system

Abstract

We provide the system. [Solution] A means of receiving messages from an electronic messaging service, A means of performing natural language processing to extract schedule information from received messages, Based on the extracted schedule information, a means of registering the schedule in an electronic calendar service, A means of notifying that the schedule registration is complete, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, due to the spread of remote work and the increase in online communication between teams, scheduling using an electronic messaging service is routinely carried out. However, manually transferring the schedules and tasks determined in these messages to an electronic calendar or another task management tool is cumbersome and time - consuming, thus becoming a factor that hinders the efficiency of business operations. Also, there are problems such as transcription errors and omission of schedules being likely to occur, and it is difficult to manage information centrally. Furthermore, there is a need to save the effort of setting reminder functions individually. As a result, companies and project teams want to improve business efficiency and perform accurate information management.

Means for Solving the Problems

[0005] This invention provides a system that automatically analyzes messages received via an electronic messaging service and extracts schedule information. This system uses natural language processing technology to automatically extract the date, time, and event details from the message content and registers the schedule in an electronic calendar service. Furthermore, it can automatically set reminders based on the registered schedule, preventing information leaks and alerting users through notifications. In addition, by integrating with external applications and providing task management functionality, it automates project management, improving work efficiency and accurate information sharing. This reduces user effort and significantly improves work processes.

[0006] An "electronic messaging service" refers to a platform that allows users to send and receive text messages online.

[0007] "Schedule information" refers to information about a specific date, time, or period, as well as details of events or tasks scheduled within that timeframe.

[0008] "Natural language processing" refers to the technology that enables computers to understand and analyze the meaning of language that humans use on a daily basis.

[0009] An "electronic calendar service" refers to a tool or platform that allows users to manage their schedules in a digital format, enabling them to input and record appointments.

[0010] A "reminder" refers to a function that notifies users immediately before a specific date, time, or event, thereby reminding them of their schedule.

[0011] An "external application" refers to other software or tools that operate outside of a particular software program and can work in conjunction with it to provide specific functionalities.

[0012] "Task management" refers to the process of organizing, tracking, and completing various tasks related to a project or business.

[0013] "Automation" refers to technologies that enable systems to autonomously execute processes and tasks without the need for manual intervention. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that streamlines scheduling using an electronic messaging service. Users send text messages to an agent via the electronic messaging service, and the system automatically extracts scheduling information from the message and registers it in an electronic calendar service.

[0036] First, users send instructions regarding events or tasks through a messaging service. For example, "Project meeting next Monday at 10 AM." The server receives these messages via the messaging service's API. The received messages are processed using natural language processing by a text analysis generation AI to extract event information such as the date, time, location, and content.

[0037] The extracted information is sent to the electronic calendar service and registered as an event. During registration, the server uses an API to provide the necessary data to the electronic calendar service, and the schedule is automatically registered. At this time, with the user's permission, authentication such as OAuth 2.0 is cleared to access the user's calendar.

[0038] Additionally, reminders are set based on the schedule information. Reminders have the function of notifying users a certain amount of time before the specified date and time, allowing them to reconfirm their schedule through these notifications.

[0039] Furthermore, this system provides a function to automate task management by integrating with external applications. For example, by integrating with a project management tool, it is possible to automatically register and update tasks according to the progress of the project. This allows users to manage information consistently across multiple platforms.

[0040] As a concrete example, consider a scenario where a user sends the message, "There's a board meeting this Friday at 3 PM." This message is received by the server, and a generation AI is used to extract the information that a "board meeting" will take place "this Friday at 3 PM." The server then registers the event at the corresponding date and time through the electronic calendar and sets a reminder 30 minutes before the meeting. This allows the user to automate tasks related to that date and improve the efficiency of schedule management.

[0041] Thus, the present invention provides a system that efficiently manages information from electronic messaging services and enables automated schedule registration and reminder setting.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users send messages containing information about specific dates, times, or events through an electronic messaging service. For example, a message might say, "Team meeting tomorrow at 2 PM."

[0045] Step 2:

[0046] The server uses the Message Service API to receive messages sent from specific channels or direct messages. Webhooks are configured to retrieve messages in real time.

[0047] Step 3:

[0048] The server passes the received message to a generative AI model capable of natural language processing, which then analyzes the message's content. The analysis extracts date and time information such as the event name, date, and location.

[0049] Step 4:

[0050] The server prepares to register the analyzed schedule information using the electronic calendar service API. At this stage, it verifies the user's access rights to the calendar and, if necessary, goes through an authentication process.

[0051] Step 5:

[0052] The server adds a new schedule to the electronic calendar service based on the extracted information. This includes attributes such as the event title, date and time, and location.

[0053] Step 6:

[0054] The server automatically sets reminders based on registered events. The timing of the reminder can be set to a default time based on the user's past settings, for example, 10 minutes before the event.

[0055] Step 7:

[0056] The server confirms that the schedule registration and reminder setting were successful and sends a message to the user via the electronic messaging service to notify them of the result.

[0057] Step 8:

[0058] If necessary, the server will interact with an external task management system based on the results of message analysis and register or update the schedule as a project task.

[0059] This process reduces the user's effort and enables accurate registration and management of information.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In modern business and personal schedule management, users often struggle with efficient scheduling due to the use of multiple platforms. Furthermore, manual scheduling and reminder setting are time-consuming and labor-intensive, and prone to human error. Additionally, a lack of information integration with external applications makes maintaining information consistency across multiple systems difficult.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for receiving electronic messages, means for extracting schedule information from the received electronic messages using a generation AI, means for registering the extracted schedule information in an electronic calendar and setting reminders, and means for automatically registering or updating information in cooperation with external software. This enables users to efficiently manage their schedules, eliminates the need for manual input and settings, and realizes consistent information management across multiple platforms.

[0065] "Electronic message" refers to text information transmitted via a digital communication platform.

[0066] "Generative AI" is a type of artificial intelligence technology that uses machine learning to process and analyze natural language and extract useful information.

[0067] "Natural language processing" refers to technologies aimed at enabling computers to understand and process human language.

[0068] "Schedule information" refers to a collection of data related to a schedule, such as a specific date and time, location, and event name.

[0069] An "electronic calendar" refers to a system that records schedules of appointments and events in a digitized format.

[0070] A "reminder" refers to a function that notifies the user of an appointment at a specific time.

[0071] "External software" refers to other platforms or applications that function in conjunction with this system.

[0072] "Automatic information registration or updating" refers to the process by which a system automatically records or modifies data without human intervention.

[0073] This invention provides a system that utilizes an electronic messaging service to automatically register schedule information in an electronic calendar and set reminders, thereby streamlining schedule management for users.

[0074] Users send electronic messages to the server using messaging applications. These messaging applications typically include communication applications and business chat applications.

[0075] The server receives electronic messages provided by users through the messaging service's API. Simultaneously, it utilizes generative AI models such as BERT and GPT to process the natural language contained in the received messages. This automatically extracts important scheduling information, such as dates, times, and event details.

[0076] The extracted schedule information is registered with the electronic calendar service by the server. This process utilizes a RESTful API, allowing the server to access the electronic calendar, obtain permission from the user, and then register the event. OAuth 2.0 can be used for the authentication process, thereby protecting user privacy.

[0077] The server also automatically sets reminders based on appointments registered in the electronic calendar. It sends notifications to users before a specified time to prompt them to review and prepare for their appointments. These notifications could be sent via push notifications or email.

[0078] Furthermore, the server can integrate with external information management software to centrally manage user information. For example, by linking with task management tools and business management tools, it can automate task updates and provide unified information management.

[0079] As a concrete example, if a user sends an electronic message saying, "Please schedule a sales meeting for 10 AM next Monday," the server receives this message and uses a generative AI model to extract the information "sales meeting for 10 AM next Monday." It then registers this event in the electronic calendar and sets a reminder to send a push notification 30 minutes before the meeting.

[0080] An example of a prompt message is, "Please schedule a client visit for next Friday at 3 PM." This allows users to easily automate their schedule management.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user creates an electronic message containing instructions regarding the date and time using a messaging application and sends that message. The input is a text message created by the user, and the server receives that message as output. For example, the user sends a prompt message saying, "Please set up a sales meeting for next Wednesday at 2pm."

[0084] Step 2:

[0085] The server receives electronic messages from users through the messaging application's API. The input here is text data obtained via the messaging service, and the output is the received message itself. Specifically, the server uses the messaging platform's API to ingest the data and prepare it for parsing the message content.

[0086] Step 3:

[0087] The server uses a generative AI model to perform natural language processing based on the received electronic message. The input is the user's text message, and the output is extracted schedule information (e.g., date, time, and event name). Data processing at this stage involves the AI ​​model identifying specific keywords and phrases from the text and extracting details of specific dates and events. For example, the information "Next Wednesday at 2pm" and the event "Sales meeting" might be extracted.

[0088] Step 4:

[0089] The server uses the extracted schedule information to register appointments in the electronic calendar. The input is the schedule information extracted in step 3, and the output is the event registered in the electronic calendar. The specific operation includes a process in which the server uses the calendar service API to send the appointment information and authenticates it to ensure it is properly registered in the user's calendar.

[0090] Step 5:

[0091] The server automatically sets reminders based on registered appointments. The input is the event information added to the calendar, and the output is the reminder that is notified to the user. Specifically, the reminder is set 30 minutes before the appointment, and the actual notification arrives on the user's device as an email or push notification.

[0092] Step 6:

[0093] The server integrates with external information management software to automatically register or update necessary information. Inputs include scheduled schedule information and integration information with external software, while outputs are updated task and project data. Specific operations include, for example, accessing the API of a project management tool to add new entries to the task list.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In modern work environments, staff scheduling and shift adjustments are often done manually, leading to inefficiencies. In particular, when sudden shift changes or adjustments to work preferences are needed, there are few means to quickly reflect these changes, potentially disrupting business operations. Furthermore, complex management tasks are required for managers to grasp the overall schedule, placing a significant burden on them. To address these challenges and effectively support business operations, there is a need for automated schedule management.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for receiving messages using electronic communication technology, means for performing natural language processing to extract time and location information from the received messages, means for registering schedules in an electronic schedule management means based on the extracted time and location information, and means for making personnel allocation information necessary for supporting actual work available. This enables rapid adjustment of staff work preferences and shifts, thereby improving the efficiency of business operations.

[0099] "Electronic communication technology" refers to all technologies for exchanging digital data between distant locations, and is primarily used as a means of transmitting information through email and messaging applications.

[0100] "Means for receiving messages" refers to a method of obtaining messages sent by users via communication terminals or servers, and includes a device that has the function of converting the content of those messages into a format that can be used for subsequent processing.

[0101] "Natural language processing" is a technology that enables computers to understand and process the language that humans use in everyday life. Its primary purpose is to recognize the meaning and intent within text and extract structured information.

[0102] "Time and location information" refers to the date, time, and specific location information included in a message, which are elements used to determine the details of a schedule or event.

[0103] "Electronic schedule management means" refers to applications and systems that operate on digital devices and are software that supports time management for individuals and organizations by allowing them to add, delete, and modify appointments.

[0104] "Notification means" refers to a mechanism for notifying users of specific information or changes in status, and is primarily a device or method that transmits information visually or audibly using alerts or messages.

[0105] "Means of making personnel allocation information manageable" refers to systems and processes that process information to determine the allocation and roles of staff necessary for a task, and that support appropriate personnel adjustments based on that information.

[0106] In order to implement this invention, a system is needed in which a server, a terminal, and a user cooperate to function.

[0107] First, the server receives messages from terminals using electronic communication technology. On the terminal, users input and send work requests or schedule changes using a messaging application. The server stores the received messages and performs natural language processing using a generative AI model to analyze their content. Through this analysis, information about time and location is extracted from the messages, and this is used to register specific schedules.

[0108] The server sends the extracted information to electronic scheduling software (e.g., Google® Calendar API) and updates the schedule as a registered event. Additionally, when a schedule is registered, a notification is sent to the user's device to inform them of the registration completion.

[0109] Furthermore, the server utilizes the extracted schedule data as input information to optimize overall staffing. This process makes it possible to instantly reflect users' work preferences and shift adjustments.

[0110] As a concrete example, consider a case where a user sends a message from their device stating, "I would like to work from 9 AM to 12 PM tomorrow." This message is received by the server, and analysis is performed based on the prompt message of the generating AI model: "Analyze the work request and add it to the calendar." The extracted information is registered in the calendar using the Google Calendar API, and the user's device receives a notification that the registration is complete.

[0111] In implementing this system, messaging application APIs are utilized to efficiently process messages, and a natural language processing engine enables accurate data extraction. As a result, user schedule management is automated, leading to increased efficiency in business operations.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The terminal transmits a message indicating the user's work preferences. The input data is the user's message content, which is sent to the server via electronic communication technology. The output is the message data received by the server.

[0115] Step 2:

[0116] The server stores messages received from terminals and performs natural language processing on these messages using a generative AI model. The input is message data, and information about time and location is extracted using the prompt "Analyze work requests and add them to the calendar." The output is the extracted date, time, and location data.

[0117] Step 3:

[0118] The server sends an API request to an electronic schedule management system (e.g., Google Calendar API) to register an event based on the extracted date, time, and location information. The input data is the extracted date, time, and location information, and sending this to the electronic schedule management system adds the event. The output is confirmation data that the schedule has been registered.

[0119] Step 4:

[0120] The server receives a registration completion notification from the electronic schedule management system and sends that notification to the terminal. The input is the schedule registration confirmation data, which is then notified to the user. The output is the notification message displayed on the user's terminal.

[0121] Step 5:

[0122] The server incorporates registered schedule information into the staffing optimization process and reflects it in the plan for business support. The input data is schedule information, which is processed to generate appropriate staffing data. The output is data for the optimized staffing plan.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention improves the user experience by combining a system that analyzes user messages using an electronic messaging service to extract schedule information and streamline schedule management with an emotion engine that recognizes user emotions.

[0125] First, users send information about their schedules and tasks via text messages through an electronic messaging service. These messages may contain the user's emotions. The server receives these messages using the messaging service API and employs natural language processing and sentiment engines to analyze them.

[0126] The received message undergoes natural language processing using a generative AI model on the server, extracting date and time information such as events. Simultaneously, the emotion engine recognizes the user's emotions from the text within the message and classifies them into categories such as positive, negative, and neutral.

[0127] After the schedule information is extracted and the user's emotions are recognized, the server registers the schedule using the electronic calendar service API. At this stage, it is possible to adjust the notification method and timing based on the emotional information. For example, if the user indicates negative emotions, reminder notifications can be delivered more gently or the timing adjusted.

[0128] Furthermore, emotional information can be sent to external applications, allowing for dynamic adjustment of task priorities based on the user's emotions in conjunction with project management tools. This kind of integration enables more flexible task management tailored to the project's progress.

[0129] For example, if a user sends a message such as, "I have a sales meeting tomorrow at 3 PM, and I'm worried," the server receives this message and extracts "Sales meeting tomorrow at 3 PM" as scheduling information. The emotion engine also recognizes a negative emotion from the expression "I'm worried." Based on this information, the server can add an encouraging message when setting a reminder or set up a separate task prompting the user to consult with a senior colleague.

[0130] Thus, the system of the present invention can capture the user's emotions through information from electronic messaging services and provide support tailored to the individual user's psychological state, in addition to schedule management.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] Users send text messages containing instructions to the system using an electronic messaging service. These messages include specific dates and times, details of events, and expressions of the user's feelings related to those events.

[0134] Step 2:

[0135] The server receives messages via the electronic messaging service's API. The received messages are automatically passed to the parsing process.

[0136] Step 3:

[0137] The server uses natural language processing to analyze the message content and extract date, time, and event information. Simultaneously, it uses an emotion engine to analyze emotional expressions within the message and identify the user's emotional state. This emotion is classified into categories such as positive, negative, and neutral.

[0138] Step 4:

[0139] The server uses the extracted schedule information to register the schedule via the electronic calendar service API. At this time, the event name and date are included as detailed schedule information, and the event is added to the user's calendar.

[0140] Step 5:

[0141] The server sets reminders based on the recognized emotional state of the user. For example, if the user's emotions are negative, the server will soften the tone of the notification or adjust the frequency of the notification.

[0142] Step 6:

[0143] The server interacts with external applications and sends emotional information to the task management system. This supports the setting and adjustment of task priorities based on the user's emotions.

[0144] Step 7:

[0145] Finally, the server notifies the user that the schedule registration and sentiment-based adjustments are complete. This notification is sent via an electronic messaging service, allowing the user to check their schedule.

[0146] This processing flow allows the system to comprehensively analyze the content of electronic messages and enable flexible responses to improve the user experience.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] While conventional schedule management systems offer the functionality to extract schedule information and register it in electronic planning services, they lack the ability to provide notifications and task management that take user emotions into consideration, making it difficult to meet individual needs. Furthermore, the lack of appropriate responses and support tailored to user emotions highlights the need for improved user experience.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for receiving information from electronic communication services, means for performing language processing to extract schedule information from the received information, and means for recognizing emotions from the transmitted information. This enables flexible notification methods and adjustments to task management based on the user's emotions.

[0152] "Electronic communication services" refer to technologies for sending and receiving messages and information in digital format.

[0153] "Information" refers to data and messages that users transmit through electronic communication services.

[0154] "Language processing" refers to the technology of analyzing natural language and extracting specific information or meaning.

[0155] "Schedule information" refers to data about dates, times, and events extracted from messages and digital documents.

[0156] "Electronic planning services" refer to platforms for managing schedules and appointments in a digital format.

[0157] "Emotion recognition means" refers to technologies that identify and classify a user's emotions from messages and text.

[0158] "Means of adjusting notification methods" refers to technologies that change the content and timing of notifications according to the user's emotional state.

[0159] "External application" refers to integration with software and tools provided by third parties.

[0160] This invention is a system that analyzes information transmitted by users using electronic communication services, extracts schedule information, recognizes user emotions, and streamlines schedule management. Users transmit messages regarding appointments and tasks via electronic communication services using computer terminals or mobile devices. These messages are expected to contain information about the individual's emotions.

[0161] The server utilizes a message service API to receive information sent by users in real time. After receiving the information, it uses a generative AI model to perform natural language processing and extract scheduled information, i.e., data related to dates, times, and events, from the message. In this process, the server uses a specific language algorithm to perform complex text analysis.

[0162] Furthermore, the emotion recognition engine within the server analyzes message content to understand the user's emotional state. Emotions are categorized as positive, negative, or neutral. This categorized emotion information plays a crucial role in schedule management.

[0163] For example, if a user sends the message, "I have a very important meeting tomorrow at 3pm, and I'm feeling a little anxious," the server extracts the scheduled information, "meeting tomorrow at 3pm," from this message. At the same time, it recognizes a negative emotion from the expression "I'm feeling anxious." Based on this, when the server registers the meeting using the electronic planning service API, it can set a notification with a gentler tone or adjust the timing to send a reminder a little earlier before the meeting.

[0164] Examples of prompt statements to input into a generative AI model are as follows:

[0165] "Please extract the scheduled information and emotions from the following sentence: 'I have a very important meeting tomorrow at 3pm, and I'm feeling a little anxious.'"

[0166] In this way, it becomes possible to achieve flexible schedule management and individualized support while taking user emotions into consideration.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user sends a message from their terminal using an electronic communication service. This message contains information about their plans and feelings. The server receives the text message from the user as input, and a message reception log is generated as output.

[0170] Step 2:

[0171] The server retrieves messages received via the message service API. The input for this step is a message sent by the user. The data processing involves checking the message format and pre-processing it, preparing the message as output in a format suitable for subsequent parsing.

[0172] Step 3:

[0173] The server uses a generated AI model to perform natural language processing on incoming messages. Specifically, it extracts scheduled information (date, time, and event name) from the messages. The input for this step is pre-processed message data. Data processing includes keyword extraction and contextual analysis, and the output is the extracted scheduled information.

[0174] Step 4:

[0175] The server uses an emotion recognition engine to analyze emotional information within messages. The input is the user's message text, which is categorized as positive, negative, or neutral based on the emotion recognition algorithm. The output of this process is information about the user's emotions.

[0176] Step 5:

[0177] The server uses the electronic planning service API to register appointments in the calendar based on the extracted appointment and sentiment information. The input consists of appointment and sentiment information, and data processing combines these to configure notification settings. The output is the registered schedule and, if necessary, the adjusted notification message.

[0178] Step 6:

[0179] Through integration with external applications, the server registers or updates tasks in project management tools and other applications based on emotional information. The input here is information obtained through emotion recognition. The output includes task priorities adjusted according to the emotion, and registration data for external tools.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] Currently, there are systems that extract schedules from messages received via electronic communication services and manage them accordingly, but these systems cannot take into account the user's emotional state. Therefore, flexible schedule management that reduces the user's psychological burden and provides a better user experience is difficult. Furthermore, there is the challenge of not being able to adjust task priorities by integrating with external applications that utilize emotional information.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes means for receiving messages from an electronic communication service, means for performing natural language processing to extract schedule information from the received messages, means for acquiring emotional information using an emotional recognition engine that analyzes the emotions in the messages, and means for adjusting the notification method based on the emotional information. This enables schedule management that reflects the user's emotional state.

[0185] "Electronic communication services" are services that enable users to send and receive text messages and information via the internet or other communication networks.

[0186] "Natural language processing" is a technology that enables computers to understand and process human language, and in particular, it is a process that extracts information from text data and interprets the meaning of sentences.

[0187] An "electronic calendar service" is a system that allows users to register, manage, and share appointments online, providing a calendar function that can be accessed in a digital format.

[0188] An "emotion recognition engine" is a technology that identifies and classifies human emotions from text and audio, and then processes information based on the results.

[0189] "Adjusting notification methods" is the process of dynamically changing the content, timing, and method of notifications according to the user's situation and emotions, in order to provide the optimal user experience.

[0190] This invention involves a system installed in a home robot that analyzes messages from the user via electronic communication services to manage schedules and recognize emotions. This enables flexible schedule adjustments that take the user's emotions into consideration, as well as task management according to priority.

[0191] The server first receives messages from electronic communication services. Embedded hardware such as a Raspberry Pi is used for this purpose. The messages are parsed using natural language processing techniques to extract date, time, and event information. Python and the Natural Language Toolkit (nltk) library are used for this process.

[0192] Simultaneously, an emotion recognition engine analyzes the message and extracts emotional information. This engine classifies the message as positive, negative, or neutral, and identifies the user's psychological state.

[0193] The device registers appointments to an electronic calendar service based on the extracted schedule information. Based on the registered appointments, the system provides optimal notifications that take into account the user's emotions. For example, if negative emotions are detected, a voice notification in a gentle tone is provided.

[0194] Furthermore, the server integrates with work management tools to automatically adjust task priorities based on emotional information. For example, it uses the project management tool's API to list actionable tasks that reduce user anxiety.

[0195] For example, if a user sends a message saying, "I have an important presentation on Friday and I'm nervous," the server receives this message, extracts "presentation on Friday" as a date, and recognizes the negative emotion of nervousness. Based on this information, on the day of the presentation, it sends an encouraging voice message such as, "You can do it! Good luck!" and sets a high priority for presentation preparation.

[0196] Examples of prompt messages are as follows:

[0197] "Design a program that analyzes user messages to extract schedule and sentiment information, and then has a home robot provide appropriate voice notifications."

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server receives messages from users via electronic communication services. These received messages become input, and their contents are stored for processing in the next step.

[0201] Step 2:

[0202] The server analyzes received messages using natural language processing techniques. Specifically, it analyzes text data using Python and the Natural Language Toolkit (nltk) library, extracting date and event information from the message. This results in the output of the date and time information.

[0203] Step 3:

[0204] The server uses an emotion recognition engine to analyze the sentiment of messages. The emotion recognition engine takes a message as input, examines the vocabulary and context within the text, and classifies the sentiment as positive, negative, or neutral. The output is sentiment information.

[0205] Step 4:

[0206] The device calls the calendar API to register the event in the electronic calendar service based on the extracted schedule information. The schedule information is used as input, and an output confirms that it has been registered as a new event in the electronic calendar.

[0207] Step 5:

[0208] The server adjusts the notification method based on emotional information. In particular, if the emotional state is negative, it generates a voice notification in a gentle tone. Emotional information is used as input, and an adjusted notification message is output.

[0209] Step 6:

[0210] The device uses the task management tool's API to prioritize tasks based on emotional information. Specifically, it adds new tasks to the task management list with priorities corresponding to the emotional state. Emotional information and schedule information are input, and an updated task list is output.

[0211] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] This invention is a system that streamlines scheduling using an electronic messaging service. Users send text messages to an agent via the electronic messaging service, and the system automatically extracts scheduling information from the message and registers it in an electronic calendar service.

[0228] First, users send instructions regarding events or tasks through a messaging service. For example, "Project meeting next Monday at 10 AM." The server receives these messages via the messaging service's API. The received messages are processed using natural language processing by a text analysis generation AI to extract event information such as the date, time, location, and content.

[0229] The extracted information is sent to the electronic calendar service and registered as an event. During registration, the server uses an API to provide the necessary data to the electronic calendar service, and the schedule is automatically registered. At this time, with the user's permission, authentication such as OAuth 2.0 is cleared to access the user's calendar.

[0230] Additionally, reminders are set based on the schedule information. Reminders have the function of notifying users a certain amount of time before the specified date and time, allowing them to reconfirm their schedule through these notifications.

[0231] Furthermore, this system provides a function to automate task management by integrating with external applications. For example, by integrating with a project management tool, it is possible to automatically register and update tasks according to the progress of the project. This allows users to manage information consistently across multiple platforms.

[0232] As a concrete example, consider a scenario where a user sends the message, "There's a board meeting this Friday at 3 PM." This message is received by the server, and a generation AI is used to extract the information that a "board meeting" will take place "this Friday at 3 PM." The server then registers the event at the corresponding date and time through the electronic calendar and sets a reminder 30 minutes before the meeting. This allows the user to automate tasks related to that date and improve the efficiency of schedule management.

[0233] Thus, the present invention provides a system that efficiently manages information from electronic messaging services and enables automated schedule registration and reminder setting.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users send messages containing information about specific dates, times, or events through an electronic messaging service. For example, a message might say, "Team meeting tomorrow at 2 PM."

[0237] Step 2:

[0238] The server uses the Message Service API to receive messages sent from specific channels or direct messages. Webhooks are configured to retrieve messages in real time.

[0239] Step 3:

[0240] The server passes the received message to a generative AI model capable of natural language processing, which then analyzes the message's content. The analysis extracts date and time information such as the event name, date, and location.

[0241] Step 4:

[0242] The server prepares to register the analyzed schedule information using the electronic calendar service API. At this stage, it verifies the user's access rights to the calendar and, if necessary, goes through an authentication process.

[0243] Step 5:

[0244] The server adds a new schedule to the electronic calendar service based on the extracted information. This includes attributes such as the event title, date and time, and location.

[0245] Step 6:

[0246] The server automatically sets reminders based on registered events. The timing of the reminder can be set to a default time based on the user's past settings, for example, 10 minutes before the event.

[0247] Step 7:

[0248] The server confirms that the schedule registration and reminder setting were successful and sends a message to the user via the electronic messaging service to notify them of the result.

[0249] Step 8:

[0250] If necessary, the server will interact with an external task management system based on the results of message analysis and register or update the schedule as a project task.

[0251] This process reduces the user's effort and enables accurate registration and management of information.

[0252] (Example 1)

[0253] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0254] In modern business and personal schedule management, users often struggle with efficient scheduling due to the use of multiple platforms. Furthermore, manual scheduling and reminder setting are time-consuming and labor-intensive, and prone to human error. Additionally, a lack of information integration with external applications makes maintaining information consistency across multiple systems difficult.

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

[0256] In this invention, the server includes means for receiving electronic messages, means for extracting schedule information from the received electronic messages using a generation AI, means for registering the extracted schedule information in an electronic calendar and setting reminders, and means for automatically registering or updating information in cooperation with external software. This enables users to efficiently manage their schedules, eliminates the need for manual input and settings, and realizes consistent information management across multiple platforms.

[0257] "Electronic message" refers to text information transmitted via a digital communication platform.

[0258] "Generative AI" is a type of artificial intelligence technology that uses machine learning to process and analyze natural language and extract useful information.

[0259] "Natural language processing" refers to technologies aimed at enabling computers to understand and process human language.

[0260] "Schedule information" refers to a collection of data related to a schedule, such as a specific date and time, location, and event name.

[0261] An "electronic calendar" refers to a system that records schedules of appointments and events in a digitized format.

[0262] A "reminder" refers to a function that notifies the user of an appointment at a specific time.

[0263] "External software" refers to other platforms or applications that function in conjunction with this system.

[0264] "Automatic information registration or updating" refers to the process by which a system automatically records or modifies data without human intervention.

[0265] This invention provides a system that utilizes an electronic messaging service to automatically register schedule information in an electronic calendar and set reminders, thereby streamlining schedule management for users.

[0266] Users send electronic messages to the server using messaging applications. These messaging applications typically include communication applications and business chat applications.

[0267] The server receives electronic messages provided by users through the messaging service's API. Simultaneously, it utilizes generative AI models such as BERT and GPT to process the natural language contained in the received messages. This automatically extracts important scheduling information, such as dates, times, and event details.

[0268] The extracted schedule information is registered with the electronic calendar service by the server. This process utilizes a RESTful API, allowing the server to access the electronic calendar, obtain permission from the user, and then register the event. OAuth 2.0 can be used for the authentication process, thereby protecting user privacy.

[0269] The server also automatically sets reminders based on appointments registered in the electronic calendar. It sends notifications to users before a specified time to prompt them to review and prepare for their appointments. These notifications could be sent via push notifications or email.

[0270] Furthermore, the server can integrate with external information management software to centrally manage user information. For example, by linking with task management tools and business management tools, it can automate task updates and provide unified information management.

[0271] As a concrete example, if a user sends an electronic message saying, "Please schedule a sales meeting for 10 AM next Monday," the server receives this message and uses a generative AI model to extract the information "sales meeting for 10 AM next Monday." It then registers this event in the electronic calendar and sets a reminder to send a push notification 30 minutes before the meeting.

[0272] An example of a prompt message is, "Please schedule a client visit for next Friday at 3 PM." This allows users to easily automate their schedule management.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user creates an electronic message containing instructions regarding the date and time using a messaging application and sends that message. The input is a text message created by the user, and the server receives that message as output. For example, the user sends a prompt message saying, "Please set up a sales meeting for next Wednesday at 2pm."

[0276] Step 2:

[0277] The server receives electronic messages from users through the messaging application's API. The input here is text data obtained via the messaging service, and the output is the received message itself. Specifically, the server uses the messaging platform's API to ingest the data and prepare it for parsing the message content.

[0278] Step 3:

[0279] The server performs natural language processing using an AI model generated based on the received electronic message. The input is the user's text message, and the output is the extracted schedule information (e.g., date and time, event name). As data processing at this stage, the AI model identifies specific keywords and phrases from the text and extracts specific dates and event details. For example, information such as "next Wednesday at 2 pm" and the event "business meeting" are extracted.

[0280] Step 4:

[0281] The server uses the extracted schedule information to register the schedule in the electronic calendar. The input is the schedule information extracted in Step 3, and the output is the event registered in the electronic calendar. Specific operations include a process where the server uses the API of the calendar service to send the schedule information and performs authentication to ensure it is registered in the user's calendar.

[0282] Step 5:

[0283] The server automatically sets a reminder based on the registered schedule. The input is the event information added to the calendar, and the output is the reminder notified to the user. Specifically, the reminder is set 30 minutes before the schedule, and the actual notification reaches the user terminal as an email or push notification.

[0284] Step 6:

[0285] The server coordinates with external information management software and automatically registers or updates the necessary information. The input is the scheduled schedule information and the coordination information with external software, and the output is the updated task and project data. Specific operations include, for example, accessing the API of a project management tool and adding a new entry to the task list.

[0286] (Application Example 1)

[0287] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0288] In modern work environments, staff scheduling and shift adjustments are often done manually, leading to inefficiencies. In particular, when sudden shift changes or adjustments to work preferences are needed, there are few means to quickly reflect these changes, potentially disrupting business operations. Furthermore, complex management tasks are required for managers to grasp the overall schedule, placing a significant burden on them. To address these challenges and effectively support business operations, there is a need for automated schedule management.

[0289] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for receiving messages using electronic communication technology, means for performing natural language processing to extract time and location information from the received messages, means for registering schedules in an electronic schedule management means based on the extracted time and location information, and means for making personnel allocation information necessary for supporting actual work available. This enables rapid adjustment of staff work preferences and shifts, thereby improving the efficiency of business operations.

[0291] "Electronic communication technology" refers to all technologies for exchanging digital data between distant locations, and is primarily used as a means of transmitting information through email and messaging applications.

[0292] "Means for receiving messages" refers to a method of obtaining messages sent by users via communication terminals or servers, and includes a device that has the function of converting the content of those messages into a format that can be used for subsequent processing.

[0293] "Natural language processing" is a technology that enables computers to understand and process the language that humans use in everyday life. Its primary purpose is to recognize the meaning and intent within text and extract structured information.

[0294] "Time and location information" refers to the date, time, and specific location information included in a message, which are elements used to determine the details of a schedule or event.

[0295] "Electronic schedule management means" refers to applications and systems that operate on digital devices and are software that supports time management for individuals and organizations by allowing them to add, delete, and modify appointments.

[0296] "Notification means" refers to a mechanism for notifying users of specific information or changes in status, and is primarily a device or method that transmits information visually or audibly using alerts or messages.

[0297] "Means of making personnel allocation information manageable" refers to systems and processes that process information to determine the allocation and roles of staff necessary for a task, and that support appropriate personnel adjustments based on that information.

[0298] In order to implement this invention, a system is needed in which a server, a terminal, and a user cooperate to function.

[0299] First, the server receives messages from terminals using electronic communication technology. On the terminal, users input and send work requests or schedule changes using a messaging application. The server stores the received messages and performs natural language processing using a generative AI model to analyze their content. Through this analysis, information about time and location is extracted from the messages, and this is used to register specific schedules.

[0300] The server sends the extracted information to electronic schedule management software (e.g., Google Calendar API) and updates the schedule as an event to be registered. Also, when registering the schedule, a notification for informing the user of the completion of registration is sent to the terminal.

[0301] Furthermore, the server utilizes the extracted schedule data as input information for optimizing the overall staffing arrangement. Through this process, it becomes possible to immediately reflect the user's work preferences and shift adjustments.

[0302] As a specific example, consider the case where a user sends a message from the terminal stating "I hope to work from 9:00 am to 12:00 pm tomorrow". This message is received by the server and analyzed based on the prompt sentence of the generative AI model "Analyze the work preference and add it to the calendar." The extracted information is registered in the calendar using the Google Calendar API, and a registration completion notification is sent to the user terminal.

[0303] In the implementation of this system, the API of the messaging application is utilized to efficiently process the messages, and the natural language processing engine enables accurate data extraction. As a result, the system is configured to automate the user's schedule management and improve the efficiency of business operations.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The terminal sends the message of the work preference input by the user. The input data is the content of the user's message, and this data is sent to the server via electronic communication technology. The output is the message data received by the server.

[0307] Step 2:

[0308] The server stores messages received from terminals and performs natural language processing on these messages using a generative AI model. The input is message data, and information about time and location is extracted using the prompt "Analyze work requests and add them to the calendar." The output is the extracted date, time, and location data.

[0309] Step 3:

[0310] The server sends an API request to an electronic schedule management system (e.g., Google Calendar API) to register an event based on the extracted date, time, and location information. The input data is the extracted date, time, and location information, and sending this to the electronic schedule management system adds the event. The output is confirmation data that the schedule has been registered.

[0311] Step 4:

[0312] The server receives a registration completion notification from the electronic schedule management system and sends that notification to the terminal. The input is the schedule registration confirmation data, which is then notified to the user. The output is the notification message displayed on the user's terminal.

[0313] Step 5:

[0314] The server incorporates registered schedule information into the staffing optimization process and reflects it in the plan for business support. The input data is schedule information, which is processed to generate appropriate staffing data. The output is data for the optimized staffing plan.

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

[0316] This invention improves the user experience by combining a system that analyzes user messages using an electronic messaging service to extract schedule information and streamline schedule management with an emotion engine that recognizes user emotions.

[0317] First, users send information about their schedules and tasks via text messages through an electronic messaging service. These messages may contain the user's emotions. The server receives these messages using the messaging service API and employs natural language processing and sentiment engines to analyze them.

[0318] The received message undergoes natural language processing using a generative AI model on the server, extracting date and time information such as events. Simultaneously, the emotion engine recognizes the user's emotions from the text within the message and classifies them into categories such as positive, negative, and neutral.

[0319] After the schedule information is extracted and the user's emotions are recognized, the server registers the schedule using the electronic calendar service API. At this stage, it is possible to adjust the notification method and timing based on the emotional information. For example, if the user indicates negative emotions, reminder notifications can be delivered more gently or the timing adjusted.

[0320] Furthermore, emotional information can be sent to external applications, allowing for dynamic adjustment of task priorities based on the user's emotions in conjunction with project management tools. This kind of integration enables more flexible task management tailored to the project's progress.

[0321] For example, if a user sends a message such as, "I have a sales meeting tomorrow at 3 PM, and I'm worried," the server receives this message and extracts "Sales meeting tomorrow at 3 PM" as scheduling information. The emotion engine also recognizes a negative emotion from the expression "I'm worried." Based on this information, the server can add an encouraging message when setting a reminder or set up a separate task prompting the user to consult with a senior colleague.

[0322] Thus, the system of the present invention can capture the user's emotions through information from electronic messaging services and provide support tailored to the individual user's psychological state, in addition to schedule management.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] Users send text messages containing instructions to the system using an electronic messaging service. These messages include specific dates and times, details of events, and expressions of the user's feelings related to those events.

[0326] Step 2:

[0327] The server receives messages via the electronic messaging service's API. The received messages are automatically passed to the parsing process.

[0328] Step 3:

[0329] The server uses natural language processing to analyze the message content and extract date, time, and event information. Simultaneously, it uses an emotion engine to analyze emotional expressions within the message and identify the user's emotional state. This emotion is classified into categories such as positive, negative, and neutral.

[0330] Step 4:

[0331] The server uses the extracted schedule information to register the schedule via the electronic calendar service API. At this time, the event name and date are included as detailed schedule information, and the event is added to the user's calendar.

[0332] Step 5:

[0333] The server sets reminders based on the recognized emotional state of the user. For example, if the user's emotions are negative, the server will soften the tone of the notification or adjust the frequency of the notification.

[0334] Step 6:

[0335] The server interacts with external applications and sends emotional information to the task management system. This supports the setting and adjustment of task priorities based on the user's emotions.

[0336] Step 7:

[0337] Finally, the server notifies the user that the schedule registration and sentiment-based adjustments are complete. This notification is sent via an electronic messaging service, allowing the user to check their schedule.

[0338] This processing flow allows the system to comprehensively analyze the content of electronic messages and enable flexible responses to improve the user experience.

[0339] (Example 2)

[0340] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0341] While conventional schedule management systems offer the functionality to extract schedule information and register it in electronic planning services, they lack the ability to provide notifications and task management that take user emotions into consideration, making it difficult to meet individual needs. Furthermore, the lack of appropriate responses and support tailored to user emotions highlights the need for improved user experience.

[0342] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0343] In this invention, the server includes means for receiving information from electronic communication services, means for performing language processing to extract schedule information from the received information, and means for recognizing emotions from the transmitted information. This enables flexible notification methods and adjustments to task management based on the user's emotions.

[0344] "Electronic communication services" refer to technologies for sending and receiving messages and information in digital format.

[0345] "Information" refers to data and messages that users transmit through electronic communication services.

[0346] "Language processing" refers to the technology of analyzing natural language and extracting specific information or meaning.

[0347] "Schedule information" refers to data about dates, times, and events extracted from messages and digital documents.

[0348] "Electronic planning services" refer to platforms for managing schedules and appointments in a digital format.

[0349] "Emotion recognition means" refers to technologies that identify and classify a user's emotions from messages and text.

[0350] "Means of adjusting notification methods" refers to technologies that change the content and timing of notifications according to the user's emotional state.

[0351] "External application" refers to integration with software and tools provided by third parties.

[0352] This invention is a system that analyzes information transmitted by users using electronic communication services, extracts schedule information, recognizes user emotions, and streamlines schedule management. Users transmit messages regarding appointments and tasks via electronic communication services using computer terminals or mobile devices. These messages are expected to contain information about the individual's emotions.

[0353] The server utilizes a message service API to receive information sent by users in real time. After receiving the information, it uses a generative AI model to perform natural language processing and extract scheduled information, i.e., data related to dates, times, and events, from the message. In this process, the server uses a specific language algorithm to perform complex text analysis.

[0354] Furthermore, the emotion recognition engine within the server analyzes message content to understand the user's emotional state. Emotions are categorized as positive, negative, or neutral. This categorized emotion information plays a crucial role in schedule management.

[0355] For example, if a user sends the message, "I have a very important meeting tomorrow at 3pm, and I'm feeling a little anxious," the server extracts the scheduled information, "meeting tomorrow at 3pm," from this message. At the same time, it recognizes a negative emotion from the expression "I'm feeling anxious." Based on this, when the server registers the meeting using the electronic planning service API, it can set a notification with a gentler tone or adjust the timing to send a reminder a little earlier before the meeting.

[0356] Examples of prompt statements to input into a generative AI model are as follows:

[0357] "Please extract the scheduled information and emotions from the following sentence: 'I have a very important meeting tomorrow at 3pm, and I'm feeling a little anxious.'"

[0358] In this way, it becomes possible to achieve flexible schedule management and individualized support while taking user emotions into consideration.

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

[0360] Step 1:

[0361] The user sends a message from their terminal using an electronic communication service. This message contains information about their plans and feelings. The server receives the text message from the user as input, and a message reception log is generated as output.

[0362] Step 2:

[0363] The server retrieves messages received via the message service API. The input for this step is a message sent by the user. The data processing involves checking the message format and pre-processing it, preparing the message as output in a format suitable for subsequent parsing.

[0364] Step 3:

[0365] The server uses a generated AI model to perform natural language processing on incoming messages. Specifically, it extracts scheduled information (date, time, and event name) from the messages. The input for this step is pre-processed message data. Data processing includes keyword extraction and contextual analysis, and the output is the extracted scheduled information.

[0366] Step 4:

[0367] The server uses an emotion recognition engine to analyze emotional information within messages. The input is the user's message text, which is categorized as positive, negative, or neutral based on the emotion recognition algorithm. The output of this process is information about the user's emotions.

[0368] Step 5:

[0369] The server uses the electronic planning service API to register appointments in the calendar based on the extracted appointment and sentiment information. The input consists of appointment and sentiment information, and data processing combines these to configure notification settings. The output is the registered schedule and, if necessary, the adjusted notification message.

[0370] Step 6:

[0371] Through integration with external applications, the server registers or updates tasks in project management tools and other applications based on emotional information. The input here is information obtained through emotion recognition. The output includes task priorities adjusted according to the emotion, and registration data for external tools.

[0372] (Application Example 2)

[0373] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0374] Currently, there are systems that extract schedules from messages received via electronic communication services and manage them accordingly, but these systems cannot take into account the user's emotional state. Therefore, flexible schedule management that reduces the user's psychological burden and provides a better user experience is difficult. Furthermore, there is the challenge of not being able to adjust task priorities by integrating with external applications that utilize emotional information.

[0375] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0376] In this invention, the server includes means for receiving messages from an electronic communication service, means for performing natural language processing to extract schedule information from the received messages, means for acquiring emotional information using an emotional recognition engine that analyzes the emotions in the messages, and means for adjusting the notification method based on the emotional information. This enables schedule management that reflects the user's emotional state.

[0377] "Electronic communication services" are services that enable users to send and receive text messages and information via the internet or other communication networks.

[0378] "Natural language processing" is a technology that enables computers to understand and process human language, and in particular, it is a process that extracts information from text data and interprets the meaning of sentences.

[0379] An "electronic calendar service" is a system that allows users to register, manage, and share appointments online, providing a calendar function that can be accessed in a digital format.

[0380] An "emotion recognition engine" is a technology that identifies and classifies human emotions from text and audio, and then processes information based on the results.

[0381] "Adjusting notification methods" is the process of dynamically changing the content, timing, and method of notifications according to the user's situation and emotions, in order to provide the optimal user experience.

[0382] This invention involves a system installed in a home robot that analyzes messages from the user via electronic communication services to manage schedules and recognize emotions. This enables flexible schedule adjustments that take the user's emotions into consideration, as well as task management according to priority.

[0383] The server first receives messages from electronic communication services. Embedded hardware such as a Raspberry Pi is used for this purpose. The messages are parsed using natural language processing techniques to extract date, time, and event information. Python and the Natural Language Toolkit (nltk) library are used for this process.

[0384] Simultaneously, an emotion recognition engine analyzes the message and extracts emotional information. This engine classifies the message as positive, negative, or neutral, and identifies the user's psychological state.

[0385] The device registers appointments to an electronic calendar service based on the extracted schedule information. Based on the registered appointments, the system provides optimal notifications that take into account the user's emotions. For example, if negative emotions are detected, a voice notification in a gentle tone is provided.

[0386] Furthermore, the server integrates with work management tools to automatically adjust task priorities based on emotional information. For example, it uses the project management tool's API to list actionable tasks that reduce user anxiety.

[0387] For example, if a user sends a message saying, "I have an important presentation on Friday and I'm nervous," the server receives this message, extracts "presentation on Friday" as a date, and recognizes the negative emotion of nervousness. Based on this information, on the day of the presentation, it sends an encouraging voice message such as, "You can do it! Good luck!" and sets a high priority for presentation preparation.

[0388] Examples of prompt messages are as follows:

[0389] "Design a program that analyzes user messages to extract schedule and sentiment information, and then has a home robot provide appropriate voice notifications."

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The server receives messages from users via electronic communication services. These received messages become input, and their contents are stored for processing in the next step.

[0393] Step 2:

[0394] The server analyzes received messages using natural language processing techniques. Specifically, it analyzes text data using Python and the Natural Language Toolkit (nltk) library, extracting date and event information from the message. This results in the output of the date and time information.

[0395] Step 3:

[0396] The server uses an emotion recognition engine to analyze the sentiment of messages. The emotion recognition engine takes a message as input, examines the vocabulary and context within the text, and classifies the sentiment as positive, negative, or neutral. The output is sentiment information.

[0397] Step 4:

[0398] The device calls the calendar API to register the event in the electronic calendar service based on the extracted schedule information. The schedule information is used as input, and an output confirms that it has been registered as a new event in the electronic calendar.

[0399] Step 5:

[0400] The server adjusts the notification method based on emotional information. In particular, if the emotional state is negative, it generates a voice notification in a gentle tone. Emotional information is used as input, and an adjusted notification message is output.

[0401] Step 6:

[0402] The device uses the task management tool's API to prioritize tasks based on emotional information. Specifically, it adds new tasks to the task management list with priorities corresponding to the emotional state. Emotional information and schedule information are input, and an updated task list is output.

[0403] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0414] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0415] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0416] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] This invention is a system that streamlines scheduling using an electronic messaging service. Users send text messages to an agent via the electronic messaging service, and the system automatically extracts scheduling information from the message and registers it in an electronic calendar service.

[0420] First, users send instructions regarding events or tasks through a messaging service. For example, "Project meeting next Monday at 10 AM." The server receives these messages via the messaging service's API. The received messages are processed using natural language processing by a text analysis generation AI to extract event information such as the date, time, location, and content.

[0421] The extracted information is sent to the electronic calendar service and registered as an event. During registration, the server uses an API to provide the necessary data to the electronic calendar service, and the schedule is automatically registered. At this time, with the user's permission, authentication such as OAuth 2.0 is cleared to access the user's calendar.

[0422] Additionally, reminders are set based on the schedule information. Reminders have the function of notifying users a certain amount of time before the specified date and time, allowing them to reconfirm their schedule through these notifications.

[0423] Furthermore, this system provides a function to automate task management by integrating with external applications. For example, by integrating with a project management tool, it is possible to automatically register and update tasks according to the progress of the project. This allows users to manage information consistently across multiple platforms.

[0424] As a concrete example, consider a scenario where a user sends the message, "There's a board meeting this Friday at 3 PM." This message is received by the server, and a generation AI is used to extract the information that a "board meeting" will take place "this Friday at 3 PM." The server then registers the event at the corresponding date and time through the electronic calendar and sets a reminder 30 minutes before the meeting. This allows the user to automate tasks related to that date and improve the efficiency of schedule management.

[0425] Thus, the present invention provides a system that efficiently manages information from electronic messaging services and enables automated schedule registration and reminder setting.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] Users send messages containing information about specific dates, times, or events through an electronic messaging service. For example, a message might say, "Team meeting tomorrow at 2 PM."

[0429] Step 2:

[0430] The server uses the Message Service API to receive messages sent from specific channels or direct messages. Webhooks are configured to retrieve messages in real time.

[0431] Step 3:

[0432] The server passes the received message to a generative AI model capable of natural language processing, which then analyzes the message's content. The analysis extracts date and time information such as the event name, date, and location.

[0433] Step 4:

[0434] The server prepares to register the analyzed schedule information using the electronic calendar service API. At this stage, it verifies the user's access rights to the calendar and, if necessary, goes through an authentication process.

[0435] Step 5:

[0436] The server adds a new schedule to the electronic calendar service based on the extracted information. This includes attributes such as the event title, date and time, and location.

[0437] Step 6:

[0438] The server automatically sets reminders based on registered events. The timing of the reminder can be set to a default time based on the user's past settings, for example, 10 minutes before the event.

[0439] Step 7:

[0440] The server confirms that the schedule registration and reminder setting were successful and sends a message to the user via the electronic messaging service to notify them of the result.

[0441] Step 8:

[0442] If necessary, the server will interact with an external task management system based on the results of message analysis and register or update the schedule as a project task.

[0443] This process reduces the user's effort and enables accurate registration and management of information.

[0444] (Example 1)

[0445] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0446] In modern business and personal schedule management, users often struggle with efficient scheduling due to the use of multiple platforms. Furthermore, manual scheduling and reminder setting are time-consuming and labor-intensive, and prone to human error. Additionally, a lack of information integration with external applications makes maintaining information consistency across multiple systems difficult.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes means for receiving electronic messages, means for extracting schedule information from the received electronic messages using a generation AI, means for registering the extracted schedule information in an electronic calendar and setting reminders, and means for automatically registering or updating information in cooperation with external software. This enables users to efficiently manage their schedules, eliminates the need for manual input and settings, and realizes consistent information management across multiple platforms.

[0449] "Electronic message" refers to text information transmitted via a digital communication platform.

[0450] "Generative AI" is a type of artificial intelligence technology that uses machine learning to process and analyze natural language and extract useful information.

[0451] "Natural language processing" refers to technologies aimed at enabling computers to understand and process human language.

[0452] "Schedule information" refers to a collection of data related to a schedule, such as a specific date and time, location, and event name.

[0453] An "electronic calendar" refers to a system that records schedules of appointments and events in a digitized format.

[0454] A "reminder" refers to a function that notifies the user of an appointment at a specific time.

[0455] "External software" refers to other platforms or applications that function in conjunction with this system.

[0456] "Automatic information registration or updating" refers to the process by which a system automatically records or modifies data without human intervention.

[0457] This invention provides a system that utilizes an electronic messaging service to automatically register schedule information in an electronic calendar and set reminders, thereby streamlining schedule management for users.

[0458] Users send electronic messages to the server using messaging applications. These messaging applications typically include communication applications and business chat applications.

[0459] The server receives electronic messages provided by users through the messaging service's API. Simultaneously, it utilizes generative AI models such as BERT and GPT to process the natural language contained in the received messages. This automatically extracts important scheduling information, such as dates, times, and event details.

[0460] The extracted schedule information is registered with the electronic calendar service by the server. This process utilizes a RESTful API, allowing the server to access the electronic calendar, obtain permission from the user, and then register the event. OAuth 2.0 can be used for the authentication process, thereby protecting user privacy.

[0461] The server also automatically sets reminders based on appointments registered in the electronic calendar. It sends notifications to users before a specified time to prompt them to review and prepare for their appointments. These notifications could be sent via push notifications or email.

[0462] Furthermore, the server can integrate with external information management software to centrally manage user information. For example, by linking with task management tools and business management tools, it can automate task updates and provide unified information management.

[0463] As a concrete example, if a user sends an electronic message saying, "Please schedule a sales meeting for 10 AM next Monday," the server receives this message and uses a generative AI model to extract the information "sales meeting for 10 AM next Monday." It then registers this event in the electronic calendar and sets a reminder to send a push notification 30 minutes before the meeting.

[0464] An example of a prompt message is, "Please schedule a client visit for next Friday at 3 PM." This allows users to easily automate their schedule management.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] The user creates an electronic message containing instructions regarding the date and time using a messaging application and sends that message. The input is a text message created by the user, and the server receives that message as output. For example, the user sends a prompt message saying, "Please set up a sales meeting for next Wednesday at 2pm."

[0468] Step 2:

[0469] The server receives electronic messages from users through the messaging application's API. The input here is text data obtained via the messaging service, and the output is the received message itself. Specifically, the server uses the messaging platform's API to ingest the data and prepare it for parsing the message content.

[0470] Step 3:

[0471] The server uses a generative AI model to perform natural language processing based on the received electronic message. The input is the user's text message, and the output is extracted schedule information (e.g., date, time, and event name). Data processing at this stage involves the AI ​​model identifying specific keywords and phrases from the text and extracting details of specific dates and events. For example, the information "Next Wednesday at 2pm" and the event "Sales meeting" might be extracted.

[0472] Step 4:

[0473] The server uses the extracted schedule information to register appointments in the electronic calendar. The input is the schedule information extracted in step 3, and the output is the event registered in the electronic calendar. The specific operation includes a process in which the server uses the calendar service API to send the appointment information and authenticates it to ensure it is properly registered in the user's calendar.

[0474] Step 5:

[0475] The server automatically sets reminders based on registered appointments. The input is the event information added to the calendar, and the output is the reminder that is notified to the user. Specifically, the reminder is set 30 minutes before the appointment, and the actual notification arrives on the user's device as an email or push notification.

[0476] Step 6:

[0477] The server integrates with external information management software to automatically register or update necessary information. Inputs include scheduled schedule information and integration information with external software, while outputs are updated task and project data. Specific operations include, for example, accessing the API of a project management tool to add new entries to the task list.

[0478] (Application Example 1)

[0479] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0480] In modern work environments, staff scheduling and shift adjustments are often done manually, leading to inefficiencies. In particular, when sudden shift changes or adjustments to work preferences are needed, there are few means to quickly reflect these changes, potentially disrupting business operations. Furthermore, complex management tasks are required for managers to grasp the overall schedule, placing a significant burden on them. To address these challenges and effectively support business operations, there is a need for automated schedule management.

[0481] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0482] In this invention, the server includes means for receiving messages using electronic communication technology, means for performing natural language processing to extract time and location information from the received messages, means for registering schedules in an electronic schedule management means based on the extracted time and location information, and means for making personnel allocation information necessary for supporting actual work available. This enables rapid adjustment of staff work preferences and shifts, thereby improving the efficiency of business operations.

[0483] "Electronic communication technology" refers to all technologies for exchanging digital data between distant locations, and is primarily used as a means of transmitting information through email and messaging applications.

[0484] "Means for receiving messages" refers to a method of obtaining messages sent by users via communication terminals or servers, and includes a device that has the function of converting the content of those messages into a format that can be used for subsequent processing.

[0485] "Natural language processing" is a technology that enables computers to understand and process the language that humans use in everyday life. Its primary purpose is to recognize the meaning and intent within text and extract structured information.

[0486] "Time and location information" refers to the date, time, and specific location information included in a message, which are elements used to determine the details of a schedule or event.

[0487] "Electronic schedule management means" refers to applications and systems that operate on digital devices and are software that supports time management for individuals and organizations by allowing them to add, delete, and modify appointments.

[0488] "Notification means" refers to a mechanism for notifying users of specific information or changes in status, and is primarily a device or method that transmits information visually or audibly using alerts or messages.

[0489] "Means of making personnel allocation information manageable" refers to systems and processes that process information to determine the allocation and roles of staff necessary for a task, and that support appropriate personnel adjustments based on that information.

[0490] In order to implement this invention, a system is needed in which a server, a terminal, and a user cooperate to function.

[0491] First, the server receives messages from terminals using electronic communication technology. On the terminal, users input and send work requests or schedule changes using a messaging application. The server stores the received messages and performs natural language processing using a generative AI model to analyze their content. Through this analysis, information about time and location is extracted from the messages, and this is used to register specific schedules.

[0492] The server sends the extracted information to electronic scheduling software (e.g., Google Calendar API) and updates the schedule as a registered event. Additionally, a notification is sent to the user's device upon completion of the schedule registration.

[0493] Furthermore, the server utilizes the extracted schedule data as input information to optimize overall staffing. This process makes it possible to instantly reflect users' work preferences and shift adjustments.

[0494] As a concrete example, consider a case where a user sends a message from their device stating, "I would like to work from 9 AM to 12 PM tomorrow." This message is received by the server, and analysis is performed based on the prompt message of the generating AI model: "Analyze the work request and add it to the calendar." The extracted information is registered in the calendar using the Google Calendar API, and the user's device receives a notification that the registration is complete.

[0495] In implementing this system, messaging application APIs are utilized to efficiently process messages, and a natural language processing engine enables accurate data extraction. As a result, user schedule management is automated, leading to increased efficiency in business operations.

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The terminal transmits a message indicating the user's work preferences. The input data is the user's message content, which is sent to the server via electronic communication technology. The output is the message data received by the server.

[0499] Step 2:

[0500] The server stores messages received from terminals and performs natural language processing on these messages using a generative AI model. The input is message data, and information about time and location is extracted using the prompt "Analyze work requests and add them to the calendar." The output is the extracted date, time, and location data.

[0501] Step 3:

[0502] The server sends an API request to an electronic schedule management system (e.g., Google Calendar API) to register an event based on the extracted date, time, and location information. The input data is the extracted date, time, and location information, and sending this to the electronic schedule management system adds the event. The output is confirmation data that the schedule has been registered.

[0503] Step 4:

[0504] The server receives a registration completion notification from the electronic schedule management system and sends that notification to the terminal. The input is the schedule registration confirmation data, which is then notified to the user. The output is the notification message displayed on the user's terminal.

[0505] Step 5:

[0506] The server incorporates registered schedule information into the staffing optimization process and reflects it in the plan for business support. The input data is schedule information, which is processed to generate appropriate staffing data. The output is data for the optimized staffing plan.

[0507] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0508] This invention improves the user experience by combining a system that analyzes user messages using an electronic messaging service to extract schedule information and streamline schedule management with an emotion engine that recognizes user emotions.

[0509] First, users send information about their schedules and tasks via text messages through an electronic messaging service. These messages may contain the user's emotions. The server receives these messages using the messaging service API and employs natural language processing and sentiment engines to analyze them.

[0510] The received message undergoes natural language processing using a generative AI model on the server, extracting date and time information such as events. Simultaneously, the emotion engine recognizes the user's emotions from the text within the message and classifies them into categories such as positive, negative, and neutral.

[0511] After the schedule information is extracted and the user's emotions are recognized, the server registers the schedule using the electronic calendar service API. At this stage, it is possible to adjust the notification method and timing based on the emotional information. For example, if the user indicates negative emotions, reminder notifications can be delivered more gently or the timing adjusted.

[0512] Furthermore, emotional information can be sent to external applications, allowing for dynamic adjustment of task priorities based on the user's emotions in conjunction with project management tools. This kind of integration enables more flexible task management tailored to the project's progress.

[0513] For example, if a user sends a message such as, "I have a sales meeting tomorrow at 3 PM, and I'm worried," the server receives this message and extracts "Sales meeting tomorrow at 3 PM" as scheduling information. The emotion engine also recognizes a negative emotion from the expression "I'm worried." Based on this information, the server can add an encouraging message when setting a reminder or set up a separate task prompting the user to consult with a senior colleague.

[0514] Thus, the system of the present invention can capture the user's emotions through information from electronic messaging services and provide support tailored to the individual user's psychological state, in addition to schedule management.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] Users send text messages containing instructions to the system using an electronic messaging service. These messages include specific dates and times, details of events, and expressions of the user's feelings related to those events.

[0518] Step 2:

[0519] The server receives messages via the electronic messaging service's API. The received messages are automatically passed to the parsing process.

[0520] Step 3:

[0521] The server uses natural language processing to analyze the message content and extract date, time, and event information. Simultaneously, it uses an emotion engine to analyze emotional expressions within the message and identify the user's emotional state. This emotion is classified into categories such as positive, negative, and neutral.

[0522] Step 4:

[0523] The server uses the extracted schedule information to register the schedule via the electronic calendar service API. At this time, the event name and date are included as detailed schedule information, and the event is added to the user's calendar.

[0524] Step 5:

[0525] The server sets reminders based on the recognized emotional state of the user. For example, if the user's emotions are negative, the server will soften the tone of the notification or adjust the frequency of the notification.

[0526] Step 6:

[0527] The server interacts with external applications and sends emotional information to the task management system. This supports the setting and adjustment of task priorities based on the user's emotions.

[0528] Step 7:

[0529] Finally, the server notifies the user that the schedule registration and sentiment-based adjustments are complete. This notification is sent via an electronic messaging service, allowing the user to check their schedule.

[0530] This processing flow allows the system to comprehensively analyze the content of electronic messages and enable flexible responses to improve the user experience.

[0531] (Example 2)

[0532] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0533] While conventional schedule management systems offer the functionality to extract schedule information and register it in electronic planning services, they lack the ability to provide notifications and task management that take user emotions into consideration, making it difficult to meet individual needs. Furthermore, the lack of appropriate responses and support tailored to user emotions highlights the need for improved user experience.

[0534] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0535] In this invention, the server includes means for receiving information from electronic communication services, means for performing language processing to extract schedule information from the received information, and means for recognizing emotions from the transmitted information. This enables flexible notification methods and adjustments to task management based on the user's emotions.

[0536] "Electronic communication services" refer to technologies for sending and receiving messages and information in digital format.

[0537] "Information" refers to data and messages that users transmit through electronic communication services.

[0538] "Language processing" refers to the technology of analyzing natural language and extracting specific information or meaning.

[0539] "Schedule information" refers to data about dates, times, and events extracted from messages and digital documents.

[0540] "Electronic planning services" refer to platforms for managing schedules and appointments in a digital format.

[0541] "Emotion recognition means" refers to technologies that identify and classify a user's emotions from messages and text.

[0542] "Means of adjusting notification methods" refers to technologies that change the content and timing of notifications according to the user's emotional state.

[0543] "External application" refers to integration with software and tools provided by third parties.

[0544] This invention is a system that analyzes information transmitted by users using electronic communication services, extracts schedule information, recognizes user emotions, and streamlines schedule management. Users transmit messages regarding appointments and tasks via electronic communication services using computer terminals or mobile devices. These messages are expected to contain information about the individual's emotions.

[0545] The server utilizes a message service API to receive information sent by users in real time. After receiving the information, it uses a generative AI model to perform natural language processing and extract scheduled information, i.e., data related to dates, times, and events, from the message. In this process, the server uses a specific language algorithm to perform complex text analysis.

[0546] Furthermore, the emotion recognition engine within the server analyzes message content to understand the user's emotional state. Emotions are categorized as positive, negative, or neutral. This categorized emotion information plays a crucial role in schedule management.

[0547] For example, if a user sends the message, "I have a very important meeting tomorrow at 3pm, and I'm feeling a little anxious," the server extracts the scheduled information, "meeting tomorrow at 3pm," from this message. At the same time, it recognizes a negative emotion from the expression "I'm feeling anxious." Based on this, when the server registers the meeting using the electronic planning service API, it can set a notification with a gentler tone or adjust the timing to send a reminder a little earlier before the meeting.

[0548] Examples of prompt statements to input into a generative AI model are as follows:

[0549] "Please extract the scheduled information and emotions from the following sentence: 'I have a very important meeting tomorrow at 3pm, and I'm feeling a little anxious.'"

[0550] In this way, it becomes possible to achieve flexible schedule management and individualized support while taking user emotions into consideration.

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The user sends a message from their terminal using an electronic communication service. This message contains information about their plans and feelings. The server receives the text message from the user as input, and a message reception log is generated as output.

[0554] Step 2:

[0555] The server retrieves messages received via the message service API. The input for this step is a message sent by the user. The data processing involves checking the message format and pre-processing it, preparing the message as output in a format suitable for subsequent parsing.

[0556] Step 3:

[0557] The server uses a generated AI model to perform natural language processing on incoming messages. Specifically, it extracts scheduled information (date, time, and event name) from the messages. The input for this step is pre-processed message data. Data processing includes keyword extraction and contextual analysis, and the output is the extracted scheduled information.

[0558] Step 4:

[0559] The server uses an emotion recognition engine to analyze emotional information within messages. The input is the user's message text, which is categorized as positive, negative, or neutral based on the emotion recognition algorithm. The output of this process is information about the user's emotions.

[0560] Step 5:

[0561] The server uses the electronic planning service API to register appointments in the calendar based on the extracted appointment and sentiment information. The input consists of appointment and sentiment information, and data processing combines these to configure notification settings. The output is the registered schedule and, if necessary, the adjusted notification message.

[0562] Step 6:

[0563] Through integration with external applications, the server registers or updates tasks in project management tools and other applications based on emotional information. The input here is information obtained through emotion recognition. The output includes task priorities adjusted according to the emotion, and registration data for external tools.

[0564] (Application Example 2)

[0565] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0566] Currently, there are systems that extract schedules from messages received via electronic communication services and manage them accordingly, but these systems cannot take into account the user's emotional state. Therefore, flexible schedule management that reduces the user's psychological burden and provides a better user experience is difficult. Furthermore, there is the challenge of not being able to adjust task priorities by integrating with external applications that utilize emotional information.

[0567] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0568] In this invention, the server includes means for receiving messages from an electronic communication service, means for performing natural language processing to extract schedule information from the received messages, means for acquiring emotional information using an emotional recognition engine that analyzes the emotions in the messages, and means for adjusting the notification method based on the emotional information. This enables schedule management that reflects the user's emotional state.

[0569] "Electronic communication services" are services that enable users to send and receive text messages and information via the internet or other communication networks.

[0570] "Natural language processing" is a technology that enables computers to understand and process human language, and in particular, it is a process that extracts information from text data and interprets the meaning of sentences.

[0571] An "electronic calendar service" is a system that allows users to register, manage, and share appointments online, providing a calendar function that can be accessed in a digital format.

[0572] An "emotion recognition engine" is a technology that identifies and classifies human emotions from text and audio, and then processes information based on the results.

[0573] "Adjusting notification methods" is the process of dynamically changing the content, timing, and method of notifications according to the user's situation and emotions, in order to provide the optimal user experience.

[0574] This invention involves a system installed in a home robot that analyzes messages from the user via electronic communication services to manage schedules and recognize emotions. This enables flexible schedule adjustments that take the user's emotions into consideration, as well as task management according to priority.

[0575] The server first receives messages from electronic communication services. Embedded hardware such as a Raspberry Pi is used for this purpose. The messages are parsed using natural language processing techniques to extract date, time, and event information. Python and the Natural Language Toolkit (nltk) library are used for this process.

[0576] Simultaneously, an emotion recognition engine analyzes the message and extracts emotional information. This engine classifies the message as positive, negative, or neutral, and identifies the user's psychological state.

[0577] The device registers appointments to an electronic calendar service based on the extracted schedule information. Based on the registered appointments, the system provides optimal notifications that take into account the user's emotions. For example, if negative emotions are detected, a voice notification in a gentle tone is provided.

[0578] Furthermore, the server integrates with work management tools to automatically adjust task priorities based on emotional information. For example, it uses the project management tool's API to list actionable tasks that reduce user anxiety.

[0579] For example, if a user sends a message saying, "I have an important presentation on Friday and I'm nervous," the server receives this message, extracts "presentation on Friday" as a date, and recognizes the negative emotion of nervousness. Based on this information, on the day of the presentation, it sends an encouraging voice message such as, "You can do it! Good luck!" and sets a high priority for presentation preparation.

[0580] Examples of prompt messages are as follows:

[0581] "Design a program that analyzes user messages to extract schedule and sentiment information, and then has a home robot provide appropriate voice notifications."

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The server receives messages from users via electronic communication services. These received messages become input, and their contents are stored for processing in the next step.

[0585] Step 2:

[0586] The server analyzes received messages using natural language processing techniques. Specifically, it analyzes text data using Python and the Natural Language Toolkit (nltk) library, extracting date and event information from the message. This results in the output of the date and time information.

[0587] Step 3:

[0588] The server uses an emotion recognition engine to analyze the sentiment of messages. The emotion recognition engine takes a message as input, examines the vocabulary and context within the text, and classifies the sentiment as positive, negative, or neutral. The output is sentiment information.

[0589] Step 4:

[0590] The device calls the calendar API to register the event in the electronic calendar service based on the extracted schedule information. The schedule information is used as input, and an output confirms that it has been registered as a new event in the electronic calendar.

[0591] Step 5:

[0592] The server adjusts the notification method based on emotional information. In particular, if the emotional state is negative, it generates a voice notification in a gentle tone. Emotional information is used as input, and an adjusted notification message is output.

[0593] Step 6:

[0594] The device uses the task management tool's API to prioritize tasks based on emotional information. Specifically, it adds new tasks to the task management list with priorities corresponding to the emotional state. Emotional information and schedule information are input, and an updated task list is output.

[0595] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0596] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0601] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0602] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0603] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0604] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0605] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0606] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0607] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0608] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0609] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0610] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0611] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] This invention is a system that streamlines scheduling using an electronic messaging service. Users send text messages to an agent via the electronic messaging service, and the system automatically extracts scheduling information from the message and registers it in an electronic calendar service.

[0613] First, users send instructions regarding events or tasks through a messaging service. For example, "Project meeting next Monday at 10 AM." The server receives these messages via the messaging service's API. The received messages are processed using natural language processing by a text analysis generation AI to extract event information such as the date, time, location, and content.

[0614] The extracted information is sent to the electronic calendar service and registered as an event. During registration, the server uses an API to provide the necessary data to the electronic calendar service, and the schedule is automatically registered. At this time, with the user's permission, authentication such as OAuth 2.0 is cleared to access the user's calendar.

[0615] Additionally, reminders are set based on the schedule information. Reminders have the function of notifying users a certain amount of time before the specified date and time, allowing them to reconfirm their schedule through these notifications.

[0616] Furthermore, this system provides a function to automate task management by integrating with external applications. For example, by integrating with a project management tool, it is possible to automatically register and update tasks according to the progress of the project. This allows users to manage information consistently across multiple platforms.

[0617] As a concrete example, consider a scenario where a user sends the message, "There's a board meeting this Friday at 3 PM." This message is received by the server, and a generation AI is used to extract the information that a "board meeting" will take place "this Friday at 3 PM." The server then registers the event at the corresponding date and time through the electronic calendar and sets a reminder 30 minutes before the meeting. This allows the user to automate tasks related to that date and improve the efficiency of schedule management.

[0618] Thus, the present invention provides a system that efficiently manages information from electronic messaging services and enables automated schedule registration and reminder setting.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] Users send messages containing information about specific dates, times, or events through an electronic messaging service. For example, a message might say, "Team meeting tomorrow at 2 PM."

[0622] Step 2:

[0623] The server uses the Message Service API to receive messages sent from specific channels or direct messages. Webhooks are configured to retrieve messages in real time.

[0624] Step 3:

[0625] The server passes the received message to a generative AI model capable of natural language processing, which then analyzes the message's content. The analysis extracts date and time information such as the event name, date, and location.

[0626] Step 4:

[0627] The server prepares to register the analyzed schedule information using the electronic calendar service API. At this stage, it verifies the user's access rights to the calendar and, if necessary, goes through an authentication process.

[0628] Step 5:

[0629] The server adds a new schedule to the electronic calendar service based on the extracted information. This includes attributes such as the event title, date and time, and location.

[0630] Step 6:

[0631] The server automatically sets reminders based on registered events. The timing of the reminder can be set to a default time based on the user's past settings, for example, 10 minutes before the event.

[0632] Step 7:

[0633] The server confirms that the schedule registration and reminder setting were successful and sends a message to the user via the electronic messaging service to notify them of the result.

[0634] Step 8:

[0635] If necessary, the server will interact with an external task management system based on the results of message analysis and register or update the schedule as a project task.

[0636] This process reduces the user's effort and enables accurate registration and management of information.

[0637] (Example 1)

[0638] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] In modern business and personal schedule management, users often struggle with efficient scheduling due to the use of multiple platforms. Furthermore, manual scheduling and reminder setting are time-consuming and labor-intensive, and prone to human error. Additionally, a lack of information integration with external applications makes maintaining information consistency across multiple systems difficult.

[0640] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0641] In this invention, the server includes means for receiving electronic messages, means for extracting schedule information from the received electronic messages using a generation AI, means for registering the extracted schedule information in an electronic calendar and setting reminders, and means for automatically registering or updating information in cooperation with external software. This enables users to efficiently manage their schedules, eliminates the need for manual input and settings, and realizes consistent information management across multiple platforms.

[0642] "Electronic message" refers to text information transmitted via a digital communication platform.

[0643] "Generative AI" is a type of artificial intelligence technology that uses machine learning to process and analyze natural language and extract useful information.

[0644] "Natural language processing" refers to technologies aimed at enabling computers to understand and process human language.

[0645] "Schedule information" refers to a collection of data related to a schedule, such as a specific date and time, location, and event name.

[0646] An "electronic calendar" refers to a system that records schedules of appointments and events in a digitized format.

[0647] A "reminder" refers to a function that notifies the user of an appointment at a specific time.

[0648] "External software" refers to other platforms or applications that function in conjunction with this system.

[0649] "Automatic information registration or updating" refers to the process by which a system automatically records or modifies data without human intervention.

[0650] This invention provides a system that utilizes an electronic messaging service to automatically register schedule information in an electronic calendar and set reminders, thereby streamlining schedule management for users.

[0651] Users send electronic messages to the server using messaging applications. These messaging applications typically include communication applications and business chat applications.

[0652] The server receives electronic messages provided by users through the messaging service's API. Simultaneously, it utilizes generative AI models such as BERT and GPT to process the natural language contained in the received messages. This automatically extracts important scheduling information, such as dates, times, and event details.

[0653] The extracted schedule information is registered with the electronic calendar service by the server. This process utilizes a RESTful API, allowing the server to access the electronic calendar, obtain permission from the user, and then register the event. OAuth 2.0 can be used for the authentication process, thereby protecting user privacy.

[0654] The server also automatically sets reminders based on appointments registered in the electronic calendar. It sends notifications to users before a specified time to prompt them to review and prepare for their appointments. These notifications could be sent via push notifications or email.

[0655] Furthermore, the server can integrate with external information management software to centrally manage user information. For example, by linking with task management tools and business management tools, it can automate task updates and provide unified information management.

[0656] As a concrete example, if a user sends an electronic message saying, "Please schedule a sales meeting for 10 AM next Monday," the server receives this message and uses a generative AI model to extract the information "sales meeting for 10 AM next Monday." It then registers this event in the electronic calendar and sets a reminder to send a push notification 30 minutes before the meeting.

[0657] An example of a prompt message is, "Please schedule a client visit for next Friday at 3 PM." This allows users to easily automate their schedule management.

[0658] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0659] Step 1:

[0660] The user creates an electronic message containing instructions regarding the date and time using a messaging application and sends that message. The input is a text message created by the user, and the server receives that message as output. For example, the user sends a prompt message saying, "Please set up a sales meeting for next Wednesday at 2pm."

[0661] Step 2:

[0662] The server receives electronic messages from users through the messaging application's API. The input here is text data obtained via the messaging service, and the output is the received message itself. Specifically, the server uses the messaging platform's API to ingest the data and prepare it for parsing the message content.

[0663] Step 3:

[0664] The server uses a generative AI model to perform natural language processing based on the received electronic message. The input is the user's text message, and the output is extracted schedule information (e.g., date, time, and event name). Data processing at this stage involves the AI ​​model identifying specific keywords and phrases from the text and extracting details of specific dates and events. For example, the information "Next Wednesday at 2pm" and the event "Sales meeting" might be extracted.

[0665] Step 4:

[0666] The server uses the extracted schedule information to register appointments in the electronic calendar. The input is the schedule information extracted in step 3, and the output is the event registered in the electronic calendar. The specific operation includes a process in which the server uses the calendar service API to send the appointment information and authenticates it to ensure it is properly registered in the user's calendar.

[0667] Step 5:

[0668] The server automatically sets reminders based on registered appointments. The input is the event information added to the calendar, and the output is the reminder that is notified to the user. Specifically, the reminder is set 30 minutes before the appointment, and the actual notification arrives on the user's device as an email or push notification.

[0669] Step 6:

[0670] The server integrates with external information management software to automatically register or update necessary information. Inputs include scheduled schedule information and integration information with external software, while outputs are updated task and project data. Specific operations include, for example, accessing the API of a project management tool to add new entries to the task list.

[0671] (Application Example 1)

[0672] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0673] In modern work environments, staff scheduling and shift adjustments are often done manually, leading to inefficiencies. In particular, when sudden shift changes or adjustments to work preferences are needed, there are few means to quickly reflect these changes, potentially disrupting business operations. Furthermore, complex management tasks are required for managers to grasp the overall schedule, placing a significant burden on them. To address these challenges and effectively support business operations, there is a need for automated schedule management.

[0674] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0675] In this invention, the server includes means for receiving messages using electronic communication technology, means for performing natural language processing to extract time and location information from the received messages, means for registering schedules in an electronic schedule management means based on the extracted time and location information, and means for making personnel allocation information necessary for supporting actual work available. This enables rapid adjustment of staff work preferences and shifts, thereby improving the efficiency of business operations.

[0676] "Electronic communication technology" refers to all technologies for exchanging digital data between distant locations, and is primarily used as a means of transmitting information through email and messaging applications.

[0677] "Means for receiving messages" refers to a method of obtaining messages sent by users via communication terminals or servers, and includes a device that has the function of converting the content of those messages into a format that can be used for subsequent processing.

[0678] "Natural language processing" is a technology that enables computers to understand and process the language that humans use in everyday life. Its primary purpose is to recognize the meaning and intent within text and extract structured information.

[0679] "Time and location information" refers to the date, time, and specific location information included in a message, which are elements used to determine the details of a schedule or event.

[0680] "Electronic schedule management means" refers to applications and systems that operate on digital devices and are software that supports time management for individuals and organizations by allowing them to add, delete, and modify appointments.

[0681] "Notification means" refers to a mechanism for notifying users of specific information or changes in status, and is primarily a device or method that transmits information visually or audibly using alerts or messages.

[0682] "Means of making personnel allocation information manageable" refers to systems and processes that process information to determine the allocation and roles of staff necessary for a task, and that support appropriate personnel adjustments based on that information.

[0683] In order to implement this invention, a system is needed in which a server, a terminal, and a user cooperate to function.

[0684] First, the server receives messages from terminals using electronic communication technology. On the terminal, users input and send work requests or schedule changes using a messaging application. The server stores the received messages and performs natural language processing using a generative AI model to analyze their content. Through this analysis, information about time and location is extracted from the messages, and this is used to register specific schedules.

[0685] The server sends the extracted information to electronic scheduling software (e.g., Google Calendar API) and updates the schedule as a registered event. Additionally, a notification is sent to the user's device upon completion of the schedule registration.

[0686] Furthermore, the server utilizes the extracted schedule data as input information to optimize overall staffing. This process makes it possible to instantly reflect users' work preferences and shift adjustments.

[0687] As a concrete example, consider a case where a user sends a message from their device stating, "I would like to work from 9 AM to 12 PM tomorrow." This message is received by the server, and analysis is performed based on the prompt message of the generating AI model: "Analyze the work request and add it to the calendar." The extracted information is registered in the calendar using the Google Calendar API, and the user's device receives a notification that the registration is complete.

[0688] In implementing this system, messaging application APIs are utilized to efficiently process messages, and a natural language processing engine enables accurate data extraction. As a result, user schedule management is automated, leading to increased efficiency in business operations.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The terminal transmits a message indicating the user's work preferences. The input data is the user's message content, which is sent to the server via electronic communication technology. The output is the message data received by the server.

[0692] Step 2:

[0693] The server stores messages received from terminals and performs natural language processing on these messages using a generative AI model. The input is message data, and information about time and location is extracted using the prompt "Analyze work requests and add them to the calendar." The output is the extracted date, time, and location data.

[0694] Step 3:

[0695] The server sends an API request to an electronic schedule management system (e.g., Google Calendar API) to register an event based on the extracted date, time, and location information. The input data is the extracted date, time, and location information, and sending this to the electronic schedule management system adds the event. The output is confirmation data that the schedule has been registered.

[0696] Step 4:

[0697] The server receives a registration completion notification from the electronic schedule management system and sends that notification to the terminal. The input is the schedule registration confirmation data, which is then notified to the user. The output is the notification message displayed on the user's terminal.

[0698] Step 5:

[0699] The server incorporates registered schedule information into the staffing optimization process and reflects it in the plan for business support. The input data is schedule information, which is processed to generate appropriate staffing data. The output is data for the optimized staffing plan.

[0700] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0701] This invention improves the user experience by combining a system that analyzes user messages using an electronic messaging service to extract schedule information and streamline schedule management with an emotion engine that recognizes user emotions.

[0702] First, users send information about their schedules and tasks via text messages through an electronic messaging service. These messages may contain the user's emotions. The server receives these messages using the messaging service API and employs natural language processing and sentiment engines to analyze them.

[0703] The received message undergoes natural language processing using a generative AI model on the server, extracting date and time information such as events. Simultaneously, the emotion engine recognizes the user's emotions from the text within the message and classifies them into categories such as positive, negative, and neutral.

[0704] After the schedule information is extracted and the user's emotions are recognized, the server registers the schedule using the electronic calendar service API. At this stage, it is possible to adjust the notification method and timing based on the emotional information. For example, if the user indicates negative emotions, reminder notifications can be delivered more gently or the timing adjusted.

[0705] Furthermore, emotional information can be sent to external applications, allowing for dynamic adjustment of task priorities based on the user's emotions in conjunction with project management tools. This kind of integration enables more flexible task management tailored to the project's progress.

[0706] For example, if a user sends a message such as, "I have a sales meeting tomorrow at 3 PM, and I'm worried," the server receives this message and extracts "Sales meeting tomorrow at 3 PM" as scheduling information. The emotion engine also recognizes a negative emotion from the expression "I'm worried." Based on this information, the server can add an encouraging message when setting a reminder or set up a separate task prompting the user to consult with a senior colleague.

[0707] Thus, the system of the present invention can capture the user's emotions through information from electronic messaging services and provide support tailored to the individual user's psychological state, in addition to schedule management.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] Users send text messages containing instructions to the system using an electronic messaging service. These messages include specific dates and times, details of events, and expressions of the user's feelings related to those events.

[0711] Step 2:

[0712] The server receives messages via the electronic messaging service's API. The received messages are automatically passed to the parsing process.

[0713] Step 3:

[0714] The server uses natural language processing to analyze the message content and extract date, time, and event information. Simultaneously, it uses an emotion engine to analyze emotional expressions within the message and identify the user's emotional state. This emotion is classified into categories such as positive, negative, and neutral.

[0715] Step 4:

[0716] The server uses the extracted schedule information to register the schedule via the electronic calendar service API. At this time, the event name and date are included as detailed schedule information, and the event is added to the user's calendar.

[0717] Step 5:

[0718] The server sets reminders based on the recognized emotional state of the user. For example, if the user's emotions are negative, the server will soften the tone of the notification or adjust the frequency of the notification.

[0719] Step 6:

[0720] The server interacts with external applications and sends emotional information to the task management system. This supports the setting and adjustment of task priorities based on the user's emotions.

[0721] Step 7:

[0722] Finally, the server notifies the user that the schedule registration and sentiment-based adjustments are complete. This notification is sent via an electronic messaging service, allowing the user to check their schedule.

[0723] This processing flow allows the system to comprehensively analyze the content of electronic messages and enable flexible responses to improve the user experience.

[0724] (Example 2)

[0725] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0726] While conventional schedule management systems offer the functionality to extract schedule information and register it in electronic planning services, they lack the ability to provide notifications and task management that take user emotions into consideration, making it difficult to meet individual needs. Furthermore, the lack of appropriate responses and support tailored to user emotions highlights the need for improved user experience.

[0727] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0728] In this invention, the server includes means for receiving information from electronic communication services, means for performing language processing to extract schedule information from the received information, and means for recognizing emotions from the transmitted information. This enables flexible notification methods and adjustments to task management based on the user's emotions.

[0729] "Electronic communication services" refer to technologies for sending and receiving messages and information in digital format.

[0730] "Information" refers to data and messages that users transmit through electronic communication services.

[0731] "Language processing" refers to the technology of analyzing natural language and extracting specific information or meaning.

[0732] "Schedule information" refers to data about dates, times, and events extracted from messages and digital documents.

[0733] "Electronic planning services" refer to platforms for managing schedules and appointments in a digital format.

[0734] "Emotion recognition means" refers to technologies that identify and classify a user's emotions from messages and text.

[0735] "Means of adjusting notification methods" refers to technologies that change the content and timing of notifications according to the user's emotional state.

[0736] "External application" refers to integration with software and tools provided by third parties.

[0737] This invention is a system that analyzes information transmitted by users using electronic communication services, extracts schedule information, recognizes user emotions, and streamlines schedule management. Users transmit messages regarding appointments and tasks via electronic communication services using computer terminals or mobile devices. These messages are expected to contain information about the individual's emotions.

[0738] The server utilizes a message service API to receive information sent by users in real time. After receiving the information, it uses a generative AI model to perform natural language processing and extract scheduled information, i.e., data related to dates, times, and events, from the message. In this process, the server uses a specific language algorithm to perform complex text analysis.

[0739] Furthermore, the emotion recognition engine within the server analyzes message content to understand the user's emotional state. Emotions are categorized as positive, negative, or neutral. This categorized emotion information plays a crucial role in schedule management.

[0740] For example, if a user sends the message, "I have a very important meeting tomorrow at 3pm, and I'm feeling a little anxious," the server extracts the scheduled information, "meeting tomorrow at 3pm," from this message. At the same time, it recognizes a negative emotion from the expression "I'm feeling anxious." Based on this, when the server registers the meeting using the electronic planning service API, it can set a notification with a gentler tone or adjust the timing to send a reminder a little earlier before the meeting.

[0741] Examples of prompt statements to input into a generative AI model are as follows:

[0742] "Please extract the scheduled information and emotions from the following sentence: 'I have a very important meeting tomorrow at 3pm, and I'm feeling a little anxious.'"

[0743] In this way, it becomes possible to achieve flexible schedule management and individualized support while taking user emotions into consideration.

[0744] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0745] Step 1:

[0746] The user sends a message from their terminal using an electronic communication service. This message contains information about their plans and feelings. The server receives the text message from the user as input, and a message reception log is generated as output.

[0747] Step 2:

[0748] The server retrieves messages received via the message service API. The input for this step is a message sent by the user. The data processing involves checking the message format and pre-processing it, preparing the message as output in a format suitable for subsequent parsing.

[0749] Step 3:

[0750] The server uses a generated AI model to perform natural language processing on incoming messages. Specifically, it extracts scheduled information (date, time, and event name) from the messages. The input for this step is pre-processed message data. Data processing includes keyword extraction and contextual analysis, and the output is the extracted scheduled information.

[0751] Step 4:

[0752] The server uses an emotion recognition engine to analyze emotional information within messages. The input is the user's message text, which is categorized as positive, negative, or neutral based on the emotion recognition algorithm. The output of this process is information about the user's emotions.

[0753] Step 5:

[0754] The server uses the electronic planning service API to register appointments in the calendar based on the extracted appointment and sentiment information. The input consists of appointment and sentiment information, and data processing combines these to configure notification settings. The output is the registered schedule and, if necessary, the adjusted notification message.

[0755] Step 6:

[0756] Through integration with external applications, the server registers or updates tasks in project management tools and other applications based on emotional information. The input here is information obtained through emotion recognition. The output includes task priorities adjusted according to the emotion, and registration data for external tools.

[0757] (Application Example 2)

[0758] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0759] Currently, there are systems that extract schedules from messages received via electronic communication services and manage them accordingly, but these systems cannot take into account the user's emotional state. Therefore, flexible schedule management that reduces the user's psychological burden and provides a better user experience is difficult. Furthermore, there is the challenge of not being able to adjust task priorities by integrating with external applications that utilize emotional information.

[0760] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0761] In this invention, the server includes means for receiving messages from an electronic communication service, means for performing natural language processing to extract schedule information from the received messages, means for acquiring emotional information using an emotional recognition engine that analyzes the emotions in the messages, and means for adjusting the notification method based on the emotional information. This enables schedule management that reflects the user's emotional state.

[0762] "Electronic communication services" are services that enable users to send and receive text messages and information via the internet or other communication networks.

[0763] "Natural language processing" is a technology that enables computers to understand and process human language, and in particular, it is a process that extracts information from text data and interprets the meaning of sentences.

[0764] An "electronic calendar service" is a system that allows users to register, manage, and share appointments online, providing a calendar function that can be accessed in a digital format.

[0765] An "emotion recognition engine" is a technology that identifies and classifies human emotions from text and audio, and then processes information based on the results.

[0766] "Adjusting notification methods" is the process of dynamically changing the content, timing, and method of notifications according to the user's situation and emotions, in order to provide the optimal user experience.

[0767] This invention involves a system installed in a home robot that analyzes messages from the user via electronic communication services to manage schedules and recognize emotions. This enables flexible schedule adjustments that take the user's emotions into consideration, as well as task management according to priority.

[0768] The server first receives messages from electronic communication services. Embedded hardware such as a Raspberry Pi is used for this purpose. The messages are parsed using natural language processing techniques to extract date, time, and event information. Python and the Natural Language Toolkit (nltk) library are used for this process.

[0769] Simultaneously, an emotion recognition engine analyzes the message and extracts emotional information. This engine classifies the message as positive, negative, or neutral, and identifies the user's psychological state.

[0770] The device registers appointments to an electronic calendar service based on the extracted schedule information. Based on the registered appointments, the system provides optimal notifications that take into account the user's emotions. For example, if negative emotions are detected, a voice notification in a gentle tone is provided.

[0771] Furthermore, the server integrates with work management tools to automatically adjust task priorities based on emotional information. For example, it uses the project management tool's API to list actionable tasks that reduce user anxiety.

[0772] For example, if a user sends a message saying, "I have an important presentation on Friday and I'm nervous," the server receives this message, extracts "presentation on Friday" as a date, and recognizes the negative emotion of nervousness. Based on this information, on the day of the presentation, it sends an encouraging voice message such as, "You can do it! Good luck!" and sets a high priority for presentation preparation.

[0773] Examples of prompt messages are as follows:

[0774] "Design a program that analyzes user messages to extract schedule and sentiment information, and then has a home robot provide appropriate voice notifications."

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The server receives messages from users via electronic communication services. These received messages become input, and their contents are stored for processing in the next step.

[0778] Step 2:

[0779] The server analyzes received messages using natural language processing techniques. Specifically, it analyzes text data using Python and the Natural Language Toolkit (nltk) library, extracting date and event information from the message. This results in the output of the date and time information.

[0780] Step 3:

[0781] The server uses an emotion recognition engine to analyze the sentiment of messages. The emotion recognition engine takes a message as input, examines the vocabulary and context within the text, and classifies the sentiment as positive, negative, or neutral. The output is sentiment information.

[0782] Step 4:

[0783] The device calls the calendar API to register the event in the electronic calendar service based on the extracted schedule information. The schedule information is used as input, and an output confirms that it has been registered as a new event in the electronic calendar.

[0784] Step 5:

[0785] The server adjusts the notification method based on emotional information. In particular, if the emotional state is negative, it generates a voice notification in a gentle tone. Emotional information is used as input, and an adjusted notification message is output.

[0786] Step 6:

[0787] The device uses the task management tool's API to prioritize tasks based on emotional information. Specifically, it adds new tasks to the task management list with priorities corresponding to the emotional state. Emotional information and schedule information are input, and an updated task list is output.

[0788] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0789] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0790] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0791] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0792] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0793] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0794] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0795] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0796] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0797] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0798] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0799] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0800] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0801] 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.

[0802] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0803] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0804] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0805] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0806] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0807] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0808] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0809] The following is further disclosed regarding the embodiments described above.

[0810] (Claim 1)

[0811] A means of receiving messages from an electronic messaging service,

[0812] A means of performing natural language processing to extract schedule information from received messages,

[0813] Based on the extracted schedule information, a means of registering the schedule in an electronic calendar service,

[0814] A means of notifying that the schedule registration is complete,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, further comprising means for automatically setting reminders with notification functions based on schedule information extracted from messages of an electronic messaging service.

[0818] (Claim 3)

[0819] The system according to claim 1, further comprising means for linking with task management information of an external application and automatically registering or updating tasks.

[0820] "Example 1"

[0821] (Claim 1)

[0822] Means for receiving electronic messages,

[0823] A method for performing natural language processing using generative AI to extract schedule information from received electronic messages,

[0824] Based on the extracted schedule information, a method for registering the schedule in an electronic calendar,

[0825] A means of setting reminders for registered appointments and notifying the user,

[0826] A means of automatically registering or updating information in conjunction with external software,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, which automatically sets a reminder with a notification function based on schedule information.

[0830] (Claim 3)

[0831] The system according to claim 1, which automatically registers or updates information using information from an external information management tool.

[0832] "Application Example 1"

[0833] (Claim 1)

[0834] A means of receiving messages using electronic communication technology,

[0835] A means for performing natural language processing to extract time and location information from a received message,

[0836] A means for registering an appointment in an electronic schedule management system based on extracted time and location information,

[0837] A means of notifying that the schedule registration has been completed,

[0838] A means to enable the handling of personnel allocation information necessary for supporting actual business operations,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, further comprising means for automatically setting an appointment with a notification function based on time and location information extracted from a message using electronic communication technology.

[0842] (Claim 3)

[0843] The system according to claim 1, further comprising means for linking with work instruction information of an external processing platform to automatically register or update work.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] Means for receiving information from electronic communication services,

[0847] A means of performing language processing to extract schedule information from received information,

[0848] A means of registering the schedule in the electronic planning service based on the extracted schedule information,

[0849] A means of recognizing emotions from transmitted information,

[0850] Means for adjusting the notification method based on recognized emotions,

[0851] A means of coordinating work by linking with work management information from external applications,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for dynamically setting reminders with notification functions based on emotional information.

[0855] (Claim 3)

[0856] The system according to claim 1, which adjusts operations through communication with external applications based on recognized emotions.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] A means for receiving messages from electronic communication services,

[0860] A means of performing natural language processing to extract schedule information from received messages,

[0861] Based on the extracted schedule information, a means of registering the schedule in an electronic calendar service,

[0862] A means of obtaining emotional information using an emotion recognition engine that analyzes emotions within a message,

[0863] Means for adjusting notification methods based on emotional information,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising means for automatically setting reminders with notification functions based on schedule information and sentiment information extracted from messages of an electronic communication service.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising means for linking with work management information of an external application and automatically adjusting the priority of tasks according to the user's emotional information. [Explanation of symbols]

[0869] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving messages from an electronic messaging service, A means of performing natural language processing to extract schedule information from received messages, Based on the extracted schedule information, a means of registering the schedule in an electronic calendar service, A means of notifying that the schedule registration is complete, A system that includes this.

2. The system according to claim 1, further comprising means for automatically setting reminders with notification functions based on schedule information extracted from messages of an electronic messaging service.

3. The system according to claim 1, further comprising means for linking with task management information of an external application and automatically registering or updating tasks.

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