system

Abstract

We provide the system. [Solution] A means for collecting electronic messages from an information processing device using means for acquiring communication data, A means for analyzing the content of collected electronic messages and generating a summary using a generative model, A task management unit is used to identify action items from the generated summary and organize them according to priority. A means of registering organized action items by integrating with a schedule management tool, A means of notifying the user device of summary information and organized action items using the notification function, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, it has been difficult to check a large number of unread e-mails after a long vacation or a business trip and efficiently extract and organize important information and tasks from them. Therefore, users have faced the risks of overlooking important matters and the complexity of task management. This problem is particularly prominent in large organizations and busy business environments.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system that acquires electronic messages from an information processing device and generates summaries by analyzing these messages using a generation model. This system analyzes action items from the generated summaries and organizes them based on priority using a task management unit. Furthermore, by integrating with a schedule management tool and providing a function to automatically register the organized action items, the system enables users to efficiently manage unread messages and prevent overlooking important matters.

[0006] "Communication data" refers to electronic messages and their contents that are sent and received via information processing devices or networks.

[0007] A "generative model" refers to a program or system that uses machine learning algorithms to analyze data and perform summarization and information extraction.

[0008] A "task management unit" refers to a module or system that has the function of organizing and prioritizing extracted action items.

[0009] A "schedule management tool" refers to software or applications used to manage a user's schedule and tasks.

[0010] "Notification function" refers to a system or mechanism for informing a user's device of information or messages. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention provides a system for efficiently processing unread electronic messages in an information processing device and providing important information to the user. This system consists of four main components: acquisition of communication data, generation of summaries using a generative model, organization by a task management unit and registration to a schedule management tool, and execution of a notification function.

[0033] First, the server retrieves communication data from the information processing unit. This data includes unread emails and messages from the user. Next, the server inputs the retrieved communication data into a generative model and generates a summary by analyzing its content. This summarization process extracts important conversation points, action items, deadlines, and other relevant information.

[0034] Once the summary is complete, the server uses a task management unit to organize the extracted action items based on priority. This includes features that consider the importance and deadlines of each task. The organized action items are automatically registered in the user's daily scheduling tool.

[0035] Finally, the server uses its notification function to send summary information and organized action items to the user's device. This allows the user to efficiently review an overview of important communication data through their device and proceed with tasks without overlooking anything.

[0036] As a concrete example, consider a scenario where User A returns from a one-week business trip. The server retrieves unread emails from User A's mail server and analyzes them via a generative model. For example, it generates summaries such as "check project progress," "meeting invitation," and "request for contract revisions." Next, based on these summaries, the task management unit creates a specific task list based on each summary and prioritizes them. These are then instantly registered in User A's calendar tool, and notifications are sent. User A can then check important notifications on their device and take the necessary actions immediately.

[0037] Thus, the present invention provides a means for users to easily and quickly process unread communication data and to efficiently manage information.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server uses the user's account authentication information to access the mail server and chat tools. Here, it retrieves unread messages via APIs and stores them in the system's internal data store.

[0041] Step 2:

[0042] The server inputs unread messages stored in the data store into a generative model. The generative model uses natural language processing techniques to analyze the content of each message, understand the context, and generate a summary.

[0043] Step 3:

[0044] The server automatically extracts meeting schedules and tasks requiring specific action from the generated summary. This creates a list of important information that the user should focus on.

[0045] Step 4:

[0046] The server uses a task management unit to create a task list based on the extracted information. Tasks are then prioritized according to their importance and deadlines.

[0047] Step 5:

[0048] The server registers a priority task list in the user's scheduling tool. This process is automated and is immediately reflected in the user's calendar.

[0049] Step 6:

[0050] The server notifies the user's terminal of the compiled summary information and a task list with assigned priorities. The terminal receives this information and displays it to the user.

[0051] Step 7:

[0052] Users can review summaries and task lists notified via their devices, and then check for further details or adjust tasks as needed. This allows users to quickly return to work.

[0053] (Example 1)

[0054] 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."

[0055] In modern society, users receive a large volume of electronic communications and are required to efficiently organize and respond to them quickly. However, checking a large number of unread messages, finding important information, and prioritizing and managing them is not easy. Therefore, there is a need for means to reduce the burden on users and streamline information management.

[0056] 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.

[0057] In this invention, the server includes means for collecting electronic communications from data devices using a device for acquiring communication information, means for analyzing the content of the collected electronic communications and creating a summary using a language analysis model, and means for detecting action items from the generated summary and organizing them according to priority using an activity management unit. This enables users to efficiently process large amounts of electronic communications and quickly grasp and manage important information.

[0058] A "device for acquiring communication information" refers to a device that has the function of collecting electronic communications from data devices.

[0059] A "data device" is a device used to store or process electronic communications.

[0060] "Electronic communications" refers to information sent and received in digital format, such as emails and messages.

[0061] A "language analysis model" is an algorithm or program used to analyze collected electronic communications and generate summaries.

[0062] An "activity management unit" is a component that has the function of extracting action items from a generated summary and organizing them based on priority.

[0063] An "action item" refers to a specific task or action that a user should perform based on electronic communication.

[0064] The "information transmission function" refers to a mechanism for notifying the user's terminal of summarized information and organized action items.

[0065] The present invention is a system for rapidly and efficiently processing the large amount of electronic communications that users receive on a daily basis. This system consists of a device for acquiring communication information, a language analysis model, an activity management unit, an information transmission function, and the like.

[0066] The server collects electronic communications from data devices using a device that acquires communication information. This collection process is achieved by periodically retrieving unread messages from mail servers using IMAP or POP3 protocols.

[0067] The collected electronic communications are input into a language analysis model by the server. This language analysis model utilizes natural language processing techniques such as BERT and GPT to analyze the content of the electronic communications and summarize important information. A concrete example of a prompt used in this process is, "Please summarize the important elements of this email."

[0068] Furthermore, the server extracts action items from summaries generated using activity management units and organizes them according to priority. These action items are automatically registered by the server in scheduling tools. This registration process utilizes, for example, the Google® Calendar API or the Microsoft® Outlook API.

[0069] Finally, the server uses its information transmission function to notify the user's terminal of the summary information and organized action items. The notification is sent as a push notification, which the user can check on their terminal. This allows the user to efficiently handle important tasks without overlooking any.

[0070] A concrete example would be a scenario where a user returns from a long business trip and has to deal with a large volume of unread emails. The server would summarize all the emails accumulated during the trip, list important actions, and support efficient task management.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server collects electronic communications from data devices using a device that acquires communication information. This step uses the IMAP or POP3 protocol to retrieve unread electronic messages from the user's mail server. The input requires the user's authentication information and the server's address, and the output is data of unread electronic communications. This collected data is used in the next analysis step.

[0074] Step 2:

[0075] The server inputs the acquired electronic communications into a language analysis model to generate a summary. This step uses generative AI models such as BERT or GPT to extract the key points of the electronic communications. The input consists of the collected electronic communications and a prompt such as, "Summarize the key elements of this email." The output is a summary of the important information. This summary data serves as a foundation for organizing the data into operational items.

[0076] Step 3:

[0077] The server extracts action items from the generated summary and organizes them according to priority using activity management units. This step prioritizes tasks based on their importance and deadlines within the summary. The input is the generated summary data, and the output is a list of specific action items with assigned priorities. This list is ready to be registered in the user's scheduling tool.

[0078] Step 4:

[0079] The server automatically registers organized action items into a scheduling tool. This uses APIs such as Google Calendar API and Microsoft Outlook API, integrating the action items into the user's daily calendar. The input requires a prioritized list of action items, and the output is the tasks registered in the user's calendar. This improves task visibility and management.

[0080] Step 5:

[0081] The server uses its information transmission function to notify the user's terminal of summarized information and organized action items. This information is then displayed on the user's terminal screen via push notifications. The input is a task list registered in a schedule management tool, and the output is a notification presented on the terminal in a format easily reviewable by the user. Based on this notification, the user can immediately begin taking action.

[0082] (Application Example 1)

[0083] 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."

[0084] In modern industrial sectors, robotic devices are required to process large amounts of communication data quickly and efficiently, and to automate necessary work tasks. However, current systems often rely on manual processing of communication data, limiting efficiency and accuracy. Furthermore, there is a lack of mechanisms to accurately extract critical information from the data and enable robotic devices to quickly begin action. As a result, establishing optimal work processes in the industrial sector is difficult.

[0085] 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.

[0086] In this invention, the server includes means for collecting electronic messages from an information processing device using means for acquiring communication data, means for analyzing the content of the collected electronic messages and generating a summary using a generative model, and means for executing automated action items in a robotic device and efficiently managing work tasks in the industrial sector. This enables the robotic device to efficiently process communication data and automate work processes.

[0087] "Means for acquiring communication data" refers to functions or mechanisms for collecting electronic messages from information processing equipment.

[0088] A "generative model" is a program that utilizes machine learning algorithms to analyze the content of collected electronic messages and generate summaries.

[0089] A "task management unit" is a system component that detects action items from the generated summary and organizes them according to priority.

[0090] A "schedule management tool" is software or a platform for properly registering and managing organized action items.

[0091] The "notification function" is a means of communication that informs the user's device of summary information and organized action items.

[0092] A "robot device" is a mechanical device that performs automated actions and efficiently manages work tasks in the industrial sector.

[0093] The system, as an embodiment of the present invention, realizes efficient work management of robotic devices in the industrial sector. First, the server acquires communication data from an information processing device. This communication data includes electronic messages and instructions within the factory. The server inputs the acquired data into a generative model, analyzes the data using specialized machine learning algorithms, and generates a summary. This generative model employs advanced natural language processing technology such as the OpenAI® GPT series.

[0094] The generated summaries are organized in a task management unit, and action items are identified. Once tasks in the industrial sector are efficiently organized based on priority, the organized action items are registered in a scheduling tool. This tool can leverage scheduling libraries such as APScheduler.

[0095] Next, the notification function is activated, and the necessary information is provided to the user device. At this point, the robot device can efficiently complete work tasks in the industrial field by executing automated action items.

[0096] As a concrete example, suppose a new maintenance instruction arrives on the server as an electronic message in a factory. The server retrieves the message and generates a summary. Next, it creates a task list based on the summary and schedules the maintenance work for the robotic equipment for the following day. This allows information to be shared with other maintenance staff. An example of a prompt for the generating AI model would be: "Please briefly summarize the following email content and identify the important tasks and their priorities: Email body."

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server retrieves communication data from the information processing device. In this step, it connects to the email server using the IMAP protocol and downloads new messages. The input is the server's login information and the mail server's address, and the output is unread electronic message data.

[0100] Step 2:

[0101] The server inputs the acquired electronic message data into a generative model. Here, the message is passed to a generative AI model (e.g., the GPT series) in text format for analysis and summarization. The input is text data, and the output is a summary. Specifically, the process involves extracting important keywords and elements.

[0102] Step 3:

[0103] The server passes the generated summary to the task management unit. The task management unit analyzes this summary and identifies each action item. The input is the summary text, and the output is a list of action items. This allows for prioritization and determination of the importance of each task.

[0104] Step 4:

[0105] The server registers the organized action items in the schedule management tool. In this step, APScheduler is used to add the action item list to an existing schedule. The input is the action item list, and the output is the updated schedule information.

[0106] Step 5:

[0107] The server uses a notification function to send summary information and organized action items to the terminal. Notifications are sent via the user's mobile device or the interface of an industrial robot. The input is updated schedule information and summary information, and the output is the notification displayed on the terminal.

[0108] Step 6:

[0109] The robotic device initiates automated actions based on the received action items. These actions include the execution of scheduled work tasks. The input is the automated action items, and the output is the completed work tasks. Specifically, this includes timely maintenance work and progress reporting.

[0110] 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.

[0111] This invention provides a system that enables users to efficiently manage electronic messages and, by taking into account the user's emotional state, to provide more appropriate information and task management. This system mainly consists of components such as the acquisition of communication data, summary generation using a generative model, priority setting by a task management unit, integration with a schedule management tool, and emotion recognition by an emotion engine.

[0112] The server first retrieves and stores unread communication data from the user's mail server and chat tools. This data is input into a generative model, where important information is extracted as a summary. Based on this summary, the task management unit detects action items and sets their priorities. Through this process, tasks associated with important emails and messages are registered in the scheduling tool, allowing users to manage tasks without cumbersome manual work.

[0113] Another feature of this invention is the incorporation of an emotion engine. The server collects emotion data from user operation logs and input information, and analyzes it using the emotion engine. Based on the results of this analysis, the summary information and task list presented to the user are adjusted. In addition, if it is determined that the user is experiencing stress, the priority of tasks may be dynamically changed. As a result, the user receives information that is appropriate to their emotions and situation at the time, thereby reducing their burden.

[0114] As a concrete example, consider a situation where User B is unable to concentrate during work. In this case, the server uses an emotion engine to determine User B's emotional state based on their operation speed and feedback. If the result indicates "heavy burden," the task management unit lowers the priority of low-priority tasks, leaving only high-priority ones. Based on this result, the terminal notifies User B only of the most necessary information. In this way, User B can perform their work efficiently and avoid excessive stress.

[0115] By combining the elements and methods described above, this system allows users to effectively manage unread messages while providing appropriate support tailored to their psychological state at any given time.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The server queries the user's email account or chat tool to retrieve unread messages. These messages are stored in the data store in a specified format, along with information about their content.

[0119] Step 2:

[0120] The server passes unread messages stored in the data store to a generative model, which analyzes the content and creates a summary. The generative model uses natural language processing algorithms to extract key points and next actions as a summary.

[0121] Step 3:

[0122] From the generated summary, the server uses a task management unit to extract action items and assign priorities to them. This process determines which tasks should be prioritized, taking into account deadlines and importance.

[0123] Step 4:

[0124] The server connects to the user's scheduling tool and automatically registers organized activity items. This ensures that important tasks are reflected in the user's calendar tool, automatically optimizing their daily plan.

[0125] Step 5:

[0126] The server uses an emotion engine to analyze the user's past input data and real-time actions to detect their current emotional state. The emotion engine identifies the user's emotions and uses that data to adjust the next steps.

[0127] Step 6:

[0128] Based on the emotional state obtained by the emotion engine, the server dynamically repriors the task list in the task management unit. For example, if the user is stressed, the priority of non-urgent tasks will be lowered.

[0129] Step 7:

[0130] The server sends the final summary information and the adjusted task list to the terminal. The terminal receives this information and displays it to the user using its notification function.

[0131] Step 8:

[0132] Users review summary information and task lists notified via their devices. They can then check details or adjust tasks as needed, taking effective action based on the information. This enables users to work efficiently and with less stress.

[0133] (Example 2)

[0134] 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".

[0135] In modern society, information overload makes it difficult for users to efficiently manage electronic messages. Furthermore, the provision of information without considering the user's emotional state is a problem, causing excessive stress. Therefore, there is a need for a system that can extract necessary information from a vast amount of messages and present it in a way that suits the user's emotions and situation.

[0136] 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.

[0137] In this invention, the server includes means for periodically acquiring and storing unread communication information from an information processing device, means for analyzing the stored communication information using a generative model and generating a summary containing important information, and means for dynamically adjusting the information presentation according to the user's emotional state by utilizing sentiment analysis technology. This enables the user to efficiently manage important messages and receive information appropriate to their emotional state at any given time.

[0138] An "information processing device" is a computer system used for acquiring, storing, analyzing, and notifying data.

[0139] A "generative model" is a model that uses machine learning algorithms to analyze input information and summarize its content.

[0140] "Communication information" refers to electronic messages sent and received through email and chat tools used by users.

[0141] A "task management component" is a function within an information processing device that detects action items and sets their priorities.

[0142] A "schedule management device" is a tool that allows users to register activity items within a time management system and manage them visually.

[0143] "Emotional analysis technology" is a technology that analyzes a user's emotional state based on user operation logs and input information.

[0144] "Notification technology" is a technology that transmits information to the user's device and presents the necessary content.

[0145] The system of this invention enables users to efficiently manage electronic messages and, by taking into account their emotional state, to provide more appropriate information and task management. The following describes specific embodiments for implementing this system.

[0146] The server first obtains user permission and, via an information processing device, collects unread communication information from mail servers and chat tools. Specific software used here includes mail service APIs and messaging platform APIs. The collected data is stored in a database. The database used for storage is typically a relational database management system (RDBMS), which is commonly used in modern information technology.

[0147] Next, the server sends the stored communication information to a generative AI model to generate a summary containing important information. The generation process utilizes machine learning algorithms to understand the context and extract specific information. Specific generative AI models that can be used include those with text generation capabilities; for example, instructions might be given via a prompt such as, "Summarize unread messages and extract the key points."

[0148] Furthermore, the server uses sentiment analysis technology to analyze user operation logs and input information to infer the user's emotional state. Based on the emotional state, the way information is presented is dynamically adjusted. For example, low-priority tasks are hidden for users who are experiencing stress. Sentiment recognition software is used for this analysis.

[0149] The terminal notifies the user of summary information and task lists provided by the server. The notifications are customized according to the user's current emotional state and situation, and delivered in a format best suited to their needs. This system is designed to enable users to effectively manage only important electronic messages, reducing their workload and allowing them to perform their tasks efficiently. For example, it provides notifications with adjusted priority based on emotional analysis of situations where the user is unable to concentrate during work.

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] The server retrieves unread communication information from a mail server or chat tool via an information processing device. In this process, the input is unread messages accessed via an external communication service API, and the output is the retrieved message data. This data is stored in a database so that it can be used in subsequent processes.

[0153] Step 2:

[0154] The server inputs the stored communication information into the generating AI model to generate a summary. The input at this stage is the message data stored in Step 1. The generating AI model uses machine learning algorithms to analyze this input data, extract important information, and generate a summary. The output is the summarized text information. This summary is obtained based on the prompt "Summarize unread messages and extract the important points."

[0155] Step 3:

[0156] The server sends the summarized information to the task management components to detect action items and set their priorities. The input is the summarized data generated in step 2, which is then analyzed to extract specific action items, and priorities are set based on urgency and importance. The output is a list of tasks with assigned priorities.

[0157] Step 4:

[0158] The server registers the prioritized action items with the scheduling device. The input is the task list generated in step 3, which is converted into a format suitable for the scheduling device and registered. The output of this process is task information visualized in the calendar application used by the user.

[0159] Step 5:

[0160] The server uses sentiment analysis technology to collect and analyze sentiment data from user operation logs and input information. The input consists of user operation logs and input information, and the analysis evaluates the user's emotional state. The output is an index indicating the emotional state. Based on this index, the task list presentation is dynamically adjusted.

[0161] Step 6:

[0162] The terminal receives a coordinated summary and task list from the server and notifies the user. Input is the coordinated data sent from the server, which is notified or displayed on the terminal. Output is an informational presentation that the user can visually confirm and respond to.

[0163] Step 7:

[0164] Users perform tasks based on information presented from the terminal. They process tasks according to the notified summary information and priorities. At this stage, efficient work is possible because users actively utilize passive output directly through the interface.

[0165] (Application Example 2)

[0166] 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 device 14 will be referred to as the "terminal."

[0167] In modern information processing, there is a demand for the efficient management of large volumes of electronic messages. However, conventional systems have often failed to adequately consider the user's emotional state when presenting information or managing tasks, resulting in excessive stress for users. In particular, in electronic payment services, the speed and emotional consideration of customer support are crucial. Therefore, it is necessary to achieve efficient information presentation and task management that reflects the user's psychological state.

[0168] 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.

[0169] In this invention, the server includes means for collecting electronic messages from an information processing device using means for acquiring communication data, means for analyzing the content of the collected electronic messages and generating a summary using a generation model, and means for analyzing the user's emotional state using an emotion engine and adjusting the presentation of summary information and action items based on that analysis. This enables information presentation and task management that are tailored to the user's psychological state.

[0170] "Means for acquiring communication data" refers to technologies that have the function of efficiently collecting electronic messages from information processing equipment.

[0171] A "generative model" is a technology that uses machine learning algorithms to analyze the content of collected electronic messages and generate summaries.

[0172] A "task management unit" is a technology that has the function of identifying action items from the generated summary and organizing them according to priority.

[0173] A "schedule management tool" is a technology that has the functionality to effectively register and manage organized action items.

[0174] The "notification function" is a technology for appropriately notifying user devices of summary information and organized action items.

[0175] An "emotion engine" is a technology that analyzes the user's emotional state to adjust information presentation and task priorities.

[0176] This invention provides a system that combines various technologies to enable users to efficiently manage electronic messages and to realize emotion-based information delivery and task management.

[0177] First, the server automatically collects electronic messages from the user's mail server and chat tools using communication data acquisition methods. During this process, it focuses on unread messages to quickly obtain the latest information. The acquired message data is then analyzed using a generative AI model to summarize important information. This generative model utilizes a natural language processing tool powered by machine learning algorithms to efficiently generate summaries.

[0178] Next, the task management unit extracts specific action items from the generated summary and sets their priorities. Prioritization is integrated with the scheduling tool to organize the action items appropriately and efficiently. This allows users to easily identify tasks that require immediate attention.

[0179] Furthermore, the emotion engine analyzes the user's emotional state in real time based on user operation logs and input information collected by the server. Based on the results of the emotion analysis, the server dynamically adjusts task priorities and information presentation methods, optimizing the information the user receives to match their current psychological state. The emotion engine can utilize emotion analysis tools such as Azure® Emotion API.

[0180] As a concrete example, when user A, who uses an electronic payment service, inquires about an invoice, if the sentiment engine detects user dissatisfaction, the server will present the most urgent solution as a summary. This approach allows users to resolve problems quickly and efficiently.

[0181] In this series of processes, the generative AI model summarizes the message based on the following prompt:

[0182] "Summarize user messages and perform sentiment analysis. Determine whether users are experiencing stress and provide guidelines for prioritizing the display of important information."

[0183] The present invention, through the above configuration and process, realizes electronic message management and task management that takes emotional responses into consideration.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The server uses a means of acquiring communication data to collect electronic messages from the user's mail server and chat tools. The input is a list of unread messages, which the server processes. The output is the acquired message data stored within the system. Specifically, it uses APIs to interact with various messaging platforms and saves messages to a database.

[0187] Step 2:

[0188] The server uses a generative AI model to analyze the content of collected electronic messages and generate summaries. The input is the message data obtained from step 1. The generative AI model uses natural language processing algorithms to extract important information and generate a concise summary. The output is the generated summary information. Specifically, this process includes selecting important content using a text analysis tool and creating a summary.

[0189] Step 3:

[0190] The server uses a task management unit to extract action items from the generated summary and set priorities. The input is summarized information. The task management unit prioritizes tasks based on their importance and deadlines according to the set criteria. The output is a list of prioritized actions. Specifically, it assesses the urgency of tasks and determines their order.

[0191] Step 4:

[0192] The server uses an emotion engine to analyze user operation logs and input information to identify their emotional state. Input data includes the user's recent operation logs and feedback information. The emotion engine uses an emotion analysis model to assess stress and dissatisfaction levels. The output is the emotion analysis result. The specific operation involves measuring the user's operation speed and reaction time, and estimating their emotional state based on these measurements.

[0193] Step 5:

[0194] Based on the sentiment analysis results, the server dynamically adjusts priorities and information presentation. The input is the sentiment analysis results from the previous step and a prioritized action list. The output is the adjusted task list and summary information. Specifically, the server selects information to present considering the user's emotional state and optimizes it to reduce the user's burden.

[0195] Step 6:

[0196] The device utilizes its notification function to inform the user of refined summary information and organized action items. Inputs are the refined summary information and task list. Output is a notification message to the user. Specifically, it sends a push notification to the user's device to ensure that necessary information is quickly conveyed.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] This invention provides a system for efficiently processing unread electronic messages in an information processing device and providing important information to the user. This system consists of four main components: acquisition of communication data, generation of summaries using a generative model, organization by a task management unit and registration to a schedule management tool, and execution of a notification function.

[0214] First, the server retrieves communication data from the information processing unit. This data includes unread emails and messages from the user. Next, the server inputs the retrieved communication data into a generative model and generates a summary by analyzing its content. This summarization process extracts important conversation points, action items, deadlines, and other relevant information.

[0215] Once the summary is complete, the server uses a task management unit to organize the extracted action items based on priority. This includes features that consider the importance and deadlines of each task. The organized action items are automatically registered in the user's daily scheduling tool.

[0216] Finally, the server uses its notification function to send a summary of information and organized action items to the user's device. This allows the user to efficiently review an overview of important communication data through their device and proceed with tasks without overlooking anything.

[0217] As a concrete example, consider a scenario where User A returns from a one-week business trip. The server retrieves unread emails from User A's mail server and analyzes them via a generative model. For example, it generates summaries such as "check project progress," "meeting invitation," and "request for contract revisions." Next, based on these summaries, the task management unit creates a specific task list based on each summary and prioritizes them. These are then instantly registered in User A's calendar tool, and notifications are sent. User A can then check important notifications on their device and take the necessary actions immediately.

[0218] Thus, the present invention provides a means for users to easily and quickly process unread communication data and to efficiently manage information.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server uses the user's account authentication information to access the mail server and chat tools. Here, it retrieves unread messages via APIs and stores them in the system's internal data store.

[0222] Step 2:

[0223] The server inputs unread messages stored in the data store into a generative model. The generative model uses natural language processing techniques to analyze the content of each message, understand the context, and generate a summary.

[0224] Step 3:

[0225] The server automatically extracts meeting schedules and tasks requiring specific action from the generated summary. This creates a list of important information that the user should focus on.

[0226] Step 4:

[0227] The server uses a task management unit to create a task list based on the extracted information. Tasks are then prioritized according to their importance and deadlines.

[0228] Step 5:

[0229] The server registers a priority task list in the user's scheduling tool. This process is automated and is immediately reflected in the user's calendar.

[0230] Step 6:

[0231] The server notifies the user's terminal of the compiled summary information and a task list with assigned priorities. The terminal receives this information and displays it to the user.

[0232] Step 7:

[0233] Users can review summaries and task lists notified via their devices, and then check for further details or adjust tasks as needed. This allows users to quickly return to work.

[0234] (Example 1)

[0235] 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."

[0236] In modern society, users receive a large volume of electronic communications and are required to efficiently organize and respond to them quickly. However, checking a large number of unread messages, finding important information, and prioritizing and managing them is not easy. Therefore, there is a need for means to reduce the burden on users and streamline information management.

[0237] 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.

[0238] In this invention, the server includes means for collecting electronic communications from data devices using a device for acquiring communication information, means for analyzing the content of the collected electronic communications and creating a summary using a language analysis model, and means for detecting action items from the generated summary and organizing them according to priority using an activity management unit. This enables users to efficiently process large amounts of electronic communications and quickly grasp and manage important information.

[0239] A "device for acquiring communication information" refers to a device that has the function of collecting electronic communications from data devices.

[0240] A "data device" is a device used to store or process electronic communications.

[0241] "Electronic communications" refers to information sent and received in digital format, such as emails and messages.

[0242] A "language analysis model" is an algorithm or program used to analyze collected electronic communications and generate summaries.

[0243] An "activity management unit" is a component that has the function of extracting action items from a generated summary and organizing them based on priority.

[0244] An "action item" refers to a specific task or action that a user should perform based on electronic communication.

[0245] The "information transmission function" refers to a mechanism for notifying the user's terminal of summarized information and organized action items.

[0246] The present invention is a system for rapidly and efficiently processing the large amount of electronic communications that users receive on a daily basis. This system consists of a device for acquiring communication information, a language analysis model, an activity management unit, an information transmission function, and the like.

[0247] The server collects electronic communications from data devices using a device that acquires communication information. This collection process is achieved by periodically retrieving unread messages from mail servers using IMAP or POP3 protocols.

[0248] The collected electronic communications are input into a language analysis model by the server. This language analysis model utilizes natural language processing techniques such as BERT and GPT to analyze the content of the electronic communications and summarize important information. A concrete example of a prompt used in this process is, "Please summarize the important elements of this email."

[0249] Furthermore, the server extracts action items from the summary generated using activity management units and organizes them according to priority. These action items are automatically registered by the server in the scheduling tool. This registration process uses, for example, the Google Calendar API or the Microsoft Outlook API.

[0250] Finally, the server uses its information transmission function to notify the user's terminal of the summary information and organized action items. The notification is sent as a push notification, which the user can check on their terminal. This allows the user to efficiently handle important tasks without overlooking any.

[0251] A concrete example would be a scenario where a user returns from a long business trip and has to deal with a large volume of unread emails. The server would summarize all the emails accumulated during the trip, list important actions, and support efficient task management.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The server collects electronic communications from data devices using a device that acquires communication information. This step uses the IMAP or POP3 protocol to retrieve unread electronic messages from the user's mail server. The input requires the user's authentication information and the server's address, and the output is data of unread electronic communications. This collected data is used in the next analysis step.

[0255] Step 2:

[0256] The server inputs the acquired electronic communications into a language analysis model to generate a summary. This step uses generative AI models such as BERT or GPT to extract the key points of the electronic communications. The input consists of the collected electronic communications and a prompt such as, "Summarize the key elements of this email." The output is a summary of the important information. This summary data serves as a foundation for organizing the data into operational items.

[0257] Step 3:

[0258] The server extracts action items from the generated summary and organizes them according to priority using activity management units. This step prioritizes tasks based on their importance and deadlines within the summary. The input is the generated summary data, and the output is a list of specific action items with assigned priorities. This list is ready to be registered in the user's scheduling tool.

[0259] Step 4:

[0260] The server automatically registers organized action items into a scheduling tool. This uses APIs such as Google Calendar API and Microsoft Outlook API, integrating the action items into the user's daily calendar. The input requires a prioritized list of action items, and the output is the tasks registered in the user's calendar. This improves task visibility and management.

[0261] Step 5:

[0262] The server uses its information transmission function to notify the user's terminal of summarized information and organized action items. This information is then displayed on the user's terminal screen via push notifications. The input is a task list registered in a schedule management tool, and the output is a notification presented on the terminal in a format easily reviewable by the user. Based on this notification, the user can immediately begin taking action.

[0263] (Application Example 1)

[0264] 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."

[0265] In modern industrial sectors, robotic devices are required to process large amounts of communication data quickly and efficiently, and to automate necessary work tasks. However, current systems often rely on manual processing of communication data, limiting efficiency and accuracy. Furthermore, there is a lack of mechanisms to accurately extract critical information from the data and enable robotic devices to quickly begin action. As a result, establishing optimal work processes in the industrial sector is difficult.

[0266] 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.

[0267] In this invention, the server includes means for collecting electronic messages from an information processing device using means for acquiring communication data, means for analyzing the content of the collected electronic messages and generating a summary using a generative model, and means for executing automated action items in a robotic device and efficiently managing work tasks in the industrial sector. This enables the robotic device to efficiently process communication data and automate work processes.

[0268] "Means for acquiring communication data" refers to functions or mechanisms for collecting electronic messages from information processing equipment.

[0269] A "generative model" is a program that utilizes machine learning algorithms to analyze the content of collected electronic messages and generate summaries.

[0270] A "task management unit" is a system component that detects action items from the generated summary and organizes them according to priority.

[0271] A "schedule management tool" is software or a platform for properly registering and managing organized action items.

[0272] The "notification function" is a means of communication that informs the user's device of summary information and organized action items.

[0273] A "robot device" is a mechanical device that performs automated actions and efficiently manages work tasks in the industrial sector.

[0274] The system, as an embodiment of the present invention, realizes efficient work management of robotic devices in the industrial sector. First, the server acquires communication data from an information processing device. This communication data includes electronic messages and instructions within the factory. The server inputs the acquired data into a generative model, analyzes the data using specialized machine learning algorithms, and generates a summary. This generative model employs advanced natural language processing technology such as the OpenAI GPT series.

[0275] The generated summaries are organized in a task management unit, and action items are identified. Once tasks in the industrial sector are efficiently organized based on priority, the organized action items are registered in a scheduling tool. This tool can leverage scheduling libraries such as APScheduler.

[0276] Next, the notification function is activated, and the necessary information is provided to the user device. At this point, the robot device can efficiently complete work tasks in the industrial field by executing automated action items.

[0277] As a concrete example, suppose a new maintenance instruction arrives on the server as an electronic message in a factory. The server retrieves the message and generates a summary. Next, it creates a task list based on the summary and schedules the maintenance work for the robotic equipment for the following day. This allows information to be shared with other maintenance staff. An example of a prompt for the generating AI model would be: "Please briefly summarize the following email content and identify the important tasks and their priorities: Email body."

[0278] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0279] Step 1:

[0280] The server acquires communication data from the information processing device. In this step, it connects to the email server using the IMAP protocol and downloads new messages. The input is the server login information and the address of the email server, and the output is the unread email message data.

[0281] Step 2:

[0282] The server inputs the acquired email message data into the generation model. Here, it passes the message to the generation AI model (e.g., GPT series) in text format for message analysis and summarization. The input is text data, and the output is the summary text. As a specific operation, a process of extracting important keywords and elements is performed.

[0283] Step 3:

[0284] The server passes the generated summary to the task management unit. The task management unit analyzes this summary and detects each action item. The input is the summary text, and the output is the action item list. Thereby, sorting based on priority is performed, and the importance of each task is determined.

[0285] Step 4:

[0286] The server registers the sorted action items in the schedule management tool. In this step, the APScheduler is used to add the action item list to the existing schedule. The input is the action item list, and the output is the updated schedule information.

[0287] Step 5:

[0288] The server uses a notification function to notify terminals of summary information and organized action items. Notifications are sent via the user's mobile device or the interface of an industrial robot. Inputs are updated schedule information and summary information, and output is the notification displayed on the terminal.

[0289] Step 6:

[0290] The robotic device initiates automated actions based on the received action items. These actions include the execution of scheduled work tasks. The input is the automated action items, and the output is the completed work tasks. Specifically, this includes timely maintenance work and progress reporting.

[0291] 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.

[0292] This invention provides a system that enables users to efficiently manage electronic messages and, by taking into account the user's emotional state, to provide more appropriate information and task management. This system mainly consists of components such as the acquisition of communication data, summary generation using a generative model, priority setting by a task management unit, integration with a schedule management tool, and emotion recognition by an emotion engine.

[0293] The server first retrieves and stores unread communication data from the user's mail server and chat tools. This data is input into a generative model, where important information is extracted as a summary. Based on this summary, the task management unit detects action items and sets their priorities. Through this process, tasks associated with important emails and messages are registered in the scheduling tool, allowing users to manage tasks without cumbersome manual work.

[0294] Another feature of this invention is the incorporation of an emotion engine. The server collects emotion data from user operation logs and input information, and analyzes it using the emotion engine. Based on the results of this analysis, the summary information and task list presented to the user are adjusted. In addition, if it is determined that the user is experiencing stress, the priority of tasks may be dynamically changed. As a result, the user receives information that is appropriate to their emotions and situation at the time, thereby reducing their burden.

[0295] As a concrete example, consider a situation where User B is unable to concentrate during work. In this case, the server uses an emotion engine to determine User B's emotional state based on their operation speed and feedback. If the result indicates "heavy burden," the task management unit lowers the priority of low-priority tasks, leaving only high-priority ones. Based on this result, the terminal notifies User B only of the most necessary information. In this way, User B can perform their work efficiently and avoid excessive stress.

[0296] This system combines the elements and methods described above to enable users to effectively manage unread messages while providing appropriate support tailored to their psychological state at any given time.

[0297] The following describes the processing flow.

[0298] Step 1:

[0299] The server queries the user's email account or chat tool to retrieve unread messages. These messages are stored in the data store in a specified format, along with information about their content.

[0300] Step 2:

[0301] The server passes the unread messages stored in the data store to the generation model, analyzes the content, and creates a summary. The generation model uses natural language processing algorithms to extract important points and future actions as the summary.

[0302] Step 3:

[0303] From the generated summary, the server uses the task management unit to extract action items and set priorities for them. In this process, a judgment is made on which tasks should be advanced, taking into account deadlines and importance.

[0304] Step 4:

[0305] The server connects to the user's schedule management tool and automatically registers the organized action items. As a result, important tasks are reflected in the user's calendar tool, and the daily plan is automatically optimized.

[0306] Step 5:

[0307] The server uses an emotion engine to analyze the user's past input data and real-time operations, and detect the current emotional state. The emotion engine identifies the user's emotion and uses the data for adjustment in the next step.

[0308] Step 6:

[0309] Based on the emotional state obtained by the emotion engine, the server dynamically resets the priorities of the task list in the task management unit. For example, when the user is feeling stressed, the priority of non-urgent tasks is decreased.

[0310] Step 7:

[0311] The server sends the final summary information and the adjusted task list to the terminal. The terminal receives this information and displays it to the user using the notification function.

[0312] Step 8:

[0313] Users review summary information and task lists notified via their devices. They can then check details or adjust tasks as needed, taking effective action based on the information. This enables users to work efficiently and with less stress.

[0314] (Example 2)

[0315] 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".

[0316] In modern society, information overload makes it difficult for users to efficiently manage electronic messages. Furthermore, the provision of information without considering the user's emotional state is a problem, causing excessive stress. Therefore, there is a need for a system that can extract necessary information from a vast amount of messages and present it in a way that suits the user's emotions and situation.

[0317] 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.

[0318] In this invention, the server includes means for periodically acquiring and storing unread communication information from an information processing device, means for analyzing the stored communication information using a generative model and generating a summary containing important information, and means for dynamically adjusting the information presentation according to the user's emotional state by utilizing sentiment analysis technology. This enables the user to efficiently manage important messages and receive information appropriate to their emotional state at any given time.

[0319] An "information processing device" is a computer system used for acquiring, storing, analyzing, and notifying data.

[0320] A "generative model" is a model that uses machine learning algorithms to analyze input information and summarize its content.

[0321] "Communication information" refers to electronic messages sent and received through email and chat tools used by users.

[0322] A "task management component" is a function within an information processing device that detects action items and sets their priorities.

[0323] A "schedule management device" is a tool that allows users to register activity items within a time management system and manage them visually.

[0324] "Emotional analysis technology" is a technology that analyzes a user's emotional state based on user operation logs and input information.

[0325] "Notification technology" is a technology that transmits information to the user's device and presents the necessary content.

[0326] The system of this invention enables users to efficiently manage electronic messages and, by taking into account their emotional state, to provide more appropriate information and task management. The following describes specific embodiments of this system.

[0327] The server first obtains user permission and, via an information processing device, collects unread communication information from mail servers and chat tools. Specific software used here includes mail service APIs and messaging platform APIs. The collected data is stored in a database. The database used for storage is typically a relational database management system (RDBMS), which is commonly used in modern information technology.

[0328] Next, the server sends the stored communication information to a generative AI model to generate a summary containing important information. The generation process utilizes machine learning algorithms to understand the context and extract specific information. Specific generative AI models that can be used include those with text generation capabilities; for example, instructions might be given via a prompt such as, "Summarize unread messages and extract the key points."

[0329] Furthermore, the server uses sentiment analysis technology to analyze user operation logs and input information to infer the user's emotional state. Based on the emotional state, the way information is presented is dynamically adjusted. For example, low-priority tasks are hidden for users who are experiencing stress. Sentiment recognition software is used for this analysis.

[0330] The terminal notifies the user of summary information and task lists provided by the server. The notifications are customized according to the user's current emotional state and situation, and delivered in a format best suited to their needs. This system is designed to enable users to effectively manage only important electronic messages, reducing their workload and allowing them to perform their tasks efficiently. For example, it provides notifications with adjusted priority based on emotional analysis of situations where the user is unable to concentrate during work.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] The server retrieves unread communication information from a mail server or chat tool via an information processing device. In this process, the input is unread messages accessed via an external communication service API, and the output is the retrieved message data. This data is stored in a database so that it can be used in subsequent processes.

[0334] Step 2:

[0335] The server inputs the stored communication information into the generating AI model to generate a summary. The input at this stage is the message data stored in Step 1. The generating AI model uses machine learning algorithms to analyze this input data, extract important information, and generate a summary. The output is the summarized text information. This summary is obtained based on the prompt "Summarize unread messages and extract the important points."

[0336] Step 3:

[0337] The server sends the summarized information to the task management components to detect action items and set their priorities. The input is the summarized data generated in step 2, which is then analyzed to extract specific action items, and priorities are set based on urgency and importance. The output is a list of tasks with assigned priorities.

[0338] Step 4:

[0339] The server registers the prioritized action items with the scheduling device. The input is the task list generated in step 3, which is converted into a format suitable for the scheduling device and registered. The output of this process is task information visualized in the calendar application used by the user.

[0340] Step 5:

[0341] The server uses sentiment analysis technology to collect and analyze sentiment data from user operation logs and input information. The input consists of user operation logs and input information, and the analysis evaluates the user's emotional state. The output is an index indicating the emotional state. Based on this index, the task list presentation is dynamically adjusted.

[0342] Step 6:

[0343] The terminal receives a coordinated summary and task list from the server and notifies the user. Input is the coordinated data sent from the server, which is notified or displayed on the terminal. Output is an informational presentation that the user can visually confirm and respond to.

[0344] Step 7:

[0345] Users perform tasks based on information presented from the terminal. They process tasks according to the notified summary information and priorities. At this stage, efficient work is possible because users actively utilize passive output directly through the interface.

[0346] (Application Example 2)

[0347] 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."

[0348] In modern information processing, there is a demand for the efficient management of large volumes of electronic messages. However, conventional systems have often failed to adequately consider the user's emotional state when presenting information or managing tasks, resulting in excessive stress for users. In particular, in electronic payment services, the speed and emotional consideration of customer support are crucial. Therefore, it is necessary to achieve efficient information presentation and task management that reflects the user's psychological state.

[0349] 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.

[0350] In this invention, the server includes means for collecting electronic messages from an information processing device using means for acquiring communication data, means for analyzing the content of the collected electronic messages and generating a summary using a generation model, and means for analyzing the user's emotional state using an emotion engine and adjusting the presentation of summary information and action items based on that analysis. This enables information presentation and task management that are tailored to the user's psychological state.

[0351] "Means for acquiring communication data" refers to technologies that have the function of efficiently collecting electronic messages from information processing equipment.

[0352] A "generative model" is a technology that uses machine learning algorithms to analyze the content of collected electronic messages and generate summaries.

[0353] A "task management unit" is a technology that has the function of identifying action items from the generated summary and organizing them according to priority.

[0354] A "schedule management tool" is a technology that has the functionality to effectively register and manage organized action items.

[0355] The "notification function" is a technology for appropriately notifying user devices of summary information and organized action items.

[0356] An "emotion engine" is a technology that analyzes the user's emotional state to adjust information presentation and task priorities.

[0357] This invention provides a system that combines various technologies to enable users to efficiently manage electronic messages and to provide emotion-based information and task management.

[0358] First, the server automatically collects electronic messages from the user's mail server and chat tools using communication data acquisition methods. During this process, it focuses on unread messages to quickly obtain the latest information. The acquired message data is then analyzed using a generative AI model to summarize important information. This generative model utilizes a natural language processing tool powered by machine learning algorithms to efficiently generate summaries.

[0359] Next, the task management unit extracts specific action items from the generated summary and sets their priorities. Prioritization is integrated with the scheduling tool to organize the action items appropriately and efficiently. This allows users to easily identify tasks that require immediate attention.

[0360] Furthermore, the emotion engine analyzes the user's emotional state in real time based on user operation logs and input information collected by the server. Based on the results of the emotion analysis, the server dynamically adjusts task priorities and information presentation methods, optimizing the information the user receives to match their current psychological state. The emotion engine can utilize emotion analysis tools such as the Azure Emotion API.

[0361] As a concrete example, when user A, who uses an electronic payment service, inquires about an invoice, if the sentiment engine detects user dissatisfaction, the server will present the most urgent solution as a summary. This approach allows users to resolve problems quickly and efficiently.

[0362] In this series of processes, the generative AI model summarizes the message based on the following prompt:

[0363] "Summarize user messages and perform sentiment analysis. Determine whether users are experiencing stress and provide guidelines for prioritizing the display of important information."

[0364] The present invention, through the above-described configuration and process, realizes electronic message management and task management that takes emotional responses into consideration.

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The server uses a means of acquiring communication data to collect electronic messages from the user's mail server and chat tools. The input is a list of unread messages, which the server processes. The output is the acquired message data stored within the system. Specifically, it uses APIs to interact with various messaging platforms and saves messages to a database.

[0368] Step 2:

[0369] The server uses a generative AI model to analyze the content of collected electronic messages and generate summaries. The input is the message data obtained from step 1. The generative AI model uses natural language processing algorithms to extract important information and generate a concise summary. The output is the generated summary information. Specifically, this process includes selecting important content using a text analysis tool and creating a summary.

[0370] Step 3:

[0371] The server uses a task management unit to extract action items from the generated summary and set priorities. The input is summarized information. The task management unit prioritizes tasks based on their importance and deadlines according to the set criteria. The output is a list of prioritized actions. Specifically, it assesses the urgency of tasks and determines their order.

[0372] Step 4:

[0373] The server uses an emotion engine to analyze user operation logs and input information to identify their emotional state. Input data includes the user's recent operation logs and feedback information. The emotion engine uses an emotion analysis model to assess stress and dissatisfaction levels. The output is the emotion analysis result. The specific operation involves measuring the user's operation speed and reaction time, and estimating their emotional state based on these measurements.

[0374] Step 5:

[0375] Based on the sentiment analysis results, the server dynamically adjusts priorities and information presentation. The input is the sentiment analysis results from the previous step and a prioritized action list. The output is the adjusted task list and summary information. Specifically, the server selects information to present considering the user's emotional state and optimizes it to reduce the user's burden.

[0376] Step 6:

[0377] The device utilizes its notification function to inform the user of refined summary information and organized action items. Inputs are the refined summary information and task list. Output is a notification message to the user. Specifically, it sends a push notification to the user's device to ensure that necessary information is quickly conveyed.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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".

[0394] This invention provides a system for efficiently processing unread electronic messages in an information processing device and providing important information to the user. This system consists of four main components: acquisition of communication data, generation of summaries using a generative model, organization by a task management unit and registration to a schedule management tool, and execution of a notification function.

[0395] First, the server retrieves communication data from the information processing unit. This data includes unread emails and messages from the user. Next, the server inputs the retrieved communication data into a generative model and generates a summary by analyzing its content. This summarization process extracts important conversation points, action items, deadlines, and other relevant information.

[0396] Once the summary is complete, the server uses a task management unit to organize the extracted action items based on priority. This includes features that consider the importance and deadlines of each task. The organized action items are automatically registered in the user's daily scheduling tool.

[0397] Finally, the server uses its notification function to send summary information and organized action items to the user's device. This allows the user to efficiently review an overview of important communication data through their device and proceed with tasks without overlooking anything.

[0398] As a concrete example, consider a scenario where User A returns from a one-week business trip. The server retrieves unread emails from User A's mail server and analyzes them via a generative model. For example, it generates summaries such as "check project progress," "meeting invitation," and "request for contract revisions." Next, based on these summaries, the task management unit creates a specific task list based on each summary and prioritizes them. These are then instantly registered in User A's calendar tool, and notifications are sent. User A can then check important notifications on their device and take the necessary actions immediately.

[0399] Thus, the present invention provides a means for users to easily and quickly process unread communication data and to efficiently manage information.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The server uses the user's account authentication information to access the mail server and chat tools. Here, it retrieves unread messages via APIs and stores them in the system's internal data store.

[0403] Step 2:

[0404] The server inputs unread messages stored in the data store into a generative model. The generative model uses natural language processing techniques to analyze the content of each message, understand the context, and generate a summary.

[0405] Step 3:

[0406] The server automatically extracts meeting schedules and tasks requiring specific action from the generated summary. This creates a list of important information that the user should focus on.

[0407] Step 4:

[0408] The server uses a task management unit to create a task list based on the extracted information. Tasks are then prioritized according to their importance and deadlines.

[0409] Step 5:

[0410] The server registers a priority task list in the user's scheduling tool. This process is automated and is immediately reflected in the user's calendar.

[0411] Step 6:

[0412] The server notifies the user's terminal of the compiled summary information and a task list with assigned priorities. The terminal receives this information and displays it to the user.

[0413] Step 7:

[0414] Users can review summaries and task lists notified via their devices, and then check for further details or adjust tasks as needed. This allows users to quickly return to work.

[0415] (Example 1)

[0416] 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."

[0417] In modern society, users receive a large volume of electronic communications and are required to efficiently organize and respond to them quickly. However, checking a large number of unread messages, finding important information, and prioritizing and managing them is not easy. Therefore, there is a need for means to reduce the burden on users and streamline information management.

[0418] 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.

[0419] In this invention, the server includes means for collecting electronic communications from data devices using a device for acquiring communication information, means for analyzing the content of the collected electronic communications and creating a summary using a language analysis model, and means for detecting action items from the generated summary and organizing them according to priority using an activity management unit. This enables users to efficiently process large amounts of electronic communications and quickly grasp and manage important information.

[0420] A "device for acquiring communication information" refers to a device that has the function of collecting electronic communications from data devices.

[0421] A "data device" is a device used to store or process electronic communications.

[0422] "Electronic communications" refers to information sent and received in digital format, such as emails and messages.

[0423] A "language analysis model" is an algorithm or program used to analyze collected electronic communications and generate summaries.

[0424] An "activity management unit" is a component that has the function of extracting action items from a generated summary and organizing them based on priority.

[0425] An "action item" refers to a specific task or action that a user should perform based on electronic communication.

[0426] The "information transmission function" refers to a mechanism for notifying the user's terminal of summarized information and organized action items.

[0427] The present invention is a system for rapidly and efficiently processing the large volume of electronic communications that users receive on a daily basis. This system consists of a device for acquiring communication information, a language analysis model, an activity management unit, an information transmission function, and the like.

[0428] The server collects electronic communications from data devices using a device that acquires communication information. This collection process is achieved by periodically retrieving unread messages from mail servers using IMAP or POP3 protocols.

[0429] The collected electronic communications are input into a language analysis model by the server. This language analysis model utilizes natural language processing techniques such as BERT and GPT to analyze the content of the electronic communications and summarize important information. A concrete example of a prompt used in this process is, "Please summarize the important elements of this email."

[0430] Furthermore, the server extracts action items from the summary generated using activity management units and organizes them according to priority. These action items are automatically registered by the server in the scheduling tool. This registration process uses, for example, the Google Calendar API or the Microsoft Outlook API.

[0431] Finally, the server uses its information transmission function to notify the user's terminal of the summary information and organized action items. The notification is sent as a push notification, which the user can check on their terminal. This allows the user to efficiently handle important tasks without overlooking any.

[0432] A concrete example would be a scenario where a user returns from a long business trip and has to deal with a large volume of unread emails. The server would summarize all the emails accumulated during the trip, list important actions, and support efficient task management.

[0433] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0434] Step 1:

[0435] The server collects electronic communications from data devices using a device that acquires communication information. This step uses the IMAP or POP3 protocol to retrieve unread electronic messages from the user's mail server. The input requires the user's authentication information and the server's address, and the output is data of unread electronic communications. This collected data is used in the next analysis step.

[0436] Step 2:

[0437] The server inputs the acquired electronic communications into a language analysis model to generate a summary. This step uses generative AI models such as BERT or GPT to extract the key points of the electronic communications. The input consists of the collected electronic communications and a prompt such as, "Summarize the key elements of this email." The output is a summary of the important information. This summary data serves as a foundation for organizing the data into operational items.

[0438] Step 3:

[0439] The server extracts action items from the generated summary and organizes them according to priority using activity management units. This step prioritizes tasks based on their importance and deadlines within the summary. The input is the generated summary data, and the output is a list of specific action items with assigned priorities. This list is ready to be registered in the user's scheduling tool.

[0440] Step 4:

[0441] The server automatically registers organized action items into a scheduling tool. This uses APIs such as Google Calendar API and Microsoft Outlook API, integrating the action items into the user's daily calendar. The input requires a prioritized list of action items, and the output is the tasks registered in the user's calendar. This improves task visibility and management.

[0442] Step 5:

[0443] The server uses its information transmission function to notify the user's terminal of summarized information and organized action items. This information is then displayed on the user's terminal screen via push notifications. The input is a task list registered in a schedule management tool, and the output is a notification presented on the terminal in a format easily reviewable by the user. Based on this notification, the user can immediately begin taking action.

[0444] (Application Example 1)

[0445] 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."

[0446] In modern industrial sectors, robotic devices are required to process large amounts of communication data quickly and efficiently, and to automate necessary work tasks. However, current systems often rely on manual processing of communication data, limiting efficiency and accuracy. Furthermore, there is a lack of mechanisms to accurately extract critical information from the data and enable robotic devices to quickly begin action. As a result, establishing optimal work processes in the industrial sector is difficult.

[0447] 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.

[0448] In this invention, the server includes means for collecting electronic messages from an information processing device using means for acquiring communication data, means for analyzing the content of the collected electronic messages and generating a summary using a generative model, and means for executing automated action items in a robotic device and efficiently managing work tasks in the industrial sector. This enables the robotic device to efficiently process communication data and automate work processes.

[0449] "Means for acquiring communication data" refers to functions or mechanisms for collecting electronic messages from information processing equipment.

[0450] A "generative model" is a program that utilizes machine learning algorithms to analyze the content of collected electronic messages and generate summaries.

[0451] A "task management unit" is a system component that detects action items from the generated summary and organizes them according to priority.

[0452] A "schedule management tool" is software or a platform for properly registering and managing organized action items.

[0453] The "notification function" is a means of communication that informs the user's device of summary information and organized action items.

[0454] A "robot device" is a mechanical device that performs automated actions and efficiently manages work tasks in the industrial sector.

[0455] The system, as an embodiment of the present invention, realizes efficient work management of robotic devices in the industrial sector. First, the server acquires communication data from an information processing device. This communication data includes electronic messages and instructions within the factory. The server inputs the acquired data into a generative model, analyzes the data using specialized machine learning algorithms, and generates a summary. This generative model employs advanced natural language processing technology such as the OpenAI GPT series.

[0456] The generated summaries are organized in a task management unit, and action items are identified. Once tasks in the industrial sector are efficiently organized based on priority, the organized action items are registered in a scheduling tool. This tool can leverage scheduling libraries such as APScheduler.

[0457] Next, the notification function is activated, and the necessary information is provided to the user device. At this point, the robot device can efficiently complete work tasks in the industrial field by executing automated action items.

[0458] As a concrete example, suppose a new maintenance instruction arrives on the server as an electronic message in a factory. The server retrieves the message and generates a summary. Next, it creates a task list based on the summary and schedules the maintenance work for the robotic equipment for the following day. This allows information to be shared with other maintenance staff. An example of a prompt for the generating AI model would be: "Please briefly summarize the following email content and identify the important tasks and their priorities: Email body."

[0459] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0460] Step 1:

[0461] The server retrieves communication data from the information processing device. In this step, it connects to the email server using the IMAP protocol and downloads new messages. The input is the server's login information and the mail server's address, and the output is unread electronic message data.

[0462] Step 2:

[0463] The server inputs the acquired electronic message data into a generative model. Here, the message is passed to a generative AI model (e.g., the GPT series) in text format for analysis and summarization. The input is text data, and the output is a summary. Specifically, the process involves extracting important keywords and elements.

[0464] Step 3:

[0465] The server passes the generated summary to the task management unit. The task management unit analyzes this summary and identifies each action item. The input is the summary text, and the output is a list of action items. This allows for prioritization and determination of the importance of each task.

[0466] Step 4:

[0467] The server registers the organized action items in the schedule management tool. In this step, APScheduler is used to add the action item list to an existing schedule. The input is the action item list, and the output is the updated schedule information.

[0468] Step 5:

[0469] The server uses a notification function to send summary information and organized action items to the terminal. Notifications are sent via the user's mobile device or the interface of an industrial robot. The input is updated schedule information and summary information, and the output is the notification displayed on the terminal.

[0470] Step 6:

[0471] The robotic device initiates automated actions based on the received action items. These actions include the execution of scheduled work tasks. The input is the automated action items, and the output is the completed work tasks. Specifically, this includes timely maintenance work and progress reporting.

[0472] 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.

[0473] This invention provides a system that enables users to efficiently manage electronic messages and, by taking into account the user's emotional state, to provide more appropriate information and task management. This system mainly consists of components such as the acquisition of communication data, summary generation using a generative model, priority setting by a task management unit, integration with a schedule management tool, and emotion recognition by an emotion engine.

[0474] The server first retrieves and stores unread communication data from the user's mail server and chat tools. This data is input into a generative model, where important information is extracted as a summary. Based on this summary, the task management unit detects action items and sets their priorities. Through this process, tasks associated with important emails and messages are registered in the scheduling tool, allowing users to manage tasks without cumbersome manual work.

[0475] Another feature of this invention is the incorporation of an emotion engine. The server collects emotion data from user operation logs and input information, and analyzes it using the emotion engine. Based on the results of this analysis, the summary information and task list presented to the user are adjusted. In addition, if it is determined that the user is experiencing stress, the priority of tasks may be dynamically changed. As a result, the user receives information that is appropriate to their emotions and situation at the time, thereby reducing their burden.

[0476] As a concrete example, consider a situation where User B is unable to concentrate during work. In this case, the server uses an emotion engine to determine User B's emotional state based on their operation speed and feedback. If the result indicates "heavy burden," the task management unit lowers the priority of low-priority tasks, leaving only high-priority ones. Based on this result, the terminal notifies User B only of the most necessary information. In this way, User B can perform their work efficiently and avoid excessive stress.

[0477] This system combines the elements and methods described above to enable users to effectively manage unread messages while providing appropriate support tailored to their psychological state at any given time.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The server queries the user's email account or chat tool to retrieve unread messages. These messages are stored in the data store in a specified format, along with information about their content.

[0481] Step 2:

[0482] The server passes unread messages stored in the data store to a generative model, which analyzes the content and creates a summary. The generative model uses natural language processing algorithms to extract key points and next actions as a summary.

[0483] Step 3:

[0484] From the generated summary, the server uses a task management unit to extract action items and assign priorities to them. This process determines which tasks should be prioritized, taking into account deadlines and importance.

[0485] Step 4:

[0486] The server connects to the user's scheduling tool and automatically registers organized activity items. This ensures that important tasks are reflected in the user's calendar tool, automatically optimizing their daily plan.

[0487] Step 5:

[0488] The server uses an emotion engine to analyze the user's past input data and real-time actions to detect their current emotional state. The emotion engine identifies the user's emotions and uses that data to adjust the next steps.

[0489] Step 6:

[0490] Based on the emotional state obtained by the emotion engine, the server dynamically repriors the task list in the task management unit. For example, if the user is stressed, the priority of non-urgent tasks will be lowered.

[0491] Step 7:

[0492] The server sends the final summary information and the adjusted task list to the terminal. The terminal receives this information and displays it to the user using its notification function.

[0493] Step 8:

[0494] Users review summary information and task lists notified via their devices. They can then check details or adjust tasks as needed, taking effective action based on the information. This enables users to work efficiently and with less stress.

[0495] (Example 2)

[0496] 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."

[0497] In modern society, information overload makes it difficult for users to efficiently manage electronic messages. Furthermore, the provision of information without considering the user's emotional state is a problem, causing excessive stress. Therefore, there is a need for a system that can extract necessary information from a vast amount of messages and present it in a way that suits the user's emotions and situation.

[0498] 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.

[0499] In this invention, the server includes means for periodically acquiring and storing unread communication information from an information processing device, means for analyzing the stored communication information using a generative model and generating a summary containing important information, and means for dynamically adjusting the information presentation according to the user's emotional state by utilizing sentiment analysis technology. This enables the user to efficiently manage important messages and receive information appropriate to their emotional state at any given time.

[0500] An "information processing device" is a computer system used for acquiring, storing, analyzing, and notifying data.

[0501] A "generative model" is a model that uses machine learning algorithms to analyze input information and summarize its content.

[0502] "Communication information" refers to electronic messages sent and received through email and chat tools used by users.

[0503] A "task management component" is a function within an information processing device that detects action items and sets their priorities.

[0504] A "schedule management device" is a tool that allows users to register activity items within a time management system and manage them visually.

[0505] "Emotional analysis technology" is a technology that analyzes a user's emotional state based on user operation logs and input information.

[0506] "Notification technology" is a technology that transmits information to the user's device and presents the necessary content.

[0507] The system of this invention enables users to efficiently manage electronic messages and, by taking into account their emotional state, to provide more appropriate information and task management. The following describes specific embodiments of this system.

[0508] The server first obtains user permission and, via an information processing device, collects unread communication information from mail servers and chat tools. Specific software used here includes mail service APIs and messaging platform APIs. The collected data is stored in a database. The database used for storage is typically a relational database management system (RDBMS), which is commonly used in modern information technology.

[0509] Next, the server sends the stored communication information to a generative AI model to generate a summary containing important information. The generation process utilizes machine learning algorithms to understand the context and extract specific information. Specific generative AI models that can be used include those with text generation capabilities; for example, instructions might be given via a prompt such as, "Summarize unread messages and extract the key points."

[0510] Furthermore, the server uses sentiment analysis technology to analyze user operation logs and input information to infer the user's emotional state. Based on the emotional state, the way information is presented is dynamically adjusted. For example, low-priority tasks are hidden for users who are experiencing stress. Sentiment recognition software is used for this analysis.

[0511] The terminal notifies the user of summary information and task lists provided by the server. The notifications are customized according to the user's current emotional state and situation, and delivered in a format best suited to their needs. This system is designed to enable users to effectively manage only important electronic messages, reducing their workload and allowing them to perform their tasks efficiently. For example, it provides notifications with adjusted priority based on emotional analysis of situations where the user is unable to concentrate during work.

[0512] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0513] Step 1:

[0514] The server retrieves unread communication information from a mail server or chat tool via an information processing device. In this process, the input is unread messages accessed via an external communication service API, and the output is the retrieved message data. This data is stored in a database so that it can be used in subsequent processes.

[0515] Step 2:

[0516] The server inputs the stored communication information into the generating AI model to generate a summary. The input at this stage is the message data stored in Step 1. The generating AI model uses machine learning algorithms to analyze this input data, extract important information, and generate a summary. The output is the summarized text information. This summary is obtained based on the prompt "Summarize unread messages and extract the important points."

[0517] Step 3:

[0518] The server sends the summarized information to the task management components to detect action items and set their priorities. The input is the summarized data generated in step 2, which is then analyzed to extract specific action items, and priorities are set based on urgency and importance. The output is a list of tasks with assigned priorities.

[0519] Step 4:

[0520] The server registers the prioritized action items with the scheduling device. The input is the task list generated in step 3, which is converted into a format suitable for the scheduling device and registered. The output of this process is task information visualized in the calendar application used by the user.

[0521] Step 5:

[0522] The server uses sentiment analysis technology to collect and analyze sentiment data from user operation logs and input information. The input consists of user operation logs and input information, and the analysis evaluates the user's emotional state. The output is an index indicating the emotional state. Based on this index, the task list presentation is dynamically adjusted.

[0523] Step 6:

[0524] The terminal receives a coordinated summary and task list from the server and notifies the user. Input is the coordinated data sent from the server, which is notified or displayed on the terminal. Output is an informational presentation that the user can visually confirm and respond to.

[0525] Step 7:

[0526] Users perform tasks based on information presented from the terminal. They process tasks according to the notified summary information and priorities. At this stage, efficient work is possible because users actively utilize passive output directly through the interface.

[0527] (Application Example 2)

[0528] 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."

[0529] In modern information processing, there is a demand for the efficient management of large volumes of electronic messages. However, conventional systems have often failed to adequately consider the user's emotional state when presenting information or managing tasks, resulting in excessive stress for users. In particular, in electronic payment services, the speed and emotional consideration of customer support are crucial. Therefore, it is necessary to achieve efficient information presentation and task management that reflects the user's psychological state.

[0530] 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.

[0531] In this invention, the server includes means for collecting electronic messages from an information processing device using means for acquiring communication data, means for analyzing the content of the collected electronic messages and generating a summary using a generation model, and means for analyzing the user's emotional state using an emotion engine and adjusting the presentation of summary information and action items based on that analysis. This enables information presentation and task management that are tailored to the user's psychological state.

[0532] "Means for acquiring communication data" refers to technologies that have the function of efficiently collecting electronic messages from information processing equipment.

[0533] A "generative model" is a technology that uses machine learning algorithms to analyze the content of collected electronic messages and generate summaries.

[0534] A "task management unit" is a technology that has the function of identifying action items from the generated summary and organizing them according to priority.

[0535] A "schedule management tool" is a technology that has the functionality to effectively register and manage organized action items.

[0536] The "notification function" is a technology for appropriately notifying user devices of summary information and organized action items.

[0537] An "emotion engine" is a technology that analyzes the user's emotional state to adjust information presentation and task priorities.

[0538] This invention provides a system that combines various technologies to enable users to efficiently manage electronic messages and to provide emotion-based information and task management.

[0539] First, the server automatically collects electronic messages from the user's mail server and chat tools using communication data acquisition methods. During this process, it focuses on unread messages to quickly obtain the latest information. The acquired message data is then analyzed using a generative AI model to summarize important information. This generative model utilizes a natural language processing tool powered by machine learning algorithms to efficiently generate summaries.

[0540] Next, the task management unit extracts specific action items from the generated summary and sets their priorities. Prioritization is integrated with the scheduling tool to organize the action items appropriately and efficiently. This allows users to easily identify tasks that require immediate attention.

[0541] Furthermore, the emotion engine analyzes the user's emotional state in real time based on user operation logs and input information collected by the server. Based on the results of the emotion analysis, the server dynamically adjusts task priorities and information presentation methods, optimizing the information the user receives to match their current psychological state. The emotion engine can utilize emotion analysis tools such as the Azure Emotion API.

[0542] As a concrete example, when user A, who uses an electronic payment service, inquires about an invoice, if the sentiment engine detects user dissatisfaction, the server will present the most urgent solution as a summary. This approach allows users to resolve problems quickly and efficiently.

[0543] In this series of processes, the generative AI model summarizes the message based on the following prompt:

[0544] "Summarize user messages and perform sentiment analysis. Determine whether users are experiencing stress and provide guidelines for prioritizing the display of important information."

[0545] The present invention, through the above-described configuration and process, realizes electronic message management and task management that takes emotional responses into consideration.

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The server uses a means of acquiring communication data to collect electronic messages from the user's mail server and chat tools. The input is a list of unread messages, which the server processes. The output is the acquired message data stored within the system. Specifically, it uses APIs to interact with various messaging platforms and saves messages to a database.

[0549] Step 2:

[0550] The server uses a generative AI model to analyze the content of collected electronic messages and generate summaries. The input is the message data obtained from step 1. The generative AI model uses natural language processing algorithms to extract important information and generate a concise summary. The output is the generated summary information. Specifically, this process includes selecting important content using a text analysis tool and creating a summary.

[0551] Step 3:

[0552] The server uses a task management unit to extract action items from the generated summary and set priorities. The input is summarized information. The task management unit prioritizes tasks based on their importance and deadlines according to the set criteria. The output is a list of prioritized actions. Specifically, it assesses the urgency of tasks and determines their order.

[0553] Step 4:

[0554] The server uses an emotion engine to analyze user operation logs and input information to identify their emotional state. Input data includes the user's recent operation logs and feedback information. The emotion engine uses an emotion analysis model to assess stress and dissatisfaction levels. The output is the emotion analysis result. The specific operation involves measuring the user's operation speed and reaction time, and estimating their emotional state based on these measurements.

[0555] Step 5:

[0556] Based on the sentiment analysis results, the server dynamically adjusts priorities and information presentation. The input is the sentiment analysis results from the previous step and a prioritized action list. The output is the adjusted task list and summary information. Specifically, the server selects information to present considering the user's emotional state and optimizes it to reduce the user's burden.

[0557] Step 6:

[0558] The device utilizes its notification function to inform the user of refined summary information and organized action items. Inputs are the refined summary information and task list. Output is a notification message to the user. Specifically, it sends a push notification to the user's device to ensure that necessary information is quickly conveyed.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] [Fourth Embodiment]

[0563] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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".

[0576] This invention provides a system for efficiently processing unread electronic messages in an information processing device and providing important information to the user. This system consists of four main components: acquisition of communication data, generation of summaries using a generative model, organization by a task management unit and registration to a schedule management tool, and execution of a notification function.

[0577] First, the server retrieves communication data from the information processing unit. This data includes unread emails and messages from the user. Next, the server inputs the retrieved communication data into a generative model and generates a summary by analyzing its content. This summarization process extracts important conversation points, action items, deadlines, and other relevant information.

[0578] Once the summary is complete, the server uses a task management unit to organize the extracted action items based on priority. This includes features that consider the importance and deadlines of each task. The organized action items are automatically registered in the user's daily scheduling tool.

[0579] Finally, the server uses its notification function to send summary information and organized action items to the user's device. This allows the user to efficiently review an overview of important communication data through their device and proceed with tasks without overlooking anything.

[0580] As a concrete example, consider a scenario where User A returns from a one-week business trip. The server retrieves unread emails from User A's mail server and analyzes them via a generative model. For example, it generates summaries such as "check project progress," "meeting invitation," and "request for contract revisions." Next, based on these summaries, the task management unit creates a specific task list based on each summary and prioritizes them. These are then instantly registered in User A's calendar tool, and notifications are sent. User A can then check important notifications on their device and take the necessary actions immediately.

[0581] Thus, the present invention provides a means for users to easily and quickly process unread communication data and to efficiently manage information.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The server uses the user's account authentication information to access the mail server and chat tools. Here, it retrieves unread messages via APIs and stores them in the system's internal data store.

[0585] Step 2:

[0586] The server inputs unread messages stored in the data store into a generative model. The generative model uses natural language processing techniques to analyze the content of each message, understand the context, and generate a summary.

[0587] Step 3:

[0588] The server automatically extracts meeting schedules and tasks requiring specific action from the generated summary. This creates a list of important information that the user should focus on.

[0589] Step 4:

[0590] The server uses a task management unit to create a task list based on the extracted information. Tasks are then prioritized according to their importance and deadlines.

[0591] Step 5:

[0592] The server registers a priority task list in the user's scheduling tool. This process is automated and is immediately reflected in the user's calendar.

[0593] Step 6:

[0594] The server notifies the user's terminal of the compiled summary information and a task list with assigned priorities. The terminal receives this information and displays it to the user.

[0595] Step 7:

[0596] Users can review summaries and task lists notified via their devices, and then check for further details or adjust tasks as needed. This allows users to quickly return to work.

[0597] (Example 1)

[0598] 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".

[0599] In modern society, users receive a large volume of electronic communications and are required to efficiently organize and respond to them quickly. However, checking a large number of unread messages, finding important information, and prioritizing and managing them is not easy. Therefore, there is a need for means to reduce the burden on users and streamline information management.

[0600] 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.

[0601] In this invention, the server includes means for collecting electronic communications from data devices using a device for acquiring communication information, means for analyzing the content of the collected electronic communications and creating a summary using a language analysis model, and means for detecting action items from the generated summary and organizing them according to priority using an activity management unit. This enables users to efficiently process large amounts of electronic communications and quickly grasp and manage important information.

[0602] A "device for acquiring communication information" refers to a device that has the function of collecting electronic communications from data devices.

[0603] A "data device" is a device used to store or process electronic communications.

[0604] "Electronic communications" refers to information sent and received in digital format, such as emails and messages.

[0605] A "language analysis model" is an algorithm or program used to analyze collected electronic communications and generate summaries.

[0606] An "activity management unit" is a component that has the function of extracting action items from a generated summary and organizing them based on priority.

[0607] An "action item" refers to a specific task or action that a user should perform based on electronic communication.

[0608] The "information transmission function" refers to a mechanism for notifying the user's terminal of summarized information and organized action items.

[0609] The present invention is a system for rapidly and efficiently processing the large amount of electronic communications that users receive on a daily basis. This system consists of a device for acquiring communication information, a language analysis model, an activity management unit, an information transmission function, and the like.

[0610] The server collects electronic communications from data devices using a device that acquires communication information. This collection process is achieved by periodically retrieving unread messages from mail servers using IMAP or POP3 protocols.

[0611] The collected electronic communications are input into a language analysis model by the server. This language analysis model utilizes natural language processing techniques such as BERT and GPT to analyze the content of the electronic communications and summarize important information. A concrete example of a prompt used in this process is, "Please summarize the important elements of this email."

[0612] Furthermore, the server extracts action items from the summary generated using activity management units and organizes them according to priority. These action items are automatically registered by the server in the scheduling tool. This registration process uses, for example, the Google Calendar API or the Microsoft Outlook API.

[0613] Finally, the server uses its information transmission function to notify the user's terminal of the summary information and organized action items. The notification is sent as a push notification, which the user can check on their terminal. This allows the user to efficiently handle important tasks without overlooking any.

[0614] A concrete example would be a scenario where a user returns from a long business trip and has to deal with a large volume of unread emails. The server would summarize all the emails accumulated during the trip, list important actions, and support efficient task management.

[0615] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0616] Step 1:

[0617] The server collects electronic communications from data devices using a device that acquires communication information. This step uses the IMAP or POP3 protocol to retrieve unread electronic messages from the user's mail server. The input requires the user's authentication information and the server's address, and the output is data of unread electronic communications. This collected data is used in the next analysis step.

[0618] Step 2:

[0619] The server inputs the acquired electronic communications into a language analysis model to generate a summary. This step uses generative AI models such as BERT or GPT to extract the key points of the electronic communications. The input consists of the collected electronic communications and a prompt such as, "Summarize the key elements of this email." The output is a summary of the important information. This summary data serves as a foundation for organizing the data into operational items.

[0620] Step 3:

[0621] The server extracts action items from the generated summary and organizes them according to priority using activity management units. This step prioritizes tasks based on their importance and deadlines within the summary. The input is the generated summary data, and the output is a list of specific action items with assigned priorities. This list is ready to be registered in the user's scheduling tool.

[0622] Step 4:

[0623] The server automatically registers organized action items into a scheduling tool. This uses APIs such as Google Calendar API and Microsoft Outlook API, integrating the action items into the user's daily calendar. The input requires a prioritized list of action items, and the output is the tasks registered in the user's calendar. This improves task visibility and management.

[0624] Step 5:

[0625] The server uses its information transmission function to notify the user's terminal of summarized information and organized action items. This information is then displayed on the user's terminal screen via push notifications. The input is a task list registered in a schedule management tool, and the output is a notification presented on the terminal in a format easily reviewable by the user. Based on this notification, the user can immediately begin taking action.

[0626] (Application Example 1)

[0627] 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".

[0628] In modern industrial sectors, robotic devices are required to process large amounts of communication data quickly and efficiently, and to automate necessary work tasks. However, current systems often rely on manual processing of communication data, limiting efficiency and accuracy. Furthermore, there is a lack of mechanisms to accurately extract critical information from the data and enable robotic devices to quickly begin action. As a result, establishing optimal work processes in the industrial sector is difficult.

[0629] 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.

[0630] In this invention, the server includes means for collecting electronic messages from an information processing device using means for acquiring communication data, means for analyzing the content of the collected electronic messages and generating a summary using a generative model, and means for executing automated action items in a robotic device and efficiently managing work tasks in the industrial sector. This enables the robotic device to efficiently process communication data and automate work processes.

[0631] "Means for acquiring communication data" refers to functions or mechanisms for collecting electronic messages from information processing equipment.

[0632] A "generative model" is a program that utilizes machine learning algorithms to analyze the content of collected electronic messages and generate summaries.

[0633] A "task management unit" is a system component that detects action items from the generated summary and organizes them according to priority.

[0634] A "schedule management tool" is software or a platform for properly registering and managing organized action items.

[0635] The "notification function" is a means of communication that informs the user's device of summary information and organized action items.

[0636] A "robot device" is a mechanical device that performs automated actions and efficiently manages work tasks in the industrial sector.

[0637] The system, as an embodiment of the present invention, realizes efficient work management of robotic devices in the industrial sector. First, the server acquires communication data from an information processing device. This communication data includes electronic messages and instructions within the factory. The server inputs the acquired data into a generative model, analyzes the data using specialized machine learning algorithms, and generates a summary. This generative model employs advanced natural language processing technology such as the OpenAI GPT series.

[0638] The generated summaries are organized in a task management unit, and action items are identified. Once tasks in the industrial sector are efficiently organized based on priority, the organized action items are registered in a scheduling tool. This tool can leverage scheduling libraries such as APScheduler.

[0639] Next, the notification function is activated, and the necessary information is provided to the user device. At this point, the robot device can efficiently complete work tasks in the industrial field by executing automated action items.

[0640] As a concrete example, suppose a new maintenance instruction arrives on the server as an electronic message in a factory. The server retrieves the message and generates a summary. Next, it creates a task list based on the summary and schedules the maintenance work for the robotic equipment for the following day. This allows information to be shared with other maintenance staff. An example of a prompt for the generating AI model would be: "Please briefly summarize the following email content and identify the important tasks and their priorities: Email body."

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The server retrieves communication data from the information processing device. In this step, it connects to the email server using the IMAP protocol and downloads new messages. The input is the server's login information and the mail server's address, and the output is unread electronic message data.

[0644] Step 2:

[0645] The server inputs the acquired electronic message data into a generative model. Here, the message is passed to a generative AI model (e.g., the GPT series) in text format for analysis and summarization. The input is text data, and the output is a summary. Specifically, the process involves extracting important keywords and elements.

[0646] Step 3:

[0647] The server passes the generated summary to the task management unit. The task management unit analyzes this summary and identifies each action item. The input is the summary text, and the output is a list of action items. This allows for prioritization and determination of the importance of each task.

[0648] Step 4:

[0649] The server registers the organized action items in the schedule management tool. In this step, APScheduler is used to add the action item list to an existing schedule. The input is the action item list, and the output is the updated schedule information.

[0650] Step 5:

[0651] The server uses a notification function to send summary information and organized action items to the terminal. Notifications are sent via the user's mobile device or the interface of an industrial robot. The input is updated schedule information and summary information, and the output is the notification displayed on the terminal.

[0652] Step 6:

[0653] The robotic device initiates automated actions based on the received action items. These actions include the execution of scheduled work tasks. The input is the automated action items, and the output is the completed work tasks. Specifically, this includes timely maintenance work and progress reporting.

[0654] 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.

[0655] This invention provides a system that enables users to efficiently manage electronic messages and, by taking into account the user's emotional state, to provide more appropriate information and task management. This system mainly consists of components such as the acquisition of communication data, summary generation using a generative model, priority setting by a task management unit, integration with a schedule management tool, and emotion recognition by an emotion engine.

[0656] The server first retrieves and stores unread communication data from the user's mail server and chat tools. This data is input into a generative model, where important information is extracted as a summary. Based on this summary, the task management unit detects action items and sets their priorities. Through this process, tasks associated with important emails and messages are registered in the scheduling tool, allowing users to manage tasks without cumbersome manual work.

[0657] Another feature of this invention is the incorporation of an emotion engine. The server collects emotion data from user operation logs and input information, and analyzes it using the emotion engine. Based on the results of this analysis, the summary information and task list presented to the user are adjusted. In addition, if it is determined that the user is experiencing stress, the priority of tasks may be dynamically changed. As a result, the user receives information that is appropriate to their emotions and situation at the time, thereby reducing their burden.

[0658] As a concrete example, consider a situation where User B is unable to concentrate during work. In this case, the server uses an emotion engine to determine User B's emotional state based on their operation speed and feedback. If the result indicates "heavy burden," the task management unit lowers the priority of low-priority tasks, leaving only high-priority ones. Based on this result, the terminal notifies User B only of the most necessary information. In this way, User B can perform their work efficiently and avoid excessive stress.

[0659] This system combines the elements and methods described above to enable users to effectively manage unread messages while providing appropriate support tailored to their psychological state at any given time.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The server queries the user's email account or chat tool to retrieve unread messages. These messages are stored in the data store in a specified format, along with information about their content.

[0663] Step 2:

[0664] The server passes unread messages stored in the data store to a generative model, which analyzes the content and creates a summary. The generative model uses natural language processing algorithms to extract key points and next actions as a summary.

[0665] Step 3:

[0666] From the generated summary, the server uses a task management unit to extract action items and assign priorities to them. This process determines which tasks should be prioritized, taking into account deadlines and importance.

[0667] Step 4:

[0668] The server connects to the user's scheduling tool and automatically registers organized activity items. This ensures that important tasks are reflected in the user's calendar tool, automatically optimizing their daily plan.

[0669] Step 5:

[0670] The server uses an emotion engine to analyze the user's past input data and real-time actions to detect their current emotional state. The emotion engine identifies the user's emotions and uses that data to adjust the next steps.

[0671] Step 6:

[0672] Based on the emotional state obtained by the emotion engine, the server dynamically repriors the task list in the task management unit. For example, if the user is stressed, the priority of non-urgent tasks will be lowered.

[0673] Step 7:

[0674] The server sends the final summary information and the adjusted task list to the terminal. The terminal receives this information and displays it to the user using its notification function.

[0675] Step 8:

[0676] Users review summary information and task lists notified via their devices. They can then check details or adjust tasks as needed, taking effective action based on the information. This enables users to work efficiently and with less stress.

[0677] (Example 2)

[0678] 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".

[0679] In modern society, information overload makes it difficult for users to efficiently manage electronic messages. Furthermore, the provision of information without considering the user's emotional state is a problem, causing excessive stress. Therefore, there is a need for a system that can extract necessary information from a vast amount of messages and present it in a way that suits the user's emotions and situation.

[0680] 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.

[0681] In this invention, the server includes means for periodically acquiring and storing unread communication information from an information processing device, means for analyzing the stored communication information using a generative model and generating a summary containing important information, and means for dynamically adjusting the information presentation according to the user's emotional state by utilizing sentiment analysis technology. This enables the user to efficiently manage important messages and receive information appropriate to their emotional state at any given time.

[0682] An "information processing device" is a computer system used for acquiring, storing, analyzing, and notifying data.

[0683] A "generative model" is a model that uses machine learning algorithms to analyze input information and summarize its content.

[0684] "Communication information" refers to electronic messages sent and received through email and chat tools used by users.

[0685] A "task management component" is a function within an information processing device that detects action items and sets their priorities.

[0686] A "schedule management device" is a tool that allows users to register activity items within a time management system and manage them visually.

[0687] "Emotional analysis technology" is a technology that analyzes a user's emotional state based on user operation logs and input information.

[0688] "Notification technology" is a technology that transmits information to the user's device and presents the necessary content.

[0689] The system of this invention enables users to efficiently manage electronic messages and, by taking into account their emotional state, to provide more appropriate information and task management. The following describes specific embodiments of this system.

[0690] The server first obtains user permission and, via an information processing device, collects unread communication information from mail servers and chat tools. Specific software used here includes mail service APIs and messaging platform APIs. The collected data is stored in a database. The database used for storage is typically a relational database management system (RDBMS), which is commonly used in modern information technology.

[0691] Next, the server sends the stored communication information to a generative AI model to generate a summary containing important information. The generation process utilizes machine learning algorithms to understand the context and extract specific information. Specific generative AI models that can be used include those with text generation capabilities; for example, instructions might be given via a prompt such as, "Summarize unread messages and extract the key points."

[0692] Furthermore, the server uses sentiment analysis technology to analyze user operation logs and input information to infer the user's emotional state. Based on the emotional state, the way information is presented is dynamically adjusted. For example, low-priority tasks are hidden for users who are experiencing stress. Sentiment recognition software is used for this analysis.

[0693] The terminal notifies the user of summary information and task lists provided by the server. The notifications are customized according to the user's current emotional state and situation, and delivered in a format best suited to their needs. This system is designed to enable users to effectively manage only important electronic messages, reducing their workload and allowing them to perform their tasks efficiently. For example, it provides notifications with adjusted priority based on emotional analysis of situations where the user is unable to concentrate during work.

[0694] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0695] Step 1:

[0696] The server retrieves unread communication information from a mail server or chat tool via an information processing device. In this process, the input is unread messages accessed via an external communication service API, and the output is the retrieved message data. This data is stored in a database so that it can be used in subsequent processes.

[0697] Step 2:

[0698] The server inputs the stored communication information into the generating AI model to generate a summary. The input at this stage is the message data stored in Step 1. The generating AI model uses machine learning algorithms to analyze this input data, extract important information, and generate a summary. The output is the summarized text information. This summary is obtained based on the prompt "Summarize unread messages and extract the important points."

[0699] Step 3:

[0700] The server sends the summarized information to the task management components to detect action items and set their priorities. The input is the summarized data generated in step 2, which is then analyzed to extract specific action items, and priorities are set based on urgency and importance. The output is a list of tasks with assigned priorities.

[0701] Step 4:

[0702] The server registers the prioritized action items with the scheduling device. The input is the task list generated in step 3, which is converted into a format suitable for the scheduling device and registered. The output of this process is task information visualized in the calendar application used by the user.

[0703] Step 5:

[0704] The server uses sentiment analysis technology to collect and analyze sentiment data from user operation logs and input information. The input consists of user operation logs and input information, and the analysis evaluates the user's emotional state. The output is an index indicating the emotional state. Based on this index, the task list presentation is dynamically adjusted.

[0705] Step 6:

[0706] The terminal receives a coordinated summary and task list from the server and notifies the user. Input is the coordinated data sent from the server, which is notified or displayed on the terminal. Output is an informational presentation that the user can visually confirm and respond to.

[0707] Step 7:

[0708] Users perform tasks based on information presented from the terminal. They process tasks according to the notified summary information and priorities. At this stage, efficient work is possible because users actively utilize passive output directly through the interface.

[0709] (Application Example 2)

[0710] 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".

[0711] In modern information processing, there is a demand for the efficient management of large volumes of electronic messages. However, conventional systems have often failed to adequately consider the user's emotional state when presenting information or managing tasks, resulting in excessive stress for users. In particular, in electronic payment services, the speed and emotional consideration of customer support are crucial. Therefore, it is necessary to achieve efficient information presentation and task management that reflects the user's psychological state.

[0712] 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.

[0713] In this invention, the server includes means for collecting electronic messages from an information processing device using means for acquiring communication data, means for analyzing the content of the collected electronic messages and generating a summary using a generation model, and means for analyzing the user's emotional state using an emotion engine and adjusting the presentation of summary information and action items based on that analysis. This enables information presentation and task management that are tailored to the user's psychological state.

[0714] "Means for acquiring communication data" refers to technologies that have the function of efficiently collecting electronic messages from information processing equipment.

[0715] A "generative model" is a technology that uses machine learning algorithms to analyze the content of collected electronic messages and generate summaries.

[0716] A "task management unit" is a technology that has the function of identifying action items from the generated summary and organizing them according to priority.

[0717] A "schedule management tool" is a technology that has the functionality to effectively register and manage organized action items.

[0718] The "notification function" is a technology for appropriately notifying user devices of summary information and organized action items.

[0719] An "emotion engine" is a technology that analyzes the user's emotional state to adjust information presentation and task priorities.

[0720] This invention provides a system that combines various technologies to enable users to efficiently manage electronic messages and to provide emotion-based information and task management.

[0721] First, the server automatically collects electronic messages from the user's mail server and chat tools using communication data acquisition methods. During this process, it focuses on unread messages to quickly obtain the latest information. The acquired message data is then analyzed using a generative AI model to summarize important information. This generative model utilizes a natural language processing tool powered by machine learning algorithms to efficiently generate summaries.

[0722] Next, the task management unit extracts specific action items from the generated summary and sets their priorities. Prioritization is integrated with the scheduling tool to organize the action items appropriately and efficiently. This allows users to easily identify tasks that require immediate attention.

[0723] Furthermore, the emotion engine analyzes the user's emotional state in real time based on user operation logs and input information collected by the server. Based on the results of the emotion analysis, the server dynamically adjusts task priorities and information presentation methods, optimizing the information the user receives to match their current psychological state. The emotion engine can utilize emotion analysis tools such as the Azure Emotion API.

[0724] As a concrete example, when user A, who uses an electronic payment service, inquires about an invoice, if the sentiment engine detects user dissatisfaction, the server will present the most urgent solution as a summary. This approach allows users to resolve problems quickly and efficiently.

[0725] In this series of processes, the generative AI model summarizes the message based on the following prompt:

[0726] "Summarize user messages and perform sentiment analysis. Determine whether users are experiencing stress and provide guidelines for prioritizing the display of important information."

[0727] The present invention, through the above-described configuration and process, realizes electronic message management and task management that takes emotional responses into consideration.

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The server uses a means of acquiring communication data to collect electronic messages from the user's mail server and chat tools. The input is a list of unread messages, which the server processes. The output is the acquired message data stored within the system. Specifically, it uses APIs to interact with various messaging platforms and saves messages to a database.

[0731] Step 2:

[0732] The server uses a generative AI model to analyze the content of collected electronic messages and generate summaries. The input is the message data obtained from step 1. The generative AI model uses natural language processing algorithms to extract important information and generate a concise summary. The output is the generated summary information. Specifically, this process includes selecting important content using a text analysis tool and creating a summary.

[0733] Step 3:

[0734] The server uses a task management unit to extract action items from the generated summary and set priorities. The input is summarized information. The task management unit prioritizes tasks based on their importance and deadlines according to the set criteria. The output is a list of prioritized actions. Specifically, it assesses the urgency of tasks and determines their order.

[0735] Step 4:

[0736] The server uses an emotion engine to analyze user operation logs and input information to identify their emotional state. Input data includes the user's recent operation logs and feedback information. The emotion engine uses an emotion analysis model to assess stress and dissatisfaction levels. The output is the emotion analysis result. The specific operation involves measuring the user's operation speed and reaction time, and estimating their emotional state based on these measurements.

[0737] Step 5:

[0738] Based on the sentiment analysis results, the server dynamically adjusts priorities and information presentation. The input is the sentiment analysis results from the previous step and a prioritized action list. The output is the adjusted task list and summary information. Specifically, the server selects information to present considering the user's emotional state and optimizes it to reduce the user's burden.

[0739] Step 6:

[0740] The device utilizes its notification function to inform the user of refined summary information and organized action items. Inputs are the refined summary information and task list. Output is a notification message to the user. Specifically, it sends a push notification to the user's device to ensure that necessary information is quickly conveyed.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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."

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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 as being incorporated by reference.

[0762] The following is further disclosed regarding the embodiments described above.

[0763] (Claim 1)

[0764] A means for collecting electronic messages from an information processing device using means for acquiring communication data,

[0765] A means for analyzing the content of collected electronic messages and generating a summary using a generative model,

[0766] A task management unit is used to identify action items from the generated summary and organize them according to priority.

[0767] A means of registering organized action items by integrating with a schedule management tool,

[0768] A means of notifying the user device of summary information and organized action items using the notification function,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, in which a generative model uses a machine learning algorithm to understand the context of an electronic message and optimize the extraction of specific information.

[0772] (Claim 3)

[0773] The system according to claim 1, in which a task management unit determines priority by considering multiple criteria based on deadlines and importance when setting priorities.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A means for collecting electronic communications from a data device using a device that acquires communication information,

[0777] A means for analyzing the content of collected electronic communications using a language analysis model and creating a summary,

[0778] A means of using activity management units to detect action items from the generated summary and organize them according to priority,

[0779] A means of registering organized action items in conjunction with a time management tool,

[0780] A means of notifying the user terminal of summarized information and organized action items by utilizing the information transmission function,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, in which a language analysis model uses an automated learning algorithm to understand the context of electronic communications and optimize the extraction of specific information.

[0784] (Claim 3)

[0785] The system according to claim 1, in which an activity management unit determines priority by considering multiple criteria based on deadlines and importance.

[0786] "Application Example 1"

[0787] (Claim 1)

[0788] A means for collecting electronic messages from an information processing device using means for acquiring communication data,

[0789] A means for analyzing the content of collected electronic messages and generating a summary using a generative model,

[0790] A task management unit is used to identify action items from the generated summary and organize them according to priority.

[0791] A means of registering organized action items by integrating with a schedule management tool,

[0792] A means of notifying the user device of summary information and organized action items using the notification function,

[0793] In robotic devices, a means for executing automated action items and efficiently managing work tasks in the industrial sector,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, in which a generative model uses a machine learning algorithm to understand the context of an electronic message and optimize the extraction of specific information.

[0797] (Claim 3)

[0798] The system according to claim 1, in which a task management unit determines priority by considering multiple criteria based on deadlines and importance when setting priorities.

[0799] "Example 2 of combining an emotion engine"

[0800] (Claim 1)

[0801] A means for periodically acquiring and storing unread communication information from an information processing device,

[0802] A means for analyzing stored communication information using a generative model and generating a summary containing important information,

[0803] A means for detecting action items from a generated summary using task management components and determining their priority,

[0804] A means for registering action items based on determined priorities, in conjunction with a schedule management device,

[0805] A means of dynamically adjusting the presentation of information according to the user's emotional state by utilizing emotion analysis technology,

[0806] A means of transmitting summary information and adjusted action items to a terminal using notification technology,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, wherein the generative model uses advanced data analysis techniques to understand the context of communication information and streamline the extraction of specific information.

[0810] (Claim 3)

[0811] The system according to claim 1, wherein when a task management component sets a priority, it flexibly determines the priority based on emotional state and working conditions.

[0812] "Application example 2 when combining with an emotional engine"

[0813] (Claim 1)

[0814] A means for collecting electronic messages from an information processing device using means for acquiring communication data,

[0815] A means for analyzing the content of collected electronic messages and generating a summary using a generative model,

[0816] A task management unit is used to identify action items from the generated summary and organize them according to priority.

[0817] A means of registering organized action items by integrating with a schedule management tool,

[0818] A means of notifying the user device of summary information and organized action items using the notification function,

[0819] A means for analyzing the user's emotional state using an emotion engine and adjusting the presentation of summary information and behavioral items based on that analysis,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, in which a generative model uses a machine learning algorithm to understand the context of an electronic message and optimize the extraction of specific information.

[0823] (Claim 3)

[0824] The system according to claim 1, in which a task management unit determines priority by considering multiple criteria based on deadlines and importance when setting priorities. [Explanation of Symbols]

[0825] 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 for collecting electronic messages from an information processing device using means for acquiring communication data, A means for analyzing the content of collected electronic messages and generating a summary using a generative model, A task management unit is used to identify action items from the generated summary and organize them according to priority. A means of registering organized action items by integrating with a schedule management tool, A means of notifying the user device of summary information and organized action items using the notification function, A system that includes this.

2. The system according to claim 1, wherein a generative model uses a machine learning algorithm to understand the context of an electronic message and optimize the extraction of specific information.

3. The system according to claim 1, in which a task management unit determines priority by considering multiple criteria based on deadlines and importance when setting priorities.

Images