system

Abstract

We provide the system. [Solution] A means of receiving the goal and deadline entered by the user, A method for identifying necessary tasks based on the received goals and deadlines using a generative AI model, A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period, A means of displaying the schedule to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] When considering a new business or releasing an in-house tool, it is difficult to comprehensively identify the tasks necessary to achieve the project goals and assemble an appropriate schedule. Also, in-house application operations such as legal consultations and approval applications, as well as rules, need to be understood and considered in planning, but it is difficult for many employees to efficiently perform these tasks as they are tasks they are engaged in for the first time. As a result, there are problems such as omissions and delays in tasks, and the project does not proceed smoothly.

Means for Solving the Problems

[0005] The present invention solves the above problems with a system that includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generation AI model, means for organizing the tasks identified by the generation AI model into a schedule based on a predetermined period, and means for displaying the schedule to the user. This system further includes means for including internal application tasks such as legal consultations and approval requests as tasks, and means for generating multiple questions based on the goals and deadlines entered by the user and receiving answers from the user. As a result, it is possible to comprehensively identify the tasks necessary for project achievement and construct an efficient schedule that also takes internal application tasks into consideration.

[0006] A "user" is someone who operates the system and inputs goals and deadlines.

[0007] A "goal" refers to a specific achievement target for a project or business, which is entered by the user into the system.

[0008] "Due date" refers to the deadline by which a project or task must be completed, and is entered by the user into the system.

[0009] A "generative AI model" is an artificial intelligence module that identifies necessary tasks based on user input data and proposes an appropriate schedule.

[0010] A "task" refers to an individual activity or process necessary to achieve a project or goal.

[0011] A "schedule" is a list of tasks placed within a specific period, indicating the order in which they should be performed and their deadlines.

[0012] "Means" refers to the mechanisms or methods established to achieve a specific function or action.

[0013] "Internal application procedures" refer to activities that include internal rules and approval processes necessary for project progress, such as legal consultations and proposal submissions.

[0014] A "means for generating questions" is a system that creates questions to gather additional information based on the goals and deadlines entered by the user.

[0015] "Means of receiving responses" refers to functions for collecting information and responses provided by users and inputting them into the system. [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 Example 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 processor with a reference number (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 RAM (Random Access Memory) with a reference number 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] This invention relates to a system that identifies necessary tasks and proposes an efficient schedule based on the input of project goals and deadlines. Because this system utilizes a generative AI model, it can streamline project management and prevent omissions in internal application processes.

[0038] System Configuration

[0039] This system has the following configuration:

[0040] User input means

[0041] Users can enter project goals and deadlines into an input form. This input is sent from the terminal to the server.

[0042] Input data receiving means

[0043] The server saves the goals and deadlines received from the user and sends them to the generated AI model.

[0044] Generative AI Models

[0045] The generative AI model identifies necessary tasks based on the received input data and generates an appropriate schedule. This model references databases and past project data.

[0046] Schedule generation method

[0047] The server organizes the tasks generated by the AI ​​model and distributes them appropriately within the specified timeframe. It also includes tasks such as legal consultations and internal approval processes.

[0048] Schedule display methods

[0049] The organized schedule and task list are sent to the device and displayed in the user interface.

[0050] Program processing

[0051] 1. The user enters the goal and deadline.

[0052] The user enters the project goal and deadline into an input form on their device and sends it to the server. For example, they might enter "Launch a new product" as the goal and "December 31, 2023" as the deadline.

[0053] 2. The server receives the input.

[0054] The server receives user input and stores it in a database. It then sends the input data to the generated AI model.

[0055] 3. The generative AI model identifies the task.

[0056] The generation AI model identifies the tasks necessary to achieve the project based on the goals and deadlines provided by the user. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" are generated.

[0057] 4. The server generates the schedule.

[0058] The server organizes the tasks identified by the generated AI model and creates a schedule that allocates each task within the specified timeframe. It also incorporates internal application tasks such as legal consultations and approval requests into the schedule.

[0059] 5. The server sends the schedule to the terminal.

[0060] The server sends the generated schedule to the user's terminal, which then displays it. The user can review the displayed schedule and make modifications as needed.

[0061] Specific example

[0062] For example, suppose a user sets the goal as "launching a new product to market" and enters "December 31, 2023" as the deadline. The generating AI model uses this information to identify tasks such as the following, and the server organizes and displays them.

[0063] October 1st - October 15th, 2023: Market research, internal legal consultation

[0064] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0065] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0066] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0067] This invention not only allows users to significantly reduce the time and effort required for identifying tasks and creating schedules, but also enables efficient project management that takes into account internal application processes.

[0068] The following describes the processing flow.

[0069] Step 1:

[0070] The user enters the goal and deadline. The user enters the project goal (e.g., "Launch a new product") and the deadline (e.g., "December 31, 2023") into the input form on the device and clicks the submit button.

[0071] Step 2:

[0072] The server receives the input. The server receives the target and due date data sent by the user and stores it in the database. This data will be used in a later processing step.

[0073] Step 3:

[0074] The server invokes the generative AI model. The server sends the received goal and due date data to the generative AI model. It calls the generative AI model's API to request task identification based on the input data.

[0075] Step 4:

[0076] The generative AI model identifies tasks. Based on the input data, the generative AI model generates a list of tasks necessary to complete the project. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" may be included.

[0077] Step 5:

[0078] The server receives the results of the generated AI model. The server receives a list of tasks returned by the generated AI model. This list also includes the start and end dates for each task.

[0079] Step 6:

[0080] The server organizes the task list and generates a schedule. The server organizes the task list received from the generated AI model and allocates tasks to appropriate timeframes. In this process, internal application tasks such as legal consultations and approval requests are also included as tasks.

[0081] Step 7:

[0082] The server sends the generated schedule to the terminal. The server then sends the final schedule and detailed information about each task to the user's terminal.

[0083] Step 8:

[0084] The terminal displays the schedule. The terminal displays the schedule received from the server in the user interface. The user can review the displayed schedule and make modifications or additions as needed.

[0085] Step 9:

[0086] The user reviews and modifies the schedule. The user checks the displayed schedule and makes any necessary corrections or additions. They then resubmit the revised information to the server to finalize the schedule.

[0087] Through each of the steps described above, users can efficiently organize tasks and manage projects. This system helps prevent tasks from being overlooked, especially for first-time users, and supports efficient project progress.

[0088] (Example 1)

[0089] 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."

[0090] In project management, a system that automatically identifies necessary tasks and generates an efficient schedule simply by the user entering goals and deadlines is extremely useful. However, with current systems, users must manually identify each task and set the schedule, which is time-consuming and labor-intensive. Furthermore, it is difficult to incorporate internal tasks such as legal consultations and approval processes into the schedule, potentially leading to these tasks being overlooked. These challenges need to be addressed.

[0091] 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.

[0092] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for receiving and storing input data from the user, means for transmitting the stored data to a generating AI model, means for using the generating AI model to identify necessary tasks based on the received goals and deadlines, means for organizing the tasks identified by the generating AI model into a schedule based on a predetermined period, and means for transmitting the generated schedule to the user's terminal and displaying the schedule to the user. As a result, the user can significantly reduce the time and effort spent on project management and automatically generate an efficient schedule. Furthermore, internal tasks such as legal consultations and approval applications are also included in the schedule, ensuring that tasks are managed without omissions.

[0093] "User" refers to any person who uses this system.

[0094] "Goals" refer to the specific objectives that users intend to achieve within a project.

[0095] "Due date" refers to the deadline for achieving a goal.

[0096] A "server" refers to the central computer system that receives, stores, and processes various types of data within this system.

[0097] A "terminal" refers to a device used by a user to access a server and perform input or browsing.

[0098] "Input data" refers to information, including goals and deadlines, that a user sends to the server using their device.

[0099] A "generative AI model" refers to an artificial intelligence model that identifies necessary tasks based on input data and generates a schedule.

[0100] A "database" refers to a system used by a server to store input data.

[0101] A "prompt message" refers to the text format used when sending input data to a generative AI model.

[0102] A "task" refers to an individual task that needs to be performed in order to achieve a goal.

[0103] A "schedule" refers to a plan that appropriately allocates the identified tasks within a predetermined period.

[0104] "Means of display" refers to the function of transmitting the schedule generated by the server to the user's terminal and presenting it visually.

[0105] This invention is a system that identifies necessary tasks based on goals and deadlines entered by the user in project management and automatically generates an efficient schedule. Because this system utilizes a generation AI model, it can improve the efficiency of project management and prevent omissions in internal operations.

[0106] System Configuration

[0107] This system consists of the following components.

[0108] User input means

[0109] Users input project goals and deadlines using their devices. Specifically, they access a dedicated web application from their device's browser and fill in the goals and deadlines in the input form.

[0110] Input data receiving means

[0111] The server receives the target and deadline data submitted by the user and stores it in the database.

[0112] Generative AI Models

[0113] The server sends data stored in the database to the generating AI model. The generating AI model identifies the necessary tasks based on the input data and generates a schedule.

[0114] Schedule generation method

[0115] The server organizes the task list received from the generated AI model and generates a schedule based on a predetermined period. It also includes a means to include internal tasks in the schedule.

[0116] Schedule display methods

[0117] The server sends the generated schedule to the user's terminal, which then displays it.

[0118] Hardware and software to be used

[0119] hardware

[0120] Server, User terminal

[0121] software

[0122] AI models (e.g., GPT-3®), database systems, and front-end software for interface display (e.g., React).

[0123] Specific example

[0124] Here is a specific example of a user setting "New Product Market Launch" as their goal and entering "December 31, 2023" as the deadline.

[0125] 1. The user enters "New Product Market Launch" and "December 31, 2023" into the input form on their device and submits it.

[0126] 2. The server receives the input data and saves it to the database.

[0127] 3. The server sends the following prompt message to the generated AI model.

[0128] Project objective: Market launch of a new product

[0129] Project deadline: December 31, 2023

[0130] Identify the tasks necessary to achieve this goal and create an appropriate schedule.

[0131] 4. The generative AI model identifies tasks such as "market research," "product design," "manufacturing planning," and "marketing planning."

[0132] 5. The server organizes these tasks within the specified timeframe and generates a schedule. For example, "Market Research" might be set from October 1st to October 15th, and "Product Design" from October 16th to November 15th.

[0133] 6. The server sends the generated schedule to the user's terminal, which then displays it. The user can review it and make modifications as needed.

[0134] Thus, the system of the present invention enables users to significantly reduce the time and effort required for project management, and to manage tasks efficiently and without omission.

[0135] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0136] Step 1:

[0137] The user enters the goal and deadline.

[0138] Users access a dedicated web application on their device and fill in the project goals and deadlines in an input form. Specifically, they enter "New product launch" and "December 31, 2023" in their browser and click the submit button.

[0139] Input: Goals and deadlines

[0140] Output: Input data sent to the server

[0141] Step 2:

[0142] The server receives and saves the input.

[0143] The server receives goal and deadline data sent by the user. The received data is stored in a database. Specifically, it parses the HTTP request and writes its contents to the database.

[0144] Input: Input data submitted by the user

[0145] Output: Data stored in the database

[0146] Step 3:

[0147] The server sends data to the generated AI model.

[0148] The server retrieves data stored in the database and sends it to the generated AI model as a prompt message in the appropriate format.

[0149] Input: Input data stored in the database

[0150] Output: Prompt message sent to the generated AI model

[0151] Examples of specific prompt messages:

[0152] Project objective: Market launch of a new product

[0153] Project deadline: December 31, 2023

[0154] Identify the tasks necessary to achieve this goal and create an appropriate schedule.

[0155] Step 4:

[0156] The generative AI model identifies the tasks

[0157] The generative AI model receives prompt messages from the server and identifies the necessary tasks based on the goals and deadlines. The generative AI model refers to its built-in database and past project data to list the relevant tasks.

[0158] Input: Prompt message sent from the server

[0159] Output: List of identified tasks

[0160] Examples of specific tasks:

[0161] "Market research," "product design," "manufacturing planning," "marketing planning," etc.

[0162] Step 5:

[0163] The server generates the schedule.

[0164] The server receives a task list from the generated AI model and organizes and assigns each task within the specified timeframe. Specifically, it sets appropriate start and end dates for each task and creates a schedule that includes internal tasks such as legal consultations and approval applications.

[0165] Input: Task list received from the generated AI model

[0166] Output: Generated schedule

[0167] Specific example schedule:

[0168] October 1st - October 15th, 2023: Market research, internal legal consultation

[0169] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0170] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0171] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0172] Step 6:

[0173] The server sends the schedule to the terminal.

[0174] The server sends the generated schedule to the user's terminal. The server generates schedule data as an HTTP response and sends it to the terminal in the specified format. The terminal's web application receives this data and displays it in the user interface.

[0175] Input: Generated schedule

[0176] Output: Schedule sent to the user's terminal

[0177] These steps enable users to achieve efficient project management and, in particular, prevent internal tasks from being overlooked.

[0178] (Application Example 1)

[0179] 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."

[0180] In recent years, automation and efficiency in factories have become increasingly important. However, managing complex projects and manufacturing processes presents many challenges, often leading to human error and decreased efficiency. In particular, scheduling and task management waste significant time and resources when done manually. Furthermore, administrative tasks such as internal application processing and legal consultations are also important tasks that cannot be ignored, but managing them centrally is not easy. Therefore, there is a need for a system that streamlines complex project management and integrates with factory automation equipment.

[0181] 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.

[0182] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for being installed in factory automation equipment and optimizing factory operations based on the generated schedule, and means for displaying the schedule to the user. This enables both efficient project management and optimization of factory automation operations.

[0183] "A means of receiving user-entered goals and deadlines" refers to a method for users to fill in project goals and deadlines in an input form and send them to the server.

[0184] "A method for identifying necessary tasks based on received goals and deadlines using a generative AI model" refers to the process of using a generative AI model to extract the tasks required for a project based on the received goals and deadlines.

[0185] "A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period" refers to the process of appropriately allocating the tasks identified by the generative AI model within a specified period and arranging them as a schedule.

[0186] "A means of optimizing factory operations based on a generated schedule, which is installed on automated equipment within a factory" refers to the process by which software installed on automated equipment within a factory optimizes operations based on a generated schedule.

[0187] "Means of displaying the schedule to the user" refers to the process of sending the generated schedule to the user's terminal and displaying it on the user's interface.

[0188] "Methods for including internal application tasks such as legal consultations and approval requests" refers to a process that includes necessary internal application tasks, such as legal consultations and approval requests, in the project schedule.

[0189] "A means of generating multiple questions based on user-entered goals and deadlines, and receiving responses from users" refers to a process of generating questions related to the goals and deadlines entered by the user, and collecting the user's responses to those questions.

[0190] This invention relates to a system for streamlining factory operations based on user-inputted goals and deadlines. The system includes a program that uses a generative AI model to identify tasks and generate an optimal schedule.

[0191] System Configuration

[0192] 1. User input means:

[0193] Users can enter project goals and deadlines into an input form. For example, they might use a tablet or a dedicated control panel to enter "Setup of new product line" and "2023-12-31". This input is then sent from the terminal to the server.

[0194] 2. Means for receiving input data:

[0195] The server saves the goals and deadlines received from the user and sends them to the generated AI model.

[0196] 3. Generative AI Models:

[0197] The generative AI model identifies necessary tasks based on the received input data and generates an appropriate schedule. This model references databases and past project data. The generative AI model used includes the OpenAI® API.

[0198] 4. Means of generating a schedule:

[0199] The server organizes the tasks identified by the generated AI model and creates a schedule that allocates each task within the specified timeframe. It also incorporates internal application tasks such as legal consultations and approval requests into the schedule.

[0200] 5. Means of integration with factory automation equipment:

[0201] Software installed on automated equipment within the factory optimizes factory operations based on the generated schedule. This integration brings project management and factory automation together.

[0202] 6. Means of displaying the schedule:

[0203] The organized schedule and task list are sent to the device and displayed in the user interface. The user can review the displayed schedule and make modifications as needed.

[0204] Hardware and software

[0205] Hardware:

[0206] factory control computer

[0207] Tablet devices

[0208] Dedicated control panel

[0209] software:

[0210] Python program

[0211] OpenAI API

[0212] dateutil package

[0213] Specific examples and prompt statements

[0214] Project objective: Set up a new product line; deadline: 2023-12-31. Please propose the necessary tasks and schedule.

[0215] Based on this prompt, the AI ​​model generates the following schedule.

[0216] Example of a generated schedule

[0217] October 1st - October 10th, 2023: Current situation analysis and material preparation

[0218] October 11-20, 2023: Mechanical design and installation

[0219] October 21, 2023 - November 10, 2023: Software setup and initial testing

[0220] November 11, 2023 - December 10, 2023: Continuous operation testing and optimization

[0221] December 11th - December 30th, 2023: Final adjustments and quality inspection

[0222] December 31, 2023: New product line begins operation.

[0223] This not only significantly reduces the time and effort users spend identifying project tasks and creating schedules, but also enables more efficient automation of factory operations.

[0224] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0225] Step 1:

[0226] The user enters the goal and deadline. The user uses a tablet or dedicated control panel to enter the project goal and deadline into an input form and submits it to the server. For example, they might enter "Set up a new product line" and "2023-12-31". Once the data is submitted, the server receives it and saves it to the database.

[0227] Step 2:

[0228] The server receives input and processes the data. After receiving the goal and deadline from the user, the server converts the data into a format for sending to the generated AI model. This data processing includes converting to text format and generating prompt statements.

[0229] Step 3:

[0230] The server sends a prompt to the generative AI model. Based on the processed data, the server generates a prompt and sends it to the generative AI model (OpenAI API). For example, it might send the prompt "Project goal: Setup of a new product line, deadline: 2023-12-31. Please suggest the necessary tasks and schedule." to the generative AI model.

[0231] Step 4:

[0232] The generative AI model generates tasks and schedules. The model receives prompts, identifies the necessary tasks, and generates appropriate schedules. This process involves data calculations and database lookups within the model. For example, it might output tasks such as "Current Situation Analysis and Material Preparation" and "Machine Design and Installation," along with appropriate timeframes for each task.

[0233] Step 5:

[0234] The server receives and organizes the generated schedule. The server receives tasks and schedules output from the generating AI model and organizes them based on predetermined timeframes. Furthermore, it completes the schedule, including internal application tasks such as legal consultations and approval requests. This process includes data processing such as task allocation and reordering.

[0235] Step 6:

[0236] The schedule is sent to the factory's automated equipment. The server sends the organized schedule to the factory's automated equipment. The software installed in the automated equipment starts optimizing factory operations based on the received schedule. This allows the machines to work together efficiently.

[0237] Step 7:

[0238] The user is shown the schedule. Finally, the generated schedule and task list are sent to the device and displayed in the user interface. The user can review the displayed schedule and make modifications as needed.

[0239] 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.

[0240] This invention relates to a system that recognizes a user's emotional state and uses that state to identify project tasks and optimize the schedule. The system receives goals and deadlines entered by the user, uses a generative AI model to identify tasks, proposes an appropriate schedule, and uses an emotion engine to make adjustments based on the user's emotional state.

[0241] System Configuration

[0242] This system has the following configuration:

[0243] User input means

[0244] Users can enter project goals and deadlines into an input form. The input data is sent from the terminal to the server.

[0245] Input data receiving means

[0246] The server receives goal and deadline data submitted by the user and stores it in a database. It then sends this data to the AI ​​model for generating data.

[0247] Generative AI Models

[0248] The generation AI model identifies the necessary tasks based on the received goals and deadlines and generates an appropriate schedule. This model references databases and past project data.

[0249] Schedule generation method

[0250] The server organizes the tasks responded to by the generated AI model and allocates each task to the schedule based on its duration. This also includes internal application tasks such as legal consultations and approval requests.

[0251] Emotional Engine

[0252] The emotion engine collects and analyzes emotional data from user input and actions. Based on this, it recognizes the emotional state and makes necessary adjustments.

[0253] Schedule display methods

[0254] The organized schedule and task list are sent to the device and displayed in the user interface.

[0255] Program processing

[0256] 1. The user enters the goal and deadline.

[0257] The user enters the project goal and deadline into an input form on their device and sends it to the server. For example, the goal might be "market launch of a new product," and the deadline might be "December 31, 2023."

[0258] 2. The server receives the input.

[0259] The server receives user input and stores it in a database. It then sends the input data to the generated AI model.

[0260] 3. The generative AI model identifies the task.

[0261] The AI ​​model generates a list of tasks necessary to complete a project, based on the goals and deadlines provided by the user. These tasks may include, for example, "market research," "product design," "manufacturing planning," and "marketing planning."

[0262] 4. The emotion engine collects user emotion data.

[0263] The emotion engine analyzes the user's emotional state from their behavior and input data. For example, if a user is feeling stressed, the emotion engine will recognize this.

[0264] 5. Organize the task list generated by the server and create a schedule.

[0265] The server organizes the task list received from the generated AI model and distributes it within appropriate timeframes. During this process, it also considers the user's emotional state and adjusts the schedule to reduce stress.

[0266] 6. The server sends the final schedule to the terminal.

[0267] The server sends the generated schedule to the user's terminal, which then displays it. The user can review the displayed schedule and make modifications or additions as needed.

[0268] Specific example

[0269] For example, if a user sets the goal as "launching a new product to market" and enters "December 31, 2023" as the deadline, the generating AI model will identify the following tasks.

[0270] October 1st - October 15th, 2023: Market research, internal legal consultation

[0271] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0272] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0273] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0274] To reduce the user's stress level, the emotion engine makes adjustments, such as splitting the "market research" task into shorter segments if it detects high stress levels.

[0275] This invention allows users to efficiently organize tasks and manage projects, as well as receive support based on their emotional state, enabling them to perform tasks more effectively.

[0276] The following describes the processing flow.

[0277] Step 1:

[0278] The user enters the goal and deadline. The user enters the project goal (e.g., "Launch a new product") and the deadline (e.g., "December 31, 2023") into the input form on their device and clicks the submit button.

[0279] Step 2:

[0280] The server receives the input data. The server receives the target and due date data sent by the user and stores it in a secure database.

[0281] Step 3:

[0282] The server invokes the generating AI model. The server sends the received goal and deadline data to the generating AI model via API and requests that it identify the necessary tasks.

[0283] Step 4:

[0284] The generative AI model identifies tasks. The generative AI model generates a task list required for project achievement. For example, tasks such as "market research", "product design", "manufacturing planning", and "marketing plan" are included.

[0285] Step 5:

[0286] The server receives the results of the generative AI model. The server receives the task list sent from the generative AI model and the start date and end date of each task.

[0287] Step 6:

[0288] The server organizes the task list and generates a schedule. The server organizes the received task list and creates a schedule based on a predetermined period. At this time, legal consultations and report applications are also included in the tasks as in-house application operations.

[0289] Step 7:

[0290] The emotion engine collects the user's emotion data. The emotion engine analyzes the input data and behavior data collected from the user's terminal and recognizes the user's emotional state.

[0291] Step 8:

[0292] The server receives the results of the emotion engine and adjusts the schedule. The server adjusts the schedule based on the user's emotional state received from the emotion engine. For example, when the user's stress level is high, the tasks are split and flexible deadlines are set.

[0293] Step 9:

[0294] The server sends the final schedule to the terminal. The server sends the adjusted schedule and task list to the user's terminal.

[0295] Step 10:

[0296] The terminal displays the schedule. The terminal displays the schedule and task list received from the server in the user interface.

[0297] Step 11:

[0298] The user reviews and modifies the schedule. The user reviews the schedule and makes any necessary corrections or additions. They then resubmit the revised information to the server to finalize the schedule.

[0299] Through each of the above steps, users can efficiently organize tasks and manage projects while receiving support based on their emotional state. In particular, the use of an emotional engine can reduce the user's psychological burden and improve the effectiveness of task completion.

[0300] (Example 2)

[0301] 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".

[0302] In project management, efficiently identifying tasks and generating appropriate schedules is crucial. However, traditional systems often fail to consider the user's emotional state, leading to stress. Furthermore, internal application processes are sometimes not properly incorporated into the schedule. This can result in project delays and increased user burden. Current technology is insufficient to address these challenges.

[0303] 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.

[0304] In this invention, the server includes means for receiving the goals and deadlines input by the user, means for identifying the necessary tasks based on the received goals and deadlines using a generative AI model, means for scheduling the tasks identified by the generative AI model based on a predetermined period, means for analyzing the user's emotional state using an emotion engine and reflecting it in the schedule, and means for displaying the schedule to the user. Thereby, efficient and appropriate schedule management considering the user's emotional state becomes possible. Also, by having means for including in-house application operations in the tasks, the progress of the project can be smoothed.

[0305] The "user" is the entity that inputs goals and deadlines using the system.

[0306] The "goal" is the ultimate endpoint aimed at achieving a project or task.

[0307] The "deadline" is the final due date set to achieve the goal.

[0308] "Means" refers to the methods or devices used to achieve a goal.

[0309] The "generative AI model" is an artificial intelligence algorithm for generating necessary tasks and creating a schedule based on the input data.

[0310] A "task" is the specific work content or activity necessary to achieve a goal.

[0311] A "schedule" indicates the plan or time allocation for executing tasks.

[0312] The "emotion engine" is an algorithm or system for analyzing the user's emotional state and reflecting it in the schedule.

[0313] The "display means" is a device or method for visually presenting information to the user.

[0314] "Internal application procedures" refer to the internal processes required to obtain approvals and permissions necessary for the progress of a project.

[0315] This invention is a system that receives user-inputted goals and deadlines, uses a generation AI model based on them to identify necessary tasks, and generates and displays a schedule that takes the user's emotional state into account using an emotion engine. This system is configured so that the server, terminal, and user work together in cooperation with each other.

[0316] User input means

[0317] The user enters the project goal and deadline into an input form on the device. The entered data is sent to the server via the device's communication module. For example, the user enters "New product launch" as the goal and "December 31, 2023" as the deadline.

[0318] Server receiving method

[0319] The server receives target and deadline data sent from the terminal and stores that data in a database. The server also converts the received data into a prompt message and sends it to the generating AI model.

[0320] Generative AI Models

[0321] The generative AI model identifies the necessary tasks based on prompt messages sent from the server. It utilizes past project data and a trained database to generate an optimal task list.

[0322] Emotional Engine

[0323] The emotion engine collects and analyzes emotional states from user input data and behavior. For example, if a user is feeling stressed, the emotion engine recognizes this state and sends that information to the server. The emotion engine analyzes emotional states using natural language processing and emotion recognition algorithms.

[0324] Schedule generation and display

[0325] The server organizes tasks and generates an appropriate schedule based on the task list received from the generation AI model and sentiment data from the sentiment engine. For example, if the user is experiencing high stress levels, the server adjusts the schedule by splitting tasks into shorter timeframes. The generated schedule is then sent to the device, which displays the schedule.

[0326] Specific example

[0327] If a user enters "New Product Market Launch" as their goal and "December 31, 2023" as their deadline, the AI ​​model will generate the following task list:

[0328] October 1st - October 15th, 2023: Market research, internal legal consultation

[0329] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0330] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0331] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0332] Furthermore, the emotion engine detects the user's stress level, and if it determines, for example, that the user is highly stressed, it adjusts the schedule by splitting the "market research" task into shorter periods. The server sends these adjustments to the terminal, which then displays the schedule to the user.

[0333] Example of a prompt

[0334] If the goal is "market launch of a new product" and the deadline is set as "December 31, 2023," then by inputting the following prompt message into the AI ​​model, a list of necessary tasks can be generated.

[0335] Prompt message:

[0336] Objective: Launch a new product into market

[0337] Date: December 31, 2023

[0338] Identify the necessary tasks and create an appropriate schedule. Also, consider emotional data and adjust the schedule to reduce stress.

[0339] By using this prompt to input into the AI ​​model, a task list and schedule necessary to complete the project will be generated.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] The user enters the goal and deadline.

[0343] The user enters the project's goal and deadline into an input form on their device. For example, they might enter "New product launch" as the goal and "December 31, 2023" as the deadline. By clicking the submit button through the input form, this data is sent to the server.

[0344] Input: Goals and deadlines

[0345] Output: Data sent to the server

[0346] Step 2:

[0347] The server receives input.

[0348] The server receives target and deadline data sent from the terminal. The received data is stored in a database. This information is also passed to the AI ​​model as a prompt. Through this process, the user's input data is stored within the system and used for subsequent processing.

[0349] Input: Goals and deadlines from the device

[0350] Output: Data stored in the database, and prompts sent to the generated AI model.

[0351] Step 3:

[0352] Generative AI models identify tasks

[0353] When the server sends a prompt to the generation AI model, the model generates a list of necessary tasks based on the goals and deadlines. This model uses past project data and pre-trained information. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" may be generated.

[0354] Input: Prompt message

[0355] Output: List of identified tasks

[0356] Step 4:

[0357] The emotion engine collects user emotion data.

[0358] The emotion engine analyzes the user's emotional state from user input data and behavioral data. Using natural language processing and emotion recognition algorithms, it determines the user's stress level and emotional state, and sends this information to the server. For example, if a user is feeling stressed, it accurately recognizes that state.

[0359] Input: User input data, behavioral data

[0360] Output: Analyzed sentiment data

[0361] Step 5:

[0362] The server organizes the task list it generates and creates a schedule.

[0363] The server organizes tasks and generates an appropriate schedule based on the task list received from the generating AI model and sentiment data from the sentiment engine. For example, if the server detects that the user is experiencing high stress levels, it might break down a task like "market research" and set shorter timeframes. Then, it places all tasks according to priority and deadline.

[0364] Input: Task list, sentiment data

[0365] Output: Organized schedule

[0366] Step 6:

[0367] The server sends the final schedule to the terminal.

[0368] The server sends the generated schedule to the user's terminal. The terminal displays the received schedule in the user interface, allowing the user to review it. The user can review the displayed schedule and make modifications or additions as needed.

[0369] Input: Organized schedule

[0370] Output: Schedule displayed on another device

[0371] (Application Example 2)

[0372] 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".

[0373] In modern factories, worker stress and fatigue significantly impact productivity. Managing production lines is complex and requires efficient task allocation, but scheduling without considering employees' emotional states has its limitations. Furthermore, efficiently identifying tasks based on production targets and deadlines and organizing them into an optimal schedule is not easy and requires considerable effort. There is a need for methods to improve productivity by identifying employees' emotional states and adjusting schedules accordingly.

[0374] 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.

[0375] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for adjusting the schedule based on the user's emotional state using an emotion engine that collects and analyzes the user's emotional data, and means for displaying the schedule and task list to the user. This enables flexible schedule adjustments that take into account the user's emotional state, making it possible to simultaneously improve production efficiency and reduce worker stress.

[0376] "A means of receiving user-entered goals and deadlines" refers to an interface that allows users to input project goals and deadlines into the system via input forms or similar methods.

[0377] "A method for identifying necessary tasks based on received goals and deadlines using a generative AI model" refers to a processing unit that utilizes a generative AI model to extract specific tasks that should be performed based on the input goals and deadlines.

[0378] "A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period" refers to a processing device that has the function of appropriately distributing the extracted tasks over a certain period and organizing them into an optimal schedule.

[0379] An "emotion engine that collects and analyzes user emotion data" is a system that understands and analyzes a user's emotional state based on their behavior and input data.

[0380] A "means for adjusting schedules based on the user's emotional state" refers to a device that has the function of modifying or adjusting schedules based on collected emotional data, taking into account the user's stress level and fatigue level.

[0381] "Means for displaying schedules and task lists to the user" refers to display devices or display equipment that visually provide the user with a list of the final generated schedules and tasks through a user interface.

[0382] This invention relates to a system that recognizes a user's emotional state and uses that to identify project tasks and optimize the schedule. In particular, when applied to factory robots, it enables the management of factory lines and schedule adjustments that take into account the emotional state of employees. This system primarily has the following configuration and functions.

[0383] System Configuration

[0384] 1. User input means

[0385] The user uses a device (e.g., a tablet or PC) to input production targets and deadlines, providing an interface for this input. This input data is then sent to the server.

[0386] 2. Means for receiving input data

[0387] The server receives goal and deadline data submitted by the user and stores it in a database. This data is then sent to the generating AI model.

[0388] 3. Generative AI Models

[0389] The generative AI model identifies the necessary tasks based on the received goals and deadlines and generates an appropriate schedule. The generative AI model references past databases and data from similar projects.

[0390] 4. Emotional Engine

[0391] The emotion engine analyzes the user's emotional state from their behavior and input data. Based on this, it adjusts the generated schedule. For example, if the stress level is high, it will break down tasks and set shorter timeframes.

[0392] 5. Means of displaying the schedule

[0393] The final generated schedule and task list are sent to the user's device and displayed on the display. The user can review it and make modifications or additions as needed.

[0394] Explanation of the program's processing

[0395] This system operates using a program specifically designed for factory management. When a user enters goals and deadlines, the server receives this data and sends it to a generating AI model. The generating AI model references past project data to generate a task list. Subsequently, an emotion engine collects and analyzes the user's emotional data, adjusting the schedule based on their emotional state. The final generated schedule is then sent to and displayed on the user's terminal.

[0396] This system operates by combining multiple hardware and software components. Specifically, it includes data processing servers, a cloud computing environment for running generative AI models, sensors and log analysis software for collecting sentiment data, and terminal applications that provide a user interface.

[0397] For example, if a user sets the goal as "start production of a new product" and enters "January 31, 2024" as the deadline, the generating AI model will identify tasks such as "production line design," "machine layout," "commissioning," and "full-scale operation." The emotion engine then analyzes the stress levels of the employees and makes adjustments, such as splitting tasks as needed. The resulting schedule might look something like this:

[0398] "December 1st - December 15th, 2023: Production line design"

[0399] "December 16, 2023 - December 22, 2023: Machine layout"

[0400] "December 23rd - December 29th, 2023: Trial operation"

[0401] "December 30, 2023 - January 5, 2024: Full operation"

[0402] Example of a prompt

[0403] "The user has entered the goal 'Start production of a new product' and the deadline 'January 31, 2024'. Please generate a list of tasks necessary to achieve this goal."

[0404] This system allows factory managers to efficiently organize tasks and flexibly adjust schedules while taking emotional states into consideration. This can lead to increased productivity and improved working conditions.

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The user enters the goal and deadline.

[0408] The user uses a device (e.g., PC or tablet) to enter the project goal and deadline into an input form and submit it to the server. Specifically, they would enter "Start production of new product" and "January 31, 2024". The input data is collected and received by the server. The input data consists of the project goal and deadline.

[0409] Step 2:

[0410] The server receives the input and saves it to the database.

[0411] The server receives goal and deadline data submitted by the user and stores it in a database. This stored data is then used for processing by subsequent generative AI models and emotion engines.

[0412] Step 3:

[0413] The server sends input data to the generated AI model, which then identifies the tasks.

[0414] The server sends the target and deadline data stored in the database to the generating AI model. The generating AI model refers to the database and past project data to generate a list of necessary tasks. For example, tasks such as "production line design," "machine layout," "commissioning," and "production operation" are identified. This task list is then returned to the server.

[0415] Step 4:

[0416] The emotion engine collects and analyzes user emotion data.

[0417] The server collects and analyzes user emotional data through an emotion engine. Specifically, it measures stress levels and fatigue levels from the user's operation history and input data to determine their emotional state. For example, if a user is fatigued or stressed, that data is analyzed by the emotion engine. This emotional data is used to adjust the task schedule.

[0418] Step 5:

[0419] The server generates a schedule based on the generated task list and makes adjustments based on sentiment data.

[0420] The server generates a schedule that allocates tasks to predetermined periods based on the task list received from the generating AI model and the emotional data obtained from the emotion engine. For example, if the user's stress level is high, adjustments are made, such as dividing the task into multiple parts. Specifically, the "production line design" task might be split from 14 days into two 7-day tasks. This schedule is then finalized as the final schedule.

[0421] Step 6:

[0422] The server sends the final schedule to the terminal and displays it to the user.

[0423] The server sends the completed schedule and task list to the user's terminal. The terminal receives this and displays it to the user through a user interface. The user reviews the displayed schedule and makes modifications or additions as needed. This display interface is provided in a visually easy-to-understand format.

[0424] Through these steps, a system is created that allows users to efficiently organize tasks and adjust their schedules based on their emotional state.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Second Embodiment]

[0429] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] This invention relates to a system that identifies necessary tasks and proposes an efficient schedule based on the input of project goals and deadlines. Because this system utilizes a generative AI model, it can streamline project management and prevent omissions in internal application processes.

[0442] System Configuration

[0443] This system has the following configuration:

[0444] User input means

[0445] Users can enter project goals and deadlines into an input form. This input is sent from the terminal to the server.

[0446] Input data receiving means

[0447] The server saves the goals and deadlines received from the user and sends them to the generated AI model.

[0448] Generative AI Models

[0449] The generative AI model identifies necessary tasks based on the received input data and generates an appropriate schedule. This model references databases and past project data.

[0450] Schedule generation method

[0451] The server organizes the tasks generated by the AI ​​model and distributes them appropriately within the specified timeframe. It also includes tasks such as legal consultations and internal approval processes.

[0452] Schedule display methods

[0453] The organized schedule and task list are sent to the device and displayed in the user interface.

[0454] Program processing

[0455] 1. The user enters the goal and deadline.

[0456] The user enters the project goal and deadline into an input form on their device and sends it to the server. For example, they might enter "Launch a new product" as the goal and "December 31, 2023" as the deadline.

[0457] 2. The server receives the input.

[0458] The server receives user input and stores it in a database. It then sends the input data to the generated AI model.

[0459] 3. The generative AI model identifies the task.

[0460] The generating AI model identifies the tasks necessary to achieve the project based on the goals and deadlines provided by the user. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" are generated.

[0461] 4. The server generates the schedule.

[0462] The server organizes the tasks identified by the generated AI model and creates a schedule that allocates each task within the specified timeframe. It also incorporates internal application tasks such as legal consultations and approval requests into the schedule.

[0463] 5. The server sends the schedule to the terminal.

[0464] The server sends the generated schedule to the user's terminal, which then displays it. The user can review the displayed schedule and make modifications as needed.

[0465] Specific example

[0466] For example, suppose a user enters "market launch of a new product" as their goal and "December 31, 2023" as the deadline. The generating AI model uses this information to identify tasks such as the following, and the server organizes and displays them.

[0467] October 1st - October 15th, 2023: Market research, internal legal consultation

[0468] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0469] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0470] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0471] This invention not only allows users to significantly reduce the time and effort required for identifying tasks and creating schedules, but also enables efficient project management that takes into account internal application processes.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The user enters the goal and deadline. The user enters the project goal (e.g., "Launch a new product") and the deadline (e.g., "December 31, 2023") into the input form on the device and clicks the submit button.

[0475] Step 2:

[0476] The server receives the input. The server receives the target and due date data sent by the user and stores it in the database. This data will be used in a later processing step.

[0477] Step 3:

[0478] The server invokes the generative AI model. The server sends the received goal and due date data to the generative AI model. It calls the generative AI model's API to request task identification based on the input data.

[0479] Step 4:

[0480] The generative AI model identifies tasks. Based on the input data, the generative AI model generates a list of tasks necessary to complete the project. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" may be included.

[0481] Step 5:

[0482] The server receives the results of the generated AI model. The server receives a list of tasks returned by the generated AI model. This list also includes the start and end dates for each task.

[0483] Step 6:

[0484] The server organizes the task list and generates a schedule. The server organizes the task list received from the generated AI model and allocates tasks to appropriate timeframes. In this process, internal application tasks such as legal consultations and approval requests are also included as tasks.

[0485] Step 7:

[0486] The server sends the generated schedule to the terminal. The server then sends the final schedule and detailed information about each task to the user's terminal.

[0487] Step 8:

[0488] The terminal displays the schedule. The terminal displays the schedule received from the server in the user interface. The user can review the displayed schedule and make modifications or additions as needed.

[0489] Step 9:

[0490] The user reviews and modifies the schedule. The user checks the displayed schedule and makes any necessary corrections or additions. They then resubmit the revised information to the server to finalize the schedule.

[0491] Through each of the steps described above, users can efficiently organize tasks and manage projects. This system helps prevent tasks from being overlooked, especially for first-time users, and supports efficient project progress.

[0492] (Example 1)

[0493] 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".

[0494] In project management, a system that automatically identifies necessary tasks and generates an efficient schedule simply by the user entering goals and deadlines is extremely useful. However, with current systems, users must manually identify each task and set the schedule, which is time-consuming and labor-intensive. Furthermore, it is difficult to incorporate internal tasks such as legal consultations and approval processes into the schedule, potentially leading to these tasks being overlooked. These challenges need to be addressed.

[0495] 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.

[0496] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for receiving and storing input data from the user, means for transmitting the stored data to a generating AI model, means for using the generating AI model to identify necessary tasks based on the received goals and deadlines, means for organizing the tasks identified by the generating AI model into a schedule based on a predetermined period, and means for transmitting the generated schedule to the user's terminal and displaying the schedule to the user. As a result, the user can significantly reduce the time and effort spent on project management and automatically generate an efficient schedule. Furthermore, internal tasks such as legal consultations and approval applications are also included in the schedule, ensuring that tasks are managed without omissions.

[0497] "User" refers to any person who uses this system.

[0498] "Goals" refer to the specific objectives that users intend to achieve within a project.

[0499] "Due date" refers to the deadline for achieving a goal.

[0500] A "server" refers to the central computer system that receives, stores, and processes various types of data within this system.

[0501] A "terminal" refers to a device used by a user to access a server and perform input or browsing.

[0502] "Input data" refers to information, including goals and deadlines, that a user sends to the server using their device.

[0503] A "generative AI model" refers to an artificial intelligence model that identifies necessary tasks based on input data and generates a schedule.

[0504] A "database" refers to a system used by a server to store input data.

[0505] A "prompt message" refers to the text format used when sending input data to a generative AI model.

[0506] A "task" refers to an individual task that needs to be performed in order to achieve a goal.

[0507] A "schedule" refers to a plan that appropriately allocates the identified tasks within a predetermined period.

[0508] "Means of display" refers to the function of transmitting the schedule generated by the server to the user's terminal and presenting it visually.

[0509] This invention is a system that identifies necessary tasks based on goals and deadlines entered by the user in project management and automatically generates an efficient schedule. Because this system utilizes a generation AI model, it can improve the efficiency of project management and prevent omissions in internal operations.

[0510] System Configuration

[0511] This system consists of the following components.

[0512] User input means

[0513] Users input project goals and deadlines using their devices. Specifically, they access a dedicated web application from their device's browser and fill in the goals and deadlines in the input form.

[0514] Input data receiving means

[0515] The server receives the target and deadline data submitted by the user and stores it in the database.

[0516] Generative AI Models

[0517] The server sends data stored in the database to the generating AI model. The generating AI model identifies the necessary tasks based on the input data and generates a schedule.

[0518] Schedule generation method

[0519] The server organizes the task list received from the generated AI model and generates a schedule based on a predetermined period. It also includes a means to include internal tasks in the schedule.

[0520] Schedule display methods

[0521] The server sends the generated schedule to the user's terminal, which then displays it.

[0522] Hardware and software to be used

[0523] hardware

[0524] Server, User terminal

[0525] software

[0526] AI models (e.g., GPT-3), database systems, and front-end software for interface display (e.g., React).

[0527] Specific example

[0528] Here's a concrete example of a user setting "New Product Market Launch" as their goal and entering "December 31, 2023" as the deadline.

[0529] 1. The user enters "New Product Market Launch" and "December 31, 2023" into the input form on their device and submits it.

[0530] 2. The server receives the input data and saves it to the database.

[0531] 3. The server sends the following prompt message to the generated AI model.

[0532] Project objective: Market launch of a new product

[0533] Project deadline: December 31, 2023

[0534] Identify the tasks necessary to achieve this goal and create an appropriate schedule.

[0535] 4. The generative AI model identifies tasks such as "market research," "product design," "manufacturing planning," and "marketing planning."

[0536] 5. The server organizes these tasks within the specified timeframe and generates a schedule. For example, "Market Research" might be set from October 1st to October 15th, and "Product Design" from October 16th to November 15th.

[0537] 6. The server sends the generated schedule to the user's terminal, which then displays it. The user can review it and make modifications as needed.

[0538] Thus, the system of the present invention enables users to significantly reduce the time and effort required for project management, and to manage tasks efficiently and without omission.

[0539] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0540] Step 1:

[0541] The user enters the goal and deadline.

[0542] Users access a dedicated web application on their device and fill in the project goals and deadlines in an input form. Specifically, they enter "New product launch" and "December 31, 2023" in their browser and click the submit button.

[0543] Input: Goals and deadlines

[0544] Output: Input data sent to the server

[0545] Step 2:

[0546] The server receives and saves the input.

[0547] The server receives goal and deadline data sent by the user. The received data is stored in a database. Specifically, it parses the HTTP request and writes its contents to the database.

[0548] Input: Input data submitted by the user

[0549] Output: Data stored in the database

[0550] Step 3:

[0551] The server sends data to the generated AI model.

[0552] The server retrieves data stored in the database and sends it to the generated AI model as a prompt message in the appropriate format.

[0553] Input: Input data stored in the database

[0554] Output: Prompt message sent to the generated AI model

[0555] Examples of specific prompt messages:

[0556] Project objective: Market launch of a new product

[0557] Project deadline: December 31, 2023

[0558] Identify the tasks necessary to achieve this goal and create an appropriate schedule.

[0559] Step 4:

[0560] The generative AI model identifies the tasks

[0561] The generative AI model receives prompt messages from the server and identifies the necessary tasks based on the goals and deadlines. The generative AI model refers to its built-in database and past project data to list the relevant tasks.

[0562] Input: Prompt message sent from the server

[0563] Output: List of identified tasks

[0564] Examples of specific tasks:

[0565] "Market research," "product design," "manufacturing planning," "marketing planning," etc.

[0566] Step 5:

[0567] The server generates the schedule.

[0568] The server receives a task list from the generated AI model and organizes and assigns each task within the specified timeframe. Specifically, it sets appropriate start and end dates for each task and creates a schedule that includes internal tasks such as legal consultations and approval applications.

[0569] Input: Task list received from the generated AI model

[0570] Output: Generated schedule

[0571] Specific example schedule:

[0572] October 1st - October 15th, 2023: Market research, internal legal consultation

[0573] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0574] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0575] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0576] Step 6:

[0577] The server sends the schedule to the terminal.

[0578] The server sends the generated schedule to the user's terminal. The server generates schedule data as an HTTP response and sends it to the terminal in the specified format. The terminal's web application receives this data and displays it in the user interface.

[0579] Input: Generated schedule

[0580] Output: Schedule sent to the user's terminal

[0581] These steps enable users to achieve efficient project management and, in particular, prevent internal tasks from being overlooked.

[0582] (Application Example 1)

[0583] 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."

[0584] In recent years, automation and efficiency in factories have become increasingly important. However, managing complex projects and manufacturing processes presents many challenges, often leading to human error and decreased efficiency. In particular, scheduling and task management waste significant time and resources when done manually. Furthermore, administrative tasks such as internal application processing and legal consultations are also important tasks that cannot be ignored, but managing them centrally is not easy. Therefore, there is a need for a system that streamlines complex project management and integrates with factory automation equipment.

[0585] 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.

[0586] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for being installed in factory automation equipment and optimizing factory operations based on the generated schedule, and means for displaying the schedule to the user. This enables both efficient project management and optimization of factory automation operations.

[0587] "A means of receiving user-entered goals and deadlines" refers to a method for users to fill in project goals and deadlines in an input form and send them to the server.

[0588] "A method for identifying necessary tasks based on received goals and deadlines using a generative AI model" refers to the process of using a generative AI model to extract the tasks required for a project based on the received goals and deadlines.

[0589] "A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period" refers to the process of appropriately allocating the tasks identified by the generative AI model within a specified period and arranging them as a schedule.

[0590] "A means of optimizing factory operations based on a generated schedule, which is installed on automated equipment within a factory" refers to the process by which software installed on automated equipment within a factory optimizes operations based on a generated schedule.

[0591] "Means of displaying the schedule to the user" refers to the process of sending the generated schedule to the user's terminal and displaying it on the user's interface.

[0592] "Methods for including internal application tasks such as legal consultations and approval requests" refers to a process that includes necessary internal application tasks, such as legal consultations and approval requests, in the project schedule.

[0593] "A means of generating multiple questions based on user-entered goals and deadlines, and receiving responses from users" refers to a process of generating questions related to the goals and deadlines entered by the user, and collecting the user's responses to those questions.

[0594] This invention relates to a system for streamlining factory operations based on user-inputted goals and deadlines. The system includes a program that uses a generative AI model to identify tasks and generate an optimal schedule.

[0595] System Configuration

[0596] 1. User input means:

[0597] Users can enter project goals and deadlines into an input form. For example, they might use a tablet or a dedicated control panel to enter "Setup of new product line" and "2023-12-31". This input is then sent from the terminal to the server.

[0598] 2. Means for receiving input data:

[0599] The server saves the goals and deadlines received from the user and sends them to the generated AI model.

[0600] 3. Generative AI Models:

[0601] The generative AI model identifies necessary tasks based on the received input data and generates an appropriate schedule. This model references databases and past project data. The generative AI model used includes the OpenAI API.

[0602] 4. Means of generating a schedule:

[0603] The server organizes the tasks identified by the generated AI model and creates a schedule that allocates each task within the specified timeframe. It also incorporates internal application tasks such as legal consultations and approval requests into the schedule.

[0604] 5. Means of integration with factory automation equipment:

[0605] Software installed on automated equipment within the factory optimizes factory operations based on the generated schedule. This integration brings project management and factory automation together.

[0606] 6. Means of displaying the schedule:

[0607] The organized schedule and task list are sent to the device and displayed in the user interface. The user can review the displayed schedule and make modifications as needed.

[0608] Hardware and software

[0609] Hardware:

[0610] factory control computer

[0611] Tablet devices

[0612] Dedicated control panel

[0613] software:

[0614] Python program

[0615] OpenAI API

[0616] dateutil package

[0617] Specific examples and prompt statements

[0618] Project objective: Set up a new product line; deadline: 2023-12-31. Please propose the necessary tasks and schedule.

[0619] Based on this prompt, the AI ​​model generates the following schedule.

[0620] Example of a generated schedule

[0621] October 1st - October 10th, 2023: Current situation analysis and material preparation

[0622] October 11-20, 2023: Mechanical design and installation

[0623] October 21, 2023 - November 10, 2023: Software setup and initial testing

[0624] November 11, 2023 - December 10, 2023: Continuous operation testing and optimization

[0625] December 11th - December 30th, 2023: Final adjustments and quality inspection

[0626] December 31, 2023: New product line begins operation.

[0627] This not only significantly reduces the time and effort users spend identifying project tasks and creating schedules, but also enables more efficient automation of factory operations.

[0628] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0629] Step 1:

[0630] The user enters the goal and deadline. The user uses a tablet or dedicated control panel to enter the project goal and deadline into an input form and submits it to the server. For example, they might enter "Set up a new product line" and "2023-12-31". Once the data is submitted, the server receives it and saves it to the database.

[0631] Step 2:

[0632] The server receives input and processes the data. After receiving the goal and deadline from the user, the server converts the data into a format for sending to the generated AI model. This data processing includes conversion to text format and generation of prompt statements.

[0633] Step 3:

[0634] The server sends a prompt to the generative AI model. Based on the processed data, the server generates a prompt and sends it to the generative AI model (OpenAI API). For example, it might send the prompt "Project goal: Setup of a new product line, deadline: 2023-12-31. Please suggest the necessary tasks and schedule." to the generative AI model.

[0635] Step 4:

[0636] The generative AI model generates tasks and schedules. The model receives prompts, identifies the necessary tasks, and generates appropriate schedules. This process involves data calculations and database lookups within the model. For example, it might output tasks such as "Current Situation Analysis and Material Preparation" and "Machine Design and Installation," along with appropriate timeframes for each task.

[0637] Step 5:

[0638] The server receives and organizes the generated schedule. The server receives tasks and schedules output from the generating AI model and organizes them based on predetermined timeframes. Furthermore, it completes the schedule, including internal application tasks such as legal consultations and approval requests. This process includes data processing such as task allocation and reordering.

[0639] Step 6:

[0640] The schedule is sent to the factory's automated equipment. The server sends the organized schedule to the factory's automated equipment. The software installed in the automated equipment starts optimizing factory operations based on the received schedule. This allows the machines to work together efficiently.

[0641] Step 7:

[0642] The user is shown the schedule. Finally, the generated schedule and task list are sent to the device and displayed in the user interface. The user can review the displayed schedule and make modifications as needed.

[0643] 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.

[0644] This invention relates to a system that recognizes a user's emotional state and uses that state to identify project tasks and optimize the schedule. The system receives goals and deadlines entered by the user, uses a generative AI model to identify tasks, proposes an appropriate schedule, and uses an emotion engine to make adjustments based on the user's emotional state.

[0645] System Configuration

[0646] This system has the following configuration:

[0647] User input means

[0648] Users can enter project goals and deadlines into an input form. The input data is sent from the terminal to the server.

[0649] Input data receiving means

[0650] The server receives goal and deadline data submitted by the user and stores it in a database. It then sends this data to the AI ​​model for generating data.

[0651] Generative AI Models

[0652] The generation AI model identifies the necessary tasks based on the received goals and deadlines and generates an appropriate schedule. This model references databases and past project data.

[0653] Schedule generation method

[0654] The server organizes the tasks responded to by the generated AI model and allocates each task to the schedule based on its duration. This also includes internal application tasks such as legal consultations and approval requests.

[0655] Emotional Engine

[0656] The emotion engine collects and analyzes emotional data from user input and actions. Based on this, it recognizes the emotional state and makes necessary adjustments.

[0657] Schedule display methods

[0658] The organized schedule and task list are sent to the device and displayed in the user interface.

[0659] Program processing

[0660] 1. The user enters the goal and deadline.

[0661] The user enters the project goal and deadline into an input form on their device and sends it to the server. For example, the goal might be "market launch of a new product," and the deadline might be "December 31, 2023."

[0662] 2. The server receives the input.

[0663] The server receives user input and stores it in a database. It then sends the input data to the generated AI model.

[0664] 3. The generative AI model identifies the task.

[0665] The AI ​​model generates a list of tasks necessary to complete a project, based on the goals and deadlines provided by the user. These tasks may include, for example, "market research," "product design," "manufacturing planning," and "marketing planning."

[0666] 4. The emotion engine collects user emotion data.

[0667] The emotion engine analyzes the user's emotional state from their behavior and input data. For example, if a user is feeling stressed, the emotion engine will recognize this.

[0668] 5. Organize the task list generated by the server and create a schedule.

[0669] The server organizes the task list received from the generated AI model and distributes it within appropriate timeframes. During this process, it also considers the user's emotional state and adjusts the schedule to reduce stress.

[0670] 6. The server sends the final schedule to the terminal.

[0671] The server sends the generated schedule to the user's terminal, which then displays it. The user can review the displayed schedule and make modifications or additions as needed.

[0672] Specific example

[0673] For example, if a user sets the goal as "launching a new product to market" and enters "December 31, 2023" as the deadline, the generating AI model will identify the following tasks.

[0674] October 1st - October 15th, 2023: Market research, internal legal consultation

[0675] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0676] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0677] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0678] To reduce the user's stress level, the emotion engine makes adjustments, such as splitting the "market research" task into shorter segments if it detects high stress levels.

[0679] This invention allows users to efficiently organize tasks and manage projects, as well as receive support based on their emotional state, enabling them to perform tasks more effectively.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The user enters the goal and deadline. The user enters the project goal (e.g., "Launch a new product") and the deadline (e.g., "December 31, 2023") into the input form on their device and clicks the submit button.

[0683] Step 2:

[0684] The server receives the input data. The server receives the target and due date data sent by the user and stores it in a secure database.

[0685] Step 3:

[0686] The server invokes the generating AI model. The server sends the received goal and deadline data to the generating AI model via API and requests that it identify the necessary tasks.

[0687] Step 4:

[0688] The generative AI model identifies the tasks. The generative AI model generates a list of tasks necessary to complete the project. For example, these tasks might include "market research," "product design," "manufacturing planning," and "marketing planning."

[0689] Step 5:

[0690] The server receives the results of the generated AI model. The server receives the task list and the start and end dates of each task returned from the generated AI model.

[0691] Step 6:

[0692] The server organizes the task list and generates a schedule. The server organizes the received task list and creates a schedule based on a specified period. In this process, tasks such as legal consultations and approval requests are also included as internal application tasks.

[0693] Step 7:

[0694] The emotion engine collects user emotional data. The emotion engine analyzes input data and behavioral data collected from the user's device to recognize the user's emotional state.

[0695] Step 8:

[0696] The server receives the results from the emotion engine and adjusts the schedule. Based on the user's emotional state received from the emotion engine, the server adjusts the schedule. For example, if the user's stress level is high, the server may split tasks or set flexible deadlines.

[0697] Step 9:

[0698] The server sends the final schedule to the terminal. The server then sends the adjusted schedule and task list to the user's terminal.

[0699] Step 10:

[0700] The terminal displays the schedule. The terminal displays the schedule and task list received from the server in the user interface.

[0701] Step 11:

[0702] The user reviews and modifies the schedule. The user reviews the schedule and makes any necessary corrections or additions. They then resubmit the revised information to the server to finalize the schedule.

[0703] Through each of the above steps, users can efficiently organize tasks and manage projects while receiving support based on their emotional state. In particular, the use of an emotional engine can reduce the user's psychological burden and improve the effectiveness of task completion.

[0704] (Example 2)

[0705] 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".

[0706] In project management, efficiently identifying tasks and generating appropriate schedules is crucial. However, traditional systems often fail to consider the user's emotional state, leading to stress. Furthermore, internal application processes are sometimes not properly incorporated into the schedule. This can result in project delays and increased user burden. Current technology is insufficient to address these challenges.

[0707] 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.

[0708] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for analyzing the user's emotional state using an emotion engine and reflecting it in the schedule, and means for displaying the schedule to the user. This enables efficient and appropriate schedule management that takes the user's emotional state into consideration. Furthermore, by including means for internal application procedures as tasks, the progress of the project can be made smoother.

[0709] A "user" is the entity that uses the system to input goals and deadlines.

[0710] A "goal" is the ultimate destination or target for achieving a project or task.

[0711] A "deadline" is the final deadline set to achieve a goal.

[0712] "Means" refers to the methods or devices used to achieve a goal.

[0713] A "generative AI model" is an artificial intelligence algorithm that generates necessary tasks and creates schedules based on input data.

[0714] A "task" is a specific set of tasks or activities necessary to achieve a goal.

[0715] A "schedule" is a plan or time allocation for carrying out a task.

[0716] An "emotion engine" is an algorithm or system that analyzes a user's emotional state and reflects it in the schedule.

[0717] "Display means" refers to devices or methods that visually present information to a user.

[0718] "Internal application procedures" refer to the internal processes required to obtain approvals and permissions necessary for the progress of a project.

[0719] This invention is a system that receives user-inputted goals and deadlines, uses a generation AI model based on them to identify necessary tasks, and generates and displays a schedule that takes the user's emotional state into account using an emotion engine. This system is configured so that the server, terminal, and user work together in cooperation with each other.

[0720] User input means

[0721] The user enters the project goal and deadline into an input form on the device. The entered data is sent to the server via the device's communication module. For example, the user enters "New product launch" as the goal and "December 31, 2023" as the deadline.

[0722] Server receiving method

[0723] The server receives target and deadline data sent from the terminal and stores that data in a database. The server also converts the received data into a prompt message and sends it to the generating AI model.

[0724] Generative AI Models

[0725] The generative AI model identifies the necessary tasks based on prompt messages sent from the server. It utilizes past project data and a trained database to generate an optimal task list.

[0726] Emotional Engine

[0727] The emotion engine collects and analyzes emotional states from user input data and behavior. For example, if a user is feeling stressed, the emotion engine recognizes this state and sends that information to the server. The emotion engine analyzes emotional states using natural language processing and emotion recognition algorithms.

[0728] Schedule generation and display

[0729] The server organizes tasks and generates an appropriate schedule based on the task list received from the generation AI model and sentiment data from the sentiment engine. For example, if the user is experiencing high stress levels, the server adjusts the schedule by splitting tasks into shorter timeframes. The generated schedule is then sent to the device, which displays the schedule.

[0730] Specific example

[0731] If a user enters "New Product Market Launch" as their goal and "December 31, 2023" as their deadline, the AI ​​model will generate the following task list:

[0732] October 1st - October 15th, 2023: Market research, internal legal consultation

[0733] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0734] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0735] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0736] Furthermore, the emotion engine detects the user's stress level, and if it determines, for example, that the user is highly stressed, it adjusts the schedule by splitting the "market research" task into shorter periods. The server sends these adjustments to the terminal, which then displays the schedule to the user.

[0737] Example of a prompt

[0738] If the goal is "market launch of a new product" and the deadline is set as "December 31, 2023," then by inputting the following prompt message into the AI ​​model, a list of necessary tasks can be generated.

[0739] Prompt message:

[0740] Objective: Launch a new product into market

[0741] Date: December 31, 2023

[0742] Identify the necessary tasks and create an appropriate schedule. Also, consider emotional data and adjust the schedule to reduce stress.

[0743] By using this prompt to input into the AI ​​model, a task list and schedule necessary to complete the project will be generated.

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

[0745] Step 1:

[0746] The user enters the goal and deadline.

[0747] The user enters the project's goal and deadline into an input form on their device. For example, they might enter "New product launch" as the goal and "December 31, 2023" as the deadline. By clicking the submit button through the input form, this data is sent to the server.

[0748] Input: Goals and deadlines

[0749] Output: Data sent to the server

[0750] Step 2:

[0751] The server receives input.

[0752] The server receives target and deadline data sent from the terminal. The received data is stored in a database. This information is also passed to the AI ​​model as a prompt. Through this process, the user's input data is stored within the system and used for subsequent processing.

[0753] Input: Goals and deadlines from the device

[0754] Output: Data stored in the database, and prompt messages sent to the generated AI model.

[0755] Step 3:

[0756] Generative AI models identify tasks

[0757] When the server sends a prompt to the generation AI model, the model generates a list of necessary tasks based on the goals and deadlines. This model uses past project data and pre-trained information. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" may be generated.

[0758] Input: Prompt message

[0759] Output: List of identified tasks

[0760] Step 4:

[0761] The emotion engine collects user emotion data.

[0762] The emotion engine analyzes the user's emotional state from user input data and behavioral data. Using natural language processing and emotion recognition algorithms, it determines the user's stress level and emotional state, and sends this information to the server. For example, if a user is feeling stressed, it accurately recognizes that state.

[0763] Input: User input data, behavioral data

[0764] Output: Analyzed sentiment data

[0765] Step 5:

[0766] The server organizes the task list it generates and creates a schedule.

[0767] The server organizes tasks and generates an appropriate schedule based on the task list received from the generating AI model and sentiment data from the sentiment engine. For example, if the server detects that the user is experiencing high stress levels, it might break down a task like "market research" and set shorter timeframes. Then, it places all tasks according to priority and deadline.

[0768] Input: Task list, sentiment data

[0769] Output: Organized schedule

[0770] Step 6:

[0771] The server sends the final schedule to the terminal.

[0772] The server sends the generated schedule to the user's terminal. The terminal displays the received schedule in the user interface, allowing the user to review it. The user can review the displayed schedule and make modifications or additions as needed.

[0773] Input: Organized schedule

[0774] Output: Schedule displayed on another device

[0775] (Application Example 2)

[0776] 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."

[0777] In modern factories, worker stress and fatigue significantly impact productivity. Managing production lines is complex and requires efficient task allocation, but scheduling without considering employees' emotional states has its limitations. Furthermore, efficiently identifying tasks based on production targets and deadlines and organizing them into an optimal schedule is not easy and requires considerable effort. There is a need for methods to improve productivity by identifying employees' emotional states and adjusting schedules accordingly.

[0778] 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.

[0779] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for adjusting the schedule based on the user's emotional state using an emotion engine that collects and analyzes the user's emotional data, and means for displaying the schedule and task list to the user. This enables flexible schedule adjustments that take into account the user's emotional state, making it possible to simultaneously improve production efficiency and reduce worker stress.

[0780] "A means of receiving user-entered goals and deadlines" refers to an interface that allows users to input project goals and deadlines into the system via input forms or similar methods.

[0781] "A method for identifying necessary tasks based on received goals and deadlines using a generative AI model" refers to a processing unit that utilizes a generative AI model to extract specific tasks that should be performed based on the input goals and deadlines.

[0782] "A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period" refers to a processing device that has the function of appropriately distributing the extracted tasks over a certain period and organizing them into an optimal schedule.

[0783] An "emotion engine that collects and analyzes user emotion data" is a system that understands and analyzes a user's emotional state based on their behavior and input data.

[0784] A "means for adjusting schedules based on the user's emotional state" refers to a device that has the function of modifying or adjusting schedules based on collected emotional data, taking into account the user's stress level and fatigue level.

[0785] "Means for displaying schedules and task lists to the user" refers to display devices or display equipment that visually provide the user with a list of the final generated schedules and tasks through a user interface.

[0786] This invention relates to a system that recognizes a user's emotional state and uses that to identify project tasks and optimize the schedule. In particular, when applied to factory robots, it enables the management of factory lines and schedule adjustments that take into account the emotional state of employees. This system primarily has the following configuration and functions.

[0787] System Configuration

[0788] 1. User input means

[0789] The user uses a device (e.g., a tablet or PC) to input production targets and deadlines, providing an interface for this purpose. This input data is then sent to the server.

[0790] 2. Means for receiving input data

[0791] The server receives goal and deadline data submitted by the user and stores it in a database. This data is then sent to the generating AI model.

[0792] 3. Generative AI Models

[0793] The generative AI model identifies the necessary tasks based on the received goals and deadlines and generates an appropriate schedule. The generative AI model references past databases and data from similar projects.

[0794] 4. Emotional Engine

[0795] The emotion engine analyzes the user's emotional state from their behavior and input data. Based on this, it adjusts the generated schedule. For example, if the stress level is high, it will break down tasks and set shorter timeframes.

[0796] 5. Means of displaying the schedule

[0797] The final generated schedule and task list are sent to the user's device and displayed on the display. The user can review it and make modifications or additions as needed.

[0798] Explanation of the program's processing

[0799] This system operates using a program specifically designed for factory management. When a user enters goals and deadlines, the server receives this data and sends it to a generating AI model. The generating AI model references past project data to generate a task list. Subsequently, an emotion engine collects and analyzes the user's emotional data, adjusting the schedule based on their emotional state. The final generated schedule is then sent to and displayed on the user's terminal.

[0800] This system operates by combining multiple hardware and software components. Specifically, it includes data processing servers, a cloud computing environment for running generative AI models, sensors and log analysis software for collecting sentiment data, and terminal applications that provide a user interface.

[0801] For example, if a user sets the goal as "start production of a new product" and enters "January 31, 2024" as the deadline, the generating AI model will identify tasks such as "production line design," "machine layout," "commissioning," and "full-scale operation." The emotion engine then analyzes the stress levels of the employees and makes adjustments, such as splitting tasks as needed. The resulting schedule might look something like this:

[0802] "December 1st - December 15th, 2023: Production line design"

[0803] "December 16th - December 22nd, 2023: Machine Layout"

[0804] "December 23rd - December 29th, 2023: Trial operation"

[0805] "December 30, 2023 - January 5, 2024: Full operation"

[0806] Example of a prompt

[0807] "The user has entered the goal 'Start production of a new product' and the deadline 'January 31, 2024'. Please generate a list of tasks necessary to achieve this goal."

[0808] This system allows factory managers to efficiently organize tasks and flexibly adjust schedules while taking emotional states into consideration. This can lead to increased productivity and improved working conditions.

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] The user enters the goal and deadline.

[0812] The user uses a device (e.g., PC or tablet) to enter the project goal and deadline into an input form and submit it to the server. Specifically, they would enter "Start production of new product" and "January 31, 2024". The input data is collected and received by the server. The input data consists of the project goal and deadline.

[0813] Step 2:

[0814] The server receives the input and saves it to the database.

[0815] The server receives goal and deadline data submitted by the user and stores it in a database. This stored data is then used for processing by subsequent generative AI models and emotion engines.

[0816] Step 3:

[0817] The server sends input data to the generated AI model, which then identifies the tasks.

[0818] The server sends the target and deadline data stored in the database to the generating AI model. The generating AI model refers to the database and past project data to generate a list of necessary tasks. For example, tasks such as "production line design," "machine layout," "commissioning," and "production operation" are identified. This task list is then returned to the server.

[0819] Step 4:

[0820] The emotion engine collects and analyzes user emotion data.

[0821] The server collects and analyzes user emotional data through an emotion engine. Specifically, it measures stress levels and fatigue levels from the user's operation history and input data to determine their emotional state. For example, if a user is fatigued or stressed, that data is analyzed by the emotion engine. This emotional data is used to adjust the task schedule.

[0822] Step 5:

[0823] The server generates a schedule based on the generated task list and makes adjustments based on sentiment data.

[0824] The server generates a schedule that allocates tasks to predetermined periods based on the task list received from the generating AI model and the emotional data obtained from the emotion engine. For example, if the user's stress level is high, adjustments are made, such as dividing the task into multiple parts. Specifically, the "production line design" task might be split from 14 days into two 7-day tasks. This schedule is then finalized as the final schedule.

[0825] Step 6:

[0826] The server sends the final schedule to the terminal and displays it to the user.

[0827] The server sends the completed schedule and task list to the user's terminal. The terminal receives this and displays it to the user through a user interface. The user reviews the displayed schedule and makes modifications or additions as needed. This display interface is provided in a visually easy-to-understand format.

[0828] Through these steps, a system is created that allows users to efficiently organize tasks and adjust their schedules based on their emotional state.

[0829] 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.

[0830] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0831] 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.

[0832] [Third Embodiment]

[0833] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0834] 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.

[0835] 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).

[0836] 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.

[0837] 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.

[0838] 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).

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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".

[0845] This invention relates to a system that identifies necessary tasks and proposes an efficient schedule based on the input of project goals and deadlines. Because this system utilizes a generative AI model, it can streamline project management and prevent omissions in internal application processes.

[0846] System Configuration

[0847] This system has the following configuration:

[0848] User input means

[0849] Users can enter project goals and deadlines into an input form. This input is sent from the terminal to the server.

[0850] Input data receiving means

[0851] The server saves the goals and deadlines received from the user and sends them to the generated AI model.

[0852] Generative AI Models

[0853] The generative AI model identifies necessary tasks based on the received input data and generates an appropriate schedule. This model references databases and past project data.

[0854] Schedule generation method

[0855] The server organizes the tasks generated by the AI ​​model and distributes them appropriately within the specified timeframe. It also includes tasks such as legal consultations and internal approval processes.

[0856] Schedule display methods

[0857] The organized schedule and task list are sent to the device and displayed in the user interface.

[0858] Program processing

[0859] 1. The user enters the goal and deadline.

[0860] The user enters the project goal and deadline into an input form on their device and sends it to the server. For example, they might enter "Launch a new product" as the goal and "December 31, 2023" as the deadline.

[0861] 2. The server receives the input.

[0862] The server receives user input and stores it in a database. It then sends the input data to the generated AI model.

[0863] 3. The generative AI model identifies the task.

[0864] The generation AI model identifies the tasks necessary to achieve the project based on the goals and deadlines provided by the user. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" are generated.

[0865] 4. The server generates the schedule.

[0866] The server organizes the tasks identified by the generated AI model and creates a schedule that allocates each task within the specified timeframe. It also incorporates internal application tasks such as legal consultations and approval requests into the schedule.

[0867] 5. The server sends the schedule to the terminal.

[0868] The server sends the generated schedule to the user's terminal, which then displays it. The user can review the displayed schedule and make modifications as needed.

[0869] Specific example

[0870] For example, suppose a user sets the goal as "launching a new product to market" and enters "December 31, 2023" as the deadline. The generating AI model uses this information to identify tasks such as the following, and the server organizes and displays them.

[0871] October 1st - October 15th, 2023: Market research, internal legal consultation

[0872] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0873] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0874] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0875] This invention not only allows users to significantly reduce the time and effort required for identifying tasks and creating schedules, but also enables efficient project management that takes into account internal application processes.

[0876] The following describes the processing flow.

[0877] Step 1:

[0878] The user enters the goal and deadline. The user enters the project goal (e.g., "Launch a new product") and the deadline (e.g., "December 31, 2023") into the input form on the device and clicks the submit button.

[0879] Step 2:

[0880] The server receives the input. The server receives the target and due date data sent by the user and stores it in the database. This data will be used in a later processing step.

[0881] Step 3:

[0882] The server invokes the generative AI model. The server sends the received goal and due date data to the generative AI model. It calls the generative AI model's API to request task identification based on the input data.

[0883] Step 4:

[0884] The generative AI model identifies tasks. Based on the input data, the generative AI model generates a list of tasks necessary to complete the project. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" may be included.

[0885] Step 5:

[0886] The server receives the results of the generated AI model. The server receives a list of tasks returned by the generated AI model. This list also includes the start and end dates for each task.

[0887] Step 6:

[0888] The server organizes the task list and generates a schedule. The server organizes the task list received from the generated AI model and allocates tasks to appropriate timeframes. In this process, internal application tasks such as legal consultations and approval requests are also included as tasks.

[0889] Step 7:

[0890] The server sends the generated schedule to the terminal. The server then sends the final schedule and detailed information about each task to the user's terminal.

[0891] Step 8:

[0892] The terminal displays the schedule. The terminal displays the schedule received from the server in the user interface. The user can review the displayed schedule and make modifications or additions as needed.

[0893] Step 9:

[0894] The user reviews and modifies the schedule. The user checks the displayed schedule and makes any necessary corrections or additions. They then resubmit the revised information to the server to finalize the schedule.

[0895] Through each of the steps described above, users can efficiently organize tasks and manage projects. This system helps prevent tasks from being overlooked, especially for first-time users, and supports efficient project progress.

[0896] (Example 1)

[0897] 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."

[0898] In project management, a system that automatically identifies necessary tasks and generates an efficient schedule simply by the user entering goals and deadlines is extremely useful. However, with current systems, users must manually identify each task and set the schedule, which is time-consuming and labor-intensive. Furthermore, it is difficult to incorporate internal tasks such as legal consultations and approval processes into the schedule, potentially leading to these tasks being overlooked. These challenges need to be addressed.

[0899] 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.

[0900] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for receiving and storing input data from the user, means for transmitting the stored data to a generating AI model, means for using the generating AI model to identify necessary tasks based on the received goals and deadlines, means for organizing the tasks identified by the generating AI model into a schedule based on a predetermined period, and means for transmitting the generated schedule to the user's terminal and displaying the schedule to the user. As a result, the user can significantly reduce the time and effort spent on project management and automatically generate an efficient schedule. Furthermore, internal tasks such as legal consultations and approval applications are also included in the schedule, ensuring that tasks are managed without omissions.

[0901] "User" refers to any person who uses this system.

[0902] "Goals" refer to the specific objectives that users intend to achieve within a project.

[0903] "Due date" refers to the deadline for achieving a goal.

[0904] A "server" refers to the central computer system that receives, stores, and processes various types of data within this system.

[0905] A "terminal" refers to a device used by a user to access a server and perform input or browsing.

[0906] "Input data" refers to information, including goals and deadlines, that a user sends to the server using their device.

[0907] A "generative AI model" refers to an artificial intelligence model that identifies necessary tasks based on input data and generates a schedule.

[0908] A "database" refers to a system used by a server to store input data.

[0909] A "prompt message" refers to the text format used when sending input data to a generative AI model.

[0910] A "task" refers to an individual task that needs to be performed in order to achieve a goal.

[0911] A "schedule" refers to a plan that appropriately allocates the identified tasks within a predetermined period.

[0912] "Means of display" refers to the function of transmitting the schedule generated by the server to the user's terminal and presenting it visually.

[0913] This invention is a system that identifies necessary tasks based on goals and deadlines entered by the user in project management and automatically generates an efficient schedule. Because this system utilizes a generation AI model, it can improve the efficiency of project management and prevent omissions in internal operations.

[0914] System Configuration

[0915] This system consists of the following components.

[0916] User input means

[0917] Users input project goals and deadlines using their devices. Specifically, they access a dedicated web application from their device's browser and fill in the goals and deadlines in the input form.

[0918] Input data receiving means

[0919] The server receives the target and deadline data submitted by the user and stores it in the database.

[0920] Generative AI Models

[0921] The server sends data stored in the database to the generating AI model. The generating AI model identifies the necessary tasks based on the input data and generates a schedule.

[0922] Schedule generation method

[0923] The server organizes the task list received from the generated AI model and generates a schedule based on a predetermined period. It also includes a means to include internal tasks in the schedule.

[0924] Schedule display methods

[0925] The server sends the generated schedule to the user's terminal, which then displays it.

[0926] Hardware and software to be used

[0927] hardware

[0928] Server, User terminal

[0929] software

[0930] AI models (e.g., GPT-3), database systems, and front-end software for interface display (e.g., React).

[0931] Specific example

[0932] Here is a specific example of a user setting "New Product Market Launch" as their goal and entering "December 31, 2023" as the deadline.

[0933] 1. The user enters "New Product Market Launch" and "December 31, 2023" into the input form on their device and submits it.

[0934] 2. The server receives the input data and saves it to the database.

[0935] 3. The server sends the following prompt message to the generated AI model.

[0936] Project objective: Market launch of a new product

[0937] Project deadline: December 31, 2023

[0938] Identify the tasks necessary to achieve this goal and create an appropriate schedule.

[0939] 4. The generative AI model identifies tasks such as "market research," "product design," "manufacturing planning," and "marketing planning."

[0940] 5. The server organizes these tasks within the specified timeframe and generates a schedule. For example, "Market Research" might be set from October 1st to October 15th, and "Product Design" from October 16th to November 15th.

[0941] 6. The server sends the generated schedule to the user's terminal, which then displays it. The user can review it and make modifications as needed.

[0942] Thus, the system of the present invention enables users to significantly reduce the time and effort required for project management, and to manage tasks efficiently and without omission.

[0943] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0944] Step 1:

[0945] The user enters the goal and deadline.

[0946] Users access a dedicated web application on their device and fill in the project goals and deadlines in an input form. Specifically, they enter "New product launch" and "December 31, 2023" in their browser and click the submit button.

[0947] Input: Goals and deadlines

[0948] Output: Input data sent to the server

[0949] Step 2:

[0950] The server receives and saves the input.

[0951] The server receives goal and deadline data sent by the user. The received data is stored in a database. Specifically, it parses the HTTP request and writes its contents to the database.

[0952] Input: Input data submitted by the user

[0953] Output: Data stored in the database

[0954] Step 3:

[0955] The server sends data to the generated AI model.

[0956] The server retrieves data stored in the database and sends it to the generated AI model as a prompt message in the appropriate format.

[0957] Input: Input data stored in the database

[0958] Output: Prompt message sent to the generated AI model

[0959] Examples of specific prompt messages:

[0960] Project objective: Market launch of a new product

[0961] Project deadline: December 31, 2023

[0962] Identify the tasks necessary to achieve this goal and create an appropriate schedule.

[0963] Step 4:

[0964] The generative AI model identifies the tasks

[0965] The generative AI model receives prompt messages from the server and identifies the necessary tasks based on the goals and deadlines. The generative AI model refers to its built-in database and past project data to list the relevant tasks.

[0966] Input: Prompt message sent from the server

[0967] Output: List of identified tasks

[0968] Examples of specific tasks:

[0969] "Market research," "product design," "manufacturing planning," "marketing planning," etc.

[0970] Step 5:

[0971] The server generates the schedule.

[0972] The server receives a task list from the generated AI model and organizes and assigns each task within the specified timeframe. Specifically, it sets appropriate start and end dates for each task and creates a schedule that includes internal tasks such as legal consultations and approval applications.

[0973] Input: Task list received from the generated AI model

[0974] Output: Generated schedule

[0975] Specific example schedule:

[0976] October 1st - October 15th, 2023: Market research, internal legal consultation

[0977] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[0978] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[0979] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[0980] Step 6:

[0981] The server sends the schedule to the terminal.

[0982] The server sends the generated schedule to the user's terminal. The server generates schedule data as an HTTP response and sends it to the terminal in the specified format. The terminal's web application receives this data and displays it in the user interface.

[0983] Input: Generated schedule

[0984] Output: Schedule sent to the user's terminal

[0985] These steps enable users to achieve efficient project management and, in particular, prevent internal tasks from being overlooked.

[0986] (Application Example 1)

[0987] 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."

[0988] In recent years, automation and efficiency in factories have become increasingly important. However, managing complex projects and manufacturing processes presents many challenges, often leading to human error and decreased efficiency. In particular, scheduling and task management waste significant time and resources when done manually. Furthermore, administrative tasks such as internal application processing and legal consultations are also important tasks that cannot be ignored, but managing them centrally is not easy. Therefore, there is a need for a system that streamlines complex project management and integrates with factory automation equipment.

[0989] 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.

[0990] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for being installed in factory automation equipment and optimizing factory operations based on the generated schedule, and means for displaying the schedule to the user. This enables both efficient project management and optimization of factory automation operations.

[0991] "A means of receiving user-entered goals and deadlines" refers to a method for users to fill in project goals and deadlines in an input form and send them to the server.

[0992] "A method for identifying necessary tasks based on received goals and deadlines using a generative AI model" refers to the process of using a generative AI model to extract the tasks required for a project based on the received goals and deadlines.

[0993] "A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period" refers to the process of appropriately allocating the tasks identified by the generative AI model within a specified period and arranging them as a schedule.

[0994] "A means of optimizing factory operations based on a generated schedule, which is installed on automated equipment within a factory" refers to the process by which software installed on automated equipment within a factory optimizes operations based on a generated schedule.

[0995] "Means of displaying the schedule to the user" refers to the process of sending the generated schedule to the user's terminal and displaying it on the user's interface.

[0996] "Methods for including internal application tasks such as legal consultations and approval requests" refers to a process that includes necessary internal application tasks, such as legal consultations and approval requests, in the project schedule.

[0997] "A means of generating multiple questions based on user-entered goals and deadlines, and receiving responses from users" refers to a process of generating questions related to the goals and deadlines entered by the user, and collecting the user's responses to those questions.

[0998] This invention relates to a system for streamlining factory operations based on user-inputted goals and deadlines. The system includes a program that uses a generative AI model to identify tasks and generate an optimal schedule.

[0999] System Configuration

[1000] 1. User input means:

[1001] Users can enter project goals and deadlines into an input form. For example, they might use a tablet or a dedicated control panel to enter "Setup of new product line" and "2023-12-31". This input is then sent from the terminal to the server.

[1002] 2. Means for receiving input data:

[1003] The server saves the goals and deadlines received from the user and sends them to the generated AI model.

[1004] 3. Generative AI Models:

[1005] The generative AI model identifies necessary tasks based on the received input data and generates an appropriate schedule. This model references databases and past project data. The generative AI model used includes the OpenAI API.

[1006] 4. Means of generating a schedule:

[1007] The server organizes the tasks identified by the generated AI model and creates a schedule that allocates each task within the specified timeframe. It also incorporates internal application tasks such as legal consultations and approval requests into the schedule.

[1008] 5. Means of integration with factory automation equipment:

[1009] Software installed on automated equipment within the factory optimizes factory operations based on the generated schedule. This integration brings project management and factory automation together.

[1010] 6. Means of displaying the schedule:

[1011] The organized schedule and task list are sent to the device and displayed in the user interface. The user can review the displayed schedule and make modifications as needed.

[1012] Hardware and software

[1013] Hardware:

[1014] factory control computer

[1015] Tablet devices

[1016] Dedicated control panel

[1017] software:

[1018] Python program

[1019] OpenAI API

[1020] dateutil package

[1021] Specific examples and prompt statements

[1022] Project objective: Set up a new product line; deadline: 2023-12-31. Please propose the necessary tasks and schedule.

[1023] Based on this prompt, the AI ​​model generates the following schedule.

[1024] Example of a generated schedule

[1025] October 1st - October 10th, 2023: Current situation analysis and material preparation

[1026] October 11-20, 2023: Mechanical design and installation

[1027] October 21, 2023 - November 10, 2023: Software setup and initial testing

[1028] November 11, 2023 - December 10, 2023: Continuous operation testing and optimization

[1029] December 11th - December 30th, 2023: Final adjustments and quality inspection

[1030] December 31, 2023: New product line begins operation.

[1031] This not only significantly reduces the time and effort users spend identifying project tasks and creating schedules, but also enables more efficient automation of factory operations.

[1032] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1033] Step 1:

[1034] The user enters the goal and deadline. The user uses a tablet or dedicated control panel to enter the project goal and deadline into an input form and submits it to the server. For example, they might enter "Set up a new product line" and "2023-12-31". Once the data is submitted, the server receives it and saves it to the database.

[1035] Step 2:

[1036] The server receives input and processes the data. After receiving the goal and deadline from the user, the server converts the data into a format for sending to the generated AI model. This data processing includes conversion to text format and generation of prompt statements.

[1037] Step 3:

[1038] The server sends a prompt to the generative AI model. Based on the processed data, the server generates a prompt and sends it to the generative AI model (OpenAI API). For example, it might send the prompt "Project goal: Setup of a new product line, deadline: 2023-12-31. Please suggest the necessary tasks and schedule." to the generative AI model.

[1039] Step 4:

[1040] The generative AI model generates tasks and schedules. The model receives prompts, identifies the necessary tasks, and generates appropriate schedules. This process involves data calculations and database lookups within the model. For example, it might output tasks such as "Current Situation Analysis and Material Preparation" and "Machine Design and Installation," along with appropriate timeframes for each task.

[1041] Step 5:

[1042] The server receives and organizes the generated schedule. The server receives tasks and schedules output from the generating AI model and organizes them based on predetermined timeframes. Furthermore, it completes the schedule, including internal application tasks such as legal consultations and approval requests. This process includes data processing such as task allocation and reordering.

[1043] Step 6:

[1044] The schedule is sent to the factory's automated equipment. The server sends the organized schedule to the factory's automated equipment. The software installed in the automated equipment starts optimizing factory operations based on the received schedule. This allows the machines to work together efficiently.

[1045] Step 7:

[1046] The user is shown the schedule. Finally, the generated schedule and task list are sent to the device and displayed in the user interface. The user can review the displayed schedule and make modifications as needed.

[1047] 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.

[1048] This invention relates to a system that recognizes a user's emotional state and uses that state to identify project tasks and optimize the schedule. The system receives goals and deadlines entered by the user, uses a generative AI model to identify tasks, proposes an appropriate schedule, and uses an emotion engine to make adjustments based on the user's emotional state.

[1049] System Configuration

[1050] This system has the following configuration:

[1051] User input means

[1052] Users can enter project goals and deadlines into an input form. The input data is sent from the terminal to the server.

[1053] Input data receiving means

[1054] The server receives goal and deadline data submitted by the user and stores it in a database. It then sends this data to the AI ​​model for generating data.

[1055] Generative AI Models

[1056] The generation AI model identifies the necessary tasks based on the received goals and deadlines and generates an appropriate schedule. This model references databases and past project data.

[1057] Schedule generation method

[1058] The server organizes the tasks responded to by the generated AI model and allocates each task to the schedule based on its duration. This also includes internal application tasks such as legal consultations and approval requests.

[1059] Emotional Engine

[1060] The emotion engine collects and analyzes emotional data from user input and actions. Based on this, it recognizes the emotional state and makes necessary adjustments.

[1061] Schedule display methods

[1062] The organized schedule and task list are sent to the device and displayed in the user interface.

[1063] Program processing

[1064] 1. The user enters the goal and deadline.

[1065] The user enters the project goal and deadline into an input form on their device and sends it to the server. For example, the goal might be "market launch of a new product," and the deadline might be "December 31, 2023."

[1066] 2. The server receives the input.

[1067] The server receives user input and stores it in a database. It then sends the input data to the generated AI model.

[1068] 3. The generative AI model identifies the task.

[1069] The AI ​​model generates a list of tasks necessary to complete a project, based on the goals and deadlines provided by the user. These tasks may include, for example, "market research," "product design," "manufacturing planning," and "marketing planning."

[1070] 4. The emotion engine collects user emotion data.

[1071] The emotion engine analyzes the user's emotional state from their behavior and input data. For example, if a user is feeling stressed, the emotion engine will recognize this.

[1072] 5. Organize the task list generated by the server and create a schedule.

[1073] The server organizes the task list received from the generated AI model and distributes it within appropriate timeframes. During this process, it also considers the user's emotional state and adjusts the schedule to reduce stress.

[1074] 6. The server sends the final schedule to the terminal.

[1075] The server sends the generated schedule to the user's terminal, which then displays it. The user can review the displayed schedule and make modifications or additions as needed.

[1076] Specific example

[1077] For example, if a user sets the goal as "launching a new product to market" and enters "December 31, 2023" as the deadline, the generating AI model will identify the following tasks.

[1078] October 1st - October 15th, 2023: Market research, internal legal consultation

[1079] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[1080] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[1081] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[1082] To reduce the user's stress level, the emotion engine makes adjustments, such as splitting the "market research" task into shorter segments if it detects high stress levels.

[1083] This invention allows users to efficiently organize tasks and manage projects, as well as receive support based on their emotional state, enabling them to perform tasks more effectively.

[1084] The following describes the processing flow.

[1085] Step 1:

[1086] The user enters the goal and deadline. The user enters the project goal (e.g., "Launch a new product") and the deadline (e.g., "December 31, 2023") into the input form on their device and clicks the submit button.

[1087] Step 2:

[1088] The server receives the input data. The server receives the target and due date data sent by the user and stores it in a secure database.

[1089] Step 3:

[1090] The server invokes the generating AI model. The server sends the received goal and deadline data to the generating AI model via API and requests that it identify the necessary tasks.

[1091] Step 4:

[1092] The generative AI model identifies the tasks. The generative AI model generates a list of tasks necessary to complete the project. For example, these tasks might include "market research," "product design," "manufacturing planning," and "marketing planning."

[1093] Step 5:

[1094] The server receives the results of the generated AI model. The server receives the task list and the start and end dates of each task returned from the generated AI model.

[1095] Step 6:

[1096] The server organizes the task list and generates a schedule. The server organizes the received task list and creates a schedule based on a specified period. In this process, tasks such as legal consultations and approval requests are also included as internal application tasks.

[1097] Step 7:

[1098] The emotion engine collects user emotional data. The emotion engine analyzes input data and behavioral data collected from the user's device to recognize the user's emotional state.

[1099] Step 8:

[1100] The server receives the results from the emotion engine and adjusts the schedule. Based on the user's emotional state received from the emotion engine, the server adjusts the schedule. For example, if the user's stress level is high, the server may split tasks or set flexible deadlines.

[1101] Step 9:

[1102] The server sends the final schedule to the terminal. The server then sends the adjusted schedule and task list to the user's terminal.

[1103] Step 10:

[1104] The terminal displays the schedule. The terminal displays the schedule and task list received from the server in the user interface.

[1105] Step 11:

[1106] The user reviews and modifies the schedule. The user reviews the schedule and makes any necessary corrections or additions. They then resubmit the revised information to the server to finalize the schedule.

[1107] Through each of the above steps, users can efficiently organize tasks and manage projects while receiving support based on their emotional state. In particular, the use of an emotional engine can reduce the user's psychological burden and improve the effectiveness of task completion.

[1108] (Example 2)

[1109] 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."

[1110] In project management, efficiently identifying tasks and generating appropriate schedules is crucial. However, traditional systems often fail to consider the user's emotional state, leading to stress. Furthermore, internal application processes are sometimes not properly incorporated into the schedule. This can result in project delays and increased user burden. Current technology is insufficient to address these challenges.

[1111] 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.

[1112] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for analyzing the user's emotional state using an emotion engine and reflecting it in the schedule, and means for displaying the schedule to the user. This enables efficient and appropriate schedule management that takes the user's emotional state into consideration. Furthermore, by including means for internal application procedures as tasks, the progress of the project can be made smoother.

[1113] A "user" is the entity that uses the system to input goals and deadlines.

[1114] A "goal" is the ultimate destination or target for achieving a project or task.

[1115] A "deadline" is the final deadline set to achieve a goal.

[1116] "Means" refers to the methods or devices used to achieve a goal.

[1117] A "generative AI model" is an artificial intelligence algorithm that generates necessary tasks and creates schedules based on input data.

[1118] A "task" is a specific set of tasks or activities necessary to achieve a goal.

[1119] A "schedule" is a plan or time allocation for carrying out a task.

[1120] An "emotion engine" is an algorithm or system that analyzes a user's emotional state and reflects it in the schedule.

[1121] "Display means" refers to devices or methods that visually present information to a user.

[1122] "Internal application procedures" refer to the internal processes required to obtain approvals and permissions necessary for the progress of a project.

[1123] This invention is a system that receives user-inputted goals and deadlines, uses a generation AI model based on them to identify necessary tasks, and generates and displays a schedule that takes the user's emotional state into account using an emotion engine. This system is configured so that the server, terminal, and user work together in cooperation with each other.

[1124] User input means

[1125] The user enters the project goal and deadline into an input form on the device. The entered data is sent to the server via the device's communication module. For example, the user enters "New product launch" as the goal and "December 31, 2023" as the deadline.

[1126] Server receiving method

[1127] The server receives target and deadline data sent from the terminal and stores that data in a database. The server also converts the received data into a prompt message and sends it to the generating AI model.

[1128] Generative AI Models

[1129] The generative AI model identifies the necessary tasks based on prompt messages sent from the server. It utilizes past project data and a trained database to generate an optimal task list.

[1130] Emotional Engine

[1131] The emotion engine collects and analyzes emotional states from user input data and behavior. For example, if a user is feeling stressed, the emotion engine recognizes this state and sends that information to the server. The emotion engine analyzes emotional states using natural language processing and emotion recognition algorithms.

[1132] Schedule generation and display

[1133] The server organizes tasks and generates an appropriate schedule based on the task list received from the generation AI model and sentiment data from the sentiment engine. For example, if the user is experiencing high stress levels, the server adjusts the schedule by splitting tasks into shorter timeframes. The generated schedule is then sent to the device, which displays the schedule.

[1134] Specific example

[1135] If a user enters "New Product Market Launch" as their goal and "December 31, 2023" as their deadline, the AI ​​model will generate the following task list:

[1136] October 1st - October 15th, 2023: Market research, internal legal consultation

[1137] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[1138] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[1139] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[1140] Furthermore, the emotion engine detects the user's stress level, and if it determines, for example, that the user is highly stressed, it adjusts the schedule by splitting the "market research" task into shorter periods. The server sends these adjustments to the terminal, which then displays the schedule to the user.

[1141] Example of a prompt

[1142] If the goal is "market launch of a new product" and the deadline is set as "December 31, 2023," then by inputting the following prompt message into the AI ​​model, a list of necessary tasks can be generated.

[1143] Prompt message:

[1144] Objective: Launch a new product into market

[1145] Date: December 31, 2023

[1146] Identify the necessary tasks and create an appropriate schedule. Also, consider emotional data and adjust the schedule to reduce stress.

[1147] By using this prompt to input into the AI ​​model, a task list and schedule necessary to complete the project will be generated.

[1148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1149] Step 1:

[1150] The user enters the goal and deadline.

[1151] The user enters the project's goal and deadline into an input form on their device. For example, they might enter "New product launch" as the goal and "December 31, 2023" as the deadline. By clicking the submit button through the input form, this data is sent to the server.

[1152] Input: Goals and deadlines

[1153] Output: Data sent to the server

[1154] Step 2:

[1155] The server receives input.

[1156] The server receives target and deadline data sent from the terminal. The received data is stored in a database. This information is also passed to the AI ​​model as a prompt. Through this process, the user's input data is stored within the system and used for subsequent processing.

[1157] Input: Goals and deadlines from the device

[1158] Output: Data stored in the database, and prompt messages sent to the generated AI model.

[1159] Step 3:

[1160] Generative AI models identify tasks

[1161] When the server sends a prompt to the generation AI model, the model generates a list of necessary tasks based on the goals and deadlines. This model uses past project data and pre-trained information. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" may be generated.

[1162] Input: Prompt message

[1163] Output: List of identified tasks

[1164] Step 4:

[1165] The emotion engine collects user emotion data.

[1166] The emotion engine analyzes the user's emotional state from user input data and behavioral data. Using natural language processing and emotion recognition algorithms, it determines the user's stress level and emotional state, and sends this information to the server. For example, if a user is feeling stressed, it accurately recognizes that state.

[1167] Input: User input data, behavioral data

[1168] Output: Analyzed sentiment data

[1169] Step 5:

[1170] The server organizes the task list it generates and creates a schedule.

[1171] The server organizes tasks and generates an appropriate schedule based on the task list received from the generating AI model and sentiment data from the sentiment engine. For example, if the server detects that the user is experiencing high stress levels, it might break down a task like "market research" and set shorter timeframes. Then, it places all tasks according to priority and deadline.

[1172] Input: Task list, sentiment data

[1173] Output: Organized schedule

[1174] Step 6:

[1175] The server sends the final schedule to the terminal.

[1176] The server sends the generated schedule to the user's terminal. The terminal displays the received schedule in the user interface, allowing the user to review it. The user can review the displayed schedule and make modifications or additions as needed.

[1177] Input: Organized schedule

[1178] Output: Schedule displayed on another device

[1179] (Application Example 2)

[1180] 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."

[1181] In modern factories, worker stress and fatigue significantly impact productivity. Managing production lines is complex and requires efficient task allocation, but scheduling without considering employees' emotional states has its limitations. Furthermore, efficiently identifying tasks based on production targets and deadlines and organizing them into an optimal schedule is not easy and requires considerable effort. There is a need for methods to improve productivity by identifying employees' emotional states and adjusting schedules accordingly.

[1182] 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.

[1183] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for adjusting the schedule based on the user's emotional state using an emotion engine that collects and analyzes the user's emotional data, and means for displaying the schedule and task list to the user. This enables flexible schedule adjustments that take into account the user's emotional state, making it possible to simultaneously improve production efficiency and reduce worker stress.

[1184] "A means of receiving user-entered goals and deadlines" refers to an interface that allows users to input project goals and deadlines into the system via input forms or similar methods.

[1185] "A method for identifying necessary tasks based on received goals and deadlines using a generative AI model" refers to a processing unit that utilizes a generative AI model to extract specific tasks that should be performed based on the input goals and deadlines.

[1186] "A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period" refers to a processing device that has the function of appropriately distributing the extracted tasks over a certain period and organizing them into an optimal schedule.

[1187] An "emotion engine that collects and analyzes user emotion data" is a system that understands and analyzes a user's emotional state based on their behavior and input data.

[1188] A "means for adjusting schedules based on the user's emotional state" refers to a device that has the function of modifying or adjusting schedules based on collected emotional data, taking into account the user's stress level and fatigue level.

[1189] "Means for displaying schedules and task lists to the user" refers to display devices or display equipment that visually provide the user with a list of the final generated schedules and tasks through a user interface.

[1190] This invention relates to a system that recognizes a user's emotional state and uses that to identify project tasks and optimize the schedule. In particular, when applied to factory robots, it enables the management of factory lines and schedule adjustments that take into account the emotional state of employees. This system primarily has the following configuration and functions.

[1191] System Configuration

[1192] 1. User input means

[1193] The user uses a device (e.g., a tablet or PC) to input production targets and deadlines, providing an interface for this input. This input data is then sent to the server.

[1194] 2. Means for receiving input data

[1195] The server receives goal and deadline data submitted by the user and stores it in a database. This data is then sent to the generating AI model.

[1196] 3. Generative AI Models

[1197] The generative AI model identifies the necessary tasks based on the received goals and deadlines and generates an appropriate schedule. The generative AI model references past databases and data from similar projects.

[1198] 4. Emotional Engine

[1199] The emotion engine analyzes the user's emotional state from their behavior and input data. Based on this, it adjusts the generated schedule. For example, if the stress level is high, it will break down tasks and set shorter timeframes.

[1200] 5. Means of displaying the schedule

[1201] The final generated schedule and task list are sent to the user's device and displayed on the display. The user can review it and make modifications or additions as needed.

[1202] Explanation of the program's processing

[1203] This system operates using a program specifically designed for factory management. When a user enters goals and deadlines, the server receives this data and sends it to a generating AI model. The generating AI model references past project data to generate a task list. Subsequently, an emotion engine collects and analyzes the user's emotional data, adjusting the schedule based on their emotional state. The final generated schedule is then sent to and displayed on the user's terminal.

[1204] This system operates by combining multiple hardware and software components. Specifically, it includes data processing servers, a cloud computing environment for running generative AI models, sensors and log analysis software for collecting sentiment data, and terminal applications that provide a user interface.

[1205] For example, if a user sets the goal as "start production of a new product" and enters "January 31, 2024" as the deadline, the generating AI model will identify tasks such as "production line design," "machine layout," "commissioning," and "full-scale operation." The emotion engine then analyzes the stress levels of the employees and makes adjustments, such as splitting tasks as needed. The resulting schedule might look something like this:

[1206] "December 1st - December 15th, 2023: Production line design"

[1207] "December 16, 2023 - December 22, 2023: Machine layout"

[1208] "December 23rd - December 29th, 2023: Trial operation"

[1209] "December 30, 2023 - January 5, 2024: Full operation"

[1210] Example of a prompt

[1211] "The user has entered the goal 'Start production of a new product' and the deadline 'January 31, 2024'. Please generate a list of tasks necessary to achieve this goal."

[1212] This system allows factory managers to efficiently organize tasks and flexibly adjust schedules while taking emotional states into consideration. This can lead to increased productivity and improved working conditions.

[1213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1214] Step 1:

[1215] The user enters the goal and deadline.

[1216] The user uses a device (e.g., PC or tablet) to enter the project goal and deadline into an input form and submit it to the server. Specifically, they would enter "Start production of new product" and "January 31, 2024". The input data is collected and received by the server. The input data consists of the project goal and deadline.

[1217] Step 2:

[1218] The server receives the input and saves it to the database.

[1219] The server receives goal and deadline data submitted by the user and stores it in a database. This stored data is then used for processing by subsequent generative AI models and emotion engines.

[1220] Step 3:

[1221] The server sends input data to the generated AI model, which then identifies the tasks.

[1222] The server sends the target and deadline data stored in the database to the generating AI model. The generating AI model refers to the database and past project data to generate a list of necessary tasks. For example, tasks such as "production line design," "machine layout," "commissioning," and "production operation" are identified. This task list is then returned to the server.

[1223] Step 4:

[1224] The emotion engine collects and analyzes user emotion data.

[1225] The server collects and analyzes user emotional data through an emotion engine. Specifically, it measures stress levels and fatigue levels from the user's operation history and input data to determine their emotional state. For example, if a user is fatigued or stressed, that data is analyzed by the emotion engine. This emotional data is used to adjust the task schedule.

[1226] Step 5:

[1227] The server generates a schedule based on the generated task list and makes adjustments based on sentiment data.

[1228] The server generates a schedule that allocates tasks to predetermined periods based on the task list received from the generating AI model and the emotional data obtained from the emotion engine. For example, if the user's stress level is high, adjustments are made, such as dividing the task into multiple parts. Specifically, the "production line design" task might be split from 14 days into two 7-day tasks. This schedule is then finalized as the final schedule.

[1229] Step 6:

[1230] The server sends the final schedule to the terminal and displays it to the user.

[1231] The server sends the completed schedule and task list to the user's terminal. The terminal receives this and displays it to the user through a user interface. The user reviews the displayed schedule and makes modifications or additions as needed. This display interface is provided in a visually easy-to-understand format.

[1232] Through these steps, a system is created that allows users to efficiently organize tasks and adjust their schedules based on their emotional state.

[1233] 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.

[1234] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1235] 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.

[1236] [Fourth Embodiment]

[1237] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1238] 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.

[1239] 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).

[1240] 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.

[1241] 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.

[1242] 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).

[1243] 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.

[1244] 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.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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".

[1250] This invention relates to a system that identifies necessary tasks and proposes an efficient schedule based on the input of project goals and deadlines. Because this system utilizes a generative AI model, it can streamline project management and prevent omissions in internal application processes.

[1251] System Configuration

[1252] This system has the following configuration:

[1253] User input means

[1254] Users can enter project goals and deadlines into an input form. This input is sent from the terminal to the server.

[1255] Input data receiving means

[1256] The server saves the goals and deadlines received from the user and sends them to the generated AI model.

[1257] Generative AI Models

[1258] The generative AI model identifies necessary tasks based on the received input data and generates an appropriate schedule. This model references databases and past project data.

[1259] Schedule generation method

[1260] The server organizes the tasks generated by the AI ​​model and distributes them appropriately within the specified timeframe. It also includes tasks such as legal consultations and internal approval processes.

[1261] Schedule display methods

[1262] The organized schedule and task list are sent to the device and displayed in the user interface.

[1263] Program processing

[1264] 1. The user enters the goal and deadline.

[1265] The user enters the project goal and deadline into an input form on their device and sends it to the server. For example, they might enter "Launch a new product" as the goal and "December 31, 2023" as the deadline.

[1266] 2. The server receives the input.

[1267] The server receives user input and stores it in a database. It then sends the input data to the generated AI model.

[1268] 3. The generative AI model identifies the task.

[1269] The generation AI model identifies the tasks necessary to achieve the project based on the goals and deadlines provided by the user. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" are generated.

[1270] 4. The server generates the schedule.

[1271] The server organizes the tasks identified by the generated AI model and creates a schedule that allocates each task within the specified timeframe. It also incorporates internal application tasks such as legal consultations and approval requests into the schedule.

[1272] 5. The server sends the schedule to the terminal.

[1273] The server sends the generated schedule to the user's terminal, which then displays it. The user can review the displayed schedule and make modifications as needed.

[1274] Specific example

[1275] For example, suppose a user sets the goal as "launching a new product to market" and enters "December 31, 2023" as the deadline. The generating AI model uses this information to identify tasks such as the following, and the server organizes and displays them.

[1276] October 1st - October 15th, 2023: Market research, internal legal consultation

[1277] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[1278] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[1279] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[1280] This invention not only allows users to significantly reduce the time and effort required for identifying tasks and creating schedules, but also enables efficient project management that takes into account internal application processes.

[1281] The following describes the processing flow.

[1282] Step 1:

[1283] The user enters the goal and deadline. The user enters the project goal (e.g., "Launch a new product") and the deadline (e.g., "December 31, 2023") into the input form on the device and clicks the submit button.

[1284] Step 2:

[1285] The server receives the input. The server receives the target and due date data sent by the user and stores it in the database. This data will be used in a later processing step.

[1286] Step 3:

[1287] The server invokes the generative AI model. The server sends the received goal and due date data to the generative AI model. It calls the generative AI model's API to request task identification based on the input data.

[1288] Step 4:

[1289] The generative AI model identifies tasks. Based on the input data, the generative AI model generates a list of tasks necessary to complete the project. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" may be included.

[1290] Step 5:

[1291] The server receives the results of the generated AI model. The server receives a list of tasks returned by the generated AI model. This list also includes the start and end dates for each task.

[1292] Step 6:

[1293] The server organizes the task list and generates a schedule. The server organizes the task list received from the generated AI model and allocates tasks to appropriate timeframes. In this process, internal application tasks such as legal consultations and approval requests are also included as tasks.

[1294] Step 7:

[1295] The server sends the generated schedule to the terminal. The server then sends the final schedule and detailed information about each task to the user's terminal.

[1296] Step 8:

[1297] The terminal displays the schedule. The terminal displays the schedule received from the server in the user interface. The user can review the displayed schedule and make modifications or additions as needed.

[1298] Step 9:

[1299] The user reviews and modifies the schedule. The user checks the displayed schedule and makes any necessary corrections or additions. They then resubmit the revised information to the server to finalize the schedule.

[1300] Through each of the steps described above, users can efficiently organize tasks and manage projects. This system helps prevent tasks from being overlooked, especially for first-time users, and supports efficient project progress.

[1301] (Example 1)

[1302] 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".

[1303] In project management, a system that automatically identifies necessary tasks and generates an efficient schedule simply by the user entering goals and deadlines is extremely useful. However, with current systems, users must manually identify each task and set the schedule, which is time-consuming and labor-intensive. Furthermore, it is difficult to incorporate internal tasks such as legal consultations and approval processes into the schedule, potentially leading to these tasks being overlooked. These challenges need to be addressed.

[1304] 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.

[1305] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for receiving and storing input data from the user, means for transmitting the stored data to a generating AI model, means for using the generating AI model to identify necessary tasks based on the received goals and deadlines, means for organizing the tasks identified by the generating AI model into a schedule based on a predetermined period, and means for transmitting the generated schedule to the user's terminal and displaying the schedule to the user. As a result, the user can significantly reduce the time and effort spent on project management and automatically generate an efficient schedule. Furthermore, internal tasks such as legal consultations and approval applications are also included in the schedule, ensuring that tasks are managed without omissions.

[1306] "User" refers to any person who uses this system.

[1307] "Goals" refer to the specific objectives that users intend to achieve within a project.

[1308] "Due date" refers to the deadline for achieving a goal.

[1309] A "server" refers to the central computer system that receives, stores, and processes various types of data within this system.

[1310] A "terminal" refers to a device used by a user to access a server and perform input or browsing.

[1311] "Input data" refers to information, including goals and deadlines, that a user sends to the server using their device.

[1312] A "generative AI model" refers to an artificial intelligence model that identifies necessary tasks based on input data and generates a schedule.

[1313] A "database" refers to a system used by a server to store input data.

[1314] A "prompt message" refers to the text format used when sending input data to a generative AI model.

[1315] A "task" refers to an individual task that needs to be performed in order to achieve a goal.

[1316] A "schedule" refers to a plan that appropriately allocates the identified tasks within a predetermined period.

[1317] "Means of display" refers to the function of transmitting the schedule generated by the server to the user's terminal and presenting it visually.

[1318] This invention is a system that identifies necessary tasks based on goals and deadlines entered by the user in project management and automatically generates an efficient schedule. Because this system utilizes a generation AI model, it can improve the efficiency of project management and prevent omissions in internal operations.

[1319] System Configuration

[1320] This system consists of the following components.

[1321] User input means

[1322] Users input project goals and deadlines using their devices. Specifically, they access a dedicated web application from their device's browser and fill in the goals and deadlines in the input form.

[1323] Input data receiving means

[1324] The server receives the target and deadline data submitted by the user and stores it in the database.

[1325] Generative AI Models

[1326] The server sends data stored in the database to the generating AI model. The generating AI model identifies the necessary tasks based on the input data and generates a schedule.

[1327] Schedule generation method

[1328] The server organizes the task list received from the generated AI model and generates a schedule based on a predetermined period. It also includes a means to include internal tasks in the schedule.

[1329] Schedule display methods

[1330] The server sends the generated schedule to the user's terminal, which then displays it.

[1331] Hardware and software to be used

[1332] hardware

[1333] Server, User terminal

[1334] software

[1335] AI models (e.g., GPT-3), database systems, and front-end software for interface display (e.g., React).

[1336] Specific example

[1337] Here is a specific example of a user setting "New Product Market Launch" as their goal and entering "December 31, 2023" as the deadline.

[1338] 1. The user enters "New Product Market Launch" and "December 31, 2023" into the input form on their device and submits it.

[1339] 2. The server receives the input data and saves it to the database.

[1340] 3. The server sends the following prompt message to the generated AI model.

[1341] Project objective: Market launch of a new product

[1342] Project deadline: December 31, 2023

[1343] Identify the tasks necessary to achieve this goal and create an appropriate schedule.

[1344] 4. The generative AI model identifies tasks such as "market research," "product design," "manufacturing planning," and "marketing planning."

[1345] 5. The server organizes these tasks within the specified timeframe and generates a schedule. For example, "Market Research" might be set from October 1st to October 15th, and "Product Design" from October 16th to November 15th.

[1346] 6. The server sends the generated schedule to the user's terminal, which then displays it. The user can review it and make modifications as needed.

[1347] Thus, the system of the present invention enables users to significantly reduce the time and effort required for project management, and to manage tasks efficiently and without omission.

[1348] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1349] Step 1:

[1350] The user enters the goal and deadline.

[1351] Users access a dedicated web application on their device and fill in the project goals and deadlines in an input form. Specifically, they enter "New product launch" and "December 31, 2023" in their browser and click the submit button.

[1352] Input: Goals and deadlines

[1353] Output: Input data sent to the server

[1354] Step 2:

[1355] The server receives and saves the input.

[1356] The server receives goal and deadline data sent by the user. The received data is stored in a database. Specifically, it parses the HTTP request and writes its contents to the database.

[1357] Input: Input data submitted by the user

[1358] Output: Data stored in the database

[1359] Step 3:

[1360] The server sends data to the generated AI model.

[1361] The server retrieves data stored in the database and sends it to the generated AI model as a prompt message in the appropriate format.

[1362] Input: Input data stored in the database

[1363] Output: Prompt message sent to the generated AI model

[1364] Examples of specific prompt messages:

[1365] Project objective: Market launch of a new product

[1366] Project deadline: December 31, 2023

[1367] Identify the tasks necessary to achieve this goal and create an appropriate schedule.

[1368] Step 4:

[1369] The generative AI model identifies the tasks

[1370] The generative AI model receives prompt messages from the server and identifies the necessary tasks based on the goals and deadlines. The generative AI model refers to its built-in database and past project data to list the relevant tasks.

[1371] Input: Prompt message sent from the server

[1372] Output: List of identified tasks

[1373] Examples of specific tasks:

[1374] "Market research," "product design," "manufacturing planning," "marketing planning," etc.

[1375] Step 5:

[1376] The server generates the schedule.

[1377] The server receives a task list from the generated AI model and organizes and assigns each task within the specified timeframe. Specifically, it sets appropriate start and end dates for each task and creates a schedule that includes internal tasks such as legal consultations and approval applications.

[1378] Input: Task list received from the generated AI model

[1379] Output: Generated schedule

[1380] Specific example schedule:

[1381] October 1st - October 15th, 2023: Market research, internal legal consultation

[1382] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[1383] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[1384] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[1385] Step 6:

[1386] The server sends the schedule to the terminal.

[1387] The server sends the generated schedule to the user's terminal. The server generates schedule data as an HTTP response and sends it to the terminal in the specified format. The terminal's web application receives this data and displays it in the user interface.

[1388] Input: Generated schedule

[1389] Output: Schedule sent to the user's terminal

[1390] These steps enable users to achieve efficient project management and, in particular, prevent internal tasks from being overlooked.

[1391] (Application Example 1)

[1392] 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".

[1393] In recent years, automation and efficiency in factories have become increasingly important. However, managing complex projects and manufacturing processes presents many challenges, often leading to human error and decreased efficiency. In particular, scheduling and task management waste significant time and resources when done manually. Furthermore, administrative tasks such as internal application processing and legal consultations are also important tasks that cannot be ignored, but managing them centrally is not easy. Therefore, there is a need for a system that streamlines complex project management and integrates with factory automation equipment.

[1394] 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.

[1395] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for being installed in factory automation equipment and optimizing factory operations based on the generated schedule, and means for displaying the schedule to the user. This enables both efficient project management and optimization of factory automation operations.

[1396] "A means of receiving user-entered goals and deadlines" refers to a method for users to fill in project goals and deadlines in an input form and send them to the server.

[1397] "A method for identifying necessary tasks based on received goals and deadlines using a generative AI model" refers to the process of using a generative AI model to extract the tasks required for a project based on the received goals and deadlines.

[1398] "A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period" refers to the process of appropriately allocating the tasks identified by the generative AI model within a specified period and arranging them as a schedule.

[1399] "A means of optimizing factory operations based on a generated schedule, which is installed on automated equipment within a factory" refers to the process by which software installed on automated equipment within a factory optimizes operations based on a generated schedule.

[1400] "Means of displaying the schedule to the user" refers to the process of sending the generated schedule to the user's terminal and displaying it on the user's interface.

[1401] "Methods for including internal application tasks such as legal consultations and approval requests" refers to a process that includes necessary internal application tasks, such as legal consultations and approval requests, in the project schedule.

[1402] "A means of generating multiple questions based on user-entered goals and deadlines, and receiving responses from users" refers to a process of generating questions related to the goals and deadlines entered by the user, and collecting the user's responses to those questions.

[1403] This invention relates to a system for streamlining factory operations based on user-inputted goals and deadlines. The system includes a program that uses a generative AI model to identify tasks and generate an optimal schedule.

[1404] System Configuration

[1405] 1. User input means:

[1406] Users can enter project goals and deadlines into an input form. For example, they might use a tablet or a dedicated control panel to enter "Setup of new product line" and "2023-12-31". This input is then sent from the terminal to the server.

[1407] 2. Means for receiving input data:

[1408] The server saves the goals and deadlines received from the user and sends them to the generated AI model.

[1409] 3. Generative AI Models:

[1410] The generative AI model identifies necessary tasks based on the received input data and generates an appropriate schedule. This model references databases and past project data. The generative AI model used includes the OpenAI API.

[1411] 4. Means of generating a schedule:

[1412] The server organizes the tasks identified by the generated AI model and creates a schedule that allocates each task within the specified timeframe. It also incorporates internal application tasks such as legal consultations and approval requests into the schedule.

[1413] 5. Means of integration with factory automation equipment:

[1414] Software installed on automated equipment within the factory optimizes factory operations based on the generated schedule. This integration brings project management and factory automation together.

[1415] 6. Means of displaying the schedule:

[1416] The organized schedule and task list are sent to the device and displayed in the user interface. The user can review the displayed schedule and make modifications as needed.

[1417] Hardware and software

[1418] Hardware:

[1419] factory control computer

[1420] Tablet devices

[1421] Dedicated control panel

[1422] software:

[1423] Python program

[1424] OpenAI API

[1425] dateutil package

[1426] Specific examples and prompt statements

[1427] Project objective: Set up a new product line; deadline: 2023-12-31. Please propose the necessary tasks and schedule.

[1428] Based on this prompt, the AI ​​model generates the following schedule.

[1429] Example of a generated schedule

[1430] October 1st - October 10th, 2023: Current situation analysis and material preparation

[1431] October 11-20, 2023: Mechanical design and installation

[1432] October 21, 2023 - November 10, 2023: Software setup and initial testing

[1433] November 11, 2023 - December 10, 2023: Continuous operation testing and optimization

[1434] December 11th - December 30th, 2023: Final adjustments and quality inspection

[1435] December 31, 2023: New product line begins operation.

[1436] This not only significantly reduces the time and effort users spend identifying project tasks and creating schedules, but also enables more efficient automation of factory operations.

[1437] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1438] Step 1:

[1439] The user enters the goal and deadline. The user uses a tablet or dedicated control panel to enter the project goal and deadline into an input form and submits it to the server. For example, they might enter "Set up a new product line" and "2023-12-31". Once the data is submitted, the server receives it and saves it to the database.

[1440] Step 2:

[1441] The server receives input and processes the data. After receiving the goal and deadline from the user, the server converts the data into a format for sending to the generated AI model. This data processing includes conversion to text format and generation of prompt statements.

[1442] Step 3:

[1443] The server sends a prompt to the generative AI model. Based on the processed data, the server generates a prompt and sends it to the generative AI model (OpenAI API). For example, it might send the prompt "Project goal: Setup of a new product line, deadline: 2023-12-31. Please suggest the necessary tasks and schedule." to the generative AI model.

[1444] Step 4:

[1445] The generative AI model generates tasks and schedules. The model receives prompts, identifies the necessary tasks, and generates appropriate schedules. This process involves data calculations and database lookups within the model. For example, it might output tasks such as "Current Situation Analysis and Material Preparation" and "Machine Design and Installation," along with appropriate timeframes for each task.

[1446] Step 5:

[1447] The server receives and organizes the generated schedule. The server receives tasks and schedules output from the generating AI model and organizes them based on predetermined timeframes. Furthermore, it completes the schedule, including internal application tasks such as legal consultations and approval requests. This process includes data processing such as task allocation and reordering.

[1448] Step 6:

[1449] The schedule is sent to the factory's automated equipment. The server sends the organized schedule to the factory's automated equipment. The software installed in the automated equipment starts optimizing factory operations based on the received schedule. This allows the machines to work together efficiently.

[1450] Step 7:

[1451] The user is shown the schedule. Finally, the generated schedule and task list are sent to the device and displayed in the user interface. The user can review the displayed schedule and make modifications as needed.

[1452] 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.

[1453] This invention relates to a system that recognizes a user's emotional state and uses that state to identify project tasks and optimize the schedule. The system receives goals and deadlines entered by the user, uses a generative AI model to identify tasks, proposes an appropriate schedule, and uses an emotion engine to make adjustments based on the user's emotional state.

[1454] System Configuration

[1455] This system has the following configuration:

[1456] User input means

[1457] Users can enter project goals and deadlines into an input form. The input data is sent from the terminal to the server.

[1458] Input data receiving means

[1459] The server receives goal and deadline data submitted by the user and stores it in a database. It then sends this data to the AI ​​model for generating data.

[1460] Generative AI Models

[1461] The generation AI model identifies the necessary tasks based on the received goals and deadlines and generates an appropriate schedule. This model references databases and past project data.

[1462] Schedule generation method

[1463] The server organizes the tasks responded to by the generated AI model and allocates each task to the schedule based on its duration. This also includes internal application tasks such as legal consultations and approval requests.

[1464] Emotional Engine

[1465] The emotion engine collects and analyzes emotional data from user input and actions. Based on this, it recognizes the emotional state and makes necessary adjustments.

[1466] Schedule display methods

[1467] The organized schedule and task list are sent to the device and displayed in the user interface.

[1468] Program processing

[1469] 1. The user enters the goal and deadline.

[1470] The user enters the project goal and deadline into an input form on their device and sends it to the server. For example, the goal might be "market launch of a new product," and the deadline might be "December 31, 2023."

[1471] 2. The server receives the input.

[1472] The server receives user input and stores it in a database. It then sends the input data to the generated AI model.

[1473] 3. The generative AI model identifies the task.

[1474] The AI ​​model generates a list of tasks necessary to complete a project, based on the goals and deadlines provided by the user. These tasks may include, for example, "market research," "product design," "manufacturing planning," and "marketing planning."

[1475] 4. The emotion engine collects user emotion data.

[1476] The emotion engine analyzes the user's emotional state from their behavior and input data. For example, if a user is feeling stressed, the emotion engine will recognize this.

[1477] 5. Organize the task list generated by the server and create a schedule.

[1478] The server organizes the task list received from the generated AI model and distributes it within appropriate timeframes. During this process, it also considers the user's emotional state and adjusts the schedule to reduce stress.

[1479] 6. The server sends the final schedule to the terminal.

[1480] The server sends the generated schedule to the user's terminal, which then displays it. The user can review the displayed schedule and make modifications or additions as needed.

[1481] Specific example

[1482] For example, if a user sets the goal as "launching a new product to market" and enters "December 31, 2023" as the deadline, the generating AI model will identify the following tasks.

[1483] October 1st - October 15th, 2023: Market research, internal legal consultation

[1484] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[1485] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[1486] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[1487] To reduce the user's stress level, the emotion engine makes adjustments, such as splitting the "market research" task into shorter segments if it detects high stress levels.

[1488] This invention allows users to efficiently organize tasks and manage projects, as well as receive support based on their emotional state, enabling them to perform tasks more effectively.

[1489] The following describes the processing flow.

[1490] Step 1:

[1491] The user enters the goal and deadline. The user enters the project goal (e.g., "Launch a new product") and the deadline (e.g., "December 31, 2023") into the input form on their device and clicks the submit button.

[1492] Step 2:

[1493] The server receives the input data. The server receives the target and due date data sent by the user and stores it in a secure database.

[1494] Step 3:

[1495] The server invokes the generating AI model. The server sends the received goal and deadline data to the generating AI model via API and requests that it identify the necessary tasks.

[1496] Step 4:

[1497] The generative AI model identifies the tasks. The generative AI model generates a list of tasks necessary to complete the project. For example, these tasks might include "market research," "product design," "manufacturing planning," and "marketing planning."

[1498] Step 5:

[1499] The server receives the results of the generated AI model. The server receives the task list and the start and end dates of each task returned from the generated AI model.

[1500] Step 6:

[1501] The server organizes the task list and generates a schedule. The server organizes the received task list and creates a schedule based on a specified period. In this process, tasks such as legal consultations and approval requests are also included as internal application tasks.

[1502] Step 7:

[1503] The emotion engine collects user emotional data. The emotion engine analyzes input data and behavioral data collected from the user's device to recognize the user's emotional state.

[1504] Step 8:

[1505] The server receives the results from the emotion engine and adjusts the schedule. Based on the user's emotional state received from the emotion engine, the server adjusts the schedule. For example, if the user's stress level is high, the server may split tasks or set flexible deadlines.

[1506] Step 9:

[1507] The server sends the final schedule to the terminal. The server then sends the adjusted schedule and task list to the user's terminal.

[1508] Step 10:

[1509] The terminal displays the schedule. The terminal displays the schedule and task list received from the server in the user interface.

[1510] Step 11:

[1511] The user reviews and modifies the schedule. The user reviews the schedule and makes any necessary corrections or additions. They then resubmit the revised information to the server to finalize the schedule.

[1512] Through each of the above steps, users can efficiently organize tasks and manage projects while receiving support based on their emotional state. In particular, the use of an emotional engine can reduce the user's psychological burden and improve the effectiveness of task completion.

[1513] (Example 2)

[1514] 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".

[1515] In project management, efficiently identifying tasks and generating appropriate schedules is crucial. However, traditional systems often fail to consider the user's emotional state, leading to stress. Furthermore, internal application processes are sometimes not properly incorporated into the schedule. This can result in project delays and increased user burden. Current technology is insufficient to address these challenges.

[1516] 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.

[1517] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for analyzing the user's emotional state using an emotion engine and reflecting it in the schedule, and means for displaying the schedule to the user. This enables efficient and appropriate schedule management that takes the user's emotional state into consideration. Furthermore, by including means for internal application procedures as tasks, the progress of the project can be made smoother.

[1518] A "user" is the entity that uses the system to input goals and deadlines.

[1519] A "goal" is the ultimate destination or target for achieving a project or task.

[1520] A "deadline" is the final deadline set to achieve a goal.

[1521] "Means" refers to the methods or devices used to achieve a goal.

[1522] A "generative AI model" is an artificial intelligence algorithm that generates necessary tasks and creates schedules based on input data.

[1523] A "task" is a specific set of tasks or activities necessary to achieve a goal.

[1524] A "schedule" is a plan or time allocation for carrying out a task.

[1525] An "emotion engine" is an algorithm or system that analyzes a user's emotional state and reflects it in the schedule.

[1526] "Display means" refers to devices or methods that visually present information to a user.

[1527] "Internal application procedures" refer to the internal processes required to obtain approvals and permissions necessary for the progress of a project.

[1528] This invention is a system that receives user-inputted goals and deadlines, uses a generation AI model based on them to identify necessary tasks, and generates and displays a schedule that takes the user's emotional state into account using an emotion engine. This system is configured so that the server, terminal, and user work together in cooperation with each other.

[1529] User input means

[1530] The user enters the project goal and deadline into an input form on the device. The entered data is sent to the server via the device's communication module. For example, the user enters "New product launch" as the goal and "December 31, 2023" as the deadline.

[1531] Server receiving method

[1532] The server receives target and deadline data sent from the terminal and stores that data in a database. The server also converts the received data into a prompt message and sends it to the generating AI model.

[1533] Generative AI Models

[1534] The generative AI model identifies the necessary tasks based on prompt messages sent from the server. It utilizes past project data and a trained database to generate an optimal task list.

[1535] Emotional Engine

[1536] The emotion engine collects and analyzes emotional states from user input data and behavior. For example, if a user is feeling stressed, the emotion engine recognizes this state and sends that information to the server. The emotion engine analyzes emotional states using natural language processing and emotion recognition algorithms.

[1537] Schedule generation and display

[1538] The server organizes tasks and generates an appropriate schedule based on the task list received from the generation AI model and sentiment data from the sentiment engine. For example, if the user is experiencing high stress levels, the server adjusts the schedule by splitting tasks into shorter timeframes. The generated schedule is then sent to the device, which displays the schedule.

[1539] Specific example

[1540] If a user enters "New Product Market Launch" as their goal and "December 31, 2023" as their deadline, the AI ​​model will generate the following task list:

[1541] October 1st - October 15th, 2023: Market research, internal legal consultation

[1542] October 16, 2023 - November 15, 2023: Product Design, Approval Application

[1543] November 16, 2023 - December 15, 2023: Manufacturing planning, marketing strategy preparation

[1544] December 16th - December 31st, 2023: Final checks, preparation for market launch.

[1545] Furthermore, the emotion engine detects the user's stress level, and if it determines, for example, that the user is highly stressed, it adjusts the schedule by splitting the "market research" task into shorter periods. The server sends these adjustments to the terminal, which then displays the schedule to the user.

[1546] Example of a prompt

[1547] If the goal is "market launch of a new product" and the deadline is set as "December 31, 2023," then by inputting the following prompt message into the AI ​​model, a list of necessary tasks can be generated.

[1548] Prompt message:

[1549] Objective: Launch a new product into market

[1550] Date: December 31, 2023

[1551] Identify the necessary tasks and create an appropriate schedule. Also, consider emotional data and adjust the schedule to reduce stress.

[1552] By using this prompt to input into the AI ​​model, a task list and schedule necessary to complete the project will be generated.

[1553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1554] Step 1:

[1555] The user enters the goal and deadline.

[1556] The user enters the project's goal and deadline into an input form on their device. For example, they might enter "New product launch" as the goal and "December 31, 2023" as the deadline. By clicking the submit button through the input form, this data is sent to the server.

[1557] Input: Goals and deadlines

[1558] Output: Data sent to the server

[1559] Step 2:

[1560] The server receives input.

[1561] The server receives target and deadline data sent from the terminal. The received data is stored in a database. This information is also passed to the AI ​​model as a prompt. Through this process, the user's input data is stored within the system and used for subsequent processing.

[1562] Input: Goals and deadlines from the device

[1563] Output: Data stored in the database, and prompt messages sent to the generated AI model.

[1564] Step 3:

[1565] Generative AI models identify tasks

[1566] When the server sends a prompt to the generation AI model, the model generates a list of necessary tasks based on the goals and deadlines. This model uses past project data and pre-trained information. For example, tasks such as "market research," "product design," "manufacturing planning," and "marketing plan" may be generated.

[1567] Input: Prompt message

[1568] Output: List of identified tasks

[1569] Step 4:

[1570] The emotion engine collects user emotion data.

[1571] The emotion engine analyzes the user's emotional state from user input data and behavioral data. Using natural language processing and emotion recognition algorithms, it determines the user's stress level and emotional state, and sends this information to the server. For example, if a user is feeling stressed, it accurately recognizes that state.

[1572] Input: User input data, behavioral data

[1573] Output: Analyzed sentiment data

[1574] Step 5:

[1575] The server organizes the task list it generates and creates a schedule.

[1576] The server organizes tasks and generates an appropriate schedule based on the task list received from the generating AI model and sentiment data from the sentiment engine. For example, if the server detects that the user is experiencing high stress levels, it might break down a task like "market research" and set shorter timeframes. Then, it places all tasks according to priority and deadline.

[1577] Input: Task list, sentiment data

[1578] Output: Organized schedule

[1579] Step 6:

[1580] The server sends the final schedule to the terminal.

[1581] The server sends the generated schedule to the user's terminal. The terminal displays the received schedule in the user interface, allowing the user to review it. The user can review the displayed schedule and make modifications or additions as needed.

[1582] Input: Organized schedule

[1583] Output: Schedule displayed on another device

[1584] (Application Example 2)

[1585] 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".

[1586] In modern factories, worker stress and fatigue significantly impact productivity. Managing production lines is complex and requires efficient task allocation, but scheduling without considering employees' emotional states has its limitations. Furthermore, efficiently identifying tasks based on production targets and deadlines and organizing them into an optimal schedule is not easy and requires considerable effort. There is a need for methods to improve productivity by identifying employees' emotional states and adjusting schedules accordingly.

[1587] 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.

[1588] In this invention, the server includes means for receiving goals and deadlines entered by the user, means for identifying necessary tasks based on the received goals and deadlines using a generative AI model, means for organizing the tasks identified by the generative AI model into a schedule based on a predetermined period, means for adjusting the schedule based on the user's emotional state using an emotion engine that collects and analyzes the user's emotional data, and means for displaying the schedule and task list to the user. This enables flexible schedule adjustments that take into account the user's emotional state, making it possible to simultaneously improve production efficiency and reduce worker stress.

[1589] "A means of receiving user-entered goals and deadlines" refers to an interface that allows users to input project goals and deadlines into the system via input forms or similar methods.

[1590] "A method for identifying necessary tasks based on received goals and deadlines using a generative AI model" refers to a processing unit that utilizes a generative AI model to extract specific tasks that should be performed based on the input goals and deadlines.

[1591] "A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period" refers to a processing device that has the function of appropriately distributing the extracted tasks over a certain period and organizing them into an optimal schedule.

[1592] An "emotion engine that collects and analyzes user emotion data" is a system that understands and analyzes a user's emotional state based on their behavior and input data.

[1593] A "means for adjusting schedules based on the user's emotional state" refers to a device that has the function of modifying or adjusting schedules based on collected emotional data, taking into account the user's stress level and fatigue level.

[1594] "Means for displaying schedules and task lists to the user" refers to display devices or display equipment that visually provide the user with a list of the final generated schedules and tasks through a user interface.

[1595] This invention relates to a system that recognizes a user's emotional state and uses that to identify project tasks and optimize the schedule. In particular, when applied to factory robots, it enables the management of factory lines and schedule adjustments that take into account the emotional state of employees. This system primarily has the following configuration and functions.

[1596] System Configuration

[1597] 1. User input means

[1598] The user uses a device (e.g., a tablet or PC) to input production targets and deadlines, providing an interface for this input. This input data is then sent to the server.

[1599] 2. Means for receiving input data

[1600] The server receives goal and deadline data submitted by the user and stores it in a database. This data is then sent to the generating AI model.

[1601] 3. Generative AI Models

[1602] The generative AI model identifies the necessary tasks based on the received goals and deadlines and generates an appropriate schedule. The generative AI model references past databases and data from similar projects.

[1603] 4. Emotional Engine

[1604] The emotion engine analyzes the user's emotional state from their behavior and input data. Based on this, it adjusts the generated schedule. For example, if the stress level is high, it will break down tasks and set shorter timeframes.

[1605] 5. Means of displaying the schedule

[1606] The final generated schedule and task list are sent to the user's device and displayed on the display. The user can review it and make modifications or additions as needed.

[1607] Explanation of the program's processing

[1608] This system operates using a program specifically designed for factory management. When a user enters goals and deadlines, the server receives this data and sends it to a generating AI model. The generating AI model references past project data to generate a task list. Subsequently, an emotion engine collects and analyzes the user's emotional data, adjusting the schedule based on their emotional state. The final generated schedule is then sent to and displayed on the user's terminal.

[1609] This system operates by combining multiple hardware and software components. Specifically, it includes data processing servers, a cloud computing environment for running generative AI models, sensors and log analysis software for collecting sentiment data, and terminal applications that provide a user interface.

[1610] For example, if a user sets the goal as "start production of a new product" and enters "January 31, 2024" as the deadline, the generating AI model will identify tasks such as "production line design," "machine layout," "commissioning," and "full-scale operation." The emotion engine then analyzes the stress levels of the employees and makes adjustments, such as splitting tasks as needed. The resulting schedule might look something like this:

[1611] "December 1st - December 15th, 2023: Production line design"

[1612] "December 16, 2023 - December 22, 2023: Machine layout"

[1613] "December 23rd - December 29th, 2023: Trial operation"

[1614] "December 30, 2023 - January 5, 2024: Full operation"

[1615] Example of a prompt

[1616] "The user has entered the goal 'Start production of a new product' and the deadline 'January 31, 2024'. Please generate a list of tasks necessary to achieve this goal."

[1617] This system allows factory managers to efficiently organize tasks and flexibly adjust schedules while taking emotional states into consideration. This can lead to increased productivity and improved working conditions.

[1618] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1619] Step 1:

[1620] The user enters the goal and deadline.

[1621] The user uses a device (e.g., PC or tablet) to enter the project goal and deadline into an input form and submit it to the server. Specifically, they would enter "Start production of new product" and "January 31, 2024". The input data is collected and received by the server. The input data consists of the project goal and deadline.

[1622] Step 2:

[1623] The server receives the input and saves it to the database.

[1624] The server receives goal and deadline data submitted by the user and stores it in a database. This stored data is then used for processing by subsequent generative AI models and emotion engines.

[1625] Step 3:

[1626] The server sends input data to the generated AI model, which then identifies the tasks.

[1627] The server sends the target and deadline data stored in the database to the generating AI model. The generating AI model refers to the database and past project data to generate a list of necessary tasks. For example, tasks such as "production line design," "machine layout," "commissioning," and "production operation" are identified. This task list is then returned to the server.

[1628] Step 4:

[1629] The emotion engine collects and analyzes user emotion data.

[1630] The server collects and analyzes user emotional data through an emotion engine. Specifically, it measures stress levels and fatigue levels from the user's operation history and input data to determine their emotional state. For example, if a user is fatigued or stressed, that data is analyzed by the emotion engine. This emotional data is used to adjust the task schedule.

[1631] Step 5:

[1632] The server generates a schedule based on the generated task list and makes adjustments based on sentiment data.

[1633] The server generates a schedule that allocates tasks to predetermined periods based on the task list received from the generating AI model and the emotional data obtained from the emotion engine. For example, if the user's stress level is high, adjustments are made, such as dividing the task into multiple parts. Specifically, the "production line design" task might be split from 14 days into two 7-day tasks. This schedule is then finalized as the final schedule.

[1634] Step 6:

[1635] The server sends the final schedule to the terminal and displays it to the user.

[1636] The server sends the completed schedule and task list to the user's terminal. The terminal receives this and displays it to the user through a user interface. The user reviews the displayed schedule and makes modifications or additions as needed. This display interface is provided in a visually easy-to-understand format.

[1637] Through these steps, a system is created that allows users to efficiently organize tasks and adjust their schedules based on their emotional state.

[1638] 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.

[1639] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1640] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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."

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1659] The following is further disclosed regarding the embodiments described above.

[1660] (Claim 1)

[1661] A means of receiving the goals and deadlines entered by the user,

[1662] A method for identifying necessary tasks based on the received goals and deadlines using a generative AI model,

[1663] A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period,

[1664] A means of displaying the schedule to the user,

[1665] A system that includes this.

[1666] (Claim 2)

[1667] The system according to claim 1, which has means for including internal application tasks such as legal consultations and approval requests as tasks.

[1668] (Claim 3)

[1669] The system according to claim 1, further comprising means for generating multiple questions based on goals and deadlines entered by the user and for receiving responses from the user.

[1670] "Example 1"

[1671] (Claim 1)

[1672] A means of receiving the goals and deadlines entered by the user,

[1673] A means for the server to receive and store input data from the user,

[1674] A means for the server to send stored data to a generating AI model,

[1675] A method for identifying necessary tasks based on the received goals and deadlines using a generative AI model,

[1676] A means of organizing tasks identified by the server-generated AI model into a schedule based on a predetermined period,

[1677] A means of sending the schedule generated by the server to the user's terminal and displaying the schedule to the user,

[1678] A system that includes this.

[1679] (Claim 2)

[1680] The system according to claim 1, which has means for including internal tasks such as legal consultations and approval applications as tasks.

[1681] (Claim 3)

[1682] The system according to claim 1, further comprising means for generating multiple questions based on goals and deadlines entered by the user and for receiving responses from the user.

[1683] "Application Example 1"

[1684] (Claim 1)

[1685] A means of receiving the goals and deadlines entered by the user,

[1686] A method for identifying necessary tasks based on the received goals and deadlines using a generative AI model,

[1687] A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period,

[1688] A means of optimizing factory operations based on a generated schedule, which is installed in the automated equipment within the factory.

[1689] A means of displaying the schedule to the user,

[1690] A system that includes this.

[1691] (Claim 2)

[1692] The system according to claim 1, which has means for including internal application tasks such as legal consultations and approval requests as tasks.

[1693] (Claim 3)

[1694] The system according to claim 1, further comprising means for generating multiple questions based on goals and deadlines entered by the user and for receiving responses from the user.

[1695] "Example 2 of combining an emotion engine"

[1696] (Claim 1)

[1697] A means of receiving the goals and deadlines entered by the user,

[1698] A method for identifying necessary tasks based on the received goals and deadlines using a generative AI model,

[1699] A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period,

[1700] A means of analyzing the user's emotional state using an emotion engine and reflecting it in the schedule,

[1701] A means of displaying the schedule to the user,

[1702] A system that includes this.

[1703] (Claim 2)

[1704] The system according to claim 1, having means for including internal application procedures as tasks.

[1705] (Claim 3)

[1706] The system according to claim 1, further comprising means for generating multiple questions based on goals and deadlines entered by the user and for receiving responses from the user.

[1707] "Application example 2 when combining with an emotional engine"

[1708] (Claim 1)

[1709] A means of receiving the goals and deadlines entered by the user,

[1710] A method for identifying necessary tasks based on the received goals and deadlines using a generative AI model,

[1711] A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period,

[1712] It includes an emotion engine that collects and analyzes user emotion data, and means for adjusting the schedule based on the user's emotional state.

[1713] A means of displaying the schedule and task list to the user,

[1714] A system that includes this.

[1715] (Claim 2)

[1716] The system according to claim 1, which has means for including internal application tasks such as legal consultations and approval requests as tasks.

[1717] (Claim 3)

[1718] The system according to claim 1, further comprising means for generating multiple questions based on goals and deadlines entered by the user and for receiving responses from the user. [Explanation of symbols]

[1719] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving the goals and deadlines entered by the user, A method for identifying necessary tasks based on the received goals and deadlines using a generative AI model, A means of organizing tasks identified by a generative AI model into a schedule based on a predetermined period, A means of displaying the schedule to the user, A system that includes this.

2. The system according to claim 1, which has means for including internal application tasks such as legal consultations and approval requests as tasks.

3. The system according to claim 1, further comprising means for generating multiple questions based on the goals and deadlines entered by the user and for receiving responses from the user.

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