system

Abstract

We provide the system. [Solution] An information processing means that acquires input information for a task, calculates backward from the deadline for the task, and generates a proposal for the actions required for the task. A user interface means that notifies the user of the action suggestion generated by the information processing means, A progress management system that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated, A predictive input method that analyzes user history data, predicts the next necessary action, and automatically generates a prompt, A visualization method that visualizes action suggestions and work progress information and displays them in real time on a visual device used by the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the need to manage multiple tasks simultaneously is increasing, but efficient management is not easy. In particular, tasks with deadlines tend to be postponed, and often important actions are delayed, thereby hindering the overall progress. Also, the burden in document creation and prior preparation is large, consuming a lot of time and labor. Furthermore, the judgments and prompt inputs necessary to smoothly progress such tasks can also be a burden on the user. Therefore, it is required to solve these problems and improve the work efficiency of the user.

Means for Solving the Problems

[0005] This invention provides information processing means to support users' task management by working backward from task deadlines and suggesting appropriate actions. Furthermore, it reduces the burden of document creation by using progress management means that analyze progress and identify parts of meeting materials and presentation materials that can be automatically generated. In addition, it provides predictive input means that predict necessary actions based on the user's history data and automatically generate prompts, thereby smoothing task progress and reducing the burden of prompt input. In this way, this invention centrally manages multiple tasks and improves the user's work efficiency.

[0006] A "task" is a series of actions or tasks that must be completed by a specific deadline.

[0007] "Input information" refers to data regarding task details and deadlines that users provide to the system through various interfaces.

[0008] A "deadline" is the date and time by which a particular task must be completed.

[0009] "Working backward" refers to setting a schedule by working backward from a target completion time to plan and act accordingly.

[0010] "Action suggestions" refer to specific instructions or guidance that a system provides to a user regarding actions to effectively complete a particular task.

[0011] "Information processing means" refers to components including hardware and software for analyzing input data and providing appropriate output to the user.

[0012] "User interface means" refers to a visual or tactile input and output interface used by a user to interact with a system.

[0013] "Progress" is an evaluation criterion that shows how well a particular task or project is progressing towards its planned steps and deadlines.

[0014] "Analysis" refers to the process of examining information in detail and identifying important components and patterns.

[0015] "Automated generation" is a process in which a computer independently creates the necessary data and documents with minimal human intervention.

[0016] A "predictive input method" is a component of a system that automatically generates future actions and information using past data and patterns. [Brief explanation of the drawing]

[0017] [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]Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0020] In the following embodiments, a labeled 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.

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

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

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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."

[0025] [First Embodiment]

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

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0032] 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.

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

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system for streamlining task management, in which a server, terminals, and users work together. The server receives task information entered by users through terminals and performs efficient task management based on that information.

[0039] The server first identifies the task's deadline based on the input information. Next, it uses a reverse-engineer algorithm to calculate the appropriate start time for the user to begin the task. Based on the calculation result, it generates an action suggestion and sends it to the terminal. The terminal uses its notification function to display the action suggestion sent from the server to the user. This process enables the user to continuously progress on the task within the deadline.

[0040] Furthermore, the device tracks the progress of meeting materials and presentations that the user is creating. While the user is actively working on them, the device sends this information to the server. The server analyzes the progress information and assists with material creation by identifying sections that can be automatically generated. In this case, the server forms automatically generated section drafts and provides them to the user via the device. The user can review the proposed drafts, make modifications as needed, and use them.

[0041] In addition, the terminal collects the user's past history data and transfers it to the server. The server analyzes this data to predict the user's behavior patterns and automatically generates the next necessary prompt. The generated prompt is sent to the terminal and displayed to the user. In this way, the user can save time on manual input and proceed with tasks smoothly.

[0042] In a specific case, suppose a user enters the task "Prepare materials for next week's meeting." The server works backward and generates a notification suggesting, "It is recommended that you create an outline of the materials this afternoon," and sends this suggestion to the user via their device. At the same time, it monitors the progress of the materials and helps streamline the user's work by suggesting, "The data analysis section can be automatically generated."

[0043] As described above, this invention directly supports task management, reduces the burden on the user, and improves productivity.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user uses a terminal to enter task information. The task includes detailed information and deadlines. The terminal then sends this information to the server.

[0047] Step 2:

[0048] The server extracts the deadline from the received task information and begins working backward. The backward calculation algorithm identifies the intermediate steps and start times required to complete the task.

[0049] Step 3:

[0050] The server generates action suggestions based on the reverse calculation results. These suggestions include specific actions and schedules to help the user efficiently complete the task.

[0051] Step 4:

[0052] The server sends the generated action suggestions to the device. The device displays the action suggestions to the user through its notification function and prompts them to take the necessary action.

[0053] Step 5:

[0054] The device collects progress data on meeting materials and presentations that the user is involved with. This data is updated each time the user edits a document.

[0055] Step 6:

[0056] The server analyzes the progress data sent from the terminal to determine which parts of the document are incomplete and which can be automatically generated.

[0057] Step 7:

[0058] The server creates automatically generated sections based on the analysis results and sends their contents to the terminal as suggestions.

[0059] Step 8:

[0060] The terminal displays suggestions received from the server to the user, who then reviews them and can adopt or modify them.

[0061] Step 9:

[0062] The terminal continuously monitors the user's work history and behavioral patterns, and sends the data to the server.

[0063] Step 10:

[0064] The server analyzes the historical data accumulated so far and uses a predictive model to identify the user's next necessary action.

[0065] Step 11:

[0066] The server automatically generates a prompt based on the predicted action and sends it to the terminal.

[0067] Step 12:

[0068] The terminal supports the user in completing tasks by displaying prompts and guiding them to the next necessary action.

[0069] (Example 1)

[0070] 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."

[0071] In today's busy work environment, it is difficult for users to efficiently manage their tasks and execute them at the appropriate time. Furthermore, tracking and optimizing progress is required in document creation and project management, but doing so manually is time-consuming and labor-intensive. In addition, predicting the next course of action based on past activity history is not easy, hindering user productivity improvements.

[0072] 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.

[0073] In this invention, the server includes information processing means that acquire task input information, calculate backward from the task's deadline, and generate suggestions for actions necessary for the task; user interface means that notify the user of the action suggestions generated by the information processing means; and progress tracking means that monitor the user's progress and identify the parts of document creation that can be automatically generated based on the progress information. As a result, the user can manage tasks automatically and efficiently, receive suggestions and support for document creation at the optimal timing, and improve productivity.

[0074] "Task input information" refers to detailed information about jobs and activities that users register with the system, including what needs to be done and the deadline.

[0075] "Working backward" is the process of finding the optimal starting point for an action by working backward from a given termination condition.

[0076] An "information processing device" is an element that has the function of analyzing input data, performing various calculations based on that analysis, and generating outputs such as action suggestions.

[0077] "User interface means" refers to the means by which a system and a user exchange information, including screen displays and interactive functions for showing notifications and suggestions.

[0078] A "progress tracking tool" is an element that has the functionality to monitor the progress of tasks and projects that a user is working on, and to collect and analyze that data.

[0079] "Automatically generated portions" refer to sections of documents or data that the system can automatically create based on predetermined rules or algorithms.

[0080] "Historical data" refers to records of activities and information entered by users in the past, and is the data that is subject to analysis.

[0081] A "predictive support tool" is an element equipped with the function of analyzing past data and proposing future actions based on that analysis.

[0082] A "prompt" is a short instruction or question that a system displays to a user, used to guide the user's next action.

[0083] This invention is a system for streamlining task management, and it operates through the collaboration of a server, terminals, and users.

[0084] The server utilizes a generative AI model to analyze the task information entered by the user and generate suggestions to support the initiation of action at the appropriate time. Specifically, it executes algorithms using programming languages ​​such as "Python" and "R" to calculate deadlines from the input data. Furthermore, the server uses machine learning frameworks such as "TENSORFLOW®" to analyze progress information and identify parts of document creation that can be automatically generated.

[0085] The device has an interface function that notifies the user of these action suggestions and automatically generated sections of materials. By providing users with necessary information in a timely manner using the notification function, users can manage their tasks efficiently.

[0086] Furthermore, the terminal collects the user's past behavior history and sends it to the server. Based on this history data, the server can analyze the user's behavior patterns using database solutions such as SQL and BigQuery, and automatically generate the next necessary prompts. This allows the user to skip manual input and proceed with tasks more smoothly.

[0087] As a concrete example, let's consider a scenario where a user enters the prompt, "Please help me prepare for the next project meeting. Please suggest a specific schedule and progress management plan." In this case, the server will suggest an optimal schedule and notify the user about the parts of the document creation that can be automatically generated, thereby supporting efficient work execution.

[0088] Through the mechanism described above, the present invention can reduce the burden on users and improve productivity.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The server receives task information entered by the user through the terminal.

[0092] Input: Task name, due date, details

[0093] Processing: The server saves the received task information to the database.

[0094] Output: Confirmation message for saved task information

[0095] Specific operation: When the user enters "Create report, due date: Friday" into the terminal, the terminal sends this information to the server, which then saves it.

[0096] Step 2:

[0097] The server applies a reverse calculation algorithm based on the stored task information.

[0098] Input: Task deadline information

[0099] Processing: The server uses an algorithm to calculate the optimal start time for the action.

[0100] Output: Suggestion for action start date and time

[0101] Specific operation: The server calculates that "for a Friday deadline, it is desirable to start preparations from Wednesday" and creates a proposal accordingly.

[0102] Step 3:

[0103] The server transmits the calculated action start date and time to the terminal via the user interface.

[0104] Input: Suggestion for the start date and time of the action

[0105] Processing: The server sends the generated suggestions to the terminal, and the terminal notifies the user of the received information.

[0106] Output: Notification of the start date and time of the action displayed to the user.

[0107] Specific action: The device displays a pop-up notification saying, "Please begin preparations at 10:00 AM on Wednesday."

[0108] Step 4:

[0109] The terminal tracks the progress of the materials the user is working on and sends the data to the server.

[0110] Input: Document progress

[0111] Processing: The terminal records progress and prepares data to send to the server.

[0112] Output: Sending progress data to the server

[0113] Specific operation: When the user completes a portion of the document, the device records the progress and sends it to the server as "Introduction Complete," etc.

[0114] Step 5:

[0115] The server analyzes the progress data and identifies sections that can be automatically generated.

[0116] Input: Progress data

[0117] Processing: The server uses a generative AI model to analyze the data and identify incomplete sections of the document.

[0118] Output: Suggestions for automatically generated sections

[0119] Specific action: The server creates a suggestion that "a data analysis section can be automatically generated" and sends it to the terminal.

[0120] Step 6:

[0121] The terminal receives a proposal from the server and notifies the user.

[0122] Input: Suggestions for automatically generated sections

[0123] Processing: Display the received suggestions to the user.

[0124] Output: Notification of automatically generated suggestions displayed to the user

[0125] Specific action: The device displays a notification saying, "There is a proposed automated data analysis. Do you want to use it?"

[0126] Step 7:

[0127] The device collects user history data and sends it to the server.

[0128] Input: User history data

[0129] Processing: The terminal organizes the collected historical data and prepares it for transmission to the server.

[0130] Output: Sending history data to the server

[0131] Specific operation: The device collects the "task history for the past 6 months" and periodically sends it to the server.

[0132] Step 8:

[0133] The server analyzes the historical data and generates the next necessary prompt.

[0134] Input: Historical data

[0135] Processing: The server uses analysis software to recognize patterns and generate prompts.

[0136] Output: Generated prompt

[0137] Specific action: The server generates a prompt saying "We recommend you create a report conclusion next" and sends it to the terminal.

[0138] Step 9:

[0139] The terminal displays the generated prompts to the user to assist with the task.

[0140] Input: Generated prompt

[0141] Processing: The terminal displays a prompt to prompt the user for the next action.

[0142] Output: Prompt displayed to the user

[0143] Specific action: The terminal displays a prompt to the user saying, "Please begin creating your conclusion as the next step."

[0144] (Application Example 1)

[0145] 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."

[0146] In manufacturing environments and work sites, there is a need for systems that enable workers to efficiently handle frequently occurring tasks and maintenance work, while also allowing them to grasp progress in real time and accurately perform the next necessary tasks. However, existing systems have been insufficient in terms of tracking workers' progress in real time and visually indicating the next actions to be taken, limiting the improvement of work efficiency.

[0147] 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.

[0148] In this invention, the server includes an information processing means that acquires input information for a task, calculates backward from the task's deadline, and generates suggestions for actions required for the task; a user interface means that notifies the user of the action suggestions generated by the information processing means; a progress management means that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated; and a visualization means that visualizes the action suggestions and work progress information and displays them in real time on a visual device used by the user. This enables workers to efficiently carry out their work while checking tasks and progress on-site in real time.

[0149] "Task input information" refers to the information necessary for a user to complete a specific task or job within the system.

[0150] "Working backward from the deadline" is the process of calculating the appropriate start time for action by working backward from the completion deadline of a set task.

[0151] "Action suggestions" refer to the system proposing specific actions that users can take to efficiently complete tasks by the deadline.

[0152] An "information processing device" is a device that performs analysis and calculations based on the input information of a task and generates action suggestions.

[0153] A "user interface means" is an interface device used to convey information and suggestions generated by a system to the user.

[0154] A "progress management system" is a device that monitors the progress of a user's work, identifies problem areas, and makes suggestions for improvement.

[0155] "Visualization means" are tools or devices used to visually display action suggestions or work progress information.

[0156] "Visual devices" refer to equipment used by users to visually receive information from a system.

[0157] A system for carrying out this invention includes a cloud server, a visual device used by the user (e.g., smart glasses), and a communication network for coordinating the server and the visual device.

[0158] The server receives task information entered by the user through their visual device. The server then applies a reverse-engineer algorithm based on the task's deadline to calculate the action start date and time. This calculation uses the Python programming language. Based on the calculated action start date and time, action suggestions are generated and notified to the user's visual device in real time. The visual device uses software such as the Oculus SDK to visually display the information to the user.

[0159] The terminal monitors the user's progress and sends progress data to the server via a progress management system. The server analyzes this data, identifies incomplete or automatically regenerative parts, and makes suggestions. These suggestions are also communicated to the user via a visual device, making it easier for the user to understand the next steps.

[0160] Furthermore, the server analyzes the user's past history data and predicts behavioral patterns to generate prompts indicating the next necessary action. These prompts are generated by an AI model and provided to the user via the terminal. An example of a prompt message might be: "Please decide what to do as your next task. Based on your history, we will generate the most appropriate action suggestion."

[0161] As a concrete example, this system can be used by workers in a manufacturing plant to efficiently carry out their daily tasks by checking the previous day's work list and priorities on a visual device. Furthermore, for tasks that are behind schedule, a message such as "Assembly of part X is behind schedule. The next task to be performed is Y." will be displayed on the visual device, assisting workers in taking immediate action.

[0162] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0163] Step 1:

[0164] The server receives task input information transmitted from the user through a visual device. The input data includes task details, deadlines, etc., and the server records this information in a database and then starts processing.

[0165] Step 2:

[0166] The server executes a reverse calculation algorithm based on the deadline information of the received task. This algorithm calculates the appropriate start time for action from the completion deadline and generates this as an action suggestion using Python. The generated suggestion is temporarily stored in memory.

[0167] Step 3:

[0168] Action suggestions generated by the server are transmitted to the terminal via the network through the user interface. The terminal displays the received data in real time through the API of its visual device, indicating the user the next action to take.

[0169] Step 4:

[0170] The terminal continuously monitors the user's work progress and sends progress data to the server. It also transmits data collected using sensors and input devices regarding the results and progress of the tasks performed by the user.

[0171] Step 5:

[0172] The server analyzes the received progress data and identifies incomplete sections. Furthermore, it generates a proposal that includes sections that can be automatically generated by the progress management system and sends it to the terminal. The server then uses data analysis tools to efficiently generate the proposal.

[0173] Step 6:

[0174] The server predicts behavioral patterns based on the user's historical data and generates prompts indicating the next necessary action using an AI model. The generated prompt messages are then sent to the terminal and displayed to the user.

[0175] Step 7:

[0176] Users review instructions and suggestions displayed on the visual device and perform tasks. By accepting or adjusting suggestions on the visual device, they can improve the efficiency and accuracy of their work.

[0177] 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.

[0178] This invention combines an emotion engine with a task management support system to improve task efficiency while considering the user's emotional state. The system consists of a server, a terminal, an emotion engine, and a user, all of which work in coordination with each other.

[0179] First, the user inputs task information through their device. This information, including task details and deadlines, is sent from the device to the server. The server writes the received information and applies a reverse calculation algorithm to determine the start date and time for each task. In this process, the server also considers the user's sentiment information provided by the sentiment engine and adjusts the suggestions accordingly.

[0180] The emotion engine utilizes facial recognition technology and voice analysis to analyze the user's facial expressions and voice tone in real time on the device. Based on these results, it sends data about the user's emotional state to the server. The server uses this data to modify the task suggestions, thereby encouraging the user to complete tasks in a positive way.

[0181] For example, if the server determines that a user is experiencing stress, it will adjust task priorities and suggest starting with less demanding tasks. Conversely, if the user shows high motivation, it can even suggest more challenging tasks earlier. In this way, the system alleviates the user's psychological burden and enables efficient task management.

[0182] Regarding progress management, the server analyzes progress data received from the terminal to identify incomplete sections and sections that can be automatically generated, and sends suggestions to the user. The emotion engine then provides further support, such as encouraging the user to actively utilize the automatic generation function if they are feeling fatigued.

[0183] This invention dynamically adjusts task management according to the user's emotional state, ensuring that work progresses in the most optimal way for each individual situation. This system allows users to improve productivity and reduce stress.

[0184] The following describes the processing flow.

[0185] Step 1:

[0186] The user enters detailed information and deadlines for tasks they want to manage via their device. The device then sends this information to the server.

[0187] Step 2:

[0188] The server processes the received task information and calculates the task's start date and time by working backward from the deadline. It calculates the necessary processes based on the deadline entered by the user.

[0189] Step 3:

[0190] The device is equipped with an emotion engine that collects real-time emotional data from the user. This emotional data is primarily obtained through facial recognition technology and voice analysis.

[0191] Step 4:

[0192] The device uses an emotion engine to acquire emotional data, which is then sent to a server. The server analyzes the user's emotional state in real time.

[0193] Step 5:

[0194] The server takes into account emotional data provided by the emotion engine when generating task suggestions through reverse engineering. If the user is experiencing stress, it prioritizes lighter tasks.

[0195] Step 6:

[0196] The server sends a pre-configured action suggestion to the device. The device notifies the user and presents suggestions tailored to their emotional state.

[0197] Step 7:

[0198] The user reviews the suggestion and starts the task if necessary. The user confirms that their emotional state is appropriate.

[0199] Step 8:

[0200] Data regarding document creation and progress is sent from the terminal to the server. The server analyzes this data to identify incomplete sections and parts that can be automatically generated.

[0201] Step 9:

[0202] The server suggests automatically generated sections to the user as solutions to progress issues, and the emotion engine provides additional support as needed.

[0203] Step 10:

[0204] The server and terminal continuously monitor the user's emotional state and use this information to suggest tasks and manage progress. They continue to provide suggestions that take into account the user's psychological burden and motivation.

[0205] In this way, the entire system can adapt to the user's emotions and provide optimal task management tailored to individual circumstances.

[0206] (Example 2)

[0207] 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".

[0208] In modern society, efficiently managing a wide variety of tasks is crucial. However, dynamic task management that takes into account the user's emotional state is not yet fully realized, and in certain situations, it can increase the user's burden. Therefore, there is a need for methods that reduce stress and achieve efficient task management while considering the user's emotional state.

[0209] 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.

[0210] In this invention, the server includes an information processing means that acquires input information for a task, calculates backward from the task's deadline, and generates suggestions for actions required for the task; a progress management means that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated; and an emotion consideration means that analyzes the user's emotional state and adjusts action suggestions based on emotional data. This enables dynamic adjustment of task priorities according to the user's emotional state.

[0211] An "information processing device" is a device that has the function of acquiring input information for a task, working backward from the deadline, and generating suggestions for the actions necessary for that task.

[0212] "Dialogue device means" refers to an interface device for notifying the user of action suggestions generated by the information processing means.

[0213] A "progress management tool" is a device that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated.

[0214] An "emotion-considering device" is a device that analyzes the user's emotional state and adjusts behavioral suggestions based on that data.

[0215] An "algorithmic means" is a computational procedure used to dynamically change the priority of tasks based on emotional information obtained from a data processing device.

[0216] This invention provides a system that streamlines task management while taking into account the user's emotional state. Its main components include a server, a terminal, and an emotion engine. This allows task suggestions to adapt to the user's emotions, resulting in smooth task management.

[0217] The server is the core of the system. It processes task information received from terminals and functions as an information processing tool. Specifically, it calculates the task start date and time by working backward from the task deadline. The server utilizes database software to store and manage the received task information, which is later used to generate action suggestions.

[0218] The terminal is the primary interface for users to interact with the system. On the terminal, as the user inputs task information, the emotion engine operates in real time. The emotion engine analyzes facial expressions and voice tone using facial recognition and voice analysis technologies, and sends emotion data to the server. This process utilizes image processing algorithms and voice analysis software.

[0219] The server, upon receiving user emotional data, uses this data as an emotional consideration to inform task suggestions. Based on the emotional state, task priorities are adjusted, and considerations are made such as starting with less demanding tasks. For example, if a user is experiencing stress, suggestions that minimize the workload will be made. On the other hand, if a user is highly motivated, it may be possible to suggest more difficult tasks earlier in the process.

[0220] The generative AI model assists in analyzing data and generating action suggestions on the server. An example of a prompt to input into this model is, "If the user is feeling stressed, suggest prioritizing less demanding tasks." In this process, the AI ​​model uses sentiment data and task information to generate more personalized suggestions.

[0221] In this way, the present invention provides a new form of task management that incorporates the user's emotional state, enabling efficient and less stressful work performance.

[0222] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0223] Step 1:

[0224] The user enters task information using a terminal. Specifically, they enter the task name, due date, and detailed information through the interface on the terminal. This input data is sent to the server via an HTTP request.

[0225] Step 2:

[0226] The server saves task information received from the terminal to the database. Input data includes the task name, due date, etc. The server inserts this data into the database using SQL queries. As output, new task data is saved in the database.

[0227] Step 3:

[0228] The device activates an emotion engine to analyze the user's real-time facial expressions and voice tone. It uses image data from the built-in camera and audio data from the microphone as input. The emotion engine analyzes this data using image processing algorithms and audio analysis software to evaluate the user's emotional state. The output is user emotional state data.

[0229] Step 4:

[0230] The terminal sends the analyzed emotional state data to the server. The server receives this data and prepares to perform task management that reflects the emotional data. The input is the emotional state data, and the output includes the state in which the data is ready to be used as emotional data within the server.

[0231] Step 5:

[0232] The server generates task action suggestions while considering emotional data. It uses task information stored in a database and emotional data received from the terminal as input data. The server calculates suggestions using a generative AI model and adjusts task priorities according to the emotional state. Specifically, it provides suggestions to reduce the burden on users experiencing stress. The output is the adjusted action suggestions.

[0233] Step 6:

[0234] The server sends the generated action suggestions to the terminal, and the terminal notifies the user. The input is the adjusted action suggestions sent from the server. The terminal displays the suggestions to the user through pop-up notifications or an interface. The output is the presentation of the action suggestions in a visible form for the user.

[0235] (Application Example 2)

[0236] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0237] Modern consumers face a variety of tasks on a daily basis, and are required to manage them efficiently. However, conventional task management systems have difficulty flexibly responding to consumers' emotional states and various situations within the home, resulting in problems in efficiently carrying out tasks while reducing stress. This invention aims to provide a more comfortable and efficient task management system by dynamically adjusting task priorities based on the consumer's emotional state.

[0238] 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.

[0239] In this invention, the server includes information processing means that acquire task input information, calculate backward from the deadline, and generate suggestions for actions necessary for the task; emotion analysis means that analyze the user's emotional state and adjust task priorities based on that state; and progress management means that analyze the user's progress, identify incomplete parts, and suggest parts that can be automatically generated. This enables optimal task management in accordance with the user's emotional state.

[0240] "Information processing means" refers to a device or program that has the function of acquiring input information for a task, calculating backward from the deadline, and generating suggestions for actions necessary for the task.

[0241] A "consumer interface means" is a device or program for notifying consumers of action suggestions generated by an information processing means.

[0242] An "emotional analysis tool" is a device or program that analyzes the emotional state of a person and adjusts the priority of tasks based on that state.

[0243] A "progress management tool" is a device or program that analyzes the progress of a user, identifies incomplete parts, and suggests parts that can be automatically generated.

[0244] A "predictive input means" is a device or program that analyzes a consumer's historical data, predicts the next necessary action, and automatically generates text.

[0245] The system for implementing this invention consists of multiple components: a server, a terminal, an emotion analysis engine, and a user. The server is responsible for task management and suggestion, and provides an optimal task management strategy based on the user's emotional state obtained by the emotion analysis engine. The terminal functions as an interface for the user to input task information and provide feedback on suggested tasks.

[0246] Specifically, the server first obtains task input information from the terminal and uses information processing tools to calculate backward from the deadline. This process utilizes algorithms written in programming languages ​​such as Python. Next, the emotion analysis engine uses software tools like OpenCV and TensorFlow to analyze changes in voice and facial expressions, understanding the user's emotional state in real time. Based on this, the emotion analysis tool optimizes task priorities according to the user's current state.

[0247] The progress management system analyzes incomplete tasks based on user progress data and suggests automatically generated parts as needed. The predictive input system predicts the next necessary actions based on past historical data and automatically generates suggestions to help users complete tasks more efficiently.

[0248] For example, when a user is engaged in the task of gardening, if the emotion analysis engine determines that the user is experiencing stress, the system will suggest relaxation-related tasks in parallel to alleviate the burden. Furthermore, if the user shows high motivation, suggestions will be made to encourage them to start a new project.

[0249] An example of a prompt using a generative AI model might be: "Please create a voice analysis model that determines whether the user is experiencing stress. It needs to analyze changes in the speed and tone of the user's voice and estimate their emotional state based on that."

[0250] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0251] Step 1:

[0252] The server receives task information entered by the user from the terminal. The input information includes task details and deadlines, and the server stores this information in a database. The server organizes the basic task information and prepares to apply a reverse calculation algorithm.

[0253] Step 2:

[0254] The server uses a reverse-engineer algorithm to calculate an action plan leading up to the task deadline. It uses the task deadline and the current date and time as input to calculate the start date and time for each task. The calculated schedule is then compiled into action suggestions aimed at improving the efficiency of task progress.

[0255] Step 3:

[0256] The terminal notifies the user of action suggestions from the server. These notifications are displayed on the user's smartphone or computer. The user interface receives the action suggestions and allows the user to set reminders, add them to their calendar, and perform other actions to carry them out.

[0257] Step 4:

[0258] The emotion analysis engine analyzes the user's voice and facial expression data acquired through the device. This real-time collected emotion data is used to determine the user's emotional state. The determined emotional state is then used to prioritize tasks.

[0259] Step 5:

[0260] The server receives the emotion analysis results and adjusts task priorities based on this information. If stress is detected, it prioritizes less burdensome tasks; if high motivation is detected, it suggests more difficult tasks. This provides task suggestions that are optimal for the user.

[0261] Step 6:

[0262] The server analyzes the user's progress and identifies incomplete tasks. User progress data is used as input, including, for example, task completion rates and delays. Based on this information, the server generates automatically reproducible parts and additional suggestions, which are then presented to the user.

[0263] Step 7:

[0264] The predictive input method analyzes the user's historical data and predicts the next necessary action. Based on past behavioral history, a generative AI model creates prompts and provides suggestions to support the user's next steps. This enables the user to complete tasks efficiently.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] [Second Embodiment]

[0269] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0270] 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.

[0271] 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).

[0272] 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.

[0273] 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.

[0274] 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).

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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".

[0281] This invention is a system for streamlining task management, in which a server, terminals, and users work together. The server receives task information entered by users through terminals and performs efficient task management based on that information.

[0282] First, based on the input information of the task, the server identifies the deadline of the task. Next, using the reverse calculation algorithm, it calculates the action start date and time so that the user can start the action at an appropriate time. Based on the calculation result, it generates an action proposal and sends this to the terminal. The terminal uses the notification function to display the action proposal sent from the server to the user. Through this process, the user can continuously progress the task within the deadline.

[0283] Furthermore, the terminal tracks the progress of meeting materials or presentations being created by the user. While the user is working forward, the terminal sends this information to the server. The server analyzes the progress information and assists in creating the materials by identifying sections that can be automatically generated. In this case, the server forms an automatically generated section plan and provides this to the user via the terminal. The user can check the proposed plan, make corrections as necessary, and use it.

[0284] In addition, the terminal collects the user's past history data and transfers it to the server. The server analyzes these data to predict the user's behavior pattern and then automatically generates the required prompts. The generated prompts are sent to the terminal and displayed to the user. In this way, the user can save the effort of their own input and smoothly progress the task.

[0285] As a specific case, assume that the user inputs the task of "preparing materials for next week's meeting". The server generates a notification such as "It is recommended to create an outline of the materials this afternoon" by reverse calculation and proposes it to the user via the terminal. At the same time, it monitors the progress of the materials and proposes "The data analysis part can be automatically generated" to assist in improving the efficiency of the user's work.

[0286] As described above, this invention directly supports task management, reduces the burden on the user, and improves productivity.

[0287] The following describes the processing flow.

[0288] Step 1:

[0289] The user uses a terminal to enter task information. The task includes detailed information and deadlines. The terminal then sends this information to the server.

[0290] Step 2:

[0291] The server extracts the deadline from the received task information and begins working backward. The backward calculation algorithm identifies the intermediate steps and start times required to complete the task.

[0292] Step 3:

[0293] The server generates action suggestions based on the reverse calculation results. These suggestions include specific actions and schedules to help the user efficiently complete the task.

[0294] Step 4:

[0295] The server sends the generated action suggestions to the device. The device displays the action suggestions to the user through its notification function and prompts them to take the necessary action.

[0296] Step 5:

[0297] The device collects progress data on meeting materials and presentations that the user is involved with. This data is updated each time the user edits a document.

[0298] Step 6:

[0299] The server analyzes the progress data sent from the terminal to determine which parts of the document are incomplete and which can be automatically generated.

[0300] Step 7:

[0301] The server creates a section that can be automatically generated based on the analysis results and sends the content to the terminal as a proposal.

[0302] Step 8:

[0303] The terminal displays the proposal received from the server to the user, and the user checks and adopts or modifies it.

[0304] Step 9:

[0305] The terminal continuously monitors the user's work history and behavior patterns and sends data to the server.

[0306] Step 10:

[0307] The server analyzes the historical data accumulated so far and uses a prediction model to identify the user's next required action.

[0308] Step 11:

[0309] The server automatically generates a prompt based on the predicted action and sends it to the terminal.

[0310] Step 12:

[0311] The terminal displays the prompt to the user and guides the next required action to support the user's task execution.

[0312] (Example 1)

[0313] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0314] In today's busy work environment, it is difficult for users to efficiently manage their tasks and execute them at the appropriate time. Furthermore, tracking and optimizing progress is required in document creation and project management, but doing so manually is time-consuming and labor-intensive. In addition, predicting the next course of action based on past activity history is not easy, hindering user productivity improvements.

[0315] 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.

[0316] In this invention, the server includes information processing means that acquire task input information, calculate backward from the task's deadline, and generate suggestions for actions necessary for the task; user interface means that notify the user of the action suggestions generated by the information processing means; and progress tracking means that monitor the user's progress and identify the parts of document creation that can be automatically generated based on the progress information. As a result, the user can manage tasks automatically and efficiently, receive suggestions and support for document creation at the optimal timing, and improve productivity.

[0317] "Task input information" refers to detailed information about jobs and activities that users register with the system, including what needs to be done and the deadline.

[0318] "Working backward" is the process of finding the optimal starting point for an action by working backward from a given termination condition.

[0319] An "information processing device" is an element that has the function of analyzing input data, performing various calculations based on that analysis, and generating outputs such as action suggestions.

[0320] "User interface means" refers to the means by which a system and a user exchange information, including screen displays and interactive functions for showing notifications and suggestions.

[0321] A "progress tracking tool" is an element that has the functionality to monitor the progress of tasks and projects that a user is working on, and to collect and analyze that data.

[0322] "Automatically generated portions" refer to sections of documents or data that the system can automatically create based on predetermined rules or algorithms.

[0323] "Historical data" refers to records of activities and information entered by users in the past, and is the data that is subject to analysis.

[0324] A "predictive support tool" is an element equipped with the function of analyzing past data and proposing future actions based on that analysis.

[0325] A "prompt" is a short instruction or question that a system displays to a user, used to guide the user's next action.

[0326] This invention is a system for streamlining task management, and it operates through the collaboration of a server, terminals, and users.

[0327] The server utilizes a generative AI model to analyze the task information entered by the user and generate suggestions to support the initiation of action at the appropriate time. Specifically, it executes algorithms using programming languages ​​such as Python and R to calculate deadlines from the input data. Furthermore, the server uses machine learning frameworks such as TensorFlow to analyze progress information and identify parts of document creation that can be automatically generated.

[0328] The device has an interface function that notifies the user of these action suggestions and automatically generated sections of materials. By providing users with necessary information in a timely manner using the notification function, users can manage their tasks efficiently.

[0329] Furthermore, the terminal collects the user's past behavior history and sends it to the server. Based on this history data, the server can analyze the user's behavior patterns using database solutions such as SQL and BigQuery, and automatically generate the next necessary prompts. This allows the user to skip manual input and proceed with tasks more smoothly.

[0330] As a concrete example, let's consider a scenario where a user enters the prompt, "Please help me prepare for the next project meeting. Please suggest a specific schedule and progress management plan." In this case, the server will suggest an optimal schedule and notify the user about the parts of the document creation that can be automatically generated, thereby supporting efficient work execution.

[0331] Through the mechanism described above, the present invention can reduce the burden on users and improve productivity.

[0332] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0333] Step 1:

[0334] The server receives task information entered by the user through the terminal.

[0335] Input: Task name, due date, details

[0336] Processing: The server saves the received task information to the database.

[0337] Output: Confirmation message for saved task information

[0338] Specific operation: When the user enters "Create report, due date: Friday" into the terminal, the terminal sends this information to the server, which then saves it.

[0339] Step 2:

[0340] The server applies a reverse calculation algorithm based on the stored task information.

[0341] Input: Task deadline information

[0342] Processing: The server uses an algorithm to calculate the optimal start time for the action.

[0343] Output: Suggestion for action start date and time

[0344] Specific operation: The server calculates that "for a Friday deadline, it is desirable to start preparations from Wednesday" and creates a proposal accordingly.

[0345] Step 3:

[0346] The server transmits the calculated action start date and time to the terminal via the user interface.

[0347] Input: Suggestion for the start date and time of the action

[0348] Processing: The server sends the generated suggestions to the terminal, and the terminal notifies the user of the received information.

[0349] Output: Notification of the start date and time of the action displayed to the user.

[0350] Specific action: The device displays a pop-up notification saying, "Please begin preparations at 10:00 AM on Wednesday."

[0351] Step 4:

[0352] The terminal tracks the progress of the materials the user is working on and sends the data to the server.

[0353] Input: Document progress

[0354] Processing: The terminal records progress and prepares data to send to the server.

[0355] Output: Sending progress data to the server

[0356] Specific operation: When the user completes a portion of the document, the device records the progress and sends it to the server as "Introduction Complete," etc.

[0357] Step 5:

[0358] The server analyzes the progress data and identifies sections that can be automatically generated.

[0359] Input: Progress data

[0360] Processing: The server uses a generative AI model to analyze the data and identify incomplete sections of the document.

[0361] Output: Suggestions for automatically generated sections

[0362] Specific action: The server creates a suggestion that "a data analysis section can be automatically generated" and sends it to the terminal.

[0363] Step 6:

[0364] The terminal receives a proposal from the server and notifies the user.

[0365] Input: Suggestions for automatically generated sections

[0366] Processing: Display the received suggestions to the user.

[0367] Output: Notification of automatically generated suggestions displayed to the user

[0368] Specific action: The device displays a notification saying, "There is a proposed automated data analysis. Do you want to use it?"

[0369] Step 7:

[0370] The device collects user history data and sends it to the server.

[0371] Input: User history data

[0372] Processing: The terminal organizes the collected historical data and prepares it for transmission to the server.

[0373] Output: Sending history data to the server

[0374] Specific operation: The device collects the "task history for the past 6 months" and periodically sends it to the server.

[0375] Step 8:

[0376] The server analyzes the historical data and generates the next necessary prompt.

[0377] Input: Historical data

[0378] Processing: The server uses analysis software to recognize patterns and generate prompts.

[0379] Output: Generated prompt

[0380] Specific action: The server generates a prompt saying "We recommend you create a report conclusion next" and sends it to the terminal.

[0381] Step 9:

[0382] The terminal displays the generated prompts to the user to assist with the task.

[0383] Input: Generated prompt

[0384] Processing: The terminal displays a prompt to prompt the user for the next action.

[0385] Output: Prompt displayed to the user

[0386] Specific action: The terminal displays a prompt to the user saying, "Please begin creating your conclusion as the next step."

[0387] (Application Example 1)

[0388] 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."

[0389] In manufacturing environments and work sites, there is a need for systems that enable workers to efficiently handle frequently occurring tasks and maintenance work, while also allowing them to grasp progress in real time and accurately perform the next necessary tasks. However, existing systems have been insufficient in terms of tracking workers' progress in real time and visually indicating the next actions to be taken, limiting the improvement of work efficiency.

[0390] 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.

[0391] In this invention, the server includes an information processing means that acquires input information for a task, calculates backward from the task's deadline, and generates suggestions for actions required for the task; a user interface means that notifies the user of the action suggestions generated by the information processing means; a progress management means that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated; and a visualization means that visualizes the action suggestions and work progress information and displays them in real time on a visual device used by the user. This enables workers to efficiently carry out their work while checking tasks and progress on-site in real time.

[0392] "Task input information" refers to the information necessary for a user to complete a specific task or job within the system.

[0393] "Working backward from the deadline" is the process of calculating the appropriate start time for action by working backward from the completion deadline of a set task.

[0394] "Action suggestions" refer to the system proposing specific actions that users can take to efficiently complete tasks by the deadline.

[0395] An "information processing device" is a device that performs analysis and calculations based on the input information of a task and generates action suggestions.

[0396] A "user interface means" is an interface device used to convey information and suggestions generated by a system to the user.

[0397] A "progress management system" is a device that monitors the progress of a user's work, identifies problem areas, and makes suggestions for improvement.

[0398] "Visualization means" are tools or devices used to visually display action suggestions or work progress information.

[0399] "Visual devices" refer to equipment used by users to visually receive information from a system.

[0400] A system for carrying out this invention includes a cloud server, a visual device used by the user (e.g., smart glasses), and a communication network for coordinating the server and the visual device.

[0401] The server receives task information entered by the user through their visual device. The server then applies a reverse-engineer algorithm based on the task's deadline to calculate the action start date and time. This calculation uses the Python programming language. Based on the calculated action start date and time, action suggestions are generated and notified to the user's visual device in real time. The visual device uses software such as the Oculus SDK to visually display the information to the user.

[0402] The terminal monitors the user's progress and sends progress data to the server via a progress management system. The server analyzes this data, identifies incomplete or automatically regenerative parts, and makes suggestions. These suggestions are also communicated to the user via a visual device, making it easier for the user to understand the next steps.

[0403] Furthermore, the server analyzes the user's past history data and predicts behavioral patterns to generate prompts indicating the next necessary action. These prompts are generated by an AI model and provided to the user via the terminal. An example of a prompt message might be: "Please decide what to do as your next task. Based on your history, we will generate the most appropriate action suggestion."

[0404] As a concrete example, this system can be used by workers in a manufacturing plant to efficiently carry out their daily tasks by checking the previous day's work list and priorities on a visual device. Furthermore, for tasks that are behind schedule, a message such as "Assembly of part X is behind schedule. The next task to be performed is Y." will be displayed on the visual device, assisting workers in taking immediate action.

[0405] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0406] Step 1:

[0407] The server receives task input information transmitted from the user through a visual device. The input data includes task details, deadlines, etc., and the server records this information in a database and then starts processing.

[0408] Step 2:

[0409] The server executes a reverse calculation algorithm based on the deadline information of the received task. This algorithm calculates the appropriate start time for action from the completion deadline and generates this as an action suggestion using Python. The generated suggestion is temporarily stored in memory.

[0410] Step 3:

[0411] Action suggestions generated by the server are transmitted to the terminal via the network through the user interface. The terminal displays the received data in real time through the API of its visual device, indicating the user the next action to take.

[0412] Step 4:

[0413] The terminal continuously monitors the user's work progress and sends progress data to the server. It also transmits data collected using sensors and input devices regarding the results and progress of the tasks performed by the user.

[0414] Step 5:

[0415] The server analyzes the received progress data and identifies incomplete sections. Furthermore, it generates a proposal that includes sections that can be automatically generated by the progress management system and sends it to the terminal. The server then uses data analysis tools to efficiently generate the proposal.

[0416] Step 6:

[0417] The server predicts behavioral patterns based on the user's historical data and generates prompts indicating the next necessary action using an AI model. The generated prompt messages are then sent to the terminal and displayed to the user.

[0418] Step 7:

[0419] Users review instructions and suggestions displayed on the visual device and perform tasks. By accepting or adjusting suggestions on the visual device, they can improve the efficiency and accuracy of their work.

[0420] 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.

[0421] This invention combines an emotion engine with a task management support system to improve task efficiency while considering the user's emotional state. The system consists of a server, a terminal, an emotion engine, and a user, all of which work in coordination with each other.

[0422] First, the user inputs task information through their device. This information, including task details and deadlines, is sent from the device to the server. The server writes the received information and applies a reverse calculation algorithm to determine the start date and time for each task. In this process, the server also considers the user's sentiment information provided by the sentiment engine and adjusts the suggestions accordingly.

[0423] The emotion engine utilizes facial recognition technology and voice analysis to analyze the user's facial expressions and voice tone in real time on the device. Based on these results, it sends data about the user's emotional state to the server. The server uses this data to modify the task suggestions, thereby encouraging the user to complete tasks in a positive way.

[0424] For example, if the server determines that a user is experiencing stress, it will adjust task priorities and suggest starting with less demanding tasks. Conversely, if the user shows high motivation, it can even suggest more challenging tasks earlier. In this way, the system alleviates the user's psychological burden and enables efficient task management.

[0425] Regarding progress management, the server analyzes progress data received from the terminal to identify incomplete sections and sections that can be automatically generated, and sends suggestions to the user. The emotion engine then provides further support, such as encouraging the user to actively utilize the automatic generation function if they are feeling fatigued.

[0426] This invention dynamically adjusts task management according to the user's emotional state, ensuring that work progresses in the most optimal way for each individual situation. This system allows users to improve productivity and reduce stress.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The user enters detailed information and deadlines for tasks they want to manage via their device. The device then sends this information to the server.

[0430] Step 2:

[0431] The server processes the received task information and calculates the task's start date and time by working backward from the deadline. It calculates the necessary processes based on the deadline entered by the user.

[0432] Step 3:

[0433] The device is equipped with an emotion engine that collects real-time emotional data from the user. This emotional data is primarily obtained through facial recognition technology and voice analysis.

[0434] Step 4:

[0435] The device uses an emotion engine to acquire emotional data, which is then sent to a server. The server analyzes the user's emotional state in real time.

[0436] Step 5:

[0437] The server takes into account emotional data provided by the emotion engine when generating task suggestions through reverse engineering. If the user is experiencing stress, it prioritizes lighter tasks.

[0438] Step 6:

[0439] The server sends a pre-configured action suggestion to the device. The device notifies the user and presents suggestions tailored to their emotional state.

[0440] Step 7:

[0441] The user reviews the suggestion and starts the task if necessary. The user confirms that their emotional state is appropriate.

[0442] Step 8:

[0443] Data regarding document creation and progress is sent from the terminal to the server. The server analyzes this data to identify incomplete sections and parts that can be automatically generated.

[0444] Step 9:

[0445] The server suggests automatically generated sections to the user as solutions to progress issues, and the emotion engine provides additional support as needed.

[0446] Step 10:

[0447] The server and terminal continuously monitor the user's emotional state and use this information to suggest tasks and manage progress. They continue to provide suggestions that take into account the user's psychological burden and motivation.

[0448] In this way, the entire system can adapt to the user's emotions and provide optimal task management tailored to individual circumstances.

[0449] (Example 2)

[0450] 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".

[0451] In modern society, efficiently managing a wide variety of tasks is crucial. However, dynamic task management that takes into account the user's emotional state is not yet fully realized, and in certain situations, it can increase the user's burden. Therefore, there is a need for methods that reduce stress and achieve efficient task management while considering the user's emotional state.

[0452] 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.

[0453] In this invention, the server includes an information processing means that acquires input information for a task, calculates backward from the task's deadline, and generates suggestions for actions required for the task; a progress management means that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated; and an emotion consideration means that analyzes the user's emotional state and adjusts action suggestions based on emotional data. This enables dynamic adjustment of task priorities according to the user's emotional state.

[0454] An "information processing device" is a device that has the function of acquiring input information for a task, working backward from the deadline, and generating suggestions for the actions necessary for that task.

[0455] "Dialogue device means" refers to an interface device for notifying the user of action suggestions generated by the information processing means.

[0456] A "progress management tool" is a device that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated.

[0457] An "emotion-considering device" is a device that analyzes the user's emotional state and adjusts behavioral suggestions based on that data.

[0458] An "algorithmic means" is a computational procedure used to dynamically change the priority of tasks based on emotional information obtained from a data processing device.

[0459] This invention provides a system that streamlines task management while taking into account the user's emotional state. Its main components include a server, a terminal, and an emotion engine. This allows task suggestions to adapt to the user's emotions, resulting in smooth task management.

[0460] The server is the core of the system. It processes task information received from terminals and functions as an information processing tool. Specifically, it calculates the task start date and time by working backward from the task deadline. The server utilizes database software to store and manage the received task information, which is later used to generate action suggestions.

[0461] The terminal is the primary interface for users to interact with the system. On the terminal, as the user inputs task information, the emotion engine operates in real time. The emotion engine analyzes facial expressions and voice tone using facial recognition and voice analysis technologies, and sends emotion data to the server. This process utilizes image processing algorithms and voice analysis software.

[0462] The server, upon receiving user emotional data, uses this data as an emotional consideration to inform task suggestions. Based on the emotional state, task priorities are adjusted, and considerations are made such as starting with less demanding tasks. For example, if a user is experiencing stress, suggestions that minimize the workload will be made. On the other hand, if a user is highly motivated, it may be possible to suggest more difficult tasks earlier in the process.

[0463] The generative AI model assists in analyzing data and generating action suggestions on the server. An example of a prompt to input into this model is, "If the user is feeling stressed, suggest prioritizing less demanding tasks." In this process, the AI ​​model uses sentiment data and task information to generate more personalized suggestions.

[0464] In this way, the present invention provides a new form of task management that incorporates the user's emotional state, enabling efficient and less stressful work performance.

[0465] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0466] Step 1:

[0467] The user enters task information using a terminal. Specifically, they enter the task name, due date, and detailed information through the interface on the terminal. This input data is sent to the server via an HTTP request.

[0468] Step 2:

[0469] The server saves task information received from the terminal to the database. Input data includes the task name, due date, etc. The server inserts this data into the database using SQL queries. As output, new task data is saved in the database.

[0470] Step 3:

[0471] The device activates an emotion engine to analyze the user's real-time facial expressions and voice tone. It uses image data from the built-in camera and audio data from the microphone as input. The emotion engine analyzes this data using image processing algorithms and audio analysis software to evaluate the user's emotional state. The output is user emotional state data.

[0472] Step 4:

[0473] The terminal sends the analyzed emotional state data to the server. The server receives this data and prepares to perform task management that reflects the emotional data. The input is the emotional state data, and the output includes the state in which the data is ready to be used as emotional data within the server.

[0474] Step 5:

[0475] The server generates task action suggestions while considering emotional data. It uses task information stored in a database and emotional data received from the terminal as input data. The server calculates suggestions using a generative AI model and adjusts task priorities according to the emotional state. Specifically, it provides suggestions to reduce the burden on users experiencing stress. The output is the adjusted action suggestions.

[0476] Step 6:

[0477] The server sends the generated action suggestions to the terminal, and the terminal notifies the user. The input is the adjusted action suggestions sent from the server. The terminal displays the suggestions to the user through pop-up notifications or an interface. The output is the presentation of the action suggestions in a visible form for the user.

[0478] (Application Example 2)

[0479] 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."

[0480] Modern consumers face a variety of tasks on a daily basis, and are required to manage them efficiently. However, conventional task management systems have difficulty flexibly responding to consumers' emotional states and various situations within the home, resulting in problems in efficiently carrying out tasks while reducing stress. This invention aims to provide a more comfortable and efficient task management system by dynamically adjusting task priorities based on the consumer's emotional state.

[0481] 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.

[0482] In this invention, the server includes information processing means that acquire task input information, calculate backward from the deadline, and generate suggestions for actions necessary for the task; emotion analysis means that analyze the user's emotional state and adjust task priorities based on that state; and progress management means that analyze the user's progress, identify incomplete parts, and suggest parts that can be automatically generated. This enables optimal task management in accordance with the user's emotional state.

[0483] "Information processing means" refers to a device or program that has the function of acquiring input information for a task, calculating backward from the deadline, and generating suggestions for actions necessary for the task.

[0484] A "consumer interface means" is a device or program for notifying consumers of action suggestions generated by an information processing means.

[0485] An "emotional analysis tool" is a device or program that analyzes the emotional state of a person and adjusts the priority of tasks based on that state.

[0486] A "progress management tool" is a device or program that analyzes the progress of a user, identifies incomplete parts, and suggests parts that can be automatically generated.

[0487] A "predictive input means" is a device or program that analyzes a consumer's historical data, predicts the next necessary action, and automatically generates text.

[0488] The system for implementing this invention consists of multiple components: a server, a terminal, an emotion analysis engine, and a user. The server is responsible for task management and suggestion, and provides an optimal task management strategy based on the user's emotional state obtained by the emotion analysis engine. The terminal functions as an interface for the user to input task information and provide feedback on suggested tasks.

[0489] Specifically, the server first obtains task input information from the terminal and uses information processing tools to calculate backward from the deadline. This process utilizes algorithms written in programming languages ​​such as Python. Next, the emotion analysis engine uses software tools like OpenCV and TensorFlow to analyze changes in voice and facial expressions, understanding the user's emotional state in real time. Based on this, the emotion analysis tool optimizes task priorities according to the user's current state.

[0490] The progress management system analyzes incomplete tasks based on user progress data and suggests automatically generated parts as needed. The predictive input system predicts the next necessary actions based on past historical data and automatically generates suggestions to help users complete tasks more efficiently.

[0491] For example, when a user is engaged in the task of gardening, if the emotion analysis engine determines that the user is experiencing stress, the system will suggest relaxation-related tasks in parallel to alleviate the burden. Furthermore, if the user shows high motivation, suggestions will be made to encourage them to start a new project.

[0492] An example of a prompt using a generative AI model might be: "Please create a voice analysis model that determines whether the user is experiencing stress. It needs to analyze changes in the speed and tone of the user's voice and estimate their emotional state based on that."

[0493] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0494] Step 1:

[0495] The server receives task information entered by the user from the terminal. The input information includes task details and deadlines, and the server stores this information in a database. The server organizes the basic task information and prepares to apply a reverse calculation algorithm.

[0496] Step 2:

[0497] The server uses a reverse-engineer algorithm to calculate an action plan leading up to the task deadline. It uses the task deadline and the current date and time as input to calculate the start date and time for each task. The calculated schedule is then compiled into action suggestions aimed at improving the efficiency of task progress.

[0498] Step 3:

[0499] The terminal notifies the user of action suggestions from the server. These notifications are displayed on the user's smartphone or computer. The user interface receives the action suggestions and allows the user to set reminders, add them to their calendar, and perform other actions to carry them out.

[0500] Step 4:

[0501] The emotion analysis engine analyzes the user's voice and facial expression data acquired through the device. This real-time collected emotion data is used to determine the user's emotional state. The determined emotional state is then used to prioritize tasks.

[0502] Step 5:

[0503] The server receives the emotion analysis results and adjusts task priorities based on this information. If stress is detected, it prioritizes less burdensome tasks; if high motivation is detected, it suggests more difficult tasks. This provides task suggestions that are optimal for the user.

[0504] Step 6:

[0505] The server analyzes the user's progress and identifies incomplete tasks. User progress data is used as input, including, for example, task completion rates and delays. Based on this information, the server generates automatically reproducible parts and additional suggestions, which are then presented to the user.

[0506] Step 7:

[0507] The predictive input method analyzes the user's historical data and predicts the next necessary action. Based on past behavioral history, a generative AI model creates prompts and provides suggestions to support the user's next steps. This enables the user to complete tasks efficiently.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] [Third Embodiment]

[0512] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0513] 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.

[0514] 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).

[0515] 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.

[0516] 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.

[0517] 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).

[0518] 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.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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".

[0524] This invention is a system for streamlining task management, in which a server, terminals, and users work together. The server receives task information entered by users through terminals and performs efficient task management based on that information.

[0525] The server first identifies the task's deadline based on the input information. Next, it uses a reverse-engineer algorithm to calculate the appropriate start time for the user to begin the task. Based on the calculation result, it generates an action suggestion and sends it to the terminal. The terminal uses its notification function to display the action suggestion sent from the server to the user. This process enables the user to continuously progress on the task within the deadline.

[0526] Furthermore, the device tracks the progress of meeting materials and presentations that the user is creating. While the user is actively working on them, the device sends this information to the server. The server analyzes the progress information and assists with material creation by identifying sections that can be automatically generated. In this case, the server forms automatically generated section drafts and provides them to the user via the device. The user can review the proposed drafts, make modifications as needed, and use them.

[0527] In addition, the terminal collects the user's past history data and transfers it to the server. The server analyzes this data to predict the user's behavior patterns and automatically generates the next necessary prompt. The generated prompt is sent to the terminal and displayed to the user. In this way, the user can save time on manual input and proceed with tasks smoothly.

[0528] In a specific case, suppose a user enters the task "Prepare materials for next week's meeting." The server works backward and generates a notification suggesting, "It is recommended that you create an outline of the materials this afternoon," and sends this suggestion to the user via their device. At the same time, it monitors the progress of the materials and helps streamline the user's work by suggesting, "The data analysis section can be automatically generated."

[0529] As described above, this invention directly supports task management, reduces the burden on the user, and improves productivity.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The user uses a terminal to enter task information. The task includes detailed information and deadlines. The terminal then sends this information to the server.

[0533] Step 2:

[0534] The server extracts the deadline from the received task information and begins working backward. The backward calculation algorithm identifies the intermediate steps and start times required to complete the task.

[0535] Step 3:

[0536] The server generates action suggestions based on the reverse calculation results. These suggestions include specific actions and schedules to help the user efficiently complete the task.

[0537] Step 4:

[0538] The server sends the generated action suggestions to the device. The device displays the action suggestions to the user through its notification function and prompts them to take the necessary action.

[0539] Step 5:

[0540] The device collects progress data on meeting materials and presentations that the user is involved with. This data is updated each time the user edits a document.

[0541] Step 6:

[0542] The server analyzes the progress data sent from the terminal to determine which parts of the document are incomplete and which can be automatically generated.

[0543] Step 7:

[0544] The server creates automatically generated sections based on the analysis results and sends their contents to the terminal as suggestions.

[0545] Step 8:

[0546] The terminal displays suggestions received from the server to the user, who then reviews them and can adopt or modify them.

[0547] Step 9:

[0548] The terminal continuously monitors the user's work history and behavioral patterns, and sends the data to the server.

[0549] Step 10:

[0550] The server analyzes the historical data accumulated so far and uses a predictive model to identify the user's next necessary action.

[0551] Step 11:

[0552] The server automatically generates a prompt based on the predicted action and sends it to the terminal.

[0553] Step 12:

[0554] The terminal supports the user in completing tasks by displaying prompts and guiding them to the next necessary action.

[0555] (Example 1)

[0556] 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."

[0557] In today's busy work environment, it is difficult for users to efficiently manage their tasks and execute them at the appropriate time. Furthermore, tracking and optimizing progress is required in document creation and project management, but doing so manually is time-consuming and labor-intensive. In addition, predicting the next course of action based on past activity history is not easy, hindering user productivity improvements.

[0558] 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.

[0559] In this invention, the server includes information processing means that acquire task input information, calculate backward from the task's deadline, and generate suggestions for actions necessary for the task; user interface means that notify the user of the action suggestions generated by the information processing means; and progress tracking means that monitor the user's progress and identify the parts of document creation that can be automatically generated based on the progress information. As a result, the user can manage tasks automatically and efficiently, receive suggestions and support for document creation at the optimal timing, and improve productivity.

[0560] "Task input information" refers to detailed information about jobs and activities that users register with the system, including what needs to be done and the deadline.

[0561] "Working backward" is the process of finding the optimal starting point for an action by working backward from a given termination condition.

[0562] An "information processing device" is an element that has the function of analyzing input data, performing various calculations based on that analysis, and generating outputs such as action suggestions.

[0563] "User interface means" refers to the means by which a system and a user exchange information, including screen displays and interactive functions for showing notifications and suggestions.

[0564] A "progress tracking tool" is an element that has the functionality to monitor the progress of tasks and projects that a user is working on, and to collect and analyze that data.

[0565] "Automatically generated portions" refer to sections of documents or data that the system can automatically create based on predetermined rules or algorithms.

[0566] "Historical data" refers to records of activities and information entered by users in the past, and is the data that is subject to analysis.

[0567] A "predictive support tool" is an element equipped with the function of analyzing past data and proposing future actions based on that analysis.

[0568] A "prompt" is a short instruction or question that a system displays to a user, used to guide the user's next action.

[0569] This invention is a system for streamlining task management, and it operates through the collaboration of a server, terminals, and users.

[0570] The server utilizes a generative AI model to analyze the task information entered by the user and generate suggestions to support the initiation of action at the appropriate time. Specifically, it executes algorithms using programming languages ​​such as Python and R to calculate deadlines from the input data. Furthermore, the server uses machine learning frameworks such as TensorFlow to analyze progress information and identify parts of document creation that can be automatically generated.

[0571] The device has an interface function that notifies the user of these action suggestions and automatically generated sections of materials. By providing users with necessary information in a timely manner using the notification function, users can manage their tasks efficiently.

[0572] Furthermore, the terminal collects the user's past behavior history and sends it to the server. Based on this history data, the server can analyze the user's behavior patterns using database solutions such as SQL and BigQuery, and automatically generate the next necessary prompts. This allows the user to skip manual input and proceed with tasks more smoothly.

[0573] As a concrete example, let's consider a scenario where a user enters the prompt, "Please help me prepare for the next project meeting. Please suggest a specific schedule and progress management plan." In this case, the server will suggest an optimal schedule and notify the user about the parts of the document creation that can be automatically generated, thereby supporting efficient work execution.

[0574] Through the mechanism described above, the present invention can reduce the burden on users and improve productivity.

[0575] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0576] Step 1:

[0577] The server receives task information entered by the user through the terminal.

[0578] Input: Task name, due date, details

[0579] Processing: The server saves the received task information to the database.

[0580] Output: Confirmation message for saved task information

[0581] Specific operation: When the user enters "Create report, due date: Friday" into the terminal, the terminal sends this information to the server, which then saves it.

[0582] Step 2:

[0583] The server applies a reverse calculation algorithm based on the stored task information.

[0584] Input: Task deadline information

[0585] Processing: The server uses an algorithm to calculate the optimal start time for the action.

[0586] Output: Suggestion for action start date and time

[0587] Specific operation: The server calculates that "for a Friday deadline, it is desirable to start preparations from Wednesday" and creates a proposal accordingly.

[0588] Step 3:

[0589] The server transmits the calculated action start date and time to the terminal via the user interface.

[0590] Input: Suggestion for the start date and time of the action

[0591] Processing: The server sends the generated suggestions to the terminal, and the terminal notifies the user of the received information.

[0592] Output: Notification of the start date and time of the action displayed to the user.

[0593] Specific action: The device displays a pop-up notification saying, "Please begin preparations at 10:00 AM on Wednesday."

[0594] Step 4:

[0595] The terminal tracks the progress of the materials the user is working on and sends the data to the server.

[0596] Input: Document progress

[0597] Processing: The terminal records progress and prepares data to send to the server.

[0598] Output: Sending progress data to the server

[0599] Specific operation: When the user completes a portion of the document, the device records the progress and sends it to the server as "Introduction Complete," etc.

[0600] Step 5:

[0601] The server analyzes the progress data and identifies sections that can be automatically generated.

[0602] Input: Progress data

[0603] Processing: The server uses a generative AI model to analyze the data and identify incomplete sections of the document.

[0604] Output: Suggestions for automatically generated sections

[0605] Specific action: The server creates a suggestion that "a data analysis section can be automatically generated" and sends it to the terminal.

[0606] Step 6:

[0607] The terminal receives a proposal from the server and notifies the user.

[0608] Input: Suggestions for automatically generated sections

[0609] Processing: Display the received suggestions to the user.

[0610] Output: Notification of automatically generated suggestions displayed to the user

[0611] Specific action: The device displays a notification saying, "There is a proposed automated data analysis. Do you want to use it?"

[0612] Step 7:

[0613] The device collects user history data and sends it to the server.

[0614] Input: User history data

[0615] Processing: The terminal organizes the collected historical data and prepares it for transmission to the server.

[0616] Output: Sending history data to the server

[0617] Specific operation: The device collects the "task history for the past 6 months" and periodically sends it to the server.

[0618] Step 8:

[0619] The server analyzes the historical data and generates the next necessary prompt.

[0620] Input: Historical data

[0621] Processing: The server uses analysis software to recognize patterns and generate prompts.

[0622] Output: Generated prompt

[0623] Specific action: The server generates a prompt saying "We recommend you create a report conclusion next" and sends it to the terminal.

[0624] Step 9:

[0625] The terminal displays the generated prompts to the user to assist with the task.

[0626] Input: Generated prompt

[0627] Processing: The terminal displays a prompt to prompt the user for the next action.

[0628] Output: Prompt displayed to the user

[0629] Specific action: The terminal displays a prompt to the user saying, "Please begin creating your conclusion as the next step."

[0630] (Application Example 1)

[0631] 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."

[0632] In manufacturing environments and work sites, there is a need for systems that enable workers to efficiently handle frequently occurring tasks and maintenance work, while also allowing them to grasp progress in real time and accurately perform the next necessary tasks. However, existing systems have been insufficient in terms of tracking workers' progress in real time and visually indicating the next actions to be taken, limiting the improvement of work efficiency.

[0633] 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.

[0634] In this invention, the server includes an information processing means that acquires input information for a task, calculates backward from the task's deadline, and generates suggestions for actions required for the task; a user interface means that notifies the user of the action suggestions generated by the information processing means; a progress management means that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated; and a visualization means that visualizes the action suggestions and work progress information and displays them in real time on a visual device used by the user. This enables workers to efficiently carry out their work while checking tasks and progress on-site in real time.

[0635] "Task input information" refers to the information necessary for a user to complete a specific task or job within the system.

[0636] "Working backward from the deadline" is the process of calculating the appropriate start time for action by working backward from the completion deadline of a set task.

[0637] "Action suggestions" refer to the system proposing specific actions that users can take to efficiently complete tasks by the deadline.

[0638] An "information processing device" is a device that performs analysis and calculations based on the input information of a task and generates action suggestions.

[0639] A "user interface means" is an interface device used to convey information and suggestions generated by a system to the user.

[0640] A "progress management system" is a device that monitors the progress of a user's work, identifies problem areas, and makes suggestions for improvement.

[0641] "Visualization means" are tools or devices used to visually display action suggestions or work progress information.

[0642] "Visual devices" refer to equipment used by users to visually receive information from a system.

[0643] A system for carrying out this invention includes a cloud server, a visual device used by the user (e.g., smart glasses), and a communication network for coordinating the server and the visual device.

[0644] The server receives task information entered by the user through their visual device. The server then applies a reverse-engineer algorithm based on the task's deadline to calculate the action start date and time. This calculation uses the Python programming language. Based on the calculated action start date and time, action suggestions are generated and notified to the user's visual device in real time. The visual device uses software such as the Oculus SDK to visually display the information to the user.

[0645] The terminal monitors the user's progress and sends progress data to the server via a progress management system. The server analyzes this data, identifies incomplete or automatically regenerative parts, and makes suggestions. These suggestions are also communicated to the user via a visual device, making it easier for the user to understand the next steps.

[0646] Furthermore, the server analyzes the user's past history data and predicts behavioral patterns to generate prompts indicating the next necessary action. These prompts are generated by an AI model and provided to the user via the terminal. An example of a prompt message might be: "Please decide what to do as your next task. Based on your history, we will generate the most appropriate action suggestion."

[0647] As a concrete example, this system can be used by workers in a manufacturing plant to efficiently carry out their daily tasks by checking the previous day's work list and priorities on a visual device. Furthermore, for tasks that are behind schedule, a message such as "Assembly of part X is behind schedule. The next task to be performed is Y." will be displayed on the visual device, assisting workers in taking immediate action.

[0648] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0649] Step 1:

[0650] The server receives task input information transmitted from the user through a visual device. The input data includes task details, deadlines, etc., and the server records this information in a database and then starts processing.

[0651] Step 2:

[0652] The server executes a reverse calculation algorithm based on the deadline information of the received task. This algorithm calculates the appropriate start time for action from the completion deadline and generates this as an action suggestion using Python. The generated suggestion is temporarily stored in memory.

[0653] Step 3:

[0654] Action suggestions generated by the server are transmitted to the terminal via the network through the user interface. The terminal displays the received data in real time through the API of its visual device, indicating the user the next action to take.

[0655] Step 4:

[0656] The terminal continuously monitors the user's work progress and sends progress data to the server. It also transmits data collected using sensors and input devices regarding the results and progress of the tasks performed by the user.

[0657] Step 5:

[0658] The server analyzes the received progress data and identifies incomplete sections. Furthermore, it generates a proposal that includes sections that can be automatically generated by the progress management system and sends it to the terminal. The server then uses data analysis tools to efficiently generate the proposal.

[0659] Step 6:

[0660] The server predicts behavioral patterns based on the user's historical data and generates prompts indicating the next necessary action using an AI model. The generated prompt messages are then sent to the terminal and displayed to the user.

[0661] Step 7:

[0662] Users review instructions and suggestions displayed on the visual device and perform tasks. By accepting or adjusting suggestions on the visual device, they can improve the efficiency and accuracy of their work.

[0663] 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.

[0664] This invention combines an emotion engine with a task management support system to improve task efficiency while considering the user's emotional state. The system consists of a server, a terminal, an emotion engine, and a user, all of which work in coordination with each other.

[0665] First, the user inputs task information through their device. This information, including task details and deadlines, is sent from the device to the server. The server writes the received information and applies a reverse calculation algorithm to determine the start date and time for each task. In this process, the server also considers the user's sentiment information provided by the sentiment engine and adjusts the suggestions accordingly.

[0666] The emotion engine utilizes facial recognition technology and voice analysis to analyze the user's facial expressions and voice tone in real time on the device. Based on these results, it sends data about the user's emotional state to the server. The server uses this data to modify the task suggestions, thereby encouraging the user to complete tasks in a positive way.

[0667] For example, if the server determines that a user is experiencing stress, it will adjust task priorities and suggest starting with less demanding tasks. Conversely, if the user shows high motivation, it can even suggest more challenging tasks earlier. In this way, the system alleviates the user's psychological burden and enables efficient task management.

[0668] Regarding progress management, the server analyzes progress data received from the terminal to identify incomplete sections and sections that can be automatically generated, and sends suggestions to the user. The emotion engine then provides further support, such as encouraging the user to actively utilize the automatic generation function if they are feeling fatigued.

[0669] This invention dynamically adjusts task management according to the user's emotional state, ensuring that work progresses in the most optimal way for each individual situation. This system allows users to improve productivity and reduce stress.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The user enters detailed information and deadlines for tasks they want to manage via their device. The device then sends this information to the server.

[0673] Step 2:

[0674] The server processes the received task information and calculates the task's start date and time by working backward from the deadline. It calculates the necessary processes based on the deadline entered by the user.

[0675] Step 3:

[0676] The device is equipped with an emotion engine that collects real-time emotional data from the user. This emotional data is primarily obtained through facial recognition technology and voice analysis.

[0677] Step 4:

[0678] The device uses an emotion engine to acquire emotional data, which is then sent to a server. The server analyzes the user's emotional state in real time.

[0679] Step 5:

[0680] The server takes into account emotional data provided by the emotion engine when generating task suggestions through reverse engineering. If the user is experiencing stress, it prioritizes lighter tasks.

[0681] Step 6:

[0682] The server sends a pre-configured action suggestion to the device. The device notifies the user and presents suggestions tailored to their emotional state.

[0683] Step 7:

[0684] The user reviews the suggestion and starts the task if necessary. The user confirms that their emotional state is appropriate.

[0685] Step 8:

[0686] Data regarding document creation and progress is sent from the terminal to the server. The server analyzes this data to identify incomplete sections and parts that can be automatically generated.

[0687] Step 9:

[0688] The server suggests automatically generated sections to the user as solutions to progress issues, and the emotion engine provides additional support as needed.

[0689] Step 10:

[0690] The server and terminal continuously monitor the user's emotional state and use this information to suggest tasks and manage progress. They continue to provide suggestions that take into account the user's psychological burden and motivation.

[0691] In this way, the entire system can adapt to the user's emotions and provide optimal task management tailored to individual circumstances.

[0692] (Example 2)

[0693] 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."

[0694] In modern society, efficiently managing a wide variety of tasks is crucial. However, dynamic task management that takes into account the user's emotional state is not yet fully realized, and in certain situations, it can increase the user's burden. Therefore, there is a need for methods that reduce stress and achieve efficient task management while considering the user's emotional state.

[0695] 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.

[0696] In this invention, the server includes an information processing means that acquires input information for a task, calculates backward from the task's deadline, and generates suggestions for actions required for the task; a progress management means that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated; and an emotion consideration means that analyzes the user's emotional state and adjusts action suggestions based on emotional data. This enables dynamic adjustment of task priorities according to the user's emotional state.

[0697] An "information processing device" is a device that has the function of acquiring input information for a task, working backward from the deadline, and generating suggestions for the actions necessary for that task.

[0698] "Dialogue device means" refers to an interface device for notifying the user of action suggestions generated by the information processing means.

[0699] A "progress management tool" is a device that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated.

[0700] An "emotion-considering device" is a device that analyzes the user's emotional state and adjusts behavioral suggestions based on that data.

[0701] An "algorithmic means" is a computational procedure used to dynamically change the priority of tasks based on emotional information obtained from a data processing device.

[0702] This invention provides a system that streamlines task management while taking into account the user's emotional state. Its main components include a server, a terminal, and an emotion engine. This allows task suggestions to adapt to the user's emotions, resulting in smooth task management.

[0703] The server is the core of the system. It processes task information received from terminals and functions as an information processing tool. Specifically, it calculates the task start date and time by working backward from the task deadline. The server utilizes database software to store and manage the received task information, which is later used to generate action suggestions.

[0704] The terminal is the primary interface for users to interact with the system. On the terminal, as the user inputs task information, the emotion engine operates in real time. The emotion engine analyzes facial expressions and voice tone using facial recognition and voice analysis technologies, and sends emotion data to the server. This process utilizes image processing algorithms and voice analysis software.

[0705] The server, upon receiving user emotional data, uses this data as an emotional consideration to inform task suggestions. Based on the emotional state, task priorities are adjusted, and considerations are made such as starting with less demanding tasks. For example, if a user is experiencing stress, suggestions that minimize the workload will be made. On the other hand, if a user is highly motivated, it may be possible to suggest more difficult tasks earlier in the process.

[0706] The generative AI model assists in analyzing data and generating action suggestions on the server. An example of a prompt to input into this model is, "If the user is feeling stressed, suggest prioritizing less demanding tasks." In this process, the AI ​​model uses sentiment data and task information to generate more personalized suggestions.

[0707] In this way, the present invention provides a new form of task management that incorporates the user's emotional state, enabling efficient and less stressful work performance.

[0708] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0709] Step 1:

[0710] The user enters task information using a terminal. Specifically, they enter the task name, due date, and detailed information through the interface on the terminal. This input data is sent to the server via an HTTP request.

[0711] Step 2:

[0712] The server saves task information received from the terminal to the database. Input data includes the task name, due date, etc. The server inserts this data into the database using SQL queries. As output, new task data is saved in the database.

[0713] Step 3:

[0714] The device activates an emotion engine to analyze the user's real-time facial expressions and voice tone. It uses image data from the built-in camera and audio data from the microphone as input. The emotion engine analyzes this data using image processing algorithms and audio analysis software to evaluate the user's emotional state. The output is user emotional state data.

[0715] Step 4:

[0716] The terminal sends the analyzed emotional state data to the server. The server receives this data and prepares to perform task management that reflects the emotional data. The input is the emotional state data, and the output includes the state in which the data is ready to be used as emotional data within the server.

[0717] Step 5:

[0718] The server generates task action suggestions while considering emotional data. It uses task information stored in a database and emotional data received from the terminal as input data. The server calculates suggestions using a generative AI model and adjusts task priorities according to the emotional state. Specifically, it provides suggestions to reduce the burden on users experiencing stress. The output is the adjusted action suggestions.

[0719] Step 6:

[0720] The server sends the generated action suggestions to the terminal, and the terminal notifies the user. The input is the adjusted action suggestions sent from the server. The terminal displays the suggestions to the user through pop-up notifications or an interface. The output is the presentation of the action suggestions in a visible form for the user.

[0721] (Application Example 2)

[0722] 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."

[0723] Modern consumers face a variety of tasks on a daily basis, and are required to manage them efficiently. However, conventional task management systems have difficulty flexibly responding to consumers' emotional states and various situations within the home, resulting in problems in efficiently carrying out tasks while reducing stress. This invention aims to provide a more comfortable and efficient task management system by dynamically adjusting task priorities based on the consumer's emotional state.

[0724] 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.

[0725] In this invention, the server includes information processing means that acquire task input information, calculate backward from the deadline, and generate suggestions for actions necessary for the task; emotion analysis means that analyze the user's emotional state and adjust task priorities based on that state; and progress management means that analyze the user's progress, identify incomplete parts, and suggest parts that can be automatically generated. This enables optimal task management in accordance with the user's emotional state.

[0726] "Information processing means" refers to a device or program that has the function of acquiring input information for a task, calculating backward from the deadline, and generating suggestions for actions necessary for the task.

[0727] A "consumer interface means" is a device or program for notifying consumers of action suggestions generated by an information processing means.

[0728] An "emotional analysis tool" is a device or program that analyzes the emotional state of a person and adjusts the priority of tasks based on that state.

[0729] A "progress management tool" is a device or program that analyzes the progress of a user, identifies incomplete parts, and suggests parts that can be automatically generated.

[0730] A "predictive input means" is a device or program that analyzes a consumer's historical data, predicts the next necessary action, and automatically generates text.

[0731] The system for implementing this invention consists of multiple components: a server, a terminal, an emotion analysis engine, and a user. The server is responsible for task management and suggestion, and provides an optimal task management strategy based on the user's emotional state obtained by the emotion analysis engine. The terminal functions as an interface for the user to input task information and provide feedback on suggested tasks.

[0732] Specifically, the server first obtains task input information from the terminal and uses information processing tools to calculate backward from the deadline. This process utilizes algorithms written in programming languages ​​such as Python. Next, the emotion analysis engine uses software tools like OpenCV and TensorFlow to analyze changes in voice and facial expressions, understanding the user's emotional state in real time. Based on this, the emotion analysis tool optimizes task priorities according to the user's current state.

[0733] The progress management system analyzes incomplete tasks based on user progress data and suggests automatically generated parts as needed. The predictive input system predicts the next necessary actions based on past historical data and automatically generates suggestions to help users complete tasks more efficiently.

[0734] For example, when a user is engaged in the task of gardening, if the emotion analysis engine determines that the user is experiencing stress, the system will suggest relaxation-related tasks in parallel to alleviate the burden. Furthermore, if the user shows high motivation, suggestions will be made to encourage them to start a new project.

[0735] An example of a prompt using a generative AI model might be: "Please create a voice analysis model that determines whether the user is experiencing stress. It needs to analyze changes in the speed and tone of the user's voice and estimate their emotional state based on that."

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The server receives task information entered by the user from the terminal. The input information includes task details and deadlines, and the server stores this information in a database. The server organizes the basic task information and prepares to apply a reverse calculation algorithm.

[0739] Step 2:

[0740] The server uses a reverse-engineer algorithm to calculate an action plan leading up to the task deadline. It uses the task deadline and the current date and time as input to calculate the start date and time for each task. The calculated schedule is then compiled into action suggestions aimed at improving the efficiency of task progress.

[0741] Step 3:

[0742] The terminal notifies the user of action suggestions from the server. These notifications are displayed on the user's smartphone or computer. The user interface receives the action suggestions and allows the user to set reminders, add them to their calendar, and perform other actions to carry them out.

[0743] Step 4:

[0744] The emotion analysis engine analyzes the user's voice and facial expression data acquired through the device. This real-time collected emotion data is used to determine the user's emotional state. The determined emotional state is then used to prioritize tasks.

[0745] Step 5:

[0746] The server receives the emotion analysis results and adjusts task priorities based on this information. If stress is detected, it prioritizes less burdensome tasks; if high motivation is detected, it suggests more difficult tasks. This provides task suggestions that are optimal for the user.

[0747] Step 6:

[0748] The server analyzes the user's progress and identifies incomplete tasks. User progress data is used as input, including, for example, task completion rates and delays. Based on this information, the server generates automatically reproducible parts and additional suggestions, which are then presented to the user.

[0749] Step 7:

[0750] The predictive input method analyzes the user's historical data and predicts the next necessary action. Based on past behavioral history, a generative AI model creates prompts and provides suggestions to support the user's next steps. This enables the user to complete tasks efficiently.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] [Fourth Embodiment]

[0755] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0756] 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.

[0757] 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).

[0758] 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.

[0759] 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.

[0760] 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).

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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".

[0768] This invention is a system for streamlining task management, in which a server, terminals, and users work together. The server receives task information entered by users through terminals and performs efficient task management based on that information.

[0769] The server first identifies the task's deadline based on the input information. Next, it uses a reverse-engineer algorithm to calculate the appropriate start time for the user to begin the task. Based on the calculation result, it generates an action suggestion and sends it to the terminal. The terminal uses its notification function to display the action suggestion sent from the server to the user. This process enables the user to continuously progress on the task within the deadline.

[0770] Furthermore, the device tracks the progress of meeting materials and presentations that the user is creating. While the user is actively working on them, the device sends this information to the server. The server analyzes the progress information and assists with material creation by identifying sections that can be automatically generated. In this case, the server forms automatically generated section drafts and provides them to the user via the device. The user can review the proposed drafts, make modifications as needed, and use them.

[0771] In addition, the terminal collects the user's past history data and transfers it to the server. The server analyzes this data to predict the user's behavior patterns and automatically generates the next necessary prompt. The generated prompt is sent to the terminal and displayed to the user. In this way, the user can save time on manual input and proceed with tasks smoothly.

[0772] In a specific case, suppose a user enters the task "Prepare materials for next week's meeting." The server works backward and generates a notification suggesting, "It is recommended that you create an outline of the materials this afternoon," and sends this suggestion to the user via their device. At the same time, it monitors the progress of the materials and helps streamline the user's work by suggesting, "The data analysis section can be automatically generated."

[0773] As described above, this invention directly supports task management, reduces the burden on the user, and improves productivity.

[0774] The following describes the processing flow.

[0775] Step 1:

[0776] The user uses a terminal to enter task information. The task includes detailed information and deadlines. The terminal then sends this information to the server.

[0777] Step 2:

[0778] The server extracts the deadline from the received task information and begins working backward. The backward calculation algorithm identifies the intermediate steps and start times required to complete the task.

[0779] Step 3:

[0780] The server generates action suggestions based on the reverse calculation results. These suggestions include specific actions and schedules to help the user efficiently complete the task.

[0781] Step 4:

[0782] The server sends the generated action suggestions to the device. The device displays the action suggestions to the user through its notification function and prompts them to take the necessary action.

[0783] Step 5:

[0784] The device collects progress data on meeting materials and presentations that the user is involved with. This data is updated each time the user edits a document.

[0785] Step 6:

[0786] The server analyzes the progress data sent from the terminal to determine which parts of the document are incomplete and which can be automatically generated.

[0787] Step 7:

[0788] The server creates automatically generated sections based on the analysis results and sends their contents to the terminal as suggestions.

[0789] Step 8:

[0790] The terminal displays suggestions received from the server to the user, who then reviews them and can adopt or modify them.

[0791] Step 9:

[0792] The terminal continuously monitors the user's work history and behavioral patterns, and sends the data to the server.

[0793] Step 10:

[0794] The server analyzes the historical data accumulated so far and uses a predictive model to identify the user's next necessary action.

[0795] Step 11:

[0796] The server automatically generates a prompt based on the predicted action and sends it to the terminal.

[0797] Step 12:

[0798] The terminal supports the user in completing tasks by displaying prompts and guiding them to the next necessary action.

[0799] (Example 1)

[0800] 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".

[0801] In today's busy work environment, it is difficult for users to efficiently manage their tasks and execute them at the appropriate time. Furthermore, tracking and optimizing progress is required in document creation and project management, but doing so manually is time-consuming and labor-intensive. In addition, predicting the next course of action based on past activity history is not easy, hindering user productivity improvements.

[0802] 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.

[0803] In this invention, the server includes information processing means that acquire task input information, calculate backward from the task's deadline, and generate suggestions for actions necessary for the task; user interface means that notify the user of the action suggestions generated by the information processing means; and progress tracking means that monitor the user's progress and identify the parts of document creation that can be automatically generated based on the progress information. As a result, the user can manage tasks automatically and efficiently, receive suggestions and support for document creation at the optimal timing, and improve productivity.

[0804] "Task input information" refers to detailed information about jobs and activities that users register with the system, including what needs to be done and the deadline.

[0805] "Working backward" is the process of finding the optimal starting point for an action by working backward from a given termination condition.

[0806] An "information processing device" is an element that has the function of analyzing input data, performing various calculations based on that analysis, and generating outputs such as action suggestions.

[0807] "User interface means" refers to the means by which a system and a user exchange information, including screen displays and interactive functions for showing notifications and suggestions.

[0808] A "progress tracking tool" is an element that has the functionality to monitor the progress of tasks and projects that a user is working on, and to collect and analyze that data.

[0809] "Automatically generated portions" refer to sections of documents or data that the system can automatically create based on predetermined rules or algorithms.

[0810] "Historical data" refers to records of activities and information entered by users in the past, and is the data that is subject to analysis.

[0811] A "predictive support tool" is an element equipped with the function of analyzing past data and proposing future actions based on that analysis.

[0812] A "prompt" is a short instruction or question that a system displays to a user, used to guide the user's next action.

[0813] This invention is a system for streamlining task management, and it operates through the collaboration of a server, terminals, and users.

[0814] The server utilizes a generative AI model to analyze the task information entered by the user and generate suggestions to support the initiation of action at the appropriate time. Specifically, it executes algorithms using programming languages ​​such as Python and R to calculate deadlines from the input data. Furthermore, the server uses machine learning frameworks such as TensorFlow to analyze progress information and identify parts of document creation that can be automatically generated.

[0815] The device has an interface function that notifies the user of these action suggestions and automatically generated sections of materials. By providing users with necessary information in a timely manner using the notification function, users can manage their tasks efficiently.

[0816] Furthermore, the terminal collects the user's past behavior history and sends it to the server. Based on this history data, the server can analyze the user's behavior patterns using database solutions such as SQL and BigQuery, and automatically generate the next necessary prompts. This allows the user to skip manual input and proceed with tasks more smoothly.

[0817] As a concrete example, let's consider a scenario where a user enters the prompt, "Please help me prepare for the next project meeting. Please suggest a specific schedule and progress management plan." In this case, the server will suggest an optimal schedule and notify the user about the parts of the document creation that can be automatically generated, thereby supporting efficient work execution.

[0818] Through the mechanism described above, the present invention can reduce the burden on users and improve productivity.

[0819] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0820] Step 1:

[0821] The server receives task information entered by the user through the terminal.

[0822] Input: Task name, due date, details

[0823] Processing: The server saves the received task information to the database.

[0824] Output: Confirmation message for saved task information

[0825] Specific operation: When the user enters "Create report, due date: Friday" into the terminal, the terminal sends this information to the server, which then saves it.

[0826] Step 2:

[0827] The server applies a reverse calculation algorithm based on the stored task information.

[0828] Input: Task deadline information

[0829] Processing: The server uses an algorithm to calculate the optimal start time for the action.

[0830] Output: Suggestion for action start date and time

[0831] Specific operation: The server calculates that "for a Friday deadline, it is desirable to start preparations from Wednesday" and creates a proposal accordingly.

[0832] Step 3:

[0833] The server transmits the calculated action start date and time to the terminal via the user interface.

[0834] Input: Suggestion for the start date and time of the action

[0835] Processing: The server sends the generated suggestions to the terminal, and the terminal notifies the user of the received information.

[0836] Output: Notification of the start date and time of the action displayed to the user.

[0837] Specific action: The device displays a pop-up notification saying, "Please begin preparations at 10:00 AM on Wednesday."

[0838] Step 4:

[0839] The terminal tracks the progress of the materials the user is working on and sends the data to the server.

[0840] Input: Document progress

[0841] Processing: The terminal records progress and prepares data to send to the server.

[0842] Output: Sending progress data to the server

[0843] Specific operation: When the user completes a portion of the document, the device records the progress and sends it to the server as "Introduction Complete," etc.

[0844] Step 5:

[0845] The server analyzes the progress data and identifies sections that can be automatically generated.

[0846] Input: Progress data

[0847] Processing: The server uses a generative AI model to analyze the data and identify incomplete sections of the document.

[0848] Output: Suggestions for automatically generated sections

[0849] Specific action: The server creates a suggestion that "a data analysis section can be automatically generated" and sends it to the terminal.

[0850] Step 6:

[0851] The terminal receives a proposal from the server and notifies the user.

[0852] Input: Suggestions for automatically generated sections

[0853] Processing: Display the received suggestions to the user.

[0854] Output: Notification of automatically generated suggestions displayed to the user

[0855] Specific action: The device displays a notification saying, "There is a proposed automated data analysis. Do you want to use it?"

[0856] Step 7:

[0857] The device collects user history data and sends it to the server.

[0858] Input: User history data

[0859] Processing: The terminal organizes the collected historical data and prepares it for transmission to the server.

[0860] Output: Sending history data to the server

[0861] Specific operation: The device collects the "task history for the past 6 months" and periodically sends it to the server.

[0862] Step 8:

[0863] The server analyzes the historical data and generates the next necessary prompt.

[0864] Input: Historical data

[0865] Processing: The server uses analysis software to recognize patterns and generate prompts.

[0866] Output: Generated prompt

[0867] Specific action: The server generates a prompt saying "We recommend you create a report conclusion next" and sends it to the terminal.

[0868] Step 9:

[0869] The terminal displays the generated prompts to the user to assist with the task.

[0870] Input: Generated prompt

[0871] Processing: The terminal displays a prompt to prompt the user for the next action.

[0872] Output: Prompt displayed to the user

[0873] Specific action: The terminal displays a prompt to the user saying, "Please begin creating your conclusion as the next step."

[0874] (Application Example 1)

[0875] 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".

[0876] In manufacturing environments and work sites, there is a need for systems that enable workers to efficiently handle frequently occurring tasks and maintenance work, while also allowing them to grasp progress in real time and accurately perform the next necessary tasks. However, existing systems have been insufficient in terms of tracking workers' progress in real time and visually indicating the next actions to be taken, limiting the improvement of work efficiency.

[0877] 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.

[0878] In this invention, the server includes an information processing means that acquires input information for a task, calculates backward from the task's deadline, and generates suggestions for actions required for the task; a user interface means that notifies the user of the action suggestions generated by the information processing means; a progress management means that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated; and a visualization means that visualizes the action suggestions and work progress information and displays them in real time on a visual device used by the user. This enables workers to efficiently carry out their work while checking tasks and progress on-site in real time.

[0879] "Task input information" refers to the information necessary for a user to complete a specific task or job within the system.

[0880] "Working backward from the deadline" is the process of calculating the appropriate start time for action by working backward from the completion deadline of a set task.

[0881] "Action suggestions" refer to the system proposing specific actions that users can take to efficiently complete tasks by the deadline.

[0882] An "information processing device" is a device that performs analysis and calculations based on the input information of a task and generates action suggestions.

[0883] A "user interface means" is an interface device used to convey information and suggestions generated by a system to the user.

[0884] A "progress management system" is a device that monitors the progress of a user's work, identifies problem areas, and makes suggestions for improvement.

[0885] "Visualization means" are tools or devices used to visually display action suggestions or work progress information.

[0886] "Visual devices" refer to equipment used by users to visually receive information from a system.

[0887] A system for carrying out this invention includes a cloud server, a visual device used by the user (e.g., smart glasses), and a communication network for coordinating the server and the visual device.

[0888] The server receives task information entered by the user through their visual device. The server then applies a reverse-engineer algorithm based on the task's deadline to calculate the action start date and time. This calculation uses the Python programming language. Based on the calculated action start date and time, action suggestions are generated and notified to the user's visual device in real time. The visual device uses software such as the Oculus SDK to visually display the information to the user.

[0889] The terminal monitors the user's progress and sends progress data to the server via a progress management system. The server analyzes this data, identifies incomplete or automatically regenerative parts, and makes suggestions. These suggestions are also communicated to the user via a visual device, making it easier for the user to understand the next steps.

[0890] Furthermore, the server analyzes the user's past history data and predicts behavioral patterns to generate prompts indicating the next necessary action. These prompts are generated by an AI model and provided to the user via the terminal. An example of a prompt message might be: "Please decide what to do as your next task. Based on your history, we will generate the most appropriate action suggestion."

[0891] As a concrete example, this system can be used by workers in a manufacturing plant to efficiently carry out their daily tasks by checking the previous day's work list and priorities on a visual device. Furthermore, for tasks that are behind schedule, a message such as "Assembly of part X is behind schedule. The next task to be performed is Y." will be displayed on the visual device, assisting workers in taking immediate action.

[0892] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0893] Step 1:

[0894] The server receives task input information transmitted from the user through a visual device. The input data includes task details, deadlines, etc., and the server records this information in a database and then starts processing.

[0895] Step 2:

[0896] The server executes a reverse calculation algorithm based on the deadline information of the received task. This algorithm calculates the appropriate start time for action from the completion deadline and generates this as an action suggestion using Python. The generated suggestion is temporarily stored in memory.

[0897] Step 3:

[0898] Action suggestions generated by the server are transmitted to the terminal via the network through the user interface. The terminal displays the received data in real time through the API of its visual device, indicating the user the next action to take.

[0899] Step 4:

[0900] The terminal continuously monitors the user's work progress and sends progress data to the server. It also transmits data collected using sensors and input devices regarding the results and progress of the tasks performed by the user.

[0901] Step 5:

[0902] The server analyzes the received progress data and identifies incomplete sections. Furthermore, it generates a proposal that includes sections that can be automatically generated by the progress management system and sends it to the terminal. The server then uses data analysis tools to efficiently generate the proposal.

[0903] Step 6:

[0904] The server predicts behavioral patterns based on the user's historical data and generates prompts indicating the next necessary action using an AI model. The generated prompt messages are then sent to the terminal and displayed to the user.

[0905] Step 7:

[0906] Users review instructions and suggestions displayed on the visual device and perform tasks. By accepting or adjusting suggestions on the visual device, they can improve the efficiency and accuracy of their work.

[0907] 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.

[0908] This invention combines an emotion engine with a task management support system to improve task efficiency while considering the user's emotional state. The system consists of a server, a terminal, an emotion engine, and a user, all of which work in coordination with each other.

[0909] First, the user inputs task information through their device. This information, including task details and deadlines, is sent from the device to the server. The server writes the received information and applies a reverse calculation algorithm to determine the start date and time for each task. In this process, the server also considers the user's sentiment information provided by the sentiment engine and adjusts the suggestions accordingly.

[0910] The emotion engine utilizes facial recognition technology and voice analysis to analyze the user's facial expressions and voice tone in real time on the device. Based on these results, it sends data about the user's emotional state to the server. The server uses this data to modify the task suggestions, thereby encouraging the user to complete tasks in a positive way.

[0911] For example, if the server determines that a user is experiencing stress, it will adjust task priorities and suggest starting with less demanding tasks. Conversely, if the user shows high motivation, it can even suggest more challenging tasks earlier. In this way, the system alleviates the user's psychological burden and enables efficient task management.

[0912] Regarding progress management, the server analyzes progress data received from the terminal to identify incomplete sections and sections that can be automatically generated, and sends suggestions to the user. The emotion engine then provides further support, such as encouraging the user to actively utilize the automatic generation function if they are feeling fatigued.

[0913] This invention dynamically adjusts task management according to the user's emotional state, ensuring that work progresses in the most optimal way for each individual situation. This system allows users to improve productivity and reduce stress.

[0914] The following describes the processing flow.

[0915] Step 1:

[0916] The user enters detailed information and deadlines for tasks they want to manage via their device. The device then sends this information to the server.

[0917] Step 2:

[0918] The server processes the received task information and calculates the task's start date and time by working backward from the deadline. It calculates the necessary processes based on the deadline entered by the user.

[0919] Step 3:

[0920] The device is equipped with an emotion engine that collects real-time emotional data from the user. This emotional data is primarily obtained through facial recognition technology and voice analysis.

[0921] Step 4:

[0922] The device uses an emotion engine to acquire emotional data, which is then sent to a server. The server analyzes the user's emotional state in real time.

[0923] Step 5:

[0924] The server takes into account emotional data provided by the emotion engine when generating task suggestions through reverse engineering. If the user is experiencing stress, it prioritizes lighter tasks.

[0925] Step 6:

[0926] The server sends a pre-configured action suggestion to the device. The device notifies the user and presents suggestions tailored to their emotional state.

[0927] Step 7:

[0928] The user reviews the suggestion and starts the task if necessary. The user confirms that their emotional state is appropriate.

[0929] Step 8:

[0930] Data regarding document creation and progress is sent from the terminal to the server. The server analyzes this data to identify incomplete sections and parts that can be automatically generated.

[0931] Step 9:

[0932] The server suggests automatically generated sections to the user as solutions to progress issues, and the emotion engine provides additional support as needed.

[0933] Step 10:

[0934] The server and terminal continuously monitor the user's emotional state and use this information to suggest tasks and manage progress. They continue to provide suggestions that take into account the user's psychological burden and motivation.

[0935] In this way, the entire system can adapt to the user's emotions and provide optimal task management tailored to individual circumstances.

[0936] (Example 2)

[0937] 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".

[0938] In modern society, efficiently managing a wide variety of tasks is crucial. However, dynamic task management that takes into account the user's emotional state is not yet fully realized, and in certain situations, it can increase the user's burden. Therefore, there is a need for methods that reduce stress and achieve efficient task management while considering the user's emotional state.

[0939] 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.

[0940] In this invention, the server includes an information processing means that acquires input information for a task, calculates backward from the task's deadline, and generates suggestions for actions required for the task; a progress management means that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated; and an emotion consideration means that analyzes the user's emotional state and adjusts action suggestions based on emotional data. This enables dynamic adjustment of task priorities according to the user's emotional state.

[0941] An "information processing device" is a device that has the function of acquiring input information for a task, working backward from the deadline, and generating suggestions for the actions necessary for that task.

[0942] "Dialogue device means" refers to an interface device for notifying the user of action suggestions generated by the information processing means.

[0943] A "progress management tool" is a device that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated.

[0944] An "emotion-considering device" is a device that analyzes the user's emotional state and adjusts behavioral suggestions based on that data.

[0945] An "algorithmic means" is a computational procedure used to dynamically change the priority of tasks based on emotional information obtained from a data processing device.

[0946] This invention provides a system that streamlines task management while taking into account the user's emotional state. Its main components include a server, a terminal, and an emotion engine. This allows task suggestions to adapt to the user's emotions, resulting in smooth task management.

[0947] The server is the core of the system. It processes task information received from terminals and functions as an information processing tool. Specifically, it calculates the task start date and time by working backward from the task deadline. The server utilizes database software to store and manage the received task information, which is later used to generate action suggestions.

[0948] The terminal is the primary interface for users to interact with the system. On the terminal, as the user inputs task information, the emotion engine operates in real time. The emotion engine analyzes facial expressions and voice tone using facial recognition and voice analysis technologies, and sends emotion data to the server. This process utilizes image processing algorithms and voice analysis software.

[0949] The server, upon receiving user emotional data, uses this data as an emotional consideration to inform task suggestions. Based on the emotional state, task priorities are adjusted, and considerations are made such as starting with less demanding tasks. For example, if a user is experiencing stress, suggestions that minimize the workload will be made. On the other hand, if a user is highly motivated, it may be possible to suggest more difficult tasks earlier in the process.

[0950] The generative AI model assists in analyzing data and generating action suggestions on the server. An example of a prompt to input into this model is, "If the user is feeling stressed, suggest prioritizing less demanding tasks." In this process, the AI ​​model uses sentiment data and task information to generate more personalized suggestions.

[0951] In this way, the present invention provides a new form of task management that incorporates the user's emotional state, enabling efficient and less stressful work performance.

[0952] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0953] Step 1:

[0954] The user enters task information using a terminal. Specifically, they enter the task name, due date, and detailed information through the interface on the terminal. This input data is sent to the server via an HTTP request.

[0955] Step 2:

[0956] The server saves task information received from the terminal to the database. Input data includes the task name, due date, etc. The server inserts this data into the database using SQL queries. As output, new task data is saved in the database.

[0957] Step 3:

[0958] The device activates an emotion engine to analyze the user's real-time facial expressions and voice tone. It uses image data from the built-in camera and audio data from the microphone as input. The emotion engine analyzes this data using image processing algorithms and audio analysis software to evaluate the user's emotional state. The output is user emotional state data.

[0959] Step 4:

[0960] The terminal sends the analyzed emotional state data to the server. The server receives this data and prepares to perform task management that reflects the emotional data. The input is the emotional state data, and the output includes the state in which the data is ready to be used as emotional data within the server.

[0961] Step 5:

[0962] The server generates task action suggestions while considering emotional data. It uses task information stored in a database and emotional data received from the terminal as input data. The server calculates suggestions using a generative AI model and adjusts task priorities according to the emotional state. Specifically, it provides suggestions to reduce the burden on users experiencing stress. The output is the adjusted action suggestions.

[0963] Step 6:

[0964] The server sends the generated action suggestions to the terminal, and the terminal notifies the user. The input is the adjusted action suggestions sent from the server. The terminal displays the suggestions to the user through pop-up notifications or an interface. The output is the presentation of the action suggestions in a visible form for the user.

[0965] (Application Example 2)

[0966] 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".

[0967] Modern consumers face a variety of tasks on a daily basis, and are required to manage them efficiently. However, conventional task management systems have difficulty flexibly responding to consumers' emotional states and various situations within the home, resulting in problems in efficiently carrying out tasks while reducing stress. This invention aims to provide a more comfortable and efficient task management system by dynamically adjusting task priorities based on the consumer's emotional state.

[0968] 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.

[0969] In this invention, the server includes information processing means that acquire task input information, calculate backward from the deadline, and generate suggestions for actions necessary for the task; emotion analysis means that analyze the user's emotional state and adjust task priorities based on that state; and progress management means that analyze the user's progress, identify incomplete parts, and suggest parts that can be automatically generated. This enables optimal task management in accordance with the user's emotional state.

[0970] "Information processing means" refers to a device or program that has the function of acquiring input information for a task, calculating backward from the deadline, and generating suggestions for actions necessary for the task.

[0971] A "consumer interface means" is a device or program for notifying consumers of action suggestions generated by an information processing means.

[0972] An "emotional analysis tool" is a device or program that analyzes the emotional state of a person and adjusts the priority of tasks based on that state.

[0973] A "progress management tool" is a device or program that analyzes the progress of a user, identifies incomplete parts, and suggests parts that can be automatically generated.

[0974] A "predictive input means" is a device or program that analyzes a consumer's historical data, predicts the next necessary action, and automatically generates text.

[0975] The system for implementing this invention consists of multiple components: a server, a terminal, an emotion analysis engine, and a user. The server is responsible for task management and suggestion, and provides an optimal task management strategy based on the user's emotional state obtained by the emotion analysis engine. The terminal functions as an interface for the user to input task information and provide feedback on suggested tasks.

[0976] Specifically, the server first obtains task input information from the terminal and uses information processing tools to calculate backward from the deadline. This process utilizes algorithms written in programming languages ​​such as Python. Next, the emotion analysis engine uses software tools like OpenCV and TensorFlow to analyze changes in voice and facial expressions, understanding the user's emotional state in real time. Based on this, the emotion analysis tool optimizes task priorities according to the user's current state.

[0977] The progress management system analyzes incomplete tasks based on user progress data and suggests automatically generated parts as needed. The predictive input system predicts the next necessary actions based on past historical data and automatically generates suggestions to help users complete tasks more efficiently.

[0978] For example, when a user is engaged in the task of gardening, if the emotion analysis engine determines that the user is experiencing stress, the system will suggest relaxation-related tasks in parallel to alleviate the burden. Furthermore, if the user shows high motivation, suggestions will be made to encourage them to start a new project.

[0979] An example of a prompt using a generative AI model might be: "Please create a voice analysis model that determines whether the user is experiencing stress. It needs to analyze changes in the speed and tone of the user's voice and estimate their emotional state based on that."

[0980] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0981] Step 1:

[0982] The server receives task information entered by the user from the terminal. The input information includes task details and deadlines, and the server stores this information in a database. The server organizes the basic task information and prepares to apply a reverse calculation algorithm.

[0983] Step 2:

[0984] The server uses a reverse-engineer algorithm to calculate an action plan leading up to the task deadline. It uses the task deadline and the current date and time as input to calculate the start date and time for each task. The calculated schedule is then compiled into action suggestions aimed at improving the efficiency of task progress.

[0985] Step 3:

[0986] The terminal notifies the user of action suggestions from the server. These notifications are displayed on the user's smartphone or computer. The user interface receives the action suggestions and allows the user to set reminders, add them to their calendar, and perform other actions to carry them out.

[0987] Step 4:

[0988] The emotion analysis engine analyzes the user's voice and facial expression data acquired through the device. This real-time collected emotion data is used to determine the user's emotional state. The determined emotional state is then used to prioritize tasks.

[0989] Step 5:

[0990] The server receives the emotion analysis results and adjusts task priorities based on this information. If stress is detected, it prioritizes less burdensome tasks; if high motivation is detected, it suggests more difficult tasks. This provides task suggestions that are optimal for the user.

[0991] Step 6:

[0992] The server analyzes the user's progress and identifies incomplete tasks. User progress data is used as input, including, for example, task completion rates and delays. Based on this information, the server generates automatically reproducible parts and additional suggestions, which are then presented to the user.

[0993] Step 7:

[0994] The predictive input method analyzes the user's historical data and predicts the next necessary action. Based on past behavioral history, a generative AI model creates prompts and provides suggestions to support the user's next steps. This enables the user to complete tasks efficiently.

[0995] 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.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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.

[1003] 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."

[1004] 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.

[1005] 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.

[1006] 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] 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.

[1015] 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.

[1016] The following is further disclosed regarding the embodiments described above.

[1017] (Claim 1)

[1018] An information processing means that acquires input information for a task, calculates backward from the deadline for the task, and generates a proposal for the actions required for the task.

[1019] A user interface means that notifies the user of the action suggestion generated by the information processing means,

[1020] A progress management system that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated,

[1021] A predictive input method that analyzes user history data, predicts the next necessary action, and automatically generates a prompt,

[1022] A system that includes this.

[1023] (Claim 2)

[1024] The progress management means determines the portion of meeting materials and presentation materials that can be automatically generated, according to claim 1.

[1025] (Claim 3)

[1026] The system according to claim 1, further comprising means for displaying a prompt generated by the predictive input means to the user.

[1027] "Example 1"

[1028] (Claim 1)

[1029] An information processing means that acquires input information for a task, calculates backward from the deadline for the task, and generates a proposal for the actions required for the task.

[1030] A user interface means that notifies the user of the action suggestion generated by the information processing means,

[1031] A progress tracking means that monitors the user's progress and identifies the parts of document creation that can be automatically generated based on the progress information,

[1032] A predictive support system that analyzes user history data, predicts the next necessary action, and automatically generates prompts based on that prediction,

[1033] A system that includes this.

[1034] (Claim 2)

[1035] The progress tracking means identifies and provides to the user portions of documents and visual materials that can be automatically generated.

[1036] (Claim 3)

[1037] The system according to claim 1, further comprising means for displaying prompts generated by the predictive support means to the user.

[1038] "Application Example 1"

[1039] (Claim 1)

[1040] An information processing means that acquires input information for a task, calculates backward from the deadline for the task, and generates a proposal for the actions required for the task.

[1041] A user interface means that notifies the user of the action suggestion generated by the information processing means,

[1042] A progress management system that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated,

[1043] A predictive input method that analyzes user history data, predicts the next necessary action, and automatically generates a prompt,

[1044] A visualization method that visualizes action suggestions and work progress information and displays them in real time on a visual device used by the user,

[1045] A system that includes this.

[1046] (Claim 2)

[1047] The system according to claim 1, wherein the progress management means not only determines which parts of meeting materials and presentation materials can be automatically generated, but also analyzes progress data at the work site and proposes the next work procedure to be performed.

[1048] (Claim 3)

[1049] The system according to claim 1, further comprising means for displaying prompts generated by the predictive input means to the user, and having a function for instructing the user's visual device to perform the next necessary tasks or maintenance information.

[1050] "Example 2 of combining an emotion engine"

[1051] (Claim 1)

[1052] An information processing means that acquires input information for a task, calculates backward from the deadline for the task, and generates a proposal for the actions required for the task.

[1053] A dialogue device means that notifies the user of the action suggestion generated by the information processing means,

[1054] A progress management system that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated,

[1055] An emotional consideration tool that analyzes the user's emotional state and adjusts behavioral suggestions based on emotional data,

[1056] An algorithm that dynamically changes task priorities based on emotional information acquired from a data processing device,

[1057] A system that includes this.

[1058] (Claim 2)

[1059] The progress management means determines the portion of document data and information dissemination materials that can be automatically generated, according to claim 1.

[1060] (Claim 3)

[1061] The system according to claim 1, further comprising means for displaying action suggestions adjusted by the emotion-taking means to the user.

[1062] "Application example 2 when combining with an emotional engine"

[1063] (Claim 1)

[1064] An information processing means that acquires input information for a task, calculates backward from the deadline for the task, and generates a proposal for the actions required for the task.

[1065] A consumer interface means that notifies consumers of the action suggestions generated by the aforementioned information processing means,

[1066] An emotion analysis tool that analyzes the emotional state of consumers and adjusts task priorities based on that state,

[1067] A progress management system that analyzes the progress of users, identifies unfinished parts, and proposes parts that can be automatically generated,

[1068] A predictive input method that analyzes consumer history data, predicts the next necessary action, and automatically generates generated text,

[1069] A system that includes this.

[1070] (Claim 2)

[1071] The system according to claim 1, wherein the progress management means determines not only meeting materials and presentation materials, but also parts of household tasks that can be automatically generated.

[1072] (Claim 3)

[1073] The system according to claim 1, further comprising means for displaying task suggestions generated by the emotion analysis means to the consumer. [Explanation of symbols]

[1074] 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. An information processing means that acquires input information for a task, calculates backward from the deadline for the task, and generates a proposal for the actions required for the task. A user interface means that notifies the user of the action suggestion generated by the information processing means, A progress management system that analyzes the user's progress, identifies incomplete parts, and suggests parts that can be automatically generated, A predictive input method that analyzes user history data, predicts the next necessary action, and automatically generates a prompt, A visualization method that visualizes action suggestions and work progress information and displays them in real time on a visual device used by the user, A system that includes this.

2. The system according to claim 1, wherein the progress management means not only determines which parts of meeting materials and presentation materials can be automatically generated, but also analyzes progress data at the work site and proposes the next work procedure to be performed.

3. The system according to claim 1, further comprising means for displaying prompts generated by the predictive input means to the user, and having a function for instructing the user's visual device to perform the next necessary tasks or maintenance information.

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