule information.

[1978] json

[1979] {

[1980] "event": "meeting",

[1981] "date": "YYYY-MM-DD",

[1982] "time": "14:00",

[1983] "participant": "Bob"

[1984] }

[1985] Step 4: Data integration and analysis

[1986] The server accesses external calendar and weather APIs to retrieve and integrate the required information.

[1987] Specific behavior:

[1988] Input: Parsed detailed schedule information.

[1989] Data processing: The server accesses the calendar API to retrieve existing schedule information and check for duplicates. It also accesses the weather API to retrieve the weather forecast for the relevant day.

[1990] Output: Schedule and weather information with no overlaps. For example, the weather forecast information is: "Cloudy".

[1991] Step 5: Run the Emotion Engine

[1992] The device uses facial recognition and voice analysis technologies to recognize the user's emotional state.

[1993] Specific behavior:

[1994] Input: User's facial expression and voice data.

[1995] Data calculation: Data is collected via the device's built-in camera and microphone and analyzed by a local emotion recognition module.

[1996] Output: Recognized emotional state data.

[1997] The server uses an emotion engine to adjust the schedule based on the user's emotional state.

[1998] Specific behavior:

[1999] Input: Recognized emotional state data and detailed schedule information.

[2000] Data calculation: The server inputs data into the emotion engine and adjusts the schedule based on emotions.

[2001] Output: Adjusted schedule information. For example, if the user is stressed, postpone less urgent tasks.

[2002] Step 6: Schedule optimization

[2003] The server integrates all the data and automatically generates the optimal schedule.

[2004] Specific behavior:

[2005] Input: Adjusted schedule information, duplicate check results, and weather information.

[2006] Data calculation: The server runs the optimization algorithm to integrate the data and generate the optimal schedule.

[2007] Output: Optimized proposed schedule.

[2008] Step 7: Conversational Interaction

[2009] The device displays the optimized schedule information and asks the user for confirmation.

[2010] Specific behavior:

[2011] Input: Optimized proposed schedule.

[2012] Data processing: The device displays a user interface and displays a dialogue prompt such as "I'd like to set up a meeting with Bob for Monday at 2pm. Would you like to confirm?"

[2013] Output: The user's response (e.g., "Yes").

[2014] Step 8: Confirm schedule and notify

[2015] The server determines the final schedule information and reflects it on the user's calendar.

[2016] Specific behavior:

[2017] Input: User's acknowledgment (Yes).

[2018] Data processing: The server updates the schedule information using the calendar API.

[2019] Output: The confirmed schedule information is reflected in the user's calendar.

[2020] The device will notify the user that the schedule has been confirmed.

[2021] Specific behavior:

[2022] Input: Confirmed schedule information.

[2023] Data processing: The terminal generates and displays a notification message.

[2024] Output: The user receives a notification that says "A meeting with Bob has been scheduled for next Monday at 2 PM."

[2025] The above is the specific operation of each processing step and the flow of the data processing and data calculation that accompanies it.

[2026] (Application example 2)

[2027] 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."

[2028] Conventional schedule management systems simply manage a user's schedule information but are unable to consider the user's emotional state. This makes it difficult to flexibly adjust schedules to accommodate changes in stress and emotional state, preventing stress reduction and efficient schedule management for users. Furthermore, they lack the functionality to integrate information from weather forecasts and external data sources to optimize schedules. The present invention aims to solve these problems and improve the quality of life for users by providing a schedule management system that considers the user's emotional state.

[2029] 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.

[2030] In this invention, the server includes means for performing face recognition and voice analysis to recognize the emotional state of the user, means for proposing and adjusting a schedule based on the emotional state using an emotion engine, and means for prompting the user to confirm and correct the schedule in an interactive format, thereby enabling schedule management that flexibly responds to the emotional state of the user.

[2031] "User" means an individual or corporation that uses the system to manage schedules.

[2032] "Schedule information" refers to information such as the date, time, content, location, and participants of a scheduled event entered by the user.

[2033] A "server" is a computer system that processes and stores data over the Internet.

[2034] "Natural language processing" is the technology that enables computers to understand, interpret, and process human language.

[2035] An "external data source" is a service or database that provides data outside of the system, such as a weather API or a calendar API.

[2036] "Facial recognition" is a technology that analyzes a user's facial image to identify an individual.

[2037] "Voice analysis" is a technology that analyzes a user's voice to determine their emotional state and intentions.

[2038] An "emotion engine" is software or a system that analyzes a user's emotional state and determines an appropriate response based on the results.

[2039] "Interactive" refers to the way in which a system and a user exchange information with each other through input and output.

[2040] "Notification" refers to the system providing information to the user regarding schedule confirmation or changes.

[2041] This invention is a system that improves the efficiency of a user's schedule and task management, and by combining it with an emotion engine, enables flexible responses according to the user's emotional state. The detailed configuration and processing for specifically implementing this system are described below.

[2042] System configuration

[2043] User device: Refers to a smartphone, PC, etc., and is the device through which the user inputs schedule information.

[2044] Server: A computer system that processes and stores data over the Internet.

[2045] Facial recognition technology: Technology that analyzes a user's facial image to identify the individual and recognize their emotional state.

[2046] Voice analysis technology: Technology that analyzes the user's voice to determine their emotional state and intentions.

[2047] Emotion engine: Software or a system that determines an appropriate response based on the user's emotional state.

[2048] Calendar API: An external data source used to integrate existing schedule information.

[2049] Weather API: An external data source used to obtain weather forecast information.

[2050] Generative AI model: Software that performs natural language processing, including models used for analysis (e.g., the GPT series).

[2051] A natural language description of the program's processing

[2052] 1. Obtaining user information

[2053] Users enter schedule information into their device's calendar app, such as "Schedule a meeting for next Monday at 2 PM."

[2054] 2. Transmission of information

[2055] The terminal sends this schedule information to the server. The data sent will have the following format, for example:

[2056] json

[2057] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2058] 3. Performing Natural Language Processing

[2059] The server uses a generative AI model to parse the submitted schedule information, converting it into specific dates and times and providing event details.

[2060] json

[2061] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob"}

[2062] 4. Data integration and analysis

[2063] The server uses the calendar API to retrieve existing schedule information and check whether it overlaps with the new schedule information, and at the same time accesses the weather API to retrieve the weather forecast for the following Monday.

[2064] 5. Running the Emotion Engine

[2065] The device uses voice and facial recognition technology to understand the user's emotional state, for example, determining whether the user is feeling stressed.

[2066] 6. Schedule optimization

[2067] The server optimizes the schedule by taking into account the acquired calendar information, weather forecast information, and the user's emotional state. For example, if the weather is bad, the server suggests that the user hold a meeting indoors.

[2068] 7. Conversational Interaction

[2069] The device interactively asks the user to confirm, "I'd like to set up a meeting with Bob on Monday at 2 PM. Would you like to confirm?" If the user answers "yes," it proceeds to the next step.

[2070] 8. Schedule confirmation and notification

[2071] The server determines the final schedule information and reflects it in the user's calendar. The reflected information is as follows:

[2072] json

[2073] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob", "location": "Online"}

[2074] The device will send a notification to the user saying, "A meeting with Bob has been scheduled for next Monday at 2 PM."

[2075] Specific examples

[2076] For example, let us consider the case where a user performs an operation such as "schedule an online meeting at 10:00 AM next Tuesday."

[2077] 1. Obtaining user information: The user enters "Online meeting next Tuesday at 10 AM" into the device's calendar app.

[2078] 2. Sending information: The terminal sends this information to the server. The data sent is in the following format:

[2079] json

[2080] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[2081] 3. Perform natural language processing: The server parses this information and converts it into specific dates and times, as well as identifying the type of event and participants.

[2082] json

[2083] {"event": "Online Meeting", "date": "YYYY-MM-DD", "time": "10:00", "participant": "N / A"}

[2084] 4. Data integration and analysis: The server uses the calendar API to check for overlapping events and retrieves weather forecast information from the weather API.

[2085] 5. Execution of emotion engine: The device analyzes the user's voice and facial expressions to recognize their emotions. For example, it determines that the user is not feeling stressed.

[2086] 6. Schedule optimization: The server determines the optimal schedule based on this information.

[2087] 7. Conversational interaction: The device asks the user for confirmation: "I'm going to schedule an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[2088] 8. Schedule confirmation and notification: The server confirms the schedule information after receiving the user's confirmation, and updates the user's calendar. This schedule information is then notified to the device.

[2089] Example prompts for generative AI models

[2090] A user types, "I want to order dinner at 7 PM." The weather is rainy, and the user is stressed. Suggest the best time and menu to order.

[2091] This system allows users to manage their schedules efficiently and stress-free, and in particular allows for flexible responses that take into account emotional states.

[2092] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2093] Step 1: Get user information

[2094] A user enters an event into the device's calendar app. For example, "Schedule a meeting next Monday at 2 PM." The input information is as follows:

[2095] input:

[2096] json

[2097] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2098] Output: Schedule information stored directly on the user's device.

[2099] Step 2: Submit your information

[2100] The device sends the acquired schedule information to the server. This sending process uses the following HTTP POST request. The input data is the schedule information acquired in step 1:

[2101] input:

[2102] json

[2103] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2104] Output: The schedule information sent to the server.

[2105] Step 3: Performing Natural Language Processing

[2106] The server uses a generative AI model to parse the submitted schedule information, first converting it into formal dates and times and then extracting event details. The input data is the data received in step 2:

[2107] input:

[2108] json

[2109] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2110] The analysis produces the following data:

[2111] output:

[2112] json

[2113] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob"}

[2114] Step 4: Data integration and analysis

[2115] The server accesses external data sources (Calendar API and Weather API) to retrieve existing schedule information and weather forecast information. The input data is the data obtained in step 3 and the new data retrieved from the API:

[2116] Input: Data obtained from the Calendar API and Weather API

[2117] Output: New schedule and weather forecast information without overlaps

[2118] Step 5: Run the Emotion Engine

[2119] The device performs facial recognition and voice analysis to recognize the user's emotions. The input data is the user's facial image and voice data:

[2120] Input: User's facial image and voice data

[2121] Output: Perceived user emotional state (e.g., "stressed")

[2122] Step 6: Schedule optimization

[2123] The server optimizes the schedule by integrating calendar information, weather forecast information, and the user's emotional state. For example, it suggests indoor meetings when the weather forecast is bad, and postpones less important tasks when the user is stressed. The input data are the data obtained in steps 4 and 5:

[2124] Input: All integrated data for optimization

[2125] Output: Optimized schedule proposal (e.g. "Indoor meeting proposal")

[2126] Step 7: Conversational Interaction

[2127] The device interactively asks the user, "I'd like to schedule a meeting with Bob at 2 PM on Monday. Would you like to confirm?" and collects the result. The input data is a schedule optimization proposal:

[2128] Input: Optimized schedule proposal

[2129] Output: User confirmation result (e.g. "Yes")

[2130] Step 8: Confirm schedule and notify

[2131] The server receives the user's confirmation result, finalizes the schedule information, and updates the user's calendar. The terminal then sends a notification to the user. The input data is the user's confirmation result:

[2132] Input: User confirmation result

[2133] Output: Confirmed schedule information and notification message (e.g., "Your meeting with Bob has been scheduled for next Monday at 2 PM.")

[2134] The above is the specific processing content of each step.

[2135] 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.

[2136] 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.

[2137] 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.

[2138] [Fourth embodiment]

[2139] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2140] 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.

[2141] 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).

[2142] 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.

[2143] 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.

[2144] 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).

[2145] 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.

[2146] 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.

[2147] 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.

[2148] 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.

[2149] 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.

[2150] 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.

[2151] 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."

[2152] This invention is an AI tool for streamlining user schedule and task management. The system acquires the user's schedule information, analyzes it using natural language processing technology, a generative AI model, and optimizes the schedule by integrating information from external data sources. It also prompts the user to confirm and correct the information interactively, and then finalizes and notifies the user of the final schedule information.

[2153] A natural language description of the program's processing

[2154] Retrieving User Information

[2155] A user types into a device (e.g., a smartphone or PC) "Meeting with Bob next Monday at 2 PM." That information is stored on the device.

[2156] Sending information

[2157] The device sends the acquired schedule information to the server. The data sent is in the following format:

[2158] json

[2159] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2160] Performing natural language processing

[2161] The server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information, and the analysis results in detailed information such as:

[2162] json

[2163] {

[2164] "event": "meeting",

[2165] "date": "YYYY-MM-DD",

[2166] "time": "14:00",

[2167] "participant": "Bob"

[2168] }

[2169] Data Integration and Analysis

[2170] The server accesses an external calendar API to check for overlaps with other events. At the same time, it uses a weather API to get the weather forecast for the following Monday. For example, it may predict a cloudy day. This information is used to optimize the schedule.

[2171] Schedule Optimization

[2172] The server then uses this information to optimize the schedule. For example, it can refer to the weather forecast and suggest indoor meetings if an outdoor event is not suitable for the day of the meeting. It also optimizes the time to avoid conflicts with other tasks or appointments.

[2173] Conversational Interaction

[2174] The device interactively asks the user for confirmation: "I'd like to set up a meeting with Bob for Monday at 2 PM. Confirm?" and waits for the user to confirm or amend. If the user answers "yes," it proceeds to the next step.

[2175] Schedule confirmation and notification

[2176] The server confirms the final schedule information and updates the user's calendar. The confirmed schedule information is then sent to the device, informing the user that "A meeting with Bob has been scheduled for next Monday at 2:00 PM."

[2177] Specific examples

[2178] For example, a specific example will be given of a case where a user performs an operation to "schedule an online meeting at 10:00 AM next Tuesday."

[2179] Retrieving User Information

[2180] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[2181] Sending information

[2182] The device sends this information to the server. The data sent is in the following format:

[2183] json

[2184] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[2185] Performing natural language processing

[2186] The server parses this information and converts it into a concrete date and time.

[2187] json

[2188] {

[2189] "event": "online meeting",

[2190] "date": "YYYY-MM-DD",

[2191] "time": "10:00"

[2192] }

[2193] Data Integration and Analysis

[2194] The server uses the calendar API to check for overlaps with other events and also retrieves weather information from the weather API, e.g., a "sunny" forecast.

[2195] Schedule Optimization

[2196] The server determines the optimal schedule based on this information.

[2197] Conversational Interaction

[2198] The device asks the user, "We're setting up an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[2199] Schedule confirmation and notification

[2200] The server confirms the final schedule information and updates the calendar on the user's device. The notification reads, "An online meeting has been scheduled for next Tuesday at 10:00 AM."

[2201] The above is an embodiment of the present invention. This system allows users to manage their schedules efficiently and stress-free.

[2202] The processing flow will be explained below.

[2203] Step 1:

[2204] A user enters "Meeting with Bob next Monday at 2 PM" into the calendar app on their device (e.g., smartphone or PC).

[2205] Step 2:

[2206] The terminal stores the entered schedule information and sends it to the server. The data sent is in the following format:

[2207] json

[2208] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2209] Step 3:

[2210] The server uses a generative AI model (e.g., GPT series) to analyze the received schedule information. The analysis results in detailed information such as:

[2211] json

[2212] {

[2213] "event": "meeting",

[2214] "date": "YYYY-MM-DD",

[2215] "time": "14:00",

[2216] "participant": "Bob"

[2217] }

[2218] Step 4:

[2219] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[2220] Step 5:

[2221] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[2222] Step 6:

[2223] The server takes into account calendar information and weather forecast information to optimize the schedule. For example, if an event is scheduled outdoors, the server considers the weather forecast and suggests an indoor meeting.

[2224] Step 7:

[2225] Based on the device's optimized schedule information, the user is prompted interactively to confirm, "I'm going to schedule a meeting with Bob for Monday at 2 p.m. Would you like to confirm?"

[2226] Step 8:

[2227] The user presses a confirmation button through a dialogue interface or answers "yes" by voice input.

[2228] Step 9:

[2229] The server receives confirmation from the user, finalizes the schedule information, and updates the user's calendar. The updated information looks like this:

[2230] json

[2231] {

[2232] "event": "meeting",

[2233] "date": "YYYY-MM-DD",

[2234] "time": "14:00",

[2235] "participant": "Bob",

[2236] "location": "online"

[2237] }

[2238] Step 10:

[2239] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[2240] The above are the specific operations performed at each step. In this way, the terminal, server, and user work together to achieve efficient and optimal schedule management.

[2241] Example 1

[2242] 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."

[2243] Conventional schedule management systems require users to manually input, check, and correct schedules, which is time-consuming and labor-intensive. Furthermore, they lack the ability to integrate external data, such as weather information and overlap checks with other appointments, making schedule optimization difficult. Furthermore, they are unable to handle natural language input, making user input complex and unintuitive.

[2244] 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.

[2245] In this invention, the server includes means for acquiring schedule information from a user, means for transmitting the acquired schedule information to an information processing device, means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants, means for integrating information acquired from external data sources and adjusting and optimizing the schedule, means for interactively prompting the user for confirmation and correction, means for finalizing the schedule information and notifying the user's terminal, means for analyzing the schedule information using a generative AI model, and means for performing natural language processing using prompt sentences to identify the date and time. This allows the user to efficiently manage their schedule, and realizes optimized schedule management integrated with external data.

[2246] "Schedule information" refers to data about the date, time, type of event, and participants that a user enters as an appointment.

[2247] The term "information processing device" refers to a device or system in general that has the function of receiving, analyzing, and transmitting schedule information.

[2248] "Natural language processing" is a computer technology for analyzing human language and understanding or generating meaning.

[2249] "External Data Sources" refers to external data providers, including calendar APIs and weather APIs, that the server accesses and uses for schedule optimization.

[2250] "Schedule adjustment and optimization" is the process of suggesting optimal times and settings, taking into account the user's existing schedule and external data.

[2251] "Means for interactively prompting confirmation and correction" refers to a function that interactively prompts the user to confirm and correct the schedule contents.

[2252] A "generative AI model" is an artificial intelligence technology that includes large-scale learning models used for natural language processing.

[2253] A "prompt" is text that is input to a generative AI model to instruct it on a specific task.

[2254] "Terminal" refers to the device (such as a smartphone or PC) that a user uses to input and receive schedule information.

[2255] This invention is an advanced scheduling system for streamlining users' schedules and task management. This system is based on a large-scale natural language processing technology called a generative AI model.

[2256] Hardware and software used

[2257] Hardware

[2258] 1. Terminal: A device through which a user inputs schedule information. Examples include smartphones and personal computers (PCs).

[2259] 2. Server: A central management system for analyzing schedule information and integrating external data.

[2260] software

[2261] 1. Natural language processing technology: Generative AI models (e.g., GPT series) are used to analyze the schedule information entered by the user.

[2262] 2. Calendar API: Used to check for overlaps with other events.

[2263] 3. Weather API: Used to obtain weather information required for schedule optimization.

[2264] Data processing and calculation

[2265] 1. Obtaining user information

[2266] A user enters "Meeting next Monday at 2 PM" into the device's calendar app. This information is stored on the device.

[2267] 2. Transmission of information

[2268] The terminal transmits the acquired schedule information to the server.

[2269] For example, data is sent in the following format:

[2270] json

[2271] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2272] 3. Performing Natural Language Processing

[2273] The server analyzes the schedule information using a generative AI model, for example, using the following prompt:

[2274] Example prompt: "Convert 'next Monday at 2 PM' into a specific date and time."

[2275] Examples of what can be obtained as a result of the analysis:

[2276] json

[2277] {

[2278] "event": "meeting",

[2279] "date": "YYYY-MM-DD",

[2280] "time": "14:00",

[2281] "participant": "Bob"

[2282] }

[2283] 4. Data integration and analysis

[2284] The server uses an external calendar API to check for overlaps with other events, and at the same time, retrieves the weather forecast for the day of the meeting using a weather API.

[2285] For example, a "cloudy" forecast is obtained.

[2286] 5. Schedule optimization

[2287] The server optimizes schedules based on weather forecasts and other schedules, for example suggesting indoor meetings if the weather is bad.

[2288] The schedule is updated as a result of the optimization.

[2289] 6. Conversational Interaction

[2290] The device interactively asks the user for confirmation: "I'm going to schedule a meeting for Monday at 2 PM. Would you like to confirm?" and waits for the user to confirm or correct the request.

[2291] 7. Schedule confirmation and notification

[2292] The server determines the final schedule information and notifies the user's device.

[2293] The user is notified of the confirmed schedule information, stating, "A meeting has been scheduled for next Monday at 2:00 p.m."

[2294] Specific examples

[2295] For example, if a user wants to "schedule an online meeting next Tuesday at 10:00 AM":

[2296] The user inputs specific plans into the device.

[2297] The device sends this information to the server.

[2298] The server analyzes it using a generative AI model and converts it into a specific date and time.

[2299] The server uses the calendar and weather APIs to optimize the schedule.

[2300] The device interactively asks the user for confirmation, and the user replies "yes."

[2301] The server confirms the final schedule and reflects it on the user's calendar.

[2302] As described above, the system of the present invention provides users with efficient and stress-free schedule management.

[2303] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2304] Step 1:

[2305] Retrieving User Information

[2306] A user enters "Meeting at 2 PM next Monday" into the calendar app on their device (e.g., smartphone or PC). This entry is saved in the device's database.

[2307] Input: Scheduling information entered by the user (e.g., "Meeting next Monday at 2 PM").

[2308] Output: Schedule information stored in the device database.

[2309] Specific behavior:

[2310] The user launches a calendar app and enters a new event.

[2311] Suppose the input is "Meeting next Monday at 2pm."

[2312] This input is stored in an internal database.

[2313] Step 2:

[2314] Sending information

[2315] The device sends the saved schedule information to the server. The data sent is in JSON format.

[2316] Input: Schedule information stored on the device.

[2317] Output: Schedule information sent to the server in JSON format.

[2318] Specific behavior:

[2319] The device organizes the saved schedule information into JSON format.

[2320] For example, the following JSON is generated:

[2321] json

[2322] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2323] The JSON data is sent over the internet to a server.

[2324] Step 3:

[2325] Performing natural language processing

[2326] The server uses a generative AI model to parse the submitted schedule information, converting dates and times into specific formats using prompts.

[2327] Input: Schedule information in JSON format.

[2328] Output: Schedule information converted into concrete dates and times.

[2329] Specific behavior:

[2330] The server parses the received JSON data.

[2331] Enter the following prompt into the generative AI model: "Convert 'next Monday at 2 PM' into a specific date and time."

[2332] The generative AI model performs the analysis and generates data such as:

[2333] json

[2334] {

[2335] "event": "meeting",

[2336] "date": "YYYY-MM-DD",

[2337] "time": "14:00",

[2338] "participant": "Bob"

[2339] }

[2340] Step 4:

[2341] Data Integration and Analysis

[2342] The server accesses external calendar and weather APIs to check for duplicates and obtain weather information.

[2343] Input: Parsed schedule information.

[2344] Output: Results of overlap check with other events and weather information.

[2345] Specific behavior:

[2346] The server sends a request to the external calendar API to check for conflicts with other events for the user.

[2347] Send a request to the weather API to get weather information for the day the meeting is scheduled.

[2348] For example, a "cloudy" forecast is obtained.

[2349] Step 5:

[2350] Schedule Optimization

[2351] Optimize your schedule based on the information obtained by the server. Suggest the best schedule based on weather forecasts and other schedules.

[2352] Input: Duplicate check results and weather information.

[2353] Output: Optimized schedule information.

[2354] Specific behavior:

[2355] The server calculates the optimal schedule based on weather information and other schedules.

[2356] For example, if the weather is bad, suggest meeting indoors.

[2357] The schedule is updated as a result of the optimization.

[2358] Step 6:

[2359] Conversational Interaction

[2360] The device interactively asks the user for confirmation: "I'd like to schedule a meeting for Monday at 2 PM. Would you like to confirm?"

[2361] Input: Optimized schedule information.

[2362] Output: User confirmation or correction instructions.

[2363] Specific behavior:

[2364] The device displays a notification to the user.

[2365] The user responds with "yes" or "no."

[2366] If the answer is "no", a form is displayed that allows the user to enter a new time.

[2367] Step 7:

[2368] Schedule confirmation and notification

[2369] The server determines the final schedule information and notifies the user's device.

[2370] Input: User confirmed or corrected schedule information.

[2371] Output: Confirmed schedule information and notifications.

[2372] Specific behavior:

[2373] The server saves the confirmed schedule to the user's calendar.

[2374] Send a notification to your device saying "A meeting has been scheduled for next Monday at 2 PM."

[2375] The user's calendar will update to show the new schedule.

[2376] (Application example 1)

[2377] 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."

[2378] Conventional schedule management systems do not provide sufficient support for streamlining user schedule and task management. Especially for high-risk jobs like security guarding, real-time schedule optimization and risk information integration are necessary. This allows for more effective patrol and surveillance, but conventional systems cannot meet these needs. Furthermore, a lack of visual or audio feedback makes it difficult to respond quickly.

[2379] 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.

[2380] In this invention, the server includes means for acquiring schedule information from a user, means for transmitting the acquired schedule information to the server, means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and location, means for integrating information acquired from external data sources to adjust and optimize the schedule, means for interactively prompting the user to confirm and correct the information, means for finalizing the schedule information and notifying the user's device, means for proposing optimal monitoring routes and patrol schedules, means for optimizing patrol routes by integrating risk assessment information, means for managing monitoring points in security areas based on location information, and means for providing visual or audio feedback, thereby enabling effective and efficient schedule management for security guards and optimization of patrol work.

[2381] A "user" is a person or professional who uses the system to manage schedules and tasks.

[2382] "Schedule information" refers to data about appointments and tasks, including dates, times, event types, locations, etc.

[2383] A "server" is a computer system that analyzes, integrates, and optimizes schedule information.

[2384] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[2385] "External Data Source" means a source of externally provided data, including a calendar API or weather API.

[2386] "Optimization" means adjusting schedules and tasks efficiently and effectively.

[2387] "Interaction style" refers to the way in which users and systems interact with each other to exchange information.

[2388] A "surveillance route" is the route that security guards follow when patrolling.

[2389] A "patrol schedule" is a plan for security guards to patrol specific locations at specific times.

[2390] "Risk assessment information" refers to data that predicts and provides information about security risks.

[2391] A "patrol route" is a pre-determined route that a security guard follows when patrolling.

[2392] "Location information" is data that indicates the geographical information of the user or the monitored area.

[2393] A "vigilance area" is a location or area that security guards should monitor.

[2394] "Visual feedback" refers to the display of information provided through a display or smart glasses.

[2395] "Audio feedback" refers to information provided through audio output.

[2396] The system for realizing this invention allows users to efficiently manage their schedules, and in particular, optimizes patrol and surveillance operations in security services. The system is implemented by combining the following hardware and software.

[2397] The system uses smart glasses or head-mounted displays (terminals) to obtain the user's schedule information. When the user gives voice commands or inputs to these devices, the information is sent to a cloud server. The software used includes generative AI models (e.g., GPT series) as well as calendar APIs, weather APIs, and risk assessment APIs.

[2398] Processing Description

[2399] Retrieving User Information

[2400] The user commands the smart glasses to "start monitoring the east gate at 2:00 p.m. next Tuesday" by voice, which is then converted into text by the device.

[2401] Sending information

[2402] The device sends the schedule information it has acquired to the cloud server. The data sent will be in the following format:

[2403] json

[2404] {"event": "monitoring", "time": "2pm", "location": "east gate"}

[2405] Performing natural language processing

[2406] The cloud server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information and convert it into specific time and location information. The analysis results are as follows:

[2407] json

[2408] {

[2409] "event": "monitoring",

[2410] "time": "14:00",

[2411] "location": "East Gate"

[2412] }

[2413] Data Integration and Analysis

[2414] The cloud server uses the calendar API and risk assessment API to integrate other schedules with risk information. For example, it can obtain information such as "There is a high possibility that suspicious individuals will be seen around the east gate."

[2415] Schedule optimization

[2416] Based on this information, the cloud server will suggest the most effective surveillance route and time to the user, optimizing areas to be patrolled in addition to the East Gate.

[2417] Conversational Interaction

[2418] The device will ask the user aloud, "We will begin monitoring the East and North Gates at 2:00 p.m. next Tuesday. Would you like to confirm?" If the user responds "Yes," it will proceed to the next step.

[2419] Schedule confirmation and notification

[2420] The cloud server finalizes the monitoring schedule and notifies the user's device, which then provides visual and audio feedback, stating, "Monitoring of the East and North Gates has been set for next Tuesday at 2:00 PM."

[2421] Specific examples

[2422] Here is a specific example of a case where a user commands the smart glasses to "set up a patrol of the south exit at 4:00 p.m. next Wednesday."

[2423] Retrieving User Information

[2424] The user types into the terminal, "Patrol the south exit next Wednesday at 4pm."

[2425] Sending information

[2426] The device sends this information to the cloud server in the following format:

[2427] json

[2428] {"event": "tour", "time": "4pm", "location": "south exit"}

[2429] Performing natural language processing

[2430] The cloud server analyzes the information and converts it into:

[2431] json

[2432] {

[2433] "event": "tour",

[2434] "time": "16:00",

[2435] "location": "South Exit"

[2436] }

[2437] Data Integration and Analysis

[2438] The cloud server uses the risk assessment API to obtain risk information such as "There have been reports of suspicious individuals being seen around the south exit."

[2439] Schedule Optimization

[2440] The cloud server proposes the optimal route and time, and optimization is performed including the surrounding area.

[2441] Conversational Interaction

[2442] The terminal asks, "Patrol of the South Exit and surrounding area will be scheduled for next Wednesday at 4pm. Confirm?"

[2443] Schedule confirmation and notification

[2444] If the user responds "Yes," the cloud server finalizes the patrol schedule and notifies the device, displaying the message, "Patrol of the south exit and surrounding area has been scheduled for next Wednesday at 4 p.m."

[2445] Prompt Sentence Examples

[2446] Based on the command "Start monitoring the East Gate at 2:00 PM next Tuesday," please integrate other schedules and risk information to propose the optimal monitoring schedule.

[2447] This will enable effective and efficient schedule management for security guards and optimization of patrol operations.

[2448] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2449] Step 1:

[2450] The user commands the smart glasses by voice, "Start monitoring the east gate at 2:00 PM next Tuesday." The device converts this voice command into text data and recognizes the content as "schedule information." The input is a voice command, and the output is schedule information in text format. In this step, voice recognition technology is used to convert the voice data into text data.

[2451] Step 2:

[2452] The device sends the acquired schedule information to the cloud server. The data sent is in the following format:

[2453] json

[2454] {"event": "monitoring", "time": "2pm", "location": "east gate"}

[2455] The input is schedule information in text format, and the output is data transmission to a cloud server.

[2456] Step 3:

[2457] The cloud server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information. The analysis results in specific time and location information, which may take the following format:

[2458] json

[2459] {

[2460] "event": "monitoring",

[2461] "time": "14:00",

[2462] "location": "East Gate"

[2463] }

[2464] The input is schedule information in text format, and the output is parsed schedule data. In this step, a generative AI model is used for natural language processing.

[2465] Step 4:

[2466] The cloud server calls the calendar API and risk assessment API, and integrates other schedule and risk information based on the acquired schedule information. For example, it acquires information that "there is a high possibility that a suspicious person will be seen around the east gate." The input is the analyzed schedule data, and the output is the integrated risk assessment data and schedule data. In this step, the API is called to integrate and analyze the data.

[2467] Step 5:

[2468] The cloud server uses this integrated data to propose the most effective surveillance route and time. For example, it may propose additional areas to patrol besides the East Gate. The input is the integrated risk assessment data and schedule data, and the output is an optimized surveillance schedule proposal. This step uses data analysis and optimization algorithms.

[2469] Step 6:

[2470] The terminal asks the user by voice, "Monitoring of the East and North Gates will begin at 2:00 PM next Tuesday. Do you want to confirm?" If the user answers "Yes," it proceeds to the next step. The input is the optimized monitoring schedule proposal, and the output is the user's response. This step uses a conversational interface.

[2471] Step 7:

[2472] After receiving the user's response, the cloud server finalizes the monitoring schedule and notifies the user's device. The device provides visual or audio feedback, such as "Monitoring of the East and North Gates has been set for next Tuesday at 2:00 PM." The input is the user's confirmation response, and the output is notification of the finalized schedule information. This step uses the database update and notification functions.

[2473] 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.

[2474] This invention is a system that streamlines user schedule and task management and, by combining it with an emotion engine, enables flexible responses according to the user's emotional state. This system acquires the user's schedule information, analyzes it using natural language processing technology, and integrates information from external data sources to optimize the schedule. It also recognizes the user's emotions, proposes and adjusts the schedule, and notifies the user of the final schedule information.

[2475] A natural language description of the program's processing

[2476] Retrieving User Information

[2477] A user enters "Meeting with Bob at 2 PM next Monday" into the calendar app on their device (e.g., smartphone or PC). That information is stored on the device.

[2478] Sending information

[2479] The terminal sends the entered schedule information to the server. The data sent is in the following format:

[2480] json

[2481] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2482] Performing natural language processing

[2483] The server uses a generative AI model (e.g., GPT series) to analyze the submitted schedule information, and the analysis results in detailed information such as:

[2484] json

[2485] {

[2486] "event": "meeting",

[2487] "date": "YYYY-MM-DD",

[2488] "time": "14:00",

[2489] "participant": "Bob"

[2490] }

[2491] Data Integration and Analysis

[2492] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[2493] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[2494] Running the Emotion Engine

[2495] The device uses facial recognition and voice analysis technologies to recognize the user's emotional state.

[2496] The server uses an emotion engine to adjust the schedule based on the user's perceived emotional state. For example, if the user is feeling stressed, the system will suggest postponing less urgent tasks.

[2497] Schedule Optimization

[2498] The server optimizes the schedule by taking into account calendar information, weather forecast information, and the user's emotional state. For example, it suggests indoor meetings based on the weather forecast and also makes suggestions that match the user's emotional state.

[2499] Conversational Interaction

[2500] Based on the device-optimized schedule information, the user is interactively asked to confirm, "I'm going to schedule a meeting with Bob for Monday at 2 PM. Would you like to confirm?" If the user answers "yes," the process proceeds to the next step.

[2501] Schedule confirmation and notification

[2502] The server determines the final schedule information and updates the user's calendar. The updated information looks like this:

[2503] json

[2504] {

[2505] "event": "meeting",

[2506] "date": "YYYY-MM-DD",

[2507] "time": "14:00",

[2508] "participant": "Bob",

[2509] "location": "online"

[2510] }

[2511] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[2512] Specific examples

[2513] For example, a specific example will be given below in which a user performs an operation to "schedule an online meeting at 10:00 AM next Tuesday."

[2514] Retrieving User Information

[2515] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[2516] Sending information

[2517] The device sends this information to the server. The data sent is in the following format:

[2518] json

[2519] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[2520] Performing natural language processing

[2521] The server parses this information and converts it into a concrete date and time, as well as the type of event and participants:

[2522] json

[2523] {

[2524] "event": "online meeting",

[2525] "date": "YYYY-MM-DD",

[2526] "time": "10:00",

[2527] "participant": "N / A"

[2528] }

[2529] Data Integration and Analysis

[2530] The server uses the calendar API to check for overlapping events and also retrieves weather forecast information from the weather API.

[2531] Running the Emotion Engine

[2532] The device analyzes the user's voice and facial expressions to recognize their emotions. For example, it determines that the user is not feeling stressed.

[2533] Schedule Optimization

[2534] The server determines the optimal schedule based on this information.

[2535] Conversational Interaction

[2536] The device will ask the user for confirmation: "An online meeting will be scheduled for next Tuesday at 10:00 AM. Would you like to confirm?"

[2537] Schedule confirmation and notification

[2538] The server receives the user's confirmation and finalizes the schedule information, which is then reflected in the user's calendar. This schedule information is then notified to the device.

[2539] The above is an embodiment of the present invention. This system allows users to manage their schedules efficiently and stress-free. In particular, by taking into account the user's emotional state, more flexible responses are possible, improving the quality of daily life.

[2540] The processing flow will be explained below.

[2541] Specific steps of the program's processing

[2542] Step 1:

[2543] A user speaks or texts "Meet with Bob next Monday at 2 PM" into the calendar app on their device (e.g., smartphone or PC).

[2544] Step 2:

[2545] The terminal processes the entered schedule information and sends it to the server. The data sent is in the following format:

[2546] json

[2547] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2548] Step 3:

[2549] The server uses a generative AI model (GPT series) to analyze the received schedule information. This converts the input "next Monday" or "2 PM" into a specific date and time, and also identifies the type of event and its attendees. The analysis results are as follows:

[2550] json

[2551] {

[2552] "event": "meeting",

[2553] "date": "YYYY-MM-DD",

[2554] "time": "14:00",

[2555] "participant": "Bob"

[2556] }

[2557] Step 4:

[2558] The server accesses an external calendar API, retrieves existing schedule information, and checks whether it overlaps with the new schedule information.

[2559] Step 5:

[2560] The server accesses the weather API and gets the weather forecast for the following Monday, for example, "cloudy."

[2561] Step 6:

[2562] The device uses an emotion engine (face recognition technology and voice analysis technology) to recognize the user's emotional state. For example, it analyzes the user's facial expressions and voice using a camera or microphone to identify the user's emotional state.

[2563] Step 7:

[2564] The server uses an emotion engine to adjust the schedule based on the user's perceived emotional state. For example, if the user is feeling stressed, the server suggests adjusting meeting times to allow time for relaxation.

[2565] Step 8:

[2566] The server takes into account calendar information, weather forecast information, and emotional state to optimize the schedule. For example, it prioritizes important tasks and suggests appropriate locations depending on the weather.

[2567] Step 9:

[2568] Based on the device-optimized schedule information, the user is interactively asked to confirm, "I'm going to schedule a meeting with Bob next Monday at 2 p.m. Would you like to confirm?"

[2569] Step 10:

[2570] The user confirms by answering "yes" in the dialogue interface.

[2571] Step 11:

[2572] The server receives confirmation from the user, finalizes the schedule information, and updates the user's calendar. The finalized information looks like this:

[2573] json

[2574] {

[2575] "event": "meeting",

[2576] "date": "YYYY-MM-DD",

[2577] "time": "14:00",

[2578] "participant": "Bob",

[2579] "location": "online"

[2580] }

[2581] Step 12:

[2582] The device sends the user a notification saying, "You have a meeting with Bob scheduled for next Monday at 2 PM."

[2583] Specific examples

[2584] For example, a specific example will be given below in which a user performs an operation such as "schedule an online meeting at 10:00 AM next Tuesday."

[2585] Step 1:

[2586] A user enters "Online meeting next Tuesday at 10 AM" into their device's calendar app.

[2587] Step 2:

[2588] The device sends this information to the server. The data sent is in the following format:

[2589] json

[2590] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[2591] Step 3:

[2592] The server parses this information and converts it into a concrete date and time, as well as the type of event and participants:

[2593] json

[2594] {

[2595] "event": "online meeting",

[2596] "date": "YYYY-MM-DD",

[2597] "time": "10:00",

[2598] "participant": "N / A"

[2599] }

[2600] Step 4:

[2601] The server uses the calendar API to check for overlapping events and also retrieves weather forecast information from the weather API.

[2602] Step 5:

[2603] The device analyzes the user's voice and facial expressions to recognize their emotions, for example, rating the user as "relaxed."

[2604] Step 6:

[2605] The server determines the optimal schedule based on this information, taking into account the user's relaxed emotional state and setting a schedule with ample time.

[2606] Step 7:

[2607] The device prompts the user for confirmation: "I'm setting up an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[2608] Step 8:

[2609] The user checks and corrects as necessary.

[2610] Step 9:

[2611] The server finalizes the schedule information and sends notifications to the user's device.

[2612] The above is a specific embodiment for carrying out the present invention, and this system enables the user to manage their schedule efficiently and stress-free, and to respond flexibly according to their emotional state.

[2613] Example 2

[2614] 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."

[2615] Modern users are required to efficiently manage a variety of tasks and schedules in their busy daily lives. However, traditional schedule management systems do not take into account the user's emotional state, making it difficult to provide appropriate schedule suggestions and adjustments for users who feel stressed or tense. Furthermore, they often lack the functionality to integrate with external data sources and provide optimal schedules. This leads to problems that reduce the user's quality of life.

[2616] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring schedule information from a user; means for transmitting the acquired schedule information to the server; means for analyzing the transmitted schedule information using natural language processing and identifying the date, time, event type, and participants; means for integrating information acquired from external data sources to adjust and optimize the schedule; means for recognizing the user's emotional state; means for adjusting the schedule based on the user's emotional state using an emotion engine; means for interactively prompting the user to confirm and correct the information; and means for finalizing the schedule information and notifying the user's device. This makes it possible to provide an optimal schedule by integrating information from external data sources while taking the user's emotional state into consideration.

[2617] "User" refers to a person who uses the system to manage schedules and tasks.

[2618] "Terminal" means a device that a user uses to access and operate the system, including a smartphone, personal computer, tablet, etc.

[2619] "Server" refers to the central computer system that analyzes schedule information submitted by users and integrates it with external data sources.

[2620] "Schedule information" refers to information about upcoming meetings, events, etc. that a user enters into a terminal.

[2621] "Natural language processing" refers to the technology that allows a computer to understand and analyze strings of characters written in natural language.

[2622] "External data sources" refers to external services or databases that the server accesses to obtain information, including calendar APIs and weather APIs.

[2623] "Schedule adjustment" refers to appropriately changing or optimizing the schedule based on the acquired information.

[2624] "Emotional state" refers to the user's psychological state, including states such as stress, fatigue, and joy.

[2625] An "emotion engine" is a software component that analyzes a user's emotional state and makes schedule suggestions and adjustments based on that.

[2626] "Interaction style" refers to the way in which a system interacts with a user, including speech recognition and text input.

[2627] "Schedule optimization" refers to improving the quality of a user's life by efficiently and rationally organizing and adjusting the user's schedule.

[2628] "Notifications" refers to alerts or messages that inform users of final schedule information.

[2629] This invention is a system that improves the efficiency of a user's schedule and task management and, by combining it with an emotion engine, enables flexible responses according to the user's emotional state. This system is realized using multiple hardware and software components. Specific hardware and software configurations, as well as the data processing and data calculations performed by each, are described below.

[2630] Hardware and software used

[2631] Device:

[2632] Refers to devices that are directly operated by users, such as smartphones, personal computers, and tablets.

[2633] It uses a built-in camera and microphone to enable facial and voice recognition technology.

[2634] server:

[2635] This refers to the central processing unit that processes the schedule information sent by users and analyzes and integrates the data.

[2636] It has the ability to access external data sources such as weather APIs and calendar APIs.

[2637] It has natural language processing capabilities using an emotion engine and generative AI models (e.g., GPT series).

[2638] Data processing and calculation

[2639] Retrieving User Information

[2640] A user enters schedule information into the device's calendar app, for example, "Meeting with Bob next Monday at 2 PM."

[2641] The device receives this information and temporarily stores it.

[2642] Sending information

[2643] The device generates an HTTP request and sends the entered schedule information to the server's API endpoint. The sent data is in JSON format.

[2644] Performing natural language processing

[2645] The server analyzes the submitted schedule information using a generative AI model (e.g., GPT series) to identify specific dates and times, event types, and participants.

[2646] Data Integration and Analysis

[2647] The server obtains the necessary information from the calendar API and weather API, and integrates the weather information and overlaps it with the obtained schedule information.

[2648] Running the Emotion Engine

[2649] The device uses the built-in camera and microphone to recognize the user's emotional state using facial recognition and voice analysis technologies.

[2650] The server uses an emotion engine to adjust the schedule based on the user's emotional state, for example, suggesting more relaxing tasks if the user is feeling stressed.

[2651] Schedule Optimization

[2652] The server integrates all data and automatically generates an appropriate schedule, taking into account the user's emotional state and external data to propose the optimal schedule.

[2653] Conversational Interaction

[2654] The device displays the optimized schedule information and displays an interactive prompt to the user, such as "Do you want to confirm?" If the user responds "yes," the device proceeds to the next step.

[2655] Schedule confirmation and notification

[2656] The server determines the final schedule information and reflects it on the user's calendar.

[2657] The terminal notifies the user of the confirmed schedule information.

[2658] Specific examples

[2659] For example, consider the case where a user requests an online meeting at 10 AM next Tuesday. In this case, the user enters "online meeting at 10 AM next Tuesday" into the device's calendar app. The device receives this input and sends it to the server. The server analyzes it using a generative AI model and obtains the necessary data from the calendar API and weather API. Next, it analyzes the user's emotional state and generates an optimal schedule. The device then asks the user for confirmation, updates the calendar with the final schedule, and notifies the user.

[2660] An example of a prompt might be:

[2661] "We've set up an online meeting for next Tuesday at 10am. Would you like to confirm?"

[2662] In this way, the present invention can provide efficient and flexible schedule management and improve the quality of life of users.

[2663] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2664] Step 1: Get user information

[2665] A user enters "Meeting with Bob next Monday at 2 PM" into their device's calendar app.

[2666] Specific behavior:

[2667] Input: The user enters event information into a calendar app.

[2668] Data processing: The terminal receives the input and temporarily stores this schedule information in an internal database.

[2669] Output: Schedule information stored on the device.

[2670] Step 2: Submit your information

[2671] The terminal transmits the input schedule information to the server.

[2672] Specific behavior:

[2673] Input: Schedule information stored on the device.

[2674] Data processing: The device converts the schedule information into a JSON-formatted HTTP request.

[2675] Output: Schedule information in JSON format is sent to the server.

[2676] json

[2677] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2678] Step 3: Performing Natural Language Processing

[2679] The server analyzes the sent schedule information using a generative AI model (e.g., GPT series).

[2680] Specific behavior:

[2681] Input: JSON formatted schedule information sent from the device to the server.

[2682] Data calculation: The server sends analytical prompts to the generative AI model, which then uses natural language processing to parse this information to identify specific dates, times, and participants.

[2683] Output: Parsed detailed schedule information.

[2684] json

[2685] {

[2686] "event": "meeting",

[2687] "date": "YYYY-MM-DD",

[2688] "time": "14:00",

[2689] "participant": "Bob"

[2690] }

[2691] Step 4: Data integration and analysis

[2692] The server accesses external calendar and weather APIs to retrieve and integrate the required information.

[2693] Specific behavior:

[2694] Input: Parsed detailed schedule information.

[2695] Data processing: The server accesses the calendar API to retrieve existing schedule information and check for duplicates. It also accesses the weather API to retrieve the weather forecast for the relevant day.

[2696] Output: Schedule and weather information with no overlaps. For example, the weather forecast information is: "Cloudy".

[2697] Step 5: Run the Emotion Engine

[2698] The device uses facial recognition and voice analysis technologies to recognize the user's emotional state.

[2699] Specific behavior:

[2700] Input: User's facial expression and voice data.

[2701] Data calculation: Data is collected via the device's built-in camera and microphone and analyzed by a local emotion recognition module.

[2702] Output: Recognized emotional state data.

[2703] The server uses an emotion engine to adjust the schedule based on the user's emotional state.

[2704] Specific behavior:

[2705] Input: Recognized emotional state data and detailed schedule information.

[2706] Data calculation: The server inputs data into the emotion engine and adjusts the schedule based on emotions.

[2707] Output: Adjusted schedule information. For example, if the user is stressed, postpone less urgent tasks.

[2708] Step 6: Schedule optimization

[2709] The server integrates all the data and automatically generates the optimal schedule.

[2710] Specific behavior:

[2711] Input: Adjusted schedule information, duplicate check results, and weather information.

[2712] Data calculation: The server runs the optimization algorithm to integrate the data and generate the optimal schedule.

[2713] Output: Optimized proposed schedule.

[2714] Step 7: Conversational Interaction

[2715] The device displays the optimized schedule information and asks the user for confirmation.

[2716] Specific behavior:

[2717] Input: Optimized proposed schedule.

[2718] Data processing: The device displays a user interface and displays a dialogue prompt such as "I'd like to set up a meeting with Bob for Monday at 2pm. Would you like to confirm?"

[2719] Output: The user's response (e.g., "Yes").

[2720] Step 8: Confirm schedule and notify

[2721] The server determines the final schedule information and reflects it on the user's calendar.

[2722] Specific behavior:

[2723] Input: User's acknowledgment (Yes).

[2724] Data processing: The server updates the schedule information using the calendar API.

[2725] Output: The confirmed schedule information is reflected in the user's calendar.

[2726] The device will notify the user that the schedule has been confirmed.

[2727] Specific behavior:

[2728] Input: Confirmed schedule information.

[2729] Data processing: The terminal generates and displays a notification message.

[2730] Output: The user receives a notification that says "A meeting with Bob has been scheduled for next Monday at 2 PM."

[2731] The above is the specific operation of each processing step and the flow of the data processing and data calculation that accompanies it.

[2732] (Application example 2)

[2733] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2734] Conventional schedule management systems simply manage a user's schedule information but are unable to consider the user's emotional state. This makes it difficult to flexibly adjust schedules to accommodate changes in stress and emotional state, preventing stress reduction and efficient schedule management for users. Furthermore, they lack the functionality to integrate information from weather forecasts and external data sources to optimize schedules. The present invention aims to solve these problems and improve the quality of life for users by providing a schedule management system that considers the user's emotional state.

[2735] 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.

[2736] In this invention, the server includes means for performing face recognition and voice analysis to recognize the emotional state of the user, means for proposing and adjusting a schedule based on the emotional state using an emotion engine, and means for prompting the user to confirm and correct the schedule in an interactive format, thereby enabling schedule management that flexibly responds to the emotional state of the user.

[2737] "User" means an individual or corporation that uses the system to manage schedules.

[2738] "Schedule information" refers to information such as the date, time, content, location, and participants of a scheduled event entered by the user.

[2739] A "server" is a computer system that processes and stores data over the Internet.

[2740] "Natural language processing" is the technology that enables computers to understand, interpret, and process human language.

[2741] An "external data source" is a service or database that provides data outside of the system, such as a weather API or a calendar API.

[2742] "Facial recognition" is a technology that analyzes a user's facial image to identify an individual.

[2743] "Voice analysis" is a technology that analyzes a user's voice to determine their emotional state and intentions.

[2744] An "emotion engine" is software or a system that analyzes a user's emotional state and determines an appropriate response based on the results.

[2745] "Interactive" refers to the way in which a system and a user exchange information with each other through input and output.

[2746] "Notification" refers to the system providing information to the user regarding schedule confirmation or changes.

[2747] This invention is a system that improves the efficiency of a user's schedule and task management, and by combining it with an emotion engine, enables flexible responses according to the user's emotional state. The detailed configuration and processing for specifically implementing this system are described below.

[2748] System configuration

[2749] User device: Refers to a smartphone, PC, etc., and is the device through which the user inputs schedule information.

[2750] Server: A computer system that processes and stores data over the Internet.

[2751] Facial recognition technology: Technology that analyzes a user's facial image to identify the individual and recognize their emotional state.

[2752] Voice analysis technology: Technology that analyzes the user's voice to determine their emotional state and intentions.

[2753] Emotion engine: Software or a system that determines an appropriate response based on the user's emotional state.

[2754] Calendar API: An external data source used to integrate existing schedule information.

[2755] Weather API: An external data source used to obtain weather forecast information.

[2756] Generative AI model: Software that performs natural language processing, including models used for analysis (e.g., the GPT series).

[2757] A natural language description of the program's processing

[2758] 1. Obtaining user information

[2759] Users enter schedule information into their device's calendar app, such as "Schedule a meeting for next Monday at 2 PM."

[2760] 2. Transmission of information

[2761] The terminal sends this schedule information to the server. The data sent will have the following format, for example:

[2762] json

[2763] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2764] 3. Performing Natural Language Processing

[2765] The server uses a generative AI model to parse the submitted schedule information, converting it into specific dates and times and providing event details.

[2766] json

[2767] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob"}

[2768] 4. Data integration and analysis

[2769] The server uses the calendar API to retrieve existing schedule information and check whether it overlaps with the new schedule information, and at the same time accesses the weather API to retrieve the weather forecast for the following Monday.

[2770] 5. Running the Emotion Engine

[2771] The device uses voice and facial recognition technology to understand the user's emotional state, for example, determining whether the user is feeling stressed.

[2772] 6. Schedule optimization

[2773] The server optimizes the schedule by taking into account the acquired calendar information, weather forecast information, and the user's emotional state. For example, if the weather is bad, the server suggests that the user hold a meeting indoors.

[2774] 7. Conversational Interaction

[2775] The device interactively asks the user to confirm, "I'd like to set up a meeting with Bob on Monday at 2 PM. Would you like to confirm?" If the user answers "yes," it proceeds to the next step.

[2776] 8. Schedule confirmation and notification

[2777] The server determines the final schedule information and reflects it in the user's calendar. The reflected information is as follows:

[2778] json

[2779] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob", "location": "Online"}

[2780] The device will send a notification to the user saying, "A meeting with Bob has been scheduled for next Monday at 2 PM."

[2781] Specific examples

[2782] For example, let us consider the case where a user performs an operation such as "schedule an online meeting at 10:00 AM next Tuesday."

[2783] 1. Obtaining user information: The user enters "Online meeting next Tuesday at 10 AM" into the device's calendar app.

[2784] 2. Sending information: The terminal sends this information to the server. The data sent is in the following format:

[2785] json

[2786] {"event": "Online meeting", "date": "Next Tuesday", "time": "10:00 AM"}

[2787] 3. Perform natural language processing: The server parses this information and converts it into specific dates and times, as well as identifying the type of event and participants.

[2788] json

[2789] {"event": "Online Meeting", "date": "YYYY-MM-DD", "time": "10:00", "participant": "N / A"}

[2790] 4. Data integration and analysis: The server uses the calendar API to check for overlapping events and retrieves weather forecast information from the weather API.

[2791] 5. Execution of emotion engine: The device analyzes the user's voice and facial expressions to recognize their emotions. For example, it determines that the user is not feeling stressed.

[2792] 6. Schedule optimization: The server determines the optimal schedule based on this information.

[2793] 7. Conversational interaction: The device asks the user for confirmation: "I'm going to schedule an online meeting for next Tuesday at 10:00 AM. Would you like to confirm?"

[2794] 8. Schedule confirmation and notification: The server confirms the schedule information after receiving the user's confirmation, and updates the user's calendar. This schedule information is then notified to the device.

[2795] Example prompts for generative AI models

[2796] A user types, "I want to order dinner at 7 PM." The weather is rainy, and the user is stressed. Suggest the best time and menu to order.

[2797] This system allows users to manage their schedules efficiently and stress-free, and in particular allows for flexible responses that take into account emotional states.

[2798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2799] Step 1: Get user information

[2800] A user enters an event into the device's calendar app. For example, "Schedule a meeting next Monday at 2 PM." The input information is as follows:

[2801] input:

[2802] json

[2803] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2804] Output: Schedule information stored directly on the user's device.

[2805] Step 2: Submit your information

[2806] The device sends the acquired schedule information to the server. This sending process uses the following HTTP POST request. The input data is the schedule information acquired in step 1:

[2807] input:

[2808] json

[2809] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2810] Output: The schedule information sent to the server.

[2811] Step 3: Performing Natural Language Processing

[2812] The server uses a generative AI model to parse the submitted schedule information, first converting it into formal dates and times and then extracting event details. The input data is the data received in step 2:

[2813] input:

[2814] json

[2815] {"event": "Meeting", "date": "Next Monday", "time": "2 PM", "participant": "Bob"}

[2816] The analysis produces the following data:

[2817] output:

[2818] json

[2819] {"event": "Meeting", "date": "YYYY-MM-DD", "time": "14:00", "participant": "Bob"}

[2820] Step 4: Data integration and analysis

[2821] The server accesses external data sources (Calendar API and Weather API) to retrieve existing schedule information and weather forecast information. The input data is the data obtained in step 3 and the new data retrieved from the API:

[2822] Input: Data obtained from the Calendar API and Weather API

[2823] Output: New schedule and weather forecast information without overlaps

[2824] Step 5: Run the Emotion Engine

[2825] The device performs facial recognition and voice analysis to recognize the user's emotions. The input data is the user's facial image and voice data:

[2826] Input: User's facial image and voice data

[2827] Output: Perceived user emotional state (e.g., "stressed")

[2828] Step 6: Schedule optimization

[2829] The server optimizes the schedule by integrating calendar information, weather forecast information, and the user's emotional state. For example, it suggests indoor meetings when the weather forecast is bad, and postpones less important tasks when the user is stressed. The input data are the data obtained in steps 4 and 5:

[2830] Input: All integrated data for optimization

[2831] Output: Optimized schedule proposal (e.g. "Indoor meeting proposal")

[2832] Step 7: Conversational Interaction

[2833] The device interactively asks the user, "I'd like to schedule a meeting with Bob at 2 PM on Monday. Would you like to confirm?" and collects the result. The input data is a schedule optimization proposal:

[2834] Input: Optimized schedule proposal

[2835] Output: User confirmation result (e.g. "Yes")

[2836] Step 8: Confirm schedule and notify

[2837] The server receives the user's confirmation result, finalizes the schedule information, and updates the user's calendar. The terminal then sends a notification to the user. The input data is the user's confirmation result:

[2838] Input: User confirmation result ...

Claims

1. a means for obtaining schedule information from a user; means for transmitting the acquired schedule information to a server; a means for analyzing the submitted schedule information using natural language processing to identify the date, time, type of event, and participants; a means of integrating information obtained from external data sources to adjust and optimize schedules; A means to prompt the user interactively for review and correction; The system includes a means for finalizing the schedule information and notifying the user's device.

2. 10. The system of claim 1, further comprising means for using a calendar API to check for date and time overlaps.

3. The system of claim 1 , further comprising means for using a weather API to obtain weather forecast information.

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