System

Abstract

A system is provided.SOLUTION: A system comprising: input means for a user to register a task and a deadline; means for a server to record task and deadline information received from the input means; means for the server to acquire past record data and calculate approximate man-hours and a priority related to the task; means for the server to acquire a current schedule of the user and generate an optimal schedule of the task; and means for the server to notify the user of the generated schedule.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's work environment, businesspeople expend a great deal of resources managing and coordinating a constant stream of tasks. This is due to the need to coordinate schedules, the complexity of tasks, and the changing team members. This makes it difficult for businesspeople to focus on creative work, creating a need for efficient task management systems. However, current task management tools face challenges, such as being difficult to customize to meet individual user needs and lacking mechanisms for effectively utilizing past performance data. [Means for solving the problem]

[0005] In this invention, an input means is provided for users to register tasks and deadlines, and a server is prepared to record the input information. The server acquires past performance data and calculates estimated man-hours and priorities related to the registered tasks. The server then acquires the user's current schedule data and generates an optimal schedule based on the calculated man-hours and priorities. The generated schedule is notified to the user, and the server includes means for analyzing and identifying relevant departments and useful information and recommending them to the user. Using AI algorithms, more accurate task management can be achieved, providing an environment where business people can focus on creative work.

[0006] A "user" is an entity that uses the system to manage tasks and adjust schedules.

[0007] A "task" is a job or work that a user must perform, and refers to a specific action or project.

[0008] The "deadline" refers to a deadline set by the user for completing a task, and indicates that the task must be completed by a specific date and time.

[0009] "Input means" refers to an interface that allows a user to register information such as tasks and deadlines into the system.

[0010] The "server" is a central system that receives, stores, and analyzes information registered by users, and makes necessary schedule adjustments and recommends information.

[0011] "Performance data" refers to records of the time required to complete tasks performed in the past, the number of man-hours required, related information, etc., and is used for analyzing tasks.

[0012] The "estimated man-hours" is the estimated work time required to complete a specific task, calculated by the server based on past performance data.

[0013] "Priority" is an index that indicates whether the server evaluates the importance and urgency of a task and whether the task should be executed with priority compared to other tasks.

[0014] "Schedule generation means" refers to the process in which the server calculates and proposes an optimal work schedule based on the user's current schedule and information on new tasks.

[0015] "Recommendation means" refers to the function of the server to identify and notify the user of relevant departments and useful information.

[0016] "AI algorithm" refers to the artificial intelligence technology that the server uses to calculate the approximate labor hours and priority of tasks with high accuracy. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is a generation AI system that calculates estimated man-hours and priorities based on past performance data and adjusts schedules when users register tasks and deadlines.

[0039] System Overview

[0040] The system consists of the following main components:

[0041] 1. User Input Method

[0042] 2. Server data reception and storage function

[0043] 3. Server past performance data acquisition function

[0044] 4. AI algorithms that calculate effort and priority

[0045] 5. User schedule adjustment function

[0046] 6. Related information recommendation function

[0047] Program processing overview

[0048] Registering tasks and deadlines

[0049] A user registers a task

[0050] The user inputs a new task and its due date through their own device. For example, they can register "Complete the design of the new website by December 1st."

[0051] The device sends task information to the server

[0052] The user's device sends the input task and delivery date information to the server. Specifically, the input information is sent to the server's endpoint via the API.

[0053] Data recording and acquisition

[0054] The server receives and stores task information

[0055] The server stores the task and deadline information received from the device in a database. For example, it records data such as "Complete the design of the new website by December 1st."

[0056] The server acquires past performance data

[0057] The server retrieves past performance data related to the registered task from the database, specifically by filtering performance data of similar website design tasks that have been completed in the past.

[0058] Calculating effort and priority

[0059] The server uses AI to calculate the man-hours and priority

[0060] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority of the new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[0061] Schedule adjustment

[0062] The server retrieves the user's current schedule

[0063] The server retrieves the user's current schedule from a database and, if necessary, may also connect with the calendar app used by the user.

[0064] The server proposes the optimal schedule

[0065] The server will suggest start and end dates for the new task based on the effort and priority of the task and the user's current schedule. For example, it might suggest that the best time to complete the new website design task is between November 20th and November 30th.

[0066] The server notifies the user of the proposed schedule

[0067] The server transmits the proposed schedule information to the user's terminal, which is notified of the proposed schedule.

[0068] Related information recommendations

[0069] The server analyzes and obtains relevant information

[0070] The server analyzes past performance data and identifies related departments and reference materials. For example, it identifies information such as "Yamada from the design department was involved in a similar task."

[0071] The server recommends related information to the user

[0072] Based on the analysis results, the server sends relevant information to the user's device and makes recommendations. The user will receive a notification such as, "It would be a good idea to contact Yamada-san, the person in charge. Also, please refer to the materials from the previous project."

[0073] Specific examples

[0074] When a user registers a task such as "Create a new website design and have it completed by December 1st," the system works as follows:

[0075] 1. The device sends task information to the server.

[0076] 2. The server receives and stores the information and obtains past performance data.

[0077] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[0078] 4. The server identifies relevant information (e.g., people involved in past projects and reference materials) and recommends it to the user.

[0079] This system allows business people to significantly reduce the time they spend on task management and scheduling, allowing them to focus on more creative work.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] The user inputs the task and its due date. The user inputs task information such as "Complete the design of the new website by December 1st" from their own device.

[0083] Step 2:

[0084] The device sends task information to the server. The user's device sends the entered task and deadline information to the server's endpoint using the API.

[0085] Step 3:

[0086] The server receives the task information and stores it in a database. Specifically, the server records information such as "Complete the design of the new website by December 1st" in the database.

[0087] Step 4:

[0088] The server obtains past performance data. The server obtains past performance data related to the registered task from the database by filtering.

[0089] Step 5:

[0090] The server uses AI to calculate the estimated man-hours and priority. Based on the acquired performance data, the server uses an AI algorithm to calculate the estimated man-hours and priority of a new task. For example, the AI ​​might determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[0091] Step 6:

[0092] The server retrieves the user's current schedule. The server retrieves the user's existing schedule data from a database or a linked calendar app.

[0093] Step 7:

[0094] The server generates an optimal schedule. Taking into account the man-hours and priority of the new task and the user's current schedule, the server calculates the start and end dates and times of the task and generates an optimal schedule. For example, it may determine that "it is optimal to assign the new website design task between November 20th and November 30th."

[0095] Step 8:

[0096] The server notifies the user of the generated schedule. The server transmits the generated schedule information to the user's terminal and notifies the user.

[0097] Step 9:

[0098] The server analyzes and acquires related information. Based on past performance data, the server identifies related departments and reference materials. For example, it identifies information such as "Yamada from the design department was involved in a similar task."

[0099] Step 10:

[0100] The server recommends related information to the user. The server then sends the identified related information to the user's device and notifies them. For example, the user might receive a notification such as, "It would be a good idea to contact Mr. Yamada, the person in charge. Also, please refer to the materials from the previous project."

[0101] Through these steps, the system efficiently supports users in task management and schedule adjustment, providing an environment in which business people can concentrate on creative work.

[0102] Example 1

[0103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] In today's business environment, efficient management of multiple tasks and their deadlines is required. However, doing this manually is time-consuming, labor-intensive, and inefficient. Furthermore, there is a lack of systems that can calculate appropriate man-hours and priorities based on performance data from similar past tasks and propose optimal schedules. Furthermore, it is difficult to provide relevant information and reference materials in a timely manner for task completion.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0106] In this invention, the server includes: an input means for a user to register tasks and deadlines; a means for the server to record the task and deadline information received from the input means; a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; a means for the server to recommend relevant departments and useful information to the user; a means for the server to store and filter past performance data using a database; a means for a terminal to communicate with the server via an API; a means for the server to acquire the user's schedule in cooperation with an external calendar service; and a means for the server to calculate estimated man-hours and priorities using an AI algorithm. This allows users to efficiently manage tasks and adjust their schedules, and quickly obtain necessary related information.

[0107] A "user" is a person or organization that uses this system to input tasks and their due dates, and receives schedule management and related information.

[0108] The "server" is a computer system that records task and delivery date information received from users, retrieves past performance data from a database, calculates labor hours and priorities using AI algorithms, generates optimal schedules, and provides notification and recommendation functions.

[0109] "Input means" refers to an interface or device that allows a user to register task and delivery date information, and examples include a keyboard, a touch screen, and a dedicated application.

[0110] The "recording means" is a function for storing the task and deadline information received by the server in a database, and also checks the consistency of the data.

[0111] "Performance data" is data relating to similar tasks that have been completed in the past, and includes information such as the task name, man-hours, completion date, and people involved.

[0112] "Estimated man-hours" is an estimate of the total time required to complete a task, calculated by an AI algorithm based on past performance data.

[0113] "Priority" is an assessment of the importance or urgency of a particular task, and indicates how much priority it needs to have compared to other tasks.

[0114] A "schedule" is a specific plan of the start and end dates of a task and the work to be done during that time.

[0115] "Notification means" refers to a method for notifying the user of schedules and related information generated by the server, and specifically includes push notifications, emails, display messages, etc.

[0116] The "recommendation function" is a function in which the server analyzes the database and suggests information and reference materials that may be useful to the user.

[0117] A "database" is a structured data repository that stores received task information and past performance data and allows for searching and retrieval as needed.

[0118] An "API" is an interface for exchanging data between a terminal and a server, and communication is carried out according to a standardized protocol.

[0119] An "external calendar service" is a calendar application provided by a third party, such as Google Calendar or Outlook Calendar, that the server works with to obtain the user's current schedule.

[0120] An "AI algorithm" is a machine learning model that analyzes past data and estimates the effort and priority of new tasks.

[0121] MODE FOR CARRYING OUT THE INVENTION

[0122] This invention is an AI generation system for managing tasks and deadlines, which uses past performance data based on tasks entered by a user, calculates approximate man-hours and priorities, and provides optimal schedules and related information. Specific embodiments of the system are described below.

[0123] System Overview

[0124] The system mainly consists of the following components:

[0125] 1. User Input Method

[0126] 2. Server data reception and storage function

[0127] 3. Server past performance data acquisition function

[0128] 4. AI algorithms that calculate effort and priority

[0129] 5. User schedule adjustment function

[0130] 6. Related information recommendation function

[0131] Hardware and software used

[0132] 1. User's device (PC, tablet, smartphone, etc.)

[0133] 2. Servers (including cloud and on-premise environments)

[0134] 3. Database (MySQL, PostgreSQL, etc.)

[0135] 4. AI algorithms (TensorFlow, PyTorch, etc.)

[0136] 5. Calendar services (Google Calendar, Outlook Calendar, etc.)

[0137] Program processing overview

[0138] Registering tasks and deadlines

[0139] Users use their devices to input new tasks and their due dates. The input data is sent to the server via the API. For example, a user might input "Complete the design of the new website by December 1st."

[0140] Data recording and acquisition

[0141] The server receives the task and delivery date information sent from the terminal and stores it in a database.The server then retrieves past performance data from the database.Specifically, it filters and extracts past data related to similar tasks.

[0142] Calculating effort and priority

[0143] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority for new tasks. The AI ​​algorithm is built using TensorFlow and PyTorch and analyzes past data. The calculation results include, "Based on past performance, this task will require approximately 50 hours and is a high priority."

[0144] Schedule adjustment

[0145] The server obtains the user's current schedule. If necessary, it connects with an external calendar service to obtain schedule information. The server then generates an optimal schedule based on the effort and priority of the new task and the user's current schedule, and proposes it to the user. For example, it might suggest that "it would be best to proceed with the design of the new website from November 20th to November 30th."

[0146] Notifications and Recommendations

[0147] The server notifies the user of the generated schedule information. At the same time, the server analyzes past performance data, identifies relevant information (such as people involved in past projects and reference materials), and makes recommendations to the user.

[0148] Specific examples

[0149] For example, if a user registers a task such as "Create a new website design and have it completed by December 1st," the system will act as follows:

[0150] 1. The user enters the task and due date on the terminal.

[0151] 2. The device sends the input data to the server.

[0152] 3. The server receives the data and stores it in a database.

[0153] 4. The server retrieves performance data for similar tasks in the past.

[0154] 5. The server uses AI to calculate the estimated effort and priority of the new task.

[0155] 6. The server retrieves the user's current schedule.

[0156] 7. The server proposes an optimal schedule.

[0157] 8. The server notifies the user of the proposed schedule.

[0158] 9. The server analyzes and retrieves the relevant information.

[0159] 10. The server recommends relevant information to the user.

[0160] Prompt Sentence Examples

[0161] "We have a task to design a new website. The deadline is December 1st. Based on past performance data for similar tasks, please tell us the approximate man-hours and priority. Also, please suggest the optimal schedule."

[0162] Based on this prompt, the system provides the necessary information and suggests the optimal schedule for the user.

[0163] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0164] Step 1:

[0165] The user enters the task and due date

[0166] The user inputs a new task and its due date using an input form on the terminal or a dedicated application.

[0167] Input: Tasks and deadlines, such as "Complete new website design by December 1st."

[0168] Output: Task and due date data in text format.

[0169] Step 2:

[0170] The device sends the input data to the server

[0171] The terminal uses the API to send the task and deadline information entered by the user to the server.

[0172] Input: Task and due date data entered by the user.

[0173] Data processing: Convert data into JSON format.

[0174] Output: JSON data sent to the server's API endpoint.

[0175] Step 3:

[0176] The server receives the data and stores it in the database

[0177] The server receives the task and deadline data sent via the API and stores it in a database.

[0178] Input: Task and due date data received by the server in JSON format.

[0179] Data processing: Convert JSON data into structured data.

[0180] Output: Task and due date data stored in a database.

[0181] Step 4:

[0182] The server obtains performance data for similar tasks from the past.

[0183] The server searches the database for past performance data using a query and obtains data related to the registered task.

[0184] Input: Saved task and due date data.

[0185] Data processing: Use queries to search for similar tasks from a database.

[0186] Output: A list of historical performance data.

[0187] Step 5:

[0188] The server uses AI to calculate the estimated man-hours and priority of new tasks.

[0189] The server inputs past performance data acquired into an AI algorithm to calculate the estimated man-hours and priority of new tasks.

[0190] Input: A list of historical performance data.

[0191] Data Calculation: Data analysis using AI algorithms.

[0192] Output: Estimated effort and priority of the new task (e.g. "50 hours required, high priority").

[0193] Step 6:

[0194] The server retrieves the user's current schedule

[0195] The server contacts a database or external calendar service to retrieve the user's current schedule.

[0196] Input: User credentials and / or calendar service API key.

[0197] Data processing: Communication with external calendar services.

[0198] Output: Current schedule data.

[0199] Step 7:

[0200] The server proposes the optimal schedule for new tasks.

[0201] The server proposes optimal start and end dates based on the current schedule and the man-hours and priority of the new task.

[0202] Inputs: New task effort and priority, current schedule data.

[0203] Data calculation: Execution of the schedule generation algorithm.

[0204] Output: The optimal schedule for the new task (e.g., "November 20th to November 30th is optimal").

[0205] Step 8:

[0206] The server notifies the user of the generated schedule.

[0207] The server notifies the user's terminal of the proposed schedule.

[0208] Input: The optimal schedule for the new task.

[0209] Data processing: generating notification messages.

[0210] Output: Push notification or email to user device.

[0211] Step 9:

[0212] The server analyzes and retrieves the relevant information

[0213] The server analyzes past performance data and identifies relevant departments and useful information.

[0214] Input: Historical performance data.

[0215] Data operations: Running data analysis algorithms.

[0216] Output: A list of relevant information (e.g., "Yamada from the design department was involved").

[0217] Step 10:

[0218] The server recommends relevant information to the user

[0219] The server recommends related information to the user based on the analysis results.

[0220] Input: A list of relevant information.

[0221] Data calculation: Generate recommended messages.

[0222] Output: Recommendation notification to the user device (e.g., "We recommend contacting Yamada-san, the person in charge").

[0223] (Application example 1)

[0224] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0225] Logistics centers have numerous tasks (such as receiving, picking, and shipping goods), and each task requires strict delivery date management. However, there are not enough methods in place to properly manage and efficiently handle these numerous tasks and delivery dates. This can lead to reduced work efficiency, which can result in a deterioration in overall logistics performance. Furthermore, it is difficult for employees to correctly estimate the priority and approximate man-hours required for each task and set an optimal schedule. An appropriate system is needed to solve these problems.

[0226] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0227] In this invention, the server includes: an input means for a user to register tasks and delivery dates; a means for the server to record the task and delivery date information received from the input means; a means for the server to acquire past performance data and calculate approximate man-hours and priorities related to the tasks; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; a means for the server to recommend relevant departments and useful information to the user; a means for the logistics center to register logistics work, calculate approximate man-hours and priorities based on past performance data, and propose an optimal schedule; and a means for a logistics center employee to input task information using a smartphone or a head-mounted display and for the system to propose a schedule based on this information. This enables efficient management of each task and appropriate scheduling at the logistics center.

[0228] A "task" refers to a specific unit of work or processing carried out at a logistics center.

[0229] "Delivery date" refers to the deadline by which each task at a logistics center must be completed.

[0230] "Input means" refers to a device or interface that allows a user to register tasks and deadlines in the system.

[0231] "Server" refers to a computer system that records received task and delivery date information, and acquires, analyzes, and notifies data.

[0232] "Past performance data" refers to data relating to previous similar tasks, and is information used to calculate the estimated man-hours and priorities.

[0233] "Estimated effort" refers to a rough estimate of the time required to complete a task, calculated based on past performance data.

[0234] "Priority" refers to an indicator that shows the importance and urgency of a task.

[0235] A "schedule" refers to a work plan, including start and finish times and dates for tasks.

[0236] "Recommendation" refers to suggesting relevant information to a user.

[0237] A "logistics center" refers to a facility where logistics operations such as receiving, storing, picking, and shipping goods are carried out.

[0238] "Smartphone" refers to a mobile phone-type information terminal used to input task information.

[0239] "Head-mounted display" refers to a head-mounted display device used to input task information.

[0240] System Overview

[0241] This invention is a system for efficient task management and scheduling in a logistics center. The system consists of the following main components:

[0242] 1. User Input Method

[0243] 2. Server data reception and storage function

[0244] 3. Server past performance data acquisition function

[0245] 4. AI algorithms that calculate effort and priority

[0246] 5. User schedule adjustment function

[0247] 6. Related information recommendation function

[0248] Program processing overview

[0249] Registering tasks and deadlines

[0250] A user registers a task

[0251] Users use a smartphone or head-mounted display (HMD) to register tasks and deadlines in the system. For example, they might enter "Complete product receiving work by December 1, 2023."

[0252] The device sends task information to the server

[0253] The registered information is sent from the terminal to the server. Specifically, task and delivery date information is sent to the server's endpoint via API.

[0254] Data recording and acquisition

[0255] The server receives and stores task information

[0256] The server records the received task and delivery date information in a database. For example, data such as "Complete product receiving work by December 1, 2023" is saved.

[0257] The server acquires past performance data

[0258] The server then retrieves from the database past performance data related to the registered task, for example, data on similar past "warehouse-receiving" tasks.

[0259] Calculating effort and priority

[0260] The server uses AI to calculate the man-hours and priority

[0261] The server inputs the acquired performance data into an AI algorithm to calculate the estimated man-hours and priority. This uses data such as past work time and importance. For example, based on past data, it can calculate that "warehousing work takes an average of 5 hours and has high priority."

[0262] Schedule adjustment

[0263] The server retrieves the user's current schedule

[0264] The server then retrieves the user's current schedule, which may involve retrieving the user's calendar information from a database.

[0265] The server proposes the optimal schedule

[0266] The server will propose an optimal schedule based on the man-hours and priority of the new task and the current schedule. For example, it may suggest that "it would be optimal to carry out the inventory work from November 20, 2023 to November 30, 2023."

[0267] The server notifies the user of the proposed schedule

[0268] The proposed schedule information is sent from the server to the user's terminal and notified.

[0269] Related information recommendations

[0270] The server analyzes and obtains relevant information

[0271] The server analyzes past performance data and identifies relevant information, such as the people involved in the previous task.

[0272] The server recommends related information to the user

[0273] Based on the analysis results, the server sends relevant information to the user's device as a recommendation, such as "We recommend contacting the employee who handled the previous task."

[0274] Specific examples

[0275] When a user registers a task such as "Complete product receiving work by December 1, 2023," the system behaves as follows:

[0276] 1. The device sends task information to the server.

[0277] 2. The server receives and stores the information and obtains past performance data.

[0278] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[0279] 4. The server identifies relevant information (e.g., people involved in past projects and reference materials) and recommends it to the user.

[0280] Example prompt sentence:

[0281] When a user registers a task such as "Complete product receiving work by December 1, 2023," the system works as follows:

[0282] 1. The device sends task information to the server.

[0283] 2. The server receives and stores the information and obtains past performance data.

[0284] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes an optimal schedule.

[0285] 4. The server identifies relevant information and recommends it to the user.

[0286] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0287] Step 1:

[0288] Registering tasks and deadlines

[0289] The user uses a smartphone or head-mounted display (HMD) to input a new task, "Goods Receipt Work," and its due date, "December 1, 2023." The information entered by the user is sent from the device to the server via API. The input data includes the "Task Name" and "Delivery Date" fields. The server then receives the task information and is ready to start the system.

[0290] Step 2:

[0291] Data recording and acquisition

[0292] The server receives the task and delivery date information sent from the terminal and stores this information in a database. Next, the server retrieves past performance data related to the registered task from the database. Specifically, it filters and retrieves data related to "warehouse entry work" that was performed in the past. This allows the server to prepare the original data, which will serve as the basis for the next calculation.

[0293] Step 3:

[0294] Calculating effort and priority

[0295] The server inputs the acquired performance data into an AI algorithm. The server processes the data by inputting data such as past work time and importance into the AI, and calculates the approximate man-hours and priority of the task. For example, it obtains an output such as "warehouse entry work takes an average of 5 hours and is a high priority." This allows the server to perform a detailed evaluation of the task.

[0296] Step 4:

[0297] Schedule adjustment

[0298] The server retrieves the user's current schedule data from the database. The server generates an optimal schedule for tasks based on the man-hours and priorities calculated by the AI ​​and the current schedule. For example, it suggests that a new inventory task should be optimally carried out from November 20, 2023 to November 30, 2023. This information is intended for use by the user.

[0299] Step 5:

[0300] Proposal schedule notification

[0301] The server sends the generated optimal schedule information to the user's device, where a notification is displayed prompting the user to confirm the new schedule. This allows the user to confirm the proposed schedule and prepare to put it into action.

[0302] Step 6:

[0303] Related information recommendations

[0304] The server analyzes past performance data and identifies relevant information, such as "the person involved in the previous inventory work" and "reference materials." Based on the results of this analysis, the server notifies the user's device of related information as recommendations. The user can use the presented information to help them complete their tasks.

[0305] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0306] This invention relates to a generative AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state when the user registers tasks and deadlines. This system allows users to efficiently manage tasks and adjust schedules, and also uses an emotion engine to provide support that is sensitive to the user's emotional state.

[0307] System Overview

[0308] The system consists of the following main components:

[0309] 1. User Input Method

[0310] 2. Server data reception and storage function

[0311] 3. Server past performance data acquisition function

[0312] 4. Emotion Engine

[0313] 5. AI algorithms to calculate effort and priority

[0314] 6. FUZA's schedule adjustment function

[0315] 7. Recommendation function for related departments and useful information

[0316] Program processing overview

[0317] Registering tasks and deadlines

[0318] A user registers a task

[0319] The user inputs a new task and its due date using their own terminal. For example, they may register "Complete the design of a new website by December 1st."

[0320] The device sends task information to the server

[0321] The user's terminal transmits the input task and deadline information to the server via the API.

[0322] Data recording and acquisition

[0323] The server receives and stores task information

[0324] The server stores the task and deadline information received from the terminal in a database. For example, it records information such as "Complete the design of the new website by December 1st."

[0325] The server acquires past performance data

[0326] The server retrieves past performance data related to the registered task from the database.

[0327] Calculating effort and priority

[0328] The server uses AI to calculate the man-hours and priority

[0329] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority of a new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[0330] Emotion Engine

[0331] The server recognizes the user's emotions through the emotion engine.

[0332] The server uses an emotion engine to analyze the user's emotional state based on data acquired from the user's device. For example, it uses facial recognition and voice analysis technology to determine whether the user is feeling stressed.

[0333] Priority adjustment based on emotion engine

[0334] The server adjusts the traditional priorities based on the user's emotional state, for example, prioritizing less demanding tasks if the user is feeling stressed.

[0335] Recommendations based on emotion engines

[0336] The server considers the user's emotional state and recommends appropriate resources and support information. For example, if the user is feeling stressed, it will suggest breaks and relaxation methods to reduce stress.

[0337] Schedule adjustment

[0338] The server retrieves the user's current schedule

[0339] The server retrieves the user's current schedule from a database or calendar app.

[0340] The server generates the optimal schedule

[0341] The server considers the effort, priority, and emotional state of the new task to generate optimal start and end dates and times for the task.

[0342] The server notifies the user of the proposed schedule

[0343] The server transmits the generated schedule information to the user's terminal and notifies the user.

[0344] Related information recommendations

[0345] The server analyzes and obtains relevant information

[0346] Analyze past performance data and identify related departments and materials. For example, identify "people from the design department were involved in similar tasks."

[0347] The server recommends related information to the user

[0348] The identified relevant information is sent to the user's device and a notification is sent, such as "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials."

[0349] Specific examples

[0350] When a user registers a task such as "Create a new website design and have it completed by December 1st," the system works as follows:

[0351] 1. The device sends task information to the server.

[0352] 2. The server receives the information and retrieves past performance data.

[0353] 3. The server uses AI to calculate the estimated man-hours and priority, and uses an emotion engine to analyze the user's emotional state.

[0354] 4. The server adjusts priorities based on emotional states and generates an optimal schedule.

[0355] 5. The server notifies the user of the generated schedule and simultaneously recommends related information.

[0356] This system allows users to significantly reduce the time they spend on task management and schedule adjustment, and also provides support that takes into account their emotional state.

[0357] The processing flow will be explained below.

[0358] Step 1:

[0359] The user inputs a task and its due date. For example, the user inputs "Complete the design of the new website by December 1st" on their device.

[0360] Step 2:

[0361] The device sends task information to the server. The user's device sends the entered task and deadline information to the server's endpoint via API.

[0362] Step 3:

[0363] The server receives the task information and stores it in a database. The server records information such as "Complete the design of the new website by December 1st" in the database.

[0364] Step 4:

[0365] The server obtains past performance data. The server obtains past performance data related to the registered task from the database by filtering.

[0366] Step 5:

[0367] The server uses AI to calculate the man-hours and priority. The server inputs the acquired performance data into an AI algorithm and calculates the approximate man-hours and priority of a new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[0368] Step 6:

[0369] The server recognizes the user's emotions through an emotion engine. The server uses data acquired from the user's device (face recognition, voice analysis, etc.) to analyze the user's stress level and emotions. For example, it determines that the user is feeling stressed.

[0370] Step 7:

[0371] The server adjusts the priority based on the emotion engine. If the user's emotional state is stressful, the server increases the priority of less burdensome tasks. For example, the server may adjust the priority of a task by saying, "The user is feeling stressed, so set the priority of this task low."

[0372] Step 8:

[0373] The server retrieves the user's current schedule. The server retrieves the user's current schedule data from a database or calendar application. For example, it retrieves information about existing meetings and breaks in the current schedule.

[0374] Step 9:

[0375] The server generates an optimal schedule. The server calculates the start and end dates and times of the tasks, taking into account the man-hours, priority, emotional state of the user, and the current schedule of the new tasks. For example, it determines that "it is optimal to assign the new website design task between November 20th and November 30th."

[0376] Step 10:

[0377] The server notifies the user of the generated schedule. The server sends the generated schedule information to the user's terminal and notifies the user. The user's terminal displays "The task is scheduled to start on November 20th and be completed on November 30th."

[0378] Step 11:

[0379] The server analyzes and acquires related information. Based on past performance data, the server identifies related departments and reference materials. For example, it identifies that "a person in the design department was involved in a similar task."

[0380] Step 12:

[0381] The server recommends related information to the user. The server then sends the identified related information to the user's device and notifies them. For example, the user may receive a notification such as, "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials."

[0382] This detailed processing step allows the system to efficiently support users in task management and schedule adjustment, and also provides support that takes into account their emotional state.

[0383] Example 2

[0384] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0385] Conventional task management systems lack specific support for users to efficiently manage their tasks. Furthermore, because they prioritize tasks without taking into account the user's emotional state, there is a risk of increasing stress and strain on the user. Furthermore, because they lack a mechanism for fully utilizing past performance data, it is difficult to estimate the amount of work required for a task or calculate an appropriate schedule. This makes it difficult for users to obtain appropriate resources and information, hindering efficient task completion.

[0386] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: an input means for a user to register tasks and deadlines; a means for the server to record task and deadline information received from the input means; a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks; an emotion analysis means for the server to recognize the user's emotional state; a means for the server to adjust priorities using the emotion analysis means and an AI algorithm; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; and a means for the server to recommend relevant departments and useful information to the user. This enables users to efficiently manage tasks and adjust their schedules and receive support that takes their emotional state into consideration, thereby reducing stress and burden. It also enables users to quickly obtain appropriate resources and related information.

[0387] A "user" is an entity that uses this system to register tasks and deadlines.

[0388] "Input means" refers to a device or software that allows a user to input information about tasks and deadlines.

[0389] The "server" is a central device that processes and stores task information and performance data, and runs AI algorithms and emotion analysis.

[0390] A "task" refers to the specific details of the work or activity that a user must perform.

[0391] "Delivery date" refers to the estimated date and time for completing a task set by the user.

[0392] The "recording means" is a device or software that stores the received task and delivery date information in a storage device such as a database.

[0393] "Past performance data" refers to historical information about similar or related tasks previously performed.

[0394] "Estimated effort" refers to an estimate of the time required to complete a particular task.

[0395] "Priority" refers to an evaluation of importance or urgency for determining the execution order among multiple tasks.

[0396] "Emotional state" refers to the user's psychological and emotional state, including stress level, satisfaction, etc.

[0397] An "emotion analysis means" is a device or software that uses facial recognition or voice analysis techniques to analyze a user's emotional state.

[0398] An "AI algorithm" is a computational method that uses machine learning and data analysis to derive optimal results.

[0399] A "schedule generation means" is a device or software that determines the optimal start and end dates and times for tasks based on estimated man-hours, priority, and the user's emotional state.

[0400] The "notification means" is a device or software that notifies the user of the generated schedule and recommendation information.

[0401] A "recommendation means" is a device or software that selects and suggests resources and information that are useful to the user.

[0402] "Relevant departments" refers to other departments or agencies involved in the performance of the task.

[0403] "Useful information" refers to past materials and knowledge that are useful in completing a task.

[0404] This invention is a generation AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state when the user registers tasks and deadlines. The purpose of this system is to support users in efficiently managing tasks and adjusting schedules. Furthermore, it uses emotional analysis means to provide support that is sensitive to the user's emotional state.

[0405] System configuration

[0406] The system consists of the following main components:

[0407] 1. User Input Method

[0408] 2. Server data reception and storage function

[0409] 3. Server past performance data acquisition function

[0410] 4. Emotion analysis method

[0411] 5. AI algorithms to calculate effort and priority

[0412] 6. User schedule adjustment function

[0413] 7. Recommendation function for related departments and useful information

[0414] Hardware and Software Configuration

[0415] Users input data via devices such as PCs and smartphones. Task and delivery date information is entered via dedicated web and mobile applications. On the server side, multiple software modules operate to receive and store data, import performance data, run AI algorithms, and perform sentiment analysis. These include database management systems (e.g., MySQL), AI modules (e.g., TensorFlow), and sentiment analysis tools (e.g., OpenFace and Google Cloud Speech-to-Text API).

[0416] Overview of program processing

[0417] When a user registers a task, the device sends the entered task and delivery date information to the server. The server receives the information and stores it in a database. The server then retrieves past performance data and applies an AI algorithm to calculate the estimated effort and priority of the new task. It then recognizes the user's emotional state through emotion analysis and adjusts task priorities accordingly. Finally, the server notifies the user of the generated optimal schedule and, if necessary, recommends related departments and useful information.

[0418] Specific examples

[0419] For example, if a user registers a task such as "The design of a new website needs to be completed by December 1st," the system will act as follows:

[0420] 1. The user's device sends task information to the server.

[0421] 2. The server receives the information and retrieves past performance data.

[0422] 3. The server uses AI to calculate the estimated man-hours and priority, and uses emotion analysis tools to analyze the user's emotional state.

[0423] 4. The server adjusts priorities based on emotional states and generates an optimal schedule.

[0424] 5. The server notifies the user of the generated schedule and simultaneously recommends related information.

[0425] This system allows users to significantly reduce the time they spend on task management and schedule adjustment, and also provides support that takes into account their emotional state.

[0426] Prompt Sentence Examples

[0427] For example, you can generate a description of the system above by providing the following prompt to a generative AI model:

[0428] "Please explain how a system works, where a user registers tasks and generates a schedule based on the estimated effort and priority of the tasks, as well as their emotional state."

[0429] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0430] Step 1:

[0431] The user inputs a new task and its due date. The input is done using a dedicated web application or mobile application. For example, the user inputs "Complete the design of a new website by December 1st." This input data (task content and due date) is collected by the device and sent to the next step.

[0432] Step 2:

[0433] The device sends the entered task and delivery date information to the server via API. Specifically, the device uses an HTTP POST request to send the input data to a specified endpoint on the server. The input data consists of a JSON object containing the task details and delivery date. Based on this request, the server receives the data and begins processing in the next step.

[0434] Step 3:

[0435] The server saves the received task and deadline information in the database. The server stores the information in a temporary buffer and then performs an INSERT operation on the database. This permanently saves the input data (task details and deadline) in the database. For example, the information "Complete the design of the new website by December 1st" is recorded in the database.

[0436] Step 4:

[0437] The server retrieves past performance data from a database. The server executes an SQL query to search and retrieve historical information about similar or related tasks that have been performed in the past. Past data related to the input data (new task) is retrieved. For example, the time and results of past website design tasks are retrieved.

[0438] Step 5:

[0439] The server inputs past performance data acquired into an AI algorithm to calculate the estimated effort and priority of a new task. An AI algorithm (e.g., TensorFlow model) is used to calculate the estimated effort and priority from past data. The input is performance data, and the output is result data such as "approximately 50 hours required" or "high priority."

[0440] Step 6:

[0441] In order for the server to recognize the user's emotional state, it inputs data collected from the user's device (e.g., facial recognition data, voice data) into an emotion analysis tool. Using an emotion analysis tool (e.g., OpenFace or Google Cloud Speech-to-Text API), it analyzes the user's facial expressions and tone of voice, and outputs the user's emotional state (e.g., the user is feeling stressed).

[0442] Step 7:

[0443] The server adjusts the priorities using emotion analysis methods and AI algorithms. Based on the results of the emotion analysis, the AI ​​adjusts the initial priorities. For example, if the user is feeling stressed, it will lower the priority and prioritize less demanding tasks. The input is the emotional state and the initial priorities, and the output is the adjusted priorities.

[0444] Step 8:

[0445] The server retrieves the user's current schedule information. It uses Dell's Google Calendar API and / or Outlook Calendar API to retrieve the user's current schedule and saves it as current schedule data. This data includes existing meetings and scheduled tasks.

[0446] Step 9:

[0447] The server considers the estimated effort, priority, and the user's emotional state of the task, and uses an AI algorithm to generate the optimal start and end dates and times for the task. It then applies a schedule generation algorithm to calculate the optimal schedule. The input is task information, emotional state, and current schedule data, and the output is the optimal start and end dates and times for the task.

[0448] Step 10:

[0449] The server sends the generated schedule information to the user's device and notifies them. The server notifies the user of the optimal schedule via push notification or email. The user receives the start and end dates and times of specific tasks.

[0450] Step 11:

[0451] The server analyzes past performance data and identifies related departments and useful information. It identifies related departments and available materials based on past data. For example, it identifies "people from the design department were involved in similar tasks."

[0452] Step 12:

[0453] The server sends the identified relevant information to the user's device and notifies them. The server then provides specific advice such as, "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials." The input is the relevant information, and the output is the notification content to the user.

[0454] (Application example 2)

[0455] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0456] Conventional task management systems do not adjust schedules taking into account the user's emotional state, which means they are unable to reduce user stress and excessive workloads. Furthermore, while appropriate task allocation and shift adjustment according to the emotional state of staff members is important in daily operations at brick-and-mortar stores, no efficient method for achieving this has been provided.

[0457] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for the user to register tasks and deadlines, a means for the server to record the task and deadline information received from the input means, a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks, a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks, a means for the server to notify the user of the generated schedule, a means for the server to recommend relevant departments and useful information to the user, a means for the server to acquire the user's emotional state and adjust task priorities based on the emotional state, and a means for the server to suggest relaxation methods to the user based on the emotional state. This enables efficient task management and schedule adjustment that takes the user's emotional state into consideration.

[0458] "Input means for users to register tasks and deadlines" refers to a device or interface that users use to input new tasks and their deadlines into the system.

[0459] The "means for recording the task and deadline information received by the server from the input means" refers to a function in which the server stores information on the task and deadline sent from the user in a database.

[0460] "Means for the server to acquire past performance data and calculate the estimated man-hours and priority associated with the task" refers to an algorithm or function that allows the server to collect data on past related tasks and, based on that data, calculate the required time and execution priority of a new task.

[0461] "Means for the server to obtain the user's current schedule and generate an optimal schedule for the task" refers to the function by which the server obtains the user's current schedule information and determines the optimal execution time for the new task based on that information.

[0462] The "means by which the server notifies the user of the generated schedule" refers to a communication means or interface for notifying the user of the schedule information generated by the server.

[0463] "Means for the server to recommend related departments and useful information to the user" refers to a function for the server to recommend departments and useful information related to a task to the user.

[0464] "Means for the server to acquire the user's emotional state and adjust task priorities based on that emotional state" refers to a function in which the server collects user emotional data and changes the order in which tasks are executed based on that data.

[0465] "Means for the server to suggest relaxation methods to the user based on the emotional state" refers to a function in which the server takes into account the emotional state of the user and suggests appropriate relaxation methods or breaks.

[0466] This invention relates to a generative AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state, once the user registers tasks and deadlines. This system is designed to efficiently manage staff and adjust schedules in physical stores, and consists of the following main components:

[0467] User input method

[0468] A user uses an input device such as a smartphone or tablet to register a new task and its due date. For example, consider the case where a store staff member registers a task such as "Complete product inventory check by December 1st."

[0469] A means of recording task information

[0470] The server receives task and delivery date information from the user and records it in the database. The task information is sent to the server via API and immediately saved. For example, an "inventory check task" and its "delivery date information" are recorded in the database.

[0471] Acquisition and analysis of performance data

[0472] The server retrieves past performance data from the database and calculates the estimated effort and priority associated with a new task using an AI algorithm. For example, based on data from similar past tasks, it might calculate that "inventory checks will take approximately 20 hours and have a medium priority."

[0473] Schedule optimization

[0474] The server retrieves the user's current schedule information from a calendar app or database, and generates an optimal schedule for new tasks based on this information. The task priority and effort are taken into account. For example, "An inventory check task is added to the schedule, and the start and end times are set."

[0475] Schedule notification method

[0476] The server notifies the user of the generated schedule via push notification, email notification, etc. For example, "A new schedule is sent to the staff member's smartphone."

[0477] Related information recommendations

[0478] The server analyzes past performance data, identifies relevant departments and useful information, and makes recommendations to users. This includes the server recommending specific personnel and related materials. For example, "recommending materials from the design department and project materials from the previous inventory check."

[0479] Acquiring and analyzing emotional states

[0480] The server acquires the user's emotional state and analyzes it using an emotion engine. Facial recognition and voice analysis are used to acquire emotions. For example, if a staff member is feeling stressed, the server will detect this.

[0481] Adjusting task priorities

[0482] The server adjusts task priorities based on the emotional state of the staff member. For example, if the staff member's stress level is high, the server will lower the priority of the task.

[0483] Suggestions for relaxation methods

[0484] The server suggests relaxation methods to users based on their emotional state, for example, "to staff who are feeling stressed, it suggests break times and relaxation methods."

[0485] As a concrete example, consider the following scenario.

[0486] A store staff member registers a task to "check product inventory by December 1st."

[0487] Based on past inventory confirmation data, the server calculates the required man-hours as "20 hours" and determines the priority to be "medium."

[0488] Additionally, an emotion engine is used to analyze the emotional state of staff, and if stress levels are high, the priority is adjusted to "low."

[0489] The optimal schedule is generated and notified to store staff's smartphones.

[0490] Suggestions for rest and relaxation will be made as needed.

[0491] Example prompt sentence:

[0492] "A user has registered the task 'Check product inventory by December 1st.' Based on past performance data, the required man-hours are calculated to be 20 hours, and the priority is medium. Please generate the optimal schedule taking into account the user's emotional state."

[0493] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0494] Step 1:

[0495] The user uses an input means for registering tasks and deadlines to input task information. The input is made using a smartphone or tablet, and includes the task name, deadline, and other detailed information. For example, the user registers a task such as "Check product inventory by December 1st."

[0496] Input: Task name, due date, details

[0497] Data processing: structuring task information

[0498] Output: Structured task information

[0499] Step 2:

[0500] The device sends the entered task and deadline information to the server, which then sends this information via API and reaches the server in real time.

[0501] Input: Structured task information

[0502] Data processing: Data transfer to server via API

[0503] Output: Task information saved on the server

[0504] Step 3:

[0505] The server stores the received task and deadline information in a database, which records the user ID, task name, and deadline.

[0506] Input: Task information received by the server

[0507] Data processing: storing information in a database

[0508] Output: Task information stored in the database

[0509] Step 4:

[0510] The server retrieves historical performance data. This data is retrieved directly from the database and includes information such as effort and priority of previously related tasks.

[0511] Input: User ID

[0512] Data processing: Extraction of past performance data

[0513] Output: Past performance data

[0514] Step 5:

[0515] The server calculates the estimated man-hours and priority of new tasks based on past performance data. Here, an AI algorithm is used to analyze the data. For example, based on past data on inventory check tasks, it calculates that "20 hours will be required," and the priority is also determined.

[0516] Input: Past performance data, task information

[0517] Data processing: Analysis using AI algorithms

[0518] Output: Estimated man-hours, priority

[0519] Step 6:

[0520] The server retrieves the user's current schedule. This can be done from a calendar app or a database.

[0521] Input: User ID

[0522] Data processing: Getting the current schedule

[0523] Output: Current schedule information

[0524] Step 7:

[0525] The server generates an optimal schedule for the new task, determining when to execute the task based on the user's current schedule and the newly calculated estimated effort and priority.

[0526] Input: Current schedule information, estimated man-hours, priority

[0527] Data processing: generating optimal schedules

[0528] Output: New task schedule

[0529] Step 8:

[0530] The server notifies the user's device of the generated schedule. The device receives the notification and notifies the user. Notifications can be sent via push notifications or email.

[0531] Input: New task schedule

[0532] Data Processing: Notification Format

[0533] Output: Schedule notification to user

[0534] Step 9:

[0535] The server recommends relevant departments and useful information to the user, analyzes past performance data to identify relevant departments and materials, and notifies the user.

[0536] Input: Past performance data

[0537] Data processing: analysis of relevant information

[0538] Output: Related information recommendations

[0539] Step 10:

[0540] The server acquires the user's emotional state and analyzes it with an emotion engine, using facial recognition and voice analysis to determine stress levels, etc.

[0541] Input: User emotion data

[0542] Data processing: Analysis using emotion engine

[0543] Output: Emotional state

[0544] Step 11:

[0545] The server adjusts the priority of tasks based on the user's emotional state. For example, if the user's stress level is high, the server lowers the priority to reduce the burden.

[0546] Input: Emotional state, task information

[0547] Data processing: Adjusting task priorities

[0548] Output: Adjusted task priorities

[0549] Step 12:

[0550] The server suggests relaxation methods to the user based on their emotional state, including suggestions for break times and relaxation methods.

[0551] Input: Emotional state

[0552] Data processing: Deciding on relaxation method

[0553] Output: Relaxation suggestions

[0554] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0555] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0556] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0557] [Second embodiment]

[0558] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0559] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0560] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0561] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0562] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0563] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0564] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0565] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0566] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0567] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0568] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0569] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0570] This invention is a generation AI system that calculates estimated man-hours and priorities based on past performance data and adjusts schedules when users register tasks and deadlines.

[0571] System Overview

[0572] The system consists of the following main components:

[0573] 1. User Input Method

[0574] 2. Server data reception and storage function

[0575] 3. Server past performance data acquisition function

[0576] 4. AI algorithms that calculate effort and priority

[0577] 5. User schedule adjustment function

[0578] 6. Related information recommendation function

[0579] Program processing overview

[0580] Registering tasks and deadlines

[0581] A user registers a task

[0582] The user inputs a new task and its due date through their own device. For example, they can register "Complete the design of the new website by December 1st."

[0583] The device sends task information to the server

[0584] The user's device sends the input task and delivery date information to the server. Specifically, the input information is sent to the server's endpoint via the API.

[0585] Data recording and acquisition

[0586] The server receives and stores task information

[0587] The server stores the task and deadline information received from the device in a database. For example, it records data such as "Complete the design of the new website by December 1st."

[0588] The server acquires past performance data

[0589] The server retrieves past performance data related to the registered task from the database, specifically by filtering performance data of similar website design tasks that have been completed in the past.

[0590] Calculating effort and priority

[0591] The server uses AI to calculate the man-hours and priority

[0592] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority of the new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[0593] Schedule adjustment

[0594] The server retrieves the user's current schedule

[0595] The server retrieves the user's current schedule from a database and, if necessary, may also connect with the calendar app used by the user.

[0596] The server proposes the optimal schedule

[0597] The server will suggest start and end dates for the new task based on the effort and priority of the task and the user's current schedule. For example, it might suggest that the best time to complete the new website design task is between November 20th and November 30th.

[0598] The server notifies the user of the proposed schedule

[0599] The server transmits the proposed schedule information to the user's terminal, which is notified of the proposed schedule.

[0600] Related information recommendations

[0601] The server analyzes and obtains relevant information

[0602] The server analyzes past performance data and identifies related departments and reference materials. For example, it identifies information such as "Yamada from the design department was involved in a similar task."

[0603] The server recommends related information to the user

[0604] Based on the analysis results, the server sends relevant information to the user's device and makes recommendations. The user will receive a notification such as, "It would be a good idea to contact Yamada-san, the person in charge. Also, please refer to the materials from the previous project."

[0605] Specific examples

[0606] When a user registers a task such as "Create a new website design and have it completed by December 1st," the system works as follows:

[0607] 1. The device sends task information to the server.

[0608] 2. The server receives and stores the information and obtains past performance data.

[0609] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[0610] 4. The server identifies relevant information (e.g., people involved in past projects and reference materials) and recommends it to the user.

[0611] This system allows business people to significantly reduce the time they spend on task management and scheduling, allowing them to focus on more creative work.

[0612] The processing flow will be explained below.

[0613] Step 1:

[0614] The user inputs the task and its due date. The user inputs task information such as "Complete the design of the new website by December 1st" from their own device.

[0615] Step 2:

[0616] The device sends task information to the server. The user's device sends the entered task and deadline information to the server's endpoint using the API.

[0617] Step 3:

[0618] The server receives the task information and stores it in a database. Specifically, the server records information such as "Complete the design of the new website by December 1st" in the database.

[0619] Step 4:

[0620] The server obtains past performance data. The server obtains past performance data related to the registered task from the database by filtering.

[0621] Step 5:

[0622] The server uses AI to calculate the estimated man-hours and priority. Based on the acquired performance data, the server uses an AI algorithm to calculate the estimated man-hours and priority of a new task. For example, the AI ​​might determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[0623] Step 6:

[0624] The server retrieves the user's current schedule. The server retrieves the user's existing schedule data from a database or a linked calendar app.

[0625] Step 7:

[0626] The server generates an optimal schedule. Taking into account the man-hours and priority of the new task and the user's current schedule, the server calculates the start and end dates and times of the task and generates an optimal schedule. For example, it may determine that "it is optimal to assign the new website design task between November 20th and November 30th."

[0627] Step 8:

[0628] The server notifies the user of the generated schedule. The server transmits the generated schedule information to the user's terminal and notifies the user.

[0629] Step 9:

[0630] The server analyzes and acquires related information. Based on past performance data, the server identifies related departments and reference materials. For example, it identifies information such as "Yamada from the design department was involved in a similar task."

[0631] Step 10:

[0632] The server recommends related information to the user. The server then sends the identified related information to the user's device and notifies them. For example, the user might receive a notification such as, "It would be a good idea to contact Mr. Yamada, the person in charge. Also, please refer to the materials from the previous project."

[0633] Through these steps, the system efficiently supports users in task management and schedule adjustment, providing an environment in which business people can concentrate on creative work.

[0634] Example 1

[0635] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0636] In today's business environment, efficient management of multiple tasks and their deadlines is required. However, doing this manually is time-consuming, labor-intensive, and inefficient. Furthermore, there is a lack of systems that can calculate appropriate man-hours and priorities based on performance data from similar past tasks and propose optimal schedules. Furthermore, it is difficult to provide relevant information and reference materials in a timely manner for task completion.

[0637] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0638] In this invention, the server includes: an input means for a user to register tasks and deadlines; a means for the server to record the task and deadline information received from the input means; a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; a means for the server to recommend relevant departments and useful information to the user; a means for the server to store and filter past performance data using a database; a means for a terminal to communicate with the server via an API; a means for the server to acquire the user's schedule in cooperation with an external calendar service; and a means for the server to calculate estimated man-hours and priorities using an AI algorithm. This allows users to efficiently manage tasks and adjust their schedules, and quickly obtain necessary related information.

[0639] A "user" is a person or organization that uses this system to input tasks and their due dates, and receives schedule management and related information.

[0640] The "server" is a computer system that records task and delivery date information received from users, retrieves past performance data from a database, calculates labor hours and priorities using AI algorithms, generates optimal schedules, and provides notification and recommendation functions.

[0641] "Input means" refers to an interface or device that allows a user to register task and delivery date information, and examples include a keyboard, a touch screen, and a dedicated application.

[0642] The "recording means" is a function for storing the task and deadline information received by the server in a database, and also checks the consistency of the data.

[0643] "Performance data" is data relating to similar tasks that have been completed in the past, and includes information such as the task name, man-hours, completion date, and people involved.

[0644] "Estimated man-hours" is an estimate of the total time required to complete a task, calculated by an AI algorithm based on past performance data.

[0645] "Priority" is an assessment of the importance or urgency of a particular task, and indicates how much priority it needs to have compared to other tasks.

[0646] A "schedule" is a specific plan of the start and end dates of a task and the work to be done during that time.

[0647] "Notification means" refers to a method for notifying the user of schedules and related information generated by the server, and specifically includes push notifications, emails, display messages, etc.

[0648] The "recommendation function" is a function in which the server analyzes the database and suggests information and reference materials that may be useful to the user.

[0649] A "database" is a structured data repository that stores received task information and past performance data and allows for searching and retrieval as needed.

[0650] An "API" is an interface for exchanging data between a terminal and a server, and communication is carried out according to a standardized protocol.

[0651] An "external calendar service" is a calendar application provided by a third party, such as Google Calendar or Outlook Calendar, that the server works with to obtain the user's current schedule.

[0652] An "AI algorithm" is a machine learning model that analyzes past data and estimates the effort and priority of new tasks.

[0653] MODE FOR CARRYING OUT THE INVENTION

[0654] This invention is an AI generation system for managing tasks and deadlines, which uses past performance data based on tasks entered by a user, calculates approximate man-hours and priorities, and provides optimal schedules and related information. Specific embodiments of the system are described below.

[0655] System Overview

[0656] The system mainly consists of the following components:

[0657] 1. User Input Method

[0658] 2. Server data reception and storage function

[0659] 3. Server past performance data acquisition function

[0660] 4. AI algorithms that calculate effort and priority

[0661] 5. User schedule adjustment function

[0662] 6. Related information recommendation function

[0663] Hardware and software used

[0664] 1. User's device (PC, tablet, smartphone, etc.)

[0665] 2. Servers (including cloud and on-premise environments)

[0666] 3. Database (MySQL, PostgreSQL, etc.)

[0667] 4. AI algorithms (TensorFlow, PyTorch, etc.)

[0668] 5. Calendar services (Google Calendar, Outlook Calendar, etc.)

[0669] Program processing overview

[0670] Registering tasks and deadlines

[0671] Users use their devices to input new tasks and their due dates. The input data is sent to the server via the API. For example, a user might input "Complete the design of the new website by December 1st."

[0672] Data recording and acquisition

[0673] The server receives the task and delivery date information sent from the terminal and stores it in a database.The server then retrieves past performance data from the database.Specifically, it filters and extracts past data related to similar tasks.

[0674] Calculating effort and priority

[0675] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority for new tasks. The AI ​​algorithm is built using TensorFlow and PyTorch and analyzes past data. The calculation results include, "Based on past performance, this task will require approximately 50 hours and is a high priority."

[0676] Schedule adjustment

[0677] The server obtains the user's current schedule. If necessary, it connects with an external calendar service to obtain schedule information. The server then generates an optimal schedule based on the effort and priority of the new task and the user's current schedule, and proposes it to the user. For example, it might suggest that "it would be best to proceed with the design of the new website from November 20th to November 30th."

[0678] Notifications and Recommendations

[0679] The server notifies the user of the generated schedule information. At the same time, the server analyzes past performance data, identifies relevant information (such as people involved in past projects and reference materials), and makes recommendations to the user.

[0680] Specific examples

[0681] For example, if a user registers a task such as "Create a new website design and have it completed by December 1st," the system will act as follows:

[0682] 1. The user enters the task and due date on the terminal.

[0683] 2. The device sends the input data to the server.

[0684] 3. The server receives the data and stores it in a database.

[0685] 4. The server retrieves performance data for similar tasks in the past.

[0686] 5. The server uses AI to calculate the estimated effort and priority of the new task.

[0687] 6. The server retrieves the user's current schedule.

[0688] 7. The server proposes an optimal schedule.

[0689] 8. The server notifies the user of the proposed schedule.

[0690] 9. The server analyzes and retrieves the relevant information.

[0691] 10. The server recommends relevant information to the user.

[0692] Prompt Sentence Examples

[0693] "We have a task to design a new website. The deadline is December 1st. Based on past performance data for similar tasks, please tell us the approximate man-hours and priority. Also, please suggest the optimal schedule."

[0694] Based on this prompt, the system provides the necessary information and suggests the optimal schedule for the user.

[0695] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0696] Step 1:

[0697] The user enters the task and due date

[0698] The user inputs a new task and its due date using an input form on the terminal or a dedicated application.

[0699] Input: Tasks and deadlines, such as "Complete new website design by December 1st."

[0700] Output: Task and due date data in text format.

[0701] Step 2:

[0702] The device sends the input data to the server

[0703] The terminal uses the API to send the task and deadline information entered by the user to the server.

[0704] Input: Task and due date data entered by the user.

[0705] Data processing: Convert data into JSON format.

[0706] Output: JSON data sent to the server's API endpoint.

[0707] Step 3:

[0708] The server receives the data and stores it in the database

[0709] The server receives the task and deadline data sent via the API and stores it in a database.

[0710] Input: Task and due date data received by the server in JSON format.

[0711] Data processing: Convert JSON data into structured data.

[0712] Output: Task and due date data stored in a database.

[0713] Step 4:

[0714] The server obtains performance data for similar tasks from the past.

[0715] The server searches the database for past performance data using a query and obtains data related to the registered task.

[0716] Input: Saved task and due date data.

[0717] Data processing: Use queries to search for similar tasks from a database.

[0718] Output: A list of historical performance data.

[0719] Step 5:

[0720] The server uses AI to calculate the estimated man-hours and priority of new tasks.

[0721] The server inputs past performance data acquired into an AI algorithm to calculate the estimated man-hours and priority of new tasks.

[0722] Input: A list of historical performance data.

[0723] Data Calculation: Data analysis using AI algorithms.

[0724] Output: Estimated effort and priority of the new task (e.g. "50 hours required, high priority").

[0725] Step 6:

[0726] The server retrieves the user's current schedule

[0727] The server contacts a database or external calendar service to retrieve the user's current schedule.

[0728] Input: User credentials and / or calendar service API key.

[0729] Data processing: Communication with external calendar services.

[0730] Output: Current schedule data.

[0731] Step 7:

[0732] The server proposes the optimal schedule for new tasks.

[0733] The server proposes optimal start and end dates based on the current schedule and the man-hours and priority of the new task.

[0734] Inputs: New task effort and priority, current schedule data.

[0735] Data calculation: Execution of the schedule generation algorithm.

[0736] Output: The optimal schedule for the new task (e.g., "November 20th to November 30th is optimal").

[0737] Step 8:

[0738] The server notifies the user of the generated schedule.

[0739] The server notifies the user's terminal of the proposed schedule.

[0740] Input: The optimal schedule for the new task.

[0741] Data processing: generating notification messages.

[0742] Output: Push notification or email to user device.

[0743] Step 9:

[0744] The server analyzes and retrieves the relevant information

[0745] The server analyzes past performance data and identifies relevant departments and useful information.

[0746] Input: Historical performance data.

[0747] Data operations: Running data analysis algorithms.

[0748] Output: A list of relevant information (e.g., "Yamada from the design department was involved").

[0749] Step 10:

[0750] The server recommends relevant information to the user

[0751] The server recommends related information to the user based on the analysis results.

[0752] Input: A list of relevant information.

[0753] Data calculation: Generate recommended messages.

[0754] Output: Recommendation notification to the user device (e.g., "We recommend contacting Yamada-san, the person in charge").

[0755] (Application example 1)

[0756] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0757] Logistics centers have numerous tasks (such as receiving, picking, and shipping goods), and each task requires strict delivery date management. However, there are not enough methods in place to properly manage and efficiently handle these numerous tasks and delivery dates. This can lead to reduced work efficiency, which can result in a deterioration in overall logistics performance. Furthermore, it is difficult for employees to correctly estimate the priority and approximate man-hours required for each task and set an optimal schedule. An appropriate system is needed to solve these problems.

[0758] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0759] In this invention, the server includes: an input means for a user to register tasks and delivery dates; a means for the server to record the task and delivery date information received from the input means; a means for the server to acquire past performance data and calculate approximate man-hours and priorities related to the tasks; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; a means for the server to recommend relevant departments and useful information to the user; a means for the logistics center to register logistics work, calculate approximate man-hours and priorities based on past performance data, and propose an optimal schedule; and a means for a logistics center employee to input task information using a smartphone or a head-mounted display and for the system to propose a schedule based on this information. This enables efficient management of each task and appropriate scheduling at the logistics center.

[0760] A "task" refers to a specific unit of work or processing carried out at a logistics center.

[0761] "Delivery date" refers to the deadline by which each task at a logistics center must be completed.

[0762] "Input means" refers to a device or interface that allows a user to register tasks and deadlines in the system.

[0763] "Server" refers to a computer system that records received task and delivery date information, and acquires, analyzes, and notifies data.

[0764] "Past performance data" refers to data relating to previous similar tasks, and is information used to calculate the estimated man-hours and priorities.

[0765] "Estimated effort" refers to a rough estimate of the time required to complete a task, calculated based on past performance data.

[0766] "Priority" refers to an indicator that shows the importance and urgency of a task.

[0767] A "schedule" refers to a work plan, including start and finish times and dates for tasks.

[0768] "Recommendation" refers to suggesting relevant information to a user.

[0769] A "logistics center" refers to a facility where logistics operations such as receiving, storing, picking, and shipping goods are carried out.

[0770] "Smartphone" refers to a mobile phone-type information terminal used to input task information.

[0771] "Head-mounted display" refers to a head-mounted display device used to input task information.

[0772] System Overview

[0773] This invention is a system for efficient task management and scheduling in a logistics center. The system consists of the following main components:

[0774] 1. User Input Method

[0775] 2. Server data reception and storage function

[0776] 3. Server past performance data acquisition function

[0777] 4. AI algorithms that calculate effort and priority

[0778] 5. User schedule adjustment function

[0779] 6. Related information recommendation function

[0780] Program processing overview

[0781] Registering tasks and deadlines

[0782] A user registers a task

[0783] Users use a smartphone or head-mounted display (HMD) to register tasks and deadlines in the system. For example, they might enter "Complete product receiving work by December 1, 2023."

[0784] The device sends task information to the server

[0785] The registered information is sent from the terminal to the server. Specifically, task and delivery date information is sent to the server's endpoint via API.

[0786] Data recording and acquisition

[0787] The server receives and stores task information

[0788] The server records the received task and delivery date information in a database. For example, data such as "Complete product receiving work by December 1, 2023" is saved.

[0789] The server acquires past performance data

[0790] The server then retrieves from the database past performance data related to the registered task, for example, data on similar past "warehouse-receiving" tasks.

[0791] Calculating effort and priority

[0792] The server uses AI to calculate the man-hours and priority

[0793] The server inputs the acquired performance data into an AI algorithm to calculate the estimated man-hours and priority. This uses data such as past work time and importance. For example, based on past data, it can calculate that "warehousing work takes an average of 5 hours and has high priority."

[0794] Schedule adjustment

[0795] The server retrieves the user's current schedule

[0796] The server then retrieves the user's current schedule, which may involve retrieving the user's calendar information from a database.

[0797] The server proposes the optimal schedule

[0798] The server will propose an optimal schedule based on the man-hours and priority of the new task and the current schedule. For example, it may suggest that "it would be optimal to carry out the inventory work from November 20, 2023 to November 30, 2023."

[0799] The server notifies the user of the proposed schedule

[0800] The proposed schedule information is sent from the server to the user's terminal and notified.

[0801] Related information recommendations

[0802] The server analyzes and obtains relevant information

[0803] The server analyzes past performance data and identifies relevant information, such as the people involved in the previous task.

[0804] The server recommends related information to the user

[0805] Based on the analysis results, the server sends relevant information to the user's device as a recommendation, such as "We recommend contacting the employee who handled the previous task."

[0806] Specific examples

[0807] When a user registers a task such as "Complete product receiving work by December 1, 2023," the system behaves as follows:

[0808] 1. The device sends task information to the server.

[0809] 2. The server receives and stores the information and obtains past performance data.

[0810] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[0811] 4. The server identifies relevant information (e.g., people involved in past projects and reference materials) and recommends it to the user.

[0812] Example prompt sentence:

[0813] When a user registers a task such as "Complete product receiving work by December 1, 2023," the system works as follows:

[0814] 1. The device sends task information to the server.

[0815] 2. The server receives and stores the information and obtains past performance data.

[0816] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes an optimal schedule.

[0817] 4. The server identifies relevant information and recommends it to the user.

[0818] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0819] Step 1:

[0820] Registering tasks and deadlines

[0821] The user uses a smartphone or head-mounted display (HMD) to input a new task, "Goods Receipt Work," and its due date, "December 1, 2023." The information entered by the user is sent from the device to the server via API. The input data includes the "Task Name" and "Delivery Date" fields. The server then receives the task information and is ready to start the system.

[0822] Step 2:

[0823] Data recording and acquisition

[0824] The server receives the task and delivery date information sent from the terminal and stores this information in a database. Next, the server retrieves past performance data related to the registered task from the database. Specifically, it filters and retrieves data related to "warehouse entry work" that was performed in the past. This allows the server to prepare the original data, which will serve as the basis for the next calculation.

[0825] Step 3:

[0826] Calculating effort and priority

[0827] The server inputs the acquired performance data into an AI algorithm. The server processes the data by inputting data such as past work time and importance into the AI, and calculates the approximate man-hours and priority of the task. For example, it obtains an output such as "warehouse entry work takes an average of 5 hours and is a high priority." This allows the server to perform a detailed evaluation of the task.

[0828] Step 4:

[0829] Schedule adjustment

[0830] The server retrieves the user's current schedule data from the database. The server generates an optimal schedule for tasks based on the man-hours and priorities calculated by the AI ​​and the current schedule. For example, it suggests that a new inventory task should be optimally carried out from November 20, 2023 to November 30, 2023. This information is intended for use by the user.

[0831] Step 5:

[0832] Proposal schedule notification

[0833] The server sends the generated optimal schedule information to the user's device, where a notification is displayed prompting the user to confirm the new schedule. This allows the user to confirm the proposed schedule and prepare to put it into action.

[0834] Step 6:

[0835] Related information recommendations

[0836] The server analyzes past performance data and identifies relevant information, such as "the person involved in the previous inventory work" and "reference materials." Based on the results of this analysis, the server notifies the user's device of related information as recommendations. The user can use the presented information to help them complete their tasks.

[0837] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0838] This invention relates to a generative AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state when the user registers tasks and deadlines. This system allows users to efficiently manage tasks and adjust schedules, and also uses an emotion engine to provide support that is sensitive to the user's emotional state.

[0839] System Overview

[0840] The system consists of the following main components:

[0841] 1. User Input Method

[0842] 2. Server data reception and storage function

[0843] 3. Server past performance data acquisition function

[0844] 4. Emotion Engine

[0845] 5. AI algorithms to calculate effort and priority

[0846] 6. FUZA's schedule adjustment function

[0847] 7. Recommendation function for related departments and useful information

[0848] Program processing overview

[0849] Registering tasks and deadlines

[0850] A user registers a task

[0851] The user inputs a new task and its due date using their own terminal. For example, they may register "Complete the design of a new website by December 1st."

[0852] The device sends task information to the server

[0853] The user's terminal transmits the input task and deadline information to the server via the API.

[0854] Data recording and acquisition

[0855] The server receives and stores task information

[0856] The server stores the task and deadline information received from the terminal in a database. For example, it records information such as "Complete the design of the new website by December 1st."

[0857] The server acquires past performance data

[0858] The server retrieves past performance data related to the registered task from the database.

[0859] Calculating effort and priority

[0860] The server uses AI to calculate the man-hours and priority

[0861] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority of a new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[0862] Emotion Engine

[0863] The server recognizes the user's emotions through the emotion engine.

[0864] The server uses an emotion engine to analyze the user's emotional state based on data acquired from the user's device. For example, it uses facial recognition and voice analysis technology to determine whether the user is feeling stressed.

[0865] Priority adjustment based on emotion engine

[0866] The server adjusts the traditional priorities based on the user's emotional state, for example, prioritizing less demanding tasks if the user is feeling stressed.

[0867] Recommendations based on emotion engines

[0868] The server considers the user's emotional state and recommends appropriate resources and support information. For example, if the user is feeling stressed, it will suggest breaks and relaxation methods to reduce stress.

[0869] Schedule adjustment

[0870] The server retrieves the user's current schedule

[0871] The server retrieves the user's current schedule from a database or calendar app.

[0872] The server generates the optimal schedule

[0873] The server considers the effort, priority, and emotional state of the new task to generate optimal start and end dates and times for the task.

[0874] The server notifies the user of the proposed schedule

[0875] The server transmits the generated schedule information to the user's terminal and notifies the user.

[0876] Related information recommendations

[0877] The server analyzes and obtains relevant information

[0878] Analyze past performance data and identify related departments and materials. For example, identify "people from the design department were involved in similar tasks."

[0879] The server recommends related information to the user

[0880] The identified relevant information is sent to the user's device and a notification is sent, such as "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials."

[0881] Specific examples

[0882] When a user registers a task such as "Create a new website design and have it completed by December 1st," the system works as follows:

[0883] 1. The device sends task information to the server.

[0884] 2. The server receives the information and retrieves past performance data.

[0885] 3. The server uses AI to calculate the estimated man-hours and priority, and uses an emotion engine to analyze the user's emotional state.

[0886] 4. The server adjusts priorities based on emotional states and generates an optimal schedule.

[0887] 5. The server notifies the user of the generated schedule and simultaneously recommends related information.

[0888] This system allows users to significantly reduce the time they spend on task management and schedule adjustment, and also provides support that takes into account their emotional state.

[0889] The processing flow will be explained below.

[0890] Step 1:

[0891] The user inputs a task and its due date. For example, the user inputs "Complete the design of the new website by December 1st" on their device.

[0892] Step 2:

[0893] The device sends task information to the server. The user's device sends the entered task and deadline information to the server's endpoint via API.

[0894] Step 3:

[0895] The server receives the task information and stores it in a database. The server records information such as "Complete the design of the new website by December 1st" in the database.

[0896] Step 4:

[0897] The server obtains past performance data. The server obtains past performance data related to the registered task from the database by filtering.

[0898] Step 5:

[0899] The server uses AI to calculate the man-hours and priority. The server inputs the acquired performance data into an AI algorithm and calculates the approximate man-hours and priority of a new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[0900] Step 6:

[0901] The server recognizes the user's emotions through an emotion engine. The server uses data acquired from the user's device (face recognition, voice analysis, etc.) to analyze the user's stress level and emotions. For example, it determines that the user is feeling stressed.

[0902] Step 7:

[0903] The server adjusts the priority based on the emotion engine. If the user's emotional state is stressful, the server increases the priority of less burdensome tasks. For example, the server may adjust the priority of a task by saying, "The user is feeling stressed, so set the priority of this task low."

[0904] Step 8:

[0905] The server retrieves the user's current schedule. The server retrieves the user's current schedule data from a database or calendar application. For example, it retrieves information about existing meetings and breaks in the current schedule.

[0906] Step 9:

[0907] The server generates an optimal schedule. The server calculates the start and end dates and times of the tasks, taking into account the man-hours, priority, emotional state of the user, and the current schedule of the new tasks. For example, it determines that "it is optimal to assign the new website design task between November 20th and November 30th."

[0908] Step 10:

[0909] The server notifies the user of the generated schedule. The server sends the generated schedule information to the user's terminal and notifies the user. The user's terminal displays "The task is scheduled to start on November 20th and be completed on November 30th."

[0910] Step 11:

[0911] The server analyzes and acquires related information. Based on past performance data, the server identifies related departments and reference materials. For example, it identifies that "a person in the design department was involved in a similar task."

[0912] Step 12:

[0913] The server recommends related information to the user. The server then sends the identified related information to the user's device and notifies them. For example, the user may receive a notification such as, "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials."

[0914] This detailed processing step allows the system to efficiently support users in task management and schedule adjustment, and also provides support that takes into account their emotional state.

[0915] Example 2

[0916] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0917] Conventional task management systems lack specific support for users to efficiently manage their tasks. Furthermore, because they prioritize tasks without taking into account the user's emotional state, there is a risk of increasing stress and strain on the user. Furthermore, because they lack a mechanism for fully utilizing past performance data, it is difficult to estimate the amount of work required for a task or calculate an appropriate schedule. This makes it difficult for users to obtain appropriate resources and information, hindering efficient task completion.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: an input means for a user to register tasks and deadlines; a means for the server to record task and deadline information received from the input means; a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks; an emotion analysis means for the server to recognize the user's emotional state; a means for the server to adjust priorities using the emotion analysis means and an AI algorithm; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; and a means for the server to recommend relevant departments and useful information to the user. This enables users to efficiently manage tasks and adjust their schedules and receive support that takes their emotional state into consideration, thereby reducing stress and burden. It also enables users to quickly obtain appropriate resources and related information.

[0919] A "user" is an entity that uses this system to register tasks and deadlines.

[0920] "Input means" refers to a device or software that allows a user to input information about tasks and deadlines.

[0921] The "server" is a central device that processes and stores task information and performance data, and runs AI algorithms and emotion analysis.

[0922] A "task" refers to the specific details of the work or activity that a user must perform.

[0923] "Delivery date" refers to the estimated date and time for completing a task set by the user.

[0924] The "recording means" is a device or software that stores the received task and delivery date information in a storage device such as a database.

[0925] "Past performance data" refers to historical information about similar or related tasks previously performed.

[0926] "Estimated effort" refers to an estimate of the time required to complete a particular task.

[0927] "Priority" refers to an evaluation of importance or urgency for determining the execution order among multiple tasks.

[0928] "Emotional state" refers to the user's psychological and emotional state, including stress level, satisfaction, etc.

[0929] An "emotion analysis means" is a device or software that uses facial recognition or voice analysis techniques to analyze a user's emotional state.

[0930] An "AI algorithm" is a computational method that uses machine learning and data analysis to derive optimal results.

[0931] A "schedule generation means" is a device or software that determines the optimal start and end dates and times for tasks based on estimated man-hours, priority, and the user's emotional state.

[0932] The "notification means" is a device or software that notifies the user of the generated schedule and recommendation information.

[0933] A "recommendation means" is a device or software that selects and suggests resources and information that are useful to the user.

[0934] "Relevant departments" refers to other departments or agencies involved in the performance of the task.

[0935] "Useful information" refers to past materials and knowledge that are useful in completing a task.

[0936] This invention is a generation AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state when the user registers tasks and deadlines. The purpose of this system is to support users in efficiently managing tasks and adjusting schedules. Furthermore, it uses emotional analysis means to provide support that is sensitive to the user's emotional state.

[0937] System configuration

[0938] The system consists of the following main components:

[0939] 1. User Input Method

[0940] 2. Server data reception and storage function

[0941] 3. Server past performance data acquisition function

[0942] 4. Emotion analysis method

[0943] 5. AI algorithms to calculate effort and priority

[0944] 6. User schedule adjustment function

[0945] 7. Recommendation function for related departments and useful information

[0946] Hardware and Software Configuration

[0947] Users input data via devices such as PCs and smartphones. Task and delivery date information is entered via dedicated web and mobile applications. On the server side, multiple software modules operate to receive and store data, import performance data, run AI algorithms, and perform sentiment analysis. These include database management systems (e.g., MySQL), AI modules (e.g., TensorFlow), and sentiment analysis tools (e.g., OpenFace and Google Cloud Speech-to-Text API).

[0948] Overview of program processing

[0949] When a user registers a task, the device sends the entered task and delivery date information to the server. The server receives the information and stores it in a database. The server then retrieves past performance data and applies an AI algorithm to calculate the estimated effort and priority of the new task. It then recognizes the user's emotional state through emotion analysis and adjusts task priorities accordingly. Finally, the server notifies the user of the generated optimal schedule and, if necessary, recommends related departments and useful information.

[0950] Specific examples

[0951] For example, if a user registers a task such as "The design of a new website needs to be completed by December 1st," the system will act as follows:

[0952] 1. The user's device sends task information to the server.

[0953] 2. The server receives the information and retrieves past performance data.

[0954] 3. The server uses AI to calculate the estimated man-hours and priority, and uses emotion analysis tools to analyze the user's emotional state.

[0955] 4. The server adjusts priorities based on emotional states and generates an optimal schedule.

[0956] 5. The server notifies the user of the generated schedule and simultaneously recommends related information.

[0957] This system allows users to significantly reduce the time they spend on task management and schedule adjustment, and also provides support that takes into account their emotional state.

[0958] Prompt Sentence Examples

[0959] For example, you can generate a description of the system above by providing the following prompt to a generative AI model:

[0960] "Please explain how a system works, where a user registers tasks and generates a schedule based on the estimated effort and priority of the tasks, as well as their emotional state."

[0961] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0962] Step 1:

[0963] The user inputs a new task and its due date. The input is done using a dedicated web application or mobile application. For example, the user inputs "Complete the design of a new website by December 1st." This input data (task content and due date) is collected by the device and sent to the next step.

[0964] Step 2:

[0965] The device sends the entered task and delivery date information to the server via API. Specifically, the device uses an HTTP POST request to send the input data to a specified endpoint on the server. The input data consists of a JSON object containing the task details and delivery date. Based on this request, the server receives the data and begins processing in the next step.

[0966] Step 3:

[0967] The server saves the received task and deadline information in the database. The server stores the information in a temporary buffer and then performs an INSERT operation on the database. This permanently saves the input data (task details and deadline) in the database. For example, the information "Complete the design of the new website by December 1st" is recorded in the database.

[0968] Step 4:

[0969] The server retrieves past performance data from a database. The server executes an SQL query to search and retrieve historical information about similar or related tasks that have been performed in the past. Past data related to the input data (new task) is retrieved. For example, the time and results of past website design tasks are retrieved.

[0970] Step 5:

[0971] The server inputs past performance data acquired into an AI algorithm to calculate the estimated effort and priority of a new task. An AI algorithm (e.g., TensorFlow model) is used to calculate the estimated effort and priority from past data. The input is performance data, and the output is result data such as "approximately 50 hours required" or "high priority."

[0972] Step 6:

[0973] In order for the server to recognize the user's emotional state, it inputs data collected from the user's device (e.g., facial recognition data, voice data) into an emotion analysis tool. Using an emotion analysis tool (e.g., OpenFace or Google Cloud Speech-to-Text API), it analyzes the user's facial expressions and tone of voice, and outputs the user's emotional state (e.g., the user is feeling stressed).

[0974] Step 7:

[0975] The server adjusts the priorities using emotion analysis methods and AI algorithms. Based on the results of the emotion analysis, the AI ​​adjusts the initial priorities. For example, if the user is feeling stressed, it will lower the priority and prioritize less demanding tasks. The input is the emotional state and the initial priorities, and the output is the adjusted priorities.

[0976] Step 8:

[0977] The server retrieves the user's current schedule information. It uses Dell's Google Calendar API and / or Outlook Calendar API to retrieve the user's current schedule and saves it as current schedule data. This data includes existing meetings and scheduled tasks.

[0978] Step 9:

[0979] The server considers the estimated effort, priority, and the user's emotional state of the task, and uses an AI algorithm to generate the optimal start and end dates and times for the task. It then applies a schedule generation algorithm to calculate the optimal schedule. The input is task information, emotional state, and current schedule data, and the output is the optimal start and end dates and times for the task.

[0980] Step 10:

[0981] The server sends the generated schedule information to the user's device and notifies them. The server notifies the user of the optimal schedule via push notification or email. The user receives the start and end dates and times of specific tasks.

[0982] Step 11:

[0983] The server analyzes past performance data and identifies related departments and useful information. It identifies related departments and available materials based on past data. For example, it identifies "people from the design department were involved in similar tasks."

[0984] Step 12:

[0985] The server sends the identified relevant information to the user's device and notifies them. The server then provides specific advice such as, "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials." The input is the relevant information, and the output is the notification content to the user.

[0986] (Application example 2)

[0987] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0988] Conventional task management systems do not adjust schedules taking into account the user's emotional state, which means they are unable to reduce user stress and excessive workloads. Furthermore, while appropriate task allocation and shift adjustment according to the emotional state of staff members is important in daily operations at brick-and-mortar stores, no efficient method for achieving this has been provided.

[0989] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for the user to register tasks and deadlines, a means for the server to record the task and deadline information received from the input means, a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks, a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks, a means for the server to notify the user of the generated schedule, a means for the server to recommend relevant departments and useful information to the user, a means for the server to acquire the user's emotional state and adjust task priorities based on the emotional state, and a means for the server to suggest relaxation methods to the user based on the emotional state. This enables efficient task management and schedule adjustment that takes the user's emotional state into consideration.

[0990] "Input means for users to register tasks and deadlines" refers to a device or interface that users use to input new tasks and their deadlines into the system.

[0991] The "means for recording the task and deadline information received by the server from the input means" refers to a function in which the server stores information on the task and deadline sent from the user in a database.

[0992] "Means for the server to acquire past performance data and calculate the estimated man-hours and priority associated with the task" refers to an algorithm or function that allows the server to collect data on past related tasks and, based on that data, calculate the required time and execution priority of a new task.

[0993] "Means for the server to obtain the user's current schedule and generate an optimal schedule for the task" refers to the function by which the server obtains the user's current schedule information and determines the optimal execution time for the new task based on that information.

[0994] The "means by which the server notifies the user of the generated schedule" refers to a communication means or interface for notifying the user of the schedule information generated by the server.

[0995] "Means for the server to recommend related departments and useful information to the user" refers to a function for the server to recommend departments and useful information related to a task to the user.

[0996] "Means for the server to acquire the user's emotional state and adjust task priorities based on that emotional state" refers to a function in which the server collects user emotional data and changes the order in which tasks are executed based on that data.

[0997] "Means for the server to suggest relaxation methods to the user based on the emotional state" refers to a function in which the server takes into account the emotional state of the user and suggests appropriate relaxation methods or breaks.

[0998] This invention relates to a generative AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state, once the user registers tasks and deadlines. This system is designed to efficiently manage staff and adjust schedules in physical stores, and consists of the following main components:

[0999] User input method

[1000] A user uses an input device such as a smartphone or tablet to register a new task and its due date. For example, consider the case where a store staff member registers a task such as "Complete product inventory check by December 1st."

[1001] A means of recording task information

[1002] The server receives task and delivery date information from the user and records it in the database. The task information is sent to the server via API and immediately saved. For example, an "inventory check task" and its "delivery date information" are recorded in the database.

[1003] Acquisition and analysis of performance data

[1004] The server retrieves past performance data from the database and calculates the estimated effort and priority associated with a new task using an AI algorithm. For example, based on data from similar past tasks, it might calculate that "inventory checks will take approximately 20 hours and have a medium priority."

[1005] Schedule optimization

[1006] The server retrieves the user's current schedule information from a calendar app or database, and generates an optimal schedule for new tasks based on this information. The task priority and effort are taken into account. For example, "An inventory check task is added to the schedule, and the start and end times are set."

[1007] Schedule notification method

[1008] The server notifies the user of the generated schedule via push notification, email notification, etc. For example, "A new schedule is sent to the staff member's smartphone."

[1009] Related information recommendations

[1010] The server analyzes past performance data, identifies relevant departments and useful information, and makes recommendations to users. This includes the server recommending specific personnel and related materials. For example, "recommending materials from the design department and project materials from the previous inventory check."

[1011] Acquiring and analyzing emotional states

[1012] The server acquires the user's emotional state and analyzes it using an emotion engine. Facial recognition and voice analysis are used to acquire emotions. For example, if a staff member is feeling stressed, the server will detect this.

[1013] Adjusting task priorities

[1014] The server adjusts task priorities based on the emotional state of the staff member. For example, if the staff member's stress level is high, the server will lower the priority of the task.

[1015] Suggestions for relaxation methods

[1016] The server suggests relaxation methods to users based on their emotional state, for example, "to staff who are feeling stressed, it suggests break times and relaxation methods."

[1017] As a concrete example, consider the following scenario.

[1018] A store staff member registers a task to "check product inventory by December 1st."

[1019] Based on past inventory confirmation data, the server calculates the required man-hours as "20 hours" and determines the priority to be "medium."

[1020] Additionally, an emotion engine is used to analyze the emotional state of staff, and if stress levels are high, the priority is adjusted to "low."

[1021] The optimal schedule is generated and notified to store staff's smartphones.

[1022] Suggestions for rest and relaxation will be made as needed.

[1023] Example prompt sentence:

[1024] "A user has registered the task 'Check product inventory by December 1st.' Based on past performance data, the required man-hours are calculated to be 20 hours, and the priority is medium. Please generate the optimal schedule taking into account the user's emotional state."

[1025] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1026] Step 1:

[1027] The user uses an input means for registering tasks and deadlines to input task information. The input is made using a smartphone or tablet, and includes the task name, deadline, and other detailed information. For example, the user registers a task such as "Check product inventory by December 1st."

[1028] Input: Task name, due date, details

[1029] Data processing: structuring task information

[1030] Output: Structured task information

[1031] Step 2:

[1032] The device sends the entered task and deadline information to the server, which then sends this information via API and reaches the server in real time.

[1033] Input: Structured task information

[1034] Data processing: Data transfer to server via API

[1035] Output: Task information saved on the server

[1036] Step 3:

[1037] The server stores the received task and deadline information in a database, which records the user ID, task name, and deadline.

[1038] Input: Task information received by the server

[1039] Data processing: storing information in a database

[1040] Output: Task information stored in the database

[1041] Step 4:

[1042] The server retrieves historical performance data. This data is retrieved directly from the database and includes information such as effort and priority of previously related tasks.

[1043] Input: User ID

[1044] Data processing: Extraction of past performance data

[1045] Output: Past performance data

[1046] Step 5:

[1047] The server calculates the estimated man-hours and priority of new tasks based on past performance data. Here, an AI algorithm is used to analyze the data. For example, based on past data on inventory check tasks, it calculates that "20 hours will be required," and the priority is also determined.

[1048] Input: Past performance data, task information

[1049] Data processing: Analysis using AI algorithms

[1050] Output: Estimated man-hours, priority

[1051] Step 6:

[1052] The server retrieves the user's current schedule. This can be done from a calendar app or a database.

[1053] Input: User ID

[1054] Data processing: Getting the current schedule

[1055] Output: Current schedule information

[1056] Step 7:

[1057] The server generates an optimal schedule for the new task, determining when to execute the task based on the user's current schedule and the newly calculated estimated effort and priority.

[1058] Input: Current schedule information, estimated man-hours, priority

[1059] Data processing: generating optimal schedules

[1060] Output: New task schedule

[1061] Step 8:

[1062] The server notifies the user's device of the generated schedule. The device receives the notification and notifies the user. Notifications can be sent via push notifications or email.

[1063] Input: New task schedule

[1064] Data Processing: Notification Format

[1065] Output: Schedule notification to user

[1066] Step 9:

[1067] The server recommends relevant departments and useful information to the user, analyzes past performance data to identify relevant departments and materials, and notifies the user.

[1068] Input: Past performance data

[1069] Data processing: analysis of relevant information

[1070] Output: Related information recommendations

[1071] Step 10:

[1072] The server acquires the user's emotional state and analyzes it with an emotion engine, using facial recognition and voice analysis to determine stress levels, etc.

[1073] Input: User emotion data

[1074] Data processing: Analysis using emotion engine

[1075] Output: Emotional state

[1076] Step 11:

[1077] The server adjusts the priority of tasks based on the user's emotional state. For example, if the user's stress level is high, the server lowers the priority to reduce the burden.

[1078] Input: Emotional state, task information

[1079] Data processing: Adjusting task priorities

[1080] Output: Adjusted task priorities

[1081] Step 12:

[1082] The server suggests relaxation methods to the user based on their emotional state, including suggestions for break times and relaxation methods.

[1083] Input: Emotional state

[1084] Data processing: Deciding on relaxation method

[1085] Output: Relaxation suggestions

[1086] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1088] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1089] [Third embodiment]

[1090] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1093] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1094] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1098] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1100] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1101] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1102] This invention is a generation AI system that calculates estimated man-hours and priorities based on past performance data and adjusts schedules when users register tasks and deadlines.

[1103] System Overview

[1104] The system consists of the following main components:

[1105] 1. User Input Method

[1106] 2. Server data reception and storage function

[1107] 3. Server past performance data acquisition function

[1108] 4. AI algorithms that calculate effort and priority

[1109] 5. User schedule adjustment function

[1110] 6. Related information recommendation function

[1111] Program processing overview

[1112] Registering tasks and deadlines

[1113] A user registers a task

[1114] The user inputs a new task and its due date through their own device. For example, they can register "Complete the design of the new website by December 1st."

[1115] The device sends task information to the server

[1116] The user's device sends the input task and delivery date information to the server. Specifically, the input information is sent to the server's endpoint via the API.

[1117] Data recording and acquisition

[1118] The server receives and stores task information

[1119] The server stores the task and deadline information received from the device in a database. For example, it records data such as "Complete the design of the new website by December 1st."

[1120] The server acquires past performance data

[1121] The server retrieves past performance data related to the registered task from the database, specifically by filtering performance data of similar website design tasks that have been completed in the past.

[1122] Calculating effort and priority

[1123] The server uses AI to calculate the man-hours and priority

[1124] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority of the new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[1125] Schedule adjustment

[1126] The server retrieves the user's current schedule

[1127] The server retrieves the user's current schedule from a database and, if necessary, may also connect with the calendar app used by the user.

[1128] The server proposes the optimal schedule

[1129] The server will suggest start and end dates for the new task based on the effort and priority of the task and the user's current schedule. For example, it might suggest that the best time to complete the new website design task is between November 20th and November 30th.

[1130] The server notifies the user of the proposed schedule

[1131] The server transmits the proposed schedule information to the user's terminal, which is notified of the proposed schedule.

[1132] Related information recommendations

[1133] The server analyzes and obtains relevant information

[1134] The server analyzes past performance data and identifies related departments and reference materials. For example, it identifies information such as "Yamada from the design department was involved in a similar task."

[1135] The server recommends related information to the user

[1136] Based on the analysis results, the server sends relevant information to the user's device and makes recommendations. The user will receive a notification such as, "It would be a good idea to contact Yamada-san, the person in charge. Also, please refer to the materials from the previous project."

[1137] Specific examples

[1138] When a user registers a task such as "Create a new website design and have it completed by December 1st," the system works as follows:

[1139] 1. The device sends task information to the server.

[1140] 2. The server receives and stores the information and obtains past performance data.

[1141] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[1142] 4. The server identifies relevant information (e.g., people involved in past projects and reference materials) and recommends it to the user.

[1143] This system allows business people to significantly reduce the time they spend on task management and scheduling, allowing them to focus on more creative work.

[1144] The processing flow will be explained below.

[1145] Step 1:

[1146] The user inputs the task and its due date. The user inputs task information such as "Complete the design of the new website by December 1st" from their own device.

[1147] Step 2:

[1148] The device sends task information to the server. The user's device sends the entered task and deadline information to the server's endpoint using the API.

[1149] Step 3:

[1150] The server receives the task information and stores it in a database. Specifically, the server records information such as "Complete the design of the new website by December 1st" in the database.

[1151] Step 4:

[1152] The server obtains past performance data. The server obtains past performance data related to the registered task from the database by filtering.

[1153] Step 5:

[1154] The server uses AI to calculate the estimated man-hours and priority. Based on the acquired performance data, the server uses an AI algorithm to calculate the estimated man-hours and priority of a new task. For example, the AI ​​might determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[1155] Step 6:

[1156] The server retrieves the user's current schedule. The server retrieves the user's existing schedule data from a database or a linked calendar app.

[1157] Step 7:

[1158] The server generates an optimal schedule. Taking into account the man-hours and priority of the new task and the user's current schedule, the server calculates the start and end dates and times of the task and generates an optimal schedule. For example, it may determine that "it is optimal to assign the new website design task between November 20th and November 30th."

[1159] Step 8:

[1160] The server notifies the user of the generated schedule. The server transmits the generated schedule information to the user's terminal and notifies the user.

[1161] Step 9:

[1162] The server analyzes and acquires related information. Based on past performance data, the server identifies related departments and reference materials. For example, it identifies information such as "Yamada from the design department was involved in a similar task."

[1163] Step 10:

[1164] The server recommends related information to the user. The server then sends the identified related information to the user's device and notifies them. For example, the user might receive a notification such as, "It would be a good idea to contact Mr. Yamada, the person in charge. Also, please refer to the materials from the previous project."

[1165] Through these steps, the system efficiently supports users in task management and schedule adjustment, providing an environment in which business people can concentrate on creative work.

[1166] Example 1

[1167] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1168] In today's business environment, efficient management of multiple tasks and their deadlines is required. However, doing this manually is time-consuming, labor-intensive, and inefficient. Furthermore, there is a lack of systems that can calculate appropriate man-hours and priorities based on performance data from similar past tasks and propose optimal schedules. Furthermore, it is difficult to provide relevant information and reference materials in a timely manner for task completion.

[1169] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1170] In this invention, the server includes: an input means for a user to register tasks and deadlines; a means for the server to record the task and deadline information received from the input means; a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; a means for the server to recommend relevant departments and useful information to the user; a means for the server to store and filter past performance data using a database; a means for a terminal to communicate with the server via an API; a means for the server to acquire the user's schedule in cooperation with an external calendar service; and a means for the server to calculate estimated man-hours and priorities using an AI algorithm. This allows users to efficiently manage tasks and adjust their schedules, and quickly obtain necessary related information.

[1171] A "user" is a person or organization that uses this system to input tasks and their due dates, and receives schedule management and related information.

[1172] The "server" is a computer system that records task and delivery date information received from users, retrieves past performance data from a database, calculates labor hours and priorities using AI algorithms, generates optimal schedules, and provides notification and recommendation functions.

[1173] "Input means" refers to an interface or device that allows a user to register task and delivery date information, and examples include a keyboard, a touch screen, and a dedicated application.

[1174] The "recording means" is a function for storing the task and deadline information received by the server in a database, and also checks the consistency of the data.

[1175] "Performance data" is data relating to similar tasks that have been completed in the past, and includes information such as the task name, man-hours, completion date, and people involved.

[1176] "Estimated man-hours" is an estimate of the total time required to complete a task, calculated by an AI algorithm based on past performance data.

[1177] "Priority" is an assessment of the importance or urgency of a particular task, and indicates how much priority it needs to have compared to other tasks.

[1178] A "schedule" is a specific plan of the start and end dates of a task and the work to be done during that time.

[1179] "Notification means" refers to a method for notifying the user of the schedule and related information generated by the server, and specifically includes push notifications, emails, display messages, etc.

[1180] The "recommendation function" is a function in which the server analyzes the database and suggests information and reference materials that may be useful to the user.

[1181] A "database" is a structured data repository that stores received task information and past performance data and allows for searching and retrieval as needed.

[1182] An "API" is an interface for exchanging data between a terminal and a server, and communication is carried out according to a standardized protocol.

[1183] An "external calendar service" is a calendar application provided by a third party, such as Google Calendar or Outlook Calendar, that the server works with to obtain the user's current schedule.

[1184] An "AI algorithm" is a machine learning model that analyzes past data and estimates the effort and priority of new tasks.

[1185] MODE FOR CARRYING OUT THE INVENTION

[1186] This invention is an AI generation system for managing tasks and deadlines, which uses past performance data based on tasks entered by a user, calculates estimated man-hours and priorities, and provides optimal schedules and related information. Specific embodiments of the system are described below.

[1187] System Overview

[1188] The system mainly consists of the following components:

[1189] 1. User Input Method

[1190] 2. Server data reception and storage function

[1191] 3. Server past performance data acquisition function

[1192] 4. AI algorithms that calculate effort and priority

[1193] 5. User schedule adjustment function

[1194] 6. Related information recommendation function

[1195] Hardware and software used

[1196] 1. User's device (PC, tablet, smartphone, etc.)

[1197] 2. Servers (including cloud and on-premise environments)

[1198] 3. Database (MySQL, PostgreSQL, etc.)

[1199] 4. AI algorithms (TensorFlow, PyTorch, etc.)

[1200] 5. Calendar services (Google Calendar, Outlook Calendar, etc.)

[1201] Program processing overview

[1202] Registering tasks and deadlines

[1203] Users use their devices to input new tasks and their due dates. The input data is sent to the server via the API. For example, a user might input "Complete the design of the new website by December 1st."

[1204] Data recording and acquisition

[1205] The server receives the task and delivery date information sent from the terminal and stores it in a database.The server then retrieves past performance data from the database.Specifically, it filters and extracts past data related to similar tasks.

[1206] Calculating effort and priority

[1207] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority for new tasks. The AI ​​algorithm is built using TensorFlow and PyTorch and analyzes past data. The calculation results include, "Based on past performance, this task will require approximately 50 hours and is a high priority."

[1208] Schedule adjustment

[1209] The server obtains the user's current schedule. If necessary, it connects with an external calendar service to obtain schedule information. The server then generates an optimal schedule based on the effort and priority of the new task and the user's current schedule, and proposes it to the user. For example, it might suggest that "it would be best to proceed with the design of the new website from November 20th to November 30th."

[1210] Notifications and Recommendations

[1211] The server notifies the user of the generated schedule information. At the same time, the server analyzes past performance data, identifies related information (such as people involved in past projects and reference materials), and makes recommendations to the user.

[1212] Specific examples

[1213] For example, if a user registers a task such as "Create a new website design and have it completed by December 1st," the system will act as follows:

[1214] 1. The user enters the task and due date on the terminal.

[1215] 2. The device sends the input data to the server.

[1216] 3. The server receives the data and stores it in a database.

[1217] 4. The server retrieves performance data for similar tasks in the past.

[1218] 5. The server uses AI to calculate the estimated effort and priority of the new task.

[1219] 6. The server retrieves the user's current schedule.

[1220] 7. The server proposes an optimal schedule.

[1221] 8. The server notifies the user of the proposed schedule.

[1222] 9. The server analyzes and retrieves the relevant information.

[1223] 10. The server recommends relevant information to the user.

[1224] Prompt Sentence Examples

[1225] "We have a task to design a new website. The deadline is December 1st. Based on past performance data for similar tasks, please tell us the approximate man-hours and priority. Also, please suggest the optimal schedule."

[1226] Based on this prompt, the system provides the necessary information and suggests the optimal schedule for the user.

[1227] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1228] Step 1:

[1229] The user enters the task and due date

[1230] The user inputs a new task and its due date using an input form on the terminal or a dedicated application.

[1231] Input: Tasks and deadlines, such as "Complete new website design by December 1st."

[1232] Output: Task and due date data in text format.

[1233] Step 2:

[1234] The device sends the input data to the server

[1235] The terminal uses the API to send the task and deadline information entered by the user to the server.

[1236] Input: Task and due date data entered by the user.

[1237] Data processing: Convert data into JSON format.

[1238] Output: JSON data sent to the server's API endpoint.

[1239] Step 3:

[1240] The server receives the data and stores it in the database

[1241] The server receives the task and deadline data sent via the API and stores it in a database.

[1242] Input: Task and due date data received by the server in JSON format.

[1243] Data processing: Convert JSON data into structured data.

[1244] Output: Task and due date data stored in a database.

[1245] Step 4:

[1246] The server obtains performance data for similar tasks from the past.

[1247] The server searches the database for past performance data using a query and obtains data related to the registered task.

[1248] Input: Saved task and due date data.

[1249] Data processing: Use queries to search for similar tasks from a database.

[1250] Output: A list of historical performance data.

[1251] Step 5:

[1252] The server uses AI to calculate the estimated man-hours and priority of new tasks.

[1253] The server inputs past performance data acquired into an AI algorithm to calculate the estimated man-hours and priority of new tasks.

[1254] Input: A list of historical performance data.

[1255] Data Calculation: Data analysis using AI algorithms.

[1256] Output: Estimated effort and priority of the new task (e.g. "50 hours required, high priority").

[1257] Step 6:

[1258] The server retrieves the user's current schedule

[1259] The server contacts a database or external calendar service to retrieve the user's current schedule.

[1260] Input: User credentials and / or calendar service API key.

[1261] Data processing: Communication with external calendar services.

[1262] Output: Current schedule data.

[1263] Step 7:

[1264] The server proposes the optimal schedule for new tasks.

[1265] The server proposes optimal start and end dates based on the current schedule and the man-hours and priority of the new task.

[1266] Inputs: New task effort and priority, current schedule data.

[1267] Data calculation: Execution of the schedule generation algorithm.

[1268] Output: The optimal schedule for the new task (e.g., "November 20th to November 30th is optimal").

[1269] Step 8:

[1270] The server notifies the user of the generated schedule.

[1271] The server notifies the user's terminal of the proposed schedule.

[1272] Input: The optimal schedule for the new task.

[1273] Data processing: generating notification messages.

[1274] Output: Push notification or email to user device.

[1275] Step 9:

[1276] The server analyzes and retrieves the relevant information

[1277] The server analyzes past performance data and identifies relevant departments and useful information.

[1278] Input: Historical performance data.

[1279] Data operations: Running data analysis algorithms.

[1280] Output: A list of relevant information (e.g., "Yamada from the design department was involved").

[1281] Step 10:

[1282] The server recommends relevant information to the user

[1283] The server recommends related information to the user based on the analysis results.

[1284] Input: A list of relevant information.

[1285] Data calculation: Generate recommended messages.

[1286] Output: Recommendation notification to the user device (e.g., "We recommend contacting Yamada-san, the person in charge").

[1287] (Application example 1)

[1288] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1289] Logistics centers have numerous tasks (such as receiving, picking, and shipping goods), and each task requires strict delivery date management. However, there are not enough methods in place to properly manage and efficiently handle these numerous tasks and delivery dates. This can lead to reduced work efficiency, which can result in a deterioration in overall logistics performance. Furthermore, it is difficult for employees to correctly estimate the priority and approximate man-hours required for each task and set an optimal schedule. An appropriate system is needed to solve these problems.

[1290] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1291] In this invention, the server includes: an input means for a user to register tasks and delivery dates; a means for the server to record the task and delivery date information received from the input means; a means for the server to acquire past performance data and calculate approximate man-hours and priorities related to the tasks; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; a means for the server to recommend relevant departments and useful information to the user; a means for the logistics center to register logistics work, calculate approximate man-hours and priorities based on past performance data, and propose an optimal schedule; and a means for a logistics center employee to input task information using a smartphone or a head-mounted display and for the system to propose a schedule based on this information. This enables efficient management of each task and appropriate scheduling at the logistics center.

[1292] A "task" refers to a specific unit of work or processing carried out at a logistics center.

[1293] "Delivery date" refers to the deadline by which each task at a logistics center must be completed.

[1294] "Input means" refers to a device or interface that allows a user to register tasks and deadlines in the system.

[1295] "Server" refers to a computer system that records received task and delivery date information, and acquires, analyzes, and notifies data.

[1296] "Past performance data" refers to data relating to previous similar tasks, and is information used to calculate the estimated man-hours and priorities.

[1297] "Estimated effort" refers to a rough estimate of the time required to complete a task, calculated based on past performance data.

[1298] "Priority" refers to an indicator that shows the importance and urgency of a task.

[1299] A "schedule" refers to a work plan, including start and finish times and dates for tasks.

[1300] "Recommendation" refers to suggesting relevant information to a user.

[1301] A "logistics center" refers to a facility where logistics operations such as receiving, storing, picking, and shipping goods are carried out.

[1302] "Smartphone" refers to a mobile phone-type information terminal used to input task information.

[1303] "Head-mounted display" refers to a head-mounted display device used to input task information.

[1304] System Overview

[1305] This invention is a system for efficient task management and scheduling in a logistics center. The system consists of the following main components:

[1306] 1. User Input Method

[1307] 2. Server data reception and storage function

[1308] 3. Server past performance data acquisition function

[1309] 4. AI algorithms that calculate effort and priority

[1310] 5. User schedule adjustment function

[1311] 6. Related information recommendation function

[1312] Program processing overview

[1313] Registering tasks and deadlines

[1314] A user registers a task

[1315] Users use a smartphone or head-mounted display (HMD) to register tasks and deadlines in the system. For example, they might enter "Complete product receiving work by December 1, 2023."

[1316] The device sends task information to the server

[1317] The registered information is sent from the terminal to the server. Specifically, task and delivery date information is sent to the server's endpoint via API.

[1318] Data recording and acquisition

[1319] The server receives and stores task information

[1320] The server records the received task and delivery date information in a database. For example, data such as "Complete product receiving work by December 1, 2023" is saved.

[1321] The server acquires past performance data

[1322] The server then retrieves from the database past performance data related to the registered task, for example, data on similar past "warehouse-receiving" tasks.

[1323] Calculating effort and priority

[1324] The server uses AI to calculate the man-hours and priority

[1325] The server inputs the acquired performance data into an AI algorithm to calculate the estimated man-hours and priority. This uses data such as past work time and importance. For example, based on past data, it can calculate that "warehousing work takes an average of 5 hours and has high priority."

[1326] Schedule adjustment

[1327] The server retrieves the user's current schedule

[1328] The server then retrieves the user's current schedule, which may involve retrieving the user's calendar information from a database.

[1329] The server proposes the optimal schedule

[1330] The server will propose an optimal schedule based on the man-hours and priority of the new task and the current schedule. For example, it may suggest that "it would be optimal to carry out the inventory work from November 20, 2023 to November 30, 2023."

[1331] The server notifies the user of the proposed schedule

[1332] The proposed schedule information is sent from the server to the user's terminal and notified.

[1333] Related information recommendations

[1334] The server analyzes and obtains relevant information

[1335] The server analyzes past performance data and identifies relevant information, such as the people involved in the previous task.

[1336] The server recommends related information to the user

[1337] Based on the analysis results, the server sends relevant information to the user's device as a recommendation, such as "We recommend contacting the employee who handled the previous task."

[1338] Specific examples

[1339] When a user registers a task such as "Complete product receiving work by December 1, 2023," the system works as follows:

[1340] 1. The device sends task information to the server.

[1341] 2. The server receives and stores the information and obtains past performance data.

[1342] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[1343] 4. The server identifies relevant information (e.g., people involved in past projects and reference materials) and recommends it to the user.

[1344] Example prompt sentence:

[1345] When a user registers a task such as "Complete product receiving work by December 1, 2023," the system works as follows:

[1346] 1. The device sends task information to the server.

[1347] 2. The server receives and stores the information and obtains past performance data.

[1348] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[1349] 4. The server identifies relevant information and recommends it to the user.

[1350] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1351] Step 1:

[1352] Registering tasks and deadlines

[1353] The user uses a smartphone or head-mounted display (HMD) to input a new task, "Goods Receipt Work," and its due date, "December 1, 2023." The information entered by the user is sent from the device to the server via API. The input data includes the "Task Name" and "Delivery Date" fields. The server then receives the task information and is ready to start the system.

[1354] Step 2:

[1355] Data recording and acquisition

[1356] The server receives the task and delivery date information sent from the terminal and stores this information in a database. Next, the server retrieves past performance data related to the registered task from the database. Specifically, it filters and retrieves data related to "warehouse entry work" that was performed in the past. This allows the server to prepare the original data, which will serve as the basis for the next calculation.

[1357] Step 3:

[1358] Calculating effort and priority

[1359] The server inputs the acquired performance data into an AI algorithm. The server processes the data by inputting data such as past work time and importance into the AI, and calculates the approximate man-hours and priority of the task. For example, it obtains an output such as "warehouse entry work takes an average of 5 hours and is a high priority." This allows the server to perform a detailed evaluation of the task.

[1360] Step 4:

[1361] Schedule adjustment

[1362] The server retrieves the user's current schedule data from the database. The server generates an optimal schedule for tasks based on the man-hours and priorities calculated by the AI ​​and the current schedule. For example, it suggests that a new inventory task should be optimally carried out from November 20, 2023 to November 30, 2023. This information is intended for use by the user.

[1363] Step 5:

[1364] Proposal schedule notification

[1365] The server sends the generated optimal schedule information to the user's device, where a notification is displayed prompting the user to confirm the new schedule. This allows the user to confirm the proposed schedule and prepare to put it into action.

[1366] Step 6:

[1367] Related information recommendations

[1368] The server analyzes past performance data and identifies relevant information, such as "the person involved in the previous inventory work" and "reference materials." Based on the results of this analysis, the server notifies the user's device of related information as recommendations. The user can use the presented information to help them complete their tasks.

[1369] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1370] This invention relates to a generative AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state when the user registers tasks and deadlines. This system allows users to efficiently manage tasks and adjust schedules, and also uses an emotion engine to provide support that is sensitive to the user's emotional state.

[1371] System Overview

[1372] The system consists of the following main components:

[1373] 1. User Input Method

[1374] 2. Server data reception and storage function

[1375] 3. Server past performance data acquisition function

[1376] 4. Emotion Engine

[1377] 5. AI algorithms to calculate effort and priority

[1378] 6. FUZA's schedule adjustment function

[1379] 7. Recommendation function for related departments and useful information

[1380] Program processing overview

[1381] Registering tasks and deadlines

[1382] A user registers a task

[1383] The user inputs a new task and its due date using their own terminal. For example, they may register "Complete the design of the new website by December 1st."

[1384] The device sends task information to the server

[1385] The user's terminal transmits the input task and deadline information to the server via the API.

[1386] Data recording and acquisition

[1387] The server receives and stores task information

[1388] The server stores the task and deadline information received from the terminal in a database. For example, it records information such as "Complete the design of the new website by December 1st."

[1389] The server acquires past performance data

[1390] The server retrieves past performance data related to the registered task from the database.

[1391] Calculating effort and priority

[1392] The server uses AI to calculate the man-hours and priority

[1393] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority of a new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[1394] Emotion Engine

[1395] The server recognizes the user's emotions through the emotion engine.

[1396] The server uses an emotion engine to analyze the user's emotional state based on data acquired from the user's device. For example, it uses facial recognition and voice analysis technology to determine whether the user is feeling stressed.

[1397] Priority adjustment based on emotion engine

[1398] The server adjusts the traditional priorities based on the user's emotional state, for example, prioritizing less demanding tasks if the user is feeling stressed.

[1399] Recommendations based on emotion engines

[1400] The server considers the user's emotional state and recommends appropriate resources and support information. For example, if the user is feeling stressed, it will suggest breaks and relaxation methods to reduce stress.

[1401] Schedule adjustment

[1402] The server retrieves the user's current schedule

[1403] The server retrieves the user's current schedule from a database or calendar app.

[1404] The server generates the optimal schedule

[1405] The server considers the effort, priority, and emotional state of the new task to generate optimal start and end dates and times for the task.

[1406] The server notifies the user of the proposed schedule

[1407] The server transmits the generated schedule information to the user's terminal and notifies the user.

[1408] Related information recommendations

[1409] The server analyzes and obtains relevant information

[1410] Analyze past performance data and identify related departments and materials. For example, identify "people from the design department were involved in similar tasks."

[1411] The server recommends related information to the user

[1412] The identified relevant information is sent to the user's device and a notification is sent, such as "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials."

[1413] Specific examples

[1414] When a user registers a task such as "Create a new website design and have it completed by December 1st," the system works as follows:

[1415] 1. The device sends task information to the server.

[1416] 2. The server receives the information and retrieves past performance data.

[1417] 3. The server uses AI to calculate the estimated man-hours and priority, and uses an emotion engine to analyze the user's emotional state.

[1418] 4. The server adjusts priorities based on emotional states and generates an optimal schedule.

[1419] 5. The server notifies the user of the generated schedule and simultaneously recommends related information.

[1420] This system allows users to significantly reduce the time they spend on task management and schedule adjustment, and also provides support that takes into account their emotional state.

[1421] The processing flow will be explained below.

[1422] Step 1:

[1423] The user inputs a task and its due date. For example, the user inputs "Complete the design of the new website by December 1st" on their device.

[1424] Step 2:

[1425] The device sends task information to the server. The user's device sends the entered task and deadline information to the server's endpoint via API.

[1426] Step 3:

[1427] The server receives the task information and stores it in a database. The server records information such as "Complete the design of the new website by December 1st" in the database.

[1428] Step 4:

[1429] The server obtains past performance data. The server obtains past performance data related to the registered task from the database by filtering.

[1430] Step 5:

[1431] The server uses AI to calculate the man-hours and priority. The server inputs the acquired performance data into an AI algorithm and calculates the approximate man-hours and priority of a new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[1432] Step 6:

[1433] The server recognizes the user's emotions through an emotion engine. The server uses data acquired from the user's device (face recognition, voice analysis, etc.) to analyze the user's stress level and emotions. For example, it determines that the user is feeling stressed.

[1434] Step 7:

[1435] The server adjusts the priority based on the emotion engine. If the user's emotional state is stressful, the server increases the priority of less burdensome tasks. For example, the server may adjust the priority of a task by saying, "The user is feeling stressed, so set the priority of this task low."

[1436] Step 8:

[1437] The server retrieves the user's current schedule. The server retrieves the user's current schedule data from a database or calendar application. For example, it retrieves information about existing meetings and breaks in the current schedule.

[1438] Step 9:

[1439] The server generates an optimal schedule. The server calculates the start and end dates and times of the tasks, taking into account the man-hours, priority, emotional state of the user, and the current schedule of the new tasks. For example, it determines that "it is optimal to assign the new website design task between November 20th and November 30th."

[1440] Step 10:

[1441] The server notifies the user of the generated schedule. The server sends the generated schedule information to the user's terminal and notifies the user. The user's terminal displays "The task is scheduled to start on November 20th and be completed on November 30th."

[1442] Step 11:

[1443] The server analyzes and acquires related information. Based on past performance data, the server identifies related departments and reference materials. For example, it identifies that "a person in the design department was involved in a similar task."

[1444] Step 12:

[1445] The server recommends related information to the user. The server then sends the identified related information to the user's device and notifies them. For example, the user may receive a notification such as, "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials."

[1446] This detailed processing step allows the system to efficiently support users in task management and schedule adjustment, and also provides support that takes into account their emotional state.

[1447] Example 2

[1448] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1449] Conventional task management systems lack specific support for users to efficiently manage their tasks. Furthermore, because they prioritize tasks without taking into account the user's emotional state, there is a risk of increasing stress and strain on the user. Furthermore, because they lack a mechanism for fully utilizing past performance data, it is difficult to estimate the amount of work required for a task or calculate an appropriate schedule. This makes it difficult for users to obtain appropriate resources and information, hindering efficient task completion.

[1450] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: an input means for a user to register tasks and deadlines; a means for the server to record task and deadline information received from the input means; a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks; an emotion analysis means for the server to recognize the user's emotional state; a means for the server to adjust priorities using the emotion analysis means and an AI algorithm; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; and a means for the server to recommend relevant departments and useful information to the user. This enables users to efficiently manage tasks and adjust their schedules and receive support that takes their emotional state into consideration, thereby reducing stress and burden. It also enables users to quickly obtain appropriate resources and related information.

[1451] A "user" is an entity that uses this system to register tasks and deadlines.

[1452] "Input means" refers to a device or software that allows a user to input information about tasks and deadlines.

[1453] The "server" is a central device that processes and stores task information and performance data, and runs AI algorithms and emotion analysis.

[1454] A "task" refers to the specific details of the work or activity that a user must perform.

[1455] "Delivery date" refers to the estimated date and time for completing a task set by the user.

[1456] The "recording means" is a device or software that stores the received task and delivery date information in a storage device such as a database.

[1457] "Past performance data" refers to historical information about similar or related tasks previously performed.

[1458] "Estimated effort" refers to an estimate of the time required to complete a particular task.

[1459] "Priority" refers to an evaluation of importance or urgency for determining the execution order among multiple tasks.

[1460] "Emotional state" refers to the user's psychological and emotional state, including stress level, satisfaction, etc.

[1461] An "emotion analysis means" is a device or software that uses facial recognition or voice analysis techniques to analyze a user's emotional state.

[1462] An "AI algorithm" is a computational method that uses machine learning and data analysis to derive optimal results.

[1463] A "schedule generation means" is a device or software that determines the optimal start and end dates and times for tasks based on estimated man-hours, priority, and the user's emotional state.

[1464] The "notification means" is a device or software that notifies the user of the generated schedule and recommendation information.

[1465] A "recommendation means" is a device or software that selects and suggests resources and information that are useful to the user.

[1466] "Relevant departments" refers to other departments or agencies involved in the performance of the task.

[1467] "Useful information" refers to past materials and knowledge that are useful in completing a task.

[1468] This invention is a generation AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state when the user registers tasks and deadlines. The purpose of this system is to support users in efficiently managing tasks and adjusting schedules. Furthermore, it uses emotional analysis means to provide support that is sensitive to the user's emotional state.

[1469] System configuration

[1470] The system consists of the following main components:

[1471] 1. User Input Method

[1472] 2. Server data reception and storage function

[1473] 3. Server past performance data acquisition function

[1474] 4. Emotion analysis method

[1475] 5. AI algorithms to calculate effort and priority

[1476] 6. User schedule adjustment function

[1477] 7. Recommendation function for related departments and useful information

[1478] Hardware and Software Configuration

[1479] Users input data via devices such as PCs and smartphones. Task and delivery date information is entered via dedicated web and mobile applications. On the server side, multiple software modules operate to receive and store data, import performance data, run AI algorithms, and perform sentiment analysis. These include database management systems (e.g., MySQL), AI modules (e.g., TensorFlow), and sentiment analysis tools (e.g., OpenFace and Google Cloud Speech-to-Text API).

[1480] Overview of program processing

[1481] When a user registers a task, the device sends the entered task and delivery date information to the server. The server receives the information and stores it in a database. The server then retrieves past performance data and applies an AI algorithm to calculate the estimated effort and priority of the new task. It then recognizes the user's emotional state through emotion analysis and adjusts task priorities accordingly. Finally, the server notifies the user of the generated optimal schedule and, if necessary, recommends related departments and useful information.

[1482] Specific examples

[1483] For example, if a user registers a task such as "The design of a new website needs to be completed by December 1st," the system will act as follows:

[1484] 1. The user's device sends task information to the server.

[1485] 2. The server receives the information and retrieves past performance data.

[1486] 3. The server uses AI to calculate the estimated man-hours and priority, and uses emotion analysis tools to analyze the user's emotional state.

[1487] 4. The server adjusts priorities based on emotional states and generates an optimal schedule.

[1488] 5. The server notifies the user of the generated schedule and simultaneously recommends related information.

[1489] This system allows users to significantly reduce the time they spend on task management and schedule adjustment, and also provides support that takes into account their emotional state.

[1490] Prompt Sentence Examples

[1491] For example, you can generate a description of the system above by providing the following prompt to a generative AI model:

[1492] "Please explain how a system works, where a user registers tasks and generates a schedule based on the estimated effort and priority of the tasks, as well as their emotional state."

[1493] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1494] Step 1:

[1495] The user inputs a new task and its due date. The input is done using a dedicated web application or mobile application. For example, the user inputs "Complete the design of a new website by December 1st." This input data (task content and due date) is collected by the device and sent to the next step.

[1496] Step 2:

[1497] The device sends the entered task and delivery date information to the server via API. Specifically, the device uses an HTTP POST request to send the input data to a specified endpoint on the server. The input data consists of a JSON object containing the task details and delivery date. Based on this request, the server receives the data and begins processing in the next step.

[1498] Step 3:

[1499] The server saves the received task and deadline information in the database. The server stores the information in a temporary buffer and then performs an INSERT operation on the database. This permanently saves the input data (task details and deadline) in the database. For example, the information "Complete the design of the new website by December 1st" is recorded in the database.

[1500] Step 4:

[1501] The server retrieves past performance data from a database. The server executes an SQL query to search and retrieve historical information about similar or related tasks that have been performed in the past. Past data related to the input data (new task) is retrieved. For example, the time and results of past website design tasks are retrieved.

[1502] Step 5:

[1503] The server inputs past performance data acquired into an AI algorithm to calculate the estimated effort and priority of a new task. An AI algorithm (e.g., TensorFlow model) is used to calculate the estimated effort and priority from past data. The input is performance data, and the output is result data such as "approximately 50 hours required" or "high priority."

[1504] Step 6:

[1505] In order for the server to recognize the user's emotional state, it inputs data collected from the user's device (e.g., facial recognition data, voice data) into an emotion analysis tool. Using an emotion analysis tool (e.g., OpenFace or Google Cloud Speech-to-Text API), it analyzes the user's facial expressions and tone of voice, and outputs the user's emotional state (e.g., the user is feeling stressed).

[1506] Step 7:

[1507] The server adjusts the priorities using emotion analysis methods and AI algorithms. Based on the results of the emotion analysis, the AI ​​adjusts the initial priorities. For example, if the user is feeling stressed, it will lower the priority and prioritize less demanding tasks. The input is the emotional state and the initial priorities, and the output is the adjusted priorities.

[1508] Step 8:

[1509] The server retrieves the user's current schedule information. It uses Dell's Google Calendar API and / or Outlook Calendar API to retrieve the user's current schedule and saves it as current schedule data. This data includes existing meetings and scheduled tasks.

[1510] Step 9:

[1511] The server considers the estimated effort, priority, and the user's emotional state of the task, and uses an AI algorithm to generate the optimal start and end dates and times for the task. It then applies a schedule generation algorithm to calculate the optimal schedule. The input is task information, emotional state, and current schedule data, and the output is the optimal start and end dates and times for the task.

[1512] Step 10:

[1513] The server sends the generated schedule information to the user's device and notifies them. The server notifies the user of the optimal schedule via push notification or email. The user receives the start and end dates and times of specific tasks.

[1514] Step 11:

[1515] The server analyzes past performance data and identifies related departments and useful information. It identifies related departments and available materials based on past data. For example, it identifies "people from the design department were involved in similar tasks."

[1516] Step 12:

[1517] The server sends the identified relevant information to the user's device and notifies them. The server then provides specific advice such as, "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials." The input is the relevant information, and the output is the notification content to the user.

[1518] (Application example 2)

[1519] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1520] Conventional task management systems do not adjust schedules taking into account the user's emotional state, which means they are unable to reduce user stress and excessive workloads. Furthermore, while appropriate task allocation and shift adjustment according to the emotional state of staff members is important in daily operations at brick-and-mortar stores, no efficient method for achieving this has been provided.

[1521] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for the user to register tasks and deadlines, a means for the server to record the task and deadline information received from the input means, a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks, a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks, a means for the server to notify the user of the generated schedule, a means for the server to recommend relevant departments and useful information to the user, a means for the server to acquire the user's emotional state and adjust task priorities based on the emotional state, and a means for the server to suggest relaxation methods to the user based on the emotional state. This enables efficient task management and schedule adjustment that takes the user's emotional state into consideration.

[1522] "Input means for users to register tasks and deadlines" refers to a device or interface that users use to input new tasks and their deadlines into the system.

[1523] The "means for recording the task and deadline information received by the server from the input means" refers to a function in which the server stores information on the task and deadline sent from the user in a database.

[1524] "Means for the server to acquire past performance data and calculate the estimated man-hours and priority associated with the task" refers to an algorithm or function that allows the server to collect data on past related tasks and, based on that data, calculate the required time and execution priority of a new task.

[1525] "Means for the server to obtain the user's current schedule and generate an optimal schedule for the task" refers to the function by which the server obtains the user's current schedule information and determines the optimal execution time for the new task based on that information.

[1526] The "means by which the server notifies the user of the generated schedule" refers to a communication means or interface for notifying the user of the schedule information generated by the server.

[1527] "Means for the server to recommend related departments and useful information to the user" refers to a function for the server to recommend departments and useful information related to a task to the user.

[1528] "Means for the server to acquire the user's emotional state and adjust task priorities based on that emotional state" refers to a function in which the server collects user emotional data and changes the order in which tasks are executed based on that data.

[1529] "Means for the server to suggest relaxation methods to the user based on the emotional state" refers to a function in which the server takes into account the emotional state of the user and suggests appropriate relaxation methods or breaks.

[1530] This invention relates to a generative AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state, once the user registers tasks and deadlines. This system is designed to efficiently manage staff and adjust schedules in physical stores, and consists of the following main components:

[1531] User input method

[1532] A user uses an input device such as a smartphone or tablet to register a new task and its due date. For example, consider the case where a store staff member registers a task such as "Complete product inventory check by December 1st."

[1533] A means of recording task information

[1534] The server receives task and delivery date information from the user and records it in the database. The task information is sent to the server via API and immediately saved. For example, an "inventory check task" and its "delivery date information" are recorded in the database.

[1535] Acquisition and analysis of performance data

[1536] The server retrieves past performance data from the database and calculates the estimated effort and priority associated with a new task using an AI algorithm. For example, based on data from similar past tasks, it might calculate that "inventory checks will take approximately 20 hours and have a medium priority."

[1537] Schedule optimization

[1538] The server retrieves the user's current schedule information from a calendar app or database, and generates an optimal schedule for new tasks based on this information. The task priority and effort are taken into account. For example, "An inventory check task is added to the schedule, and the start and end times are set."

[1539] Schedule notification method

[1540] The server notifies the user of the generated schedule via push notification, email notification, etc. For example, "A new schedule is sent to the staff member's smartphone."

[1541] Related information recommendations

[1542] The server analyzes past performance data, identifies relevant departments and useful information, and makes recommendations to users. This includes the server recommending specific personnel and related materials. For example, "recommending materials from the design department and project materials from the previous inventory check."

[1543] Acquiring and analyzing emotional states

[1544] The server acquires the user's emotional state and analyzes it using an emotion engine. Facial recognition and voice analysis are used to acquire emotions. For example, if a staff member is feeling stressed, the server will detect this.

[1545] Adjusting task priorities

[1546] The server adjusts task priorities based on the emotional state of the staff member. For example, if the staff member's stress level is high, the server will lower the priority of the task.

[1547] Suggestions for relaxation methods

[1548] The server suggests relaxation methods to users based on their emotional state, for example, "to staff who are feeling stressed, it suggests break times and relaxation methods."

[1549] As a concrete example, consider the following scenario.

[1550] A store staff member registers a task to "check product inventory by December 1st."

[1551] Based on past inventory confirmation data, the server calculates the required man-hours as "20 hours" and determines the priority to be "medium."

[1552] Additionally, an emotion engine is used to analyze the emotional state of staff, and if stress levels are high, the priority is adjusted to "low."

[1553] The optimal schedule is generated and notified to store staff's smartphones.

[1554] Suggestions for rest and relaxation will be made as needed.

[1555] Example prompt sentence:

[1556] "A user has registered the task 'Check product inventory by December 1st.' Based on past performance data, the required man-hours are calculated to be 20 hours, and the priority is medium. Please generate the optimal schedule taking into account the user's emotional state."

[1557] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1558] Step 1:

[1559] The user uses an input means for registering tasks and deadlines to input task information. The input is made using a smartphone or tablet, and includes the task name, deadline, and other detailed information. For example, the user registers a task such as "Check product inventory by December 1st."

[1560] Input: Task name, due date, details

[1561] Data processing: structuring task information

[1562] Output: Structured task information

[1563] Step 2:

[1564] The device sends the entered task and deadline information to the server, which then sends this information via API and reaches the server in real time.

[1565] Input: Structured task information

[1566] Data processing: Data transfer to server via API

[1567] Output: Task information saved on the server

[1568] Step 3:

[1569] The server stores the received task and deadline information in a database, which records the user ID, task name, and deadline.

[1570] Input: Task information received by the server

[1571] Data processing: storing information in a database

[1572] Output: Task information stored in the database

[1573] Step 4:

[1574] The server retrieves historical performance data. This data comes directly from the database and includes information such as effort and priority of previously related tasks.

[1575] Input: User ID

[1576] Data processing: Extraction of past performance data

[1577] Output: Past performance data

[1578] Step 5:

[1579] The server calculates the estimated man-hours and priority of new tasks based on past performance data. Here, an AI algorithm is used to analyze the data. For example, based on past data on inventory check tasks, it calculates that "20 hours will be required," and the priority is also determined.

[1580] Input: Past performance data, task information

[1581] Data processing: Analysis using AI algorithms

[1582] Output: Estimated man-hours, priority

[1583] Step 6:

[1584] The server retrieves the user's current schedule. This can be done from a calendar app or a database.

[1585] Input: User ID

[1586] Data processing: Getting the current schedule

[1587] Output: Current schedule information

[1588] Step 7:

[1589] The server generates an optimal schedule for the new task, determining when to execute the task based on the user's current schedule and the newly calculated estimated effort and priority.

[1590] Input: Current schedule information, estimated man-hours, priority

[1591] Data processing: generating optimal schedules

[1592] Output: New task schedule

[1593] Step 8:

[1594] The server notifies the user's device of the generated schedule. The device receives the notification and notifies the user. Notifications can be sent via push notifications or email.

[1595] Input: New task schedule

[1596] Data Processing: Notification Format

[1597] Output: Schedule notification to user

[1598] Step 9:

[1599] The server recommends relevant departments and useful information to the user, analyzes past performance data to identify relevant departments and materials, and notifies the user.

[1600] Input: Past performance data

[1601] Data processing: analysis of relevant information

[1602] Output: Related information recommendations

[1603] Step 10:

[1604] The server acquires the user's emotional state and analyzes it with an emotion engine, using facial recognition and voice analysis to determine stress levels, etc.

[1605] Input: User emotion data

[1606] Data processing: Analysis using emotion engine

[1607] Output: Emotional state

[1608] Step 11:

[1609] The server adjusts the priority of tasks based on the user's emotional state. For example, if the user's stress level is high, the server lowers the priority to reduce the burden.

[1610] Input: Emotional state, task information

[1611] Data processing: Adjusting task priority

[1612] Output: Adjusted task priorities

[1613] Step 12:

[1614] The server suggests relaxation methods to the user based on their emotional state, including suggestions for break times and relaxation methods.

[1615] Input: Emotional state

[1616] Data processing: Deciding on relaxation method

[1617] Output: Relaxation suggestions

[1618] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1619] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1620] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1621] [Fourth embodiment]

[1622] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1623] 7, a 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.

[1624] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1625] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1626] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1627] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1628] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1629] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1630] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1631] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1632] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1633] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1634] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1635] This invention is a generation AI system that calculates estimated man-hours and priorities based on past performance data and adjusts schedules when users register tasks and deadlines.

[1636] System Overview

[1637] The system consists of the following main components:

[1638] 1. User Input Method

[1639] 2. Server data reception and storage function

[1640] 3. Server past performance data acquisition function

[1641] 4. AI algorithms that calculate effort and priority

[1642] 5. User schedule adjustment function

[1643] 6. Related information recommendation function

[1644] Program processing overview

[1645] Registering tasks and deadlines

[1646] A user registers a task

[1647] The user inputs a new task and its due date through their own device. For example, they can register "Complete the design of the new website by December 1st."

[1648] The device sends task information to the server

[1649] The user's device sends the input task and delivery date information to the server. Specifically, the input information is sent to the server's endpoint via the API.

[1650] Data recording and acquisition

[1651] The server receives and stores task information

[1652] The server stores the task and deadline information received from the device in a database. For example, it records data such as "Complete the design of the new website by December 1st."

[1653] The server acquires past performance data

[1654] The server retrieves past performance data related to the registered task from the database, specifically by filtering performance data of similar website design tasks that have been completed in the past.

[1655] Calculating effort and priority

[1656] The server uses AI to calculate the man-hours and priority

[1657] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority of the new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[1658] Schedule adjustment

[1659] The server retrieves the user's current schedule

[1660] The server retrieves the user's current schedule from a database and, if necessary, may also connect with the calendar app used by the user.

[1661] The server proposes the optimal schedule

[1662] The server will suggest start and end dates for the new task based on the effort and priority of the task and the user's current schedule. For example, it might suggest that the best time to complete the new website design task is between November 20th and November 30th.

[1663] The server notifies the user of the proposed schedule

[1664] The server transmits the proposed schedule information to the user's terminal, which is notified of the proposed schedule.

[1665] Related information recommendations

[1666] The server analyzes and obtains relevant information

[1667] The server analyzes past performance data and identifies related departments and reference materials. For example, it identifies information such as "Yamada from the design department was involved in a similar task."

[1668] The server recommends related information to the user

[1669] Based on the analysis results, the server sends relevant information to the user's device and makes recommendations. The user will receive a notification such as, "It would be a good idea to contact Yamada-san, the person in charge. Also, please refer to the materials from the previous project."

[1670] Specific examples

[1671] When a user registers a task such as "Create a new website design and have it completed by December 1st," the system works as follows:

[1672] 1. The device sends task information to the server.

[1673] 2. The server receives and stores the information and obtains past performance data.

[1674] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[1675] 4. The server identifies relevant information (e.g., people involved in past projects and reference materials) and recommends it to the user.

[1676] This system allows business people to significantly reduce the time they spend on task management and scheduling, allowing them to focus on more creative work.

[1677] The processing flow will be explained below.

[1678] Step 1:

[1679] The user inputs the task and its due date. The user inputs task information such as "Complete the design of the new website by December 1st" from their own device.

[1680] Step 2:

[1681] The device sends task information to the server. The user's device sends the entered task and deadline information to the server's endpoint using the API.

[1682] Step 3:

[1683] The server receives the task information and stores it in a database. Specifically, the server records information such as "Complete the design of the new website by December 1st" in the database.

[1684] Step 4:

[1685] The server obtains past performance data. The server obtains past performance data related to the registered task from the database by filtering.

[1686] Step 5:

[1687] The server uses AI to calculate the estimated man-hours and priority. Based on the acquired performance data, the server uses an AI algorithm to calculate the estimated man-hours and priority of a new task. For example, the AI ​​might determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[1688] Step 6:

[1689] The server retrieves the user's current schedule. The server retrieves the user's existing schedule data from a database or a linked calendar app.

[1690] Step 7:

[1691] The server generates an optimal schedule. Taking into account the man-hours and priority of the new task and the user's current schedule, the server calculates the start and end dates and times of the task and generates an optimal schedule. For example, it may determine that "it is optimal to assign the new website design task between November 20th and November 30th."

[1692] Step 8:

[1693] The server notifies the user of the generated schedule. The server transmits the generated schedule information to the user's terminal and notifies the user.

[1694] Step 9:

[1695] The server analyzes and acquires related information. Based on past performance data, the server identifies related departments and reference materials. For example, it identifies information such as "Yamada from the design department was involved in a similar task."

[1696] Step 10:

[1697] The server recommends related information to the user. The server then sends the identified related information to the user's device and notifies them. For example, the user might receive a notification such as, "It would be a good idea to contact Mr. Yamada, the person in charge. Also, please refer to the materials from the previous project."

[1698] Through these steps, the system efficiently supports users in task management and schedule adjustment, providing an environment in which business people can concentrate on creative work.

[1699] Example 1

[1700] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1701] In today's business environment, efficient management of multiple tasks and their deadlines is required. However, doing this manually is time-consuming, labor-intensive, and inefficient. Furthermore, there is a lack of systems that can calculate appropriate man-hours and priorities based on performance data from similar past tasks and propose optimal schedules. Furthermore, it is difficult to provide relevant information and reference materials in a timely manner for task completion.

[1702] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1703] In this invention, the server includes: an input means for a user to register tasks and deadlines; a means for the server to record the task and deadline information received from the input means; a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; a means for the server to recommend relevant departments and useful information to the user; a means for the server to store and filter past performance data using a database; a means for a terminal to communicate with the server via an API; a means for the server to acquire the user's schedule in cooperation with an external calendar service; and a means for the server to calculate estimated man-hours and priorities using an AI algorithm. This allows users to efficiently manage tasks and adjust their schedules, and quickly obtain necessary related information.

[1704] A "user" is a person or organization that uses this system to input tasks and their due dates, and receives schedule management and related information.

[1705] The "server" is a computer system that records task and delivery date information received from users, retrieves past performance data from a database, calculates labor hours and priorities using AI algorithms, generates optimal schedules, and provides notification and recommendation functions.

[1706] "Input means" refers to an interface or device that allows a user to register task and delivery date information, and examples include a keyboard, a touch screen, and a dedicated application.

[1707] The "recording means" is a function for storing the task and deadline information received by the server in a database, and also checks the consistency of the data.

[1708] "Performance data" is data relating to similar tasks that have been completed in the past, and includes information such as the task name, man-hours, completion date, and people involved.

[1709] "Estimated man-hours" is an estimate of the total time required to complete a task, calculated by an AI algorithm based on past performance data.

[1710] "Priority" is an assessment of the importance or urgency of a particular task, and indicates how much priority it needs to have compared to other tasks.

[1711] A "schedule" is a specific plan of the start and end dates of a task and the work to be done during that time.

[1712] "Notification means" refers to a method for notifying the user of the schedule and related information generated by the server, and specifically includes push notifications, emails, display messages, etc.

[1713] The "recommendation function" is a function in which the server analyzes the database and suggests information and reference materials that may be useful to the user.

[1714] A "database" is a structured data repository that stores received task information and past performance data and allows for searching and retrieval as needed.

[1715] An "API" is an interface for exchanging data between a terminal and a server, and communication is carried out according to a standardized protocol.

[1716] An "external calendar service" is a calendar application provided by a third party, such as Google Calendar or Outlook Calendar, that the server works with to obtain the user's current schedule.

[1717] An "AI algorithm" is a machine learning model that analyzes past data and estimates the effort and priority of new tasks.

[1718] MODE FOR CARRYING OUT THE INVENTION

[1719] This invention is an AI generation system for managing tasks and deadlines, which uses past performance data based on tasks entered by a user, calculates estimated man-hours and priorities, and provides optimal schedules and related information. Specific embodiments of the system are described below.

[1720] System Overview

[1721] The system mainly consists of the following components:

[1722] 1. User Input Method

[1723] 2. Server data reception and storage function

[1724] 3. Server past performance data acquisition function

[1725] 4. AI algorithms that calculate effort and priority

[1726] 5. User schedule adjustment function

[1727] 6. Related information recommendation function

[1728] Hardware and software used

[1729] 1. User's device (PC, tablet, smartphone, etc.)

[1730] 2. Servers (including cloud and on-premise environments)

[1731] 3. Database (MySQL, PostgreSQL, etc.)

[1732] 4. AI algorithms (TensorFlow, PyTorch, etc.)

[1733] 5. Calendar services (Google Calendar, Outlook Calendar, etc.)

[1734] Program processing overview

[1735] Registering tasks and deadlines

[1736] Users use their devices to input new tasks and their due dates. The input data is sent to the server via the API. For example, a user might input "Complete the design of the new website by December 1st."

[1737] Data recording and acquisition

[1738] The server receives the task and delivery date information sent from the terminal and stores it in a database.The server then retrieves past performance data from the database.Specifically, it filters and extracts past data related to similar tasks.

[1739] Calculating effort and priority

[1740] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority for new tasks. The AI ​​algorithm is built using TensorFlow and PyTorch and analyzes past data. The calculation results include, "Based on past performance, this task will require approximately 50 hours and is a high priority."

[1741] Schedule adjustment

[1742] The server obtains the user's current schedule. If necessary, it connects with an external calendar service to obtain schedule information. The server then generates an optimal schedule based on the effort and priority of the new task and the user's current schedule, and proposes it to the user. For example, it might suggest that "it would be best to proceed with the design of the new website from November 20th to November 30th."

[1743] Notifications and Recommendations

[1744] The server notifies the user of the generated schedule information. At the same time, the server analyzes past performance data, identifies related information (such as people involved in past projects and reference materials), and makes recommendations to the user.

[1745] Specific examples

[1746] For example, if a user registers a task such as "Create a new website design and have it completed by December 1st," the system will act as follows:

[1747] 1. The user enters the task and due date on the terminal.

[1748] 2. The device sends the input data to the server.

[1749] 3. The server receives the data and stores it in a database.

[1750] 4. The server retrieves performance data for similar tasks in the past.

[1751] 5. The server uses AI to calculate the estimated effort and priority of the new task.

[1752] 6. The server retrieves the user's current schedule.

[1753] 7. The server proposes an optimal schedule.

[1754] 8. The server notifies the user of the proposed schedule.

[1755] 9. The server analyzes and retrieves the relevant information.

[1756] 10. The server recommends relevant information to the user.

[1757] Prompt Sentence Examples

[1758] "We have a task to design a new website. The deadline is December 1st. Based on past performance data for similar tasks, please tell us the approximate man-hours and priority. Also, please suggest the optimal schedule."

[1759] Based on this prompt, the system provides the necessary information and suggests the optimal schedule for the user.

[1760] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1761] Step 1:

[1762] The user enters the task and due date

[1763] The user inputs a new task and its due date using an input form on the terminal or a dedicated application.

[1764] Input: Tasks and deadlines, such as "Complete new website design by December 1st."

[1765] Output: Task and due date data in text format.

[1766] Step 2:

[1767] The device sends the input data to the server

[1768] The terminal uses the API to send the task and deadline information entered by the user to the server.

[1769] Input: Task and due date data entered by the user.

[1770] Data processing: Convert data into JSON format.

[1771] Output: JSON data sent to the server's API endpoint.

[1772] Step 3:

[1773] The server receives the data and stores it in the database

[1774] The server receives the task and deadline data sent via the API and stores it in a database.

[1775] Input: Task and due date data received by the server in JSON format.

[1776] Data processing: Convert JSON data into structured data.

[1777] Output: Task and due date data stored in a database.

[1778] Step 4:

[1779] The server obtains performance data for similar tasks from the past.

[1780] The server searches the database for past performance data using a query and obtains data related to the registered task.

[1781] Input: Saved task and due date data.

[1782] Data processing: Use queries to search for similar tasks from a database.

[1783] Output: A list of historical performance data.

[1784] Step 5:

[1785] The server uses AI to calculate the estimated man-hours and priority of new tasks.

[1786] The server inputs past performance data acquired into an AI algorithm to calculate the estimated man-hours and priority of new tasks.

[1787] Input: A list of historical performance data.

[1788] Data Calculation: Data analysis using AI algorithms.

[1789] Output: Estimated effort and priority of the new task (e.g. "50 hours required, high priority").

[1790] Step 6:

[1791] The server retrieves the user's current schedule

[1792] The server contacts a database or external calendar service to retrieve the user's current schedule.

[1793] Input: User credentials and / or calendar service API key.

[1794] Data processing: Communication with external calendar services.

[1795] Output: Current schedule data.

[1796] Step 7:

[1797] The server proposes the optimal schedule for new tasks.

[1798] The server proposes optimal start and end dates based on the current schedule and the man-hours and priority of the new task.

[1799] Inputs: New task effort and priority, current schedule data.

[1800] Data calculation: Execution of the schedule generation algorithm.

[1801] Output: The optimal schedule for the new task (e.g., "November 20th to November 30th is optimal").

[1802] Step 8:

[1803] The server notifies the user of the generated schedule.

[1804] The server notifies the user's terminal of the proposed schedule.

[1805] Input: The optimal schedule for the new task.

[1806] Data processing: generating notification messages.

[1807] Output: Push notification or email to user device.

[1808] Step 9:

[1809] The server analyzes and retrieves the relevant information

[1810] The server analyzes past performance data and identifies relevant departments and useful information.

[1811] Input: Historical performance data.

[1812] Data operations: Running data analysis algorithms.

[1813] Output: A list of relevant information (e.g., "Yamada from the design department was involved").

[1814] Step 10:

[1815] The server recommends relevant information to the user

[1816] The server recommends related information to the user based on the analysis results.

[1817] Input: A list of relevant information.

[1818] Data calculation: Generate recommended messages.

[1819] Output: Recommendation notification to the user device (e.g., "We recommend contacting Yamada-san, the person in charge").

[1820] (Application example 1)

[1821] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1822] Logistics centers have numerous tasks (such as receiving, picking, and shipping goods), and each task requires strict delivery date management. However, there are not enough methods in place to properly manage and efficiently handle these numerous tasks and delivery dates. This can lead to reduced work efficiency, which can result in a deterioration in overall logistics performance. Furthermore, it is difficult for employees to correctly estimate the priority and approximate man-hours required for each task and set an optimal schedule. An appropriate system is needed to solve these problems.

[1823] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1824] In this invention, the server includes: an input means for a user to register tasks and delivery dates; a means for the server to record the task and delivery date information received from the input means; a means for the server to acquire past performance data and calculate approximate man-hours and priorities related to the tasks; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; a means for the server to recommend relevant departments and useful information to the user; a means for the logistics center to register logistics work, calculate approximate man-hours and priorities based on past performance data, and propose an optimal schedule; and a means for a logistics center employee to input task information using a smartphone or a head-mounted display and for the system to propose a schedule based on this information. This enables efficient management of each task and appropriate scheduling at the logistics center.

[1825] A "task" refers to a specific unit of work or processing carried out at a logistics center.

[1826] "Delivery date" refers to the deadline by which each task at a logistics center must be completed.

[1827] "Input means" refers to a device or interface that allows a user to register tasks and deadlines in the system.

[1828] "Server" refers to a computer system that records received task and delivery date information, and acquires, analyzes, and notifies data.

[1829] "Past performance data" refers to data relating to previous similar tasks, and is information used to calculate the estimated man-hours and priorities.

[1830] "Estimated effort" refers to a rough estimate of the time required to complete a task, calculated based on past performance data.

[1831] "Priority" refers to an indicator that shows the importance and urgency of a task.

[1832] A "schedule" refers to a work plan, including start and finish times and dates for tasks.

[1833] "Recommendation" refers to suggesting relevant information to a user.

[1834] A "logistics center" refers to a facility where logistics operations such as receiving, storing, picking, and shipping goods are carried out.

[1835] "Smartphone" refers to a mobile phone-type information terminal used to input task information.

[1836] "Head-mounted display" refers to a head-mounted display device used to input task information.

[1837] System Overview

[1838] This invention is a system for efficient task management and scheduling in a logistics center. The system consists of the following main components:

[1839] 1. User Input Method

[1840] 2. Server data reception and storage function

[1841] 3. Server past performance data acquisition function

[1842] 4. AI algorithms that calculate effort and priority

[1843] 5. User schedule adjustment function

[1844] 6. Related information recommendation function

[1845] Program processing overview

[1846] Registering tasks and deadlines

[1847] A user registers a task

[1848] Users use a smartphone or head-mounted display (HMD) to register tasks and deadlines in the system. For example, they might enter "Complete product receiving work by December 1, 2023."

[1849] The device sends task information to the server

[1850] The registered information is sent from the terminal to the server. Specifically, task and delivery date information is sent to the server's endpoint via API.

[1851] Data recording and acquisition

[1852] The server receives and stores task information

[1853] The server records the received task and delivery date information in a database. For example, data such as "Complete product receiving work by December 1, 2023" is saved.

[1854] The server acquires past performance data

[1855] The server then retrieves from the database past performance data related to the registered task, for example, data on similar past "warehouse-receiving" tasks.

[1856] Calculating effort and priority

[1857] The server uses AI to calculate the man-hours and priority

[1858] The server inputs the acquired performance data into an AI algorithm to calculate the estimated man-hours and priority. This uses data such as past work time and importance. For example, based on past data, it can calculate that "warehousing work takes an average of 5 hours and has high priority."

[1859] Schedule adjustment

[1860] The server retrieves the user's current schedule

[1861] The server then retrieves the user's current schedule, which may involve retrieving the user's calendar information from a database.

[1862] The server proposes the optimal schedule

[1863] The server will propose an optimal schedule based on the man-hours and priority of the new task and the current schedule. For example, it may suggest that "it would be optimal to carry out the inventory work from November 20, 2023 to November 30, 2023."

[1864] The server notifies the user of the proposed schedule

[1865] The proposed schedule information is sent from the server to the user's terminal and notified.

[1866] Related information recommendations

[1867] The server analyzes and obtains relevant information

[1868] The server analyzes past performance data and identifies relevant information, such as the people involved in the previous task.

[1869] The server recommends related information to the user

[1870] Based on the analysis results, the server sends relevant information to the user's device as a recommendation, such as "We recommend contacting the employee who handled the previous task."

[1871] Specific examples

[1872] When a user registers a task such as "Complete product receiving work by December 1, 2023," the system works as follows:

[1873] 1. The device sends task information to the server.

[1874] 2. The server receives and stores the information and obtains past performance data.

[1875] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[1876] 4. The server identifies relevant information (e.g., people involved in past projects and reference materials) and recommends it to the user.

[1877] Example prompt sentence:

[1878] When a user registers a task such as "Complete product receiving work by December 1, 2023," the system works as follows:

[1879] 1. The device sends task information to the server.

[1880] 2. The server receives and stores the information and obtains past performance data.

[1881] 3. The server uses AI to calculate estimated man-hours and priorities, and proposes the optimal schedule.

[1882] 4. The server identifies relevant information and recommends it to the user.

[1883] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1884] Step 1:

[1885] Registering tasks and deadlines

[1886] The user uses a smartphone or head-mounted display (HMD) to input a new task, "Goods Receipt Work," and its due date, "December 1, 2023." The information entered by the user is sent from the device to the server via API. The input data includes the "Task Name" and "Delivery Date" fields. The server then receives the task information and is ready to start the system.

[1887] Step 2:

[1888] Data recording and acquisition

[1889] The server receives the task and delivery date information sent from the terminal and stores this information in a database. Next, the server retrieves past performance data related to the registered task from the database. Specifically, it filters and retrieves data related to "warehouse entry work" that was performed in the past. This allows the server to prepare the original data, which will serve as the basis for the next calculation.

[1890] Step 3:

[1891] Calculating effort and priority

[1892] The server inputs the acquired performance data into an AI algorithm. The server processes the data by inputting data such as past work time and importance into the AI, and calculates the approximate man-hours and priority of the task. For example, it obtains an output such as "warehouse entry work takes an average of 5 hours and is a high priority." This allows the server to perform a detailed evaluation of the task.

[1893] Step 4:

[1894] Schedule adjustment

[1895] The server retrieves the user's current schedule data from the database. The server generates an optimal schedule for tasks based on the man-hours and priorities calculated by the AI ​​and the current schedule. For example, it suggests that a new inventory task should be optimally carried out from November 20, 2023 to November 30, 2023. This information is intended for use by the user.

[1896] Step 5:

[1897] Proposal schedule notification

[1898] The server sends the generated optimal schedule information to the user's device, where a notification is displayed prompting the user to confirm the new schedule. This allows the user to confirm the proposed schedule and prepare to put it into action.

[1899] Step 6:

[1900] Related information recommendations

[1901] The server analyzes past performance data and identifies relevant information, such as "the person involved in the previous inventory work" and "reference materials." Based on the results of this analysis, the server notifies the user's device of related information as recommendations. The user can use the presented information to help them complete their tasks.

[1902] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1903] This invention relates to a generative AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state when the user registers tasks and deadlines. This system allows users to efficiently manage tasks and adjust schedules, and also uses an emotion engine to provide support that is sensitive to the user's emotional state.

[1904] System Overview

[1905] The system consists of the following main components:

[1906] 1. User Input Method

[1907] 2. Server data reception and storage function

[1908] 3. Server past performance data acquisition function

[1909] 4. Emotion Engine

[1910] 5. AI algorithms to calculate effort and priority

[1911] 6. FUZA's schedule adjustment function

[1912] 7. Recommendation function for related departments and useful information

[1913] Program processing overview

[1914] Registering tasks and deadlines

[1915] A user registers a task

[1916] The user inputs a new task and its due date using their own terminal. For example, they may register "Complete the design of the new website by December 1st."

[1917] The device sends task information to the server

[1918] The user's terminal transmits the input task and deadline information to the server via the API.

[1919] Data recording and acquisition

[1920] The server receives and stores task information

[1921] The server stores the task and deadline information received from the terminal in a database. For example, it records information such as "Complete the design of the new website by December 1st."

[1922] The server acquires past performance data

[1923] The server retrieves past performance data related to the registered task from the database.

[1924] Calculating effort and priority

[1925] The server uses AI to calculate the man-hours and priority

[1926] The server inputs the acquired performance data into an AI algorithm to calculate the approximate man-hours and priority of a new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[1927] Emotion Engine

[1928] The server recognizes the user's emotions through the emotion engine.

[1929] The server uses an emotion engine to analyze the user's emotional state based on data acquired from the user's device. For example, it uses facial recognition and voice analysis technology to determine whether the user is feeling stressed.

[1930] Priority adjustment based on emotion engine

[1931] The server adjusts the traditional priorities based on the user's emotional state, for example, prioritizing less demanding tasks if the user is feeling stressed.

[1932] Recommendations based on emotion engines

[1933] The server considers the user's emotional state and recommends appropriate resources and support information. For example, if the user is feeling stressed, it will suggest breaks and relaxation methods to reduce stress.

[1934] Schedule adjustment

[1935] The server retrieves the user's current schedule

[1936] The server retrieves the user's current schedule from a database or calendar app.

[1937] The server generates the optimal schedule

[1938] The server considers the effort, priority, and emotional state of the new task to generate optimal start and end dates and times for the task.

[1939] The server notifies the user of the proposed schedule

[1940] The server transmits the generated schedule information to the user's terminal and notifies the user.

[1941] Related information recommendations

[1942] The server analyzes and obtains relevant information

[1943] Analyze past performance data and identify related departments and materials. For example, identify "people from the design department were involved in similar tasks."

[1944] The server recommends related information to the user

[1945] The identified relevant information is sent to the user's device and a notification is sent, such as "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials."

[1946] Specific examples

[1947] When a user registers a task such as "Create a new website design and have it completed by December 1st," the system works as follows:

[1948] 1. The device sends task information to the server.

[1949] 2. The server receives the information and retrieves past performance data.

[1950] 3. The server uses AI to calculate the estimated man-hours and priority, and uses an emotion engine to analyze the user's emotional state.

[1951] 4. The server adjusts priorities based on emotional states and generates an optimal schedule.

[1952] 5. The server notifies the user of the generated schedule and simultaneously recommends related information.

[1953] This system allows users to significantly reduce the time they spend on task management and schedule adjustment, and also provides support that takes into account their emotional state.

[1954] The processing flow will be explained below.

[1955] Step 1:

[1956] The user inputs a task and its due date. For example, the user inputs "Complete the design of the new website by December 1st" on their device.

[1957] Step 2:

[1958] The device sends task information to the server. The user's device sends the entered task and deadline information to the server's endpoint via API.

[1959] Step 3:

[1960] The server receives the task information and stores it in a database. The server records information such as "Complete the design of the new website by December 1st" in the database.

[1961] Step 4:

[1962] The server obtains past performance data. The server obtains past performance data related to the registered task from the database by filtering.

[1963] Step 5:

[1964] The server uses AI to calculate the man-hours and priority. The server inputs the acquired performance data into an AI algorithm and calculates the approximate man-hours and priority of a new task. For example, the AI ​​may determine that "based on past performance, this task will require approximately 50 hours and is a high priority."

[1965] Step 6:

[1966] The server recognizes the user's emotions through an emotion engine. The server uses data acquired from the user's device (face recognition, voice analysis, etc.) to analyze the user's stress level and emotions. For example, it determines that the user is feeling stressed.

[1967] Step 7:

[1968] The server adjusts the priority based on the emotion engine. If the user's emotional state is stressful, the server increases the priority of less burdensome tasks. For example, the server may adjust the priority of a task by saying, "The user is feeling stressed, so set the priority of this task low."

[1969] Step 8:

[1970] The server retrieves the user's current schedule. The server retrieves the user's current schedule data from a database or calendar application. For example, it retrieves information about existing meetings and breaks in the current schedule.

[1971] Step 9:

[1972] The server generates an optimal schedule. The server calculates the start and end dates and times of the tasks, taking into account the man-hours, priority, emotional state of the user, and the current schedule of the new tasks. For example, it determines that "it is optimal to assign the new website design task between November 20th and November 30th."

[1973] Step 10:

[1974] The server notifies the user of the generated schedule. The server sends the generated schedule information to the user's terminal and notifies the user. The user's terminal displays "The task is scheduled to start on November 20th and be completed on November 30th."

[1975] Step 11:

[1976] The server analyzes and acquires related information. Based on past performance data, the server identifies related departments and reference materials. For example, it identifies that "a person in the design department was involved in a similar task."

[1977] Step 12:

[1978] The server recommends related information to the user. The server then sends the identified related information to the user's device and notifies them. For example, the user may receive a notification such as, "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials."

[1979] This detailed processing step allows the system to efficiently support users in task management and schedule adjustment, and also provides support that takes into account their emotional state.

[1980] Example 2

[1981] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1982] Conventional task management systems lack specific support for users to efficiently manage their tasks. Furthermore, because they prioritize tasks without taking into account the user's emotional state, there is a risk of increasing stress and strain on the user. Furthermore, because they lack a mechanism for fully utilizing past performance data, it is difficult to estimate the amount of work required for a task or calculate an appropriate schedule. This makes it difficult for users to obtain appropriate resources and information, hindering efficient task completion.

[1983] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: an input means for a user to register tasks and deadlines; a means for the server to record task and deadline information received from the input means; a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks; an emotion analysis means for the server to recognize the user's emotional state; a means for the server to adjust priorities using the emotion analysis means and an AI algorithm; a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks; a means for the server to notify the user of the generated schedule; and a means for the server to recommend relevant departments and useful information to the user. This enables users to efficiently manage tasks and adjust their schedules and receive support that takes their emotional state into consideration, thereby reducing stress and burden. It also enables users to quickly obtain appropriate resources and related information.

[1984] A "user" is an entity that uses this system to register tasks and deadlines.

[1985] "Input means" refers to a device or software that allows a user to input information about tasks and deadlines.

[1986] The "server" is a central device that processes and stores task information and performance data, and runs AI algorithms and emotion analysis.

[1987] A "task" refers to the specific details of the work or activity that a user must perform.

[1988] "Delivery date" refers to the estimated date and time for completing a task set by the user.

[1989] The "recording means" is a device or software that stores the received task and delivery date information in a storage device such as a database.

[1990] "Past performance data" refers to historical information about similar or related tasks previously performed.

[1991] "Estimated effort" refers to an estimate of the time required to complete a particular task.

[1992] "Priority" refers to an evaluation of importance or urgency for determining the execution order among multiple tasks.

[1993] "Emotional state" refers to the user's psychological and emotional state, including stress level, satisfaction, etc.

[1994] An "emotion analysis means" is a device or software that uses facial recognition or voice analysis techniques to analyze a user's emotional state.

[1995] An "AI algorithm" is a computational method that uses machine learning and data analysis to derive optimal results.

[1996] A "schedule generation means" is a device or software that determines the optimal start and end dates and times for tasks based on estimated man-hours, priority, and the user's emotional state.

[1997] The "notification means" is a device or software that notifies the user of the generated schedule and recommendation information.

[1998] A "recommendation means" is a device or software that selects and suggests resources and information that are useful to the user.

[1999] "Relevant departments" refers to other departments or agencies involved in the performance of the task.

[2000] "Useful information" refers to past materials and knowledge that are useful in completing a task.

[2001] This invention is a generation AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state when the user registers tasks and deadlines. The purpose of this system is to support users in efficiently managing tasks and adjusting schedules. Furthermore, it uses emotional analysis means to provide support that is sensitive to the user's emotional state.

[2002] System configuration

[2003] The system consists of the following main components:

[2004] 1. User Input Method

[2005] 2. Server data reception and storage function

[2006] 3. Server past performance data acquisition function

[2007] 4. Emotion analysis method

[2008] 5. AI algorithms to calculate effort and priority

[2009] 6. User schedule adjustment function

[2010] 7. Recommendation function for related departments and useful information

[2011] Hardware and Software Configuration

[2012] Users input data via devices such as PCs and smartphones. Task and delivery date information is entered via dedicated web and mobile applications. On the server side, multiple software modules operate to receive and store data, import performance data, run AI algorithms, and perform sentiment analysis. These include database management systems (e.g., MySQL), AI modules (e.g., TensorFlow), and sentiment analysis tools (e.g., OpenFace and Google Cloud Speech-to-Text API).

[2013] Overview of program processing

[2014] When a user registers a task, the device sends the entered task and delivery date information to the server. The server receives the information and stores it in a database. The server then retrieves past performance data and applies an AI algorithm to calculate the estimated effort and priority of the new task. It then recognizes the user's emotional state through emotion analysis and adjusts task priorities accordingly. Finally, the server notifies the user of the generated optimal schedule and, if necessary, recommends related departments and useful information.

[2015] Specific examples

[2016] For example, if a user registers a task such as "The design of a new website needs to be completed by December 1st," the system will act as follows:

[2017] 1. The user's device sends task information to the server.

[2018] 2. The server receives the information and retrieves past performance data.

[2019] 3. The server uses AI to calculate the estimated man-hours and priority, and uses emotion analysis tools to analyze the user's emotional state.

[2020] 4. The server adjusts priorities based on emotional states and generates an optimal schedule.

[2021] 5. The server notifies the user of the generated schedule and simultaneously recommends related information.

[2022] This system allows users to significantly reduce the time they spend on task management and schedule adjustment, and also provides support that takes into account their emotional state.

[2023] Prompt Sentence Examples

[2024] For example, you can generate a description of the system above by providing the following prompt to a generative AI model:

[2025] "Please explain how a system works, where a user registers tasks and generates a schedule based on the estimated effort and priority of the tasks, as well as their emotional state."

[2026] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2027] Step 1:

[2028] The user inputs a new task and its due date. The input is done using a dedicated web application or mobile application. For example, the user inputs "Complete the design of a new website by December 1st." This input data (task content and due date) is collected by the device and sent to the next step.

[2029] Step 2:

[2030] The device sends the entered task and delivery date information to the server via API. Specifically, the device uses an HTTP POST request to send the input data to a specified endpoint on the server. The input data consists of a JSON object containing the task details and delivery date. Based on this request, the server receives the data and begins processing in the next step.

[2031] Step 3:

[2032] The server saves the received task and deadline information in the database. The server stores the information in a temporary buffer and then performs an INSERT operation on the database. This permanently saves the input data (task details and deadline) in the database. For example, the information "Complete the design of the new website by December 1st" is recorded in the database.

[2033] Step 4:

[2034] The server retrieves past performance data from a database. The server executes an SQL query to search and retrieve historical information about similar or related tasks that have been performed in the past. Past data related to the input data (new task) is retrieved. For example, the time and results of past website design tasks are retrieved.

[2035] Step 5:

[2036] The server inputs past performance data acquired into an AI algorithm to calculate the estimated effort and priority of a new task. An AI algorithm (e.g., TensorFlow model) is used to calculate the estimated effort and priority from past data. The input is performance data, and the output is result data such as "approximately 50 hours required" or "high priority."

[2037] Step 6:

[2038] In order for the server to recognize the user's emotional state, it inputs data collected from the user's device (e.g., facial recognition data, voice data) into an emotion analysis tool. Using an emotion analysis tool (e.g., OpenFace or Google Cloud Speech-to-Text API), it analyzes the user's facial expressions and tone of voice, and outputs the user's emotional state (e.g., the user is feeling stressed).

[2039] Step 7:

[2040] The server adjusts the priorities using emotion analysis methods and AI algorithms. Based on the results of the emotion analysis, the AI ​​adjusts the initial priorities. For example, if the user is feeling stressed, it will lower the priority and prioritize less demanding tasks. The input is the emotional state and the initial priorities, and the output is the adjusted priorities.

[2041] Step 8:

[2042] The server retrieves the user's current schedule information. It uses Dell's Google Calendar API and / or Outlook Calendar API to retrieve the user's current schedule and saves it as current schedule data. This data includes existing meetings and scheduled tasks.

[2043] Step 9:

[2044] The server considers the estimated effort, priority, and the user's emotional state of the task, and uses an AI algorithm to generate the optimal start and end dates and times for the task. It then applies a schedule generation algorithm to calculate the optimal schedule. The input is task information, emotional state, and current schedule data, and the output is the optimal start and end dates and times for the task.

[2045] Step 10:

[2046] The server sends the generated schedule information to the user's device and notifies them. The server notifies the user of the optimal schedule via push notification or email. The user receives the start and end dates and times of specific tasks.

[2047] Step 11:

[2048] The server analyzes past performance data and identifies related departments and useful information. It identifies related departments and available materials based on past data. For example, it identifies "people from the design department were involved in similar tasks."

[2049] Step 12:

[2050] The server sends the identified relevant information to the user's device and notifies them. The server then provides specific advice such as, "It would be a good idea to contact the person in charge. Also, please refer to the previous project materials." The input is the relevant information, and the output is the notification content to the user.

[2051] (Application example 2)

[2052] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2053] Conventional task management systems do not adjust schedules taking into account the user's emotional state, which means they are unable to reduce user stress and excessive workloads. Furthermore, while appropriate task allocation and shift adjustment according to the emotional state of staff members is important in daily operations at brick-and-mortar stores, no efficient method for achieving this has been provided.

[2054] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for the user to register tasks and deadlines, a means for the server to record the task and deadline information received from the input means, a means for the server to acquire past performance data and calculate estimated man-hours and priorities related to the tasks, a means for the server to acquire the user's current schedule and generate an optimal schedule for the tasks, a means for the server to notify the user of the generated schedule, a means for the server to recommend relevant departments and useful information to the user, a means for the server to acquire the user's emotional state and adjust task priorities based on the emotional state, and a means for the server to suggest relaxation methods to the user based on the emotional state. This enables efficient task management and schedule adjustment that takes the user's emotional state into consideration.

[2055] "Input means for users to register tasks and deadlines" refers to a device or interface that users use to input new tasks and their deadlines into the system.

[2056] The "means for recording the task and deadline information received by the server from the input means" refers to a function in which the server stores information on the task and deadline sent from the user in a database.

[2057] "Means for the server to acquire past performance data and calculate the estimated man-hours and priority associated with the task" refers to an algorithm or function that allows the server to collect data on past related tasks and, based on that data, calculate the required time and execution priority of a new task.

[2058] "Means for the server to obtain the user's current schedule and generate an optimal schedule for the task" refers to the function by which the server obtains the user's current schedule information and determines the optimal execution time for the new task based on that information.

[2059] The "means by which the server notifies the user of the generated schedule" refers to a communication means or interface for notifying the user of the schedule information generated by the server.

[2060] "Means for the server to recommend related departments and useful information to the user" refers to a function for the server to recommend departments and useful information related to a task to the user.

[2061] "Means for the server to acquire the user's emotional state and adjust task priorities based on that emotional state" refers to a function in which the server collects user emotional data and changes the order in which tasks are executed based on that data.

[2062] "Means for the server to suggest relaxation methods to the user based on the emotional state" refers to a function in which the server takes into account the emotional state of the user and suggests appropriate relaxation methods or breaks.

[2063] This invention relates to a generative AI system that adjusts schedules by calculating estimated man-hours and priorities based on past performance data and the user's emotional state, once the user registers tasks and deadlines. This system is designed to efficiently manage staff and adjust schedules in physical stores, and consists of the following main components:

[2064] User input method

[2065] A user uses an input device such as a smartphone or tablet to register a new task and its due date. For example, consider the case where a store staff member registers a task such as "Complete product inventory check by December 1st."

[2066] A means of recording task information

[2067] The server receives task and delivery date information from the user and records it in the database. The task information is sent to the server via API and immediately saved. For example, an "inventory check task" and its "delivery date information" are recorded in the database.

[2068] Acquisition and analysis of performance data

[2069] The server retrieves past performance data from the database and calculates the estimated effort and priority associated with a new task using an AI algorithm. For example, based on data from similar past tasks, it might calculate that "inventory checks will take approximately 20 hours and have a medium priority."

[2070] Schedule optimization

[2071] The server retrieves the user's current schedule information from a calendar app or database, and generates an optimal schedule for new tasks based on this information. The task priority and effort are taken into account. For example, "An inventory check task is added to the schedule, and the start and end times are set."

[2072] Schedule notification method

[2073] The server notifies the user of the generated schedule via push notification, email notification, etc. For example, "A new schedule is sent to the staff member's smartphone."

[2074] Related information recommendations

[2075] The server analyzes past performance data, identifies relevant departments and useful information, and makes recommendations to users. This includes the server recommending specific personnel and related materials. For example, "recommending materials from the design department and project materials from the previous inventory check."

[2076] Acquiring and analyzing emotional states

[2077] The server acquires the user's emotional state and analyzes it using an emotion engine. Facial recognition and voice analysis are used to acquire emotions. For example, if a staff member is feeling stressed, the server will detect this.

[2078] Adjusting task priorities

[2079] The server adjusts task priorities based on the emotional state of the staff member. For example, if the staff member's stress level is high, the server will lower the priority of the task.

[2080] Suggestions for relaxation methods

[2081] The server suggests relaxation methods to users based on their emotional state, for example, "to staff who are feeling stressed, it suggests break times and relaxation methods."

[2082] As a concrete example, consider the following scenario.

[2083] A store staff member registers a task to "check product inventory by December 1st."

[2084] Based on past inventory confirmation data, the server calculates the required man-hours as "20 hours" and determines the priority to be "medium."

[2085] Additionally, an emotion engine is used to analyze the emotional state of staff, and if stress levels are high, the priority is adjusted to "low."

[2086] The optimal schedule is generated and notified to store staff's smartphones.

[2087] Suggestions for rest and relaxation will be made as needed.

[2088] Example prompt sentence:

[2089] "A user has registered the task 'Check product inventory by December 1st.' Based on past performance data, the required man-hours are calculated to be 20 hours, and the priority is medium. Please generate the optimal schedule taking into account the user's emotional state."

[2090] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2091] Step 1:

[2092] The user uses an input means for registering tasks and deadlines to input task information. The input is made using a smartphone or tablet, and includes the task name, deadline, and other detailed information. For example, the user registers a task such as "Check product inventory by December 1st."

[2093] Input: Task name, due date, details

[2094] Data processing: structuring task information

[2095] Output: Structured task information

[2096] Step 2:

[2097] The device sends the entered task and deadline information to the server, which then sends this information via API and reaches the server in real time.

[2098] Input: Structured task information

[2099] Data processing: Data transfer to server via API

[2100] Output: Task information saved on the server

[2101] Step 3:

[2102] The server stores the received task and deadline information in a database, which records the user ID, task name, and deadline.

[2103] Input: Task information received by the server

[2104] Data processing: storing information in a database

[2105] Output: Task information stored in the database

[2106] Step 4:

[2107] The server retrieves historical performance data. This data comes directly from the database and includes information such as effort and priority of previously related tasks.

[2108] Input: User ID

[2109] Data processing: Extraction of past performance data

[2110] Output: Past performance data

[2111] Step 5:

[2112] The server calculates the estimated man-hours and priority of new tasks based on past performance data. Here, an AI algorithm is used to analyze the data. For example, based on past data on inventory check tasks, it calculates that "20 hours will be required," and the priority is also determined.

[2113] Input: Past performance data, task information

[2114] Data processing: Analysis using AI algorithms

[2115] Output: Estimated man-hours, priority

[2116] Step 6:

[2117] The server retrieves the user's current schedule. This can be done from a calendar app or a database.

[2118] Input: User ID

[2119] Data processing: Getting the current schedule

[2120] Output: Current schedule information

[2121] Step 7:

[2122] The server generates an optimal schedule for the new task, determining when to execute the task based on the user's current schedule and the newly calculated estimated effort and priority.

[2123] Input: Current schedule information, estimated man-hours, priority

[2124] Data processing: generating optimal schedules

[2125] Output: New task schedule

[2126] Step 8:

[2127] The server notifies the user's device of the generated schedule. The device receives the notification and notifies the user. Notifications can be sent via push notifications or email.

[2128] Input: New task schedule

[2129] Data Processing: Notification Format

[2130] Output: Schedule notification to user

[2131] Step 9:

[2132] The server recommends relevant departments and useful information to the user, analyzes past performance data to identify relevant departments and materials, and notifies the user.

[2133] Input: Past performance data

[2134] Data processing: analysis of relevant information

[2135] Output: Related information recommendations

[2136] Step 10:

[2137] The server acquires the user's emotional state and analyzes it with an emotion engine, using facial recognition and voice analysis to determine stress levels, etc.

[2138] Input: User emotion data

[2139] Data processing: Analysis using emotion engine

[2140] Output: Emotional state

[2141] Step 11:

[2142] The server adjusts the priority of tasks based on the user's emotional state. For example, if the user's stress level is high, the server lowers the priority to reduce the burden.

[2143] Input: Emotional state, task information

[2144] Data processing: Adjusting task priority

[2145] Output: Adjusted task priorities

[2146] Step 12:

[2147] The server suggests relaxation methods to the user based on their emotional state, including suggestions for break times and relaxation methods.

[2148] Input: Emotional state

[2149] Data processing: Deciding on relaxation method

[2150] Output: Relaxation suggestions

[2151] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2152] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2153] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2154] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2155] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a sta...

Claims

1. an input means for a user to register a task and a due date; a means for recording the task and delivery date information received by the server from the input means; A server acquires past performance data and calculates an estimated man-hour and priority associated with the task; A means for the server to obtain the user's current schedule and generate an optimal schedule for the task; a means for the server to notify a user of the generated schedule; The system includes a means for the server to recommend related departments and useful information to the user.

2. 2. The system according to claim 1, wherein the server includes means for analyzing past performance data and identifying relevant departments and useful information.

3. 2. The system according to claim 1, wherein the server includes means for calculating the approximate man-hours and priority of a task using an AI algorithm.

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