System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving appointment information from a user; means for analyzing the appointment information to extract a date, a location, and content of the appointment; means for storing the extracted appointment information; means for suggesting a reminder or an action based on the stored appointment information; means for notifying the user of the suggested reminder or action; and means for collecting feedback from the user to improve accuracy of the suggested reminder or action.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 modern society, many users struggle with time and schedule management. Being late, forgetting important items, and the hassle of checking schedules can be particularly stressful. These problems stem from users' management methods, which rely on their minds, calendars, and notes. This results in insufficient centralization of information and the need for manual confirmation and suggestions. Furthermore, there is a lack of efficient methods for providing schedule reminders and responding to user-specific needs. There is also a lack of mechanisms for improving the system's accuracy based on user feedback. Given these circumstances, there is a need for a system that can reduce users' daily stress and enable efficient schedule management. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes: means for receiving schedule information from a user; means for analyzing the schedule information and extracting the date, time, location, and content of the schedule; means for saving the extracted schedule information; means for proposing reminders and actions based on the saved schedule information; means for notifying the user of the suggested reminders and actions; and means for collecting feedback from the user and improving the accuracy of the reminders and actions. The schedule information from the user is either text data in chat format or image data, and the means for analyzing the image data uses optical character recognition (OCR), making it possible to accommodate a wide range of input formats. This centralizes information and enables optimal reminders and action suggestions tailored to the user's specific needs. Furthermore, by incorporating user feedback, the system's accuracy and convenience are improved, resulting in efficient schedule management.

[0006] "User" means any individual person or entity using the System.

[0007] "Schedule information" is data relating to a schedule entered by a user, and includes information regarding the date, time, location, and content.

[0008] "Means for receiving" refers to a method or device for obtaining input information from a user.

[0009] "Analyzing" refers to the process of breaking down given information and interpreting it semantically.

[0010] "Means for extracting" refers to a method or device for extracting specific elements from the analyzed information.

[0011] "Storage means" refers to a method or device for recording data in a storage device or database.

[0012] "Remind" refers to the process of sending notifications or alerts to users about appointments.

[0013] The "means for suggesting an action" refers to a method or device for instructing or recommending a specific action to a user based on a schedule.

[0014] "Means for notifying" refers to a method or device for communicating information about a reminder or action to a user.

[0015] "Means for collecting feedback" refers to methods and devices for obtaining opinions and reactions from users.

[0016] "Means for improving accuracy" refers to methods and devices for improving the effectiveness of the system based on the feedback collected.

[0017] "Chat format" refers to a format in which conversations are conducted using text messages.

[0018] "Text data" refers to digital data that contains text information.

[0019] "Image data" refers to digital data that includes still images.

[0020] Optical character recognition (OCR) refers to a technology that automatically recognizes characters in an image and converts them into text data. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] An embodiment of the present invention will be described.

[0043] Overall system overview

[0044] This system allows users to easily input schedule information and suggests reminders and actions based on that schedule. The system mainly consists of the following components:

[0045] 1. Means of receiving schedule information from users

[0046] 2. A method for analyzing schedule information and extracting the date, time, location, and content of the schedule

[0047] 3. How to save the extracted schedule information

[0048] 4. A way to suggest reminders and actions based on saved schedule information

[0049] 5. How to notify users of reminders and actions

[0050] 6. A way to collect user feedback and improve the accuracy of reminders and actions

[0051] Receiving input from users

[0052] Users enter schedule information through chat text or screenshots. For example, if a user enters "I have a meeting in Shinjuku next Monday at 3 PM" into LINE chat, this information is received by the system. The device then forwards the received data to the server.

[0053] Analysis and extraction of schedule information

[0054] The server analyzes chat-style text and screenshots received from users. In the case of text, natural language processing technology is used to extract the date, time, location, and content of the appointment. For example, from the text "There is a meeting in Shinjuku at 3 PM next Monday," the information extracted is "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." In the case of image data, optical character recognition (OCR) technology is used to extract character information and analyze it in the same way.

[0055] Save schedule information

[0056] The extracted schedule information is stored in a database by the server. This schedule information is also added to the user's calendar. For example, if you use Google Calendar, you can add an event using the Google Calendar API.

[0057] Reminders and suggested actions

[0058] The server periodically scans the saved schedule information and suggests necessary reminders and actions. It uses an AI model to analyze past data and suggest optimal actions for the user. For example, if a meeting in Shinjuku is coming up, a reminder such as "There will be a meeting in Shinjuku tomorrow. Please make sure you don't forget any necessary documents" will be sent the day before the meeting.

[0059] Reminders and action notifications

[0060] The server notifies the device of the suggested reminders and actions and displays them to the user. Messages can be sent using the LINE API. For example, an hour before a meeting, a notification will be sent via LINE saying, "There will be a meeting in Shinjuku soon. Do you have the necessary materials?"

[0061] Gathering feedback and improving accuracy

[0062] The system can improve its accuracy by providing feedback on reminders and actions. Feedback is entered through chat and collected by the server. The collected feedback is then reflected in the AI ​​model to help suggest future reminders and actions.

[0063] Specific examples

[0064] Example 1: Entering schedule in chat format

[0065] A user types "I have a meeting in Shinjuku next Monday at 3 PM" into LINE. The device sends this message to the server, which extracts "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." Based on this, an event is added to Google Calendar and a reminder is sent the day before the meeting.

[0066] Example 2: Entering schedule information using screenshots

[0067] A user takes a screenshot of a map of Shinjuku Station on their device and sends it to the system. The server uses OCR technology to extract "Shinjuku Station" and generates related event information. This information is added to Google Calendar, and a related reminder is sent on the scheduled date.

[0068] In this way, the present invention provides a system that allows users to efficiently manage their schedules, and provides appropriate reminders and action suggestions when needed.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] Users enter schedule information using chat-style text messages or screenshots. For example, a user might type "I have a meeting in Shinjuku next Monday at 3 PM" into a LINE chat, or take a screenshot of a map of Shinjuku Station.

[0072] Step 2:

[0073] The device processes received text messages and screenshots. Specifically, it converts input data into an appropriate format and forwards it to the server. For example, this includes sending text data using the LINE API.

[0074] Step 3:

[0075] The server analyzes text messages and screenshots received from the device. For text data, natural language processing (NLP) is used to extract the date, time, location, and content of the appointment. For example, from "I have a meeting in Shinjuku at 3 PM next Monday," the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting" are extracted. For screenshots, optical character recognition (OCR) is used to extract the text information within the image and analyze it in the same way.

[0076] Step 4:

[0077] The server saves the extracted event information in a database. It also adds this event information to the user's calendar. For example, if you use Google Calendar, you can add an event using the Google Calendar API. This allows you to centrally manage your events in the calendar app you use.

[0078] Step 5:

[0079] The server uses the saved schedule information to send reminders and suggest actions. It uses an AI model to analyze past data and determine what notifications to send at what time. For example, the day before a meeting, it might suggest a reminder such as, "There will be a meeting in Shinjuku tomorrow. Please make sure you don't forget any necessary documents."

[0080] Step 6:

[0081] The server notifies the device of the suggested reminders and actions. It also uses the LINE API to send messages to users. For example, a notification can be sent via LINE one hour before a meeting saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0082] Step 7:

[0083] Users provide feedback on reminders and actions. User feedback is entered through chat and sent from the device to the server. This feedback is used to improve the accuracy of future reminders and action suggestions by the system.

[0084] Step 8:

[0085] The server updates the AI ​​model based on the collected feedback, improving the accuracy of future reminders and action suggestions, and providing a more personalized service.

[0086] This completes the entire process flow, allowing users to efficiently manage their schedules and receive appropriate reminders and actions when needed.

[0087] Example 1

[0088] 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."

[0089] Conventional schedule management systems have the problem that users manually input their schedules, and subsequent reminders and action suggestions are not fully optimized. Additionally, there is an insufficient mechanism for efficiently collecting user feedback and improving the accuracy of the entire system, resulting in low user convenience.

[0090] 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.

[0091] In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content, means for saving the extracted schedule information in data storage, means for suggesting reminders and actions based on the saved schedule information, means for notifying the user of the reminders and action suggestions, and means for collecting feedback from the user and improving the accuracy of the reminders and action suggestions. This allows users to easily input schedule information and efficiently receive reminders and action suggestions. In addition, the accuracy of the system can be continuously improved through feedback.

[0092] "User" means any individual or entity that uses the System to enter appointment information and receive reminders and suggestions.

[0093] "Schedule information" refers to information about the date, time, location, and content of a schedule entered by a user.

[0094] "Chat-format text data" refers to text-format message data sent and received via chat applications or messaging platforms.

[0095] "Image data" refers to visual information stored in an image file format, such as a screenshot or photograph.

[0096] "Optical character recognition technology" refers to technology for extracting character information from image data.

[0097] "Data storage" refers to a storage device used to store and manage data for long periods of time in a computer system.

[0098] "Remind" refers to the act of notifying the user of the contents of a schedule again based on the saved schedule information.

[0099] "Action suggestion" refers to the act of suggesting appropriate actions and preparations to the user based on the saved schedule information.

[0100] "Feedback" refers to the act of a user providing the system with their opinion or evaluation of a reminder or suggested action.

[0101] The following describes an embodiment of the present invention, including an overview of the system, specific examples of the hardware and software used, a process flow, and specific examples.

[0102] Overall system overview

[0103] This invention is a system that allows users to easily input schedule information and provides reminders and action suggestions based on that schedule. This system mainly consists of the following components:

[0104] 1. Means of receiving schedule information from users

[0105] 2. A method for analyzing schedule information and extracting the date, time, location, and content

[0106] 3. A means for saving the extracted schedule information to data storage

[0107] 4. A way to suggest reminders and actions based on saved schedule information

[0108] 5. Means of notifying users of reminders and actions

[0109] 6. Gathering user feedback to improve the accuracy of reminders and actions

[0110] Hardware and software used in the system

[0111] Terminal: The device used by the user, such as a smartphone or tablet, is equipped with a chat application.

[0112] Server: A server for data analysis, storage, and notification. It has an appropriate API for connecting to the database.

[0113] Natural Language Processing (NLP) engine: Used to extract date, time, location, and content from user-entered text data.

[0114] Optical Character Recognition (OCR) technology: Used to extract text information from image data such as screenshots.

[0115] Data Storage: The database used to store the extracted appointment information.

[0116] Notification system: Uses Google Calendar API and LINE API to notify users of reminders and suggestions.

[0117] Specific examples

[0118] Example 1: Chat-style text data

[0119] A user types "I have a meeting in Shinjuku next Monday at 3 PM" on LINE. This information is received by the device and forwarded to the server. The server uses an NLP engine to extract "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." The extracted information is saved in data storage and added to Google Calendar. The day before the meeting, a reminder notification is sent to the user via LINE stating, "There is a meeting in Shinjuku tomorrow. Please don't forget to bring the necessary materials."

[0120] Example 2: Screenshot image data

[0121] The user takes a screenshot of a map of Shinjuku Station on their device and sends it to the system. The server uses OCR technology to extract "Shinjuku Station" and generates related appointment information. The generated information is saved in data storage and added to Google Calendar. On the scheduled day, a notification is sent to the user via LINE saying, "There is a meeting at Shinjuku Station today. Please be careful not to leave anything behind."

[0122] Prompt Sentence Examples

[0123] The prompt for example 1's chat-style schedule entry is as follows:

[0124] "Please tell me how to enter an appointment in LINE such as 'I have a meeting in Shinjuku next Monday at 3 PM' and have the system recognize it and send the appropriate reminder."

[0125] In this way, the present invention allows users to easily manage their schedules and receive appropriate reminders and action suggestions when needed. Furthermore, by collecting feedback, the accuracy of the system can be continuously improved.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] Users can enter schedule information via chat on LINE or via screenshots. The information entered can include text such as "I have a meeting in Shinjuku at 3:00 PM next Monday" or a screenshot of a map of Shinjuku Station.

[0129] Step 2:

[0130] The device receives chat-style text data and screenshots entered by the user, then forwards the received data to the server via an HTTP request. The input data is in text or image format and is sent as the payload of the request to the server.

[0131] Step 3:

[0132] The server analyzes the received data. In the case of text data in chat format, the server uses a natural language processing (NLP) engine to extract the date, time, location, and content. For example, from the text "There is a meeting in Shinjuku at 3 PM next Monday," the server extracts "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." In the case of image data, the server uses optical character recognition (OCR) technology to extract character information and analyzes it in the same way.

[0133] Step 4:

[0134] The server saves the extracted schedule information in data storage. Specifically, it saves the analysis results in a database and then uses the Google Calendar API to add an event to the user's calendar. For example, the extracted information "Date and time: next Monday at 3:00 PM," "Location: Shinjuku," and "Content: Meeting" is saved to Google Calendar.

[0135] Step 5:

[0136] The server prepares to send reminders and suggest actions based on the saved schedule information. It scans the database at regular intervals and generates reminder notifications, such as "There's a meeting in Shinjuku tomorrow. Please don't forget to bring the necessary documents." It uses an AI model to create optimal action suggestions based on past data.

[0137] Step 6:

[0138] The server notifies the user of the generated reminders and action suggestions. Using the LINE API or other notification systems, the server sends reminders and action suggestions to the device, which then displays them to the user. For example, the day before a meeting, the server sends a LINE message saying, "There will be a meeting in Shinjuku tomorrow. Don't forget to bring the necessary documents."

[0139] Step 7:

[0140] Users provide feedback on reminders and suggested actions. For example, the provided feedback may include opinions such as "The reminder was too late." The device sends this feedback to the server, which collects it. The collected feedback is reflected in the AI ​​model and used to improve the accuracy of future reminders and suggested actions.

[0141] (Application example 1)

[0142] 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."

[0143] In modern society, users lead busy lives, making schedule management a major issue. It is particularly difficult to check or change schedules while driving, making schedule management a key priority. In these circumstances, a system that can efficiently and safely manage schedule information is needed.

[0144] 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.

[0145] In this invention, the server includes a means for analyzing the user's voice input and extracting schedule information, a means for notifying the user of the schedule information and reminders through the vehicle's in-vehicle system, and a means for receiving the schedule information from the user, thereby enabling the user to manage their schedule without using both hands.

[0146] "User" means a person who uses this system.

[0147] "Schedule information" refers to information including details of a schedule, such as date, time, location, and content.

[0148] "Voice input" means that a user provides information to a system by voice.

[0149] "Analysis" is the process of understanding input data and converting it into meaningful information.

[0150] "Storage" means recording the extracted information in a database or the like for long-term retention.

[0151] "Remind" means sending a notification to remind the user of a schedule.

[0152] "Action" means proposing specific actions or instructions.

[0153] "Notification" means sending a message to inform the user of information.

[0154] "Feedback" refers to the evaluations and opinions that users provide to the system.

[0155] "In-vehicle system" means an information processing and display system installed in a vehicle.

[0156] An embodiment of the present invention will be described. This system receives schedule information from a user via voice, analyzes it, saves it, suggests reminders and actions, notifies the user, and collects feedback. Specific embodiments are described below.

[0157] The server uses a speech recognition engine (SpeechRecognition library) to analyze the user's voice input. This speech recognition process converts the voice data into textual appointment information. Natural language processing techniques are then used to parse the text into appointment details such as date, time, location, and content.

[0158] The analyzed schedule information is stored in a database system such as AWS (registered trademark) DynamoDB. The stored schedule information is periodically scanned by an AI model on the server, and optimal reminders and actions are suggested by analyzing past data.

[0159] The in-vehicle information processing system notifies users of saved schedule information and suggested reminders, and can send reminder notifications to users' smartphones using messaging services such as the LINE API.

[0160] User feedback is used to collect ratings and opinions on reminders and actions, allowing the AI ​​model to continuously learn and improve the accuracy of future suggestions.

[0161] Specific examples

[0162] For example, if a user says in a car, "I have a meeting in Shinjuku next Monday at 3 p.m.", the speech recognition engine converts this into text, and the server analyzes the schedule information. This schedule information is then saved in a database, and a reminder notification, "I have a meeting in Shinjuku tomorrow," is sent to the user's smartphone the day before the meeting.

[0163] Prompt Sentence Examples

[0164] "Analyze the user's statement, 'I have a meeting in Shinjuku next Monday at 3 PM,' and extract the date, time, location, and content."

[0165] This system ensures safety on board while enabling efficient schedule management. By utilizing this system, passengers can check their schedules and receive reminders without using their hands.

[0166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0167] Step 1:

[0168] The user inputs voice data in the car. The user's speech (e.g., "I have a meeting in Shinjuku at 3:00 PM next Monday") is input to the terminal via a microphone in the car. The terminal then transmits this voice data to the server.

[0169] Step 2:

[0170] The server uses a speech recognition engine (SpeechRecognition library) to convert the received voice data into text data. The speech recognition engine processes the voice data and outputs it as a string (text). For example, the voice data may be recognized as "There will be a meeting in Shinjuku next Monday at 3:00 PM."

[0171] Step 3:

[0172] The server uses natural language processing (NLP) technology to analyze text data. It performs a process to extract date, time, location, and content from the input text. For example, from the text "There will be a meeting in Shinjuku at 3 PM next Monday," it extracts the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." The analysis results in structured data.

[0173] Step 4:

[0174] The server stores the extracted appointment information in a database system (e.g., AWS DynamoDB). The appointment information is stored persistently by adding structured data to DynamoDB. The stored information includes fields for date, time, location, and content.

[0175] Step 5:

[0176] The server periodically scans the saved schedule information. It uses an AI model to analyze the saved data and suggest appropriate reminders and actions. It calculates the optimal timing and content of reminders based on past data and user feedback. For example, the server suggests a reminder such as "There will be a meeting in Shinjuku tomorrow" the day before a meeting.

[0177] Step 6:

[0178] The server sends reminders and action notifications to the vehicle's in-vehicle system (e.g., infotainment system) or the user's smartphone. It uses a pre-configured notification method (e.g., LINE API) to send appropriate messages. For example, one hour before a meeting, it sends a notification message saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0179] Step 7:

[0180] Users receive notifications and provide feedback on reminders and actions. The feedback is sent to the server, where it is used as training data for the AI ​​model to improve the accuracy of future suggestions.

[0181] 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.

[0182] An embodiment of the present invention will be described.

[0183] Overall system overview

[0184] This system allows users to easily input schedule information, and suggests reminders and actions based on the schedule. It also combines an emotion engine that recognizes the user's emotions. The system mainly consists of the following components:

[0185] 1. Means of receiving schedule information from users

[0186] 2. A method for analyzing schedule information and extracting the date, time, location, and content of the schedule

[0187] 3. How to save the extracted schedule information

[0188] 4. A way to suggest reminders and actions based on saved schedule information

[0189] 5. A way to notify users of reminders and suggested actions

[0190] 6. Emotion engine that recognizes user emotions

[0191] 7. A way to collect user feedback and improve the accuracy of reminders and actions

[0192] Receiving input from users

[0193] Users enter their schedule information using chat-style text messages or screenshots—for example, "I have a meeting in Shinjuku next Monday at 3 p.m."—or take a screenshot of a map of Shinjuku Station. The device converts this data into the appropriate format and sends it to the server.

[0194] Analysis and extraction of schedule information

[0195] The server analyzes text messages and screenshots received from the device. In the case of text data, natural language processing technology is used to extract the date, time, location, and content of the appointment. For example, from the text "There will be a meeting in Shinjuku at 3 PM next Monday," the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting" is extracted. In the case of screenshots, optical character recognition (OCR) technology is used to extract the text information and perform the same analysis.

[0196] Save schedule information

[0197] The extracted schedule information is stored in a database by the server, and is also added to the user's calendar. For example, if the user uses Google Calendar, the event is added using the Google Calendar API.

[0198] Emotion recognition by emotion engine

[0199] The server is equipped with an emotion engine that analyzes input data from users to determine their emotional state. Emotions such as positive, negative, and neutral are extracted from text data. For example, a message such as "I'm busy, but I'll do my best" can be used to determine a positive emotion, while a message such as "I'm tired" can be used to determine a negative emotion.

[0200] Reminders and suggested actions

[0201] The server suggests reminders and actions based on the saved schedule information and the emotional state determined by the emotion engine. If the emotional state is positive, it suggests a motivating reminder, and if it is negative, it includes an encouraging message. For example, the day before a meeting, it might remind you, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0202] Reminders and action notifications

[0203] The server sends the suggested reminders and actions to the device and displays them to the user. A message is sent using the LINE API, so that a notification such as "There's a meeting in Shinjuku soon. Do you have the necessary materials?" is sent one hour before the meeting.

[0204] Gathering feedback and improving accuracy

[0205] Users provide feedback on reminders and actions. User feedback is entered through chat and collected by the server. This feedback is used to improve the accuracy of future reminders and action suggestions.

[0206] Specific examples

[0207] Example 1: Chat-style schedule entry and emotion recognition

[0208] A user types "I have a meeting in Shinjuku next Monday at 3 PM" into LINE, and then adds "I'm busy, but I'll do my best." The device sends this to the server, which extracts the schedule information and uses the emotion engine to recognize "positive emotions." Based on this, an event is added to Google Calendar, and a positive reminder is sent the day before the meeting: "I have a meeting in Shinjuku tomorrow. Do your best!"

[0209] Example 2: Schedule entry and emotion recognition using screenshots

[0210] The user takes a screenshot of a map of Shinjuku Station on their device and adds the message "I'm tired." The device then sends this to the server, which uses OCR to extract "Shinjuku Station," and the emotion engine recognizes "negative emotion." Based on this, an event is added to Google Calendar, and a notification is sent one hour before the meeting saying, "There's a meeting in Shinjuku soon. Please relax and do your best."

[0211] In this way, the present invention, which combines an emotion engine, can provide appropriate reminders and action suggestions according to the user's emotional state, thereby providing more personalized schedule management.

[0212] The processing flow will be explained below.

[0213] Step 1:

[0214] Users enter appointment information using chat-style text messages or screenshots, such as "I have a meeting in Shinjuku next Monday at 3 PM" or by taking a screenshot of a map of Shinjuku Station.

[0215] Step 2:

[0216] The device converts the input text message or screenshot into an appropriate format and sends it to the server. For example, it uses the LINE API to send text data or image data.

[0217] Step 3:

[0218] The server analyzes the data received from the device. In the case of text data, it uses natural language processing (NLP) technology to extract the date, time, location, and content of the appointment. For example, from "There will be a meeting in Shinjuku at 3:00 PM next Monday," it extracts the information "Date and time: 3:00 PM next Monday," "Location: Shinjuku," and "Content: Meeting."

[0219] Step 4:

[0220] In the case of image data, the server uses optical character recognition (OCR) technology to extract text information from the image and analyze it. For example, it can extract "Shinjuku Station" from a screenshot of a map of Shinjuku Station.

[0221] Step 5:

[0222] The server saves the extracted event information in a database and adds it to the user's calendar, for example, by using the Google Calendar API to add the event to Google Calendar.

[0223] Step 6:

[0224] The server uses an emotion engine to analyze the user's input data and determine their emotional state. Emotions such as positive, negative, and neutral are extracted from the text data. For example, a message such as "I'm busy, but I'll do my best" can be used to determine a positive emotion.

[0225] Step 7:

[0226] The server suggests reminders and actions based on the stored schedule information and emotional state. If the emotional state is positive, it suggests motivating reminders, and if it is negative, it includes encouraging messages. For example, the day before a meeting, it might remind the user, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0227] Step 8:

[0228] The server notifies the device of suggested reminders and actions. It also uses the LINE API to send messages to users. For example, it sends a notification one hour before a meeting saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0229] Step 9:

[0230] Users provide feedback on reminders and actions, which is entered through chat and sent from the device to the server.

[0231] Step 10:

[0232] The server updates the AI ​​model based on the collected feedback, improving the accuracy of future reminders and action suggestions, and providing a more personalized service.

[0233] In this way, the present invention, which combines an emotion engine, can provide appropriate reminders and action suggestions according to the user's emotional state, thereby providing more personalized schedule management.

[0234] Example 2

[0235] 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."

[0236] In today's busy lifestyles, it is extremely important for users to efficiently manage their schedules and receive reminders and actions based on those schedules. However, existing systems do not take the user's emotional state into account when managing schedules, and as a result, they are unable to provide appropriate reminders and actions to users. In addition, there is a lack of an intuitive way to input schedules through chat or image data, which results in low usability.

[0237] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content of the schedule, means for saving the extracted schedule information, means for proposing reminders and actions based on the saved schedule information, means for notifying the user of the suggested reminders and actions, means for recognizing the emotional state of the user based on data input by the user, and means for adjusting the content of the reminders and actions based on the emotional state. This makes it possible to suggest appropriate reminders and actions according to the user's emotional state, thereby realizing more personalized schedule management. In addition, usability can be improved by intuitive schedule input means using chat format or image data.

[0238] The "means for receiving schedule information from the user" has the function of receiving schedule information in the form of a text message or image data entered by the user and transmitting that data to the server.

[0239] "Means for analyzing schedule information and extracting the date, time, location, and content of the schedule" refers to a function that uses natural language processing technology and optical character recognition technology to identify and extract items such as the date, time, location, and content from the input schedule information.

[0240] The "means for saving the extracted schedule information" is a means having a function for saving the extracted schedule information in a database or a calendar service.

[0241] The "means for suggesting reminders and actions" has the function of generating and suggesting appropriate reminders and actions to the user based on the saved schedule information.

[0242] The "means for notifying the user of reminders and suggested actions" has the function of sending and displaying generated reminders and suggested actions on the user's device.

[0243] "Means for collecting feedback from users and improving the accuracy of reminders and actions" refers to a function that receives feedback provided by users, analyzes it, and improves the accuracy of reminders and actions.

[0244] An "emotion recognition means" is a device that has the function of recognizing an emotional state based on user input data using natural language processing and machine learning technology.

[0245] The "means for adjusting the content of reminders and actions based on emotional state" has the function of changing the content of reminders and actions according to the recognized emotional state and making more appropriate suggestions to the user.

[0246] "Text data" refers to character information entered in chat format.

[0247] "Image data" refers to information entered in the form of an image, such as a screenshot or photograph.

[0248] Optical character recognition (OCR) is a technology that analyzes characters in image data and extracts them as text information.

[0249] This invention is a system that allows users to easily input schedule information, and suggests reminders and actions based on that schedule, and also combines it with an emotion engine that recognizes the user's emotions. The main components of the system are as follows:

[0250] 1. Means of receiving schedule information from users

[0251] Users enter schedule information using chat-style text messages or screenshots. For example, a user might type, "I have a meeting in Shinjuku next Monday at 3 PM," or take a screenshot of a map of Shinjuku Station. The device receives this input data, converts it into an appropriate format, and sends it to the server. By utilizing the LINE API, text messages can be received, and text information can be extracted from screenshots using OCR technology such as PyTesseract.

[0252] 2. Analysis of schedule information

[0253] The server analyzes the received text messages and screenshots. For text data, natural language processing technology (e.g., SpaCy) is used to extract the date, time, location, and content of the event. For screenshot data, optical character recognition technology (e.g., Tesseract) is used to extract character information and perform the same analysis.

[0254] 3. Save event information

[0255] The server saves the extracted schedule information in a database. It also adds this schedule information to the user's calendar using the Google Calendar API. For example, event information such as "Meeting in Shinjuku at 3:00 PM next Monday" is reflected in the calendar.

[0256] 4. Emotion recognition

[0257] The emotion engine built into the server analyzes input data from users to determine their emotional state. The emotion engine uses natural language processing libraries such as TextBlob to extract emotions such as positive, negative, and neutral from text data. For example, a message such as "I'm busy, but I'll do my best" will determine a positive emotion, while a message such as "I'm tired" will determine a negative emotion.

[0258] 5. Reminders and action suggestions

[0259] The server suggests reminders and actions based on the stored schedule information and the emotional state determined by the emotion engine. If the emotional state is positive, it suggests a motivating reminder, and if it is negative, it includes an encouraging message. For example, the day before a meeting, it might send a reminder such as, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0260] 6. Reminders and Action Notifications

[0261] The server notifies the device of the suggested reminders and actions. It uses the LINE API to send messages so that users receive a notification one hour before the meeting, such as "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0262] 7. Gather feedback and improve accuracy

[0263] Users provide feedback on reminders and actions. The feedback is entered through chat and collected by the server. The collected feedback is used to improve the accuracy of future reminders and action suggestions.

[0264] Examples of concrete examples and prompts

[0265] Example 1: Schedule entry in chat format and emotion recognition

[0266] 1. A user types in LINE, "I have a meeting in Shinjuku next Monday at 3pm," and then sends an additional message saying, "I'm busy, but I'll do my best."

[0267] 2. The device sends this to the server.

[0268] 3. The server extracts the schedule information and the emotion engine recognizes "positive emotions."

[0269] 4. The server adds an event to Google Calendar and sends a positive reminder the day before the meeting: "There's a meeting in Shinjuku tomorrow. Good luck!"

[0270] Example 2: Schedule entry and emotion recognition using screenshots

[0271] 1. A user takes a screenshot of a map of Shinjuku Station on their device and sends the message "I'm tired."

[0272] 2. The device sends this to the server.

[0273] 3. The server uses OCR to extract "Shinjuku Station" and the emotion engine recognizes "negative emotion."

[0274] 4. The server adds the event to Google Calendar and notifies you one hour before the meeting, saying, "There's a meeting in Shinjuku soon. Relax and do your best."

[0275] Example prompt sentence:

[0276] Enter "I have a meeting in Shinjuku next Monday at 3pm."

[0277] Take a screenshot of a map of Shinjuku Station and type "tired."

[0278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0279] Step 1: Enter event information from the user

[0280] A user inputs schedule information as a text message. For example, a user might use the LINE app to input, "I have a meeting in Shinjuku next Monday at 3 PM." The text message and image data are sent to the device as input data. The device then converts the data into an appropriate format and sends it to the server.

[0281] Specifically, it receives text messages using the LINE API, extracts text data from screenshots using PyTesseract, and sends the converted data to the server.

[0282] Step 2: Analyze the schedule information

[0283] When the server receives a text message, it analyzes it using natural language processing technology (e.g., SpaCy). It extracts the date, time, location, and content from the text message received as input data. In the case of image data, it also uses optical character recognition technology (e.g., Tesseract) to extract and analyze the text information.

[0284] Specifically, from a text message such as "There will be a meeting in Shinjuku next Monday at 3 PM," SpaCy is used to extract "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." Additionally, Tesseract is used to extract the text information for "Shinjuku Station" from a screenshot.

[0285] Step 3: Save the event information

[0286] The server stores the extracted event information in a database, including the extracted date, time, location, and content, and also adds the event information to the user's calendar using the Google Calendar API.

[0287] Specifically, the Google Calendar API is used to add event information such as "Meeting in Shinjuku at 3pm next Monday" to the user's Google Calendar.

[0288] Step 4: Emotion Recognition

[0289] The server uses an emotion engine (e.g., TextBlob) to determine the emotional state of the user from the input data. It analyzes the text messages received as input data and extracts emotions such as positive, negative, or neutral.

[0290] In terms of specific actions, the message "I'm busy, but I'll do my best" is perceived as a positive emotion, while the message "I'm tired" is perceived as a negative emotion.

[0291] Step 5: Reminders and suggested actions

[0292] The server then suggests reminders and actions based on the stored schedule information and the results of emotion recognition. The suggestions are tailored to the user's emotional state: positive emotions include motivational reminders, and negative emotions include encouraging messages.

[0293] Specifically, if the emotion is positive, it generates a reminder such as "There is a meeting in Shinjuku tomorrow. Do your best!", and if the emotion is negative, it generates a reminder such as "There is a meeting in Shinjuku soon. Relax and do your best."

[0294] Step 6: Reminders and Action Notifications

[0295] The server notifies the device of the generated reminders and actions by sending messages using the LINE API, notifying the user at the appropriate time (for example, one hour before the meeting).

[0296] Specifically, the message "There's a meeting coming up in Shinjuku. Do you have the necessary materials?" is sent using the LINE API.

[0297] Step 7: Gather feedback and refine

[0298] Users provide feedback on the reminders and actions they receive. The server collects and stores the feedback in a database. The collected feedback is used to improve the accuracy of future reminders and action suggestions.

[0299] Specifically, users send feedback via LINE chat, such as "The reminder was too early," and this data is collected and applied to the machine learning model to improve the accuracy of future suggestions.

[0300] (Application example 2)

[0301] 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."

[0302] In modern society, busy users find it difficult to manage their schedules and receive reminders and action suggestions that are appropriate for their emotional state. In particular, there are limited means to receive timely, personalized information and support while shopping in physical stores. This leads to a decline in the quality of the shopping experience and a decrease in user satisfaction. To address this issue, there is a need to propose a system that combines user schedule management and emotion recognition to make users' lives more comfortable.

[0303] 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.

[0304] In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content of the schedule, means for saving the extracted schedule information, means for suggesting reminders and actions based on the saved schedule information, means for notifying the user of the suggested reminders and actions, means including an emotion recognition engine for analyzing the emotional state of the user and adjusting the reminders and actions based on the analyzed emotional state, means for collecting feedback from the user and improving the accuracy of the reminders and actions, means for notifying the user using a location information service when the user approaches a place to be visited, and means for providing product details and campaign information related to the place to be visited. This makes it possible to provide personalized reminders and actions according to the user's emotional state and improve the shopping experience.

[0305] "User" refers to a person who uses the system to input schedule information and receive reminders and suggested actions.

[0306] "Schedule information" refers to information indicating a user's future schedule, including specific dates, times, locations, and contents.

[0307] The "date, time, place, and content of the appointment" refers to the specific date, time, place, and details of the appointment extracted from the appointment information.

[0308] "Remind" refers to a notification or alert that reminds a user of an appointment.

[0309] "Action" refers to specific actions or suggestions that users should take based on schedule information and emotional state.

[0310] An "emotion recognition engine" refers to a system that analyzes input data from a user and determines their emotional state.

[0311] "Feedback" refers to the ratings and comments you provide in response to reminders and suggested actions.

[0312] "Location information services" refers to services that use GPS, Wi-Fi, etc. to identify the user's location and provide information based on that location.

[0313] "Notification" means a message or alert intended to inform the User of a reminder or suggested action.

[0314] "Product details and campaign information" refers to information about products that can be obtained at the locations or stores that users visit, as well as information about current special offers and discounts.

[0315] MODE FOR CARRYING OUT THE INVENTION

[0316] This invention provides a system that allows users to easily input schedule information, suggests reminders and actions based on the schedule, and recognizes the user's emotional state. Specific embodiments are described below. The system of the present invention is composed of the following elements:

[0317] 1. Means of receiving schedule information from users

[0318] Users use a smartphone application to input schedule information. This information is entered as chat-style text data or image data (screenshots). For example, a user might enter a message such as, "I plan to go to ABC Department Store at 2:00 PM on Saturday. I'm busy, but I'm looking forward to shopping."

[0319] 2. Analysis and extraction of schedule information

[0320] The server analyzes text messages and image data received from smartphone devices. For text data, the server uses the Google Cloud Natural Language API to analyze and extract the date, time, location, and content of the event. For image data, the server uses the Google Cloud Vision API to extract text information from the image using OCR (optical character recognition) technology.

[0321] 3. How to save schedule information

[0322] The extracted schedule information is stored in a database by the server, and at the same time, the schedule information is added to the user's calendar (e.g., Google Calendar) using the Google Calendar API.

[0323] 4. Means of determining emotional state using an emotion recognition engine

[0324] The server is equipped with an emotion recognition engine that analyzes input data from users to determine their emotional state. For example, it can recognize positive emotions from a message such as "I'm busy, but I'm looking forward to shopping."

[0325] 5. Reminders and action suggestions

[0326] The server suggests reminders and actions based on the stored schedule information and the analyzed emotional state, for example, "Enjoy a great shopping experience!" before a visit.

[0327] 6. Reminders and action notifications

[0328] Suggested reminders and actions are sent to the smartphone using Firebase Cloud Messaging. For example, when a user approaches a designated visit location, location services can be used to notify the user that "It's almost time to visit ABC Department Store."

[0329] 7. Feedback collection and refinement methods

[0330] Users provide feedback on reminders and actions. This feedback is entered through chat and collected by the server. This feedback information is used to improve the accuracy of future reminders and action suggestions.

[0331] Specific examples

[0332] Here's an example where a user enters a shopping itinerary and receives a reminder:

[0333] A user types on LINE, "I plan to go to ABC Department Store on Saturday at 2 PM. I'm busy, but I'm looking forward to shopping." The smartphone device sends this to the server, which extracts the schedule information and uses an emotion recognition engine to recognize "positive emotions." Based on this, an event is added to Google Calendar and a positive reminder is sent before the user arrives, saying, "Enjoy a great shopping experience!"

[0334] Prompt Sentence Examples

[0335] "I'm planning to go to ABC Department Store on Saturday at 2pm. I'll be busy, but I'm looking forward to shopping."

[0336] In this way, the system of the present invention can provide appropriate reminders and action suggestions based on the user's emotional state, providing a more personalized shopping experience.

[0337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0338] Step 1:

[0339] Terminal - Receiving schedule information from users

[0340] A user inputs chat-style text data or screenshots into a smartphone app. For example, they send a message saying, "I plan to go to ABC department store at 2 PM on Saturday. I'm busy, but I'm looking forward to shopping." The input data is sent to the server via the app. The server then obtains the schedule information.

[0341] Input: Text data entered by the user or screenshot

[0342] Output: Appointment information sent to the server

[0343] Specific operation: The smartphone application provides an input UI, receives text messages and images, and sends them to the server.

[0344] Step 2:

[0345] Server - Schedule information analysis

[0346] The server uses Google Cloud Natural Language API to parse the received text message and extract the date, time, location, and content of the appointment. For image data, Google Cloud Vision API is used to extract text information using OCR technology.

[0347] Input: Text data or screenshot sent to the server

[0348] Output: Extracted date, time, location, and content information

[0349] Specific operation: The server passes the received data to the analysis API and obtains the extracted data.

[0350] Step 3:

[0351] Server - Save schedule information

[0352] The extracted event information is stored in a database by the server, and the information is simultaneously added to the user's calendar (such as Google Calendar). This process uses the Google Calendar API.

[0353] Input: Extracted date, time, location, and content information

[0354] Output: Event information saved in the database, event information added to the calendar

[0355] Specific behavior: The server saves the data to the database and makes a request to the Google Calendar API.

[0356] Step 4:

[0357] Server - Emotion recognition engine analyzes emotional states

[0358] The server uses an emotion recognition engine to analyze the emotional state of the user's message, for example, recognizing a positive emotion from the message "I'm busy, but I'm looking forward to shopping."

[0359] Input: User message text

[0360] Output: Emotional state, such as positive or negative

[0361] What happens: The server passes the text to the emotion recognition engine and gets the results.

[0362] Step 5:

[0363] Server - Reminders and action suggestions

[0364] Based on the stored schedule information and the analyzed emotional state, the server will suggest reminders and actions, for example, generating a message like "Enjoy a great shopping experience!"

[0365] Input: Saved schedule information, analyzed emotional state

[0366] Output: Suggested reminders and actions

[0367] Specific operation: The server generates a message based on the data and determines the notification content.

[0368] Step 6:

[0369] Server - Reminders and action notifications

[0370] Suggested reminders and actions are sent to your smartphone using Firebase Cloud Messaging. For example, when the scheduled time approaches, you might get a notification saying, "It's almost time for your visit to ABC Department Store."

[0371] Input: Suggested reminder or action

[0372] Output: Notification sent to the user's smartphone

[0373] What happens: The server sends a notification message through Firebase Cloud Messaging.

[0374] Step 7:

[0375] Terminal - Collecting Feedback

[0376] Users can provide feedback on reminders and actions, such as rating the reminder as "helpful." This feedback is sent back to the server and used to improve the service in the future.

[0377] Input: User-provided feedback

[0378] Output: Feedback information sent to the server

[0379] What happens: The smartphone app receives the feedback and sends it to the server.

[0380] The above is a specific process flow for implementing the present invention. This system allows users to receive personalized reminders and actions, improving their shopping and schedule management experience.

[0381] 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.

[0382] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0383] 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.

[0384] [Second embodiment]

[0385] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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."

[0397] An embodiment of the present invention will be described.

[0398] Overall system overview

[0399] This system allows users to easily input schedule information and suggests reminders and actions based on that schedule. The system mainly consists of the following components:

[0400] 1. Means of receiving schedule information from users

[0401] 2. A method for analyzing schedule information and extracting the date, time, location, and content of the schedule

[0402] 3. How to save the extracted schedule information

[0403] 4. A way to suggest reminders and actions based on saved schedule information

[0404] 5. How to notify users of reminders and actions

[0405] 6. A way to collect user feedback and improve the accuracy of reminders and actions

[0406] Receiving input from users

[0407] Users enter schedule information through chat text or screenshots. For example, if a user enters "I have a meeting in Shinjuku next Monday at 3 PM" into LINE chat, this information is received by the system. The device then forwards the received data to the server.

[0408] Analysis and extraction of schedule information

[0409] The server analyzes chat-style text and screenshots received from users. In the case of text, natural language processing technology is used to extract the date, time, location, and content of the appointment. For example, from the text "There is a meeting in Shinjuku at 3 PM next Monday," the information extracted is "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." In the case of image data, optical character recognition (OCR) technology is used to extract character information and analyze it in the same way.

[0410] Save schedule information

[0411] The extracted event information is stored in a database by the server. It is also added to the user's calendar. For example, if you use Google Calendar, you can add an event using the Google Calendar API.

[0412] Reminders and suggested actions

[0413] The server periodically scans the saved schedule information and suggests necessary reminders and actions. It uses an AI model to analyze past data and suggest optimal actions for the user. For example, if a meeting in Shinjuku is coming up, a reminder such as "There will be a meeting in Shinjuku tomorrow. Please make sure you don't forget any necessary documents" will be sent the day before the meeting.

[0414] Reminders and action notifications

[0415] The server notifies the device of the suggested reminders and actions and displays them to the user. Messages can be sent using the LINE API. For example, an hour before a meeting, a notification will be sent via LINE saying, "There will be a meeting in Shinjuku soon. Do you have the necessary materials?"

[0416] Gathering feedback and improving accuracy

[0417] The system can improve its accuracy by providing feedback on reminders and actions. Feedback is entered through chat and collected by the server. The collected feedback is then reflected in the AI ​​model to help suggest future reminders and actions.

[0418] Specific examples

[0419] Example 1: Entering schedule in chat format

[0420] A user types "I have a meeting in Shinjuku next Monday at 3 PM" into LINE. The device sends this message to the server, which extracts "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." Based on this, an event is added to Google Calendar and a reminder is sent the day before the meeting.

[0421] Example 2: Entering schedule information using screenshots

[0422] A user takes a screenshot of a map of Shinjuku Station on their device and sends it to the system. The server uses OCR technology to extract "Shinjuku Station" and generates related event information. This information is added to Google Calendar, and a related reminder is sent on the scheduled date.

[0423] In this way, the present invention provides a system that allows users to efficiently manage their schedules, and provides appropriate reminders and action suggestions when needed.

[0424] The processing flow will be explained below.

[0425] Step 1:

[0426] Users enter schedule information using chat-style text messages or screenshots. For example, a user might type "I have a meeting in Shinjuku next Monday at 3 PM" into a LINE chat, or take a screenshot of a map of Shinjuku Station.

[0427] Step 2:

[0428] The device processes received text messages and screenshots. Specifically, it converts input data into an appropriate format and forwards it to the server. For example, this includes sending text data using the LINE API.

[0429] Step 3:

[0430] The server analyzes text messages and screenshots received from the device. For text data, natural language processing (NLP) is used to extract the date, time, location, and content of the appointment. For example, from "I have a meeting in Shinjuku at 3 PM next Monday," the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting" are extracted. For screenshots, optical character recognition (OCR) is used to extract the text information within the image and analyze it in the same way.

[0431] Step 4:

[0432] The server saves the extracted event information in a database. It also adds this event information to the user's calendar. For example, if you use Google Calendar, you can add an event using the Google Calendar API. This allows you to centrally manage your events in the calendar app you use.

[0433] Step 5:

[0434] The server uses the saved schedule information to send reminders and suggest actions. It uses an AI model to analyze past data and determine what notifications to send at what time. For example, the day before a meeting, it might suggest a reminder such as, "There will be a meeting in Shinjuku tomorrow. Please make sure you don't forget any necessary documents."

[0435] Step 6:

[0436] The server notifies the device of the suggested reminders and actions. It also uses the LINE API to send messages to users. For example, a notification can be sent via LINE one hour before a meeting saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0437] Step 7:

[0438] Users provide feedback on reminders and actions. User feedback is entered through chat and sent from the device to the server. This feedback is used to improve the accuracy of future reminders and action suggestions by the system.

[0439] Step 8:

[0440] The server updates the AI ​​model based on the collected feedback, improving the accuracy of future reminders and action suggestions, and providing a more personalized service.

[0441] This completes the entire process flow, allowing users to efficiently manage their schedules and receive appropriate reminders and actions when needed.

[0442] Example 1

[0443] 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."

[0444] Conventional schedule management systems have the problem that users manually input their schedules, and subsequent reminders and action suggestions are not fully optimized. Additionally, there is an insufficient mechanism for efficiently collecting user feedback and improving the accuracy of the entire system, resulting in low user convenience.

[0445] 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.

[0446] In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content, means for saving the extracted schedule information in data storage, means for suggesting reminders and actions based on the saved schedule information, means for notifying the user of the reminders and action suggestions, and means for collecting feedback from the user and improving the accuracy of the reminders and action suggestions. This allows users to easily input schedule information and efficiently receive reminders and action suggestions. In addition, the accuracy of the system can be continuously improved through feedback.

[0447] "User" means any individual or entity that uses the System to enter appointment information and receive reminders and suggestions.

[0448] "Schedule information" refers to information about the date, time, location, and content of a schedule entered by a user.

[0449] "Chat-format text data" refers to text-format message data sent and received via chat applications or messaging platforms.

[0450] "Image data" refers to visual information stored in an image file format, such as a screenshot or photograph.

[0451] "Optical character recognition technology" refers to technology for extracting character information from image data.

[0452] "Data storage" refers to a storage device used to store and manage data for long periods of time in a computer system.

[0453] "Remind" refers to the act of notifying the user of the contents of a schedule again based on the saved schedule information.

[0454] "Action suggestion" refers to the act of suggesting appropriate actions and preparations to the user based on the saved schedule information.

[0455] "Feedback" refers to the act of a user providing the system with their opinion or evaluation of a reminder or suggested action.

[0456] The following describes an embodiment of the present invention, including an overview of the system, specific examples of the hardware and software used, a process flow, and specific examples.

[0457] Overall system overview

[0458] This invention is a system that allows users to easily input schedule information and provides reminders and action suggestions based on that schedule. This system mainly consists of the following components:

[0459] 1. Means of receiving schedule information from users

[0460] 2. A method for analyzing schedule information and extracting the date, time, location, and content

[0461] 3. A means for saving the extracted schedule information to data storage

[0462] 4. A way to suggest reminders and actions based on saved schedule information

[0463] 5. Means of notifying users of reminders and actions

[0464] 6. Gathering user feedback to improve the accuracy of reminders and actions

[0465] Hardware and software used in the system

[0466] Terminal: The device used by the user, such as a smartphone or tablet, is equipped with a chat application.

[0467] Server: A server for data analysis, storage, and notification. It has an appropriate API for connecting to the database.

[0468] Natural Language Processing (NLP) engine: Used to extract date, time, location, and content from user-entered text data.

[0469] Optical Character Recognition (OCR) technology: Used to extract text information from image data such as screenshots.

[0470] Data Storage: The database used to store the extracted appointment information.

[0471] Notification system: Uses Google Calendar API and LINE API to notify users of reminders and suggestions.

[0472] Specific examples

[0473] Example 1: Chat-style text data

[0474] A user types "I have a meeting in Shinjuku next Monday at 3 PM" on LINE. This information is received by the device and forwarded to the server. The server uses an NLP engine to extract "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." The extracted information is saved in data storage and added to Google Calendar. The day before the meeting, a reminder notification is sent to the user via LINE stating, "There is a meeting in Shinjuku tomorrow. Please don't forget to bring the necessary materials."

[0475] Example 2: Screenshot image data

[0476] The user takes a screenshot of a map of Shinjuku Station on their device and sends it to the system. The server uses OCR technology to extract "Shinjuku Station" and generates related appointment information. The generated information is saved in data storage and added to Google Calendar. On the scheduled day, a notification is sent to the user via LINE saying, "There is a meeting at Shinjuku Station today. Please be careful not to leave anything behind."

[0477] Prompt Sentence Examples

[0478] The prompt for example 1's chat-style schedule entry is as follows:

[0479] "Please tell me how to enter an appointment in LINE such as 'I have a meeting in Shinjuku next Monday at 3 PM' and have the system recognize it and send the appropriate reminder."

[0480] In this way, the present invention allows users to easily manage their schedules and receive appropriate reminders and action suggestions when needed. Furthermore, by collecting feedback, the accuracy of the system can be continuously improved.

[0481] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0482] Step 1:

[0483] Users can enter schedule information via chat on LINE or via screenshots. The information entered can include text such as "I have a meeting in Shinjuku at 3:00 PM next Monday" or a screenshot of a map of Shinjuku Station.

[0484] Step 2:

[0485] The device receives chat-style text data and screenshots entered by the user, then forwards the received data to the server via an HTTP request. The input data is in text or image format and is sent as the payload of the request to the server.

[0486] Step 3:

[0487] The server analyzes the received data. In the case of text data in chat format, the server uses a natural language processing (NLP) engine to extract the date, time, location, and content. For example, from the text "There is a meeting in Shinjuku at 3 PM next Monday," the server extracts "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." In the case of image data, the server uses optical character recognition (OCR) technology to extract character information and analyzes it in the same way.

[0488] Step 4:

[0489] The server saves the extracted schedule information in data storage. Specifically, it saves the analysis results in a database and then uses the Google Calendar API to add an event to the user's calendar. For example, the extracted information "Date and time: next Monday at 3:00 PM," "Location: Shinjuku," and "Content: Meeting" is saved to Google Calendar.

[0490] Step 5:

[0491] The server prepares to send reminders and suggest actions based on the saved schedule information. It scans the database at regular intervals and generates reminder notifications, such as "There's a meeting in Shinjuku tomorrow. Please don't forget to bring the necessary documents." It uses an AI model to create optimal action suggestions based on past data.

[0492] Step 6:

[0493] The server notifies the user of the generated reminders and action suggestions. Using the LINE API or other notification systems, the server sends reminders and action suggestions to the device, which then displays them to the user. For example, the day before a meeting, the server sends a LINE message saying, "There will be a meeting in Shinjuku tomorrow. Don't forget to bring the necessary documents."

[0494] Step 7:

[0495] Users provide feedback on reminders and suggested actions. For example, the provided feedback may include opinions such as "The reminder was too late." The device sends this feedback to the server, which collects it. The collected feedback is reflected in the AI ​​model and used to improve the accuracy of future reminders and suggested actions.

[0496] (Application example 1)

[0497] 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."

[0498] In modern society, users lead busy lives, making schedule management a major issue. It is particularly difficult to check or change schedules while driving, making schedule management a key priority. In these circumstances, a system that can efficiently and safely manage schedule information is needed.

[0499] 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.

[0500] In this invention, the server includes a means for analyzing the user's voice input and extracting schedule information, a means for notifying the user of the schedule information and reminders through the vehicle's in-vehicle system, and a means for receiving the schedule information from the user, thereby enabling the user to manage their schedule without using both hands.

[0501] "User" means a person who uses this system.

[0502] "Schedule information" refers to information including details of a schedule, such as date, time, location, and content.

[0503] "Voice input" means that a user provides information to a system by voice.

[0504] "Analysis" is the process of understanding input data and converting it into meaningful information.

[0505] "Storage" means recording the extracted information in a database or the like for long-term retention.

[0506] "Remind" means sending a notification to remind the user of a schedule.

[0507] "Action" means proposing specific actions or instructions.

[0508] "Notification" means sending a message to inform the user of information.

[0509] "Feedback" refers to the evaluations and opinions that users provide to the system.

[0510] "In-vehicle system" means an information processing and display system installed in a vehicle.

[0511] An embodiment of the present invention will be described. This system receives schedule information from a user via voice, analyzes it, saves it, suggests reminders and actions, notifies the user, and collects feedback. Specific embodiments are described below.

[0512] The server uses a speech recognition engine (SpeechRecognition library) to analyze the user's voice input. This speech recognition process converts the voice data into textual appointment information. Natural language processing techniques are then used to parse the text into appointment details such as date, time, location, and content.

[0513] The analyzed schedule information is stored in a database system such as AWS's DynamoDB, which is then periodically scanned by an AI model on the server, which analyzes past data to suggest optimal reminders and actions.

[0514] The in-vehicle information processing system notifies users of saved schedule information and suggested reminders, and can send reminder notifications to users' smartphones using messaging services such as the LINE API.

[0515] User feedback is used to collect ratings and opinions on reminders and actions, allowing the AI ​​model to continuously learn and improve the accuracy of future suggestions.

[0516] Specific examples

[0517] For example, if a user says in a car, "I have a meeting in Shinjuku next Monday at 3 p.m.", the speech recognition engine converts this into text, and the server analyzes the schedule information. This schedule information is then saved in a database, and a reminder notification, "I have a meeting in Shinjuku tomorrow," is sent to the user's smartphone the day before the meeting.

[0518] Prompt Sentence Examples

[0519] "Analyze the user's statement, 'I have a meeting in Shinjuku next Monday at 3 PM,' and extract the date, time, location, and content."

[0520] This system ensures safety on board while enabling efficient schedule management. By utilizing this system, passengers can check their schedules and receive reminders without using their hands.

[0521] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0522] Step 1:

[0523] The user inputs voice data in the car. The user's speech (e.g., "I have a meeting in Shinjuku at 3:00 PM next Monday") is input to the terminal via a microphone in the car. The terminal then transmits this voice data to the server.

[0524] Step 2:

[0525] The server uses a speech recognition engine (SpeechRecognition library) to convert the received voice data into text data. The speech recognition engine processes the voice data and outputs it as a string (text). For example, the voice data may be recognized as "There will be a meeting in Shinjuku next Monday at 3:00 PM."

[0526] Step 3:

[0527] The server uses natural language processing (NLP) technology to analyze text data. It performs a process to extract date, time, location, and content from the input text. For example, from the text "There will be a meeting in Shinjuku at 3 PM next Monday," it extracts the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." The analysis results in structured data.

[0528] Step 4:

[0529] The server stores the extracted appointment information in a database system (e.g., AWS DynamoDB). The appointment information is stored persistently by adding structured data to DynamoDB. The stored information includes fields for date, time, location, and content.

[0530] Step 5:

[0531] The server periodically scans the saved schedule information. It uses an AI model to analyze the saved data and suggest appropriate reminders and actions. It calculates the optimal timing and content of reminders based on past data and user feedback. For example, the server suggests a reminder such as "There will be a meeting in Shinjuku tomorrow" the day before a meeting.

[0532] Step 6:

[0533] The server sends reminders and action notifications to the vehicle's in-vehicle system (e.g., infotainment system) or the user's smartphone. It uses a pre-configured notification method (e.g., LINE API) to send appropriate messages. For example, one hour before a meeting, it sends a notification message saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0534] Step 7:

[0535] Users receive notifications and provide feedback on reminders and actions. The feedback is sent to the server, where it is used as training data for the AI ​​model to improve the accuracy of future suggestions.

[0536] 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.

[0537] An embodiment of the present invention will be described.

[0538] Overall system overview

[0539] This system allows users to easily input schedule information, and suggests reminders and actions based on the schedule. It also combines an emotion engine that recognizes the user's emotions. The system mainly consists of the following components:

[0540] 1. Means of receiving schedule information from users

[0541] 2. A method for analyzing schedule information and extracting the date, time, location, and content of the schedule

[0542] 3. How to save the extracted schedule information

[0543] 4. A way to suggest reminders and actions based on saved schedule information

[0544] 5. A way to notify users of reminders and suggested actions

[0545] 6. Emotion engine that recognizes user emotions

[0546] 7. A way to collect user feedback and improve the accuracy of reminders and actions

[0547] Receiving input from users

[0548] Users enter their schedule information using chat-style text messages or screenshots—for example, "I have a meeting in Shinjuku next Monday at 3 p.m."—or take a screenshot of a map of Shinjuku Station. The device converts this data into the appropriate format and sends it to the server.

[0549] Analysis and extraction of schedule information

[0550] The server analyzes text messages and screenshots received from the device. In the case of text data, natural language processing technology is used to extract the date, time, location, and content of the appointment. For example, from the text "There will be a meeting in Shinjuku at 3 PM next Monday," the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting" is extracted. In the case of screenshots, optical character recognition (OCR) technology is used to extract the text information and perform the same analysis.

[0551] Save schedule information

[0552] The extracted schedule information is stored in a database by the server, and is also added to the user's calendar. For example, if the user uses Google Calendar, the event is added using the Google Calendar API.

[0553] Emotion recognition by emotion engine

[0554] The server is equipped with an emotion engine that analyzes input data from users to determine their emotional state. Emotions such as positive, negative, and neutral are extracted from text data. For example, a message such as "I'm busy, but I'll do my best" can be used to determine a positive emotion, while a message such as "I'm tired" can be used to determine a negative emotion.

[0555] Reminders and suggested actions

[0556] The server suggests reminders and actions based on the saved schedule information and the emotional state determined by the emotion engine. If the emotional state is positive, it suggests a motivating reminder, and if it is negative, it includes an encouraging message. For example, the day before a meeting, it might remind you, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0557] Reminders and action notifications

[0558] The server sends the suggested reminders and actions to the device and displays them to the user. A message is sent using the LINE API, so that a notification such as "There's a meeting in Shinjuku soon. Do you have the necessary materials?" is sent one hour before the meeting.

[0559] Gathering feedback and improving accuracy

[0560] Users provide feedback on reminders and actions. User feedback is entered through chat and collected by the server. This feedback is used to improve the accuracy of future reminders and action suggestions.

[0561] Specific examples

[0562] Example 1: Chat-style schedule entry and emotion recognition

[0563] A user types "I have a meeting in Shinjuku next Monday at 3 PM" into LINE, and then adds "I'm busy, but I'll do my best." The device sends this to the server, which extracts the schedule information and uses the emotion engine to recognize "positive emotions." Based on this, an event is added to Google Calendar, and a positive reminder is sent the day before the meeting: "I have a meeting in Shinjuku tomorrow. Do your best!"

[0564] Example 2: Schedule entry and emotion recognition using screenshots

[0565] The user takes a screenshot of a map of Shinjuku Station on their device and adds the message "I'm tired." The device then sends this to the server, which uses OCR to extract "Shinjuku Station," and the emotion engine recognizes "negative emotion." Based on this, an event is added to Google Calendar, and a notification is sent one hour before the meeting saying, "There's a meeting in Shinjuku soon. Please relax and do your best."

[0566] In this way, the present invention, which combines an emotion engine, can provide appropriate reminders and action suggestions according to the user's emotional state, thereby providing more personalized schedule management.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] Users enter appointment information using chat-style text messages or screenshots, such as "I have a meeting in Shinjuku next Monday at 3 PM" or by taking a screenshot of a map of Shinjuku Station.

[0570] Step 2:

[0571] The device converts the input text message or screenshot into an appropriate format and sends it to the server. For example, it uses the LINE API to send text data or image data.

[0572] Step 3:

[0573] The server analyzes the data received from the device. In the case of text data, it uses natural language processing (NLP) technology to extract the date, time, location, and content of the appointment. For example, from "There will be a meeting in Shinjuku at 3:00 PM next Monday," it extracts the information "Date and time: 3:00 PM next Monday," "Location: Shinjuku," and "Content: Meeting."

[0574] Step 4:

[0575] In the case of image data, the server uses optical character recognition (OCR) technology to extract text information from the image and analyze it. For example, it can extract "Shinjuku Station" from a screenshot of a map of Shinjuku Station.

[0576] Step 5:

[0577] The server saves the extracted event information in a database and adds it to the user's calendar, for example, by using the Google Calendar API to add the event to Google Calendar.

[0578] Step 6:

[0579] The server uses an emotion engine to analyze the user's input data and determine their emotional state. Emotions such as positive, negative, and neutral are extracted from the text data. For example, a message such as "I'm busy, but I'll do my best" can be used to determine a positive emotion.

[0580] Step 7:

[0581] The server suggests reminders and actions based on the stored schedule information and emotional state. If the emotional state is positive, it suggests motivating reminders, and if it is negative, it includes encouraging messages. For example, the day before a meeting, it might remind the user, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0582] Step 8:

[0583] The server notifies the device of suggested reminders and actions. It also uses the LINE API to send messages to users. For example, it sends a notification one hour before a meeting saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0584] Step 9:

[0585] Users provide feedback on reminders and actions, which is entered through chat and sent from the device to the server.

[0586] Step 10:

[0587] The server updates the AI ​​model based on the collected feedback, improving the accuracy of future reminders and action suggestions, and providing a more personalized service.

[0588] In this way, the present invention, which combines an emotion engine, can provide appropriate reminders and action suggestions according to the user's emotional state, thereby providing more personalized schedule management.

[0589] Example 2

[0590] 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."

[0591] In today's busy lifestyles, it is extremely important for users to efficiently manage their schedules and receive reminders and actions based on those schedules. However, existing systems do not take the user's emotional state into account when managing schedules, and as a result, they are unable to provide appropriate reminders and actions to users. In addition, there is a lack of an intuitive way to input schedules through chat or image data, which results in low usability.

[0592] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content of the schedule, means for saving the extracted schedule information, means for proposing reminders and actions based on the saved schedule information, means for notifying the user of the suggested reminders and actions, means for recognizing the emotional state of the user based on data input by the user, and means for adjusting the content of the reminders and actions based on the emotional state. This makes it possible to suggest appropriate reminders and actions according to the user's emotional state, thereby realizing more personalized schedule management. In addition, usability can be improved by intuitive schedule input means using chat format or image data.

[0593] The "means for receiving schedule information from the user" has the function of receiving schedule information in the form of a text message or image data entered by the user and transmitting that data to the server.

[0594] "Means for analyzing schedule information and extracting the date, time, location, and content of the schedule" refers to a function that uses natural language processing technology and optical character recognition technology to identify and extract items such as the date, time, location, and content from the input schedule information.

[0595] The "means for saving the extracted schedule information" is a means having a function for saving the extracted schedule information in a database or a calendar service.

[0596] The "means for suggesting reminders and actions" has the function of generating and suggesting appropriate reminders and actions to the user based on the saved schedule information.

[0597] The "means for notifying the user of reminders and suggested actions" has the function of sending and displaying generated reminders and suggested actions on the user's device.

[0598] "Means for collecting feedback from users and improving the accuracy of reminders and actions" refers to a function that receives feedback provided by users, analyzes it, and improves the accuracy of reminders and actions.

[0599] An "emotion recognition means" is a device that has the function of recognizing an emotional state based on user input data using natural language processing and machine learning technology.

[0600] The "means for adjusting the content of reminders and actions based on emotional state" has the function of changing the content of reminders and actions according to the recognized emotional state and making more appropriate suggestions to the user.

[0601] "Text data" refers to character information entered in chat format.

[0602] "Image data" refers to information entered in the form of an image, such as a screenshot or photograph.

[0603] Optical character recognition (OCR) is a technology that analyzes characters in image data and extracts them as text information.

[0604] This invention is a system that allows users to easily input schedule information, and suggests reminders and actions based on that schedule, and also combines it with an emotion engine that recognizes the user's emotions. The main components of the system are as follows:

[0605] 1. Means of receiving schedule information from users

[0606] Users enter schedule information using chat-style text messages or screenshots. For example, a user might type, "I have a meeting in Shinjuku next Monday at 3 PM," or take a screenshot of a map of Shinjuku Station. The device receives this input data, converts it into an appropriate format, and sends it to the server. By utilizing the LINE API, text messages can be received, and text information can be extracted from screenshots using OCR technology such as PyTesseract.

[0607] 2. Analysis of schedule information

[0608] The server analyzes the received text messages and screenshots. For text data, natural language processing technology (e.g., SpaCy) is used to extract the date, time, location, and content of the event. For screenshot data, optical character recognition technology (e.g., Tesseract) is used to extract character information and perform the same analysis.

[0609] 3. Save event information

[0610] The server saves the extracted schedule information in a database. It also adds this schedule information to the user's calendar using the Google Calendar API. For example, event information such as "Meeting in Shinjuku at 3:00 PM next Monday" is reflected in the calendar.

[0611] 4. Emotion recognition

[0612] The emotion engine built into the server analyzes input data from users to determine their emotional state. The emotion engine uses natural language processing libraries such as TextBlob to extract emotions such as positive, negative, and neutral from text data. For example, a message such as "I'm busy, but I'll do my best" will determine a positive emotion, while a message such as "I'm tired" will determine a negative emotion.

[0613] 5. Reminders and action suggestions

[0614] The server suggests reminders and actions based on the stored schedule information and the emotional state determined by the emotion engine. If the emotional state is positive, it suggests a motivating reminder, and if it is negative, it includes an encouraging message. For example, the day before a meeting, it might send a reminder such as, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0615] 6. Reminders and Action Notifications

[0616] The server notifies the device of the suggested reminders and actions. It uses the LINE API to send messages so that users receive a notification one hour before the meeting, such as "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0617] 7. Gather feedback and improve accuracy

[0618] Users provide feedback on reminders and actions. The feedback is entered through chat and collected by the server. The collected feedback is used to improve the accuracy of future reminders and action suggestions.

[0619] Examples of concrete examples and prompts

[0620] Example 1: Schedule entry in chat format and emotion recognition

[0621] 1. A user types in LINE, "I have a meeting in Shinjuku next Monday at 3pm," and then sends an additional message saying, "I'm busy, but I'll do my best."

[0622] 2. The device sends this to the server.

[0623] 3. The server extracts the schedule information and the emotion engine recognizes "positive emotions."

[0624] 4. The server adds an event to Google Calendar and sends a positive reminder the day before the meeting: "There's a meeting in Shinjuku tomorrow. Good luck!"

[0625] Example 2: Schedule entry and emotion recognition using screenshots

[0626] 1. A user takes a screenshot of a map of Shinjuku Station on their device and sends the message "I'm tired."

[0627] 2. The device sends this to the server.

[0628] 3. The server uses OCR to extract "Shinjuku Station" and the emotion engine recognizes "negative emotion."

[0629] 4. The server adds the event to Google Calendar and notifies you one hour before the meeting, saying, "There's a meeting in Shinjuku soon. Relax and do your best."

[0630] Example prompt sentence:

[0631] Enter "I have a meeting in Shinjuku next Monday at 3pm."

[0632] Take a screenshot of a map of Shinjuku Station and type "tired."

[0633] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0634] Step 1: Enter event information from the user

[0635] A user inputs schedule information as a text message. For example, a user might use the LINE app to input, "I have a meeting in Shinjuku next Monday at 3 PM." The text message and image data are sent to the device as input data. The device then converts the data into an appropriate format and sends it to the server.

[0636] Specifically, it receives text messages using the LINE API, extracts text data from screenshots using PyTesseract, and sends the converted data to the server.

[0637] Step 2: Analyze the schedule information

[0638] When the server receives a text message, it analyzes it using natural language processing technology (e.g., SpaCy). It extracts the date, time, location, and content from the text message received as input data. In the case of image data, it also uses optical character recognition technology (e.g., Tesseract) to extract and analyze the text information.

[0639] Specifically, from a text message such as "There will be a meeting in Shinjuku next Monday at 3 PM," SpaCy is used to extract "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." Additionally, Tesseract is used to extract the text information for "Shinjuku Station" from a screenshot.

[0640] Step 3: Save the event information

[0641] The server stores the extracted event information in a database, including the extracted date, time, location, and content, and also adds the event information to the user's calendar using the Google Calendar API.

[0642] Specifically, the Google Calendar API is used to add event information such as "Meeting in Shinjuku at 3pm next Monday" to the user's Google Calendar.

[0643] Step 4: Emotion Recognition

[0644] The server uses an emotion engine (e.g., TextBlob) to determine the emotional state of the user from the input data. It analyzes the text messages received as input data and extracts emotions such as positive, negative, or neutral.

[0645] In terms of specific actions, the message "I'm busy, but I'll do my best" is perceived as a positive emotion, while the message "I'm tired" is perceived as a negative emotion.

[0646] Step 5: Reminders and suggested actions

[0647] The server then suggests reminders and actions based on the stored schedule information and the results of emotion recognition. The suggestions are tailored to the user's emotional state: positive emotions include motivational reminders, and negative emotions include encouraging messages.

[0648] Specifically, if the emotion is positive, it generates a reminder such as "There is a meeting in Shinjuku tomorrow. Do your best!", and if the emotion is negative, it generates a reminder such as "There is a meeting in Shinjuku soon. Relax and do your best."

[0649] Step 6: Reminders and Action Notifications

[0650] The server notifies the device of the generated reminders and actions by sending messages using the LINE API, notifying the user at the appropriate time (for example, one hour before the meeting).

[0651] Specifically, the message "There's a meeting coming up in Shinjuku. Do you have the necessary materials?" is sent using the LINE API.

[0652] Step 7: Gather feedback and refine

[0653] Users provide feedback on the reminders and actions they receive. The server collects and stores the feedback in a database. The collected feedback is used to improve the accuracy of future reminders and action suggestions.

[0654] Specifically, users send feedback via LINE chat, such as "The reminder was too early," and this data is collected and applied to the machine learning model to improve the accuracy of future suggestions.

[0655] (Application example 2)

[0656] 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."

[0657] In modern society, busy users find it difficult to manage their schedules and receive reminders and action suggestions that are appropriate for their emotional state. In particular, there are limited means to receive timely, personalized information and support while shopping in physical stores. This leads to a decline in the quality of the shopping experience and a decrease in user satisfaction. To address this issue, there is a need to propose a system that combines user schedule management and emotion recognition to make users' lives more comfortable.

[0658] 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.

[0659] In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content of the schedule, means for saving the extracted schedule information, means for suggesting reminders and actions based on the saved schedule information, means for notifying the user of the suggested reminders and actions, means including an emotion recognition engine for analyzing the emotional state of the user and adjusting the reminders and actions based on the analyzed emotional state, means for collecting feedback from the user and improving the accuracy of the reminders and actions, means for notifying the user using a location information service when the user approaches a place to be visited, and means for providing product details and campaign information related to the place to be visited. This makes it possible to provide personalized reminders and actions according to the user's emotional state and improve the shopping experience.

[0660] "User" refers to a person who uses the system to input schedule information and receive reminders and suggested actions.

[0661] "Schedule information" refers to information indicating a user's future schedule, including specific dates, times, locations, and contents.

[0662] The "date, time, place, and content of the appointment" refers to the specific date, time, place, and details of the appointment extracted from the appointment information.

[0663] "Remind" refers to a notification or alert that reminds a user of an appointment.

[0664] "Action" refers to specific actions or suggestions that users should take based on schedule information and emotional state.

[0665] An "emotion recognition engine" refers to a system that analyzes input data from a user and determines their emotional state.

[0666] "Feedback" refers to the ratings and comments you provide in response to reminders and suggested actions.

[0667] "Location information services" refers to services that use GPS, Wi-Fi, etc. to identify the user's location and provide information based on that location.

[0668] "Notification" means a message or alert intended to inform the User of a reminder or suggested action.

[0669] "Product details and campaign information" refers to information about products that can be obtained at the locations or stores that users visit, as well as information about current special offers and discounts.

[0670] MODE FOR CARRYING OUT THE INVENTION

[0671] This invention provides a system that allows users to easily input schedule information, suggests reminders and actions based on the schedule, and recognizes the user's emotional state. Specific embodiments are described below. The system of the present invention is composed of the following elements:

[0672] 1. Means of receiving schedule information from users

[0673] Users use a smartphone application to input schedule information. This information is entered as chat-style text data or image data (screenshots). For example, a user might enter a message such as, "I plan to go to ABC Department Store at 2:00 PM on Saturday. I'm busy, but I'm looking forward to shopping."

[0674] 2. Analysis and extraction of schedule information

[0675] The server analyzes text messages and image data received from smartphone devices. For text data, the server uses the Google Cloud Natural Language API to analyze and extract the date, time, location, and content of the event. For image data, the server uses the Google Cloud Vision API to extract text information from the image using OCR (optical character recognition) technology.

[0676] 3. How to save schedule information

[0677] The extracted schedule information is stored in a database by the server, and at the same time, the schedule information is added to the user's calendar (e.g., Google Calendar) using the Google Calendar API.

[0678] 4. Means of determining emotional state using an emotion recognition engine

[0679] The server is equipped with an emotion recognition engine that analyzes input data from users to determine their emotional state. For example, it can recognize positive emotions from a message such as "I'm busy, but I'm looking forward to shopping."

[0680] 5. Reminders and action suggestions

[0681] The server suggests reminders and actions based on the stored schedule information and the analyzed emotional state, for example, "Enjoy a great shopping experience!" before a visit.

[0682] 6. Reminders and action notifications

[0683] Suggested reminders and actions are sent to the smartphone using Firebase Cloud Messaging. For example, when a user approaches a designated visit location, location services can be used to notify the user that "It's almost time to visit ABC Department Store."

[0684] 7. Feedback collection and refinement methods

[0685] Users provide feedback on reminders and actions. This feedback is entered through chat and collected by the server. This feedback information is used to improve the accuracy of future reminders and action suggestions.

[0686] Specific examples

[0687] Here's an example where a user enters a shopping itinerary and receives a reminder:

[0688] A user types on LINE, "I plan to go to ABC Department Store on Saturday at 2 PM. I'm busy, but I'm looking forward to shopping." The smartphone device sends this to the server, which extracts the schedule information and uses an emotion recognition engine to recognize "positive emotions." Based on this, an event is added to Google Calendar and a positive reminder is sent before the user arrives, saying, "Enjoy a great shopping experience!"

[0689] Prompt Sentence Examples

[0690] "I'm planning to go to ABC Department Store on Saturday at 2pm. I'll be busy, but I'm looking forward to shopping."

[0691] In this way, the system of the present invention can provide appropriate reminders and action suggestions based on the user's emotional state, providing a more personalized shopping experience.

[0692] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0693] Step 1:

[0694] Terminal - Receiving schedule information from users

[0695] A user inputs chat-style text data or screenshots into a smartphone app. For example, they send a message saying, "I plan to go to ABC department store at 2 PM on Saturday. I'm busy, but I'm looking forward to shopping." The input data is sent to the server via the app. The server then obtains the schedule information.

[0696] Input: Text data entered by the user or screenshot

[0697] Output: Appointment information sent to the server

[0698] Specific operation: The smartphone application provides an input UI, receives text messages and images, and sends them to the server.

[0699] Step 2:

[0700] Server - Schedule information analysis

[0701] The server uses Google Cloud Natural Language API to parse the received text message and extract the date, time, location, and content of the appointment. For image data, Google Cloud Vision API is used to extract text information using OCR technology.

[0702] Input: Text data or screenshot sent to the server

[0703] Output: Extracted date, time, location, and content information

[0704] Specific operation: The server passes the received data to the analysis API and obtains the extracted data.

[0705] Step 3:

[0706] Server - Save schedule information

[0707] The extracted event information is stored in a database by the server, and the information is simultaneously added to the user's calendar (such as Google Calendar). This process uses the Google Calendar API.

[0708] Input: Extracted date, time, location, and content information

[0709] Output: Event information saved in the database, event information added to the calendar

[0710] Specific behavior: The server saves the data to the database and makes a request to the Google Calendar API.

[0711] Step 4:

[0712] Server - Emotion recognition engine analyzes emotional states

[0713] The server uses an emotion recognition engine to analyze the emotional state of the user's message, for example, recognizing a positive emotion from the message "I'm busy, but I'm looking forward to shopping."

[0714] Input: User message text

[0715] Output: Emotional state, such as positive or negative

[0716] What happens: The server passes the text to the emotion recognition engine and gets the results.

[0717] Step 5:

[0718] Server - Reminders and action suggestions

[0719] Based on the stored schedule information and the analyzed emotional state, the server will suggest reminders and actions, for example, generating a message like "Enjoy a great shopping experience!"

[0720] Input: Saved schedule information, analyzed emotional state

[0721] Output: Suggested reminders and actions

[0722] Specific operation: The server generates a message based on the data and determines the notification content.

[0723] Step 6:

[0724] Server - Reminders and action notifications

[0725] Suggested reminders and actions are sent to your smartphone using Firebase Cloud Messaging. For example, when the scheduled time approaches, you might get a notification saying, "It's almost time for your visit to ABC Department Store."

[0726] Input: Suggested reminder or action

[0727] Output: Notification sent to the user's smartphone

[0728] What happens: The server sends a notification message through Firebase Cloud Messaging.

[0729] Step 7:

[0730] Terminal - Collecting Feedback

[0731] Users can provide feedback on reminders and actions, such as rating the reminder as "helpful." This feedback is sent back to the server and used to improve the service in the future.

[0732] Input: User-provided feedback

[0733] Output: Feedback information sent to the server

[0734] What happens: The smartphone app receives the feedback and sends it to the server.

[0735] The above is a specific process flow for implementing the present invention. This system allows users to receive personalized reminders and actions, improving their shopping and schedule management experience.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] [Third embodiment]

[0740] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0741] 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.

[0742] 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).

[0743] 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.

[0744] 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.

[0745] 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).

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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."

[0752] An embodiment of the present invention will be described.

[0753] Overall system overview

[0754] This system allows users to easily input schedule information and suggests reminders and actions based on that schedule. The system mainly consists of the following components:

[0755] 1. Means of receiving schedule information from users

[0756] 2. A method for analyzing schedule information and extracting the date, time, location, and content of the schedule

[0757] 3. How to save the extracted schedule information

[0758] 4. A way to suggest reminders and actions based on saved schedule information

[0759] 5. How to notify users of reminders and actions

[0760] 6. A way to collect user feedback and improve the accuracy of reminders and actions

[0761] Receiving input from users

[0762] Users enter schedule information through chat text or screenshots. For example, if a user enters "I have a meeting in Shinjuku next Monday at 3 PM" into LINE chat, this information is received by the system. The device then forwards the received data to the server.

[0763] Analysis and extraction of schedule information

[0764] The server analyzes chat-style text and screenshots received from users. In the case of text, natural language processing technology is used to extract the date, time, location, and content of the appointment. For example, from the text "There is a meeting in Shinjuku at 3 PM next Monday," the information extracted is "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." In the case of image data, optical character recognition (OCR) technology is used to extract character information and analyze it in the same way.

[0765] Save schedule information

[0766] The extracted event information is stored in a database by the server. It is also added to the user's calendar. For example, if you use Google Calendar, you can add an event using the Google Calendar API.

[0767] Reminders and suggested actions

[0768] The server periodically scans the saved schedule information and suggests necessary reminders and actions. It uses an AI model to analyze past data and suggest optimal actions for the user. For example, if a meeting in Shinjuku is coming up, a reminder such as "There will be a meeting in Shinjuku tomorrow. Please make sure you don't forget any necessary documents" will be sent the day before the meeting.

[0769] Reminders and action notifications

[0770] The server notifies the device of the suggested reminders and actions and displays them to the user. Messages can be sent using the LINE API. For example, an hour before a meeting, a notification will be sent via LINE saying, "There will be a meeting in Shinjuku soon. Do you have the necessary materials?"

[0771] Gathering feedback and improving accuracy

[0772] The system can improve its accuracy by providing feedback on reminders and actions. Feedback is entered through chat and collected by the server. The collected feedback is then reflected in the AI ​​model to help suggest future reminders and actions.

[0773] Specific examples

[0774] Example 1: Entering schedule in chat format

[0775] A user types "I have a meeting in Shinjuku next Monday at 3 PM" into LINE. The device sends this message to the server, which extracts "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." Based on this, an event is added to Google Calendar and a reminder is sent the day before the meeting.

[0776] Example 2: Entering schedule information using screenshots

[0777] A user takes a screenshot of a map of Shinjuku Station on their device and sends it to the system. The server uses OCR technology to extract "Shinjuku Station" and generates related event information. This information is added to Google Calendar, and a related reminder is sent on the scheduled date.

[0778] In this way, the present invention provides a system that allows users to efficiently manage their schedules, and provides appropriate reminders and action suggestions when needed.

[0779] The processing flow will be explained below.

[0780] Step 1:

[0781] Users enter schedule information using chat-style text messages or screenshots. For example, a user might type "I have a meeting in Shinjuku next Monday at 3 PM" into a LINE chat, or take a screenshot of a map of Shinjuku Station.

[0782] Step 2:

[0783] The device processes received text messages and screenshots. Specifically, it converts input data into an appropriate format and forwards it to the server. For example, this includes sending text data using the LINE API.

[0784] Step 3:

[0785] The server analyzes text messages and screenshots received from the device. For text data, natural language processing (NLP) is used to extract the date, time, location, and content of the appointment. For example, from "I have a meeting in Shinjuku at 3 PM next Monday," the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting" are extracted. For screenshots, optical character recognition (OCR) is used to extract the text information within the image and analyze it in the same way.

[0786] Step 4:

[0787] The server saves the extracted event information in a database. It also adds this event information to the user's calendar. For example, if you use Google Calendar, you can add an event using the Google Calendar API. This allows you to centrally manage your events in the calendar app you use.

[0788] Step 5:

[0789] The server uses the saved schedule information to send reminders and suggest actions. It uses an AI model to analyze past data and determine what notifications to send at what time. For example, the day before a meeting, it might suggest a reminder such as, "There will be a meeting in Shinjuku tomorrow. Please make sure you don't forget any necessary documents."

[0790] Step 6:

[0791] The server notifies the device of the suggested reminders and actions. It also uses the LINE API to send messages to users. For example, a notification can be sent via LINE one hour before a meeting saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0792] Step 7:

[0793] Users provide feedback on reminders and actions. User feedback is entered through chat and sent from the device to the server. This feedback is used to improve the accuracy of future reminders and action suggestions by the system.

[0794] Step 8:

[0795] The server updates the AI ​​model based on the collected feedback, improving the accuracy of future reminders and action suggestions, and providing a more personalized service.

[0796] This completes the entire process flow, allowing users to efficiently manage their schedules and receive appropriate reminders and actions when needed.

[0797] Example 1

[0798] 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."

[0799] Conventional schedule management systems have the problem that users manually input their schedules, and subsequent reminders and action suggestions are not fully optimized. Additionally, there is an insufficient mechanism for efficiently collecting user feedback and improving the accuracy of the entire system, resulting in low user convenience.

[0800] 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.

[0801] In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content, means for saving the extracted schedule information in data storage, means for suggesting reminders and actions based on the saved schedule information, means for notifying the user of the reminders and action suggestions, and means for collecting feedback from the user and improving the accuracy of the reminders and action suggestions. This allows users to easily input schedule information and efficiently receive reminders and action suggestions. In addition, the accuracy of the system can be continuously improved through feedback.

[0802] "User" means any individual or entity that uses the System to enter appointment information and receive reminders and suggestions.

[0803] "Schedule information" refers to information about the date, time, location, and content of a schedule entered by a user.

[0804] "Chat-format text data" refers to text-format message data sent and received via chat applications or messaging platforms.

[0805] "Image data" refers to visual information stored in an image file format, such as a screenshot or photograph.

[0806] "Optical character recognition technology" refers to technology for extracting character information from image data.

[0807] "Data storage" refers to a storage device used to store and manage data for long periods of time in a computer system.

[0808] "Remind" refers to the act of notifying the user of the contents of a schedule again based on the saved schedule information.

[0809] "Action suggestion" refers to the act of suggesting appropriate actions and preparations to the user based on the saved schedule information.

[0810] "Feedback" refers to the act of a user providing the system with their opinion or evaluation of a reminder or suggested action.

[0811] The following describes an embodiment of the present invention, including an overview of the system, specific examples of the hardware and software used, a process flow, and specific examples.

[0812] Overall system overview

[0813] This invention is a system that allows users to easily input schedule information and provides reminders and action suggestions based on that schedule. This system mainly consists of the following components:

[0814] 1. Means of receiving schedule information from users

[0815] 2. A method for analyzing schedule information and extracting the date, time, location, and content

[0816] 3. A means for saving the extracted schedule information to data storage

[0817] 4. A way to suggest reminders and actions based on saved schedule information

[0818] 5. Means of notifying users of reminders and actions

[0819] 6. Gathering user feedback to improve the accuracy of reminders and actions

[0820] Hardware and software used in the system

[0821] Terminal: The device used by the user, such as a smartphone or tablet, is equipped with a chat application.

[0822] Server: A server for data analysis, storage, and notification. It has an appropriate API for connecting to the database.

[0823] Natural Language Processing (NLP) engine: Used to extract date, time, location, and content from user-entered text data.

[0824] Optical Character Recognition (OCR) technology: Used to extract text information from image data such as screenshots.

[0825] Data Storage: The database used to store the extracted appointment information.

[0826] Notification system: Uses Google Calendar API and LINE API to notify users of reminders and suggestions.

[0827] Specific examples

[0828] Example 1: Chat-style text data

[0829] A user types "I have a meeting in Shinjuku next Monday at 3 PM" on LINE. This information is received by the device and forwarded to the server. The server uses an NLP engine to extract "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." The extracted information is saved in data storage and added to Google Calendar. The day before the meeting, a reminder notification is sent to the user via LINE stating, "There is a meeting in Shinjuku tomorrow. Please don't forget to bring the necessary materials."

[0830] Example 2: Screenshot image data

[0831] The user takes a screenshot of a map of Shinjuku Station on their device and sends it to the system. The server uses OCR technology to extract "Shinjuku Station" and generates related appointment information. The generated information is saved in data storage and added to Google Calendar. On the scheduled day, a notification is sent to the user via LINE saying, "There is a meeting at Shinjuku Station today. Please be careful not to leave anything behind."

[0832] Prompt Sentence Examples

[0833] The prompt for example 1's chat-style schedule entry is as follows:

[0834] "Please tell me how to enter an appointment in LINE such as 'I have a meeting in Shinjuku next Monday at 3 PM' and have the system recognize it and send the appropriate reminder."

[0835] In this way, the present invention allows users to easily manage their schedules and receive appropriate reminders and action suggestions when needed. Furthermore, by collecting feedback, the accuracy of the system can be continuously improved.

[0836] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] Users can enter schedule information via chat on LINE or via screenshots. The information entered can include text such as "I have a meeting in Shinjuku at 3:00 PM next Monday" or a screenshot of a map of Shinjuku Station.

[0839] Step 2:

[0840] The device receives chat-style text data and screenshots entered by the user, then forwards the received data to the server via an HTTP request. The input data is in text or image format and is sent as the payload of the request to the server.

[0841] Step 3:

[0842] The server analyzes the received data. In the case of text data in chat format, the server uses a natural language processing (NLP) engine to extract the date, time, location, and content. For example, from the text "There is a meeting in Shinjuku at 3 PM next Monday," the server extracts "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." In the case of image data, the server uses optical character recognition (OCR) technology to extract character information and analyzes it in the same way.

[0843] Step 4:

[0844] The server saves the extracted schedule information in data storage. Specifically, it saves the analysis results in a database and then uses the Google Calendar API to add an event to the user's calendar. For example, the extracted information "Date and time: next Monday at 3:00 PM," "Location: Shinjuku," and "Content: Meeting" is saved to Google Calendar.

[0845] Step 5:

[0846] The server prepares to send reminders and suggest actions based on the saved schedule information. It scans the database at regular intervals and generates reminder notifications, such as "There's a meeting in Shinjuku tomorrow. Please don't forget to bring the necessary documents." It uses an AI model to create optimal action suggestions based on past data.

[0847] Step 6:

[0848] The server notifies the user of the generated reminders and action suggestions. Using the LINE API or other notification systems, the server sends reminders and action suggestions to the device, which then displays them to the user. For example, the day before a meeting, the server sends a LINE message saying, "There will be a meeting in Shinjuku tomorrow. Don't forget to bring the necessary documents."

[0849] Step 7:

[0850] Users provide feedback on reminders and suggested actions. For example, the provided feedback may include opinions such as "The reminder was too late." The device sends this feedback to the server, which collects it. The collected feedback is reflected in the AI ​​model and used to improve the accuracy of future reminders and suggested actions.

[0851] (Application example 1)

[0852] 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."

[0853] In modern society, users lead busy lives, making schedule management a major issue. It is particularly difficult to check or change schedules while driving, making schedule management a key priority. In these circumstances, a system that can efficiently and safely manage schedule information is needed.

[0854] 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.

[0855] In this invention, the server includes a means for analyzing the user's voice input and extracting schedule information, a means for notifying the user of the schedule information and reminders through the vehicle's in-vehicle system, and a means for receiving the schedule information from the user, thereby enabling the user to manage their schedule without using both hands.

[0856] "User" means a person who uses this system.

[0857] "Schedule information" refers to information including details of a schedule, such as date, time, location, and content.

[0858] "Voice input" means that a user provides information to a system by voice.

[0859] "Analysis" is the process of understanding input data and converting it into meaningful information.

[0860] "Storage" means recording the extracted information in a database or the like for long-term retention.

[0861] "Remind" means sending a notification to remind the user of a schedule.

[0862] "Action" means proposing specific actions or instructions.

[0863] "Notification" means sending a message to inform the user of information.

[0864] "Feedback" refers to the evaluations and opinions that users provide to the system.

[0865] "In-vehicle system" means an information processing and display system installed in a vehicle.

[0866] An embodiment of the present invention will be described. This system receives schedule information from a user via voice, analyzes it, saves it, suggests reminders and actions, notifies the user, and collects feedback. Specific embodiments are described below.

[0867] The server uses a speech recognition engine (SpeechRecognition library) to analyze the user's voice input. This speech recognition process converts the voice data into textual appointment information. Natural language processing techniques are then used to parse the text into appointment details such as date, time, location, and content.

[0868] The analyzed schedule information is stored in a database system such as AWS's DynamoDB, which is then periodically scanned by an AI model on the server, which analyzes past data to suggest optimal reminders and actions.

[0869] The in-vehicle information processing system notifies users of saved schedule information and suggested reminders, and can send reminder notifications to users' smartphones using messaging services such as the LINE API.

[0870] User feedback is used to collect ratings and opinions on reminders and actions, allowing the AI ​​model to continuously learn and improve the accuracy of future suggestions.

[0871] Specific examples

[0872] For example, if a user says in a car, "I have a meeting in Shinjuku next Monday at 3 p.m.", the speech recognition engine converts this into text, and the server analyzes the schedule information. This schedule information is then saved in a database, and a reminder notification, "I have a meeting in Shinjuku tomorrow," is sent to the user's smartphone the day before the meeting.

[0873] Prompt Sentence Examples

[0874] "Analyze the user's statement, 'I have a meeting in Shinjuku next Monday at 3 PM,' and extract the date, time, location, and content."

[0875] This system ensures safety on board while enabling efficient schedule management. By utilizing this system, passengers can check their schedules and receive reminders without using their hands.

[0876] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0877] Step 1:

[0878] The user inputs voice data in the car. The user's speech (e.g., "I have a meeting in Shinjuku at 3:00 PM next Monday") is input to the terminal via a microphone in the car. The terminal then transmits this voice data to the server.

[0879] Step 2:

[0880] The server uses a speech recognition engine (SpeechRecognition library) to convert the received voice data into text data. The speech recognition engine processes the voice data and outputs it as a string (text). For example, the voice data may be recognized as "There will be a meeting in Shinjuku next Monday at 3:00 PM."

[0881] Step 3:

[0882] The server uses natural language processing (NLP) technology to analyze text data. It performs a process to extract date, time, location, and content from the input text. For example, from the text "There will be a meeting in Shinjuku at 3 PM next Monday," it extracts the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." The analysis results in structured data.

[0883] Step 4:

[0884] The server stores the extracted appointment information in a database system (e.g., AWS DynamoDB). The appointment information is stored persistently by adding structured data to DynamoDB. The stored information includes fields for date, time, location, and content.

[0885] Step 5:

[0886] The server periodically scans the saved schedule information. It uses an AI model to analyze the saved data and suggest appropriate reminders and actions. It calculates the optimal timing and content of reminders based on past data and user feedback. For example, the server suggests a reminder such as "There will be a meeting in Shinjuku tomorrow" the day before a meeting.

[0887] Step 6:

[0888] The server sends reminders and action notifications to the vehicle's in-vehicle system (e.g., infotainment system) or the user's smartphone. It uses a pre-configured notification method (e.g., LINE API) to send appropriate messages. For example, one hour before a meeting, it sends a notification message saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0889] Step 7:

[0890] Users receive notifications and provide feedback on reminders and actions. The feedback is sent to the server, where it is used as training data for the AI ​​model to improve the accuracy of future suggestions.

[0891] 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.

[0892] An embodiment of the present invention will be described.

[0893] Overall system overview

[0894] This system allows users to easily input schedule information, and suggests reminders and actions based on the schedule. It also combines an emotion engine that recognizes the user's emotions. The system mainly consists of the following components:

[0895] 1. Means of receiving schedule information from users

[0896] 2. A method for analyzing schedule information and extracting the date, time, location, and content of the schedule

[0897] 3. How to save the extracted schedule information

[0898] 4. A way to suggest reminders and actions based on saved schedule information

[0899] 5. A way to notify users of reminders and suggested actions

[0900] 6. Emotion engine that recognizes user emotions

[0901] 7. A way to collect user feedback and improve the accuracy of reminders and actions

[0902] Receiving input from users

[0903] Users enter their schedule information using chat-style text messages or screenshots—for example, "I have a meeting in Shinjuku next Monday at 3 p.m."—or take a screenshot of a map of Shinjuku Station. The device converts this data into the appropriate format and sends it to the server.

[0904] Analysis and extraction of schedule information

[0905] The server analyzes text messages and screenshots received from the device. In the case of text data, natural language processing technology is used to extract the date, time, location, and content of the appointment. For example, from the text "There will be a meeting in Shinjuku at 3 PM next Monday," the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting" is extracted. In the case of screenshots, optical character recognition (OCR) technology is used to extract the text information and perform the same analysis.

[0906] Save schedule information

[0907] The extracted schedule information is stored in a database by the server, and is also added to the user's calendar. For example, if the user uses Google Calendar, the event is added using the Google Calendar API.

[0908] Emotion recognition by emotion engine

[0909] The server is equipped with an emotion engine that analyzes input data from users to determine their emotional state. Emotions such as positive, negative, and neutral are extracted from text data. For example, a message such as "I'm busy, but I'll do my best" can be used to determine a positive emotion, while a message such as "I'm tired" can be used to determine a negative emotion.

[0910] Reminders and suggested actions

[0911] The server suggests reminders and actions based on the saved schedule information and the emotional state determined by the emotion engine. If the emotional state is positive, it suggests a motivating reminder, and if it is negative, it includes an encouraging message. For example, the day before a meeting, it might remind you, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0912] Reminders and action notifications

[0913] The server sends the suggested reminders and actions to the device and displays them to the user. A message is sent using the LINE API, so that a notification such as "There's a meeting in Shinjuku soon. Do you have the necessary materials?" is sent one hour before the meeting.

[0914] Gathering feedback and improving accuracy

[0915] Users provide feedback on reminders and actions. User feedback is entered through chat and collected by the server. This feedback is used to improve the accuracy of future reminders and action suggestions.

[0916] Specific examples

[0917] Example 1: Chat-style schedule entry and emotion recognition

[0918] A user types "I have a meeting in Shinjuku next Monday at 3 PM" into LINE, and then adds "I'm busy, but I'll do my best." The device sends this to the server, which extracts the schedule information and uses the emotion engine to recognize "positive emotions." Based on this, an event is added to Google Calendar, and a positive reminder is sent the day before the meeting: "I have a meeting in Shinjuku tomorrow. Do your best!"

[0919] Example 2: Schedule entry and emotion recognition using screenshots

[0920] The user takes a screenshot of a map of Shinjuku Station on their device and adds the message "I'm tired." The device then sends this to the server, which uses OCR to extract "Shinjuku Station," and the emotion engine recognizes "negative emotion." Based on this, an event is added to Google Calendar, and a notification is sent one hour before the meeting saying, "There's a meeting in Shinjuku soon. Please relax and do your best."

[0921] In this way, the present invention, which combines an emotion engine, can provide appropriate reminders and action suggestions according to the user's emotional state, thereby providing more personalized schedule management.

[0922] The processing flow will be explained below.

[0923] Step 1:

[0924] Users enter appointment information using chat-style text messages or screenshots, such as "I have a meeting in Shinjuku next Monday at 3 PM" or by taking a screenshot of a map of Shinjuku Station.

[0925] Step 2:

[0926] The device converts the input text message or screenshot into an appropriate format and sends it to the server. For example, it uses the LINE API to send text data or image data.

[0927] Step 3:

[0928] The server analyzes the data received from the device. In the case of text data, it uses natural language processing (NLP) technology to extract the date, time, location, and content of the appointment. For example, from "There will be a meeting in Shinjuku at 3:00 PM next Monday," it extracts the information "Date and time: 3:00 PM next Monday," "Location: Shinjuku," and "Content: Meeting."

[0929] Step 4:

[0930] In the case of image data, the server uses optical character recognition (OCR) technology to extract text information from the image and analyze it. For example, it can extract "Shinjuku Station" from a screenshot of a map of Shinjuku Station.

[0931] Step 5:

[0932] The server saves the extracted event information in a database and adds it to the user's calendar, for example, by using the Google Calendar API to add the event to Google Calendar.

[0933] Step 6:

[0934] The server uses an emotion engine to analyze the user's input data and determine their emotional state. Emotions such as positive, negative, and neutral are extracted from the text data. For example, a message such as "I'm busy, but I'll do my best" can be used to determine a positive emotion.

[0935] Step 7:

[0936] The server suggests reminders and actions based on the stored schedule information and emotional state. If the emotional state is positive, it suggests motivating reminders, and if it is negative, it includes encouraging messages. For example, the day before a meeting, it might remind the user, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0937] Step 8:

[0938] The server notifies the device of suggested reminders and actions. It also uses the LINE API to send messages to users. For example, it sends a notification one hour before a meeting saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0939] Step 9:

[0940] Users provide feedback on reminders and actions, which is entered through chat and sent from the device to the server.

[0941] Step 10:

[0942] The server updates the AI ​​model based on the collected feedback, improving the accuracy of future reminders and action suggestions, and providing a more personalized service.

[0943] In this way, the present invention, which combines an emotion engine, can provide appropriate reminders and action suggestions according to the user's emotional state, thereby providing more personalized schedule management.

[0944] Example 2

[0945] 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."

[0946] In today's busy lifestyles, it is extremely important for users to efficiently manage their schedules and receive reminders and actions based on those schedules. However, existing systems do not take the user's emotional state into account when managing schedules, and as a result, they are unable to provide appropriate reminders and actions to users. In addition, there is a lack of an intuitive way to input schedules through chat or image data, which results in low usability.

[0947] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content of the schedule, means for saving the extracted schedule information, means for proposing reminders and actions based on the saved schedule information, means for notifying the user of the suggested reminders and actions, means for recognizing the emotional state of the user based on data input by the user, and means for adjusting the content of the reminders and actions based on the emotional state. This makes it possible to suggest appropriate reminders and actions according to the user's emotional state, thereby realizing more personalized schedule management. In addition, usability can be improved by intuitive schedule input means using chat format or image data.

[0948] The "means for receiving schedule information from the user" has the function of receiving schedule information in the form of a text message or image data entered by the user and transmitting that data to the server.

[0949] "Means for analyzing schedule information and extracting the date, time, location, and content of the schedule" refers to a function that uses natural language processing technology and optical character recognition technology to identify and extract items such as the date, time, location, and content from the input schedule information.

[0950] The "means for saving the extracted schedule information" is a means having a function for saving the extracted schedule information in a database or a calendar service.

[0951] The "means for suggesting reminders and actions" has the function of generating and suggesting appropriate reminders and actions to the user based on the saved schedule information.

[0952] The "means for notifying the user of reminders and suggested actions" has the function of sending and displaying generated reminders and suggested actions on the user's device.

[0953] "Means for collecting feedback from users and improving the accuracy of reminders and actions" refers to a function that receives feedback provided by users, analyzes it, and improves the accuracy of reminders and actions.

[0954] An "emotion recognition means" is a device that has the function of recognizing an emotional state based on user input data using natural language processing and machine learning technology.

[0955] The "means for adjusting the content of reminders and actions based on emotional state" has the function of changing the content of reminders and actions according to the recognized emotional state and making more appropriate suggestions to the user.

[0956] "Text data" refers to character information entered in chat format.

[0957] "Image data" refers to information entered in the form of an image, such as a screenshot or photograph.

[0958] Optical character recognition (OCR) is a technology that analyzes characters in image data and extracts them as text information.

[0959] This invention is a system that allows users to easily input schedule information, and suggests reminders and actions based on that schedule, and also combines it with an emotion engine that recognizes the user's emotions. The main components of the system are as follows:

[0960] 1. Means of receiving schedule information from users

[0961] Users enter schedule information using chat-style text messages or screenshots. For example, a user might type, "I have a meeting in Shinjuku next Monday at 3 PM," or take a screenshot of a map of Shinjuku Station. The device receives this input data, converts it into an appropriate format, and sends it to the server. By utilizing the LINE API, text messages can be received, and text information can be extracted from screenshots using OCR technology such as PyTesseract.

[0962] 2. Analysis of schedule information

[0963] The server analyzes the received text messages and screenshots. For text data, natural language processing technology (e.g., SpaCy) is used to extract the date, time, location, and content of the event. For screenshot data, optical character recognition technology (e.g., Tesseract) is used to extract character information and perform the same analysis.

[0964] 3. Save event information

[0965] The server saves the extracted schedule information in a database. It also adds this schedule information to the user's calendar using the Google Calendar API. For example, event information such as "Meeting in Shinjuku at 3:00 PM next Monday" is reflected in the calendar.

[0966] 4. Emotion recognition

[0967] The emotion engine built into the server analyzes input data from users to determine their emotional state. The emotion engine uses natural language processing libraries such as TextBlob to extract emotions such as positive, negative, and neutral from text data. For example, a message such as "I'm busy, but I'll do my best" will determine a positive emotion, while a message such as "I'm tired" will determine a negative emotion.

[0968] 5. Reminders and action suggestions

[0969] The server suggests reminders and actions based on the stored schedule information and the emotional state determined by the emotion engine. If the emotional state is positive, it suggests a motivating reminder, and if it is negative, it includes an encouraging message. For example, the day before a meeting, it might send a reminder such as, "There's a meeting in Shinjuku tomorrow. Do your best!"

[0970] 6. Reminders and Action Notifications

[0971] The server notifies the device of the suggested reminders and actions. It uses the LINE API to send messages so that users receive a notification one hour before the meeting, such as "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[0972] 7. Gather feedback and improve accuracy

[0973] Users provide feedback on reminders and actions. The feedback is entered through chat and collected by the server. The collected feedback is used to improve the accuracy of future reminders and action suggestions.

[0974] Examples of concrete examples and prompts

[0975] Example 1: Schedule entry in chat format and emotion recognition

[0976] 1. A user types in LINE, "I have a meeting in Shinjuku next Monday at 3pm," and then sends an additional message saying, "I'm busy, but I'll do my best."

[0977] 2. The device sends this to the server.

[0978] 3. The server extracts the schedule information and the emotion engine recognizes "positive emotions."

[0979] 4. The server adds an event to Google Calendar and sends a positive reminder the day before the meeting: "There's a meeting in Shinjuku tomorrow. Good luck!"

[0980] Example 2: Schedule entry and emotion recognition using screenshots

[0981] 1. A user takes a screenshot of a map of Shinjuku Station on their device and sends the message "I'm tired."

[0982] 2. The device sends this to the server.

[0983] 3. The server uses OCR to extract "Shinjuku Station" and the emotion engine recognizes "negative emotion."

[0984] 4. The server adds the event to Google Calendar and notifies you one hour before the meeting, saying, "There's a meeting in Shinjuku soon. Relax and do your best."

[0985] Example prompt sentence:

[0986] Enter "I have a meeting in Shinjuku next Monday at 3pm."

[0987] Take a screenshot of a map of Shinjuku Station and type "tired."

[0988] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0989] Step 1: Enter event information from the user

[0990] A user inputs schedule information as a text message. For example, a user might use the LINE app to input, "I have a meeting in Shinjuku next Monday at 3 PM." The text message and image data are sent to the device as input data. The device then converts the data into an appropriate format and sends it to the server.

[0991] Specifically, it receives text messages using the LINE API, extracts text data from screenshots using PyTesseract, and sends the converted data to the server.

[0992] Step 2: Analyze the schedule information

[0993] When the server receives a text message, it analyzes it using natural language processing technology (e.g., SpaCy). It extracts the date, time, location, and content from the text message received as input data. In the case of image data, it also uses optical character recognition technology (e.g., Tesseract) to extract and analyze the text information.

[0994] Specifically, from a text message such as "There will be a meeting in Shinjuku next Monday at 3 PM," SpaCy is used to extract "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." Additionally, Tesseract is used to extract the text information for "Shinjuku Station" from a screenshot.

[0995] Step 3: Save the event information

[0996] The server stores the extracted event information in a database, including the extracted date, time, location, and content, and also adds the event information to the user's calendar using the Google Calendar API.

[0997] Specifically, the Google Calendar API is used to add event information such as "Meeting in Shinjuku at 3pm next Monday" to the user's Google Calendar.

[0998] Step 4: Emotion Recognition

[0999] The server uses an emotion engine (e.g., TextBlob) to determine the emotional state of the user from the input data. It analyzes the text messages received as input data and extracts emotions such as positive, negative, or neutral.

[1000] In terms of specific actions, the message "I'm busy, but I'll do my best" is perceived as a positive emotion, while the message "I'm tired" is perceived as a negative emotion.

[1001] Step 5: Reminders and suggested actions

[1002] The server then suggests reminders and actions based on the stored schedule information and the results of emotion recognition. The suggestions are tailored to the user's emotional state: positive emotions include motivational reminders, and negative emotions include encouraging messages.

[1003] Specifically, if the emotion is positive, it generates a reminder such as "There is a meeting in Shinjuku tomorrow. Do your best!", and if the emotion is negative, it generates a reminder such as "There is a meeting in Shinjuku soon. Relax and do your best."

[1004] Step 6: Reminders and Action Notifications

[1005] The server notifies the device of the generated reminders and actions by sending messages using the LINE API, notifying the user at the appropriate time (for example, one hour before the meeting).

[1006] Specifically, the message "There's a meeting coming up in Shinjuku. Do you have the necessary materials?" is sent using the LINE API.

[1007] Step 7: Gather feedback and refine

[1008] Users provide feedback on the reminders and actions they receive. The server collects and stores the feedback in a database. The collected feedback is used to improve the accuracy of future reminders and action suggestions.

[1009] Specifically, users send feedback via LINE chat, such as "The reminder was too early," and this data is collected and applied to the machine learning model to improve the accuracy of future suggestions.

[1010] (Application example 2)

[1011] 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."

[1012] In modern society, busy users find it difficult to manage their schedules and receive reminders and action suggestions that are appropriate for their emotional state. In particular, there are limited means to receive timely, personalized information and support while shopping in physical stores. This leads to a decline in the quality of the shopping experience and a decrease in user satisfaction. To address this issue, there is a need to propose a system that combines user schedule management and emotion recognition to make users' lives more comfortable.

[1013] 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.

[1014] In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content of the schedule, means for saving the extracted schedule information, means for suggesting reminders and actions based on the saved schedule information, means for notifying the user of the suggested reminders and actions, means including an emotion recognition engine for analyzing the emotional state of the user and adjusting the reminders and actions based on the analyzed emotional state, means for collecting feedback from the user and improving the accuracy of the reminders and actions, means for notifying the user using a location information service when the user approaches a place to be visited, and means for providing product details and campaign information related to the place to be visited. This makes it possible to provide personalized reminders and actions according to the user's emotional state and improve the shopping experience.

[1015] "User" refers to a person who uses the system to input schedule information and receive reminders and suggested actions.

[1016] "Schedule information" refers to information indicating a user's future schedule, including specific dates, times, locations, and contents.

[1017] The "date, time, place, and content of the appointment" refers to the specific date, time, place, and details of the appointment extracted from the appointment information.

[1018] "Remind" refers to a notification or alert that reminds a user of an appointment.

[1019] "Action" refers to specific actions or suggestions that users should take based on schedule information and emotional state.

[1020] An "emotion recognition engine" refers to a system that analyzes input data from a user and determines their emotional state.

[1021] "Feedback" refers to the ratings and comments you provide in response to reminders and suggested actions.

[1022] "Location information services" refers to services that use GPS, Wi-Fi, etc. to identify the user's location and provide information based on that location.

[1023] "Notification" means a message or alert intended to inform the User of a reminder or suggested action.

[1024] "Product details and campaign information" refers to information about products that can be obtained at the locations or stores that users visit, as well as information about current special offers and discounts.

[1025] MODE FOR CARRYING OUT THE INVENTION

[1026] This invention provides a system that allows users to easily input schedule information, suggests reminders and actions based on the schedule, and recognizes the user's emotional state. Specific embodiments are described below. The system of the present invention is composed of the following elements:

[1027] 1. Means of receiving schedule information from users

[1028] Users use a smartphone application to input schedule information. This information is entered as chat-style text data or image data (screenshots). For example, a user might enter a message such as, "I plan to go to ABC Department Store at 2:00 PM on Saturday. I'm busy, but I'm looking forward to shopping."

[1029] 2. Analysis and extraction of schedule information

[1030] The server analyzes text messages and image data received from smartphone devices. For text data, the server uses the Google Cloud Natural Language API to analyze and extract the date, time, location, and content of the event. For image data, the server uses the Google Cloud Vision API to extract text information from the image using OCR (optical character recognition) technology.

[1031] 3. How to save schedule information

[1032] The extracted schedule information is stored in a database by the server, and at the same time, the schedule information is added to the user's calendar (e.g., Google Calendar) using the Google Calendar API.

[1033] 4. Means of determining emotional state using an emotion recognition engine

[1034] The server is equipped with an emotion recognition engine that analyzes input data from users to determine their emotional state. For example, it can recognize positive emotions from a message such as "I'm busy, but I'm looking forward to shopping."

[1035] 5. Reminders and action suggestions

[1036] The server suggests reminders and actions based on the stored schedule information and the analyzed emotional state, for example, "Enjoy a great shopping experience!" before a visit.

[1037] 6. Reminders and action notifications

[1038] Suggested reminders and actions are sent to the smartphone using Firebase Cloud Messaging. For example, when a user approaches a designated visit location, location services can be used to notify the user that "It's almost time to visit ABC Department Store."

[1039] 7. Feedback collection and refinement methods

[1040] Users provide feedback on reminders and actions. This feedback is entered through chat and collected by the server. This feedback information is used to improve the accuracy of future reminders and action suggestions.

[1041] Specific examples

[1042] Here's an example where a user enters a shopping itinerary and receives a reminder:

[1043] A user types on LINE, "I plan to go to ABC Department Store on Saturday at 2 PM. I'm busy, but I'm looking forward to shopping." The smartphone device sends this to the server, which extracts the schedule information and uses an emotion recognition engine to recognize "positive emotions." Based on this, an event is added to Google Calendar and a positive reminder is sent before the user arrives, saying, "Enjoy a great shopping experience!"

[1044] Prompt Sentence Examples

[1045] "I'm planning to go to ABC Department Store on Saturday at 2pm. I'll be busy, but I'm looking forward to shopping."

[1046] In this way, the system of the present invention can provide appropriate reminders and action suggestions based on the user's emotional state, providing a more personalized shopping experience.

[1047] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1048] Step 1:

[1049] Terminal - Receiving schedule information from users

[1050] A user inputs chat-style text data or screenshots into a smartphone app. For example, they send a message saying, "I plan to go to ABC department store at 2 PM on Saturday. I'm busy, but I'm looking forward to shopping." The input data is sent to the server via the app. The server then obtains the schedule information.

[1051] Input: Text data entered by the user or screenshot

[1052] Output: Appointment information sent to the server

[1053] Specific operation: The smartphone application provides an input UI, receives text messages and images, and sends them to the server.

[1054] Step 2:

[1055] Server - Schedule information analysis

[1056] The server uses Google Cloud Natural Language API to parse the received text message and extract the date, time, location, and content of the appointment. For image data, Google Cloud Vision API is used to extract text information using OCR technology.

[1057] Input: Text data or screenshot sent to the server

[1058] Output: Extracted date, time, location, and content information

[1059] Specific operation: The server passes the received data to the analysis API and obtains the extracted data.

[1060] Step 3:

[1061] Server - Save schedule information

[1062] The extracted event information is stored in a database by the server, and the information is simultaneously added to the user's calendar (such as Google Calendar). This process uses the Google Calendar API.

[1063] Input: Extracted date, time, location, and content information

[1064] Output: Event information saved in the database, event information added to the calendar

[1065] Specific behavior: The server saves the data to the database and makes a request to the Google Calendar API.

[1066] Step 4:

[1067] Server - Emotion recognition engine analyzes emotional states

[1068] The server uses an emotion recognition engine to analyze the emotional state of the user's message, for example, recognizing a positive emotion from the message "I'm busy, but I'm looking forward to shopping."

[1069] Input: User message text

[1070] Output: Emotional state, such as positive or negative

[1071] What happens: The server passes the text to the emotion recognition engine and gets the results.

[1072] Step 5:

[1073] Server - Reminders and action suggestions

[1074] Based on the stored schedule information and the analyzed emotional state, the server will suggest reminders and actions, for example, generating a message like "Enjoy a great shopping experience!"

[1075] Input: Saved schedule information, analyzed emotional state

[1076] Output: Suggested reminders and actions

[1077] Specific operation: The server generates a message based on the data and determines the notification content.

[1078] Step 6:

[1079] Server - Reminders and action notifications

[1080] Suggested reminders and actions are sent to your smartphone using Firebase Cloud Messaging. For example, when the scheduled time approaches, you might get a notification saying, "It's almost time for your visit to ABC Department Store."

[1081] Input: Suggested reminder or action

[1082] Output: Notification sent to the user's smartphone

[1083] What happens: The server sends a notification message through Firebase Cloud Messaging.

[1084] Step 7:

[1085] Terminal - Collecting Feedback

[1086] Users can provide feedback on reminders and actions, such as rating the reminder as "helpful." This feedback is sent back to the server and used to improve the service in the future.

[1087] Input: User-provided feedback

[1088] Output: Feedback information sent to the server

[1089] What happens: The smartphone app receives the feedback and sends it to the server.

[1090] The above is a specific process flow for implementing the present invention. This system allows users to receive personalized reminders and actions, improving their shopping and schedule management experience.

[1091] 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.

[1092] 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.

[1093] 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.

[1094] [Fourth embodiment]

[1095] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1096] 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.

[1097] 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).

[1098] 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.

[1099] 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.

[1100] 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).

[1101] 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.

[1102] 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.

[1103] 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.

[1104] 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.

[1105] 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.

[1106] 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.

[1107] 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."

[1108] An embodiment of the present invention will be described.

[1109] Overall system overview

[1110] This system allows users to easily input schedule information and suggests reminders and actions based on that schedule. The system mainly consists of the following components:

[1111] 1. Means of receiving schedule information from users

[1112] 2. A method for analyzing schedule information and extracting the date, time, location, and content of the schedule

[1113] 3. How to save the extracted schedule information

[1114] 4. A way to suggest reminders and actions based on saved schedule information

[1115] 5. How to notify users of reminders and actions

[1116] 6. A way to collect user feedback and improve the accuracy of reminders and actions

[1117] Receiving input from users

[1118] Users enter schedule information through chat text or screenshots. For example, if a user enters "I have a meeting in Shinjuku next Monday at 3 PM" into LINE chat, this information is received by the system. The device then forwards the received data to the server.

[1119] Analysis and extraction of schedule information

[1120] The server analyzes chat-style text and screenshots received from users. In the case of text, natural language processing technology is used to extract the date, time, location, and content of the appointment. For example, from the text "There is a meeting in Shinjuku at 3 PM next Monday," the information extracted is "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." In the case of image data, optical character recognition (OCR) technology is used to extract character information and analyze it in the same way.

[1121] Save schedule information

[1122] The extracted event information is stored in a database by the server. It is also added to the user's calendar. For example, if you use Google Calendar, you can add an event using the Google Calendar API.

[1123] Reminders and suggested actions

[1124] The server periodically scans the saved schedule information and suggests necessary reminders and actions. It uses an AI model to analyze past data and suggest optimal actions for the user. For example, if a meeting in Shinjuku is coming up, a reminder such as "There will be a meeting in Shinjuku tomorrow. Please make sure you don't forget any necessary documents" will be sent the day before the meeting.

[1125] Reminders and action notifications

[1126] The server notifies the device of the suggested reminders and actions and displays them to the user. Messages can be sent using the LINE API. For example, an hour before a meeting, a notification will be sent via LINE saying, "There will be a meeting in Shinjuku soon. Do you have the necessary materials?"

[1127] Gathering feedback and improving accuracy

[1128] The system can improve its accuracy by providing feedback on reminders and actions. Feedback is entered through chat and collected by the server. The collected feedback is then reflected in the AI ​​model to help suggest future reminders and actions.

[1129] Specific examples

[1130] Example 1: Entering schedule in chat format

[1131] A user types "I have a meeting in Shinjuku next Monday at 3 PM" into LINE. The device sends this message to the server, which extracts "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." Based on this, an event is added to Google Calendar and a reminder is sent the day before the meeting.

[1132] Example 2: Entering schedule information using screenshots

[1133] A user takes a screenshot of a map of Shinjuku Station on their device and sends it to the system. The server uses OCR technology to extract "Shinjuku Station" and generates related event information. This information is added to Google Calendar, and a related reminder is sent on the scheduled date.

[1134] In this way, the present invention provides a system that allows users to efficiently manage their schedules, and provides appropriate reminders and action suggestions when needed.

[1135] The processing flow will be explained below.

[1136] Step 1:

[1137] Users enter schedule information using chat-style text messages or screenshots. For example, a user might type "I have a meeting in Shinjuku next Monday at 3 PM" into a LINE chat, or take a screenshot of a map of Shinjuku Station.

[1138] Step 2:

[1139] The device processes received text messages and screenshots. Specifically, it converts input data into an appropriate format and forwards it to the server. For example, this includes sending text data using the LINE API.

[1140] Step 3:

[1141] The server analyzes text messages and screenshots received from the device. For text data, natural language processing (NLP) is used to extract the date, time, location, and content of the appointment. For example, from "I have a meeting in Shinjuku at 3 PM next Monday," the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting" are extracted. For screenshots, optical character recognition (OCR) is used to extract the text information within the image and analyze it in the same way.

[1142] Step 4:

[1143] The server saves the extracted event information in a database. It also adds this event information to the user's calendar. For example, if you use Google Calendar, you can add an event using the Google Calendar API. This allows you to centrally manage your events in the calendar app you use.

[1144] Step 5:

[1145] The server uses the saved schedule information to send reminders and suggest actions. It uses an AI model to analyze past data and determine what notifications to send at what time. For example, the day before a meeting, it might suggest a reminder such as, "There will be a meeting in Shinjuku tomorrow. Please make sure you don't forget any necessary documents."

[1146] Step 6:

[1147] The server notifies the device of the suggested reminders and actions. It also uses the LINE API to send messages to users. For example, a notification can be sent via LINE one hour before a meeting saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[1148] Step 7:

[1149] Users provide feedback on reminders and actions. User feedback is entered through chat and sent from the device to the server. This feedback is used to improve the accuracy of future reminders and action suggestions by the system.

[1150] Step 8:

[1151] The server updates the AI ​​model based on the collected feedback, improving the accuracy of future reminders and action suggestions, and providing a more personalized service.

[1152] This completes the entire process flow, allowing users to efficiently manage their schedules and receive appropriate reminders and actions when needed.

[1153] Example 1

[1154] 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."

[1155] Conventional schedule management systems have the problem that users manually input their schedules, and subsequent reminders and action suggestions are not fully optimized. Additionally, there is an insufficient mechanism for efficiently collecting user feedback and improving the accuracy of the entire system, resulting in low user convenience.

[1156] 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.

[1157] In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content, means for saving the extracted schedule information in data storage, means for suggesting reminders and actions based on the saved schedule information, means for notifying the user of the reminders and action suggestions, and means for collecting feedback from the user and improving the accuracy of the reminders and action suggestions. This allows users to easily input schedule information and efficiently receive reminders and action suggestions. In addition, the accuracy of the system can be continuously improved through feedback.

[1158] "User" means any individual or entity that uses the System to enter appointment information and receive reminders and suggestions.

[1159] "Schedule information" refers to information about the date, time, location, and content of a schedule entered by a user.

[1160] "Chat-format text data" refers to text-format message data sent and received via chat applications or messaging platforms.

[1161] "Image data" refers to visual information stored in an image file format, such as a screenshot or photograph.

[1162] "Optical character recognition technology" refers to technology for extracting character information from image data.

[1163] "Data storage" refers to a storage device used to store and manage data for long periods of time in a computer system.

[1164] "Remind" refers to the act of notifying the user of the contents of a schedule again based on the saved schedule information.

[1165] "Action suggestion" refers to the act of suggesting appropriate actions and preparations to the user based on the saved schedule information.

[1166] "Feedback" refers to the act of a user providing the system with their opinion or evaluation of a reminder or suggested action.

[1167] The following describes an embodiment of the present invention, including an overview of the system, specific examples of the hardware and software used, a process flow, and specific examples.

[1168] Overall system overview

[1169] This invention is a system that allows users to easily input schedule information and provides reminders and action suggestions based on that schedule. This system mainly consists of the following components:

[1170] 1. Means of receiving schedule information from users

[1171] 2. A method for analyzing schedule information and extracting the date, time, location, and content

[1172] 3. A means for saving the extracted schedule information to data storage

[1173] 4. A way to suggest reminders and actions based on saved schedule information

[1174] 5. Means of notifying users of reminders and actions

[1175] 6. Gathering user feedback to improve the accuracy of reminders and actions

[1176] Hardware and software used in the system

[1177] Terminal: The device used by the user, such as a smartphone or tablet, is equipped with a chat application.

[1178] Server: A server for data analysis, storage, and notification. It has an appropriate API for connecting to the database.

[1179] Natural Language Processing (NLP) engine: Used to extract date, time, location, and content from user-entered text data.

[1180] Optical Character Recognition (OCR) technology: Used to extract text information from image data such as screenshots.

[1181] Data Storage: The database used to store the extracted appointment information.

[1182] Notification system: Uses Google Calendar API and LINE API to notify users of reminders and suggestions.

[1183] Specific examples

[1184] Example 1: Chat-style text data

[1185] A user types "I have a meeting in Shinjuku next Monday at 3 PM" on LINE. This information is received by the device and forwarded to the server. The server uses an NLP engine to extract "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." The extracted information is saved in data storage and added to Google Calendar. The day before the meeting, a reminder notification is sent to the user via LINE stating, "There is a meeting in Shinjuku tomorrow. Please don't forget to bring the necessary materials."

[1186] Example 2: Screenshot image data

[1187] The user takes a screenshot of a map of Shinjuku Station on their device and sends it to the system. The server uses OCR technology to extract "Shinjuku Station" and generates related appointment information. The generated information is saved in data storage and added to Google Calendar. On the scheduled day, a notification is sent to the user via LINE saying, "There is a meeting at Shinjuku Station today. Please be careful not to leave anything behind."

[1188] Prompt Sentence Examples

[1189] The prompt for example 1's chat-style schedule entry is as follows:

[1190] "Please tell me how to enter an appointment in LINE such as 'I have a meeting in Shinjuku next Monday at 3 PM' and have the system recognize it and send the appropriate reminder."

[1191] In this way, the present invention allows users to easily manage their schedules and receive appropriate reminders and action suggestions when needed. Furthermore, by collecting feedback, the accuracy of the system can be continuously improved.

[1192] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1193] Step 1:

[1194] Users can enter schedule information via chat on LINE or via screenshots. The information entered can include text such as "I have a meeting in Shinjuku at 3:00 PM next Monday" or a screenshot of a map of Shinjuku Station.

[1195] Step 2:

[1196] The device receives chat-style text data and screenshots entered by the user, then forwards the received data to the server via an HTTP request. The input data is in text or image format and is sent as the payload of the request to the server.

[1197] Step 3:

[1198] The server analyzes the received data. In the case of text data in chat format, the server uses a natural language processing (NLP) engine to extract the date, time, location, and content. For example, from the text "There is a meeting in Shinjuku at 3 PM next Monday," the server extracts "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." In the case of image data, the server uses optical character recognition (OCR) technology to extract character information and analyzes it in the same way.

[1199] Step 4:

[1200] The server saves the extracted schedule information in data storage. Specifically, it saves the analysis results in a database and then uses the Google Calendar API to add an event to the user's calendar. For example, the extracted information "Date and time: next Monday at 3:00 PM," "Location: Shinjuku," and "Content: Meeting" is saved to Google Calendar.

[1201] Step 5:

[1202] The server prepares to send reminders and suggest actions based on the saved schedule information. It scans the database at regular intervals and generates reminder notifications, such as "There's a meeting in Shinjuku tomorrow. Please don't forget to bring the necessary documents." It uses an AI model to create optimal action suggestions based on past data.

[1203] Step 6:

[1204] The server notifies the user of the generated reminders and action suggestions. Using the LINE API or other notification systems, the server sends reminders and action suggestions to the device, which then displays them to the user. For example, the day before a meeting, the server sends a LINE message saying, "There will be a meeting in Shinjuku tomorrow. Don't forget to bring the necessary documents."

[1205] Step 7:

[1206] Users provide feedback on reminders and suggested actions. For example, the provided feedback may include opinions such as "The reminder was too late." The device sends this feedback to the server, which collects it. The collected feedback is reflected in the AI ​​model and used to improve the accuracy of future reminders and suggested actions.

[1207] (Application example 1)

[1208] 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."

[1209] In modern society, users lead busy lives, making schedule management a major issue. It is particularly difficult to check or change schedules while driving, making schedule management a key priority. In these circumstances, a system that can efficiently and safely manage schedule information is needed.

[1210] 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.

[1211] In this invention, the server includes a means for analyzing the user's voice input and extracting schedule information, a means for notifying the user of the schedule information and reminders through the vehicle's in-vehicle system, and a means for receiving the schedule information from the user, thereby enabling the user to manage their schedule without using both hands.

[1212] "User" means a person who uses this system.

[1213] "Schedule information" refers to information including details of a schedule, such as date, time, location, and content.

[1214] "Voice input" means that a user provides information to a system by voice.

[1215] "Analysis" is the process of understanding input data and converting it into meaningful information.

[1216] "Storage" means recording the extracted information in a database or the like for long-term retention.

[1217] "Remind" means sending a notification to remind the user of a schedule.

[1218] "Action" means proposing specific actions or instructions.

[1219] "Notification" means sending a message to inform the user of information.

[1220] "Feedback" refers to the evaluations and opinions that users provide to the system.

[1221] "In-vehicle system" means an information processing and display system installed in a vehicle.

[1222] An embodiment of the present invention will be described. This system receives schedule information from a user via voice, analyzes it, saves it, suggests reminders and actions, notifies the user, and collects feedback. Specific embodiments are described below.

[1223] The server uses a speech recognition engine (SpeechRecognition library) to analyze the user's voice input. This speech recognition process converts the voice data into textual appointment information. Natural language processing techniques are then used to parse the text into appointment details such as date, time, location, and content.

[1224] The analyzed schedule information is stored in a database system such as AWS's DynamoDB, which is then periodically scanned by an AI model on the server, which analyzes past data to suggest optimal reminders and actions.

[1225] The in-vehicle information processing system notifies users of saved schedule information and suggested reminders, and can send reminder notifications to users' smartphones using messaging services such as the LINE API.

[1226] User feedback is used to collect ratings and opinions on reminders and actions, allowing the AI ​​model to continuously learn and improve the accuracy of future suggestions.

[1227] Specific examples

[1228] For example, if a user says in a car, "I have a meeting in Shinjuku next Monday at 3 p.m.", the speech recognition engine converts this into text, and the server analyzes the schedule information. This schedule information is then saved in a database, and a reminder notification, "I have a meeting in Shinjuku tomorrow," is sent to the user's smartphone the day before the meeting.

[1229] Prompt Sentence Examples

[1230] "Analyze the user's statement, 'I have a meeting in Shinjuku next Monday at 3 PM,' and extract the date, time, location, and content."

[1231] This system ensures safety on board while enabling efficient schedule management. By utilizing this system, passengers can check their schedules and receive reminders without using their hands.

[1232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1233] Step 1:

[1234] The user inputs voice data in the car. The user's speech (e.g., "I have a meeting in Shinjuku at 3:00 PM next Monday") is input to the terminal via a microphone in the car. The terminal then transmits this voice data to the server.

[1235] Step 2:

[1236] The server uses a speech recognition engine (SpeechRecognition library) to convert the received voice data into text data. The speech recognition engine processes the voice data and outputs it as a string (text). For example, the voice data may be recognized as "There will be a meeting in Shinjuku next Monday at 3:00 PM."

[1237] Step 3:

[1238] The server uses natural language processing (NLP) technology to analyze text data. It performs a process to extract date, time, location, and content from the input text. For example, from the text "There will be a meeting in Shinjuku at 3 PM next Monday," it extracts the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting." The analysis results in structured data.

[1239] Step 4:

[1240] The server stores the extracted appointment information in a database system (e.g., AWS DynamoDB). The appointment information is stored persistently by adding structured data to DynamoDB. The stored information includes fields for date, time, location, and content.

[1241] Step 5:

[1242] The server periodically scans the saved schedule information. It uses an AI model to analyze the saved data and suggest appropriate reminders and actions. It calculates the optimal timing and content of reminders based on past data and user feedback. For example, the server suggests a reminder such as "There will be a meeting in Shinjuku tomorrow" the day before a meeting.

[1243] Step 6:

[1244] The server sends reminders and action notifications to the vehicle's in-vehicle system (e.g., infotainment system) or the user's smartphone. It uses a pre-configured notification method (e.g., LINE API) to send appropriate messages. For example, one hour before a meeting, it sends a notification message saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[1245] Step 7:

[1246] Users receive notifications and provide feedback on reminders and actions. The feedback is sent to the server, where it is used as training data for the AI ​​model to improve the accuracy of future suggestions.

[1247] 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.

[1248] An embodiment of the present invention will be described.

[1249] Overall system overview

[1250] This system allows users to easily input schedule information, and suggests reminders and actions based on the schedule. It also combines an emotion engine that recognizes the user's emotions. The system mainly consists of the following components:

[1251] 1. Means of receiving schedule information from users

[1252] 2. A method for analyzing schedule information and extracting the date, time, location, and content of the schedule

[1253] 3. How to save the extracted schedule information

[1254] 4. A way to suggest reminders and actions based on saved schedule information

[1255] 5. A way to notify users of reminders and suggested actions

[1256] 6. Emotion engine that recognizes user emotions

[1257] 7. A way to collect user feedback and improve the accuracy of reminders and actions

[1258] Receiving input from users

[1259] Users enter their schedule information using chat-style text messages or screenshots—for example, "I have a meeting in Shinjuku next Monday at 3 p.m."—or take a screenshot of a map of Shinjuku Station. The device converts this data into the appropriate format and sends it to the server.

[1260] Analysis and extraction of schedule information

[1261] The server analyzes text messages and screenshots received from the device. In the case of text data, natural language processing technology is used to extract the date, time, location, and content of the appointment. For example, from the text "There will be a meeting in Shinjuku at 3 PM next Monday," the information "Date and time: 3 PM next Monday," "Location: Shinjuku," and "Content: Meeting" is extracted. In the case of screenshots, optical character recognition (OCR) technology is used to extract the text information and perform the same analysis.

[1262] Save schedule information

[1263] The extracted schedule information is stored in a database by the server, and is also added to the user's calendar. For example, if the user uses Google Calendar, the event is added using the Google Calendar API.

[1264] Emotion recognition by emotion engine

[1265] The server is equipped with an emotion engine that analyzes input data from users to determine their emotional state. Emotions such as positive, negative, and neutral are extracted from text data. For example, a message such as "I'm busy, but I'll do my best" can be used to determine a positive emotion, while a message such as "I'm tired" can be used to determine a negative emotion.

[1266] Reminders and suggested actions

[1267] The server suggests reminders and actions based on the saved schedule information and the emotional state determined by the emotion engine. If the emotional state is positive, it suggests a motivating reminder, and if it is negative, it includes an encouraging message. For example, the day before a meeting, it might remind you, "There's a meeting in Shinjuku tomorrow. Do your best!"

[1268] Reminders and action notifications

[1269] The server sends the suggested reminders and actions to the device and displays them to the user. A message is sent using the LINE API, so that a notification such as "There's a meeting in Shinjuku soon. Do you have the necessary materials?" is sent one hour before the meeting.

[1270] Gathering feedback and improving accuracy

[1271] Users provide feedback on reminders and actions. User feedback is entered through chat and collected by the server. This feedback is used to improve the accuracy of future reminders and action suggestions.

[1272] Specific examples

[1273] Example 1: Chat-style schedule entry and emotion recognition

[1274] A user types "I have a meeting in Shinjuku next Monday at 3 PM" into LINE, and then adds "I'm busy, but I'll do my best." The device sends this to the server, which extracts the schedule information and uses the emotion engine to recognize "positive emotions." Based on this, an event is added to Google Calendar, and a positive reminder is sent the day before the meeting: "I have a meeting in Shinjuku tomorrow. Do your best!"

[1275] Example 2: Schedule entry and emotion recognition using screenshots

[1276] The user takes a screenshot of a map of Shinjuku Station on their device and adds the message "I'm tired." The device then sends this to the server, which uses OCR to extract "Shinjuku Station," and the emotion engine recognizes "negative emotion." Based on this, an event is added to Google Calendar, and a notification is sent one hour before the meeting saying, "There's a meeting in Shinjuku soon. Please relax and do your best."

[1277] In this way, the present invention, which combines an emotion engine, can provide appropriate reminders and action suggestions according to the user's emotional state, thereby providing more personalized schedule management.

[1278] The processing flow will be explained below.

[1279] Step 1:

[1280] Users enter appointment information using chat-style text messages or screenshots, such as "I have a meeting in Shinjuku next Monday at 3 PM" or by taking a screenshot of a map of Shinjuku Station.

[1281] Step 2:

[1282] The device converts the input text message or screenshot into an appropriate format and sends it to the server. For example, it uses the LINE API to send text data or image data.

[1283] Step 3:

[1284] The server analyzes the data received from the device. In the case of text data, it uses natural language processing (NLP) technology to extract the date, time, location, and content of the appointment. For example, from "There will be a meeting in Shinjuku at 3:00 PM next Monday," it extracts the information "Date and time: 3:00 PM next Monday," "Location: Shinjuku," and "Content: Meeting."

[1285] Step 4:

[1286] In the case of image data, the server uses optical character recognition (OCR) technology to extract text information from the image and analyze it. For example, it can extract "Shinjuku Station" from a screenshot of a map of Shinjuku Station.

[1287] Step 5:

[1288] The server saves the extracted event information in a database and adds it to the user's calendar, for example, by using the Google Calendar API to add the event to Google Calendar.

[1289] Step 6:

[1290] The server uses an emotion engine to analyze the user's input data and determine their emotional state. Emotions such as positive, negative, and neutral are extracted from the text data. For example, a message such as "I'm busy, but I'll do my best" can be used to determine a positive emotion.

[1291] Step 7:

[1292] The server suggests reminders and actions based on the stored schedule information and emotional state. If the emotional state is positive, it suggests motivating reminders, and if it is negative, it includes encouraging messages. For example, the day before a meeting, it might remind the user, "There's a meeting in Shinjuku tomorrow. Do your best!"

[1293] Step 8:

[1294] The server notifies the device of suggested reminders and actions. It also uses the LINE API to send messages to users. For example, it sends a notification one hour before a meeting saying, "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[1295] Step 9:

[1296] Users provide feedback on reminders and actions, which is entered through chat and sent from the device to the server.

[1297] Step 10:

[1298] The server updates the AI ​​model based on the collected feedback, improving the accuracy of future reminders and action suggestions, and providing a more personalized service.

[1299] In this way, the present invention, which combines an emotion engine, can provide appropriate reminders and action suggestions according to the user's emotional state, thereby providing more personalized schedule management.

[1300] Example 2

[1301] 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."

[1302] In today's busy lifestyles, it is extremely important for users to efficiently manage their schedules and receive reminders and actions based on those schedules. However, existing systems do not take the user's emotional state into account when managing schedules, and as a result, they are unable to provide appropriate reminders and actions to users. In addition, there is a lack of an intuitive way to input schedules through chat or image data, which results in low usability.

[1303] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content of the schedule, means for saving the extracted schedule information, means for proposing reminders and actions based on the saved schedule information, means for notifying the user of the suggested reminders and actions, means for recognizing the emotional state of the user based on data input by the user, and means for adjusting the content of the reminders and actions based on the emotional state. This makes it possible to suggest appropriate reminders and actions according to the user's emotional state, thereby realizing more personalized schedule management. In addition, usability can be improved by intuitive schedule input means using chat format or image data.

[1304] The "means for receiving schedule information from the user" has the function of receiving schedule information in the form of a text message or image data entered by the user and transmitting that data to the server.

[1305] "Means for analyzing schedule information and extracting the date, time, location, and content of the schedule" refers to a function that uses natural language processing technology and optical character recognition technology to identify and extract items such as the date, time, location, and content from the input schedule information.

[1306] The "means for saving the extracted schedule information" is a means having a function for saving the extracted schedule information in a database or a calendar service.

[1307] The "means for suggesting reminders and actions" has the function of generating and suggesting appropriate reminders and actions to the user based on the saved schedule information.

[1308] The "means for notifying the user of reminders and suggested actions" has the function of sending and displaying generated reminders and suggested actions on the user's device.

[1309] "Means for collecting feedback from users and improving the accuracy of reminders and actions" refers to a function that receives feedback provided by users, analyzes it, and improves the accuracy of reminders and actions.

[1310] An "emotion recognition means" is a device that has the function of recognizing an emotional state based on user input data using natural language processing and machine learning technology.

[1311] The "means for adjusting the content of reminders and actions based on emotional state" has the function of changing the content of reminders and actions according to the recognized emotional state and making more appropriate suggestions to the user.

[1312] "Text data" refers to character information entered in chat format.

[1313] "Image data" refers to information entered in the form of an image, such as a screenshot or photograph.

[1314] Optical character recognition (OCR) is a technology that analyzes characters in image data and extracts them as text information.

[1315] This invention is a system that allows users to easily input schedule information, and suggests reminders and actions based on that schedule, and also combines it with an emotion engine that recognizes the user's emotions. The main components of the system are as follows:

[1316] 1. Means of receiving schedule information from users

[1317] Users enter schedule information using chat-style text messages or screenshots. For example, a user might type, "I have a meeting in Shinjuku next Monday at 3 PM," or take a screenshot of a map of Shinjuku Station. The device receives this input data, converts it into an appropriate format, and sends it to the server. By utilizing the LINE API, text messages can be received, and text information can be extracted from screenshots using OCR technology such as PyTesseract.

[1318] 2. Analysis of schedule information

[1319] The server analyzes the received text messages and screenshots. For text data, natural language processing technology (e.g., SpaCy) is used to extract the date, time, location, and content of the event. For screenshot data, optical character recognition technology (e.g., Tesseract) is used to extract character information and perform the same analysis.

[1320] 3. Save event information

[1321] The server saves the extracted schedule information in a database. It also adds this schedule information to the user's calendar using the Google Calendar API. For example, event information such as "Meeting in Shinjuku at 3:00 PM next Monday" is reflected in the calendar.

[1322] 4. Emotion recognition

[1323] The emotion engine built into the server analyzes input data from users to determine their emotional state. The emotion engine uses natural language processing libraries such as TextBlob to extract emotions such as positive, negative, and neutral from text data. For example, a message such as "I'm busy, but I'll do my best" will determine a positive emotion, while a message such as "I'm tired" will determine a negative emotion.

[1324] 5. Reminders and action suggestions

[1325] The server suggests reminders and actions based on the stored schedule information and the emotional state determined by the emotion engine. If the emotional state is positive, it suggests a motivating reminder, and if it is negative, it includes an encouraging message. For example, the day before a meeting, it might send a reminder such as, "There's a meeting in Shinjuku tomorrow. Do your best!"

[1326] 6. Reminders and Action Notifications

[1327] The server notifies the device of the suggested reminders and actions. It uses the LINE API to send messages so that users receive a notification one hour before the meeting, such as "There's a meeting in Shinjuku soon. Do you have the necessary materials?"

[1328] 7. Gather feedback and improve accuracy

[1329] Users provide feedback on reminders and actions. The feedback is entered through chat and collected by the server. The collected feedback is used to improve the accuracy of future reminders and action suggestions.

[1330] Examples of concrete examples and prompts

[1331] Example 1: Schedule entry in chat format and emotion recognition

[1332] 1. A user types in LINE, "I have a meeting in Shinjuku next Monday at 3pm," and then sends an additional message saying, "I'm busy, but I'll do my best."

[1333] 2. The device sends this to the server.

[1334] 3. The server extracts the schedule information and the emotion engine recognizes "positive emotions."

[1335] 4. The server adds an event to Google Calendar and sends a positive reminder the day before the meeting: "There's a meeting in Shinjuku tomorrow. Good luck!"

[1336] Example 2: Schedule entry and emotion recognition using screenshots

[1337] 1. A user takes a screenshot of a map of Shinjuku Station on their device and sends the message "I'm tired."

[1338] 2. The device sends this to the server.

[1339] 3. The server uses OCR to extract "Shinjuku Station" and the emotion engine recognizes "negative emotion."

[1340] 4. The server adds the event to Google Calendar and notifies you one hour before the meeting, saying, "There's a meeting in Shinjuku soon. Relax and do your best."

[1341] Example prompt sentence:

[1342] Enter "I have a meeting in Shinjuku next Monday at 3pm."

[1343] Take a screenshot of a map of Shinjuku Station and type "tired."

[1344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1345] Step 1: Enter event information from the user

[1346] A user inputs schedule information as a text message. For example, a user might use the LINE app to input, "I have a meeting in Shinjuku next Monday at 3 PM." The text message and image data are sent to the device as input data. The device then converts the data into an appropriate format and sends it to the server.

[1347] Specifically, it receives text messages using the LINE API, extracts text data from screenshots using PyTesseract, and sends the converted data to the server.

[1348] Step 2: Analyze the schedule information

[1349] When the server receives a text message, it analyzes it using natural language processing technology (e.g., SpaCy). It extracts the date, time, location, and content from the text message received as input data. In the case of image data, it also uses optical character recognition technology (e.g., Tesseract) to extract and analyze the text information.

[1350] Specifically, from a text message such as "There will be a meeting in Shinjuku next Monday at 3 PM," SpaCy is used to extract "Date and time: next Monday at 3 PM," "Location: Shinjuku," and "Content: Meeting." Additionally, Tesseract is used to extract the text information for "Shinjuku Station" from a screenshot.

[1351] Step 3: Save the event information

[1352] The server stores the extracted event information in a database, including the extracted date, time, location, and content, and also adds the event information to the user's calendar using the Google Calendar API.

[1353] Specifically, the Google Calendar API is used to add event information such as "Meeting in Shinjuku at 3pm next Monday" to the user's Google Calendar.

[1354] Step 4: Emotion Recognition

[1355] The server uses an emotion engine (e.g., TextBlob) to determine the emotional state of the user from the input data. It analyzes the text messages received as input data and extracts emotions such as positive, negative, or neutral.

[1356] In terms of specific actions, the message "I'm busy, but I'll do my best" is perceived as a positive emotion, while the message "I'm tired" is perceived as a negative emotion.

[1357] Step 5: Reminders and suggested actions

[1358] The server then suggests reminders and actions based on the stored schedule information and the results of emotion recognition. The suggestions are tailored to the user's emotional state: positive emotions include motivational reminders, and negative emotions include encouraging messages.

[1359] Specifically, if the emotion is positive, it generates a reminder such as "There is a meeting in Shinjuku tomorrow. Do your best!", and if the emotion is negative, it generates a reminder such as "There is a meeting in Shinjuku soon. Relax and do your best."

[1360] Step 6: Reminders and Action Notifications

[1361] The server notifies the device of the generated reminders and actions by sending messages using the LINE API, notifying the user at the appropriate time (for example, one hour before the meeting).

[1362] Specifically, the message "There's a meeting coming up in Shinjuku. Do you have the necessary materials?" is sent using the LINE API.

[1363] Step 7: Gather feedback and refine

[1364] Users provide feedback on the reminders and actions they receive. The server collects and stores the feedback in a database. The collected feedback is used to improve the accuracy of future reminders and action suggestions.

[1365] Specifically, users send feedback via LINE chat, such as "The reminder was too early," and this data is collected and applied to the machine learning model to improve the accuracy of future suggestions.

[1366] (Application example 2)

[1367] 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."

[1368] In modern society, busy users find it difficult to manage their schedules and receive reminders and action suggestions that are appropriate for their emotional state. In particular, there are limited means to receive timely, personalized information and support while shopping in physical stores. This leads to a decline in the quality of the shopping experience and a decrease in user satisfaction. To address this issue, there is a need to propose a system that combines user schedule management and emotion recognition to make users' lives more comfortable.

[1369] 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.

[1370] In this invention, the server includes means for receiving schedule information from a user, means for analyzing the schedule information and extracting the date, time, location, and content of the schedule, means for saving the extracted schedule information, means for suggesting reminders and actions based on the saved schedule information, means for notifying the user of the suggested reminders and actions, means including an emotion recognition engine for analyzing the emotional state of the user and adjusting the reminders and actions based on the analyzed emotional state, means for collecting feedback from the user and improving the accuracy of the reminders and actions, means for notifying the user using a location information service when the user approaches a place to be visited, and means for providing product details and campaign information related to the place to be visited. This makes it possible to provide personalized reminders and actions according to the user's emotional state and improve the shopping experience.

[1371] "User" refers to a person who uses the system to input schedule information and receive reminders and suggested actions.

[1372] "Schedule information" refers to information indicating a user's future schedule, including specific dates, times, locations, and contents.

[1373] The "date, time, place, and content of the appointment" refers to the specific date, time, place, and details of the appointment extracted from the appointment information.

[1374] "Remind" refers to a notification or alert that reminds a user of an appointment.

[1375] "Action" refers to specific actions or suggestions that users should take based on schedule information and emotional state.

[1376] An "emotion recognition engine" refers to a system that analyzes input data from a user and determines their emotional state.

[1377] "Feedback" refers to the ratings and comments you provide in response to reminders and suggested actions.

[1378] "Location information services" refers to services that use GPS, Wi-Fi, etc. to identify the user's location and provide information based on that location.

[1379] "Notification" means a message or alert intended to inform the User of a reminder or suggested action.

[1380] "Product details and campaign information" refers to information about products that can be obtained at the locations or stores that users visit, as well as information about current special offers and discounts.

[1381] MODE FOR CARRYING OUT THE INVENTION

[1382] This invention provides a system that allows users to easily input schedule information, suggests reminders and actions based on the schedule, and recognizes the user's emotional state. Specific embodiments are described below. The system of the present invention is composed of the following elements:

[1383] 1. Means of receiving schedule information from users

[1384] Users use a smartphone application to input schedule information. This information is entered as chat-style text data or image data (screenshots). For example, a user might enter a message such as, "I plan to go to ABC Department Store at 2:00 PM on Saturday. I'm busy, but I'm looking forward to shopping."

[1385] 2. Analysis and extraction of schedule information

[1386] The server analyzes text messages and image data received from smartphone devices. For text data, the server uses the Google Cloud Natural Language API to analyze and extract the date, time, location, and content of the event. For image data, the server uses the Google Cloud Vision API to extract text information from the image using OCR (optical character recognition) technology.

[1387] 3. How to save schedule information

[1388] The extracted schedule information is stored in a database by the server, and at the same time, the schedule information is added to the user's calendar (e.g., Google Calendar) using the Google Calendar API.

[1389] 4. Means of determining emotional state using an emotion recognition engine

[1390] The server is equipped with an emotion recognition engine that analyzes input data from users to determine their emotional state. For example, it can recognize positive emotions from a message such as "I'm busy, but I'm looking forward to shopping."

[1391] 5. Reminders and action suggestions

[1392] The server suggests reminders and actions based on the stored schedule information and the analyzed emotional state, for example, "Enjoy a great shopping experience!" before a visit.

[1393] 6. Reminders and action notifications

[1394] Suggested reminders and actions are sent to the smartphone using Firebase Cloud Messaging. For example, when a user approaches a designated visit location, location services can be used to notify the user that "It's almost time to visit ABC Department Store."

[1395] 7. Feedback collection and refinement methods

[1396] Users provide feedback on reminders and actions. This feedback is entered through chat and collected by the server. This feedback information is used to improve the accuracy of future reminders and action suggestions.

[1397] Specific examples

[1398] Here's an example where a user enters a shopping itinerary and receives a reminder:

[1399] A user types on LINE, "I plan to go to ABC Department Store on Saturday at 2 PM. I'm busy, but I'm looking forward to shopping." The smartphone device sends this to the server, which extracts the schedule information and uses an emotion recognition engine to recognize "positive emotions." Based on this, an event is added to Google Calendar and a positive reminder is sent before the user arrives, saying, "Enjoy a great shopping experience!"

[1400] Prompt Sentence Examples

[1401] "I'm planning to go to ABC Department Store on Saturday at 2pm. I'll be busy, but I'm looking forward to shopping."

[1402] In this way, the system of the present invention can provide appropriate reminders and action suggestions based on the user's emotional state, providing a more personalized shopping experience.

[1403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1404] Step 1:

[1405] Terminal - Receiving schedule information from users

[1406] A user inputs chat-style text data or screenshots into a smartphone app. For example, they send a message saying, "I plan to go to ABC department store at 2 PM on Saturday. I'm busy, but I'm looking forward to shopping." The input data is sent to the server via the app. The server then obtains the schedule information.

[1407] Input: Text data entered by the user or screenshot

[1408] Output: Appointment information sent to the server

[1409] Specific operation: The smartphone application provides an input UI, receives text messages and images, and sends them to the server.

[1410] Step 2:

[1411] Server - Schedule information analysis

[1412] The server uses Google Cloud Natural Language API to parse the received text message and extract the date, time, location, and content of the appointment. For image data, Google Cloud Vision API is used to extract text information using OCR technology.

[1413] Input: Text data or screenshot sent to the server

[1414] Output: Extracted date, time, location, and content information

[1415] Specific operation: The server passes the received data to the analysis API and obtains the extracted data.

[1416] Step 3:

[1417] Server - Save schedule information

[1418] The extracted event information is stored in a database by the server, and the information is simultaneously added to the user's calendar (such as Google Calendar). This process uses the Google Calendar API.

[1419] Input: Extracted date, time, location, and content information

[1420] Output: Event information saved in the database, event information added to the calendar

[1421] Specific behavior: The server saves the data to the database and makes a request to the Google Calendar API.

[1422] Step 4:

[1423] Server - Emotion recognition engine analyzes emotional states

[1424] The server uses an emotion recognition engine to analyze the emotional state of the user's message, for example, recognizing a positive emotion from the message "I'm busy, but I'm looking forward to shopping."

[1425] Input: User message text

[1426] Output: Emotional state, such as positive or negative

[1427] What happens: The server passes the text to the emotion recognition engine and gets the results.

[1428] Step 5:

[1429] Server - Reminders and action suggestions

[1430] Based on the stored schedule information and the analyzed emotional state, the server will suggest reminders and actions, for example, generating a message like "Enjoy a great shopping experience!"

[1431] Input: Saved schedule information, analyzed emotional state

[1432] Output: Suggested reminders and actions

[1433] Specific operation: The server generates a message based on the data and determines the notification content.

[1434] Step 6:

[1435] Server - Reminders and action notifications

[1436] Suggested reminders and actions are sent to your smartphone using Firebase Cloud Messaging. For example, when the scheduled time approaches, you might get a notification saying, "It's almost time for your visit to ABC Department Store."

[1437] Input: Suggested reminder or action

[1438] Output: Notification sent to the user's smartphone

[1439] What happens: The server sends a notification message through Firebase Cloud Messaging.

[1440] Step 7:

[1441] Terminal - Collecting Feedback

[1442] Users can provide feedback on reminders and actions, such as rating the reminder as "helpful." This feedback is sent back to the server and used to improve the service in the future.

[1443] Input: User-provided feedback

[1444] Output: Feedback information sent to the server

[1445] What happens: The smartphone app receives the feedback and sends it to the server.

[1446] The above is a specific process flow for implementing the present invention. This system allows users to receive personalized reminders and actions, improving their shopping and schedule management experience.

[1447] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1448] 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.

[1449] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1450] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1451] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1452] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1453] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1454] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1455] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1456] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1457] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1458] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1459] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1460] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1461] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1462] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1463] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1464] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1465] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1466] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1467] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1468] The following is further disclosed regarding the above embodiment.

[1469] (Claim 1)

[1470] means for receiving schedule information from a user;

[1471] means for analyzing the schedule information and extracting the date, time, place, and content of the schedule;

[1472] a means for storing the extracted schedule information;

[1473] A way to suggest reminders and actions based on saved schedule information,

[1474] means for notifying a user of said reminders and suggested actions;

[1475] a means for collecting user feedback to improve the accuracy of said reminders and actions;

[1476] A system including:

[1477] (Claim 2)

[1478] 2. The system according to claim 1, wherein the schedule information from the user is either text data in a chat format or image data.

[1479] (Claim 3)

[1480] 3. The system of claim 2, wherein the means for analyzing the image data uses optical character recognition (OCR).

[1481] "Example 1"

[1482] (Claim 1)

[1483] means for receiving schedule information from a user;

[1484] A means for analyzing schedule information and extracting the date, time, location, and content;

[1485] means for storing the extracted schedule information in a data storage;

[1486] A way to suggest reminders and actions based on saved schedule information,

[1487] means for notifying a user of said reminders and suggested actions;

[1488] a means for collecting user feedback to improve the accuracy of said reminders and actions;

[1489] A system including:

[1490] (Claim 2)

[1491] 2. The system according to claim 1, wherein the schedule information from the user is either text data in a chat format or image data.

[1492] (Claim 3)

[1493] 3. The system of claim 2, wherein the means for analyzing the image data uses optical character recognition techniques.

[1494] "Application Example 1"

[1495] (Claim 1)

[1496] means for receiving schedule information from a user;

[1497] means for analyzing the schedule information and extracting the date, time, place, and content of the schedule;

[1498] a means for storing the extracted schedule information;

[1499] A way to suggest reminders and actions based on saved schedule information,

[1500] means for notifying a user of said reminders and suggested actions;

[1501] a means for collecting user feedback to improve the accuracy of said reminders and actions;

[1502] means for analyzing a user's voice input and extracting schedule information;

[1503] A means of providing schedule information and reminders through the in-vehicle system;

[1504] A system including:

[1505] (Claim 2)

[1506] 2. The system according to claim 1, wherein the schedule information from the user is either text data in a chat format or image data.

[1507] (Claim 3)

[1508] 10. The system of claim 1, wherein the means for analyzing the image data uses optical character recognition (OCR).

[1509] "Example 2: Combining Emotion Engines"

[1510] (Claim 1)

[1511] means for receiving schedule information from a user;

[1512] means for analyzing the schedule information and extracting the date, time, place, and content of the schedule;

[1513] a means for storing the extracted schedule information;

[1514] A way to suggest reminders and actions based on saved schedule information,

[1515] means for notifying a user of said reminders and suggested actions;

[1516] a means for collecting user feedback to improve the accuracy of said reminders and actions;

[1517] emotion recognition means for recognizing an emotional state based on input data of a user;

[1518] means for adjusting the content of reminders and actions based on said emotional state;

[1519] A system including:

[1520] (Claim 2)

[1521] 2. The system according to claim 1, wherein the schedule information from the user is either text data in a chat format or image data.

[1522] (Claim 3)

[1523] 3. The system of claim 2, wherein the means for analyzing the image data uses optical character recognition (OCR).

[1524] "Application example 2 when combining emotion engines"

[1525] (Claim 1)

[1526] means for receiving schedule information from a user;

[1527] means for analyzing the schedule information and extracting the date, time, place, and content of the schedule;

[1528] a means for storing the extracted schedule information;

[1529] A way to suggest reminders and actions based on saved schedule information,

[1530] means for notifying a user of said reminders and suggested actions;

[1531] means for adjusting reminders and actions based on the analyzed emotional state, including an emotion recognition engine for analyzing the emotional state of the user;

[1532] a means for collecting user feedback to improve the accuracy of said reminders and actions;

[1533] A means for notifying the user using location information services when the user approaches a place to be visited;

[1534] A way to provide product details and promotions related to the places you visit;

[1535] A system including:

[1536] (Claim 2)

[1537] 2. The system according to claim 1, wherein the schedule information from the user is either text data in a chat format or image data.

[1538] (Claim 3)

[1539] 10. The system of claim 1, wherein the means for analyzing the image data uses optical character recognition (OCR). [Explanation of symbols]

[1540] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving schedule information from a user; means for analyzing the schedule information and extracting the date, time, place, and content of the schedule; a means for storing the extracted schedule information; A way to suggest reminders and actions based on saved schedule information, means for notifying a user of said reminders and suggested actions; a means for collecting user feedback to improve the accuracy of said reminders and actions; A system including:

2. 2. The system according to claim 1, wherein the schedule information from the user is either text data in a chat format or image data.

3. 3. The system of claim 2, wherein the means for analyzing the image data uses optical character recognition (OCR).

Images