system

Abstract

We provide the system. [Solution] A means for the user to input the work details and completion deadline, The server analyzes the aforementioned business content and breaks it down into specific tasks, The server provides a means for assigning a completion deadline to each of the aforementioned tasks, The server provides means for generating the list of specific tasks, A means of sending a task list generated by the server to the user terminal, The terminal displays the task list and the user manages the progress of the tasks, 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, 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 business operations, it is important to clarify and manage business contents and associated tasks. However, in conventional methods, the work of decomposing business contents into specific tasks and setting deadlines for each task is often performed manually, which has problems of requiring a great deal of labor and time. In addition, due to deficiencies in task management and insufficient visualization of progress, the efficiency of business operations may decrease. The purpose of this invention is to solve these problems and improve the efficiency of business operations and the accuracy of task management.

Means for Solving the Problems

[0005] The present invention is a system comprising: means for the user to input the content of the work and its completion deadline; means for a server to analyze the content of the work and break it down into specific tasks; means for the server to assign a completion deadline to each task; means for the server to generate a list of the specific tasks; means for the server to send the generated task list to the user terminal; and means for the terminal to display the task list and for the user to manage the progress of the tasks. This system enables the automatic analysis of the content of the work, its breakdown into specific tasks, and the setting of deadlines for each task. Furthermore, since task management can be performed through the generated task list, it is possible to improve the efficiency of work and the accuracy of management.

[0006] "Business content" refers to the business goals and action plans that the user wishes to achieve.

[0007] "Deadline" refers to the date or period during which a specific task or work must be completed.

[0008] A "user" refers to an individual or organization that uses this system to input work details and deadlines and manage task lists.

[0009] A "terminal" refers to an electronic device used by a user to input work details and deadlines, or to check and manage task lists. Examples include personal computers and smartphones.

[0010] A "server" refers to a central computer system that receives input work details and deadlines, and then analyzes and processes them.

[0011] "Analysis" refers to the process by which the server breaks down the input business content using natural language processing or other methods to extract specific tasks.

[0012] A "task" refers to a specific action or unit of work necessary to accomplish a particular task.

[0013] A "task list" refers to a list that compiles the specific tasks that have been analyzed and the deadlines set for each task in a list format.

[0014] "Natural language processing" refers to artificial intelligence technology that enables a server to understand and analyze user input as human language.

[0015] "Progress management" refers to the activities that allow users to record, check, and update the completion status of each task on their task list. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] A system for implementing the present invention includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a list of specific tasks, means for sending the task list to the user terminal, and means for the terminal to display the task list and for the user to manage the progress of the tasks.

[0038] System Overview

[0039] 1. User input:

[0040] Users enter the task details and completion deadline into an input form on their device. This can be done using text boxes or calendar widgets.

[0041] For example, a user might input the task description "Develop a new product development plan" and the completion deadline "2024-03-31".

[0042] 2. Sending input data:

[0043] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format or similar.

[0044] 3. Analysis of work content:

[0045] The server uses natural language processing (NLP) to analyze the business content based on the received data.

[0046] The analysis results in breaking down the work content into specific tasks. For example, the work content of "planning the development of a new product" can be broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0047] 4. Assigning deadlines:

[0048] The server assigns appropriate deadlines to each task based on the overall completion deadline. This is done by taking into account the time required for each task and its dependencies.

[0049] For example, the "Market Needs Survey" is assigned a deadline of 2024-01-31.

[0050] 5. Generating a task list:

[0051] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This list is also in JSON format.

[0052] 6. Sending and displaying the task list:

[0053] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user.

[0054] The display methods offered include calendar format and list format.

[0055] 7. Task management:

[0056] Users view a task list and record and update the progress of each task.

[0057] When a task is completed, the user records it on their device and manages the progress in real time.

[0058] Explanation of specific examples

[0059] Example: New product development plan

[0060] 1. User input:

[0061] Job description: "Develop development plans for new products"

[0062] Completion deadline: "2024-03-31"

[0063] 2. Sending input data:

[0064] Send the above information to the server in JSON format.

[0065] 3. Analysis of work content:

[0066] Using NLP, the process of "planning the development of a new product" was broken down into "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0067] 4. Assigning deadlines:

[0068] Market needs survey: 2024-01-31

[0069] Prototype design: 2024-02-15

[0070] Building a supply chain: 2024-02-28

[0071] Sales strategy planning: 2024-03-15

[0072] 5. Generating a task list:

[0073] Generate a task list in JSON format.

[0074] 6. Sending and displaying the task list:

[0075] The task list is sent to the device and displayed in a calendar format.

[0076] 7. Task management:

[0077] Users can record and manage the progress of each task in real time.

[0078] This system allows users to easily break down their work into specific tasks and manage them efficiently.

[0079] The following describes the processing flow.

[0080] Step 1:

[0081] The user enters the task details and completion deadline into an input form on the terminal. The input form has fields for the task details (e.g., "Develop a development plan for a new product") and the deadline (e.g., "2024-03-31"), respectively.

[0082] Step 2:

[0083] The terminal sends the entered work details and deadline to the server in a data format such as JSON. Pressing the send button sends this data to the server.

[0084] Step 3:

[0085] The server prepares the data received from the terminal (task details and deadlines) for analysis. The data is first converted to an appropriate format and then passed to the natural language processing (NLP) module.

[0086] Step 4:

[0087] The server analyzes the business content using natural language processing and breaks it down into specific tasks. For example, the business content "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0088] Step 5:

[0089] The server assigns a completion deadline to each task it breaks down. Working backward from the overall completion deadline (e.g., "2024-03-31"), it sets appropriate deadlines for each task, taking into account their duration and dependencies. For example, the deadline for "Market Needs Survey" is set to "2024-01-31".

[0090] Step 6:

[0091] The server generates a task list by listing specific tasks and their respective completion deadlines. This task list is generated in a structured data format such as JSON.

[0092] Step 7:

[0093] The server generates a task list and sends it to the terminal. The task list is formatted in a calendar or list format for easy viewing by the user.

[0094] Step 8:

[0095] The device displays a list of received tasks to the user. The user can view the task list and check the specific details and deadlines of each task. Display options include calendar view and list view.

[0096] Step 9:

[0097] Users manage the progress of each task. When a task is completed, the user marks it as complete via their device. This allows the task management progress to be updated in real time.

[0098] Through this process, a system is created that allows users to efficiently manage their work and proceed according to plan.

[0099] (Example 1)

[0100] 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."

[0101] Efficiently managing work processes is crucial for many companies and individuals, but traditional systems have made it difficult to break down work processes into specific tasks and manage their progress. In particular, there were no systems that automated the entire process from inputting and analyzing work processes to assigning tasks and managing progress. As a result, not only did work efficiency decline, but tasks were also more likely to be missed or deadlines were delayed. There is a need for a system that solves this problem and allows users to manage their work processes quickly and accurately.

[0102] 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.

[0103] In this invention, the server includes means for the user to input the work content and completion deadline; means for the server to analyze the work content and break it down into specific tasks; means for the server to assign a completion deadline to each task; means for the server to generate a list of the specific tasks; means for the server to send the generated task list to the user terminal; means for the terminal to display the task list and for the user to manage the progress of the tasks; means for the terminal to send the user's input to the server in JSON format; means for the server to analyze the received data using natural language processing; means for the server to compile the broken-down specific tasks and their deadlines into a list in JSON format; and means for the terminal to display the task list in calendar format or list format. This enables the user to quickly and accurately manage the work content and to check and update the progress of tasks in real time.

[0104] "User input" refers to the process in which users enter the details of their tasks and the deadline into an input form.

[0105] "Sending input data" refers to the process by which the terminal sends the task details and completion deadline entered by the user to the server.

[0106] "Analysis of business content" refers to the process of breaking down the business content received by the server into specific tasks using natural language processing.

[0107] "Deadline assignment" refers to the process by which the server assigns appropriate deadlines to each task based on the overall completion deadline.

[0108] "Task list generation" refers to the process by which the server generates a list of future tasks based on the analysis and deadline assignment results.

[0109] "Sending and displaying the task list" refers to the process in which the server generates a task list, sends it to the user's terminal, and the terminal displays that list to the user.

[0110] "Task management" refers to the process by which users record and update the progress of each task while referring to the displayed task list.

[0111] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and refers to a data format that is easy for humans to read and write, and easy for machines to analyze and generate.

[0112] "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0113] "Calendar format" refers to a format that displays tasks by date, similar to a calendar.

[0114] "List format" refers to a format in which tasks are displayed in a bulleted list-like manner.

[0115] The system implementing this invention is configured such that the user inputs the work content and completion deadline, the server analyzes the work content and breaks it down into specific tasks, generates a task list and sends it to the terminal, and the terminal displays the task list so that the user can manage the progress of the tasks. A specific embodiment of this system will be described in detail below.

[0116] Specifically, users first use their own devices (such as PCs, smartphones, or tablets) to enter the task details and completion deadline into an input form. The input form includes a text box and a calendar widget, which users use to describe the task in text format and select the completion deadline from the calendar.

[0117] For example, suppose a user enters the task description as "Develop a new product development plan" and the completion deadline as "2024-03-31".

[0118] Next, when the user clicks the "Submit" button, the input content is sent to the server in JSON format. This submission process uses asynchronous communication (Ajax), which improves the user experience.

[0119] On the server side, the received JSON data is analyzed using natural language processing (NLP). Specific NLP modules used include spaCy and NLTK. The server tokenizes the business content text, extracts the meaning of each word and phrase, and breaks it down into multiple specific tasks. For example, analyzing the business content "Develop a new product development plan" breaks it down into specific tasks such as "Market needs research," "Prototype design," "Supply chain construction," and "Sales strategy planning."

[0120] The server then assigns appropriate deadlines to each of the broken-down tasks based on the overall completion deadline. This process uses an algorithm that takes into account the time required for each task and its dependencies. For example, the "Market Needs Survey" task might be assigned a deadline of "2024-01-31".

[0121] Once the task list is generated, the server sends it back to the terminal in JSON format. The terminal then parses this received data and displays it on the user interface. Users can choose between a calendar format or a list format for display; for example, the calendar format can be displayed using the Google® Calendar API.

[0122] Users can view a displayed task list and record and manage the progress of each task in real time. Specifically, they can update the progress status for each task and check off completed tasks. This information is sent from the terminal to the server and stored in a database, so the progress can be checked from other devices as well.

[0123] As described above, this invention enables users to automatically break down their work into specific tasks and manage them efficiently and accurately. Implementing this system prevents task omissions and deadline delays, thereby improving work efficiency.

[0124] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0125] Step 1:

[0126] User input

[0127] The user enters the task details and completion deadline into their device. This input is done using an input form (text box and calendar widget) displayed on the device screen. For example, the user might enter "Develop a new product development plan" as the task details and select "2024-03-31" as the completion deadline. The entered data is temporarily stored in the device's memory.

[0128] Input: User-entered task details and completion deadline

[0129] Output: The task details and completion deadline are temporarily saved to the terminal's memory.

[0130] Step 2:

[0131] Sending input data

[0132] When the user clicks the "Submit" button, the device converts the entered data into JSON format and sends it to the server using an Ajax request. For example, the data is sent in the following JSON format:

[0133] json

[0134] {

[0135] "Job Description": "Develop development plans for new products"

[0136] "Completion Deadline": "2024-03-31"

[0137] }

[0138] The terminal displays the status of whether the transmission was successful or unsuccessful.

[0139] Input: The user clicked the "Submit" button.

[0140] Output: The input data is sent to the server in JSON format.

[0141] Step 3:

[0142] Analysis of business operations

[0143] The server executes a Python script based on the received JSON data to perform natural language processing (NLP). Specifically, it uses an NLP module (e.g., spaCy or NLTK) to analyze the business content and extract the meaning of each word and phrase. This breaks down the business content into multiple specific tasks. For example, "Develop a new product development plan" is broken down into "Market needs research," "Prototype design," "Supply chain construction," and "Sales strategy planning."

[0144] Input: JSON data received by the server

[0145] Output: Decomposed specific tasks (internal data list)

[0146] Step 4:

[0147] Assignment of deadlines

[0148] The server assigns appropriate deadlines to each of the broken-down tasks. It uses a Python algorithm that considers the time required and dependencies of each task. For example, the task "Market Needs Survey" is assigned the deadline "2024-01-31".

[0149] Input: Decomposed specific tasks, overall completion deadline

[0150] Output: Specific tasks with assigned deadlines (internal data list)

[0151] Step 5:

[0152] Task list generation

[0153] The server generates a task list in JSON format based on the specific tasks that have been assigned deadlines. For example, the following JSON data is generated:

[0154] json

[0155] {

[0156] "Task": [

[0157] {"Name": "Market Needs Survey", "Deadline": "2024-01-31"}

[0158] {"Name": "Prototype Design", "Deadline": "2024-02-15"},

[0159] {"Name": "Supply Chain Construction", "Deadline": "2024-02-28"},

[0160] {"Name": "Development of Sales Strategy", "Deadline": "2024-03-15"}

[0161] ]

[0162] }

[0163] Input: Specific tasks with assigned deadlines

[0164] Output: Task list in JSON format

[0165] Step 6:

[0166] Sending and displaying task lists

[0167] The server sends the generated task list to the terminal. The terminal parses the received JSON data and displays it in the user interface. Users can choose between a calendar format or a list format for display. For example, the Google Calendar API can be used to display the list in calendar format.

[0168] Input: Task list in JSON format

[0169] Output: Task list displayed on the user's device (calendar or list format)

[0170] Step 7:

[0171] Task management

[0172] Users record and manage the progress of individual tasks in real time while referring to the displayed task list. They update the task progress using the terminal interface and check off completed tasks. This information is then sent back to the server and stored in the database in real time.

[0173] Input: User progress update operation

[0174] Output: Progress information is sent from the terminal to the server and stored in the database.

[0175] The above describes the specific processing flow of this system's program.

[0176] (Application Example 1)

[0177] 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."

[0178] Traditional business management systems had a problem where, when users entered business content that was difficult to break down into specific tasks, the system could not assign appropriate tasks and deadlines to that content. Furthermore, the inability to check task progress in real time meant that users could not properly address tasks that had passed their deadlines. In addition, the difficulty in visually grasping progress contributed to decreased work efficiency.

[0179] 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.

[0180] In this invention, the server includes means for analyzing the business content entered by the user and breaking it down into specific tasks, means for automatically assigning a completion deadline to each task and sending reminders to tasks that have passed their deadline, and means for using a generative AI model for analysis and obtaining highly accurate results using prompt sentences. This makes it possible to efficiently break down business content into specific tasks and assign appropriate deadlines. In addition, through task progress management and visual display, the user can grasp the status of tasks in real time and respond efficiently.

[0181] "Job description" refers to the overall tasks or project outline that the user intends to perform.

[0182] "Completion deadline" refers to the deadline entered by the user for completing the task.

[0183] A "task" refers to a specific unit of work obtained by analyzing the content of a job.

[0184] A "list" refers to a collection of multiple tasks organized into a single list.

[0185] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0186] A "generative AI model" refers to an artificial intelligence model that uses machine learning to generate and analyze text.

[0187] A "prompt" refers to an instruction or question that is input to a generative AI model.

[0188] "Automatic assignment of deadlines" refers to the process by which the system automatically sets an appropriate completion deadline for each task.

[0189] A "reminder" refers to a function that notifies you about tasks that are due soon or have already passed their deadline.

[0190] "Calendar format" refers to a format in which tasks are visually displayed on a calendar.

[0191] "List format" refers to a format in which tasks are displayed in a sequential order.

[0192] A "user terminal" refers to a device, such as a smartphone or computer, that a user uses to access the system.

[0193] A system for implementing this invention includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a task list and sending it to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, means for automatically assigning a deadline to each task and sending a reminder to tasks that have passed their deadline, and means for using a generative AI model for analysis and using prompt statements to obtain highly accurate results.

[0194] Hardware and software to use

[0195] 1. Hardware

[0196] Servers: Cloud servers or on-premises servers are used for analyzing business processes and managing task deadlines.

[0197] User terminal: Use a smartphone or personal computer.

[0198] 2. Software

[0199] Natural Language Processing (NLP) Model: To analyze business content and break it down into specific tasks, a generative AI model (e.g., GPT-4®) is used.

[0200] Framework: For server-side processing, we use web frameworks such as Flask or Django.

[0201] Data format: Data is sent and received in formats such as JSON.

[0202] Processing flow

[0203] User actions:

[0204] Users enter the details of their tasks and their completion deadlines into an input form via a smartphone app or web app, and then press the submit button. A text box and a calendar widget for selecting dates are provided during this process.

[0205] Server processing:

[0206] The entered task details and completion deadlines are sent to the server in JSON format. The server uses an NLP model to analyze the task details and break them down into specific tasks. For example, a task like "develop a new product development plan" is broken down into specific tasks such as "market needs research," "prototype design," "supply chain construction," and "sales strategy planning." For each of the broken-down tasks, the system automatically assigns an appropriate deadline, taking into account completion deadlines and dependencies. It also has a function to send reminders for tasks that are approaching or have already passed their deadlines.

[0207] User terminal processing:

[0208] The server generates a task list and sends it to the user's terminal in JSON format. The terminal then displays the tasks and their progress in calendar or list format. Users can record and manage task progress in real time and mark completed tasks. A reminder function also provides notifications for tasks that are approaching or have passed their deadline.

[0209] Specific example

[0210] Example 1: New product development plan

[0211] 1. User input:

[0212] Job description: "Develop development plans for new products"

[0213] Completion deadline: "2024-03-31"

[0214] 2. Example of a prompt:

[0215] Break down the process of creating a new product development plan into specific tasks.

[0216] 3. Server output:

[0217] Task 1: "Market Needs Survey" (Deadline: 2024-01-31)

[0218] Task 2: "Prototype Design" (Deadline: 2024-02-15)

[0219] Task 3: "Building a Supply Chain" (Deadline: 2024-02-28)

[0220] Task 4: "Develop a sales strategy" (Deadline: 2024-03-15)

[0221] These operations enable users to effectively break down their work into specific tasks and efficiently manage and track their progress.

[0222] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0223] Step 1:

[0224] The user enters the task details and completion deadline. Specifically, they enter the task details and completion deadline into an input form on a smartphone app or web app and press the submit button. The entered task details and completion deadline are sent to the server in JSON format. Input: Task details and completion deadline, Output: JSON format data.

[0225] Step 2:

[0226] The server parses the JSON data it receives. Specifically, the server parses the input data and extracts the task details and completion deadline. Using the parsed data, it sends a prompt message to the generating AI model. Input: "Develop a new product development plan" "2024-03-31" (JSON format), Output: Parsing request (prompt message).

[0227] Step 3:

[0228] The generative AI model analyzes the business content and breaks it down into specific tasks. The generative AI model (e.g., GPT-4) uses prompts to break down the tasks. The analysis results are obtained in JSON format. Input: Prompts; Data processing: Analysis of business content and task decomposition; Output: Specific task list (JSON format).

[0229] Step 4:

[0230] The server assigns completion deadlines to each task based on the generated task list. It automatically sets appropriate deadlines for each task, taking into account the task duration and dependencies. Input: Specific task list (JSON format), Data calculation: Calculation and assignment of completion deadlines, Output: Task list with assigned deadlines.

[0231] Step 5:

[0232] The server generates a task list with assigned deadlines in JSON format and sends it to the user's terminal. The generated task list is formatted as JSON data and sent to the user's terminal. Input: Task list with assigned deadlines; Output: Task list (JSON format).

[0233] Step 6:

[0234] The device receives the task list and displays it in calendar or list format. Users can check the progress of each task while viewing the task list. Input: Task list (JSON format), Data processing: Conversion to calendar or list format, Output: Visual display.

[0235] Step 7:

[0236] Users manage the progress of each task and record completed tasks on their devices. Users mark task completion on the screen, and this information is sent to the server in real time. Input: User-updated progress; Output: Real-time progress status.

[0237] Step 8:

[0238] The server sends reminders for tasks that are approaching or have already passed their deadline. Notifications are sent to the user's device, allowing the user to take action. Input: Task progress and deadline information; Output: Reminder notification.

[0239] This allows for the effective breakdown of work content into specific tasks, and enables efficient management and tracking of their progress.

[0240] 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.

[0241] In a system implementing the present invention, the following means are added: means for the user to input the work content and completion deadline; means for the server to analyze the work content and break it down into specific tasks; means for assigning a completion deadline to each task; means for generating a list of specific tasks; means for sending the task list to the user terminal; means for the terminal to display the task list and for the user to manage the progress of the tasks; and means including an emotion engine that recognizes the user's emotions.

[0242] System Overview

[0243] 1. User input:

[0244] Users enter the task details and completion deadline into an input form on their device. This can be done using text boxes or calendar widgets.

[0245] For example, a user might input the task description "Develop a new product development plan" and the completion deadline "2024-03-31".

[0246] 2. Sending input data:

[0247] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format or similar.

[0248] 3. Analysis of work content:

[0249] The server uses natural language processing (NLP) to analyze the business content based on the received data.

[0250] The analysis results in breaking down the business content into specific tasks. For example, the business content of "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0251] 4. Assigning deadlines:

[0252] The server works backward from the overall completion deadline (e.g., "2024-03-31") and sets appropriate deadlines for each task, taking into account the time required and dependencies. For example, the "Market Needs Survey" task might be assigned a deadline of 2024-01-31.

[0253] 5. Generating a task list:

[0254] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This task list is generated in a structured data format such as JSON.

[0255] 6. Sending and displaying the task list:

[0256] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user. The display method can be either calendar or list format.

[0257] 7. Task management:

[0258] Users view a task list, recording and updating the progress of each task. When a task is completed, the user records this on their device, managing the progress in real time.

[0259] Introducing an emotional engine

[0260] 8. Recognition of emotions:

[0261] The emotion engine analyzes the user's emotions based on the data and progress the user enters. For example, it can determine whether the user is feeling stressed based on their input.

[0262] 9. Use of emotional data:

[0263] The emotion engine analyzes the user's emotional data and provides it to the server. This emotional data plays a particularly important role when the user is feeling stressed or anxious.

[0264] 10. Adjusting the task list display:

[0265] The server adjusts how the task list is displayed based on emotional data. For example, if a user is feeling stressed, it displays high-priority tasks first to reduce their workload.

[0266] 11. Adjusting task priorities:

[0267] Based on emotional data, the server readjusts task priorities. This enables optimal task management tailored to the user's emotional state.

[0268] Explanation of specific examples

[0269] Example: New product development plan

[0270] 1. User input:

[0271] Job description: "Develop development plans for new products"

[0272] Completion deadline: "2024-03-31"

[0273] 2. Sending input data:

[0274] Send the above information to the server in JSON format.

[0275] 3. Analysis of work content:

[0276] Using NLP, the process of "planning the development of a new product" was broken down into "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0277] 4. Assigning deadlines:

[0278] Market needs survey: 2024-01-31

[0279] Prototype design: 2024-02-15

[0280] Building a supply chain: 2024-02-28

[0281] Sales strategy planning: 2024-03-15

[0282] 5. Generating a task list:

[0283] Generate a task list in JSON format.

[0284] 6. Sending and Displaying Task List:

[0285] Send the task list to the terminal and display it in calendar format.

[0286] 7. Task Management:

[0287] The user records and manages the progress of each task in real time.

[0288] 8. Emotion Recognition:

[0289] The emotion engine recognizes the user's emotion from the user's input content and progress. For example, analyze that the user is feeling stressed when there are many urgent deadlines.

[0290] 9. Utilization of Emotion Data:

[0291] Provide the emotion data analyzed by the emotion engine to the server.

[0292] 10. Adjustment of Task List Display:

[0293] When the user is feeling stressed, make adjustments such as displaying high-priority tasks first.

[0294] 11. Adjustment of Task Priority:

[0295] Based on the emotion data, readjust the task priority to reduce the user's stress and enable efficient task progress.

[0296] With this system, the business content is decomposed into specific tasks and efficiently managed, and flexible task management considering the user's emotion is also realized.

[0297] The following explains the processing flow.

[0298] Step 1:

[0299] The user inputs the business content and the completion deadline into the input form of the terminal. For example, the user inputs the business content of "Develop a new product plan" and the completion deadline of "2024-03-31" into the text box and the calendar widget.

[0300] Step 2:

[0301] When the user presses the "Send" button, the terminal sends the input business content and completion deadline to the server as data in JSON format.

[0302] Step 3:

[0303] The server prepares to analyze the data received from the terminal. The data is first converted into an appropriate data format and passed to the natural language processing (NLP) module.

[0304] Step 4:

[0305] The server analyzes the business content using NLP and decomposes it into specific tasks. For example, the business content of "Develop a new product plan" is decomposed into specific tasks such as "Market needs research", "Prototype design", "Supply chain construction", and "Sales strategy formulation".

[0306] Step 5:

[0307] The server assigns a completion deadline to each task after decomposition. It calculates backward from the overall completion deadline (e.g., "2024-03-31") and sets an appropriate deadline considering the required time and dependencies of each task. For example, the deadline for "Market needs research" is set to "2024-01-31".

[0308] Step 6:

[0309] The server lists up the specific tasks and the completion deadlines of each task to generate a task list. This task list is generated as structured data in JSON format.

[0310] Step 7:

[0311] The server sends the generated task list to the terminal. The terminal prepares to display the received task list.

[0312] Step 8:

[0313] The device displays a task list to the user. The user can visually view the task list in calendar or list format.

[0314] Step 9:

[0315] The user checks the task list and records and updates the progress of each task. For example, when a task is completed, the user marks it as "completed" through their device.

[0316] Step 10:

[0317] The device sends emotional data to the emotion engine in real time based on user input and progress. The emotion engine analyzes the user's emotions from the input content (text, operation history, etc.).

[0318] Step 11:

[0319] The server receives emotional data analyzed by the emotion engine. For example, the analysis data might indicate that the user is experiencing stress.

[0320] Step 12:

[0321] The server adjusts how the task list is displayed based on emotional data. For example, if a user is feeling stressed, the server prioritizes displaying only the most important tasks.

[0322] Step 13:

[0323] The server readjusts task priorities based on sentiment data. This readjustment is intended to optimize the workload by taking the user's emotional state into consideration.

[0324] Step 14:

[0325] Users can view their readjusted task list on their device and continue managing tasks based on the new priorities. This reduces emotional burden and allows users to work more efficiently.

[0326] (Example 2)

[0327] 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".

[0328] Current task management systems offer features that break down user-entered work content into specific tasks and assign appropriate completion deadlines, but they do not consider user emotions or stress levels when displaying tasks or adjusting priorities. Therefore, it is difficult to efficiently manage tasks in work environments where users experience high levels of stress. Furthermore, there is a lack of functionality to analyze user-entered work content using natural language processing, which makes it difficult to deal with ambiguity and unclear input.

[0329] 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.

[0330] In this invention, the server includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for the server to assign a completion deadline to each task, means for the server to generate a list of the specific tasks, means for the server to transmit the generated task list to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, and means for the server to analyze the user's emotions and adjust the display and priority of tasks in order to reduce the workload. This enables the breakdown of the user's work content into specific tasks, the assignment of appropriate deadlines to each task, and flexible task management according to the user's emotional state.

[0331] "Job description" refers to the specific tasks or projects that the user intends to accomplish.

[0332] "Completion deadline" refers to the final date or time by which a particular task or work must be completed.

[0333] A "task" refers to a specific, individual work item obtained by breaking down a series of tasks.

[0334] "JSON format" refers to a lightweight data exchange format for structuring and exchanging data.

[0335] A "device" refers to an electronic device that a user can directly operate, and includes personal computers, smartphones, tablets, and other similar devices.

[0336] A "server" refers to a centralized computer system that provides or processes information over a network.

[0337] A "task list" refers to a list that compiles specific tasks and their completion deadlines.

[0338] "Natural language processing" refers to the technological field in which computers understand, analyze, and generate human language.

[0339] An "emotion engine" refers to a program that analyzes and determines a user's emotions based on their input data and behavior.

[0340] "Priority" refers to the criteria used to determine the order in which multiple tasks or work should be performed, based on their importance and urgency.

[0341] The system implementing this invention involves the user inputting the work content and completion deadline, a server analyzing this information, breaking it down into specific tasks, assigning deadlines, and generating and sending a task list. Furthermore, it can analyze the user's emotional state using an emotion engine and adjust the display and priority of tasks accordingly.

[0342] The user enters the task details and completion deadline using a terminal. The user interface uses text boxes and calendar widgets for input. Once input is complete, the terminal packages the data in JSON format and sends it to the server.

[0343] The server receives JSON data sent by the user and analyzes the business content using a natural language processing (NLP) engine (e.g., spaCy or BERT). Through this analysis, the abstract business content is broken down into concrete tasks. The server then works backward from the overall completion deadline, setting deadlines for each task, taking into account the time required and dependencies. Once the tasks and deadlines are determined, the server generates a task list in JSON format and sends it to the user's terminal.

[0344] The device analyzes the received task list and displays it in calendar or list format. The user records and updates the progress of each task based on the displayed task list. Once a task is completed, the user can mark it as completed.

[0345] Furthermore, an emotion engine is implemented to analyze the user's emotions based on user input data and task progress. The emotion engine, for example, determines whether the user is experiencing stress and provides this data to the server. Based on this emotion data, the server adjusts how the task list is displayed and prioritizes tasks according to the user's stress level. For example, if a user is experiencing high stress, high-priority tasks are displayed first to reduce their workload.

[0346] Specific example

[0347] As a concrete example, let's consider a scenario where a new product development plan is created. The user inputs the following:

[0348] Job description: "Develop development plans for new products"

[0349] Completion deadline: "2024-03-31"

[0350] When a user inputs data, the terminal sends it to the server in JSON format. The server uses an NLP engine to analyze the business content and breaks it down into tasks such as "market needs research," "prototype design," "supply chain construction," and "sales strategy planning." A specific deadline is set for each task. For example, "market needs research" might have a deadline of 2024-01-31, and "prototype design" might have a deadline of 2024-02-15.

[0351] The generated task list is sent to the device and displayed in a calendar format. The user manages their progress based on this task list and checks off completed tasks.

[0352] Example of a prompt:

[0353] Please enter the task description, "Develop a new product development plan," and the completion deadline, "2024-03-31." Based on this, the system will break down the necessary tasks and specify the completion deadline for each task.

[0354] This system not only allows for the efficient management of work by breaking down tasks into specific components and setting deadlines, but also enables flexible task management that adapts to the user's emotional state.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user enters the task details and completion deadline. The user enters the task details "Develop a new product development plan" and the completion deadline "2024-03-31" into the input form on the terminal and presses the submit button.

[0358] Input: Task description and completion deadline

[0359] Output: Data on the task details and completion deadline entered by the user.

[0360] Step 2:

[0361] The terminal packages the entered task details and completion deadline into JSON format and sends it to the server. The terminal uses HTTPS as the transmission protocol.

[0362] Input: Data of task details and completion deadlines entered by the user.

[0363] Data processing: Package the work details and completion deadline data into JSON format.

[0364] Output: Send data in JSON format to the server

[0365] Step 3:

[0366] The server parses the received JSON data and extracts the work content and completion deadline. The server then uses a natural language processing (NLP) engine (e.g., spaCy or BERT) to break down the work content into specific tasks.

[0367] Input: Data in JSON format containing task details and completion deadlines.

[0368] Data processing: Using an NLP engine, business processes are analyzed and broken down into specific tasks such as "market needs research," "prototype design," and "supply chain construction."

[0369] Output: Decomposed specific tasks

[0370] Step 4:

[0371] The server calculates the appropriate completion deadline for each specific task by working backward from the overall deadline, taking into account the time required for each task and its dependencies.

[0372] Input: Specific tasks and overall deadline

[0373] Data calculation: Use a Gantt chart scheduling algorithm to assign appropriate deadlines to each task.

[0374] Output: Specific tasks and their respective deadlines.

[0375] Step 5:

[0376] The server lists the specific tasks broken down into individual components and their respective completion deadlines, generating a task list in JSON format.

[0377] Input: Specific tasks and their respective completion deadlines.

[0378] Data processing: Package specific tasks and deadlines into JSON format.

[0379] Output: Task list in JSON format

[0380] Step 6:

[0381] The server sends the generated task list to the user's terminal. The terminal parses the received task list and displays it in calendar or list format.

[0382] Input: Task list in JSON format

[0383] Data processing: Parse task lists in JSON format and convert them to a display format.

[0384] Output: Task list displayed in calendar or list format

[0385] Step 7:

[0386] Users record and update the progress of their task list. Users enter the progress of each task on their device and mark it as completed when it is finished.

[0387] Input: Task list

[0388] Output: Updated task list and progress

[0389] Step 8:

[0390] The emotion engine analyzes the user's emotions based on their input data and task progress. For example, if a user inputs "I feel stressed," the emotion engine will analyze this as a high stress level.

[0391] Input: User input data and task progress

[0392] Data processing: Analyze emotions using an emotion engine.

[0393] Output: Analyzed sentiment data

[0394] Step 9:

[0395] The server adjusts how the task list is displayed and prioritizes tasks based on the emotional data analyzed by the emotion engine. If the user is experiencing high levels of stress, the server will make adjustments such as displaying higher-priority tasks first.

[0396] Input: Analyzed sentiment data and task list

[0397] Data Calculation: Adjust the display method of the task list and task prioritization based on sentiment data.

[0398] Output: Adjusted task list

[0399] Step 10:

[0400] The server readjusts task priorities based on emotional data. By optimizing the order of tasks according to the user's emotional state, efficient task management is achieved.

[0401] Input: Analyzed sentiment data and task list

[0402] Data processing: Reprioritize tasks based on emotional data.

[0403] Output: Re-adjusted priority task list

[0404] (Application Example 2)

[0405] 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".

[0406] Traditional business management systems simply require users to input work details and deadlines, which are then broken down into a series of tasks without considering the user's emotional state. Therefore, when users experience stress or anxiety, the system is unable to appropriately adjust task priorities or display methods, leading to decreased work efficiency and increased mental burden. Furthermore, even in task management using robots within factories, flexible responses based on emotional states are required.

[0407] 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.

[0408] In this invention, the server includes means for the user to input the work content and completion deadline, means for analyzing the work content and breaking it down into specific tasks, means for assigning completion deadlines to each task, means for generating a list of specific tasks, means for transmitting the generated task list to the user terminal, and means for recognizing the user's emotions and adjusting the display method of the task list and the priority of tasks based on emotion data. This enables robots and users in industrial areas to improve the efficiency of work management and perform flexible task management according to their emotional state.

[0409] A "user" is a person who operates the system to input the details of the work and the completion deadline, or an entity that performs that series of actions.

[0410] "Job description" refers to information that describes the specific tasks and objectives that the user is expected to accomplish.

[0411] "Completion deadline" refers to the date and time by which all tasks within a given project must be completed.

[0412] A "server" is a central computer device that analyzes business processes and breaks down and manages tasks.

[0413] A "task" is a specific set of tasks or actions derived from analyzing the content of a job.

[0414] A "task list" is a collection of information organized in a list format, consisting of broken-down tasks and their respective completion deadlines.

[0415] A "user terminal" is a device used by a user to manage their work and check and operate the progress of tasks.

[0416] "Emotion" refers to the subjective psychological state that a user experiences in response to a particular situation or task.

[0417] "Emotional data" refers to information that expresses a user's emotional state in numerical or text format.

[0418] An "emotion engine" is a system module that analyzes user emotions based on their input and actions, and generates the results.

[0419] The system for implementing this invention comprises means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a list of specific tasks, means for sending the generated task list to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, and means for recognizing the user's emotions and adjusting the display method of the task list and the priority of tasks based on emotion data.

[0420] System Overview

[0421] 1. User input:

[0422] Users input their task details and completion deadlines through a chat-like interface or form. For example, a user might input the task "Review the operation program of the factory robots" and the completion deadline "2024-06-30".

[0423] 2. Sending input data:

[0424] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format.

[0425] 3. Analysis of work content:

[0426] The server uses natural language processing (NLP) to analyze the input business content and break it down into specific tasks. This analysis utilizes a pre-trained generative AI model. For example, analyzing the business content "review the operation program of factory robots" breaks it down into specific tasks such as "review existing code," "design new functions," and "build a test environment."

[0427] 4. Assigning deadlines:

[0428] The server calculates backward from the overall completion deadline entered by the user, and sets appropriate deadlines for each task, taking into account the time required and dependencies. For example, "Review existing code" might be assigned a deadline of 2024-05-31.

[0429] 5. Generating a task list:

[0430] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This task list is generated in JSON format.

[0431] 6. Sending and displaying the task list:

[0432] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user. Calendar and list formats are available for display.

[0433] 7. Task management:

[0434] Users view a task list, recording and updating the progress of each task. When a task is completed, the user records this on their device, managing the progress in real time.

[0435] Introducing an emotional engine

[0436] 8. Emotion recognition:

[0437] The emotion engine analyzes the user's emotions based on the data and progress the user inputs. Based on the analysis results, it determines whether the user is feeling stressed, anxious, or otherwise unsettled.

[0438] 9. Use of emotional data:

[0439] The emotion engine analyzes user emotion data and provides it to the server, which then adjusts how the task list is displayed and prioritized. For example, if a user is feeling stressed, high-priority tasks are displayed first to reduce their workload.

[0440] Explanation of specific examples

[0441] Example: Review of the operation program for factory robots.

[0442] 1. User input:

[0443] Job description: "Review the operation programs of factory robots."

[0444] Completion deadline: "2024-06-30"

[0445] 2. Sending input data:

[0446] Send the above information to the server in JSON format.

[0447] 3. Analysis of work content:

[0448] Using NLP, the task of "revising the motion program of factory robots" was broken down into "reviewing existing code," "designing new functions," and "building a test environment."

[0449] 4. Assigning deadlines:

[0450] Review of existing code: 2024-05-31

[0451] Design of new features: 2024-06-15

[0452] Test environment setup: 2024-06-25

[0453] 5. Generating a task list:

[0454] Generate a task list in JSON format.

[0455] 6. Sending and displaying the task list:

[0456] The task list is sent to the device and displayed in a calendar format.

[0457] 7. Task management:

[0458] Users can record and manage the progress of each task in real time.

[0459] 8. Emotion recognition:

[0460] The emotion engine recognizes the user's emotions based on their input and progress. For example, it might analyze that a user is stressed if they have many sudden deadlines.

[0461] 9. Use of emotional data:

[0462] The emotion engine analyzes the emotion data and provides it to the server.

[0463] 10. Adjusting the task list display:

[0464] If a user is experiencing stress, adjustments will be made, such as displaying higher-priority tasks first.

[0465] 11. Adjusting task priorities:

[0466] By readjusting task priorities based on emotional data, we can reduce user stress and enable them to complete tasks more efficiently.

[0467] Example of a prompt:

[0468] Based on the invention, please break down the following tasks into specific tasks and generate a task list. Also, please explain how to adjust task priorities if the user is experiencing stress.

[0469] Job Description: Review the operation programs of factory robots.

[0470] Completion deadline: 2024-06-30

[0471] Emotional data: Stress level 7

[0472] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0473] Step 1:

[0474] The user enters the task details and completion deadline.

[0475] In terms of specific actions, the user enters the task details (e.g., "Review the operation program of the factory robot") and the completion deadline (e.g., "2024-06-30") into an input form on their smartphone or tablet device, and then presses the "Submit" button.

[0476] Input: Task description, completion deadline

[0477] Output: Data in JSON format (task details, completion deadline)

[0478] Step 2:

[0479] The server receives the input data.

[0480] Specifically, the server receives data in JSON format (task details and completion deadline) sent by the user.

[0481] Input: Data in JSON format

[0482] Output: Stored in the server's internal database.

[0483] Step 3:

[0484] The server analyzes the business content using natural language processing (NLP) and breaks it down into specific tasks.

[0485] Specifically, the server uses an NLP library (e.g., spaCy, GPT-3®) to analyze the business content within the JSON data and divides it into relevant specific tasks (e.g., "review existing code," "design new features," "build a test environment").

[0486] Input: Job description (data in JSON format)

[0487] Output: Specific task list (JSON format)

[0488] Step 4:

[0489] The server assigns a completion deadline to each task.

[0490] In terms of specific operation, the server considers the time required for each task and its dependencies, and sets an appropriate deadline by working backward from the overall completion deadline (e.g., "Review existing code: 2024-05-31", "Design new features: 2024-06-15").

[0491] Input: Specific task list, overall completion deadline

[0492] Output: A task list (in JSON format) with completion deadlines for each task.

[0493] Step 5:

[0494] The server generates a task list and sends it to the user's terminal.

[0495] Specifically, the server generates a task list with completion deadlines in JSON format and sends the generated task list to the user's terminal.

[0496] Input: Task list with completion deadlines

[0497] Output: Task list sent to the user's terminal (in JSON format)

[0498] Step 6:

[0499] The device displays a task list, and the user manages the progress of the tasks.

[0500] Specifically, the terminal receives a task list and displays it in a calendar or list format on the user interface. The user inputs progress into the terminal, recording and managing it in real time.

[0501] Input: Task list sent from the server (in JSON format)

[0502] Output: User-updated progress

[0503] Step 7:

[0504] The server uses an emotion engine to recognize the user's emotions.

[0505] Specifically, the server analyzes input content and progress data, and uses an emotion engine (e.g., IBM Watson®, Microsoft® Azure® Cognitive Services) to identify the user's emotional state (e.g., stress, anxiety).

[0506] Input: User input, progress data

[0507] Output: Sentiment data

[0508] Step 8:

[0509] The server adjusts how the task list is displayed and prioritizes tasks based on sentiment data.

[0510] Specifically, the server uses emotional data to adjust how the task list is displayed (e.g., displaying important tasks first when stress levels are high) and resets task priorities.

[0511] Input: Sentiment data, task list

[0512] Output: Adjusted task list

[0513] Step 9:

[0514] The server sends the adjusted task list back to the user's terminal.

[0515] Specifically, the server generates a coordinated task list in JSON format and sends it to the user's terminal.

[0516] Input: Adjusted task list

[0517] Output: Adjusted task list sent to the user's terminal

[0518] Example of a prompt:

[0519] Based on the invention, please break down the following tasks into specific tasks and generate a task list. Also, please explain how to adjust task priorities if the user is experiencing stress.

[0520] Job Description: Review the operation programs of factory robots.

[0521] Completion deadline: 2024-06-30

[0522] Emotional data: Stress level 7

[0523] 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.

[0524] 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.

[0525] 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.

[0526] [Second Embodiment]

[0527] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0528] 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.

[0529] 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).

[0530] 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.

[0531] 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.

[0532] 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).

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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.

[0537] 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.

[0538] 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".

[0539] A system for implementing the present invention includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a list of specific tasks, means for sending the task list to the user terminal, and means for the terminal to display the task list and for the user to manage the progress of the tasks.

[0540] System Overview

[0541] 1. User input:

[0542] Users enter the task details and completion deadline into an input form on their device. This can be done using text boxes or calendar widgets.

[0543] For example, a user might input the task description "Develop a new product development plan" and the completion deadline "2024-03-31".

[0544] 2. Sending input data:

[0545] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format or similar.

[0546] 3. Analysis of work content:

[0547] The server uses natural language processing (NLP) to analyze the business content based on the received data.

[0548] The analysis results in breaking down the work content into specific tasks. For example, the work content of "planning the development of a new product" can be broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0549] 4. Assigning deadlines:

[0550] The server assigns appropriate deadlines to each task based on the overall completion deadline. This is done by taking into account the time required for each task and its dependencies.

[0551] For example, the "Market Needs Survey" is assigned a deadline of 2024-01-31.

[0552] 5. Generating a task list:

[0553] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This list is also in JSON format.

[0554] 6. Sending and displaying the task list:

[0555] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user.

[0556] The display methods offered include calendar format and list format.

[0557] 7. Task management:

[0558] Users view a task list and record and update the progress of each task.

[0559] When a task is completed, the user records it on their device and manages the progress in real time.

[0560] Explanation of specific examples

[0561] Example: New product development plan

[0562] 1. User input:

[0563] Job description: "Develop development plans for new products"

[0564] Completion deadline: "2024-03-31"

[0565] 2. Sending input data:

[0566] Send the above information to the server in JSON format.

[0567] 3. Analysis of work content:

[0568] Using NLP, the process of "planning the development of a new product" was broken down into "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0569] 4. Assigning deadlines:

[0570] Market needs survey: 2024-01-31

[0571] Prototype design: 2024-02-15

[0572] Building a supply chain: 2024-02-28

[0573] Sales strategy planning: 2024-03-15

[0574] 5. Generating a task list:

[0575] Generate a task list in JSON format.

[0576] 6. Sending and displaying the task list:

[0577] The task list is sent to the device and displayed in a calendar format.

[0578] 7. Task management:

[0579] Users can record and manage the progress of each task in real time.

[0580] This system allows users to easily break down their work into specific tasks and manage them efficiently.

[0581] The following describes the processing flow.

[0582] Step 1:

[0583] The user enters the task details and completion deadline into an input form on the terminal. The input form has fields for the task details (e.g., "Develop a development plan for a new product") and the deadline (e.g., "2024-03-31"), respectively.

[0584] Step 2:

[0585] The terminal sends the entered work details and deadline to the server in a data format such as JSON. Pressing the send button sends this data to the server.

[0586] Step 3:

[0587] The server prepares the data received from the terminal (task details and deadlines) for analysis. The data is first converted to an appropriate format and then passed to the natural language processing (NLP) module.

[0588] Step 4:

[0589] The server analyzes the business content using natural language processing and breaks it down into specific tasks. For example, the business content "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0590] Step 5:

[0591] The server assigns a completion deadline to each task it breaks down. Working backward from the overall completion deadline (e.g., "2024-03-31"), it sets appropriate deadlines for each task, taking into account their duration and dependencies. For example, the deadline for "Market Needs Survey" is set to "2024-01-31".

[0592] Step 6:

[0593] The server generates a task list by listing specific tasks and their respective completion deadlines. This task list is generated in a structured data format such as JSON.

[0594] Step 7:

[0595] The server generates a task list and sends it to the terminal. The task list is formatted in a calendar or list format for easy viewing by the user.

[0596] Step 8:

[0597] The device displays a list of received tasks to the user. The user can view the task list and check the specific details and deadlines of each task. Display options include calendar view and list view.

[0598] Step 9:

[0599] Users manage the progress of each task. When a task is completed, the user marks it as complete via their device. This allows the task management progress to be updated in real time.

[0600] Through this process, a system is created that allows users to efficiently manage their work and proceed according to plan.

[0601] (Example 1)

[0602] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0603] Efficiently managing work processes is crucial for many companies and individuals, but traditional systems have made it difficult to break down work processes into specific tasks and manage their progress. In particular, there were no systems that automated the entire process from inputting and analyzing work processes to assigning tasks and managing progress. As a result, not only did work efficiency decline, but tasks were also more likely to be missed or deadlines were delayed. There is a need for a system that solves this problem and allows users to manage their work processes quickly and accurately.

[0604] 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.

[0605] In this invention, the server includes means for the user to input the work content and completion deadline; means for the server to analyze the work content and break it down into specific tasks; means for the server to assign a completion deadline to each task; means for the server to generate a list of the specific tasks; means for the server to send the generated task list to the user terminal; means for the terminal to display the task list and for the user to manage the progress of the tasks; means for the terminal to send the user's input to the server in JSON format; means for the server to analyze the received data using natural language processing; means for the server to compile the broken-down specific tasks and their deadlines into a list in JSON format; and means for the terminal to display the task list in calendar format or list format. This enables the user to quickly and accurately manage the work content and to check and update the progress of tasks in real time.

[0606] "User input" refers to the process in which users enter the details of their tasks and the deadline into an input form.

[0607] "Sending input data" refers to the process by which the terminal sends the task details and completion deadline entered by the user to the server.

[0608] "Analysis of business content" refers to the process of breaking down the business content received by the server into specific tasks using natural language processing.

[0609] "Deadline assignment" refers to the process by which the server assigns appropriate deadlines to each task based on the overall completion deadline.

[0610] "Task list generation" refers to the process by which the server generates a list of future tasks based on the analysis and deadline assignment results.

[0611] "Sending and displaying the task list" refers to the process in which the server generates a task list, sends it to the user's terminal, and the terminal displays that list to the user.

[0612] "Task management" refers to the process by which users record and update the progress of each task while referring to the displayed task list.

[0613] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a data format that is easy for humans to read and write, and easy for machines to analyze and generate.

[0614] "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0615] "Calendar format" refers to a format that displays tasks by date, similar to a calendar.

[0616] "List format" refers to a format in which tasks are displayed in a bulleted list-like manner.

[0617] The system implementing this invention is configured such that the user inputs the work content and completion deadline, the server analyzes the work content and breaks it down into specific tasks, generates a task list and sends it to the terminal, and the terminal displays the task list so that the user can manage the progress of the tasks. A specific embodiment of this system will be described in detail below.

[0618] Specifically, users first use their own devices (such as PCs, smartphones, or tablets) to enter the task details and completion deadline into an input form. The input form includes a text box and a calendar widget, which users use to describe the task in text format and select the completion deadline from the calendar.

[0619] For example, suppose a user enters the task description as "Develop a new product development plan" and the completion deadline as "2024-03-31".

[0620] Next, when the user clicks the "Submit" button, the input content is sent to the server in JSON format. This submission process uses asynchronous communication (Ajax), which improves the user experience.

[0621] On the server side, the received JSON data is analyzed using natural language processing (NLP). Specific NLP modules used include spaCy and NLTK. The server tokenizes the business content text, extracts the meaning of each word and phrase, and breaks it down into multiple specific tasks. For example, analyzing the business content "Develop a new product development plan" breaks it down into specific tasks such as "Market needs research," "Prototype design," "Supply chain construction," and "Sales strategy planning."

[0622] The server then assigns appropriate deadlines to each of the broken-down tasks based on the overall completion deadline. This process uses an algorithm that takes into account the time required for each task and its dependencies. For example, the "Market Needs Survey" task might be assigned a deadline of "2024-01-31".

[0623] Once the task list is generated, the server sends it back to the terminal in JSON format. The terminal then parses this received data and displays it on the user interface. Users can choose between a calendar format or a list format for display; for example, the Google Calendar API can be used to display the list in calendar format.

[0624] Users can view a displayed task list and record and manage the progress of each task in real time. Specifically, they can update the progress status for each task and check off completed tasks. This information is sent from the terminal to the server and stored in a database, so the progress can be checked from other devices as well.

[0625] As described above, this invention enables users to automatically break down their work into specific tasks and manage them efficiently and accurately. Implementing this system prevents task omissions and deadline delays, thereby improving work efficiency.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] User input

[0629] The user enters the task details and completion deadline into their device. This input is done using an input form (text box and calendar widget) displayed on the device screen. For example, the user might enter "Develop a new product development plan" as the task details and select "2024-03-31" as the completion deadline. The entered data is temporarily stored in the device's memory.

[0630] Input: User-entered task details and completion deadline

[0631] Output: The task details and completion deadline are temporarily saved to the terminal's memory.

[0632] Step 2:

[0633] Sending input data

[0634] When the user clicks the "Submit" button, the device converts the entered data into JSON format and sends it to the server using an Ajax request. For example, the data is sent in the following JSON format:

[0635] json

[0636] {

[0637] "Job Description": "Develop development plans for new products"

[0638] "Completion Deadline": "2024-03-31"

[0639] }

[0640] The terminal displays the status of whether the transmission was successful or unsuccessful.

[0641] Input: The user clicked the "Submit" button.

[0642] Output: The input data is sent to the server in JSON format.

[0643] Step 3:

[0644] Analysis of business operations

[0645] The server executes a Python script based on the received JSON data to perform natural language processing (NLP). Specifically, it uses an NLP module (e.g., spaCy or NLTK) to analyze the business content and extract the meaning of each word and phrase. This breaks down the business content into multiple specific tasks. For example, "Develop a new product development plan" is broken down into "Market needs research," "Prototype design," "Supply chain construction," and "Sales strategy planning."

[0646] Input: JSON data received by the server

[0647] Output: Decomposed specific tasks (internal data list)

[0648] Step 4:

[0649] Assignment of deadlines

[0650] The server assigns appropriate deadlines to each of the broken-down tasks. It uses a Python algorithm that considers the time required and dependencies of each task. For example, the task "Market Needs Survey" is assigned the deadline "2024-01-31".

[0651] Input: Decomposed specific tasks, overall completion deadline

[0652] Output: Specific tasks with assigned deadlines (internal data list)

[0653] Step 5:

[0654] Task list generation

[0655] The server generates a task list in JSON format based on the specific tasks that have been assigned deadlines. For example, the following JSON data is generated:

[0656] json

[0657] {

[0658] "Task": [

[0659] {"Name": "Market Needs Survey", "Deadline": "2024-01-31"}

[0660] {"Name": "Prototype Design", "Deadline": "2024-02-15"},

[0661] {"Name": "Supply Chain Construction", "Deadline": "2024-02-28"},

[0662] {"Name": "Development of Sales Strategy", "Deadline": "2024-03-15"}

[0663] ]

[0664] }

[0665] Input: Specific tasks with assigned deadlines

[0666] Output: Task list in JSON format

[0667] Step 6:

[0668] Sending and displaying task lists

[0669] The server sends the generated task list to the terminal. The terminal parses the received JSON data and displays it in the user interface. Users can choose between a calendar format or a list format for display. For example, the Google Calendar API can be used to display the list in calendar format.

[0670] Input: Task list in JSON format

[0671] Output: Task list displayed on the user's device (calendar or list format)

[0672] Step 7:

[0673] Task management

[0674] Users record and manage the progress of individual tasks in real time while referring to the displayed task list. They update the task progress using the terminal interface and check off completed tasks. This information is then sent back to the server and stored in the database in real time.

[0675] Input: User progress update operation

[0676] Output: Progress information is sent from the terminal to the server and stored in the database.

[0677] The above describes the specific processing flow of this system's program.

[0678] (Application Example 1)

[0679] 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."

[0680] Traditional business management systems had a problem where, when users entered business content that was difficult to break down into specific tasks, the system could not assign appropriate tasks and deadlines to that content. Furthermore, the inability to check task progress in real time meant that users could not properly address tasks that had passed their deadlines. In addition, the difficulty in visually grasping progress contributed to decreased work efficiency.

[0681] 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.

[0682] In this invention, the server includes means for analyzing the business content entered by the user and breaking it down into specific tasks, means for automatically assigning a completion deadline to each task and sending reminders to tasks that have passed their deadline, and means for using a generative AI model for analysis and obtaining highly accurate results using prompt sentences. This makes it possible to efficiently break down business content into specific tasks and assign appropriate deadlines. In addition, through task progress management and visual display, the user can grasp the status of tasks in real time and respond efficiently.

[0683] "Job description" refers to the overall tasks or project outline that the user intends to perform.

[0684] "Completion deadline" refers to the deadline entered by the user for completing the task.

[0685] A "task" refers to a specific unit of work obtained by analyzing the content of a job.

[0686] A "list" refers to a collection of multiple tasks organized into a single list.

[0687] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0688] A "generative AI model" refers to an artificial intelligence model that uses machine learning to generate and analyze text.

[0689] A "prompt" refers to an instruction or question that is input to a generative AI model.

[0690] "Automatic assignment of deadlines" refers to the process by which the system automatically sets an appropriate completion deadline for each task.

[0691] A "reminder" refers to a function that notifies you about tasks that are due soon or have already passed their deadline.

[0692] "Calendar format" refers to a format in which tasks are visually displayed on a calendar.

[0693] "List format" refers to a format in which tasks are displayed in a sequential order.

[0694] A "user terminal" refers to a device, such as a smartphone or computer, that a user uses to access the system.

[0695] A system for implementing this invention includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a task list and sending it to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, means for automatically assigning a deadline to each task and sending a reminder to tasks that have passed their deadline, and means for using a generative AI model for analysis and using prompt statements to obtain highly accurate results.

[0696] Hardware and software to use

[0697] 1. Hardware

[0698] Servers: Cloud servers or on-premises servers are used for analyzing business processes and managing task deadlines.

[0699] User terminal: Use a smartphone or personal computer.

[0700] 2. Software

[0701] Natural Language Processing (NLP) Model: A generative AI model (e.g., GPT-4) is used to analyze business content and break it down into specific tasks.

[0702] Framework: For server-side processing, we use web frameworks such as Flask or Django.

[0703] Data format: Data is sent and received in formats such as JSON.

[0704] Processing flow

[0705] User actions:

[0706] Users enter the details of their tasks and their completion deadlines into an input form via a smartphone app or web app, and then press the submit button. A text box and a calendar widget for selecting dates are provided during this process.

[0707] Server processing:

[0708] The entered task details and completion deadlines are sent to the server in JSON format. The server uses an NLP model to analyze the task details and break them down into specific tasks. For example, a task like "develop a new product development plan" is broken down into specific tasks such as "market needs research," "prototype design," "supply chain construction," and "sales strategy planning." For each of the broken-down tasks, the system automatically assigns an appropriate deadline, taking into account completion deadlines and dependencies. It also has a function to send reminders for tasks that are approaching or have already passed their deadlines.

[0709] User terminal processing:

[0710] The server generates a task list and sends it to the user's terminal in JSON format. The terminal then displays the tasks and their progress in calendar or list format. Users can record and manage task progress in real time and mark completed tasks. A reminder function also provides notifications for tasks that are approaching or have passed their deadline.

[0711] Specific example

[0712] Example 1: New product development plan

[0713] 1. User input:

[0714] Job description: "Develop development plans for new products"

[0715] Completion deadline: "2024-03-31"

[0716] 2. Example of a prompt:

[0717] Break down the process of creating a new product development plan into specific tasks.

[0718] 3. Server output:

[0719] Task 1: "Market Needs Survey" (Deadline: 2024-01-31)

[0720] Task 2: "Prototype Design" (Deadline: 2024-02-15)

[0721] Task 3: "Building a Supply Chain" (Deadline: 2024-02-28)

[0722] Task 4: "Develop a sales strategy" (Deadline: 2024-03-15)

[0723] These operations enable users to effectively break down their work into specific tasks and efficiently manage and track their progress.

[0724] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0725] Step 1:

[0726] The user enters the task details and completion deadline. Specifically, they enter the task details and completion deadline into an input form on a smartphone app or web app and press the submit button. The entered task details and completion deadline are sent to the server in JSON format. Input: Task details and completion deadline, Output: JSON format data.

[0727] Step 2:

[0728] The server parses the JSON data it receives. Specifically, the server parses the input data and extracts the task details and completion deadline. Using the parsed data, it sends a prompt message to the generating AI model. Input: "Develop a new product development plan" "2024-03-31" (JSON format), Output: Parsing request (prompt message).

[0729] Step 3:

[0730] The generative AI model analyzes the business content and breaks it down into specific tasks. The generative AI model (e.g., GPT-4) uses prompts to break down the tasks. The analysis results are obtained in JSON format. Input: Prompts; Data processing: Analysis of business content and task decomposition; Output: Specific task list (JSON format).

[0731] Step 4:

[0732] The server assigns completion deadlines to each task based on the generated task list. It automatically sets appropriate deadlines for each task, taking into account the task duration and dependencies. Input: Specific task list (JSON format), Data calculation: Calculation and assignment of completion deadlines, Output: Task list with assigned deadlines.

[0733] Step 5:

[0734] The server generates a task list with assigned deadlines in JSON format and sends it to the user's terminal. The generated task list is formatted as JSON data and sent to the user's terminal. Input: Task list with assigned deadlines; Output: Task list (JSON format).

[0735] Step 6:

[0736] The device receives the task list and displays it in calendar or list format. Users can check the progress of each task while viewing the task list. Input: Task list (JSON format), Data processing: Conversion to calendar or list format, Output: Visual display.

[0737] Step 7:

[0738] Users manage the progress of each task and record completed tasks on their devices. Users mark task completion on the screen, and this information is sent to the server in real time. Input: User-updated progress; Output: Real-time progress status.

[0739] Step 8:

[0740] The server sends reminders for tasks that are approaching or have already passed their deadline. Notifications are sent to the user's device, allowing the user to take action. Input: Task progress and deadline information; Output: Reminder notification.

[0741] This allows for the effective breakdown of work content into specific tasks, and enables efficient management and tracking of their progress.

[0742] 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.

[0743] In a system implementing the present invention, the following means are added: means for the user to input the work content and completion deadline; means for the server to analyze the work content and break it down into specific tasks; means for assigning a completion deadline to each task; means for generating a list of specific tasks; means for sending the task list to the user terminal; means for the terminal to display the task list and for the user to manage the progress of the tasks; and means including an emotion engine that recognizes the user's emotions.

[0744] System Overview

[0745] 1. User input:

[0746] Users enter the task details and completion deadline into an input form on their device. This can be done using text boxes or calendar widgets.

[0747] For example, a user might input the task description "Develop a new product development plan" and the completion deadline "2024-03-31".

[0748] 2. Sending input data:

[0749] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format or similar.

[0750] 3. Analysis of work content:

[0751] The server uses natural language processing (NLP) to analyze the business content based on the received data.

[0752] The analysis results in breaking down the business content into specific tasks. For example, the business content of "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0753] 4. Assigning deadlines:

[0754] The server works backward from the overall completion deadline (e.g., "2024-03-31") and sets appropriate deadlines for each task, taking into account the time required and dependencies. For example, the "Market Needs Survey" task might be assigned a deadline of 2024-01-31.

[0755] 5. Generating a task list:

[0756] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This task list is generated in a structured data format such as JSON.

[0757] 6. Sending and displaying the task list:

[0758] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user. The display method can be either calendar or list format.

[0759] 7. Task management:

[0760] Users view a task list, recording and updating the progress of each task. When a task is completed, the user records this on their device, managing the progress in real time.

[0761] Introducing an emotional engine

[0762] 8. Recognition of emotions:

[0763] The emotion engine analyzes the user's emotions based on the data and progress the user enters. For example, it can determine whether the user is feeling stressed based on their input.

[0764] 9. Use of emotional data:

[0765] The emotion engine analyzes the user's emotional data and provides it to the server. This emotional data plays a particularly important role when the user is feeling stressed or anxious.

[0766] 10. Adjusting the task list display:

[0767] The server adjusts how the task list is displayed based on emotional data. For example, if a user is feeling stressed, it displays high-priority tasks first to reduce their workload.

[0768] 11. Adjusting task priorities:

[0769] Based on emotional data, the server readjusts task priorities. This enables optimal task management tailored to the user's emotional state.

[0770] Explanation of specific examples

[0771] Example: New product development plan

[0772] 1. User input:

[0773] Job description: "Develop development plans for new products"

[0774] Completion deadline: "2024-03-31"

[0775] 2. Sending input data:

[0776] Send the above information to the server in JSON format.

[0777] 3. Analysis of work content:

[0778] Using NLP, the process of "planning the development of a new product" was broken down into "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0779] 4. Assigning deadlines:

[0780] Market needs survey: 2024-01-31

[0781] Prototype design: 2024-02-15

[0782] Building a supply chain: 2024-02-28

[0783] Sales strategy planning: 2024-03-15

[0784] 5. Generating a task list:

[0785] Generate a task list in JSON format.

[0786] 6. Sending and displaying the task list:

[0787] The task list is sent to the device and displayed in a calendar format.

[0788] 7. Task management:

[0789] Users can record and manage the progress of each task in real time.

[0790] 8. Recognition of emotions:

[0791] The emotion engine recognizes the user's emotions based on their input and progress. For example, it might analyze that a user is stressed if they have many sudden deadlines.

[0792] 9. Use of emotional data:

[0793] The emotion engine analyzes the emotion data and provides it to the server.

[0794] 10. Adjusting the task list display:

[0795] If a user is experiencing stress, adjustments will be made, such as displaying higher-priority tasks first.

[0796] 11. Adjusting task priorities:

[0797] By readjusting task priorities based on emotional data, we can reduce user stress and enable them to complete tasks more efficiently.

[0798] This system breaks down work content into specific tasks, allowing for efficient management, while also enabling flexible task management that takes user emotions into consideration.

[0799] The following describes the processing flow.

[0800] Step 1:

[0801] The user enters the task details and completion deadline into the input form on the device. For example, the user enters the task details "Develop a new product development plan" and the completion deadline "2024-03-31" into the text box and calendar widget, respectively.

[0802] Step 2:

[0803] When the user presses the "Send" button, the terminal sends the entered task details and completion deadline to the server as JSON data.

[0804] Step 3:

[0805] The server prepares the data received from the terminal for analysis. The data is first converted to an appropriate format and then passed to the natural language processing (NLP) module.

[0806] Step 4:

[0807] The server uses NLP to analyze the business process and break it down into specific tasks. For example, the business process of "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[0808] Step 5:

[0809] The server assigns a completion deadline to each task it breaks down. Working backward from the overall completion deadline (e.g., "2024-03-31"), it sets appropriate deadlines for each task, taking into account their duration and dependencies. For example, the "Market Needs Survey" task is set to have a deadline of "2024-01-31".

[0810] Step 6:

[0811] The server generates a task list by listing specific tasks and their respective completion deadlines. This task list is generated as structured data in JSON format.

[0812] Step 7:

[0813] The server sends the generated task list to the terminal. The terminal prepares to display the received task list.

[0814] Step 8:

[0815] The device displays a task list to the user. The user can visually view the task list in calendar or list format.

[0816] Step 9:

[0817] The user checks the task list and records and updates the progress of each task. For example, when a task is completed, the user marks it as "completed" through their device.

[0818] Step 10:

[0819] The device sends emotional data to the emotion engine in real time based on user input and progress. The emotion engine analyzes the user's emotions from the input content (text, operation history, etc.).

[0820] Step 11:

[0821] The server receives emotional data analyzed by the emotion engine. For example, the analysis data might indicate that the user is experiencing stress.

[0822] Step 12:

[0823] The server adjusts how the task list is displayed based on emotional data. For example, if a user is feeling stressed, the server prioritizes displaying only the most important tasks.

[0824] Step 13:

[0825] The server readjusts task priorities based on sentiment data. This readjustment is intended to optimize the workload by taking the user's emotional state into consideration.

[0826] Step 14:

[0827] Users can view their readjusted task list on their device and continue managing tasks based on the new priorities. This reduces emotional burden and allows users to work more efficiently.

[0828] (Example 2)

[0829] 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".

[0830] Current task management systems offer features that break down user-entered work content into specific tasks and assign appropriate completion deadlines, but they do not consider user emotions or stress levels when displaying tasks or adjusting priorities. Therefore, it is difficult to efficiently manage tasks in work environments where users experience high levels of stress. Furthermore, there is a lack of functionality to analyze user-entered work content using natural language processing, which makes it difficult to deal with ambiguity and unclear input.

[0831] 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.

[0832] In this invention, the server includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for the server to assign a completion deadline to each task, means for the server to generate a list of the specific tasks, means for the server to transmit the generated task list to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, and means for the server to analyze the user's emotions and adjust the display and priority of tasks in order to reduce the workload. This enables the breakdown of the user's work content into specific tasks, the assignment of appropriate deadlines to each task, and flexible task management according to the user's emotional state.

[0833] "Job description" refers to the specific tasks or projects that the user intends to accomplish.

[0834] "Completion deadline" refers to the final date or time by which a particular task or work must be completed.

[0835] A "task" refers to a specific, individual work item obtained by breaking down a series of tasks.

[0836] "JSON format" refers to a lightweight data exchange format for structuring and exchanging data.

[0837] A "device" refers to an electronic device that a user can directly operate, and includes personal computers, smartphones, tablets, and other similar devices.

[0838] A "server" refers to a centralized computer system that provides or processes information over a network.

[0839] A "task list" refers to a list that compiles specific tasks and their completion deadlines.

[0840] "Natural language processing" refers to the technological field in which computers understand, analyze, and generate human language.

[0841] An "emotion engine" refers to a program that analyzes and determines a user's emotions based on their input data and behavior.

[0842] "Priority" refers to the criteria used to determine the order in which multiple tasks or work should be performed, based on their importance and urgency.

[0843] The system implementing this invention involves the user inputting the work content and completion deadline, a server analyzing this information, breaking it down into specific tasks, assigning deadlines, and generating and sending a task list. Furthermore, it can analyze the user's emotional state using an emotion engine and adjust the display and priority of tasks accordingly.

[0844] The user enters the task details and completion deadline using a terminal. The user interface uses text boxes and calendar widgets for input. Once input is complete, the terminal packages the data in JSON format and sends it to the server.

[0845] The server receives JSON data sent by the user and analyzes the business content using a natural language processing (NLP) engine (e.g., spaCy or BERT). Through this analysis, the abstract business content is broken down into concrete tasks. The server then works backward from the overall completion deadline, setting deadlines for each task, taking into account the time required and dependencies. Once the tasks and deadlines are determined, the server generates a task list in JSON format and sends it to the user's terminal.

[0846] The device analyzes the received task list and displays it in calendar or list format. The user records and updates the progress of each task based on the displayed task list. Once a task is completed, the user can mark it as completed.

[0847] Furthermore, an emotion engine is implemented to analyze the user's emotions based on user input data and task progress. The emotion engine, for example, determines whether the user is experiencing stress and provides this data to the server. Based on this emotion data, the server adjusts how the task list is displayed and prioritizes tasks according to the user's stress level. For example, if a user is experiencing high stress, high-priority tasks are displayed first to reduce their workload.

[0848] Specific example

[0849] As a concrete example, let's consider a scenario where a new product development plan is created. The user inputs the following:

[0850] Job description: "Develop development plans for new products"

[0851] Completion deadline: "2024-03-31"

[0852] When a user inputs data, the terminal sends it to the server in JSON format. The server uses an NLP engine to analyze the business content and breaks it down into tasks such as "market needs research," "prototype design," "supply chain construction," and "sales strategy planning." A specific deadline is set for each task. For example, "market needs research" might have a deadline of 2024-01-31, and "prototype design" might have a deadline of 2024-02-15.

[0853] The generated task list is sent to the device and displayed in a calendar format. The user manages their progress based on this task list and checks off completed tasks.

[0854] Example of a prompt:

[0855] Please enter the task description, "Develop a new product development plan," and the completion deadline, "2024-03-31." Based on this, the system will break down the necessary tasks and specify the completion deadline for each task.

[0856] This system not only allows for the efficient management of work by breaking down tasks into specific components and setting deadlines, but also enables flexible task management that adapts to the user's emotional state.

[0857] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0858] Step 1:

[0859] The user enters the task details and completion deadline. The user enters the task details "Develop a new product development plan" and the completion deadline "2024-03-31" into the input form on the terminal and presses the submit button.

[0860] Input: Task description and completion deadline

[0861] Output: Data on the task details and completion deadline entered by the user.

[0862] Step 2:

[0863] The terminal packages the entered task details and completion deadline into JSON format and sends it to the server. The terminal uses HTTPS as the transmission protocol.

[0864] Input: Data of task details and completion deadlines entered by the user.

[0865] Data processing: Package the work details and completion deadline data into JSON format.

[0866] Output: Send data in JSON format to the server

[0867] Step 3:

[0868] The server parses the received JSON data and extracts the work content and completion deadline. The server then uses a natural language processing (NLP) engine (e.g., spaCy or BERT) to break down the work content into specific tasks.

[0869] Input: Data in JSON format containing task details and completion deadlines.

[0870] Data processing: Using an NLP engine, business processes are analyzed and broken down into specific tasks such as "market needs research," "prototype design," and "supply chain construction."

[0871] Output: Decomposed specific tasks

[0872] Step 4:

[0873] The server calculates the appropriate completion deadline for each specific task by working backward from the overall deadline, taking into account the time required for each task and its dependencies.

[0874] Input: Specific tasks and overall deadline

[0875] Data calculation: Use a Gantt chart scheduling algorithm to assign appropriate deadlines to each task.

[0876] Output: Specific tasks and their respective deadlines.

[0877] Step 5:

[0878] The server lists the specific tasks broken down into individual components and their respective completion deadlines, generating a task list in JSON format.

[0879] Input: Specific tasks and their respective completion deadlines.

[0880] Data processing: Package specific tasks and deadlines into JSON format.

[0881] Output: Task list in JSON format

[0882] Step 6:

[0883] The server sends the generated task list to the user's terminal. The terminal parses the received task list and displays it in calendar or list format.

[0884] Input: Task list in JSON format

[0885] Data processing: Parse task lists in JSON format and convert them to a display format.

[0886] Output: Task list displayed in calendar or list format

[0887] Step 7:

[0888] Users record and update the progress of their task list. Users enter the progress of each task on their device and mark it as completed when it is finished.

[0889] Input: Task list

[0890] Output: Updated task list and progress

[0891] Step 8:

[0892] The emotion engine analyzes the user's emotions based on their input data and task progress. For example, if a user inputs "I feel stressed," the emotion engine will analyze this as a high stress level.

[0893] Input: User input data and task progress

[0894] Data processing: Analyze emotions using an emotion engine.

[0895] Output: Analyzed sentiment data

[0896] Step 9:

[0897] The server adjusts how the task list is displayed and prioritizes tasks based on the emotional data analyzed by the emotion engine. If the user is experiencing high levels of stress, the server will make adjustments such as displaying higher-priority tasks first.

[0898] Input: Analyzed sentiment data and task list

[0899] Data Calculation: Adjust the display method of the task list and task prioritization based on sentiment data.

[0900] Output: Adjusted task list

[0901] Step 10:

[0902] The server readjusts task priorities based on emotional data. By optimizing the order of tasks according to the user's emotional state, efficient task management is achieved.

[0903] Input: Analyzed sentiment data and task list

[0904] Data processing: Reprioritize tasks based on emotional data.

[0905] Output: Re-adjusted priority task list

[0906] (Application Example 2)

[0907] 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."

[0908] Traditional business management systems simply require users to input work details and deadlines, which are then broken down into a series of tasks without considering the user's emotional state. Therefore, when users experience stress or anxiety, the system is unable to appropriately adjust task priorities or display methods, leading to decreased work efficiency and increased mental burden. Furthermore, even in task management using robots within factories, flexible responses based on emotional states are required.

[0909] 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.

[0910] In this invention, the server includes means for the user to input the work content and completion deadline, means for analyzing the work content and breaking it down into specific tasks, means for assigning completion deadlines to each task, means for generating a list of specific tasks, means for transmitting the generated task list to the user terminal, and means for recognizing the user's emotions and adjusting the display method of the task list and the priority of tasks based on emotion data. This enables robots and users in industrial areas to improve the efficiency of work management and perform flexible task management according to their emotional state.

[0911] A "user" is a person who operates the system to input the details of the work and the completion deadline, or an entity that performs that series of actions.

[0912] "Job description" refers to information that describes the specific tasks and objectives that the user is expected to accomplish.

[0913] "Completion deadline" refers to the date and time by which all tasks within a given project must be completed.

[0914] A "server" is a central computer device that analyzes business processes and breaks down and manages tasks.

[0915] A "task" is a specific set of tasks or actions derived from analyzing the content of a job.

[0916] A "task list" is a collection of information organized in a list format, consisting of broken-down tasks and their respective completion deadlines.

[0917] A "user terminal" is a device used by a user to manage their work and check and operate the progress of tasks.

[0918] "Emotion" refers to the subjective psychological state that a user experiences in response to a particular situation or task.

[0919] "Emotional data" refers to information that expresses a user's emotional state in numerical or text format.

[0920] An "emotion engine" is a system module that analyzes user emotions based on their input and actions, and generates the results.

[0921] The system for implementing this invention comprises means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a list of specific tasks, means for sending the generated task list to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, and means for recognizing the user's emotions and adjusting the display method of the task list and the priority of tasks based on emotion data.

[0922] System Overview

[0923] 1. User input:

[0924] Users input their task details and completion deadlines through a chat-like interface or form. For example, a user might input the task "Review the operation program of the factory robots" and the completion deadline "2024-06-30".

[0925] 2. Sending input data:

[0926] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format.

[0927] 3. Analysis of work content:

[0928] The server uses natural language processing (NLP) to analyze the input business content and break it down into specific tasks. This analysis utilizes a pre-trained generative AI model. For example, analyzing the business content "review the operation program of factory robots" breaks it down into specific tasks such as "review existing code," "design new functions," and "build a test environment."

[0929] 4. Assigning deadlines:

[0930] The server calculates backward from the overall completion deadline entered by the user, and sets appropriate deadlines for each task, taking into account the time required and dependencies. For example, "Review existing code" might be assigned a deadline of 2024-05-31.

[0931] 5. Generating a task list:

[0932] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This task list is generated in JSON format.

[0933] 6. Sending and displaying the task list:

[0934] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user. Calendar and list formats are available for display.

[0935] 7. Task management:

[0936] Users view a task list, recording and updating the progress of each task. When a task is completed, the user records this on their device, managing the progress in real time.

[0937] Introducing an emotional engine

[0938] 8. Emotion recognition:

[0939] The emotion engine analyzes the user's emotions based on the data and progress the user inputs. Based on the analysis results, it determines whether the user is feeling stressed, anxious, or otherwise unsettled.

[0940] 9. Use of emotional data:

[0941] The emotion engine analyzes user emotion data and provides it to the server, which then adjusts how the task list is displayed and prioritized. For example, if a user is feeling stressed, high-priority tasks are displayed first to reduce their workload.

[0942] Explanation of specific examples

[0943] Example: Review of the operation program for factory robots.

[0944] 1. User input:

[0945] Job description: "Review the operation programs of factory robots."

[0946] Completion deadline: "2024-06-30"

[0947] 2. Sending input data:

[0948] Send the above information to the server in JSON format.

[0949] 3. Analysis of work content:

[0950] Using NLP, the task of "revising the motion program of factory robots" was broken down into "reviewing existing code," "designing new functions," and "building a test environment."

[0951] 4. Assigning deadlines:

[0952] Review of existing code: 2024-05-31

[0953] Design of new features: 2024-06-15

[0954] Test environment setup: 2024-06-25

[0955] 5. Generating a task list:

[0956] Generate a task list in JSON format.

[0957] 6. Sending and displaying the task list:

[0958] The task list is sent to the device and displayed in a calendar format.

[0959] 7. Task management:

[0960] Users can record and manage the progress of each task in real time.

[0961] 8. Emotion recognition:

[0962] The emotion engine recognizes the user's emotions based on their input and progress. For example, it might analyze that a user is stressed if they have many sudden deadlines.

[0963] 9. Use of emotional data:

[0964] The emotion engine analyzes the emotion data and provides it to the server.

[0965] 10. Adjusting the task list display:

[0966] If a user is experiencing stress, adjustments will be made, such as displaying higher-priority tasks first.

[0967] 11. Adjusting task priorities:

[0968] By readjusting task priorities based on emotional data, we can reduce user stress and enable them to complete tasks more efficiently.

[0969] Example of a prompt:

[0970] Based on the invention, please break down the following tasks into specific tasks and generate a task list. Also, please explain how to adjust task priorities if the user is experiencing stress.

[0971] Job Description: Review the operation programs of factory robots.

[0972] Completion deadline: 2024-06-30

[0973] Emotional data: Stress level 7

[0974] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0975] Step 1:

[0976] The user enters the task details and completion deadline.

[0977] In terms of specific actions, the user enters the task details (e.g., "Review the operation program of the factory robot") and the completion deadline (e.g., "2024-06-30") into an input form on their smartphone or tablet device, and then presses the "Submit" button.

[0978] Input: Task description, completion deadline

[0979] Output: Data in JSON format (task details, completion deadline)

[0980] Step 2:

[0981] The server receives the input data.

[0982] Specifically, the server receives data in JSON format (task details and completion deadline) sent by the user.

[0983] Input: Data in JSON format

[0984] Output: Stored in the server's internal database.

[0985] Step 3:

[0986] The server analyzes the business content using natural language processing (NLP) and breaks it down into specific tasks.

[0987] Specifically, the server uses an NLP library (e.g., spaCy, GPT-3) to analyze the business content within the JSON data and divides it into relevant specific tasks (e.g., "review existing code," "design new features," "build a test environment").

[0988] Input: Job description (data in JSON format)

[0989] Output: Specific task list (JSON format)

[0990] Step 4:

[0991] The server assigns a completion deadline to each task.

[0992] In terms of specific operation, the server considers the time required for each task and its dependencies, and sets an appropriate deadline by working backward from the overall completion deadline (e.g., "Review existing code: 2024-05-31", "Design new features: 2024-06-15").

[0993] Input: Specific task list, overall completion deadline

[0994] Output: A task list (in JSON format) with completion deadlines for each task.

[0995] Step 5:

[0996] The server generates a task list and sends it to the user's terminal.

[0997] Specifically, the server generates a task list with completion deadlines in JSON format and sends the generated task list to the user's terminal.

[0998] Input: Task list with completion deadlines

[0999] Output: Task list sent to the user's terminal (in JSON format)

[1000] Step 6:

[1001] The device displays a task list, and the user manages the progress of the tasks.

[1002] Specifically, the terminal receives a task list and displays it in a calendar or list format on the user interface. The user inputs progress into the terminal, recording and managing it in real time.

[1003] Input: Task list sent from the server (in JSON format)

[1004] Output: User-updated progress

[1005] Step 7:

[1006] The server uses an emotion engine to recognize the user's emotions.

[1007] Specifically, the server analyzes input data and progress data, and uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to identify the user's emotional state (e.g., stress, anxiety).

[1008] Input: User input, progress data

[1009] Output: Sentiment data

[1010] Step 8:

[1011] The server adjusts how the task list is displayed and prioritizes tasks based on sentiment data.

[1012] Specifically, the server uses emotional data to adjust how the task list is displayed (e.g., displaying important tasks first when stress levels are high) and resets task priorities.

[1013] Input: Sentiment data, task list

[1014] Output: Adjusted task list

[1015] Step 9:

[1016] The server sends the adjusted task list back to the user's terminal.

[1017] Specifically, the server generates a coordinated task list in JSON format and sends it to the user's terminal.

[1018] Input: Adjusted task list

[1019] Output: Adjusted task list sent to the user's terminal

[1020] Example of a prompt:

[1021] Based on the invention, please break down the following tasks into specific tasks and generate a task list. Also, please explain how to adjust task priorities if the user is experiencing stress.

[1022] Job Description: Review the operation programs of factory robots.

[1023] Completion deadline: 2024-06-30

[1024] Emotional data: Stress level 7

[1025] 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.

[1026] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[1027] 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.

[1028] [Third Embodiment]

[1029] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1030] 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.

[1031] 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).

[1032] 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.

[1033] 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.

[1034] 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).

[1035] 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.

[1036] 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.

[1037] 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.

[1038] 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.

[1039] 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.

[1040] 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".

[1041] A system for implementing the present invention includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a list of specific tasks, means for sending the task list to the user terminal, and means for the terminal to display the task list and for the user to manage the progress of the tasks.

[1042] System Overview

[1043] 1. User input:

[1044] Users enter the task details and completion deadline into an input form on their device. This can be done using text boxes or calendar widgets.

[1045] For example, a user might input the task description "Develop a new product development plan" and the completion deadline "2024-03-31".

[1046] 2. Sending input data:

[1047] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format or similar.

[1048] 3. Analysis of work content:

[1049] The server uses natural language processing (NLP) to analyze the business content based on the received data.

[1050] The analysis results in breaking down the work content into specific tasks. For example, the work content of "planning the development of a new product" can be broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1051] 4. Assigning deadlines:

[1052] The server assigns appropriate deadlines to each task based on the overall completion deadline. This is done by taking into account the time required for each task and its dependencies.

[1053] For example, the "Market Needs Survey" is assigned a deadline of 2024-01-31.

[1054] 5. Generating a task list:

[1055] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This list is also in JSON format.

[1056] 6. Sending and displaying the task list:

[1057] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user.

[1058] The display methods offered include calendar format and list format.

[1059] 7. Task management:

[1060] Users view a task list and record and update the progress of each task.

[1061] When a task is completed, the user records it on their device and manages the progress in real time.

[1062] Explanation of specific examples

[1063] Example: New product development plan

[1064] 1. User input:

[1065] Job description: "Develop development plans for new products"

[1066] Completion deadline: "2024-03-31"

[1067] 2. Sending input data:

[1068] Send the above information to the server in JSON format.

[1069] 3. Analysis of work content:

[1070] Using NLP, the process of "planning the development of a new product" was broken down into "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1071] 4. Assigning deadlines:

[1072] Market needs survey: 2024-01-31

[1073] Prototype design: 2024-02-15

[1074] Building a supply chain: 2024-02-28

[1075] Sales strategy planning: 2024-03-15

[1076] 5. Generating a task list:

[1077] Generate a task list in JSON format.

[1078] 6. Sending and displaying the task list:

[1079] The task list is sent to the device and displayed in a calendar format.

[1080] 7. Task management:

[1081] Users can record and manage the progress of each task in real time.

[1082] This system allows users to easily break down their work into specific tasks and manage them efficiently.

[1083] The following describes the processing flow.

[1084] Step 1:

[1085] The user enters the task details and completion deadline into an input form on the terminal. The input form has fields for the task details (e.g., "Develop a development plan for a new product") and the deadline (e.g., "2024-03-31"), respectively.

[1086] Step 2:

[1087] The terminal sends the entered work details and deadline to the server in a data format such as JSON. Pressing the send button sends this data to the server.

[1088] Step 3:

[1089] The server prepares the data received from the terminal (task details and deadlines) for analysis. The data is first converted to an appropriate format and then passed to the natural language processing (NLP) module.

[1090] Step 4:

[1091] The server analyzes the business content using natural language processing and breaks it down into specific tasks. For example, the business content "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1092] Step 5:

[1093] The server assigns a completion deadline to each task it breaks down. Working backward from the overall completion deadline (e.g., "2024-03-31"), it sets appropriate deadlines for each task, taking into account their duration and dependencies. For example, the deadline for "Market Needs Survey" is set to "2024-01-31".

[1094] Step 6:

[1095] The server generates a task list by listing specific tasks and their respective completion deadlines. This task list is generated in a structured data format such as JSON.

[1096] Step 7:

[1097] The server generates a task list and sends it to the terminal. The task list is formatted in a calendar or list format for easy viewing by the user.

[1098] Step 8:

[1099] The device displays a list of received tasks to the user. The user can view the task list and check the specific details and deadlines of each task. Display options include calendar view and list view.

[1100] Step 9:

[1101] Users manage the progress of each task. When a task is completed, the user marks it as complete via their device. This allows the task management progress to be updated in real time.

[1102] Through this process, a system is created that allows users to efficiently manage their work and proceed according to plan.

[1103] (Example 1)

[1104] 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."

[1105] Efficiently managing work processes is crucial for many companies and individuals, but traditional systems have made it difficult to break down work processes into specific tasks and manage their progress. In particular, there were no systems that automated the entire process from inputting and analyzing work processes to assigning tasks and managing progress. As a result, not only did work efficiency decline, but tasks were also more likely to be missed or deadlines were delayed. There is a need for a system that solves this problem and allows users to manage their work processes quickly and accurately.

[1106] 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.

[1107] In this invention, the server includes means for the user to input the work content and completion deadline; means for the server to analyze the work content and break it down into specific tasks; means for the server to assign a completion deadline to each task; means for the server to generate a list of the specific tasks; means for the server to send the generated task list to the user terminal; means for the terminal to display the task list and for the user to manage the progress of the tasks; means for the terminal to send the user's input to the server in JSON format; means for the server to analyze the received data using natural language processing; means for the server to compile the broken-down specific tasks and their deadlines into a list in JSON format; and means for the terminal to display the task list in calendar format or list format. This enables the user to quickly and accurately manage the work content and to check and update the progress of tasks in real time.

[1108] "User input" refers to the process in which users enter the details of their tasks and the deadline into an input form.

[1109] "Sending input data" refers to the process by which the terminal sends the task details and completion deadline entered by the user to the server.

[1110] "Analysis of business content" refers to the process of breaking down the business content received by the server into specific tasks using natural language processing.

[1111] "Deadline assignment" refers to the process by which the server assigns appropriate deadlines to each task based on the overall completion deadline.

[1112] "Task list generation" refers to the process by which the server generates a list of future tasks based on the analysis and deadline assignment results.

[1113] "Sending and displaying the task list" refers to the process in which the server generates a task list, sends it to the user's terminal, and the terminal displays that list to the user.

[1114] "Task management" refers to the process by which users record and update the progress of each task while referring to the displayed task list.

[1115] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a data format that is easy for humans to read and write, and easy for machines to analyze and generate.

[1116] "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[1117] "Calendar format" refers to a format that displays tasks by date, similar to a calendar.

[1118] "List format" refers to a format in which tasks are displayed in a bulleted list-like manner.

[1119] The system implementing this invention is configured such that the user inputs the work content and completion deadline, the server analyzes the work content and breaks it down into specific tasks, generates a task list and sends it to the terminal, and the terminal displays the task list so that the user can manage the progress of the tasks. A specific embodiment of this system will be described in detail below.

[1120] Specifically, users first use their own devices (such as PCs, smartphones, or tablets) to enter the task details and completion deadline into an input form. The input form includes a text box and a calendar widget, which users use to describe the task in text format and select the completion deadline from the calendar.

[1121] For example, suppose a user enters the task description as "Develop a new product development plan" and the completion deadline as "2024-03-31".

[1122] Next, when the user clicks the "Submit" button, the input content is sent to the server in JSON format. This submission process uses asynchronous communication (Ajax), which improves the user experience.

[1123] On the server side, the received JSON data is analyzed using natural language processing (NLP). Specific NLP modules used include spaCy and NLTK. The server tokenizes the business content text, extracts the meaning of each word and phrase, and breaks it down into multiple specific tasks. For example, analyzing the business content "Develop a new product development plan" breaks it down into specific tasks such as "Market needs research," "Prototype design," "Supply chain construction," and "Sales strategy planning."

[1124] The server then assigns appropriate deadlines to each of the broken-down tasks based on the overall completion deadline. This process uses an algorithm that takes into account the time required for each task and its dependencies. For example, the "Market Needs Survey" task might be assigned a deadline of "2024-01-31".

[1125] Once the task list is generated, the server sends it back to the terminal in JSON format. The terminal then parses this received data and displays it on the user interface. Users can choose between a calendar format or a list format for display; for example, the Google Calendar API can be used to display the list in calendar format.

[1126] Users can view a displayed task list and record and manage the progress of each task in real time. Specifically, they can update the progress status for each task and check off completed tasks. This information is sent from the terminal to the server and stored in a database, so the progress can be checked from other devices as well.

[1127] As described above, this invention enables users to automatically break down their work into specific tasks and manage them efficiently and accurately. Implementing this system prevents task omissions and deadline delays, thereby improving work efficiency.

[1128] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1129] Step 1:

[1130] User input

[1131] The user enters the task details and completion deadline into their device. This input is done using an input form (text box and calendar widget) displayed on the device screen. For example, the user might enter "Develop a new product development plan" as the task details and select "2024-03-31" as the completion deadline. The entered data is temporarily stored in the device's memory.

[1132] Input: User-entered task details and completion deadline

[1133] Output: The task details and completion deadline are temporarily saved to the terminal's memory.

[1134] Step 2:

[1135] Sending input data

[1136] When the user clicks the "Submit" button, the device converts the entered data into JSON format and sends it to the server using an Ajax request. For example, the data is sent in the following JSON format:

[1137] json

[1138] {

[1139] "Job Description": "Develop development plans for new products"

[1140] "Completion Deadline": "2024-03-31"

[1141] }

[1142] The terminal displays the status of whether the transmission was successful or unsuccessful.

[1143] Input: The user clicked the "Submit" button.

[1144] Output: The input data is sent to the server in JSON format.

[1145] Step 3:

[1146] Analysis of business operations

[1147] The server executes a Python script based on the received JSON data to perform natural language processing (NLP). Specifically, it uses an NLP module (e.g., spaCy or NLTK) to analyze the business content and extract the meaning of each word and phrase. This breaks down the business content into multiple specific tasks. For example, "Develop a new product development plan" is broken down into "Market needs research," "Prototype design," "Supply chain construction," and "Sales strategy planning."

[1148] Input: JSON data received by the server

[1149] Output: Decomposed specific tasks (internal data list)

[1150] Step 4:

[1151] Assignment of deadlines

[1152] The server assigns appropriate deadlines to each of the broken-down tasks. It uses a Python algorithm that considers the time required and dependencies of each task. For example, the task "Market Needs Survey" is assigned the deadline "2024-01-31".

[1153] Input: Decomposed specific tasks, overall completion deadline

[1154] Output: Specific tasks with assigned deadlines (internal data list)

[1155] Step 5:

[1156] Task list generation

[1157] The server generates a task list in JSON format based on the specific tasks that have been assigned deadlines. For example, the following JSON data is generated:

[1158] json

[1159] {

[1160] "Task": [

[1161] {"Name": "Market Needs Survey", "Deadline": "2024-01-31"}

[1162] {"Name": "Prototype Design", "Deadline": "2024-02-15"},

[1163] {"Name": "Supply Chain Construction", "Deadline": "2024-02-28"},

[1164] {"Name": "Development of Sales Strategy", "Deadline": "2024-03-15"}

[1165] ]

[1166] }

[1167] Input: Specific tasks with assigned deadlines

[1168] Output: Task list in JSON format

[1169] Step 6:

[1170] Sending and displaying task lists

[1171] The server sends the generated task list to the terminal. The terminal parses the received JSON data and displays it in the user interface. Users can choose between a calendar format or a list format for display. For example, the Google Calendar API can be used to display the list in calendar format.

[1172] Input: Task list in JSON format

[1173] Output: Task list displayed on the user's device (calendar or list format)

[1174] Step 7:

[1175] Task management

[1176] Users record and manage the progress of individual tasks in real time while referring to the displayed task list. They update the task progress using the terminal interface and check off completed tasks. This information is then sent back to the server and stored in the database in real time.

[1177] Input: User progress update operation

[1178] Output: Progress information is sent from the terminal to the server and stored in the database.

[1179] The above describes the specific processing flow of this system's program.

[1180] (Application Example 1)

[1181] 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."

[1182] Traditional business management systems had a problem where, when users entered business content that was difficult to break down into specific tasks, the system could not assign appropriate tasks and deadlines to that content. Furthermore, the inability to check task progress in real time meant that users could not properly address tasks that had passed their deadlines. In addition, the difficulty in visually grasping progress contributed to decreased work efficiency.

[1183] 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.

[1184] In this invention, the server includes means for analyzing the business content entered by the user and breaking it down into specific tasks, means for automatically assigning a completion deadline to each task and sending reminders to tasks that have passed their deadline, and means for using a generative AI model for analysis and obtaining highly accurate results using prompt sentences. This makes it possible to efficiently break down business content into specific tasks and assign appropriate deadlines. In addition, through task progress management and visual display, the user can grasp the status of tasks in real time and respond efficiently.

[1185] "Job description" refers to the overall tasks or project outline that the user intends to perform.

[1186] "Completion deadline" refers to the deadline entered by the user for completing the task.

[1187] A "task" refers to a specific unit of work obtained by analyzing the content of a job.

[1188] A "list" refers to a collection of multiple tasks organized into a single list.

[1189] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[1190] A "generative AI model" refers to an artificial intelligence model that uses machine learning to generate and analyze text.

[1191] A "prompt" refers to an instruction or question that is input to a generative AI model.

[1192] "Automatic assignment of deadlines" refers to the process by which the system automatically sets an appropriate completion deadline for each task.

[1193] A "reminder" refers to a function that notifies you about tasks that are due soon or have already passed their deadline.

[1194] "Calendar format" refers to a format in which tasks are visually displayed on a calendar.

[1195] "List format" refers to a format in which tasks are displayed in a sequential order.

[1196] A "user terminal" refers to a device, such as a smartphone or computer, that a user uses to access the system.

[1197] A system for implementing this invention includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a task list and sending it to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, means for automatically assigning a deadline to each task and sending a reminder to tasks that have passed their deadline, and means for using a generative AI model for analysis and using prompt statements to obtain highly accurate results.

[1198] Hardware and software to use

[1199] 1. Hardware

[1200] Servers: Cloud servers or on-premises servers are used for analyzing business processes and managing task deadlines.

[1201] User terminal: Use a smartphone or personal computer.

[1202] 2. Software

[1203] Natural Language Processing (NLP) Model: A generative AI model (e.g., GPT-4) is used to analyze business content and break it down into specific tasks.

[1204] Framework: For server-side processing, we use web frameworks such as Flask or Django.

[1205] Data format: Data is sent and received in formats such as JSON.

[1206] Processing flow

[1207] User actions:

[1208] Users enter the details of their tasks and their completion deadlines into an input form via a smartphone app or web app, and then press the submit button. A text box and a calendar widget for selecting dates are provided during this process.

[1209] Server processing:

[1210] The entered task details and completion deadlines are sent to the server in JSON format. The server uses an NLP model to analyze the task details and break them down into specific tasks. For example, a task like "develop a new product development plan" is broken down into specific tasks such as "market needs research," "prototype design," "supply chain construction," and "sales strategy planning." For each of the broken-down tasks, the system automatically assigns an appropriate deadline, taking into account completion deadlines and dependencies. It also has a function to send reminders for tasks that are approaching or have already passed their deadlines.

[1211] User terminal processing:

[1212] The server generates a task list and sends it to the user's terminal in JSON format. The terminal then displays the tasks and their progress in calendar or list format. Users can record and manage task progress in real time and mark completed tasks. A reminder function also provides notifications for tasks that are approaching or have passed their deadline.

[1213] Specific example

[1214] Example 1: New product development plan

[1215] 1. User input:

[1216] Job description: "Develop development plans for new products"

[1217] Completion deadline: "2024-03-31"

[1218] 2. Example of a prompt:

[1219] Break down the process of creating a new product development plan into specific tasks.

[1220] 3. Server output:

[1221] Task 1: "Market Needs Survey" (Deadline: 2024-01-31)

[1222] Task 2: "Prototype Design" (Deadline: 2024-02-15)

[1223] Task 3: "Building a Supply Chain" (Deadline: 2024-02-28)

[1224] Task 4: "Develop a sales strategy" (Deadline: 2024-03-15)

[1225] These operations enable users to effectively break down their work into specific tasks and efficiently manage and track their progress.

[1226] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1227] Step 1:

[1228] The user enters the task details and completion deadline. Specifically, they enter the task details and completion deadline into an input form on a smartphone app or web app and press the submit button. The entered task details and completion deadline are sent to the server in JSON format. Input: Task details and completion deadline, Output: JSON format data.

[1229] Step 2:

[1230] The server parses the JSON data it receives. Specifically, the server parses the input data and extracts the task details and completion deadline. Using the parsed data, it sends a prompt message to the generating AI model. Input: "Develop a new product development plan" "2024-03-31" (JSON format), Output: Parsing request (prompt message).

[1231] Step 3:

[1232] The generative AI model analyzes the business content and breaks it down into specific tasks. The generative AI model (e.g., GPT-4) uses prompts to break down the tasks. The analysis results are obtained in JSON format. Input: Prompts; Data processing: Analysis of business content and task decomposition; Output: Specific task list (JSON format).

[1233] Step 4:

[1234] The server assigns completion deadlines to each task based on the generated task list. It automatically sets appropriate deadlines for each task, taking into account the task duration and dependencies. Input: Specific task list (JSON format), Data calculation: Calculation and assignment of completion deadlines, Output: Task list with assigned deadlines.

[1235] Step 5:

[1236] The server generates a task list with assigned deadlines in JSON format and sends it to the user's terminal. The generated task list is formatted as JSON data and sent to the user's terminal. Input: Task list with assigned deadlines; Output: Task list (JSON format).

[1237] Step 6:

[1238] The device receives the task list and displays it in calendar or list format. Users can check the progress of each task while viewing the task list. Input: Task list (JSON format), Data processing: Conversion to calendar or list format, Output: Visual display.

[1239] Step 7:

[1240] Users manage the progress of each task and record completed tasks on their devices. Users mark task completion on the screen, and this information is sent to the server in real time. Input: User-updated progress; Output: Real-time progress status.

[1241] Step 8:

[1242] The server sends reminders for tasks that are approaching or have already passed their deadline. Notifications are sent to the user's device, allowing the user to take action. Input: Task progress and deadline information; Output: Reminder notification.

[1243] This allows for the effective breakdown of work content into specific tasks, and enables efficient management and tracking of their progress.

[1244] 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.

[1245] In a system implementing the present invention, the following means are added: means for the user to input the work content and completion deadline; means for the server to analyze the work content and break it down into specific tasks; means for assigning a completion deadline to each task; means for generating a list of specific tasks; means for sending the task list to the user terminal; means for the terminal to display the task list and for the user to manage the progress of the tasks; and means including an emotion engine that recognizes the user's emotions.

[1246] System Overview

[1247] 1. User input:

[1248] Users enter the task details and completion deadline into an input form on their device. This can be done using text boxes or calendar widgets.

[1249] For example, a user might input the task description "Develop a new product development plan" and the completion deadline "2024-03-31".

[1250] 2. Sending input data:

[1251] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format or similar.

[1252] 3. Analysis of work content:

[1253] The server uses natural language processing (NLP) to analyze the business content based on the received data.

[1254] The analysis results in breaking down the business content into specific tasks. For example, the business content of "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1255] 4. Assigning deadlines:

[1256] The server works backward from the overall completion deadline (e.g., "2024-03-31") and sets appropriate deadlines for each task, taking into account the time required and dependencies. For example, the "Market Needs Survey" task might be assigned a deadline of 2024-01-31.

[1257] 5. Generating a task list:

[1258] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This task list is generated in a structured data format such as JSON.

[1259] 6. Sending and displaying the task list:

[1260] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user. The display method can be either calendar or list format.

[1261] 7. Task management:

[1262] Users view a task list, recording and updating the progress of each task. When a task is completed, the user records this on their device, managing the progress in real time.

[1263] Introducing an emotional engine

[1264] 8. Recognition of emotions:

[1265] The emotion engine analyzes the user's emotions based on the data and progress the user enters. For example, it can determine whether the user is feeling stressed based on their input.

[1266] 9. Use of emotional data:

[1267] The emotion engine analyzes the user's emotional data and provides it to the server. This emotional data plays a particularly important role when the user is feeling stressed or anxious.

[1268] 10. Adjusting the task list display:

[1269] The server adjusts how the task list is displayed based on emotional data. For example, if a user is feeling stressed, it displays high-priority tasks first to reduce their workload.

[1270] 11. Adjusting task priorities:

[1271] Based on emotional data, the server readjusts task priorities. This enables optimal task management tailored to the user's emotional state.

[1272] Explanation of specific examples

[1273] Example: New product development plan

[1274] 1. User input:

[1275] Job description: "Develop development plans for new products"

[1276] Completion deadline: "2024-03-31"

[1277] 2. Sending input data:

[1278] Send the above information to the server in JSON format.

[1279] 3. Analysis of work content:

[1280] Using NLP, the process of "planning the development of a new product" was broken down into "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1281] 4. Assigning deadlines:

[1282] Market needs survey: 2024-01-31

[1283] Prototype design: 2024-02-15

[1284] Building a supply chain: 2024-02-28

[1285] Sales strategy planning: 2024-03-15

[1286] 5. Generating a task list:

[1287] Generate a task list in JSON format.

[1288] 6. Sending and displaying the task list:

[1289] The task list is sent to the device and displayed in a calendar format.

[1290] 7. Task management:

[1291] Users can record and manage the progress of each task in real time.

[1292] 8. Recognition of emotions:

[1293] The emotion engine recognizes the user's emotions based on their input and progress. For example, it might analyze that a user is stressed if they have many sudden deadlines.

[1294] 9. Use of emotional data:

[1295] The emotion engine analyzes the emotion data and provides it to the server.

[1296] 10. Adjusting the task list display:

[1297] If a user is experiencing stress, adjustments will be made, such as displaying higher-priority tasks first.

[1298] 11. Adjusting task priorities:

[1299] By readjusting task priorities based on emotional data, we can reduce user stress and enable them to complete tasks more efficiently.

[1300] This system breaks down work content into specific tasks, allowing for efficient management, while also enabling flexible task management that takes user emotions into consideration.

[1301] The following describes the processing flow.

[1302] Step 1:

[1303] The user enters the task details and completion deadline into the input form on the device. For example, the user enters the task details "Develop a new product development plan" and the completion deadline "2024-03-31" into the text box and calendar widget, respectively.

[1304] Step 2:

[1305] When the user presses the "Send" button, the terminal sends the entered task details and completion deadline to the server as JSON data.

[1306] Step 3:

[1307] The server prepares the data received from the terminal for analysis. The data is first converted to an appropriate format and then passed to the natural language processing (NLP) module.

[1308] Step 4:

[1309] The server uses NLP to analyze the business process and break it down into specific tasks. For example, the business process of "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1310] Step 5:

[1311] The server assigns a completion deadline to each task it breaks down. Working backward from the overall completion deadline (e.g., "2024-03-31"), it sets appropriate deadlines for each task, taking into account their duration and dependencies. For example, the "Market Needs Survey" task is set to have a deadline of "2024-01-31".

[1312] Step 6:

[1313] The server generates a task list by listing specific tasks and their respective completion deadlines. This task list is generated as structured data in JSON format.

[1314] Step 7:

[1315] The server sends the generated task list to the terminal. The terminal prepares to display the received task list.

[1316] Step 8:

[1317] The device displays a task list to the user. The user can visually view the task list in calendar or list format.

[1318] Step 9:

[1319] The user checks the task list and records and updates the progress of each task. For example, when a task is completed, the user marks it as "completed" through their device.

[1320] Step 10:

[1321] The device sends emotional data to the emotion engine in real time based on user input and progress. The emotion engine analyzes the user's emotions from the input content (text, operation history, etc.).

[1322] Step 11:

[1323] The server receives emotional data analyzed by the emotion engine. For example, the analysis data might indicate that the user is experiencing stress.

[1324] Step 12:

[1325] The server adjusts how the task list is displayed based on emotional data. For example, if a user is feeling stressed, the server prioritizes displaying only the most important tasks.

[1326] Step 13:

[1327] The server readjusts task priorities based on sentiment data. This readjustment is intended to optimize the workload by taking the user's emotional state into consideration.

[1328] Step 14:

[1329] Users can view their readjusted task list on their device and continue managing tasks based on the new priorities. This reduces emotional burden and allows users to work more efficiently.

[1330] (Example 2)

[1331] 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."

[1332] Current task management systems offer features that break down user-entered work content into specific tasks and assign appropriate completion deadlines, but they do not consider user emotions or stress levels when displaying tasks or adjusting priorities. Therefore, it is difficult to efficiently manage tasks in work environments where users experience high levels of stress. Furthermore, there is a lack of functionality to analyze user-entered work content using natural language processing, which makes it difficult to deal with ambiguity and unclear input.

[1333] 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.

[1334] In this invention, the server includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for the server to assign a completion deadline to each task, means for the server to generate a list of the specific tasks, means for the server to transmit the generated task list to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, and means for the server to analyze the user's emotions and adjust the display and priority of tasks in order to reduce the workload. This enables the breakdown of the user's work content into specific tasks, the assignment of appropriate deadlines to each task, and flexible task management according to the user's emotional state.

[1335] "Job description" refers to the specific tasks or projects that the user intends to accomplish.

[1336] "Completion deadline" refers to the final date or time by which a particular task or work must be completed.

[1337] A "task" refers to a specific, individual work item obtained by breaking down a series of tasks.

[1338] "JSON format" refers to a lightweight data exchange format for structuring and exchanging data.

[1339] A "device" refers to an electronic device that a user can directly operate, and includes personal computers, smartphones, tablets, and other similar devices.

[1340] A "server" refers to a centralized computer system that provides or processes information over a network.

[1341] A "task list" refers to a list that compiles specific tasks and their completion deadlines.

[1342] "Natural language processing" refers to the technological field in which computers understand, analyze, and generate human language.

[1343] An "emotion engine" refers to a program that analyzes and determines a user's emotions based on their input data and behavior.

[1344] "Priority" refers to the criteria used to determine the order in which multiple tasks or work should be performed, based on their importance and urgency.

[1345] The system implementing this invention involves the user inputting the work content and completion deadline, a server analyzing this information, breaking it down into specific tasks, assigning deadlines, and generating and sending a task list. Furthermore, it can analyze the user's emotional state using an emotion engine and adjust the display and priority of tasks accordingly.

[1346] The user enters the task details and completion deadline using a terminal. The user interface uses text boxes and calendar widgets for input. Once input is complete, the terminal packages the data in JSON format and sends it to the server.

[1347] The server receives JSON data sent by the user and analyzes the business content using a natural language processing (NLP) engine (e.g., spaCy or BERT). Through this analysis, the abstract business content is broken down into concrete tasks. The server then works backward from the overall completion deadline, setting deadlines for each task, taking into account the time required and dependencies. Once the tasks and deadlines are determined, the server generates a task list in JSON format and sends it to the user's terminal.

[1348] The device analyzes the received task list and displays it in calendar or list format. The user records and updates the progress of each task based on the displayed task list. Once a task is completed, the user can mark it as completed.

[1349] Furthermore, an emotion engine is implemented to analyze the user's emotions based on user input data and task progress. The emotion engine, for example, determines whether the user is experiencing stress and provides this data to the server. Based on this emotion data, the server adjusts how the task list is displayed and prioritizes tasks according to the user's stress level. For example, if a user is experiencing high stress, high-priority tasks are displayed first to reduce their workload.

[1350] Specific example

[1351] As a concrete example, let's consider a scenario where a new product development plan is created. The user inputs the following:

[1352] Job description: "Develop development plans for new products"

[1353] Completion deadline: "2024-03-31"

[1354] When a user inputs data, the terminal sends it to the server in JSON format. The server uses an NLP engine to analyze the business content and breaks it down into tasks such as "market needs research," "prototype design," "supply chain construction," and "sales strategy planning." A specific deadline is set for each task. For example, "market needs research" might have a deadline of 2024-01-31, and "prototype design" might have a deadline of 2024-02-15.

[1355] The generated task list is sent to the device and displayed in a calendar format. The user manages their progress based on this task list and checks off completed tasks.

[1356] Example of a prompt:

[1357] Please enter the task description, "Develop a new product development plan," and the completion deadline, "2024-03-31." Based on this, the system will break down the necessary tasks and specify the completion deadline for each task.

[1358] This system not only allows for the efficient management of work by breaking down tasks into specific components and setting deadlines, but also enables flexible task management that adapts to the user's emotional state.

[1359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1360] Step 1:

[1361] The user enters the task details and completion deadline. The user enters the task details "Develop a new product development plan" and the completion deadline "2024-03-31" into the input form on the terminal and presses the submit button.

[1362] Input: Task description and completion deadline

[1363] Output: Data on the task details and completion deadline entered by the user.

[1364] Step 2:

[1365] The terminal packages the entered task details and completion deadline into JSON format and sends it to the server. The terminal uses HTTPS as the transmission protocol.

[1366] Input: Data of task details and completion deadlines entered by the user.

[1367] Data processing: Package the work details and completion deadline data into JSON format.

[1368] Output: Send data in JSON format to the server

[1369] Step 3:

[1370] The server parses the received JSON data and extracts the work content and completion deadline. The server then uses a natural language processing (NLP) engine (e.g., spaCy or BERT) to break down the work content into specific tasks.

[1371] Input: Data in JSON format containing task details and completion deadlines.

[1372] Data processing: Using an NLP engine, business processes are analyzed and broken down into specific tasks such as "market needs research," "prototype design," and "supply chain construction."

[1373] Output: Decomposed specific tasks

[1374] Step 4:

[1375] The server calculates the appropriate completion deadline for each specific task by working backward from the overall deadline, taking into account the time required for each task and its dependencies.

[1376] Input: Specific tasks and overall deadline

[1377] Data calculation: Use a Gantt chart scheduling algorithm to assign appropriate deadlines to each task.

[1378] Output: Specific tasks and their respective deadlines.

[1379] Step 5:

[1380] The server lists the specific tasks broken down into individual components and their respective completion deadlines, generating a task list in JSON format.

[1381] Input: Specific tasks and their respective completion deadlines.

[1382] Data processing: Package specific tasks and deadlines into JSON format.

[1383] Output: Task list in JSON format

[1384] Step 6:

[1385] The server sends the generated task list to the user's terminal. The terminal parses the received task list and displays it in calendar or list format.

[1386] Input: Task list in JSON format

[1387] Data processing: Parse task lists in JSON format and convert them to a display format.

[1388] Output: Task list displayed in calendar or list format

[1389] Step 7:

[1390] Users record and update the progress of their task list. Users enter the progress of each task on their device and mark it as completed when it is finished.

[1391] Input: Task list

[1392] Output: Updated task list and progress

[1393] Step 8:

[1394] The emotion engine analyzes the user's emotions based on their input data and task progress. For example, if a user inputs "I feel stressed," the emotion engine will analyze this as a high stress level.

[1395] Input: User input data and task progress

[1396] Data processing: Analyze emotions using an emotion engine.

[1397] Output: Analyzed sentiment data

[1398] Step 9:

[1399] The server adjusts how the task list is displayed and prioritizes tasks based on the emotional data analyzed by the emotion engine. If the user is experiencing high levels of stress, the server will make adjustments such as displaying higher-priority tasks first.

[1400] Input: Analyzed sentiment data and task list

[1401] Data Calculation: Adjust the display method of the task list and task prioritization based on sentiment data.

[1402] Output: Adjusted task list

[1403] Step 10:

[1404] The server readjusts task priorities based on emotional data. By optimizing the order of tasks according to the user's emotional state, efficient task management is achieved.

[1405] Input: Analyzed sentiment data and task list

[1406] Data processing: Reprioritize tasks based on emotional data.

[1407] Output: Re-adjusted priority task list

[1408] (Application Example 2)

[1409] 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."

[1410] Traditional business management systems simply require users to input work details and deadlines, which are then broken down into a series of tasks without considering the user's emotional state. Therefore, when users experience stress or anxiety, the system is unable to appropriately adjust task priorities or display methods, leading to decreased work efficiency and increased mental burden. Furthermore, even in task management using robots within factories, flexible responses based on emotional states are required.

[1411] 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.

[1412] In this invention, the server includes means for the user to input the work content and completion deadline, means for analyzing the work content and breaking it down into specific tasks, means for assigning completion deadlines to each task, means for generating a list of specific tasks, means for transmitting the generated task list to the user terminal, and means for recognizing the user's emotions and adjusting the display method of the task list and the priority of tasks based on emotion data. This enables robots and users in industrial areas to improve the efficiency of work management and perform flexible task management according to their emotional state.

[1413] A "user" is a person who operates the system to input the details of the work and the completion deadline, or an entity that performs that series of actions.

[1414] "Job description" refers to information that describes the specific tasks and objectives that the user is expected to accomplish.

[1415] "Completion deadline" refers to the date and time by which all tasks within a given project must be completed.

[1416] A "server" is a central computer device that analyzes business processes and breaks down and manages tasks.

[1417] A "task" is a specific set of tasks or actions derived from analyzing the content of a job.

[1418] A "task list" is a collection of information organized in a list format, consisting of broken-down tasks and their respective completion deadlines.

[1419] A "user terminal" is a device used by a user to manage their work and check and operate the progress of tasks.

[1420] "Emotion" refers to the subjective psychological state that a user experiences in response to a particular situation or task.

[1421] "Emotional data" refers to information that expresses a user's emotional state in numerical or text format.

[1422] An "emotion engine" is a system module that analyzes user emotions based on their input and actions, and generates the results.

[1423] The system for implementing this invention comprises means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a list of specific tasks, means for sending the generated task list to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, and means for recognizing the user's emotions and adjusting the display method of the task list and the priority of tasks based on emotion data.

[1424] System Overview

[1425] 1. User input:

[1426] Users input their task details and completion deadlines through a chat-like interface or form. For example, a user might input the task "Review the operation program of the factory robots" and the completion deadline "2024-06-30".

[1427] 2. Sending input data:

[1428] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format.

[1429] 3. Analysis of work content:

[1430] The server uses natural language processing (NLP) to analyze the input business content and break it down into specific tasks. This analysis utilizes a pre-trained generative AI model. For example, analyzing the business content "review the operation program of factory robots" breaks it down into specific tasks such as "review existing code," "design new functions," and "build a test environment."

[1431] 4. Assigning deadlines:

[1432] The server calculates backward from the overall completion deadline entered by the user, and sets appropriate deadlines for each task, taking into account the time required and dependencies. For example, "Review existing code" might be assigned a deadline of 2024-05-31.

[1433] 5. Generating a task list:

[1434] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This task list is generated in JSON format.

[1435] 6. Sending and displaying the task list:

[1436] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user. Calendar and list formats are available for display.

[1437] 7. Task management:

[1438] Users view a task list, recording and updating the progress of each task. When a task is completed, the user records this on their device, managing the progress in real time.

[1439] Introducing an emotional engine

[1440] 8. Emotion recognition:

[1441] The emotion engine analyzes the user's emotions based on the data and progress the user inputs. Based on the analysis results, it determines whether the user is feeling stressed, anxious, or otherwise unsettled.

[1442] 9. Use of emotional data:

[1443] The emotion engine analyzes user emotion data and provides it to the server, which then adjusts how the task list is displayed and prioritized. For example, if a user is feeling stressed, high-priority tasks are displayed first to reduce their workload.

[1444] Explanation of specific examples

[1445] Example: Review of the operation program for factory robots.

[1446] 1. User input:

[1447] Job description: "Review the operation programs of factory robots."

[1448] Completion deadline: "2024-06-30"

[1449] 2. Sending input data:

[1450] Send the above information to the server in JSON format.

[1451] 3. Analysis of work content:

[1452] Using NLP, the task of "revising the motion program of factory robots" was broken down into "reviewing existing code," "designing new functions," and "building a test environment."

[1453] 4. Assigning deadlines:

[1454] Review of existing code: 2024-05-31

[1455] Design of new features: 2024-06-15

[1456] Test environment setup: 2024-06-25

[1457] 5. Generating a task list:

[1458] Generate a task list in JSON format.

[1459] 6. Sending and displaying the task list:

[1460] The task list is sent to the device and displayed in a calendar format.

[1461] 7. Task management:

[1462] Users can record and manage the progress of each task in real time.

[1463] 8. Emotion recognition:

[1464] The emotion engine recognizes the user's emotions based on their input and progress. For example, it might analyze that a user is stressed if they have many sudden deadlines.

[1465] 9. Use of emotional data:

[1466] The emotion engine analyzes the emotion data and provides it to the server.

[1467] 10. Adjusting the task list display:

[1468] If a user is experiencing stress, adjustments will be made, such as displaying higher-priority tasks first.

[1469] 11. Adjusting task priorities:

[1470] By readjusting task priorities based on emotional data, we can reduce user stress and enable them to complete tasks more efficiently.

[1471] Example of a prompt:

[1472] Based on the invention, please break down the following tasks into specific tasks and generate a task list. Also, please explain how to adjust task priorities if the user is experiencing stress.

[1473] Job Description: Review the operation programs of factory robots.

[1474] Completion deadline: 2024-06-30

[1475] Emotional data: Stress level 7

[1476] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1477] Step 1:

[1478] The user enters the task details and completion deadline.

[1479] In terms of specific actions, the user enters the task details (e.g., "Review the operation program of the factory robot") and the completion deadline (e.g., "2024-06-30") into an input form on their smartphone or tablet device, and then presses the "Submit" button.

[1480] Input: Task description, completion deadline

[1481] Output: Data in JSON format (task details, completion deadline)

[1482] Step 2:

[1483] The server receives the input data.

[1484] Specifically, the server receives data in JSON format (task details and completion deadline) sent by the user.

[1485] Input: Data in JSON format

[1486] Output: Stored in the server's internal database.

[1487] Step 3:

[1488] The server analyzes the business content using natural language processing (NLP) and breaks it down into specific tasks.

[1489] Specifically, the server uses an NLP library (e.g., spaCy, GPT-3) to analyze the business content within the JSON data and divides it into relevant specific tasks (e.g., "review existing code," "design new features," "build a test environment").

[1490] Input: Job description (data in JSON format)

[1491] Output: Specific task list (JSON format)

[1492] Step 4:

[1493] The server assigns a completion deadline to each task.

[1494] In terms of specific operation, the server considers the time required for each task and its dependencies, and sets an appropriate deadline by working backward from the overall completion deadline (e.g., "Review existing code: 2024-05-31", "Design new features: 2024-06-15").

[1495] Input: Specific task list, overall completion deadline

[1496] Output: A task list (in JSON format) with completion deadlines for each task.

[1497] Step 5:

[1498] The server generates a task list and sends it to the user's terminal.

[1499] Specifically, the server generates a task list with completion deadlines in JSON format and sends the generated task list to the user's terminal.

[1500] Input: Task list with completion deadlines

[1501] Output: Task list sent to the user's terminal (in JSON format)

[1502] Step 6:

[1503] The device displays a task list, and the user manages the progress of the tasks.

[1504] Specifically, the terminal receives a task list and displays it in a calendar or list format on the user interface. The user inputs progress into the terminal, recording and managing it in real time.

[1505] Input: Task list sent from the server (in JSON format)

[1506] Output: User-updated progress

[1507] Step 7:

[1508] The server uses an emotion engine to recognize the user's emotions.

[1509] Specifically, the server analyzes input data and progress data, and uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to identify the user's emotional state (e.g., stress, anxiety).

[1510] Input: User input, progress data

[1511] Output: Sentiment data

[1512] Step 8:

[1513] The server adjusts how the task list is displayed and prioritizes tasks based on sentiment data.

[1514] Specifically, the server uses emotional data to adjust how the task list is displayed (e.g., displaying important tasks first when stress levels are high) and resets task priorities.

[1515] Input: Sentiment data, task list

[1516] Output: Adjusted task list

[1517] Step 9:

[1518] The server sends the adjusted task list back to the user's terminal.

[1519] Specifically, the server generates a coordinated task list in JSON format and sends it to the user's terminal.

[1520] Input: Adjusted task list

[1521] Output: Adjusted task list sent to the user's terminal

[1522] Example of a prompt:

[1523] Based on the invention, please break down the following tasks into specific tasks and generate a task list. Also, please explain how to adjust task priorities if the user is experiencing stress.

[1524] Job Description: Review the operation programs of factory robots.

[1525] Completion deadline: 2024-06-30

[1526] Emotional data: Stress level 7

[1527] 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.

[1528] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[1529] 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.

[1530] [Fourth Embodiment]

[1531] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1532] 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.

[1533] 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).

[1534] 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.

[1535] 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.

[1536] 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).

[1537] 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.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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".

[1544] A system for implementing the present invention includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a list of specific tasks, means for sending the task list to the user terminal, and means for the terminal to display the task list and for the user to manage the progress of the tasks.

[1545] System Overview

[1546] 1. User input:

[1547] Users enter the task details and completion deadline into an input form on their device. This can be done using text boxes or calendar widgets.

[1548] For example, a user might input the task description "Develop a new product development plan" and the completion deadline "2024-03-31".

[1549] 2. Sending input data:

[1550] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format or similar.

[1551] 3. Analysis of work content:

[1552] The server uses natural language processing (NLP) to analyze the business content based on the received data.

[1553] The analysis results in breaking down the work content into specific tasks. For example, the work content of "planning the development of a new product" can be broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1554] 4. Assigning deadlines:

[1555] The server assigns appropriate deadlines to each task based on the overall completion deadline. This is done by taking into account the time required for each task and its dependencies.

[1556] For example, the "Market Needs Survey" is assigned a deadline of 2024-01-31.

[1557] 5. Generating a task list:

[1558] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This list is also in JSON format.

[1559] 6. Sending and displaying the task list:

[1560] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user.

[1561] The display methods offered include calendar format and list format.

[1562] 7. Task management:

[1563] Users view a task list and record and update the progress of each task.

[1564] When a task is completed, the user records it on their device and manages the progress in real time.

[1565] Explanation of specific examples

[1566] Example: New product development plan

[1567] 1. User input:

[1568] Job description: "Develop development plans for new products"

[1569] Completion deadline: "2024-03-31"

[1570] 2. Sending input data:

[1571] Send the above information to the server in JSON format.

[1572] 3. Analysis of work content:

[1573] Using NLP, the process of "planning the development of a new product" was broken down into "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1574] 4. Assigning deadlines:

[1575] Market needs survey: 2024-01-31

[1576] Prototype design: 2024-02-15

[1577] Building a supply chain: 2024-02-28

[1578] Sales strategy planning: 2024-03-15

[1579] 5. Generating a task list:

[1580] Generate a task list in JSON format.

[1581] 6. Sending and displaying the task list:

[1582] The task list is sent to the device and displayed in a calendar format.

[1583] 7. Task management:

[1584] Users can record and manage the progress of each task in real time.

[1585] This system allows users to easily break down their work into specific tasks and manage them efficiently.

[1586] The following describes the processing flow.

[1587] Step 1:

[1588] The user enters the task details and completion deadline into an input form on the terminal. The input form has fields for the task details (e.g., "Develop a development plan for a new product") and the deadline (e.g., "2024-03-31"), respectively.

[1589] Step 2:

[1590] The terminal sends the entered work details and deadline to the server in a data format such as JSON. Pressing the send button sends this data to the server.

[1591] Step 3:

[1592] The server prepares the data received from the terminal (task details and deadlines) for analysis. The data is first converted to an appropriate format and then passed to the natural language processing (NLP) module.

[1593] Step 4:

[1594] The server analyzes the business content using natural language processing and breaks it down into specific tasks. For example, the business content "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1595] Step 5:

[1596] The server assigns a completion deadline to each task it breaks down. Working backward from the overall completion deadline (e.g., "2024-03-31"), it sets appropriate deadlines for each task, taking into account their duration and dependencies. For example, the deadline for "Market Needs Survey" is set to "2024-01-31".

[1597] Step 6:

[1598] The server generates a task list by listing specific tasks and their respective completion deadlines. This task list is generated in a structured data format such as JSON.

[1599] Step 7:

[1600] The server generates a task list and sends it to the terminal. The task list is formatted in a calendar or list format for easy viewing by the user.

[1601] Step 8:

[1602] The device displays a list of received tasks to the user. The user can view the task list and check the specific details and deadlines of each task. Display options include calendar view and list view.

[1603] Step 9:

[1604] Users manage the progress of each task. When a task is completed, the user marks it as complete via their device. This allows the task management progress to be updated in real time.

[1605] Through this process, a system is created that allows users to efficiently manage their work and proceed according to plan.

[1606] (Example 1)

[1607] 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".

[1608] Efficiently managing work processes is crucial for many companies and individuals, but traditional systems have made it difficult to break down work processes into specific tasks and manage their progress. In particular, there were no systems that automated the entire process from inputting and analyzing work processes to assigning tasks and managing progress. As a result, not only did work efficiency decline, but tasks were also more likely to be missed or deadlines were delayed. There is a need for a system that solves this problem and allows users to manage their work processes quickly and accurately.

[1609] 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.

[1610] In this invention, the server includes means for the user to input the work content and completion deadline; means for the server to analyze the work content and break it down into specific tasks; means for the server to assign a completion deadline to each task; means for the server to generate a list of the specific tasks; means for the server to send the generated task list to the user terminal; means for the terminal to display the task list and for the user to manage the progress of the tasks; means for the terminal to send the user's input to the server in JSON format; means for the server to analyze the received data using natural language processing; means for the server to compile the broken-down specific tasks and their deadlines into a list in JSON format; and means for the terminal to display the task list in calendar format or list format. This enables the user to quickly and accurately manage the work content and to check and update the progress of tasks in real time.

[1611] "User input" refers to the process in which users enter the details of their tasks and the deadline into an input form.

[1612] "Sending input data" refers to the process by which the terminal sends the task details and completion deadline entered by the user to the server.

[1613] "Analysis of business content" refers to the process of breaking down the business content received by the server into specific tasks using natural language processing.

[1614] "Deadline assignment" refers to the process by which the server assigns appropriate deadlines to each task based on the overall completion deadline.

[1615] "Task list generation" refers to the process by which the server generates a list of future tasks based on the analysis and deadline assignment results.

[1616] "Sending and displaying the task list" refers to the process in which the server generates a task list, sends it to the user's terminal, and the terminal displays that list to the user.

[1617] "Task management" refers to the process by which users record and update the progress of each task while referring to the displayed task list.

[1618] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a data format that is easy for humans to read and write, and easy for machines to analyze and generate.

[1619] "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[1620] "Calendar format" refers to a format that displays tasks by date, similar to a calendar.

[1621] "List format" refers to a format in which tasks are displayed in a bulleted list-like manner.

[1622] The system implementing this invention is configured such that the user inputs the work content and completion deadline, the server analyzes the work content and breaks it down into specific tasks, generates a task list and sends it to the terminal, and the terminal displays the task list so that the user can manage the progress of the tasks. A specific embodiment of this system will be described in detail below.

[1623] Specifically, users first use their own devices (such as PCs, smartphones, or tablets) to enter the task details and completion deadline into an input form. The input form includes a text box and a calendar widget, which users use to describe the task in text format and select the completion deadline from the calendar.

[1624] For example, suppose a user enters the task description as "Develop a new product development plan" and the completion deadline as "2024-03-31".

[1625] Next, when the user clicks the "Submit" button, the input content is sent to the server in JSON format. This submission process uses asynchronous communication (Ajax), which improves the user experience.

[1626] On the server side, the received JSON data is analyzed using natural language processing (NLP). Specific NLP modules used include spaCy and NLTK. The server tokenizes the business content text, extracts the meaning of each word and phrase, and breaks it down into multiple specific tasks. For example, analyzing the business content "Develop a new product development plan" breaks it down into specific tasks such as "Market needs research," "Prototype design," "Supply chain construction," and "Sales strategy planning."

[1627] The server then assigns appropriate deadlines to each of the broken-down tasks based on the overall completion deadline. This process uses an algorithm that takes into account the time required for each task and its dependencies. For example, the "Market Needs Survey" task might be assigned a deadline of "2024-01-31".

[1628] Once the task list is generated, the server sends it back to the terminal in JSON format. The terminal then parses this received data and displays it on the user interface. Users can choose between a calendar format or a list format for display; for example, the Google Calendar API can be used to display the list in calendar format.

[1629] Users can view a displayed task list and record and manage the progress of each task in real time. Specifically, they can update the progress status for each task and check off completed tasks. This information is sent from the terminal to the server and stored in a database, so the progress can be checked from other devices as well.

[1630] As described above, this invention enables users to automatically break down their work into specific tasks and manage them efficiently and accurately. Implementing this system prevents task omissions and deadline delays, thereby improving work efficiency.

[1631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1632] Step 1:

[1633] User input

[1634] The user enters the task details and completion deadline into their device. This input is done using an input form (text box and calendar widget) displayed on the device screen. For example, the user might enter "Develop a new product development plan" as the task details and select "2024-03-31" as the completion deadline. The entered data is temporarily stored in the device's memory.

[1635] Input: User-entered task details and completion deadline

[1636] Output: The task details and completion deadline are temporarily saved to the terminal's memory.

[1637] Step 2:

[1638] Sending input data

[1639] When the user clicks the "Submit" button, the device converts the entered data into JSON format and sends it to the server using an Ajax request. For example, the data is sent in the following JSON format:

[1640] json

[1641] {

[1642] "Job Description": "Develop development plans for new products"

[1643] "Completion Deadline": "2024-03-31"

[1644] }

[1645] The terminal displays the status of whether the transmission was successful or unsuccessful.

[1646] Input: The user clicked the "Submit" button.

[1647] Output: The input data is sent to the server in JSON format.

[1648] Step 3:

[1649] Analysis of business operations

[1650] The server executes a Python script based on the received JSON data to perform natural language processing (NLP). Specifically, it uses an NLP module (e.g., spaCy or NLTK) to analyze the business content and extract the meaning of each word and phrase. This breaks down the business content into multiple specific tasks. For example, "Develop a new product development plan" is broken down into "Market needs research," "Prototype design," "Supply chain construction," and "Sales strategy planning."

[1651] Input: JSON data received by the server

[1652] Output: Decomposed specific tasks (internal data list)

[1653] Step 4:

[1654] Assignment of deadlines

[1655] The server assigns appropriate deadlines to each of the broken-down tasks. It uses a Python algorithm that considers the time required and dependencies of each task. For example, the task "Market Needs Survey" is assigned the deadline "2024-01-31".

[1656] Input: Decomposed specific tasks, overall completion deadline

[1657] Output: Specific tasks with assigned deadlines (internal data list)

[1658] Step 5:

[1659] Task list generation

[1660] The server generates a task list in JSON format based on the specific tasks that have been assigned deadlines. For example, the following JSON data is generated:

[1661] json

[1662] {

[1663] "Task": [

[1664] {"Name": "Market Needs Survey", "Deadline": "2024-01-31"}

[1665] {"Name": "Prototype Design", "Deadline": "2024-02-15"},

[1666] {"Name": "Supply Chain Construction", "Deadline": "2024-02-28"},

[1667] {"Name": "Development of Sales Strategy", "Deadline": "2024-03-15"}

[1668] ]

[1669] }

[1670] Input: Specific tasks with assigned deadlines

[1671] Output: Task list in JSON format

[1672] Step 6:

[1673] Sending and displaying task lists

[1674] The server sends the generated task list to the terminal. The terminal parses the received JSON data and displays it in the user interface. Users can choose between a calendar format or a list format for display. For example, the Google Calendar API can be used to display the list in calendar format.

[1675] Input: Task list in JSON format

[1676] Output: Task list displayed on the user's device (calendar or list format)

[1677] Step 7:

[1678] Task management

[1679] Users record and manage the progress of individual tasks in real time while referring to the displayed task list. They update the task progress using the terminal interface and check off completed tasks. This information is then sent back to the server and stored in the database in real time.

[1680] Input: User progress update operation

[1681] Output: Progress information is sent from the terminal to the server and stored in the database.

[1682] The above describes the specific processing flow of this system's program.

[1683] (Application Example 1)

[1684] 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".

[1685] Traditional business management systems had a problem where, when users entered business content that was difficult to break down into specific tasks, the system could not assign appropriate tasks and deadlines to that content. Furthermore, the inability to check task progress in real time meant that users could not properly address tasks that had passed their deadlines. In addition, the difficulty in visually grasping progress contributed to decreased work efficiency.

[1686] 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.

[1687] In this invention, the server includes means for analyzing the business content entered by the user and breaking it down into specific tasks, means for automatically assigning a completion deadline to each task and sending reminders to tasks that have passed their deadline, and means for using a generative AI model for analysis and obtaining highly accurate results using prompt sentences. This makes it possible to efficiently break down business content into specific tasks and assign appropriate deadlines. In addition, through task progress management and visual display, the user can grasp the status of tasks in real time and respond efficiently.

[1688] "Job description" refers to the overall tasks or project outline that the user intends to perform.

[1689] "Completion deadline" refers to the deadline entered by the user for completing the task.

[1690] A "task" refers to a specific unit of work obtained by analyzing the content of a job.

[1691] A "list" refers to a collection of multiple tasks organized into a single list.

[1692] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[1693] A "generative AI model" refers to an artificial intelligence model that uses machine learning to generate and analyze text.

[1694] A "prompt" refers to an instruction or question that is input to a generative AI model.

[1695] "Automatic assignment of deadlines" refers to the process by which the system automatically sets an appropriate completion deadline for each task.

[1696] A "reminder" refers to a function that notifies you about tasks that are due soon or have already passed their deadline.

[1697] "Calendar format" refers to a format in which tasks are visually displayed on a calendar.

[1698] "List format" refers to a format in which tasks are displayed in a sequential order.

[1699] A "user terminal" refers to a device, such as a smartphone or computer, that a user uses to access the system.

[1700] A system for implementing this invention includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a task list and sending it to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, means for automatically assigning a deadline to each task and sending a reminder to tasks that have passed their deadline, and means for using a generative AI model for analysis and using prompt statements to obtain highly accurate results.

[1701] Hardware and software to use

[1702] 1. Hardware

[1703] Servers: Cloud servers or on-premises servers are used for analyzing business processes and managing task deadlines.

[1704] User terminal: Use a smartphone or personal computer.

[1705] 2. Software

[1706] Natural Language Processing (NLP) Model: A generative AI model (e.g., GPT-4) is used to analyze business content and break it down into specific tasks.

[1707] Framework: For server-side processing, we use web frameworks such as Flask or Django.

[1708] Data format: Data is sent and received in formats such as JSON.

[1709] Processing flow

[1710] User actions:

[1711] Users enter the details of their tasks and their completion deadlines into an input form via a smartphone app or web app, and then press the submit button. A text box and a calendar widget for selecting dates are provided during this process.

[1712] Server processing:

[1713] The entered task details and completion deadlines are sent to the server in JSON format. The server uses an NLP model to analyze the task details and break them down into specific tasks. For example, a task like "develop a new product development plan" is broken down into specific tasks such as "market needs research," "prototype design," "supply chain construction," and "sales strategy planning." For each of the broken-down tasks, the system automatically assigns an appropriate deadline, taking into account completion deadlines and dependencies. It also has a function to send reminders for tasks that are approaching or have already passed their deadlines.

[1714] User terminal processing:

[1715] The server generates a task list and sends it to the user's terminal in JSON format. The terminal then displays the tasks and their progress in calendar or list format. Users can record and manage task progress in real time and mark completed tasks. A reminder function also provides notifications for tasks that are approaching or have passed their deadline.

[1716] Specific example

[1717] Example 1: New product development plan

[1718] 1. User input:

[1719] Job description: "Develop development plans for new products"

[1720] Completion deadline: "2024-03-31"

[1721] 2. Example of a prompt:

[1722] Break down the process of creating a new product development plan into specific tasks.

[1723] 3. Server output:

[1724] Task 1: "Market Needs Survey" (Deadline: 2024-01-31)

[1725] Task 2: "Prototype Design" (Deadline: 2024-02-15)

[1726] Task 3: "Building a Supply Chain" (Deadline: 2024-02-28)

[1727] Task 4: "Develop a sales strategy" (Deadline: 2024-03-15)

[1728] These operations enable users to effectively break down their work into specific tasks and efficiently manage and track their progress.

[1729] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1730] Step 1:

[1731] The user enters the task details and completion deadline. Specifically, they enter the task details and completion deadline into an input form on a smartphone app or web app and press the submit button. The entered task details and completion deadline are sent to the server in JSON format. Input: Task details and completion deadline, Output: JSON format data.

[1732] Step 2:

[1733] The server parses the JSON data it receives. Specifically, the server parses the input data and extracts the task details and completion deadline. Using the parsed data, it sends a prompt message to the generating AI model. Input: "Develop a new product development plan" "2024-03-31" (JSON format), Output: Parsing request (prompt message).

[1734] Step 3:

[1735] The generative AI model analyzes the business content and breaks it down into specific tasks. The generative AI model (e.g., GPT-4) uses prompts to break down the tasks. The analysis results are obtained in JSON format. Input: Prompts; Data processing: Analysis of business content and task decomposition; Output: Specific task list (JSON format).

[1736] Step 4:

[1737] The server assigns completion deadlines to each task based on the generated task list. It automatically sets appropriate deadlines for each task, taking into account the task duration and dependencies. Input: Specific task list (JSON format), Data calculation: Calculation and assignment of completion deadlines, Output: Task list with assigned deadlines.

[1738] Step 5:

[1739] The server generates a task list with assigned deadlines in JSON format and sends it to the user's terminal. The generated task list is formatted as JSON data and sent to the user's terminal. Input: Task list with assigned deadlines; Output: Task list (JSON format).

[1740] Step 6:

[1741] The device receives the task list and displays it in calendar or list format. Users can check the progress of each task while viewing the task list. Input: Task list (JSON format), Data processing: Conversion to calendar or list format, Output: Visual display.

[1742] Step 7:

[1743] Users manage the progress of each task and record completed tasks on their devices. Users mark task completion on the screen, and this information is sent to the server in real time. Input: User-updated progress; Output: Real-time progress status.

[1744] Step 8:

[1745] The server sends reminders for tasks that are approaching or have already passed their deadline. Notifications are sent to the user's device, allowing the user to take action. Input: Task progress and deadline information; Output: Reminder notification.

[1746] This allows for the effective breakdown of work content into specific tasks, and enables efficient management and tracking of their progress.

[1747] 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.

[1748] In a system implementing the present invention, the following means are added: means for the user to input the work content and completion deadline; means for the server to analyze the work content and break it down into specific tasks; means for assigning a completion deadline to each task; means for generating a list of specific tasks; means for sending the task list to the user terminal; means for the terminal to display the task list and for the user to manage the progress of the tasks; and means including an emotion engine that recognizes the user's emotions.

[1749] System Overview

[1750] 1. User input:

[1751] Users enter the task details and completion deadline into an input form on their device. This can be done using text boxes or calendar widgets.

[1752] For example, a user might input the task description "Develop a new product development plan" and the completion deadline "2024-03-31".

[1753] 2. Sending input data:

[1754] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format or similar.

[1755] 3. Analysis of work content:

[1756] The server uses natural language processing (NLP) to analyze the business content based on the received data.

[1757] The analysis results in breaking down the business content into specific tasks. For example, the business content of "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1758] 4. Assigning deadlines:

[1759] The server works backward from the overall completion deadline (e.g., "2024-03-31") and sets appropriate deadlines for each task, taking into account the time required and dependencies. For example, the "Market Needs Survey" task might be assigned a deadline of 2024-01-31.

[1760] 5. Generating a task list:

[1761] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This task list is generated in a structured data format such as JSON.

[1762] 6. Sending and displaying the task list:

[1763] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user. The display method can be either calendar or list format.

[1764] 7. Task management:

[1765] Users view a task list, recording and updating the progress of each task. When a task is completed, the user records this on their device, managing the progress in real time.

[1766] Introducing an emotional engine

[1767] 8. Recognition of emotions:

[1768] The emotion engine analyzes the user's emotions based on the data and progress the user enters. For example, it can determine whether the user is feeling stressed based on their input.

[1769] 9. Use of emotional data:

[1770] The emotion engine analyzes the user's emotional data and provides it to the server. This emotional data plays a particularly important role when the user is feeling stressed or anxious.

[1771] 10. Adjusting the task list display:

[1772] The server adjusts how the task list is displayed based on emotional data. For example, if a user is feeling stressed, it displays high-priority tasks first to reduce their workload.

[1773] 11. Adjusting task priorities:

[1774] Based on emotional data, the server readjusts task priorities. This enables optimal task management tailored to the user's emotional state.

[1775] Explanation of specific examples

[1776] Example: New product development plan

[1777] 1. User input:

[1778] Job description: "Develop development plans for new products"

[1779] Completion deadline: "2024-03-31"

[1780] 2. Sending input data:

[1781] Send the above information to the server in JSON format.

[1782] 3. Analysis of work content:

[1783] Using NLP, the process of "planning the development of a new product" was broken down into "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1784] 4. Assigning deadlines:

[1785] Market needs survey: 2024-01-31

[1786] Prototype design: 2024-02-15

[1787] Building a supply chain: 2024-02-28

[1788] Sales strategy planning: 2024-03-15

[1789] 5. Generating a task list:

[1790] Generate a task list in JSON format.

[1791] 6. Sending and displaying the task list:

[1792] The task list is sent to the device and displayed in a calendar format.

[1793] 7. Task management:

[1794] Users can record and manage the progress of each task in real time.

[1795] 8. Recognition of emotions:

[1796] The emotion engine recognizes the user's emotions based on their input and progress. For example, it might analyze that a user is stressed if they have many sudden deadlines.

[1797] 9. Use of emotional data:

[1798] The emotion engine analyzes the emotion data and provides it to the server.

[1799] 10. Adjusting the task list display:

[1800] If a user is experiencing stress, adjustments will be made, such as displaying higher-priority tasks first.

[1801] 11. Adjusting task priorities:

[1802] By readjusting task priorities based on emotional data, we can reduce user stress and enable them to complete tasks more efficiently.

[1803] This system breaks down work content into specific tasks, allowing for efficient management, while also enabling flexible task management that takes user emotions into consideration.

[1804] The following describes the processing flow.

[1805] Step 1:

[1806] The user enters the task details and completion deadline into the input form on the device. For example, the user enters the task details "Develop a new product development plan" and the completion deadline "2024-03-31" into the text box and calendar widget, respectively.

[1807] Step 2:

[1808] When the user presses the "Send" button, the terminal sends the entered task details and completion deadline to the server as JSON data.

[1809] Step 3:

[1810] The server prepares the data received from the terminal for analysis. The data is first converted to an appropriate format and then passed to the natural language processing (NLP) module.

[1811] Step 4:

[1812] The server uses NLP to analyze the business process and break it down into specific tasks. For example, the business process of "planning the development of a new product" is broken down into specific tasks such as "researching market needs," "designing a prototype," "building a supply chain," and "developing a sales strategy."

[1813] Step 5:

[1814] The server assigns a completion deadline to each task it breaks down. Working backward from the overall completion deadline (e.g., "2024-03-31"), it sets appropriate deadlines for each task, taking into account their duration and dependencies. For example, the "Market Needs Survey" task is set to have a deadline of "2024-01-31".

[1815] Step 6:

[1816] The server generates a task list by listing specific tasks and their respective completion deadlines. This task list is generated as structured data in JSON format.

[1817] Step 7:

[1818] The server sends the generated task list to the terminal. The terminal prepares to display the received task list.

[1819] Step 8:

[1820] The device displays a task list to the user. The user can visually view the task list in calendar or list format.

[1821] Step 9:

[1822] The user checks the task list and records and updates the progress of each task. For example, when a task is completed, the user marks it as "completed" through their device.

[1823] Step 10:

[1824] The device sends emotional data to the emotion engine in real time based on user input and progress. The emotion engine analyzes the user's emotions from the input content (text, operation history, etc.).

[1825] Step 11:

[1826] The server receives emotional data analyzed by the emotion engine. For example, the analysis data might indicate that the user is experiencing stress.

[1827] Step 12:

[1828] The server adjusts how the task list is displayed based on emotional data. For example, if a user is feeling stressed, the server prioritizes displaying only the most important tasks.

[1829] Step 13:

[1830] The server readjusts task priorities based on sentiment data. This readjustment is intended to optimize the workload by taking the user's emotional state into consideration.

[1831] Step 14:

[1832] Users can view their readjusted task list on their device and continue managing tasks based on the new priorities. This reduces emotional burden and allows users to work more efficiently.

[1833] (Example 2)

[1834] 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".

[1835] Current task management systems offer features that break down user-entered work content into specific tasks and assign appropriate completion deadlines, but they do not consider user emotions or stress levels when displaying tasks or adjusting priorities. Therefore, it is difficult to efficiently manage tasks in work environments where users experience high levels of stress. Furthermore, there is a lack of functionality to analyze user-entered work content using natural language processing, which makes it difficult to deal with ambiguity and unclear input.

[1836] 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.

[1837] In this invention, the server includes means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for the server to assign a completion deadline to each task, means for the server to generate a list of the specific tasks, means for the server to transmit the generated task list to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, and means for the server to analyze the user's emotions and adjust the display and priority of tasks in order to reduce the workload. This enables the breakdown of the user's work content into specific tasks, the assignment of appropriate deadlines to each task, and flexible task management according to the user's emotional state.

[1838] "Job description" refers to the specific tasks or projects that the user intends to accomplish.

[1839] "Completion deadline" refers to the final date or time by which a particular task or work must be completed.

[1840] A "task" refers to a specific, individual work item obtained by breaking down a series of tasks.

[1841] "JSON format" refers to a lightweight data exchange format for structuring and exchanging data.

[1842] A "device" refers to an electronic device that a user can directly operate, and includes personal computers, smartphones, tablets, and other similar devices.

[1843] A "server" refers to a centralized computer system that provides or processes information over a network.

[1844] A "task list" refers to a list that compiles specific tasks and their completion deadlines.

[1845] "Natural language processing" refers to the technological field in which computers understand, analyze, and generate human language.

[1846] An "emotion engine" refers to a program that analyzes and determines a user's emotions based on their input data and behavior.

[1847] "Priority" refers to the criteria used to determine the order in which multiple tasks or work should be performed, based on their importance and urgency.

[1848] The system implementing this invention involves the user inputting the work content and completion deadline, a server analyzing this information, breaking it down into specific tasks, assigning deadlines, and generating and sending a task list. Furthermore, it can analyze the user's emotional state using an emotion engine and adjust the display and priority of tasks accordingly.

[1849] The user enters the task details and completion deadline using a terminal. The user interface uses text boxes and calendar widgets for input. Once input is complete, the terminal packages the data in JSON format and sends it to the server.

[1850] The server receives JSON data sent by the user and analyzes the business content using a natural language processing (NLP) engine (e.g., spaCy or BERT). Through this analysis, the abstract business content is broken down into concrete tasks. The server then works backward from the overall completion deadline, setting deadlines for each task, taking into account the time required and dependencies. Once the tasks and deadlines are determined, the server generates a task list in JSON format and sends it to the user's terminal.

[1851] The device analyzes the received task list and displays it in calendar or list format. The user records and updates the progress of each task based on the displayed task list. Once a task is completed, the user can mark it as completed.

[1852] Furthermore, an emotion engine is implemented to analyze the user's emotions based on user input data and task progress. The emotion engine, for example, determines whether the user is experiencing stress and provides this data to the server. Based on this emotion data, the server adjusts how the task list is displayed and prioritizes tasks according to the user's stress level. For example, if a user is experiencing high stress, high-priority tasks are displayed first to reduce their workload.

[1853] Specific example

[1854] As a concrete example, let's consider a scenario where a new product development plan is created. The user inputs the following:

[1855] Job description: "Develop development plans for new products"

[1856] Completion deadline: "2024-03-31"

[1857] When a user inputs data, the terminal sends it to the server in JSON format. The server uses an NLP engine to analyze the business content and breaks it down into tasks such as "market needs research," "prototype design," "supply chain construction," and "sales strategy planning." A specific deadline is set for each task. For example, "market needs research" might have a deadline of 2024-01-31, and "prototype design" might have a deadline of 2024-02-15.

[1858] The generated task list is sent to the device and displayed in a calendar format. The user manages their progress based on this task list and checks off completed tasks.

[1859] Example of a prompt:

[1860] Please enter the task description, "Develop a new product development plan," and the completion deadline, "2024-03-31." Based on this, the system will break down the necessary tasks and specify the completion deadline for each task.

[1861] This system not only allows for the efficient management of work by breaking down tasks into specific components and setting deadlines, but also enables flexible task management that adapts to the user's emotional state.

[1862] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1863] Step 1:

[1864] The user enters the task details and completion deadline. The user enters the task details "Develop a new product development plan" and the completion deadline "2024-03-31" into the input form on the terminal and presses the submit button.

[1865] Input: Task description and completion deadline

[1866] Output: Data on the task details and completion deadline entered by the user.

[1867] Step 2:

[1868] The terminal packages the entered task details and completion deadline into JSON format and sends it to the server. The terminal uses HTTPS as the transmission protocol.

[1869] Input: Data of task details and completion deadlines entered by the user.

[1870] Data processing: Package the work details and completion deadline data into JSON format.

[1871] Output: Send data in JSON format to the server

[1872] Step 3:

[1873] The server parses the received JSON data and extracts the work content and completion deadline. The server then uses a natural language processing (NLP) engine (e.g., spaCy or BERT) to break down the work content into specific tasks.

[1874] Input: Data in JSON format containing task details and completion deadlines.

[1875] Data processing: Using an NLP engine, business processes are analyzed and broken down into specific tasks such as "market needs research," "prototype design," and "supply chain construction."

[1876] Output: Decomposed specific tasks

[1877] Step 4:

[1878] The server calculates the appropriate completion deadline for each specific task by working backward from the overall deadline, taking into account the time required for each task and its dependencies.

[1879] Input: Specific tasks and overall deadline

[1880] Data calculation: Use a Gantt chart scheduling algorithm to assign appropriate deadlines to each task.

[1881] Output: Specific tasks and their respective deadlines.

[1882] Step 5:

[1883] The server lists the specific tasks broken down into individual components and their respective completion deadlines, generating a task list in JSON format.

[1884] Input: Specific tasks and their respective completion deadlines.

[1885] Data processing: Package specific tasks and deadlines into JSON format.

[1886] Output: Task list in JSON format

[1887] Step 6:

[1888] The server sends the generated task list to the user's terminal. The terminal parses the received task list and displays it in calendar or list format.

[1889] Input: Task list in JSON format

[1890] Data processing: Parse task lists in JSON format and convert them to a display format.

[1891] Output: Task list displayed in calendar or list format

[1892] Step 7:

[1893] Users record and update the progress of their task list. Users enter the progress of each task on their device and mark it as completed when it is finished.

[1894] Input: Task list

[1895] Output: Updated task list and progress

[1896] Step 8:

[1897] The emotion engine analyzes the user's emotions based on their input data and task progress. For example, if a user inputs "I feel stressed," the emotion engine will analyze this as a high stress level.

[1898] Input: User input data and task progress

[1899] Data processing: Analyze emotions using an emotion engine.

[1900] Output: Analyzed sentiment data

[1901] Step 9:

[1902] The server adjusts how the task list is displayed and prioritizes tasks based on the emotional data analyzed by the emotion engine. If the user is experiencing high levels of stress, the server will make adjustments such as displaying higher-priority tasks first.

[1903] Input: Analyzed sentiment data and task list

[1904] Data Calculation: Adjust the display method of the task list and task prioritization based on sentiment data.

[1905] Output: Adjusted task list

[1906] Step 10:

[1907] The server readjusts task priorities based on emotional data. By optimizing the order of tasks according to the user's emotional state, efficient task management is achieved.

[1908] Input: Analyzed sentiment data and task list

[1909] Data processing: Reprioritize tasks based on emotional data.

[1910] Output: Re-adjusted priority task list

[1911] (Application Example 2)

[1912] 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".

[1913] Traditional business management systems simply require users to input work details and deadlines, which are then broken down into a series of tasks without considering the user's emotional state. Therefore, when users experience stress or anxiety, the system is unable to appropriately adjust task priorities or display methods, leading to decreased work efficiency and increased mental burden. Furthermore, even in task management using robots within factories, flexible responses based on emotional states are required.

[1914] 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.

[1915] In this invention, the server includes means for the user to input the work content and completion deadline, means for analyzing the work content and breaking it down into specific tasks, means for assigning completion deadlines to each task, means for generating a list of specific tasks, means for transmitting the generated task list to the user terminal, and means for recognizing the user's emotions and adjusting the display method of the task list and the priority of tasks based on emotion data. This enables robots and users in industrial areas to improve the efficiency of work management and perform flexible task management according to their emotional state.

[1916] A "user" is a person who operates the system to input the details of the work and the completion deadline, or an entity that performs that series of actions.

[1917] "Job description" refers to information that describes the specific tasks and objectives that the user is expected to accomplish.

[1918] "Completion deadline" refers to the date and time by which all tasks within a given project must be completed.

[1919] A "server" is a central computer device that analyzes business processes and breaks down and manages tasks.

[1920] A "task" is a specific set of tasks or actions derived from analyzing the content of a job.

[1921] A "task list" is a collection of information organized in a list format, consisting of broken-down tasks and their respective completion deadlines.

[1922] A "user terminal" is a device used by a user to manage their work and check and operate the progress of tasks.

[1923] "Emotion" refers to the subjective psychological state that a user experiences in response to a particular situation or task.

[1924] "Emotional data" refers to information that expresses a user's emotional state in numerical or text format.

[1925] An "emotion engine" is a system module that analyzes user emotions based on their input and actions, and generates the results.

[1926] The system for implementing this invention comprises means for the user to input the work content and completion deadline, means for the server to analyze the work content and break it down into specific tasks, means for assigning a completion deadline to each task, means for generating a list of specific tasks, means for sending the generated task list to the user terminal, means for the terminal to display the task list and for the user to manage the progress of the tasks, and means for recognizing the user's emotions and adjusting the display method of the task list and the priority of tasks based on emotion data.

[1927] System Overview

[1928] 1. User input:

[1929] Users input their task details and completion deadlines through a chat-like interface or form. For example, a user might input the task "Review the operation program of the factory robots" and the completion deadline "2024-06-30".

[1930] 2. Sending input data:

[1931] When the user presses the "Submit" button, the entered task details and completion deadline are sent to the server in JSON format.

[1932] 3. Analysis of work content:

[1933] The server uses natural language processing (NLP) to analyze the input business content and break it down into specific tasks. This analysis utilizes a pre-trained generative AI model. For example, analyzing the business content "review the operation program of factory robots" breaks it down into specific tasks such as "review existing code," "design new functions," and "build a test environment."

[1934] 4. Assigning deadlines:

[1935] The server calculates backward from the overall completion deadline entered by the user, and sets appropriate deadlines for each task, taking into account the time required and dependencies. For example, "Review existing code" might be assigned a deadline of 2024-05-31.

[1936] 5. Generating a task list:

[1937] The specific tasks are broken down and their deadlines are listed, then compiled into a task list. This task list is generated in JSON format.

[1938] 6. Sending and displaying the task list:

[1939] The server sends the generated task list to the user's terminal, and the terminal displays the task list to the user. Calendar and list formats are available for display.

[1940] 7. Task management:

[1941] Users view a task list, recording and updating the progress of each task. When a task is completed, the user records this on their device, managing the progress in real time.

[1942] Introducing an emotional engine

[1943] 8. Emotion recognition:

[1944] The emotion engine analyzes the user's emotions based on the data and progress the user inputs. Based on the analysis results, it determines whether the user is feeling stressed, anxious, or otherwise unsettled.

[1945] 9. Use of emotional data:

[1946] The emotion engine analyzes user emotion data and provides it to the server, which then adjusts how the task list is displayed and prioritized. For example, if a user is feeling stressed, high-priority tasks are displayed first to reduce their workload.

[1947] Explanation of specific examples

[1948] Example: Review of the operation program for factory robots.

[1949] 1. User input:

[1950] Job description: "Review the operation programs of factory robots."

[1951] Completion deadline: "2024-06-30"

[1952] 2. Sending input data:

[1953] Send the above information to the server in JSON format.

[1954] 3. Analysis of work content:

[1955] Using NLP, the task of "revising the motion program of factory robots" was broken down into "reviewing existing code," "designing new functions," and "building a test environment."

[1956] 4. Assigning deadlines:

[1957] Review of existing code: 2024-05-31

[1958] Design of new features: 2024-06-15

[1959] Test environment setup: 2024-06-25

[1960] 5. Generating a task list:

[1961] Generate a task list in JSON format.

[1962] 6. Sending and displaying the task list:

[1963] The task list is sent to the device and displayed in a calendar format.

[1964] 7. Task management:

[1965] Users can record and manage the progress of each task in real time.

[1966] 8. Emotion recognition:

[1967] The emotion engine recognizes the user's emotions based on their input and progress. For example, it might analyze that a user is stressed if they have many sudden deadlines.

[1968] 9. Use of emotional data:

[1969] The emotion engine analyzes the emotion data and provides it to the server.

[1970] 10. Adjusting the task list display:

[1971] If a user is experiencing stress, adjustments will be made, such as displaying higher-priority tasks first.

[1972] 11. Adjusting task priorities:

[1973] By readjusting task priorities based on emotional data, we can reduce user stress and enable them to complete tasks more efficiently.

[1974] Example of a prompt:

[1975] Based on the invention, please break down the following tasks into specific tasks and generate a task list. Also, please explain how to adjust task priorities if the user is experiencing stress.

[1976] Job Description: Review the operation programs of factory robots.

[1977] Completion deadline: 2024-06-30

[1978] Emotional data: Stress level 7

[1979] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1980] Step 1:

[1981] The user enters the task details and completion deadline.

[1982] In terms of specific actions, the user enters the task details (e.g., "Review the operation program of the factory robot") and the completion deadline (e.g., "2024-06-30") into an input form on their smartphone or tablet device, and then presses the "Submit" button.

[1983] Input: Task description, completion deadline

[1984] Output: Data in JSON format (task details, completion deadline)

[1985] Step 2:

[1986] The server receives the input data.

[1987] Specifically, the server receives data in JSON format (task details and completion deadline) sent by the user.

[1988] Input: Data in JSON format

[1989] Output: Stored in the server's internal database.

[1990] Step 3:

[1991] The server analyzes the business content using natural language processing (NLP) and breaks it down into specific tasks.

[1992] Specifically, the server uses an NLP library (e.g., spaCy, GPT-3) to analyze the business content within the JSON data and divides it into relevant specific tasks (e.g., "review existing code," "design new features," "build a test environment").

[1993] Input: Job description (data in JSON format)

[1994] Output: Specific task list (JSON format)

[1995] Step 4:

[1996] The server assigns a completion deadline to each task.

[1997] In terms of specific operation, the server considers the time required for each task and its dependencies, and sets an appropriate deadline by working backward from the overall completion deadline (e.g., "Review existing code: 2024-05-31", "Design new features: 2024-06-15").

[1998] Input: Specific task list, overall completion deadline

[1999] Output: A task list (in JSON format) with completion deadlines for each task.

[2000] Step 5:

[2001] The server generates a task list and sends it to the user's terminal.

[2002] Specifically, the server generates a task list with completion deadlines in JSON format and sends the generated task list to the user's terminal.

[2003] Input: Task list with completion deadlines

[2004] Output: Task list sent to the user's terminal (in JSON format)

[2005] Step 6:

[2006] The device displays a task list, and the user manages the progress of the tasks.

[2007] Specifically, the terminal receives a task list and displays it in a calendar or list format on the user interface. The user inputs progress into the terminal, recording and managing it in real time.

[2008] Input: Task list sent from the server (in JSON format)

[2009] Output: User-updated progress

[2010] Step 7:

[2011] The server uses an emotion engine to recognize the user's emotions.

[2012] Specifically, the server analyzes input data and progress data, and uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to identify the user's emotional state (e.g., stress, anxiety).

[2013] Input: User input, progress data

[2014] Output: Sentiment data

[2015] Step 8:

[2016] The server adjusts how the task list is displayed and prioritizes tasks based on sentiment data.

[2017] Specifically, the server uses emotional data to adjust how the task list is displayed (e.g., displaying important tasks first when stress levels are high) and resets task priorities.

[2018] Input: Sentiment data, task list

[2019] Output: Adjusted task list

[2020] Step 9:

[2021] The server sends the adjusted task list back to the user's terminal.

[2022] Specifically, the server generates a coordinated task list in JSON format and sends it to the user's terminal.

[2023] Input: Adjusted task list

[2024] Output: Adjusted task list sent to the user's terminal

[2025] Example of a prompt:

[2026] Based on the invention, please break down the following tasks into specific tasks and generate a task list. Also, please explain how to adjust task priorities if the user is experiencing stress.

[2027] Job Description: Review the operation programs of factory robots.

[2028] Completion deadline: 2024-06-30

[2029] Emotional data: Stress level 7

[2030] 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.

[2031] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[2032] 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 robot 414.

[2033] 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.

[2034] 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.

[2035] 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.

[2036] 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.

[2037] 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.

[2038] 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."

[2039] 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.

[2040] 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.

[2041] 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.

[2042] 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.

[2043] 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.

[2044] 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.

[2045] 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.

[2046] 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.

[2047] 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.

[2048] 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.

[2049] 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.

[2050] 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.

[2051] The following is further disclosed regarding the embodiments described above.

[2052] (Claim 1)

[2053] A means for users to input the details of the work and the completion deadline,

[2054] The server analyzes the aforementioned business content and breaks it down into specific tasks,

[2055] The server provides a means for assigning a completion deadline to each of the aforementioned tasks,

[2056] The server provides means for generating the list of specific tasks,

[2057] A means of sending a task list generated by the server to the user terminal,

[2058] The terminal displays the task list and the user manages the progress of the tasks,

[2059] A system that includes this.

[2060] (Claim 2)

[2061] The system according to claim 1, wherein the server analyzes user input using natural language processing.

[2062] (Claim 3)

[2063] The system according to claim 1, wherein the terminal displays the task list in calendar format or list format.

[2064] "Example 1"

[2065] (Claim 1)

[2066] A means for users to input the details of the work and the completion deadline,

[2067] The server analyzes the aforementioned business content and breaks it down into specific tasks,

[2068] The server provides a means for assigning a completion deadline to each of the aforementioned tasks,

[2069] The server provides means for generating the list of specific tasks,

[2070] A means of sending a task list generated by the server to the user terminal,

[2071] The terminal displays the task list and the user manages the progress of the tasks,

[2072] A means by which the terminal sends user input to the server in JSON format,

[2073] A means of analyzing data received by the server using natural language processing,

[2074] A method for compiling the specific tasks and their deadlines, broken down by the server, into a JSON-formatted list,

[2075] A means by which the device displays the task list in calendar format or list format,

[2076] A system that includes this.

[2077] (Claim 2)

[2078] The system according to claim 1, wherein the server uses an algorithm that assigns deadlines considering the time required and dependencies of the broken-down tasks.

[2079] (Claim 3)

[2080] The system according to claim 1, which allows a user to record and manage the progress of a task in real time via a terminal.

[2081] "Application Example 1"

[2082] (Claim 1)

[2083] A means for users to input the details of the work and the completion deadline,

[2084] The server analyzes the aforementioned business content and breaks it down into specific tasks,

[2085] The server provides a means for assigning a completion deadline to each of the aforementioned tasks,

[2086] The server provides means for generating the list of specific tasks,

[2087] A means of sending a task list generated by the server to the user terminal,

[2088] The terminal displays the task list and the user manages the progress of the tasks,

[2089] A means of automatically assigning deadlines to each task and sending reminders for tasks that have passed their deadlines,

[2090] A system that includes this.

[2091] (Claim 2)

[2092] The system according to claim 1, wherein the server analyzes user input using natural language processing.

[2093] (Claim 3)

[2094] The system according to claim 1, wherein the terminal displays a task list in calendar format or list format and visually displays the progress.

[2095] (Claim 4)

[2096] The system according to claim 1, wherein the system uses an AI model generated by the system to analyze the business content and break it down into specific tasks.

[2097] (Claim 5)

[2098] The system according to claim 4, further comprising means for using prompt statements with respect to the generated AI model and obtaining analysis results with high accuracy.

[2099] "Example 2 of combining an emotion engine"

[2100] (Claim 1)

[2101] A means for users to input the details of the work and the completion deadline,

[2102] A server provides a means for analyzing the aforementioned business content and breaking it down into specific tasks,

[2103] The server provides a means for assigning a completion deadline to each of the aforementioned tasks,

[2104] The server provides means for generating the list of specific tasks,

[2105] A means of sending a task list generated by the server to the user terminal,

[2106] A means by which the terminal displays the task list and the user manages the progress of the tasks,

[2107] A means by which the server analyzes user sentiment and adjusts the display and priority of tasks to reduce workload,

[2108] A system that includes this.

[2109] (Claim 2)

[2110] The system according to claim 1, wherein the server analyzes user input using natural language processing.

[2111] (Claim 3)

[2112] The system according to claim 1, wherein the terminal displays the task list in calendar format or list format.

[2113] "Application example 2 when combining with an emotional engine"

[2114] (Claim 1)

[2115] A means for users to input the details of the work and the completion deadline,

[2116] The server analyzes the aforementioned business content and breaks it down into specific tasks,

[2117] The server provides a means for assigning a completion deadline to each of the aforementioned tasks,

[2118] The server provides means for generating the list of specific tasks,

[2119] A means of sending a task list generated by the server to the user terminal,

[2120] The terminal displays the task list and the user manages the progress of the tasks,

[2121] A means of recognizing user emotions and adjusting the way task lists are displayed and task priorities are set based on emotion data,

[2122] A system that includes this.

[2123] (Claim 2)

[2124] The system according to claim 1, wherein the server analyzes user input using natural language processing.

[2125] (Claim 3)

[2126] The system according to claim 1, wherein the terminal displays the task list in calendar format or list format.

[2127] (Claim 4)

[2128] The system according to claim 1, wherein the aforementioned emotional data is generated from the user's input and the progress of the task.

[2129] (Claim 5)

[2130] The system according to claim 1, which adjusts the display method and priority of tasks based on sentiment data. [Explanation of symbols]

[2131] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input the details of the work and the completion deadline, The server analyzes the aforementioned business content and breaks it down into specific tasks, The server provides a means for assigning a completion deadline to each of the aforementioned tasks, The server provides means for generating the list of specific tasks, A means of sending a task list generated by the server to the user terminal, The terminal displays the task list and the user manages the progress of the tasks, A system that includes this.

2. The system according to claim 1, wherein the server analyzes user input using natural language processing.

3. The system according to claim 1, wherein the terminal displays the task list in calendar format or list format.

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