system

Abstract

Provide a system. A means for inputting task information for a business; A means for analyzing, classifying, and organizing the task information; means for collecting a communication log related to the business task; A means for analyzing the communication log and identifying important contacts and schedules; A means for linking the identified schedules and contacts with a business calendar; A method for predicting annual schedules from past business data, A means of optimizing work priorities and resources based on predicted schedules; A system including:

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 corporate organizations, when an employee suddenly resigns or is transferred, there is often insufficient handover of work, making it difficult for the successor to continue the work smoothly. As a result, the risk of work delays and accidents increases, and the efficiency of the entire organization decreases. In addition, insufficient handover increases the burden on the successor, prolonging the adaptation period. To solve these problems, a method is needed to achieve an efficient and effective handover while ensuring the continuity and substitutability of work. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, a means is provided for the user to input work task information. Next, a server is provided with means for analyzing, classifying, and organizing the task information. Also, a means is provided for the server to collect communication logs related to work tasks, analyze them, and identify important contacts and schedules. A means is added for linking these identified schedules and contacts with a work calendar. Furthermore, a system is constructed that includes a means for the server to predict an annual schedule from past work data and optimize work priorities and resources based on the predicted schedule. This allows for a smooth handover even in the event of a sudden resignation or transfer, reducing the burden on successors.

[0006] The "means for inputting task information for a business" refers to an interface or device that allows a user to input information about a business, such as the task name, deadline, person in charge, and necessary materials.

[0007] "Means for analyzing, classifying, and organizing task information" refers to the process and technology by which the server analyzes the input task information, extracts the task name, deadline, person in charge, etc., and organizes them in list format.

[0008] The "means for collecting communication logs" refers to the process and database that the server uses to collect communication history such as users' emails, chats, and meeting notes.

[0009] The "means for analyzing communication logs and identifying important contacts and schedules" is a technology that enables a server to analyze collected communication logs and identify important contacts and schedules related to business.

[0010] "Means for linking with business calendars" refers to technology that automatically adds identified schedule and contact information to a business calendar application and notifies the user.

[0011] The "means for predicting the annual schedule" refers to the algorithm and calculation process by which the server analyzes past business data and predicts important future schedules and tasks.

[0012] "Means for optimizing business priorities and resources" refers to a technology that enables a server to determine business priorities and optimize resource allocation based on the identified schedule and the importance of tasks. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention is a system that uses generative AI technology to ensure smooth work handovers in the event of sudden resignation or transfer. This system provides functions such as inputting, analyzing, classifying and organizing task information, collecting and analyzing communication logs, linking with business calendars, forecasting annual schedules, and optimizing work priorities and resources.

[0035] What the program does

[0036] 1. Enter task information:

[0037] Subject: User

[0038] Users input task information into the system, including the task name, deadline, person in charge, and required materials.

[0039] Example: A user enters "Preparing year-end financial statements" as the task name, "December 31, 2023" as the deadline, and "Sato from the accounting department" as the person in charge.

[0040] 2. Analysis and classification of task information:

[0041] Subject: Server

[0042] The server receives task information entered by the user, performs text analysis, extracts detailed task information, and organizes it in a list format.

[0043] Example: The server categorizes the "preparing year-end financial statements" task and links related documents and data (e.g., historical financial data).

[0044] 3. Collecting communication logs:

[0045] Subject: Server

[0046] The server collects communication information such as users' emails, chat logs, and meeting notes.

[0047] Example: The server retrieves the minutes of the "accounting meeting" and "customer correspondence emails" from the user's mailbox.

[0048] 4. Analyzing communication logs and identifying important contacts and schedules:

[0049] Subject: Server

[0050] The server analyzes the collected communication logs and identifies important contacts and schedules related to business.

[0051] Example: A server identifies an event called "Year-End Closing Meeting (December 15, 2023)" and identifies the associated contact (e.g., accountant).

[0052] 5. Integration with business calendars:

[0053] Subject: Server

[0054] The server adds the identified schedule and contacts to the business calendar and notifies the user.

[0055] Example: The server adds "Year-end closing meeting on December 15, 2023" to the business calendar and sends a reminder to the user.

[0056] 6. Annual Schedule Forecast:

[0057] Subject: Server

[0058] The server analyzes past business data and predicts the upcoming year's schedule, setting important deadlines and tasks in advance.

[0059] Example: A server models the deadlines for financial statements at the end of each year and predicts the deadline for 2023 as December 31, 2023.

[0060] 7. Business Prioritization and Resource Optimization:

[0061] Subject: Server

[0062] The server sets the priority of operations and optimizes resources based on the predicted schedule, thereby improving business efficiency.

[0063] Example: The server sets "Preparing year-end financial statements" as a "high" priority and allocates the necessary resources (e.g., additional accounting staff) for it.

[0064] This system allows users to smoothly and effectively transfer their work even in the event of a sudden resignation or transfer, thanks to automated handover processing using AI. Successors can clearly understand tasks and schedules, allowing them to take over work quickly.

[0065] This system not only improves business continuity and reduces the burden on successors, but also contributes to improving the efficiency of the entire organization. It also prevents important tasks and deadlines from being overlooked, helping to ensure that work is carried out reliably.

[0066] The processing flow will be explained below.

[0067] Processing steps for entering task information for work

[0068] Step 1:

[0069] Subject: User

[0070] Operation: The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[0071] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[0072] Processing steps for analyzing, classifying, and organizing task information

[0073] Step 1:

[0074] Subject: Server

[0075] Operation: The server receives task information sent by the user.

[0076] Example: The server receives task information "Prepare fiscal year-end financial report."

[0077] Step 2:

[0078] Subject: Server

[0079] Operation: The server launches a natural language processing module to perform text analysis on the received task information, extracting the task name, deadline, person in charge, and required materials.

[0080] Example: The server extracts the task name "Preparing the year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[0081] Step 3:

[0082] Subject: Server

[0083] Operation: The server generates a task list based on the extracted task information and classifies and organizes each task into an appropriate category.

[0084] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[0085] Process steps for collecting communication logs

[0086] Step 1:

[0087] Subject: Server

[0088] How it works: The server starts a process to automatically collect a user's communication history, including emails, chat logs, and meeting notes.

[0089] Example: The server retrieves email logs from Google (registered trademark) Mail for the past three months.

[0090] Processing steps for analyzing communication logs and identifying important contacts and schedules

[0091] Step 1:

[0092] Subject: Server

[0093] Operation: The server analyzes the collected communication logs using natural language processing technology.

[0094] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[0095] Step 2:

[0096] Subject: Server

[0097] How it works: The server uses the analysis results to identify important business contacts and upcoming schedules.

[0098] Example: A server identifies "year-end closing meeting on December 15, 2023" and the associated contact (e.g., accountant).

[0099] Process steps for linking with business calendars

[0100] Step 1:

[0101] Subject: Server

[0102] Behavior: The server calls an API to automatically enter the specified schedule and contacts into the business calendar.

[0103] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar.

[0104] Step 2:

[0105] Subject: Server

[0106] Operation: The server sets a reminder notification for the entered schedule and notifies the user.

[0107] Example: Set the server to send a reminder notification on "December 13, 2023".

[0108] Processing steps for forecasting annual schedules

[0109] Step 1:

[0110] Subject: Server

[0111] Operation: The server launches a process to collect and analyze historical business data.

[0112] Example: A server retrieves the "financial report filing schedule for the past five years" from a database.

[0113] Step 2:

[0114] Subject: Server

[0115] How it works: The server predicts the annual schedule based on the collected data and generates a new schedule.

[0116] Example: The server predicts "December 31, 2023" as the next "year-end financial statement submission date" and registers it in the schedule.

[0117] Business Priority and Resource Optimization Process Steps

[0118] Step 1:

[0119] Subject: Server

[0120] How it works: The server prioritizes tasks based on the predicted schedule and current task information.

[0121] Example: The server sets "Prepare Year-End Financial Report" as a high priority.

[0122] Step 2:

[0123] Subject: Server

[0124] Operation: The server executes a process to optimize resource allocation based on the configured priorities.

[0125] Example: The server assigns "Additional staff in the accounting department" to the task "Prepare year-end financial report."

[0126] In this way, the present invention enables smooth business continuity through automated handover processing using AI, even in the event of a sudden resignation or transfer, thereby reducing the burden on successors and improving business efficiency across the entire company.

[0127] Example 1

[0128] 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."

[0129] Sudden resignations or transfers can cause confusion and prevent smooth work handovers. In such situations, it can be difficult for successors to quickly grasp the necessary information and take over work efficiently. Important tasks, deadlines, and contact information may be overlooked, leading to work stagnation and mistakes. Therefore, there is a need for a system that streamlines work handovers and collects and organizes all necessary information.

[0130] 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.

[0131] In this invention, the server includes means for using natural language processing when analyzing task information entered by a user, means for using a generative AI model when analyzing collected communication logs, means for predicting an annual schedule using past data with the generative AI model, and means for setting priorities for tasks based on the predicted schedule and optimizing resources through an information system. This enables a successor to smoothly and efficiently take over tasks through automated handover processing, even in the event of a sudden resignation or transfer.

[0132] "Business task information" refers to information including details of tasks related to business entered by a user.

[0133] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0134] A "generative AI model" is an algorithm that uses artificial intelligence to generate new information from data.

[0135] A "communication log" is a record of emails, chats, meeting notes, and the like that a user makes in the course of their work.

[0136] A "schedule management system" is software or a system for managing work schedules and tasks.

[0137] An "annual schedule" is a calendar that lists important dates, times, and tasks for work throughout the year.

[0138] "Priority" refers to the ranking determined based on the importance and urgency of a business task.

[0139] "Resources" refer to the personnel, equipment, time, and other resources required to carry out a task.

[0140] An "information system" is a computer system used to manage an organization's information.

[0141] "Notifications" are alerts or messages that the system sends to inform the user.

[0142] This invention is a system that uses generative AI technology to ensure smooth work handovers in the event of sudden resignation or transfer. This system provides functions such as input, analysis, classification, and organization of work task information, collection and analysis of communication logs, work calendar integration, annual schedule forecasting, and work priority and resource optimization. The following hardware and software are used to implement this system.

[0143] Hardware and software used

[0144] Server: A computer system for analyzing task information, collecting and analyzing communication logs, forecasting schedules, and optimizing resources. For example, a cloud-based server (AWS (registered trademark), Google Cloud, etc.) is used.

[0145] Natural language processing libraries: Used to analyze user-entered text. These include Python's "NLTK" and "spaCy."

[0146] Generative AI models: Used for data analysis and prediction, using machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch.

[0147] API: Used for communication log collection and calendar integration. For example, "Microsoft (registered trademark) Graph API" and "Google Calendar API."

[0148] Web browser: Used as an interface for users to enter work task information.

[0149] System processing flow and operation

[0150] Entering task information for work

[0151] Users enter task information using a dedicated input form, which functions as an interface displayed in a web browser, and users enter detailed information such as the task name, deadline, person in charge, and required materials.

[0152] Examples:

[0153] Task Name: Prepare year-end financial statements

[0154] Deadline: December 31, 2023

[0155] Person in charge: Sato from the Accounting Department

[0156] Required documents: Past financial data, accounting software documents

[0157] Sending task information to the server

[0158] When the user presses the "Send" button, the user's device sends the entered task information to the server. The transmission is securely performed using the HTTPS protocol.

[0159] Analysis, classification, and organization of task information

[0160] The server analyzes the received task information and extracts detailed information using natural language processing techniques. Based on the extracted information, the server organizes the tasks in a list format.

[0161] Specific behavior:

[0162] The server uses the Python NLTK library to parse the input text and classify it based on the characteristics of the task.

[0163] Collection of communication logs

[0164] The server collects communication information such as users' emails, chat logs, and meeting notes via a specified API.

[0165] Specific behavior:

[0166] The server uses the Microsoft Graph API to fetch relevant emails and chat logs from the user's mailbox.

[0167] Analyze communication logs and identify important contacts and schedules

[0168] The server analyzes the collected communication logs to identify important contacts and schedules, using a generative AI model for the analysis.

[0169] Specific behavior:

[0170] The server uses the Google Cloud Natural Language API to analyze text from emails and chat logs to extract important events and contact information.

[0171] Adding schedules to business calendars and notifications

[0172] The server adds the identified schedule and contacts to a business calendar (e.g., Google Calendar) and notifies the user of the information.

[0173] Specific behavior:

[0174] The server creates the event through the Google Calendar API and sends an email notification to the user.

[0175] Annual schedule forecast

[0176] The server analyzes past business data and uses a generative AI model to predict future annual schedules.

[0177] Specific behavior:

[0178] The server reads past schedule data and uses the TensorFlow library to train a model to predict future schedules.

[0179] Business prioritization and resource optimization

[0180] The server prioritizes work based on predicted schedules and uses project management tools to optimize resources.

[0181] Specific behavior:

[0182] The server uses Jira's API to prioritize tasks and assign the necessary resources (e.g., specific team members).

[0183] This system allows users to have their successors take over quickly in the event of a sudden resignation or transfer, thanks to an automated handover process that utilizes generative AI models. This prevents important tasks and deadlines from being overlooked, improving work efficiency and continuity.

[0184] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0185] Program processing flow and specific explanation

[0186] Step 1:

[0187] User input of work task information

[0188] Subject: User

[0189] Input: Task information such as task name, deadline, assignee, and required materials

[0190] Operation and output: The user enters task information related to their work using a dedicated input form. They fill in the input form with detailed information such as the task name, deadline, person in charge, and required materials, and then press the "Submit" button. This collects the task information.

[0191] Example: A user enters the task name "Preparing fiscal year-end financial statements," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[0192] Step 2:

[0193] Sending task information to the server

[0194] Subject: Device

[0195] Input: Task information entered by the user

[0196] Operation and output: When the user presses the "Submit" button, the device securely transmits the entered task information to the server using the HTTPS protocol. The information is serialized in JSON format and transmitted.

[0197] Specific operation: The device sends a POST request to the endpoint (e.g. https: / / api.company.com / tasks).

[0198] Step 3:

[0199] Analysis, classification, and organization of task information by the server

[0200] Subject: Server

[0201] Input: Received task information

[0202] Operation and output: The server analyzes the received task information and extracts detailed data using natural language processing techniques. This data extraction is performed using Python's "NLTK" and "spaCy." Based on the analyzed data, it organizes the tasks in a list format and links related documents.

[0203] Specific operation: The server analyzes the task "Preparing year-end financial statements," links related documents (such as past financial data), and organizes them into a list.

[0204] Step 4:

[0205] Collection of communication logs

[0206] Subject: Server

[0207] Input: User emails, chat logs, MTG notes, etc.

[0208] Operation and output: The server collects communication information such as user emails, chat logs, and meeting notes via API. This information is collected using the Microsoft Graph API and Slack API. The collected communication logs are stored in a database.

[0209] What it does: The server uses the Microsoft Graph API to fetch relevant emails and chat logs from the user's mailbox and save them in a database.

[0210] Step 5:

[0211] Analyze communication logs and identify important contacts and schedules

[0212] Subject: Server

[0213] Input: Collected communication logs

[0214] Operation and output: The server analyzes the collected communication logs to identify important contacts and schedules related to the work. The analysis is performed using generative AI models and natural language processing algorithms. The results are stored in a database and linked to the schedule management system.

[0215] How it works: The server uses the Google Cloud Natural Language API to analyze the text of emails and chat logs, identifying important schedules such as "End of fiscal year closing meeting (December 15, 2023)" and contact information such as accountants.

[0216] Step 6:

[0217] Adding schedules to business calendars and notifications

[0218] Subject: Server

[0219] Input: Identified schedule and contacts

[0220] Actions and Output: The server adds the identified schedules and contacts to the business calendar and sends notifications to the user using the Google Calendar API or Microsoft Outlook Calendar API. The notifications are delivered to the user via email or in-app notifications.

[0221] Specific operation: The server uses the Google Calendar API to create an event on the business calendar and notify the user of the "Year-end closing meeting on December 15, 2023."

[0222] Step 7:

[0223] Annual schedule forecast

[0224] Subject: Server

[0225] Input: Past business data

[0226] Operation and output: The server analyzes past business data and uses a generative AI model to predict the upcoming annual schedule. This prediction is made using TensorFlow and PyTorch. The prediction results are stored in a database and displayed to the user.

[0227] Specific operation: The server inputs past schedule data into the model and predicts the submission deadline for 2023 as "December 31, 2023."

[0228] Step 8:

[0229] Business prioritization and resource optimization

[0230] Subject: Server

[0231] Input: Forecasted Schedule

[0232] Operation and output: The server prioritizes tasks and optimizes resources based on the predicted schedule. This is done using the Jira API and Asana API. The optimized resource information is notified to the user.

[0233] Specific actions: The server sets "Preparing Year-End Financial Statements" to high priority, secures the necessary resources (e.g., additional accounting staff), and notifies the user.

[0234] Through these steps, the system enables quick and efficient handover of work even in the event of sudden resignation or transfer.

[0235] (Application example 1)

[0236] 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."

[0237] In logistics centers, when an employee suddenly resigns or is transferred, there is a problem of work not being smoothly handed over. This can lead to delays and mistakes, which can lead to reduced efficiency. Furthermore, task priorities and resource optimization are not achieved, negatively impacting the productivity of the entire logistics center. To solve these issues, it is necessary to organize and classify work task information, identify important contacts and schedules, forecast annual schedules, and even propose the shortest routes for logistics work and identify urgent orders.

[0238] 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.

[0239] In this invention, the server includes means for inputting business task information, means for analyzing and classifying and organizing task information, means for collecting communication logs related to business tasks, means for analyzing the communication logs and identifying important contacts and schedules, means for linking the identified schedules and contacts with a business calendar, means for predicting an annual schedule from past business data, means for optimizing business priorities and resources based on the predicted schedule, means for analyzing and organizing picking data and delivery lists for logistics work based on tasks and proposing the shortest routes, and means for identifying urgent or additional orders for logistics work based on the collected communication logs. This enables smooth business handover and improved business efficiency even in the event of a sudden resignation or transfer at a logistics center.

[0240] The "work task information input means" is an interface that allows staff at the logistics center to input their own task information into the system.

[0241] The "task information analysis means" is a system that analyzes input task information and classifies and organizes each piece of information into a specific format.

[0242] The "communication log collection means" is a system that automatically collects business-related communication data such as emails and chat logs.

[0243] The "communication log analysis means" is a system for analyzing collected communication logs and identifying important contacts and schedules.

[0244] The "business calendar linking means" is a system for synchronizing the identified schedule and contacts with the business calendar and sending necessary notifications.

[0245] The "annual schedule forecasting tool" is a system that analyzes past business data and predicts important upcoming deadlines and tasks.

[0246] The "task priority and resource optimization means" is a system that sets the priority of tasks based on a predicted schedule and optimally allocates the necessary resources.

[0247] The "Picking Data Analysis and Organization Method" is a system that analyzes picking data for logistics work based on tasks, and classifies and organizes the data for efficient product collection.

[0248] The "shortest route suggestion means" is a system that suggests efficient picking routes based on categorized and organized picking data.

[0249] The "urgent order identification means" is a system for identifying urgent orders or additional orders for logistics work based on the collected communication logs.

[0250] This invention is a system for smoothly transferring work duties at a logistics center when an employee suddenly resigns or is transferred. This system is realized by combining the following means. The main technologies used are Python programs and AWS (Amazon Web Services).

[0251] Hardware and Software Configuration

[0252] AWS EC2: Hosts application servers.

[0253] AWS Comprehend: A natural language processing (NLP) service for text analysis.

[0254] Google Calendar API: An API for integrating with business calendars.

[0255] Python: A programming language that implements the entire program.

[0256] Specific processing of the system

[0257] A means of inputting task information for work

[0258] Subject: Device

[0259] Operation: Staff at the distribution center use smartphones or tablets to enter their task information into the system, including the task name, deadline, shelf number, and required quantity.

[0260] Example: For "picking work," enter the deadline "December 15, 2023," shelf number "A23," and required quantity "10."

[0261] Task information analysis method

[0262] Subject: Server

[0263] Operation: Receives input task information and parses it using AWS Comprehend, which categorizes and organizes each piece of information into a specific format.

[0264] Example: Analyzing "picking work" and classifying and organizing products for efficient product collection.

[0265] Communication log collection method

[0266] Subject: Server

[0267] Operation: Automatically collects communication data related to the logistics center, such as emails and chat logs.

[0268] Example: Collect information about "urgent additional orders" from the logistics center chat log.

[0269] Communication log analysis method

[0270] Subject: Server

[0271] Operation: Analyze the collected communication logs and identify important contacts and schedules. Again, use AWS Comprehend.

[0272] Example: Identifying the date and time of an "urgent reorder" and the associated contact.

[0273] Business calendar integration method

[0274] Subject: Server

[0275] Interaction: Synchronize identified schedules and contacts with your business calendar using the Google Calendar API and send necessary notifications.

[0276] Example: Add "Picking work on December 15, 2023" to the business calendar and send a reminder notification.

[0277] Annual schedule forecasting tool

[0278] Subject: Server

[0279] Operation: Analyze past business data and predict the upcoming annual schedule.

[0280] Example: Forecasting tasks for December and setting a schedule for preparations for the busy end-of-year season each year.

[0281] Business priorities and resource optimization measures

[0282] Subject: Server

[0283] Operation: Prioritize work and optimally allocate necessary resources based on the forecast schedule.

[0284] Example: Optimizing resources by increasing the number of picking staff during peak periods.

[0285] Picking data analysis and organization tool

[0286] Subject: Server

[0287] Operation: Based on task information, analyzes picking data for logistics work, and classifies and organizes it for efficient product collection.

[0288] Example: A picking sheet is displayed with an optimized route based on the shelf number of each product.

[0289] Shortest route suggestion method

[0290] Subject: Server

[0291] Operation: Based on the classified and organized picking data, an efficient picking route is proposed.

[0292] Example: The route display guides picking work in the order of "shelf number A23 → B15 → C8."

[0293] Urgent Order Identification Method

[0294] Subject: Server

[0295] Operation: Identify urgent or additional logistics orders based on collected communication logs.

[0296] Example: From the collected logs, identify the "urgent additional order on December 14, 2023" and reflect the data in the system.

[0297] Prompt Sentence Examples

[0298] 1. A scene where a staff member inputs "picking work":

[0299] Prompt: "Please enter the task name, due date, shelf number, and required quantity."

[0300] 2. A scene where a log saying "A new order has been received" is collected from the communication log:

[0301] Prompt: "Please enter the log data you want to collect next."

[0302] This invention improves operational efficiency and reduces work errors and delays even when sudden resignations or transfers occur at a logistics center. It also prevents important tasks and deadlines from being missed, improving productivity throughout the organization.

[0303] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0304] Step 1:

[0305] A means of inputting task information for work

[0306] Subject: User

[0307] Operation: Logistics center staff enter their task information into the system using a smartphone or tablet.

[0308] Input: Task name, due date, shelf number, required quantity

[0309] Output: The entered task information is sent to the server.

[0310] Specific operation: For example, for "picking work," the deadline "December 15, 2023," shelf number "A23," and required quantity "10" are entered.

[0311] Step 2:

[0312] Task information analysis method

[0313] Subject: Server

[0314] Operation: Receives input task information and parses it using AWS Comprehend.

[0315] Input: Task information entered in Step 1

[0316] Output: Classified task information as the analysis result

[0317] Specific operation: Analyzes "picking work" and classifies and organizes products based on product name, required quantity, and shelf number for efficient product collection.

[0318] Step 3:

[0319] Communication log collection method

[0320] Subject: Server

[0321] Operation: Automatically collects communication data related to the logistics center, such as emails, chat logs, and work records.

[0322] Input: Communication data (email, chat log)

[0323] Output: Collected communication log data

[0324] Specific actions: For example, accessing a mail server or chat system and collecting logs such as "urgent additional order."

[0325] Step 4:

[0326] Communication log analysis method

[0327] Subject: Server

[0328] Operation: Analyze the collected communication logs using AWS Comprehend to identify important contacts and schedules.

[0329] Input: Communication log data collected in Step 3

[0330] Output: A list of important contacts and schedules

[0331] Specific operation: For example, identify the "urgent additional order date and time" and the contact information of the relevant person.

[0332] Step 5:

[0333] Business calendar integration method

[0334] Subject: Server

[0335] Interaction: Synchronize identified schedules and contacts with your business calendar using the Google Calendar API and send necessary notifications.

[0336] Input: Schedule and contacts identified in Step 4

[0337] Output: Schedules and notifications reflected in the business calendar

[0338] Specific actions: For example, add "Picking work on December 15, 2023" to the work calendar and send a reminder notification to staff.

[0339] Step 6:

[0340] Annual schedule forecasting tool

[0341] Subject: Server

[0342] Operation: Analyze past business data and predict the upcoming annual schedule.

[0343] Input: Past business data

[0344] Output: Projected annual schedule

[0345] Specific actions: Analyze tasks for the busy end-of-year period each year and set a preparation schedule for December.

[0346] Step 7:

[0347] Business priorities and resource optimization measures

[0348] Subject: Server

[0349] Operation: Prioritize work and optimally allocate necessary resources based on the forecast schedule.

[0350] Input: Annual schedule forecasted in Step 6

[0351] Output: Optimized resource allocation plan

[0352] Specific actions: For example, increasing the number of picking staff during peak periods to optimize resources.

[0353] Step 8:

[0354] Picking data analysis and organization tool

[0355] Subject: Server

[0356] Operation: Based on task information, analyzes picking data for logistics work, and classifies and organizes it for efficient product collection.

[0357] Input: Task information entered in Step 1

[0358] Output: Categorized and organized picking data

[0359] Specific operation: Based on the shelf number of each product, the picking sheet is optimized and displayed along the shortest route.

[0360] Step 9:

[0361] Shortest route suggestion method

[0362] Subject: Server

[0363] Operation: Based on the classified and organized picking data, an efficient picking route is proposed.

[0364] Input: Picking data categorized and organized in step 8

[0365] Output: Proposed picking route

[0366] Specific operation: For example, guide picking work along a route such as "shelf number A23 → B15 → C8."

[0367] Step 10:

[0368] Urgent Order Identification Method

[0369] Subject: Server

[0370] Operation: Identify urgent or additional logistics orders based on collected communication logs.

[0371] Input: Communication log data collected in Step 3

[0372] Output: Identified urgent order information

[0373] Specific operations: Identify the "urgent additional order on December 14, 2023" from the collected logs and reflect the corresponding data in the system.

[0374] 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.

[0375] This invention is a system that combines generative AI technology with an emotion engine to solve the problem of insufficient task handovers. This system achieves more effective and efficient task handovers by integrating task information input, analysis, classification and organization, communication log collection and analysis, business calendar integration, annual schedule forecasting, task priority and resource optimization, and an emotion engine that recognizes user emotions.

[0376] What the program does

[0377] 1. Enter task information:

[0378] Subject: User

[0379] The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[0380] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[0381] 2. Analysis and classification of task information:

[0382] Subject: Server

[0383] The server receives task information sent by the user, performs text analysis, and extracts the task name, deadline, person in charge, and necessary materials. It then generates a task list based on the extracted task information and classifies and organizes each task into the appropriate category.

[0384] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[0385] 3. Collecting communication logs:

[0386] Subject: Server

[0387] The server automatically collects communication history such as users' emails, chat logs, and meeting notes.

[0388] Example: The server retrieves email logs from Google Mail for the past three months.

[0389] 4. Analyzing communication logs and identifying important contacts and schedules:

[0390] Subject: Server

[0391] The server analyzes the collected communication logs using natural language processing technology to identify important contacts and schedules related to business operations.

[0392] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[0393] 5. Integration with business calendars:

[0394] Subject: Server

[0395] The server automatically enters the identified schedule and contact information into the business calendar and notifies the user. It also sets reminder notifications for the entered schedule.

[0396] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar and send a reminder to the user.

[0397] 6. Annual Schedule Forecast:

[0398] Subject: Server

[0399] The server analyzes past business data, predicts the upcoming annual schedule, and generates a new schedule.

[0400] Example: The server retrieves the "financial report submission schedule for the past five years" from the database and predicts "December 31, 2023" as the next "year-end financial report submission date."

[0401] 7. Business Prioritization and Resource Optimization:

[0402] Subject: Server

[0403] The server prioritizes tasks and optimizes resources based on the predicted schedule and current task information.

[0404] Example: A server sets "Preparing year-end financial reports" as a high priority and allocates the necessary resources (e.g., additional accounting staff) for it.

[0405] Emotion engine integration

[0406] 1. Emotion recognition using emotion engine:

[0407] Subject: Server

[0408] The server uses the user's input and behavior log to drive the emotion engine and recognize the user's emotions.

[0409] Example: A server analyzes a user's keystrokes and mouse movements to detect stress levels.

[0410] 2. Adjusting work priorities and resource optimization based on sentiment data:

[0411] Subject: Server

[0412] The server takes into account the emotional data recognized by the emotion engine and adjusts the priority of tasks and resource allocation.

[0413] Example: If a user is under high stress, the server will adjust the priority of the task to be assigned to a less demanding task.

[0414] 3. Adjust reminder notifications based on your emotions:

[0415] Subject: Server

[0416] The server adjusts the content and timing of the reminder notification based on the emotional state recognized by the emotion engine.

[0417] Example: If the user is feeling stressed, the server can flexibly adjust the arrival time of the reminder notification (for example, send it a little later).

[0418] This system allows users to smoothly continue their work even in the event of a sudden resignation or transfer, thanks to automated handover processing and support from an emotion engine. This reduces the burden on successors and improves work efficiency across the company. It also provides more human-like support by optimizing tasks and adjusting notifications based on the user's emotional state.

[0419] The processing flow will be explained below.

[0420] Processing steps for entering task information for work

[0421] Step 1:

[0422] Subject: User

[0423] Operation: The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[0424] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[0425] Processing steps for analyzing, classifying, and organizing task information

[0426] Step 1:

[0427] Subject: Server

[0428] Operation: The server receives task information sent by the user.

[0429] Example: The server receives task information "Prepare fiscal year-end financial report."

[0430] Step 2:

[0431] Subject: Server

[0432] Operation: The server launches a natural language processing module to perform text analysis on the received task information, extracting the task name, deadline, person in charge, and required materials.

[0433] Example: The server extracts the task name "Preparing the year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[0434] Step 3:

[0435] Subject: Server

[0436] Operation: The server generates a task list based on the extracted task information and classifies and organizes each task into an appropriate category.

[0437] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[0438] Process steps for collecting communication logs

[0439] Step 1:

[0440] Subject: Server

[0441] How it works: The server starts a process to automatically collect a user's communication history, including emails, chat logs, and meeting notes.

[0442] Example: The server retrieves email logs from Google Mail for the past three months.

[0443] Processing steps for analyzing communication logs and identifying important contacts and schedules

[0444] Step 1:

[0445] Subject: Server

[0446] Operation: The server analyzes the collected communication logs using natural language processing technology.

[0447] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[0448] Step 2:

[0449] Subject: Server

[0450] How it works: The server uses the analysis results to identify important business contacts and upcoming schedules.

[0451] Example: A server identifies "year-end closing meeting on December 15, 2023" and the associated contact (e.g., accountant).

[0452] Process steps for linking with business calendars

[0453] Step 1:

[0454] Subject: Server

[0455] Behavior: The server calls an API to automatically enter the specified schedule and contacts into the business calendar.

[0456] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar.

[0457] Step 2:

[0458] Subject: Server

[0459] Operation: The server sets a reminder notification for the entered schedule and notifies the user.

[0460] Example: Set the server to send a reminder notification on "December 13, 2023".

[0461] Processing steps for forecasting annual schedules

[0462] Step 1:

[0463] Subject: Server

[0464] Operation: The server launches a process to collect and analyze historical business data.

[0465] Example: A server retrieves the "financial report filing schedule for the past five years" from a database.

[0466] Step 2:

[0467] Subject: Server

[0468] How it works: The server predicts the annual schedule based on the collected data and generates a new schedule.

[0469] Example: The server predicts "December 31, 2023" as the next "year-end financial statement submission date" and registers it in the schedule.

[0470] Business Priority and Resource Optimization Process Steps

[0471] Step 1:

[0472] Subject: Server

[0473] How it works: The server prioritizes tasks based on the predicted schedule and current task information.

[0474] Example: The server sets "Prepare Year-End Financial Report" as a high priority.

[0475] Step 2:

[0476] Subject: Server

[0477] Operation: The server executes a process to optimize resource allocation based on the configured priorities.

[0478] Example: The server assigns "Additional staff in the accounting department" to the task "Prepare year-end financial report."

[0479] Processing steps for emotion engine integration

[0480] Step 1:

[0481] Subject: Server

[0482] Operation: The server uses the user's input and behavior log to drive the emotion engine and recognize the user's emotions.

[0483] Example: A server analyzes a user's keystrokes and mouse movements to detect stress levels.

[0484] Step 2:

[0485] Subject: Server

[0486] Operation: The server takes into account the emotional data recognized by the emotion engine and adjusts task priorities and resource allocation.

[0487] Example: If a user is under high stress, the server will adjust the priority of the task to be assigned to a less demanding task.

[0488] Step 3:

[0489] Subject: Server

[0490] How it works: The server adjusts the content and timing of reminder notifications based on the emotional state recognized by the emotion engine.

[0491] Example: If the user is feeling stressed, the server can flexibly adjust the arrival time of the reminder notification (for example, send it a little later).

[0492] In this way, this invention enables smooth business continuity even in the event of sudden resignation or transfer by integrating an AI-based automated handover process with an emotion engine. This reduces the burden on successors and improves business efficiency across the company. It also provides more human-like support by optimizing tasks and adjusting notifications based on the user's emotional state.

[0493] Example 2

[0494] 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."

[0495] In conventional work handovers, there have been problems with the handover not proceeding smoothly due to insufficient or inaccurate task information, insufficient understanding of communication logs, etc. Furthermore, the emotional state of the person in charge of the handover can affect work efficiency, and insufficient management of this makes it difficult to optimize work priorities and resource allocation. To solve these problems, accurate analysis of task information, appropriate analysis of communication logs, and work adjustments based on emotional data are required.

[0496] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0497] In this invention, the server includes means for inputting work task information, means for analyzing and classifying and organizing the task information, means for collecting communication logs related to the work tasks, means for analyzing the communication logs and identifying important contacts and dates, means for linking the identified dates and contacts with a work calendar, means for predicting an annual schedule from past work data, means for optimizing work priorities and resources based on the predicted schedule, means for recognizing user emotions using an emotion engine, and means for adjusting work priorities and resource allocation based on the recognized emotion data. This enables effective and efficient work handover and realizes appropriate work distribution and resource management that takes into account the user's emotional state.

[0498] "Task information for a business" is information such as the task name, deadline, person in charge, and necessary materials related to the business that is input by the user into the system.

[0499] "Means for inputting task information" refers to an interface or device that allows a user to input work task information into the system.

[0500] "Means for analyzing, classifying, and organizing task information" refers to the process or algorithm by which the server analyzes the input task information and classifies and organizes each task into an appropriate category.

[0501] "Communication log" refers to the communication history such as emails, chats, and meeting notes that are generated when a user performs their work.

[0502] "Means for collecting communication logs" refers to a function or device that allows the server to automatically collect users' emails, chat logs, meeting notes, etc.

[0503] "Means for analyzing communication logs and identifying important contacts and schedules" refers to the process or algorithm for analyzing communication logs collected by the server and identifying important business-related contacts and schedules.

[0504] "Business calendar" refers to a calendar application or system used to manage business-related schedules.

[0505] "Means for linking with business calendars" refers to the processes or algorithms by which the server automatically enters identified schedules and contacts into a business calendar and notifies the user.

[0506] "Means for predicting annual schedules" refers to the processes and algorithms that the server uses to analyze past business data and predict future annual schedules.

[0507] "Means for optimizing task priorities and resources" refers to the processes and algorithms that allow the server to prioritize tasks and optimally allocate required resources based on predicted schedules and current task information.

[0508] An "emotion engine" refers to the technology and algorithms that analyze user input and behavioral logs to recognize the user's emotions and psychological state.

[0509] "Means for recognizing emotions" refers to the process or algorithm for recognizing a user's emotions using an emotion engine.

[0510] "Means for adjusting task priorities and resource allocations based on emotional data" refers to processes or algorithms for dynamically adjusting task priorities and resource allocations based on recognized emotional data.

[0511] This invention is a system that combines generative AI technology with an emotion engine to solve the problem of insufficient task handovers. This system realizes more effective and efficient task handovers by integrating task information input, analysis, classification and organization, communication log collection and analysis, business calendar integration, annual schedule forecasting, task priority and resource optimization, and an emotion engine that recognizes user emotions.

[0512] Entering task information for work

[0513] Hardware and software: devices (computers, tablets, smartphones, etc.), business management software

[0514] What happens:

[0515] The user uses a terminal to input task information for work. The input information includes the task name, deadline, person in charge, and required materials. As a specific example, a user inputs the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[0516] Analysis, classification, and organization of task information

[0517] Hardware and software: Server, natural language processing engine

[0518] What happens:

[0519] The server receives task information sent by the user and performs text analysis using a natural language processing engine to extract the task name, deadline, person in charge, and required documents. It then generates a task list based on the extracted task information and classifies and organizes each task into appropriate categories. For example, the server might classify "Preparing the fiscal year-end financial report" into the "Financial Report" category and organize related documents and reference links.

[0520] Collection of communication logs

[0521] Hardware and software: Servers, communication log collection software (email API, chat API, etc.)

[0522] What happens:

[0523] The server automatically collects communication history such as users' emails, chat logs, meeting notes, etc. For example, the server retrieves email logs from the "email API" for the past three months.

[0524] Analyze communication logs and identify important contacts and schedules

[0525] Hardware and software: Server, natural language processing engine

[0526] What happens:

[0527] The server analyzes the collected communication logs using natural language processing technology to identify important business-related contacts and schedules. For example, the server can identify "minutes of meetings regarding fiscal year-end settlement" or "emails responding to important customers" and extract the relevant information.

[0528] Integration with business calendars

[0529] Hardware and software: Server, business calendar API (e.g. Google Calendar API)

[0530] What happens:

[0531] The server automatically enters the identified schedule and contacts into the business calendar and notifies the user. It also sets reminder notifications. As a specific example, the server uses the "Business Calendar API" to add "Year-end Closing Meeting on December 15, 2023" to the calendar and sends a reminder notification to the user.

[0532] Annual schedule forecast

[0533] Hardware and software: Servers, data analysis engines

[0534] What happens:

[0535] The server analyzes past business data and predicts the upcoming annual schedule. For example, the server retrieves the "financial report submission schedule for the past five years" from the database and predicts "December 31, 2023" as the next "year-end financial report submission date."

[0536] Business prioritization and resource optimization

[0537] Hardware and software: Servers, resource management engines

[0538] What happens:

[0539] The server prioritizes tasks based on the predicted schedule and current task information, and optimally allocates the necessary resources. For example, the server may assign "preparing the fiscal year-end financial report" as a high priority and secure the necessary resources (e.g., additional accounting staff).

[0540] Emotion engine integration

[0541] Emotion recognition by emotion engine

[0542] Hardware and software: Server, sentiment analysis engine

[0543] What happens:

[0544] The server uses the user's input and behavior logs to drive the emotion engine and recognize the user's emotions. For example, the server analyzes the user's keystrokes and mouse movements to detect their stress level.

[0545] Adjusting work priorities and resource optimization based on sentiment data

[0546] Hardware and software: Server, sentiment analysis engine

[0547] What happens:

[0548] The server adjusts task priorities and resource allocation based on the emotional data recognized by the emotion engine. For example, if a user is in a high-stress state, the server will assign less demanding tasks a high priority.

[0549] Adjusting reminder notifications based on emotions

[0550] Hardware and software: Server, sentiment analysis engine

[0551] What happens:

[0552] The server adjusts the content and timing of reminder notifications based on the emotional state recognized by the emotion engine. For example, if the user is feeling stressed, the server will adjust the time at which the reminder notification arrives to a slightly later date.

[0553] Prompt Sentence Examples

[0554] "Add a task to the work calendar to prepare the year-end financial report, and enter the person responsible and due date."

[0555] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0556] Step 1:

[0557] The user uses the terminal to input task information for the work.

[0558] Input includes the task name, deadline, person in charge, and required materials. This information is sent to the system.

[0559] Input: Task name: "Prepare fiscal year-end financial report", deadline: "December 31, 2023", person in charge: "Sato from the Accounting Department"

[0560] Output: Submitted task information

[0561] Specific behavior:

[0562] When the user fills in the task information in the input form on the terminal and presses the "send" button, the task information is sent to the server.

[0563] Step 2:

[0564] The server receives the task information sent by the user and performs text analysis.

[0565] This analysis extracts task names, deadlines, people in charge, and required materials, and stores the extracted information in a database.

[0566] Input: Submitted task information

[0567] Output: Extracted task name, due date, assignee, required materials

[0568] Specific behavior:

[0569] The server uses a "natural language processing engine" to analyze the text of the submitted task information, extract important elements, and store them in a database.

[0570] Step 3:

[0571] The server generates a list of tasks based on the analyzed task information and classifies and organizes each task into an appropriate category.

[0572] This information is visualized as a list of tasks.

[0573] Input: Extracted task name, deadline, assignee, required materials

[0574] Output: A categorized list of tasks

[0575] Specific behavior:

[0576] The server processes the task information it receives using a classification algorithm, assigns it to a category such as "financial reporting," and displays it as a list of tasks.

[0577] Step 4:

[0578] The server automatically collects communication history such as users' emails, chat logs, and meeting notes.

[0579] The collected communication logs are stored in a database.

[0580] Input: User communication history data

[0581] Output: Collected communication logs

[0582] Specific behavior:

[0583] The server uses "communication log collection software" to collect communication logs from interfaces such as "email API" for the past three months and stores them in a database.

[0584] Step 5:

[0585] The server analyzes the collected communication logs and identifies important business-related contacts and schedules.

[0586] The results of this analysis are reflected in the business calendar.

[0587] Input: Collected communication logs

[0588] Output: Important contacts and schedules

[0589] Specific behavior:

[0590] The server uses a "natural language processing engine" to analyze the collected communication logs and extract important information such as "meeting minutes" and "responses to important customers."

[0591] Step 6:

[0592] The server automatically enters the identified schedule and contacts into the business calendar and notifies the user.

[0593] Also, set reminder notifications.

[0594] Input: Important contacts and schedules

[0595] Output: Schedule entries and notifications in the business calendar

[0596] Specific behavior:

[0597] The server uses the Calendar API to add schedules such as "Meeting on December 15, 2023" to the business calendar, set reminders, and notify the user.

[0598] Step 7:

[0599] The server analyzes past business data and predicts the upcoming annual schedule.

[0600] The predicted schedule is notified to the user.

[0601] Input: Past business data

[0602] Output: Projected annual schedule

[0603] Specific behavior:

[0604] The server uses a "data analysis engine" to analyze past schedule data, generate predictions such as the "next fiscal year-end financial statement submission date," and notify the user.

[0605] Step 8:

[0606] The server prioritizes tasks based on the predicted schedule and current task information, and optimally allocates the necessary resources.

[0607] Input: Projected schedule and current task information

[0608] Output: Set work priorities and resource allocations

[0609] Specific behavior:

[0610] The server uses a "resource management engine" to set task priorities and optimally allocate resources (e.g., personnel and time) based on those priorities.

[0611] Step 9:

[0612] The server runs an emotion engine to recognize the user's emotion data.

[0613] It recognizes emotions from user input and behavior logs and saves the results.

[0614] Input: User input and action logs

[0615] Output: Recognized emotion data

[0616] Specific behavior:

[0617] The server uses an "emotion analysis engine" to analyze the user's keystrokes and mouse movements to recognize and record their stress level and emotional state.

[0618] Step 10:

[0619] The server adjusts the priority of tasks and resource allocation based on the emotion data.

[0620] This allows less taxing tasks to be prioritized when the user's stress level is high.

[0621] Input: Recognized emotion data

[0622] Output: Aligned work priorities and resource allocation

[0623] Specific behavior:

[0624] Based on the recognized emotion data, the server adjusts the priority setting algorithm, assigning low-burden tasks a higher priority, and reallocating resources.

[0625] Step 11:

[0626] The server adjusts the content and timing of the reminder notification based on the emotional state recognized by the emotion engine.

[0627] This delays the timing of the reminder notification when the user's stress level is high.

[0628] Input: Recognized emotion data

[0629] Output: Adjusted reminder notification

[0630] Specific behavior:

[0631] The server analyzes the emotional data and runs an algorithm that delays the timing of sending reminder notifications if the stress level is high.

[0632] (Application example 2)

[0633] 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."

[0634] In today's society, where there is a demand for improved work efficiency that takes into account the lack of task handovers and human emotions, the lack of smooth employee task handovers, resulting in oversights and confusion during the handover process, is a major issue. Another problem is the lack of support that takes into account the stress and emotions of employees performing the handover. Appropriate information sharing and emotional support are particularly essential for on-site work, such as in factories. Therefore, a system that integrates the efficiency of task handovers with emotion recognition is needed.

[0635] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0636] In this invention, the server includes means for inputting business task information, means for analyzing and classifying and organizing the task information, means for collecting communication history related to the business tasks, means for analyzing the communication history and identifying important contacts and dates, means for linking the identified dates and contacts with a business calendar, means for predicting an annual schedule from past business data, means for optimizing business priorities and resources based on the predicted schedule, and means for adjusting business priorities and resources based on an emotion engine that recognizes user emotion data. This makes business handover more efficient and enables optimal business support that takes human emotions into consideration.

[0637] "Business task information" refers to details of specific tasks and work to be performed in a business.

[0638] "Analysis" is the process of analyzing input data or information and extracting specific patterns or meanings.

[0639] "Classification and organization" refers to the process of dividing and organizing information according to specific categories and orders based on analyzed information.

[0640] "Communication history" is a record of business communications that a user has had, such as emails, chats, and meeting notes.

[0641] "Important Contacts" refers to the contact information of parties and personnel essential to the performance of business.

[0642] A "date" is the date and time when a particular task or event is scheduled to occur.

[0643] A "business calendar" is a calendar system for managing business schedules and plans.

[0644] An "annual plan" refers to a work schedule for one year, predicted based on past data.

[0645] "Business priority" is a criterion for determining which business tasks should be performed first, based on their importance and urgency.

[0646] "Resource optimization" refers to the optimal allocation of resources such as personnel and equipment in order to carry out business operations efficiently.

[0647] An "emotion engine" is a system that recognizes the user's emotional state and provides appropriate feedback and support.

[0648] "Emotion data" is data that expresses the user's emotional state using numerical values ​​or categories.

[0649] As an example of how to implement this invention, we will show an example of introducing a system that combines generative AI technology and an emotion engine for factory workers. Below, we will explain the specific process of this system and the technology and data processing methods used.

[0650] System Overview

[0651] The system is comprised mainly of a server and user devices (e.g., smart glasses), and supports employee work handover and efficiency through the management of work task information, emotion recognition, analysis of communication history, and integration with work calendars.

[0652] 1. Entering work task information

[0653] The user inputs task information (e.g., task name, deadline, and person in charge) through the smart glasses interface, which is then sent to the server and stored in a database.

[0654] 2. Analysis and classification of task information

[0655] The server analyzes the input task information and uses natural language processing techniques (e.g., TfidfVectorizer) to extract task names, deadlines, and assignees, then classifies the tasks into categories using KMeans clustering.

[0656] 3. Collection and analysis of communication history

[0657] The server collects communication logs such as users' emails and chat history, analyzes them using natural language processing technology, and identifies important contacts and schedules, linking them to task information.

[0658] 4. Integration with business calendars

[0659] The server automatically enters the identified important contacts and dates into the business calendar using calendar integration technologies such as the Google Calendar API, making it easier to manage task schedules.

[0660] 5. Annual schedule forecast

[0661] The server analyzes past business data and runs an algorithm to predict annual schedules, which then predicts important upcoming dates and automatically generates a new schedule.

[0662] 6. Business Prioritization and Resource Optimization

[0663] The server prioritizes tasks based on the predicted schedule and executes algorithms to optimize resources accordingly, for example, by optimally allocating resources to the necessary personnel.

[0664] 7. Emotion engine integration

[0665] The emotion engine recognizes user emotions based on user input and behavioral logs. This process uses machine learning frameworks such as Keras and TensorFlow. Based on the recognized emotion data, work priorities and resources are adjusted.

[0666] Specific examples

[0667] The user uses the smart glasses to input task information such as "Preparing the year-end financial report, 2023-12-31, Accounting Department staff."

[0668] The server collects emails and chat logs and identifies "important meeting minutes" and "customer response emails."

[0669] The server automatically adds important dates such as "year-end closing meeting" to Google Calendar and sets reminder notifications.

[0670] The generative AI model analyzes the prompt, "I'm overwhelmed with the amount of tasks I have to do right now..." and determines that the user is in a state of high stress.

[0671] This embodiment allows factory workers to efficiently take over tasks through smart glasses and receive optimal support based on emotion recognition.

[0672] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0673] Step 1:

[0674] The user uses the smart glasses interface to input task information, including the task name, deadline, and assignee, which is then sent to the server and stored in a database.

[0675] Step 2:

[0676] The server analyzes the received business task information. It uses the natural language processing technology TfidfVectorizer to perform text analysis to extract task names, deadlines, and responsible persons. Based on the analysis results, it organizes the task information and compiles it into a list.

[0677] Step 3:

[0678] The server applies the KMeans clustering algorithm to categorize the business task information. This categorizes tasks into related categories and groups similar tasks together in the task list. The input data is the analysis results, and the output data is the category information for each task.

[0679] Step 4:

[0680] The server collects communication logs such as user emails and chat history. To do this, it connects to the email server or chat server via API and obtains the log data. The collected communication logs are stored in a database.

[0681] Step 5:

[0682] The server analyzes the collected communication logs and uses natural language processing technology to identify important contacts and schedules. The important information obtained from this analysis is used in the next step. The input data is the communication logs, and the output data is the identified important contacts and schedule information.

[0683] Step 6:

[0684] The server automatically enters important contacts and dates into the business calendar using calendar integration technology such as the Google Calendar API, and the entered information is reflected in the user's calendar application.

[0685] Step 7:

[0686] The server analyzes past business data and predicts the annual schedule. This identifies important dates for the next year and generates a new schedule. The input data is past business data, and the output data is the predicted annual schedule.

[0687] Step 8:

[0688] The server prioritizes tasks based on the predicted annual schedule and runs a resource optimization algorithm, which prioritizes each task and allocates the necessary resources.

[0689] Step 9:

[0690] The server runs an emotion engine based on user input and behavior logs to recognize the user's emotions. The emotion engine uses machine learning frameworks such as Keras and TensorFlow. The input data is the user's behavior log and input data, and the output data is the recognized emotion information.

[0691] Step 10:

[0692] The server adjusts task priorities and resources based on the recognized emotion data. For example, if a user is in a high-stress state, it adjusts resource and task allocation to prioritize less demanding tasks.

[0693] The above processing steps make the handover of work more efficient and provide optimal support based on emotion recognition.

[0694] 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.

[0695] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0696] 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.

[0697] [Second embodiment]

[0698] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0699] 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.

[0700] 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).

[0701] 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.

[0702] 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.

[0703] 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).

[0704] 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. 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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."

[0710] This invention is a system that uses generative AI technology to ensure smooth work handovers in the event of sudden resignation or transfer. This system provides functions such as inputting, analyzing, classifying and organizing task information, collecting and analyzing communication logs, linking with business calendars, forecasting annual schedules, and optimizing work priorities and resources.

[0711] What the program does

[0712] 1. Enter task information:

[0713] Subject: User

[0714] Users input task information into the system, including the task name, deadline, person in charge, and required materials.

[0715] Example: A user enters "Preparing year-end financial statements" as the task name, "December 31, 2023" as the deadline, and "Sato from the accounting department" as the person in charge.

[0716] 2. Analysis and classification of task information:

[0717] Subject: Server

[0718] The server receives task information entered by the user, performs text analysis, extracts detailed task information, and organizes it in a list format.

[0719] Example: The server categorizes the "preparing year-end financial statements" task and links related documents and data (e.g., historical financial data).

[0720] 3. Collecting communication logs:

[0721] Subject: Server

[0722] The server collects communication information such as users' emails, chat logs, and meeting notes.

[0723] Example: The server retrieves the minutes of the "accounting meeting" and "customer correspondence emails" from the user's mailbox.

[0724] 4. Analyzing communication logs and identifying important contacts and schedules:

[0725] Subject: Server

[0726] The server analyzes the collected communication logs and identifies important contacts and schedules related to business.

[0727] Example: A server identifies an event called "Year-End Closing Meeting (December 15, 2023)" and identifies the associated contact (e.g., accountant).

[0728] 5. Integration with business calendars:

[0729] Subject: Server

[0730] The server adds the identified schedule and contacts to the business calendar and notifies the user.

[0731] Example: The server adds "Year-end closing meeting on December 15, 2023" to the business calendar and sends a reminder to the user.

[0732] 6. Annual Schedule Forecast:

[0733] Subject: Server

[0734] The server analyzes past business data and predicts the upcoming year's schedule, setting important deadlines and tasks in advance.

[0735] Example: A server models the deadlines for financial statements at the end of each year and predicts the deadline for 2023 as December 31, 2023.

[0736] 7. Business Prioritization and Resource Optimization:

[0737] Subject: Server

[0738] The server sets the priority of operations and optimizes resources based on the predicted schedule, thereby improving business efficiency.

[0739] Example: The server sets "Preparing year-end financial statements" as a "high" priority and allocates the necessary resources (e.g., additional accounting staff) for it.

[0740] This system allows users to smoothly and effectively transfer their work even in the event of a sudden resignation or transfer, thanks to automated handover processing using AI. Successors can clearly understand tasks and schedules, allowing them to take over work quickly.

[0741] This system not only improves business continuity and reduces the burden on successors, but also contributes to improving the efficiency of the entire organization. It also prevents important tasks and deadlines from being overlooked, helping to ensure that work is carried out reliably.

[0742] The processing flow will be explained below.

[0743] Processing steps for entering task information for work

[0744] Step 1:

[0745] Subject: User

[0746] Operation: The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[0747] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[0748] Processing steps for analyzing, classifying, and organizing task information

[0749] Step 1:

[0750] Subject: Server

[0751] Operation: The server receives task information sent by the user.

[0752] Example: The server receives task information "Prepare fiscal year-end financial report."

[0753] Step 2:

[0754] Subject: Server

[0755] Operation: The server launches a natural language processing module to perform text analysis on the received task information, extracting the task name, deadline, person in charge, and required materials.

[0756] Example: The server extracts the task name "Preparing the year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[0757] Step 3:

[0758] Subject: Server

[0759] Operation: The server generates a task list based on the extracted task information and classifies and organizes each task into an appropriate category.

[0760] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[0761] Process steps for collecting communication logs

[0762] Step 1:

[0763] Subject: Server

[0764] How it works: The server starts a process to automatically collect a user's communication history, including emails, chat logs, and meeting notes.

[0765] Example: The server retrieves email logs from Google Mail for the past three months.

[0766] Processing steps for analyzing communication logs and identifying important contacts and schedules

[0767] Step 1:

[0768] Subject: Server

[0769] Operation: The server analyzes the collected communication logs using natural language processing technology.

[0770] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[0771] Step 2:

[0772] Subject: Server

[0773] How it works: The server uses the analysis results to identify important business contacts and upcoming schedules.

[0774] Example: A server identifies "year-end closing meeting on December 15, 2023" and the associated contact (e.g., accountant).

[0775] Process steps for linking with business calendars

[0776] Step 1:

[0777] Subject: Server

[0778] Behavior: The server calls an API to automatically enter the specified schedule and contacts into the business calendar.

[0779] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar.

[0780] Step 2:

[0781] Subject: Server

[0782] Operation: The server sets a reminder notification for the entered schedule and notifies the user.

[0783] Example: Set the server to send a reminder notification on "December 13, 2023".

[0784] Processing steps for forecasting annual schedules

[0785] Step 1:

[0786] Subject: Server

[0787] Operation: The server launches a process to collect and analyze historical business data.

[0788] Example: A server retrieves the "financial report filing schedule for the past five years" from a database.

[0789] Step 2:

[0790] Subject: Server

[0791] How it works: The server predicts the annual schedule based on the collected data and generates a new schedule.

[0792] Example: The server predicts "December 31, 2023" as the next "year-end financial statement submission date" and registers it in the schedule.

[0793] Business Priority and Resource Optimization Process Steps

[0794] Step 1:

[0795] Subject: Server

[0796] How it works: The server prioritizes tasks based on the predicted schedule and current task information.

[0797] Example: The server sets "Prepare Year-End Financial Report" as a high priority.

[0798] Step 2:

[0799] Subject: Server

[0800] Operation: The server executes a process to optimize resource allocation based on the configured priorities.

[0801] Example: The server assigns "Additional staff in the accounting department" to the task "Prepare year-end financial report."

[0802] In this way, the present invention enables smooth business continuity through automated handover processing using AI, even in the event of a sudden resignation or transfer, thereby reducing the burden on successors and improving business efficiency across the entire company.

[0803] Example 1

[0804] 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."

[0805] Sudden resignations or transfers can cause confusion and prevent smooth work handovers. In such situations, it can be difficult for successors to quickly grasp the necessary information and take over work efficiently. Important tasks, deadlines, and contact information may be overlooked, leading to work stagnation and mistakes. Therefore, there is a need for a system that streamlines work handovers and collects and organizes all necessary information.

[0806] 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.

[0807] In this invention, the server includes means for using natural language processing when analyzing task information entered by a user, means for using a generative AI model when analyzing collected communication logs, means for predicting an annual schedule using past data with the generative AI model, and means for setting priorities for tasks based on the predicted schedule and optimizing resources through an information system. This enables a successor to smoothly and efficiently take over tasks through automated handover processing, even in the event of a sudden resignation or transfer.

[0808] "Business task information" refers to information including details of tasks related to business entered by a user.

[0809] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0810] A "generative AI model" is an algorithm that uses artificial intelligence to generate new information from data.

[0811] A "communication log" is a record of emails, chats, meeting notes, and the like that a user makes in the course of their work.

[0812] A "schedule management system" is software or a system for managing work schedules and tasks.

[0813] An "annual schedule" is a calendar that lists important dates, times, and tasks for work throughout the year.

[0814] "Priority" refers to the ranking determined based on the importance and urgency of a business task.

[0815] "Resources" refer to the personnel, equipment, time, and other resources required to carry out a task.

[0816] An "information system" is a computer system used to manage an organization's information.

[0817] "Notifications" are alerts or messages that the system sends to inform the user.

[0818] This invention is a system that uses generative AI technology to ensure smooth work handovers in the event of sudden resignation or transfer. This system provides functions such as input, analysis, classification, and organization of work task information, collection and analysis of communication logs, work calendar integration, annual schedule forecasting, and work priority and resource optimization. The following hardware and software are used to implement this system.

[0819] Hardware and software used

[0820] Server: A computer system for analyzing task information, collecting and analyzing communication logs, forecasting schedules, and optimizing resources. For example, a cloud-based server (AWS, Google Cloud) is used.

[0821] Natural language processing libraries: Used to analyze user-entered text. These include Python's "NLTK" and "spaCy."

[0822] Generative AI models: Used for data analysis and prediction, using machine learning libraries such as TensorFlow and PyTorch.

[0823] API: Used for communication log collection and calendar integration. For example, "Microsoft Graph API" and "Google Calendar API."

[0824] Web browser: Used as an interface for users to enter work task information.

[0825] System processing flow and operation

[0826] Entering task information for work

[0827] Users enter task information using a dedicated input form, which functions as an interface displayed in a web browser, and users enter detailed information such as the task name, deadline, person in charge, and required materials.

[0828] Examples:

[0829] Task Name: Prepare year-end financial statements

[0830] Deadline: December 31, 2023

[0831] Person in charge: Sato from the Accounting Department

[0832] Required documents: Past financial data, accounting software documents

[0833] Sending task information to the server

[0834] When the user presses the "Send" button, the user's device sends the entered task information to the server. The transmission is securely performed using the HTTPS protocol.

[0835] Analysis, classification, and organization of task information

[0836] The server analyzes the received task information and extracts detailed information using natural language processing techniques. Based on the extracted information, the server organizes the tasks in a list format.

[0837] Specific behavior:

[0838] The server uses the Python NLTK library to parse the input text and classify it based on the characteristics of the task.

[0839] Collection of communication logs

[0840] The server collects communication information such as users' emails, chat logs, and meeting notes via a specified API.

[0841] Specific behavior:

[0842] The server uses the Microsoft Graph API to fetch relevant emails and chat logs from the user's mailbox.

[0843] Analyze communication logs and identify important contacts and schedules

[0844] The server analyzes the collected communication logs to identify important contacts and schedules, using a generative AI model for the analysis.

[0845] Specific behavior:

[0846] The server uses the Google Cloud Natural Language API to analyze text from emails and chat logs to extract important events and contact information.

[0847] Adding schedules to business calendars and notifications

[0848] The server adds the identified schedule and contacts to a business calendar (e.g., Google Calendar) and notifies the user of the information.

[0849] Specific behavior:

[0850] The server creates the event through the Google Calendar API and sends an email notification to the user.

[0851] Annual schedule forecast

[0852] The server analyzes past business data and uses a generative AI model to predict future annual schedules.

[0853] Specific behavior:

[0854] The server reads past schedule data and uses the TensorFlow library to train a model to predict future schedules.

[0855] Business prioritization and resource optimization

[0856] The server prioritizes work based on predicted schedules and uses project management tools to optimize resources.

[0857] Specific behavior:

[0858] The server uses Jira's API to prioritize tasks and assign the necessary resources (e.g., specific team members).

[0859] This system allows users to have their successors take over quickly in the event of a sudden resignation or transfer, thanks to an automated handover process that utilizes generative AI models. This prevents important tasks and deadlines from being overlooked, improving work efficiency and continuity.

[0860] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0861] Program processing flow and specific explanation

[0862] Step 1:

[0863] User input of work task information

[0864] Subject: User

[0865] Input: Task information such as task name, deadline, assignee, and required materials

[0866] Operation and output: The user enters task information related to their work using a dedicated input form. They fill in the input form with detailed information such as the task name, deadline, person in charge, and required materials, and then press the "Submit" button. This collects the task information.

[0867] Example: A user enters the task name "Preparing fiscal year-end financial statements," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[0868] Step 2:

[0869] Sending task information to the server

[0870] Subject: Device

[0871] Input: Task information entered by the user

[0872] Operation and output: When the user presses the "Submit" button, the device securely transmits the entered task information to the server using the HTTPS protocol. The information is serialized in JSON format and transmitted.

[0873] Specific operation: The device sends a POST request to the endpoint (e.g. https: / / api.company.com / tasks).

[0874] Step 3:

[0875] Analysis, classification, and organization of task information by the server

[0876] Subject: Server

[0877] Input: Received task information

[0878] Operation and output: The server analyzes the received task information and extracts detailed data using natural language processing techniques. This data extraction is performed using Python's "NLTK" and "spaCy." Based on the analyzed data, it organizes the tasks in a list format and links related documents.

[0879] Specific operation: The server analyzes the task "Preparing year-end financial statements," links related documents (such as past financial data), and organizes them into a list.

[0880] Step 4:

[0881] Collection of communication logs

[0882] Subject: Server

[0883] Input: User emails, chat logs, MTG notes, etc.

[0884] Operation and output: The server collects communication information such as user emails, chat logs, and meeting notes via API. This information is collected using the Microsoft Graph API and Slack API. The collected communication logs are stored in a database.

[0885] What it does: The server uses the Microsoft Graph API to fetch relevant emails and chat logs from the user's mailbox and save them in a database.

[0886] Step 5:

[0887] Analyze communication logs and identify important contacts and schedules

[0888] Subject: Server

[0889] Input: Collected communication logs

[0890] Operation and output: The server analyzes the collected communication logs to identify important contacts and schedules related to the work. The analysis is performed using generative AI models and natural language processing algorithms. The results are stored in a database and linked to the schedule management system.

[0891] How it works: The server uses the Google Cloud Natural Language API to analyze the text of emails and chat logs, identifying important schedules such as "End of fiscal year closing meeting (December 15, 2023)" and contact information such as accountants.

[0892] Step 6:

[0893] Adding schedules to business calendars and notifications

[0894] Subject: Server

[0895] Input: Identified schedule and contacts

[0896] Actions and Output: The server adds the identified schedules and contacts to the business calendar and sends notifications to the user using the Google Calendar API or Microsoft Outlook Calendar API. The notifications are delivered to the user via email or in-app notifications.

[0897] Specific operation: The server uses the Google Calendar API to create an event on the business calendar and notify the user of the "Year-end closing meeting on December 15, 2023."

[0898] Step 7:

[0899] Annual schedule forecast

[0900] Subject: Server

[0901] Input: Past business data

[0902] Operation and output: The server analyzes past business data and uses a generative AI model to predict the upcoming annual schedule. This prediction is made using TensorFlow and PyTorch. The prediction results are stored in a database and displayed to the user.

[0903] Specific operation: The server inputs past schedule data into the model and predicts the submission deadline for 2023 as "December 31, 2023."

[0904] Step 8:

[0905] Business prioritization and resource optimization

[0906] Subject: Server

[0907] Input: Forecasted Schedule

[0908] Operation and output: The server prioritizes tasks and optimizes resources based on the predicted schedule. This is done using the Jira API and Asana API. The optimized resource information is notified to the user.

[0909] Specific actions: The server sets "Preparing Year-End Financial Statements" to high priority, secures the necessary resources (e.g., additional accounting staff), and notifies the user.

[0910] Through these steps, the system enables quick and efficient handover of work even in the event of sudden resignation or transfer.

[0911] (Application example 1)

[0912] 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."

[0913] In logistics centers, when an employee suddenly resigns or is transferred, there is a problem of work not being smoothly handed over. This can lead to delays and mistakes, which can lead to reduced efficiency. Furthermore, task priorities and resource optimization are not achieved, negatively impacting the productivity of the entire logistics center. To solve these issues, it is necessary to organize and classify work task information, identify important contacts and schedules, forecast annual schedules, and even propose the shortest routes for logistics work and identify urgent orders.

[0914] 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.

[0915] In this invention, the server includes means for inputting business task information, means for analyzing and classifying and organizing task information, means for collecting communication logs related to business tasks, means for analyzing the communication logs and identifying important contacts and schedules, means for linking the identified schedules and contacts with a business calendar, means for predicting an annual schedule from past business data, means for optimizing business priorities and resources based on the predicted schedule, means for analyzing and organizing picking data and delivery lists for logistics work based on tasks and proposing the shortest routes, and means for identifying urgent or additional orders for logistics work based on the collected communication logs. This enables smooth business handover and improved business efficiency even in the event of a sudden resignation or transfer at a logistics center.

[0916] The "work task information input means" is an interface that allows staff at the logistics center to input their own task information into the system.

[0917] The "task information analysis means" is a system that analyzes input task information and classifies and organizes each piece of information into a specific format.

[0918] The "communication log collection means" is a system that automatically collects business-related communication data such as emails and chat logs.

[0919] The "communication log analysis means" is a system for analyzing collected communication logs and identifying important contacts and schedules.

[0920] The "business calendar linking means" is a system for synchronizing the identified schedule and contacts with the business calendar and sending necessary notifications.

[0921] The "annual schedule forecasting tool" is a system that analyzes past business data and predicts important upcoming deadlines and tasks.

[0922] The "task priority and resource optimization means" is a system that sets the priority of tasks based on a predicted schedule and optimally allocates the necessary resources.

[0923] The "Picking Data Analysis and Organization Method" is a system that analyzes picking data for logistics work based on tasks, and classifies and organizes the data for efficient product collection.

[0924] The "shortest route suggestion means" is a system that suggests efficient picking routes based on categorized and organized picking data.

[0925] The "urgent order identification means" is a system for identifying urgent orders or additional orders for logistics work based on the collected communication logs.

[0926] This invention is a system for smoothly transferring work duties at a logistics center when an employee suddenly resigns or is transferred. This system is realized by combining the following means. The main technologies used are Python programs and AWS (Amazon Web Services).

[0927] Hardware and Software Configuration

[0928] AWS EC2: Hosts application servers.

[0929] AWS Comprehend: A natural language processing (NLP) service for text analysis.

[0930] Google Calendar API: An API for integrating with business calendars.

[0931] Python: A programming language that implements the entire program.

[0932] Specific processing of the system

[0933] A means of inputting task information for work

[0934] Subject: Device

[0935] Operation: Staff at the distribution center use smartphones or tablets to enter their task information into the system, including the task name, deadline, shelf number, and required quantity.

[0936] Example: For "picking work," enter the deadline "December 15, 2023," shelf number "A23," and required quantity "10."

[0937] Task information analysis method

[0938] Subject: Server

[0939] Operation: Receives input task information and parses it using AWS Comprehend, which categorizes and organizes each piece of information into a specific format.

[0940] Example: Analyzing "picking work" and classifying and organizing products for efficient product collection.

[0941] Communication log collection method

[0942] Subject: Server

[0943] Operation: Automatically collects communication data related to the logistics center, such as emails and chat logs.

[0944] Example: Collect information about "urgent additional orders" from the logistics center chat log.

[0945] Communication log analysis method

[0946] Subject: Server

[0947] Operation: Analyze the collected communication logs and identify important contacts and schedules. Again, use AWS Comprehend.

[0948] Example: Identifying the date and time of an "urgent reorder" and the associated contact.

[0949] Business calendar integration method

[0950] Subject: Server

[0951] Interaction: Synchronize identified schedules and contacts with your business calendar using the Google Calendar API and send necessary notifications.

[0952] Example: Add "Picking work on December 15, 2023" to the business calendar and send a reminder notification.

[0953] Annual schedule forecasting tool

[0954] Subject: Server

[0955] Operation: Analyze past business data and predict the upcoming annual schedule.

[0956] Example: Forecasting tasks for December and setting a schedule for preparations for the busy end-of-year season each year.

[0957] Business priorities and resource optimization measures

[0958] Subject: Server

[0959] Operation: Prioritize work and optimally allocate necessary resources based on the forecast schedule.

[0960] Example: Optimizing resources by increasing the number of picking staff during peak periods.

[0961] Picking data analysis and organization tool

[0962] Subject: Server

[0963] Operation: Based on task information, analyzes picking data for logistics work, and classifies and organizes it for efficient product collection.

[0964] Example: A picking sheet is displayed with an optimized route based on the shelf number of each product.

[0965] Shortest route suggestion method

[0966] Subject: Server

[0967] Operation: Based on the classified and organized picking data, an efficient picking route is proposed.

[0968] Example: The route display guides picking work in the order of "shelf number A23 → B15 → C8."

[0969] Urgent Order Identification Method

[0970] Subject: Server

[0971] Operation: Identify urgent or additional logistics orders based on collected communication logs.

[0972] Example: From the collected logs, identify the "urgent additional order on December 14, 2023" and reflect the data in the system.

[0973] Prompt Sentence Examples

[0974] 1. A scene where a staff member inputs "picking work":

[0975] Prompt: "Please enter the task name, due date, shelf number, and required quantity."

[0976] 2. A scene where a log saying "A new order has been received" is collected from the communication log:

[0977] Prompt: "Please enter the log data you want to collect next."

[0978] This invention improves operational efficiency and reduces work errors and delays even when sudden resignations or transfers occur at a logistics center. It also prevents important tasks and deadlines from being missed, improving productivity throughout the organization.

[0979] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0980] Step 1:

[0981] A means of inputting task information for work

[0982] Subject: User

[0983] Operation: Logistics center staff enter their task information into the system using a smartphone or tablet.

[0984] Input: Task name, due date, shelf number, required quantity

[0985] Output: The entered task information is sent to the server.

[0986] Specific operation: For example, for "picking work," the deadline "December 15, 2023," shelf number "A23," and required quantity "10" are entered.

[0987] Step 2:

[0988] Task information analysis method

[0989] Subject: Server

[0990] Operation: Receives input task information and parses it using AWS Comprehend.

[0991] Input: Task information entered in Step 1

[0992] Output: Classified task information as the analysis result

[0993] Specific operation: Analyzes "picking work" and classifies and organizes products based on product name, required quantity, and shelf number for efficient product collection.

[0994] Step 3:

[0995] Communication log collection method

[0996] Subject: Server

[0997] Operation: Automatically collects communication data related to the logistics center, such as emails, chat logs, and work records.

[0998] Input: Communication data (email, chat log)

[0999] Output: Collected communication log data

[1000] Specific actions: For example, accessing a mail server or chat system and collecting logs such as "urgent additional order."

[1001] Step 4:

[1002] Communication log analysis method

[1003] Subject: Server

[1004] Operation: Analyze the collected communication logs using AWS Comprehend to identify important contacts and schedules.

[1005] Input: Communication log data collected in Step 3

[1006] Output: A list of important contacts and schedules

[1007] Specific operation: For example, identify the "urgent additional order date and time" and the contact information of the relevant person.

[1008] Step 5:

[1009] Business calendar integration method

[1010] Subject: Server

[1011] Interaction: Synchronize identified schedules and contacts with your business calendar using the Google Calendar API and send necessary notifications.

[1012] Input: Schedule and contacts identified in Step 4

[1013] Output: Schedules and notifications reflected in the business calendar

[1014] Specific actions: For example, add "Picking work on December 15, 2023" to the work calendar and send a reminder notification to staff.

[1015] Step 6:

[1016] Annual schedule forecasting tool

[1017] Subject: Server

[1018] Operation: Analyze past business data and predict the upcoming annual schedule.

[1019] Input: Past business data

[1020] Output: Projected annual schedule

[1021] Specific actions: Analyze tasks for the busy end-of-year period each year and set a preparation schedule for December.

[1022] Step 7:

[1023] Business priorities and resource optimization measures

[1024] Subject: Server

[1025] Operation: Prioritize work and optimally allocate necessary resources based on the forecast schedule.

[1026] Input: Annual schedule forecasted in Step 6

[1027] Output: Optimized resource allocation plan

[1028] Specific actions: For example, increasing the number of picking staff during peak periods to optimize resources.

[1029] Step 8:

[1030] Picking data analysis and organization tool

[1031] Subject: Server

[1032] Operation: Based on task information, analyzes picking data for logistics work, and classifies and organizes it for efficient product collection.

[1033] Input: Task information entered in Step 1

[1034] Output: Categorized and organized picking data

[1035] Specific operation: Based on the shelf number of each product, the picking sheet is optimized and displayed along the shortest route.

[1036] Step 9:

[1037] Shortest route suggestion method

[1038] Subject: Server

[1039] Operation: Based on the classified and organized picking data, an efficient picking route is proposed.

[1040] Input: Picking data categorized and organized in step 8

[1041] Output: Proposed picking route

[1042] Specific operation: For example, guide picking work along a route such as "shelf number A23 → B15 → C8."

[1043] Step 10:

[1044] Urgent Order Identification Method

[1045] Subject: Server

[1046] Operation: Identify urgent or additional logistics orders based on collected communication logs.

[1047] Input: Communication log data collected in Step 3

[1048] Output: Identified urgent order information

[1049] Specific operations: Identify the "urgent additional order on December 14, 2023" from the collected logs and reflect the corresponding data in the system.

[1050] 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.

[1051] This invention is a system that combines generative AI technology with an emotion engine to solve the problem of insufficient task handovers. This system achieves more effective and efficient task handovers by integrating task information input, analysis, classification and organization, communication log collection and analysis, business calendar integration, annual schedule forecasting, task priority and resource optimization, and an emotion engine that recognizes user emotions.

[1052] What the program does

[1053] 1. Enter task information:

[1054] Subject: User

[1055] The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[1056] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[1057] 2. Analysis and classification of task information:

[1058] Subject: Server

[1059] The server receives task information sent by the user, performs text analysis, and extracts the task name, deadline, person in charge, and necessary materials. It then generates a task list based on the extracted task information and classifies and organizes each task into the appropriate category.

[1060] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[1061] 3. Collecting communication logs:

[1062] Subject: Server

[1063] The server automatically collects communication history such as users' emails, chat logs, and meeting notes.

[1064] Example: The server retrieves email logs from Google Mail for the past three months.

[1065] 4. Analyzing communication logs and identifying important contacts and schedules:

[1066] Subject: Server

[1067] The server analyzes the collected communication logs using natural language processing technology to identify important contacts and schedules related to business operations.

[1068] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[1069] 5. Integration with business calendars:

[1070] Subject: Server

[1071] The server automatically enters the identified schedule and contact information into the business calendar and notifies the user. It also sets reminder notifications for the entered schedule.

[1072] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar and send a reminder to the user.

[1073] 6. Annual Schedule Forecast:

[1074] Subject: Server

[1075] The server analyzes past business data, predicts the upcoming annual schedule, and generates a new schedule.

[1076] Example: The server retrieves the "financial report submission schedule for the past five years" from the database and predicts "December 31, 2023" as the next "year-end financial report submission date."

[1077] 7. Business Prioritization and Resource Optimization:

[1078] Subject: Server

[1079] The server prioritizes tasks and optimizes resources based on the predicted schedule and current task information.

[1080] Example: A server sets "Preparing year-end financial reports" as a high priority and allocates the necessary resources (e.g., additional accounting staff) for it.

[1081] Emotion engine integration

[1082] 1. Emotion recognition using emotion engine:

[1083] Subject: Server

[1084] The server uses the user's input and behavior log to drive the emotion engine and recognize the user's emotions.

[1085] Example: A server analyzes a user's keystrokes and mouse movements to detect stress levels.

[1086] 2. Adjusting work priorities and resource optimization based on sentiment data:

[1087] Subject: Server

[1088] The server takes into account the emotional data recognized by the emotion engine and adjusts the priority of tasks and resource allocation.

[1089] Example: If a user is under high stress, the server will adjust the priority of the task to be assigned to a less demanding task.

[1090] 3. Adjust reminder notifications based on your emotions:

[1091] Subject: Server

[1092] The server adjusts the content and timing of the reminder notification based on the emotional state recognized by the emotion engine.

[1093] Example: If the user is feeling stressed, the server can flexibly adjust the arrival time of the reminder notification (for example, send it a little later).

[1094] This system allows users to smoothly continue their work even in the event of a sudden resignation or transfer, thanks to automated handover processing and support from an emotion engine. This reduces the burden on successors and improves work efficiency across the company. It also provides more human-like support by optimizing tasks and adjusting notifications based on the user's emotional state.

[1095] The processing flow will be explained below.

[1096] Processing steps for entering task information for work

[1097] Step 1:

[1098] Subject: User

[1099] Operation: The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[1100] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[1101] Processing steps for analyzing, classifying, and organizing task information

[1102] Step 1:

[1103] Subject: Server

[1104] Operation: The server receives task information sent by the user.

[1105] Example: The server receives task information "Prepare fiscal year-end financial report."

[1106] Step 2:

[1107] Subject: Server

[1108] Operation: The server launches a natural language processing module to perform text analysis on the received task information, extracting the task name, deadline, person in charge, and required materials.

[1109] Example: The server extracts the task name "Preparing the year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[1110] Step 3:

[1111] Subject: Server

[1112] Operation: The server generates a task list based on the extracted task information and classifies and organizes each task into an appropriate category.

[1113] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[1114] Process steps for collecting communication logs

[1115] Step 1:

[1116] Subject: Server

[1117] How it works: The server starts a process to automatically collect a user's communication history, including emails, chat logs, and meeting notes.

[1118] Example: The server retrieves email logs from Google Mail for the past three months.

[1119] Processing steps for analyzing communication logs and identifying important contacts and schedules

[1120] Step 1:

[1121] Subject: Server

[1122] Operation: The server analyzes the collected communication logs using natural language processing technology.

[1123] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[1124] Step 2:

[1125] Subject: Server

[1126] How it works: The server uses the analysis results to identify important business contacts and upcoming schedules.

[1127] Example: A server identifies "year-end closing meeting on December 15, 2023" and the associated contact (e.g., accountant).

[1128] Process steps for linking with business calendars

[1129] Step 1:

[1130] Subject: Server

[1131] Behavior: The server calls an API to automatically enter the specified schedule and contacts into the business calendar.

[1132] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar.

[1133] Step 2:

[1134] Subject: Server

[1135] Operation: The server sets a reminder notification for the entered schedule and notifies the user.

[1136] Example: Set the server to send a reminder notification on "December 13, 2023".

[1137] Processing steps for forecasting annual schedules

[1138] Step 1:

[1139] Subject: Server

[1140] Operation: The server launches a process to collect and analyze historical business data.

[1141] Example: A server retrieves the "financial report filing schedule for the past five years" from a database.

[1142] Step 2:

[1143] Subject: Server

[1144] How it works: The server predicts the annual schedule based on the collected data and generates a new schedule.

[1145] Example: The server predicts "December 31, 2023" as the next "year-end financial statement submission date" and registers it in the schedule.

[1146] Business Priority and Resource Optimization Process Steps

[1147] Step 1:

[1148] Subject: Server

[1149] How it works: The server prioritizes tasks based on the predicted schedule and current task information.

[1150] Example: The server sets "Prepare Year-End Financial Report" as a high priority.

[1151] Step 2:

[1152] Subject: Server

[1153] Operation: The server executes a process to optimize resource allocation based on the configured priorities.

[1154] Example: The server assigns "Additional staff in the accounting department" to the task "Prepare year-end financial report."

[1155] Processing steps for emotion engine integration

[1156] Step 1:

[1157] Subject: Server

[1158] Operation: The server uses the user's input and behavior log to drive the emotion engine and recognize the user's emotions.

[1159] Example: A server analyzes a user's keystrokes and mouse movements to detect stress levels.

[1160] Step 2:

[1161] Subject: Server

[1162] Operation: The server takes into account the emotional data recognized by the emotion engine and adjusts task priorities and resource allocation.

[1163] Example: If a user is under high stress, the server will adjust the priority of the task to be assigned to a less demanding task.

[1164] Step 3:

[1165] Subject: Server

[1166] How it works: The server adjusts the content and timing of reminder notifications based on the emotional state recognized by the emotion engine.

[1167] Example: If the user is feeling stressed, the server can flexibly adjust the arrival time of the reminder notification (for example, send it a little later).

[1168] In this way, this invention enables smooth business continuity even in the event of sudden resignation or transfer by integrating an AI-based automated handover process with an emotion engine. This reduces the burden on successors and improves business efficiency across the company. It also provides more human-like support by optimizing tasks and adjusting notifications based on the user's emotional state.

[1169] Example 2

[1170] 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."

[1171] In conventional work handovers, there have been problems with the handover not proceeding smoothly due to insufficient or inaccurate task information, insufficient understanding of communication logs, etc. Furthermore, the emotional state of the person in charge of the handover can affect work efficiency, and insufficient management of this makes it difficult to optimize work priorities and resource allocation. To solve these problems, accurate analysis of task information, appropriate analysis of communication logs, and work adjustments based on emotional data are required.

[1172] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1173] In this invention, the server includes means for inputting work task information, means for analyzing and classifying and organizing the task information, means for collecting communication logs related to the work tasks, means for analyzing the communication logs and identifying important contacts and dates, means for linking the identified dates and contacts with a work calendar, means for predicting an annual schedule from past work data, means for optimizing work priorities and resources based on the predicted schedule, means for recognizing user emotions using an emotion engine, and means for adjusting work priorities and resource allocation based on the recognized emotion data. This enables effective and efficient work handover and realizes appropriate work distribution and resource management that takes into account the user's emotional state.

[1174] "Task information for a business" is information such as the task name, deadline, person in charge, and necessary materials related to the business that is input by the user into the system.

[1175] "Means for inputting task information" refers to an interface or device that allows a user to input work task information into the system.

[1176] "Means for analyzing, classifying, and organizing task information" refers to the process or algorithm by which the server analyzes the input task information and classifies and organizes each task into an appropriate category.

[1177] "Communication log" refers to the communication history such as emails, chats, and meeting notes that are generated when a user performs their work.

[1178] "Means for collecting communication logs" refers to a function or device that allows the server to automatically collect users' emails, chat logs, meeting notes, etc.

[1179] "Means for analyzing communication logs and identifying important contacts and schedules" refers to the process or algorithm for analyzing communication logs collected by the server and identifying important business-related contacts and schedules.

[1180] "Business calendar" refers to a calendar application or system used to manage business-related schedules.

[1181] "Means for linking with business calendars" refers to the processes or algorithms by which the server automatically enters identified schedules and contacts into a business calendar and notifies the user.

[1182] "Means for predicting annual schedules" refers to the processes and algorithms that the server uses to analyze past business data and predict future annual schedules.

[1183] "Means for optimizing task priorities and resources" refers to the processes and algorithms that allow the server to prioritize tasks and optimally allocate required resources based on predicted schedules and current task information.

[1184] An "emotion engine" refers to the technology and algorithms that analyze user input and behavioral logs to recognize the user's emotions and psychological state.

[1185] "Means for recognizing emotions" refers to the process or algorithm for recognizing a user's emotions using an emotion engine.

[1186] "Means for adjusting task priorities and resource allocations based on emotional data" refers to processes or algorithms for dynamically adjusting task priorities and resource allocations based on recognized emotional data.

[1187] This invention is a system that combines generative AI technology with an emotion engine to solve the problem of insufficient task handovers. This system realizes more effective and efficient task handovers by integrating task information input, analysis, classification and organization, communication log collection and analysis, business calendar integration, annual schedule forecasting, task priority and resource optimization, and an emotion engine that recognizes user emotions.

[1188] Entering task information for work

[1189] Hardware and software: devices (computers, tablets, smartphones, etc.), business management software

[1190] What happens:

[1191] The user uses a terminal to input task information for work. The input information includes the task name, deadline, person in charge, and required materials. As a specific example, a user inputs the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[1192] Analysis, classification, and organization of task information

[1193] Hardware and software: Server, natural language processing engine

[1194] What happens:

[1195] The server receives task information sent by the user and performs text analysis using a natural language processing engine to extract the task name, deadline, person in charge, and required documents. It then generates a task list based on the extracted task information and classifies and organizes each task into appropriate categories. For example, the server might classify "Preparing the fiscal year-end financial report" into the "Financial Report" category and organize related documents and reference links.

[1196] Collection of communication logs

[1197] Hardware and software: Servers, communication log collection software (email API, chat API, etc.)

[1198] What happens:

[1199] The server automatically collects communication history such as users' emails, chat logs, meeting notes, etc. For example, the server retrieves email logs from the "email API" for the past three months.

[1200] Analyze communication logs and identify important contacts and schedules

[1201] Hardware and software: Server, natural language processing engine

[1202] What happens:

[1203] The server analyzes the collected communication logs using natural language processing technology to identify important business-related contacts and schedules. For example, the server can identify "minutes of meetings regarding fiscal year-end settlement" or "emails responding to important customers" and extract the relevant information.

[1204] Integration with business calendars

[1205] Hardware and software: Server, business calendar API (e.g. Google Calendar API)

[1206] What happens:

[1207] The server automatically enters the identified schedule and contacts into the business calendar and notifies the user. It also sets reminder notifications. As a specific example, the server uses the "Business Calendar API" to add "Year-end Closing Meeting on December 15, 2023" to the calendar and sends a reminder notification to the user.

[1208] Annual schedule forecast

[1209] Hardware and software: Servers, data analysis engines

[1210] What happens:

[1211] The server analyzes past business data and predicts the upcoming annual schedule. For example, the server retrieves the "financial report submission schedule for the past five years" from the database and predicts "December 31, 2023" as the next "year-end financial report submission date."

[1212] Business prioritization and resource optimization

[1213] Hardware and software: Servers, resource management engines

[1214] What happens:

[1215] The server prioritizes tasks based on the predicted schedule and current task information, and optimally allocates the necessary resources. For example, the server may assign "preparing the fiscal year-end financial report" as a high priority and secure the necessary resources (e.g., additional accounting staff).

[1216] Emotion engine integration

[1217] Emotion recognition by emotion engine

[1218] Hardware and software: Server, sentiment analysis engine

[1219] What happens:

[1220] The server uses the user's input and behavior logs to drive the emotion engine and recognize the user's emotions. For example, the server analyzes the user's keystrokes and mouse movements to detect their stress level.

[1221] Adjusting work priorities and resource optimization based on sentiment data

[1222] Hardware and software: Server, sentiment analysis engine

[1223] What happens:

[1224] The server adjusts task priorities and resource allocation based on the emotional data recognized by the emotion engine. For example, if a user is in a high-stress state, the server will assign less demanding tasks a high priority.

[1225] Adjusting reminder notifications based on emotions

[1226] Hardware and software: Server, sentiment analysis engine

[1227] What happens:

[1228] The server adjusts the content and timing of reminder notifications based on the emotional state recognized by the emotion engine. For example, if the user is feeling stressed, the server will adjust the time at which the reminder notification arrives to a slightly later date.

[1229] Prompt Sentence Examples

[1230] "Add a task to the work calendar to prepare the year-end financial report, and enter the person responsible and due date."

[1231] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1232] Step 1:

[1233] The user uses the terminal to input task information for the work.

[1234] Input includes the task name, deadline, person in charge, and required materials. This information is sent to the system.

[1235] Input: Task name: "Prepare fiscal year-end financial report", deadline: "December 31, 2023", person in charge: "Sato from the Accounting Department"

[1236] Output: Submitted task information

[1237] Specific behavior:

[1238] When the user fills in the task information in the input form on the terminal and presses the "send" button, the task information is sent to the server.

[1239] Step 2:

[1240] The server receives the task information sent by the user and performs text analysis.

[1241] This analysis extracts task names, deadlines, people in charge, and required materials, and stores the extracted information in a database.

[1242] Input: Submitted task information

[1243] Output: Extracted task name, due date, assignee, required materials

[1244] Specific behavior:

[1245] The server uses a "natural language processing engine" to analyze the text of the submitted task information, extract important elements, and store them in a database.

[1246] Step 3:

[1247] The server generates a list of tasks based on the analyzed task information and classifies and organizes each task into an appropriate category.

[1248] This information is visualized as a list of tasks.

[1249] Input: Extracted task name, deadline, assignee, required materials

[1250] Output: A categorized list of tasks

[1251] Specific behavior:

[1252] The server processes the task information it receives using a classification algorithm, assigns it to a category such as "financial reporting," and displays it as a list of tasks.

[1253] Step 4:

[1254] The server automatically collects communication history such as users' emails, chat logs, and meeting notes.

[1255] The collected communication logs are stored in a database.

[1256] Input: User communication history data

[1257] Output: Collected communication logs

[1258] Specific behavior:

[1259] The server uses "communication log collection software" to collect communication logs from interfaces such as "email API" for the past three months and stores them in a database.

[1260] Step 5:

[1261] The server analyzes the collected communication logs and identifies important business-related contacts and schedules.

[1262] The results of this analysis are reflected in the business calendar.

[1263] Input: Collected communication logs

[1264] Output: Important contacts and schedules

[1265] Specific behavior:

[1266] The server uses a "natural language processing engine" to analyze the collected communication logs and extract important information such as "meeting minutes" and "responses to important customers."

[1267] Step 6:

[1268] The server automatically enters the identified schedule and contacts into the business calendar and notifies the user.

[1269] Also, set reminder notifications.

[1270] Input: Important contacts and schedules

[1271] Output: Schedule entries and notifications in the business calendar

[1272] Specific behavior:

[1273] The server uses the Calendar API to add schedules such as "Meeting on December 15, 2023" to the business calendar, set reminders, and notify the user.

[1274] Step 7:

[1275] The server analyzes past business data and predicts the upcoming annual schedule.

[1276] The predicted schedule is notified to the user.

[1277] Input: Past business data

[1278] Output: Projected annual schedule

[1279] Specific behavior:

[1280] The server uses a "data analysis engine" to analyze past schedule data, generate predictions such as the "next fiscal year-end financial statement submission date," and notify the user.

[1281] Step 8:

[1282] The server prioritizes tasks based on the predicted schedule and current task information, and optimally allocates the necessary resources.

[1283] Input: Projected schedule and current task information

[1284] Output: Set work priorities and resource allocations

[1285] Specific behavior:

[1286] The server uses a "resource management engine" to set task priorities and optimally allocate resources (e.g., personnel and time) based on those priorities.

[1287] Step 9:

[1288] The server runs an emotion engine to recognize the user's emotion data.

[1289] It recognizes emotions from user input and behavior logs and saves the results.

[1290] Input: User input and action logs

[1291] Output: Recognized emotion data

[1292] Specific behavior:

[1293] The server uses an "emotion analysis engine" to analyze the user's keystrokes and mouse movements to recognize and record their stress level and emotional state.

[1294] Step 10:

[1295] The server adjusts the priority of tasks and resource allocation based on the emotion data.

[1296] This allows less taxing tasks to be prioritized when the user's stress level is high.

[1297] Input: Recognized emotion data

[1298] Output: Aligned work priorities and resource allocation

[1299] Specific behavior:

[1300] Based on the recognized emotion data, the server adjusts the priority setting algorithm, assigning low-burden tasks a higher priority, and reallocating resources.

[1301] Step 11:

[1302] The server adjusts the content and timing of the reminder notification based on the emotional state recognized by the emotion engine.

[1303] This delays the timing of the reminder notification when the user's stress level is high.

[1304] Input: Recognized emotion data

[1305] Output: Adjusted reminder notification

[1306] Specific behavior:

[1307] The server analyzes the emotional data and runs an algorithm that delays the timing of sending reminder notifications if the stress level is high.

[1308] (Application example 2)

[1309] 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."

[1310] In today's society, where there is a demand for improved work efficiency that takes into account the lack of task handovers and human emotions, the lack of smooth employee task handovers, resulting in oversights and confusion during the handover process, is a major issue. Another problem is the lack of support that takes into account the stress and emotions of employees performing the handover. Appropriate information sharing and emotional support are particularly essential for on-site work, such as in factories. Therefore, a system that integrates the efficiency of task handovers with emotion recognition is needed.

[1311] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1312] In this invention, the server includes means for inputting business task information, means for analyzing and classifying and organizing the task information, means for collecting communication history related to the business tasks, means for analyzing the communication history and identifying important contacts and dates, means for linking the identified dates and contacts with a business calendar, means for predicting an annual schedule from past business data, means for optimizing business priorities and resources based on the predicted schedule, and means for adjusting business priorities and resources based on an emotion engine that recognizes user emotion data. This makes business handover more efficient and enables optimal business support that takes human emotions into consideration.

[1313] "Business task information" refers to details of specific tasks and work to be performed in a business.

[1314] "Analysis" is the process of analyzing input data or information and extracting specific patterns or meanings.

[1315] "Classification and organization" refers to the process of dividing and organizing information according to specific categories and orders based on analyzed information.

[1316] "Communication history" is a record of business communications that a user has had, such as emails, chats, and meeting notes.

[1317] "Important Contacts" refers to the contact information of parties and personnel essential to the performance of business.

[1318] A "date" is the date and time when a particular task or event is scheduled to occur.

[1319] A "business calendar" is a calendar system for managing business schedules and plans.

[1320] An "annual plan" refers to a work schedule for one year, predicted based on past data.

[1321] "Business priority" is a criterion for determining which business tasks should be performed first, based on their importance and urgency.

[1322] "Resource optimization" refers to the optimal allocation of resources such as personnel and equipment in order to carry out business operations efficiently.

[1323] An "emotion engine" is a system that recognizes the user's emotional state and provides appropriate feedback and support.

[1324] "Emotion data" is data that expresses the user's emotional state using numerical values ​​or categories.

[1325] As an example of how to implement this invention, we will show an example of introducing a system that combines generative AI technology and an emotion engine for factory workers. Below, we will explain the specific process of this system and the technology and data processing methods used.

[1326] System Overview

[1327] The system is comprised mainly of a server and user devices (e.g., smart glasses), and supports employee work handover and efficiency through the management of work task information, emotion recognition, analysis of communication history, and integration with work calendars.

[1328] 1. Entering work task information

[1329] The user inputs task information (e.g., task name, deadline, and person in charge) through the smart glasses interface, which is then sent to the server and stored in a database.

[1330] 2. Analysis and classification of task information

[1331] The server analyzes the input task information and uses natural language processing techniques (e.g., TfidfVectorizer) to extract task names, deadlines, and assignees, then classifies the tasks into categories using KMeans clustering.

[1332] 3. Collection and analysis of communication history

[1333] The server collects communication logs such as users' emails and chat history, analyzes them using natural language processing technology, and identifies important contacts and schedules, linking them to task information.

[1334] 4. Integration with business calendars

[1335] The server automatically enters the identified important contacts and dates into the business calendar using calendar integration technologies such as the Google Calendar API, making it easier to manage task schedules.

[1336] 5. Annual schedule forecast

[1337] The server analyzes past business data and runs an algorithm to predict annual schedules, which then predicts important upcoming dates and automatically generates a new schedule.

[1338] 6. Business Prioritization and Resource Optimization

[1339] The server prioritizes tasks based on the predicted schedule and executes algorithms to optimize resources accordingly, for example, by optimally allocating resources to the necessary personnel.

[1340] 7. Emotion engine integration

[1341] The emotion engine recognizes user emotions based on user input and behavioral logs. This process uses machine learning frameworks such as Keras and TensorFlow. Based on the recognized emotion data, work priorities and resources are adjusted.

[1342] Specific examples

[1343] The user uses the smart glasses to input task information such as "Preparing the year-end financial report, 2023-12-31, Accounting Department staff."

[1344] The server collects emails and chat logs and identifies "important meeting minutes" and "customer response emails."

[1345] The server automatically adds important dates such as "year-end closing meeting" to Google Calendar and sets reminder notifications.

[1346] The generative AI model analyzes the prompt, "I'm overwhelmed with the amount of tasks I have to do right now..." and determines that the user is in a state of high stress.

[1347] This embodiment allows factory workers to efficiently take over tasks through smart glasses and receive optimal support based on emotion recognition.

[1348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1349] Step 1:

[1350] The user uses the smart glasses interface to input task information, including the task name, deadline, and assignee, which is then sent to the server and stored in a database.

[1351] Step 2:

[1352] The server analyzes the received business task information. It uses the natural language processing technology TfidfVectorizer to perform text analysis to extract task names, deadlines, and responsible persons. Based on the analysis results, it organizes the task information and compiles it into a list.

[1353] Step 3:

[1354] The server applies the KMeans clustering algorithm to categorize the business task information. This categorizes tasks into related categories and groups similar tasks together in the task list. The input data is the analysis results, and the output data is the category information for each task.

[1355] Step 4:

[1356] The server collects communication logs such as user emails and chat history. To do this, it connects to the email server or chat server via API and obtains the log data. The collected communication logs are stored in a database.

[1357] Step 5:

[1358] The server analyzes the collected communication logs and uses natural language processing technology to identify important contacts and schedules. The important information obtained from this analysis is used in the next step. The input data is the communication logs, and the output data is the identified important contacts and schedule information.

[1359] Step 6:

[1360] The server automatically enters important contacts and dates into the business calendar using calendar integration technology such as the Google Calendar API, and the entered information is reflected in the user's calendar application.

[1361] Step 7:

[1362] The server analyzes past business data and predicts the annual schedule. This identifies important dates for the next year and generates a new schedule. The input data is past business data, and the output data is the predicted annual schedule.

[1363] Step 8:

[1364] The server prioritizes tasks based on the predicted annual schedule and runs a resource optimization algorithm, which prioritizes each task and allocates the necessary resources.

[1365] Step 9:

[1366] The server runs an emotion engine based on user input and behavior logs to recognize the user's emotions. The emotion engine uses machine learning frameworks such as Keras and TensorFlow. The input data is the user's behavior log and input data, and the output data is the recognized emotion information.

[1367] Step 10:

[1368] The server adjusts task priorities and resources based on the recognized emotion data. For example, if a user is in a high-stress state, it adjusts resource and task allocation to prioritize less demanding tasks.

[1369] The above processing steps make the handover of work more efficient and provide optimal support based on emotion recognition.

[1370] 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.

[1371] 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.

[1372] 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.

[1373] [Third embodiment]

[1374] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1375] 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.

[1376] 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).

[1377] 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.

[1378] 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.

[1379] 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).

[1380] 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. 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.

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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."

[1386] This invention is a system that uses generative AI technology to ensure smooth work handovers in the event of sudden resignation or transfer. This system provides functions such as inputting, analyzing, classifying and organizing task information, collecting and analyzing communication logs, linking with business calendars, forecasting annual schedules, and optimizing work priorities and resources.

[1387] What the program does

[1388] 1. Enter task information:

[1389] Subject: User

[1390] Users input task information into the system, including the task name, deadline, person in charge, and required materials.

[1391] Example: A user enters "Preparing year-end financial statements" as the task name, "December 31, 2023" as the deadline, and "Sato from the accounting department" as the person in charge.

[1392] 2. Analysis and classification of task information:

[1393] Subject: Server

[1394] The server receives task information entered by the user, performs text analysis, extracts detailed task information, and organizes it in a list format.

[1395] Example: The server categorizes the "preparing year-end financial statements" task and links related documents and data (e.g., historical financial data).

[1396] 3. Collecting communication logs:

[1397] Subject: Server

[1398] The server collects communication information such as users' emails, chat logs, and meeting notes.

[1399] Example: The server retrieves the minutes of the "accounting meeting" and "customer correspondence emails" from the user's mailbox.

[1400] 4. Analyzing communication logs and identifying important contacts and schedules:

[1401] Subject: Server

[1402] The server analyzes the collected communication logs and identifies important contacts and schedules related to business.

[1403] Example: A server identifies an event called "Year-End Closing Meeting (December 15, 2023)" and identifies the associated contact (e.g., accountant).

[1404] 5. Integration with business calendars:

[1405] Subject: Server

[1406] The server adds the identified schedule and contacts to the business calendar and notifies the user.

[1407] Example: The server adds "Year-end closing meeting on December 15, 2023" to the business calendar and sends a reminder to the user.

[1408] 6. Annual Schedule Forecast:

[1409] Subject: Server

[1410] The server analyzes past business data and predicts the upcoming year's schedule, setting important deadlines and tasks in advance.

[1411] Example: A server models the deadlines for financial statements at the end of each year and predicts the deadline for 2023 as December 31, 2023.

[1412] 7. Business Prioritization and Resource Optimization:

[1413] Subject: Server

[1414] The server sets the priority of operations and optimizes resources based on the predicted schedule, thereby improving business efficiency.

[1415] Example: The server sets "Preparing year-end financial statements" as a "high" priority and allocates the necessary resources (e.g., additional accounting staff) for it.

[1416] This system allows users to smoothly and effectively transfer their work even in the event of a sudden resignation or transfer, thanks to automated handover processing using AI. Successors can clearly understand tasks and schedules, allowing them to take over work quickly.

[1417] This system not only improves business continuity and reduces the burden on successors, but also contributes to improving the efficiency of the entire organization. It also prevents important tasks and deadlines from being overlooked, helping to ensure that work is carried out reliably.

[1418] The processing flow will be explained below.

[1419] Processing steps for entering task information for work

[1420] Step 1:

[1421] Subject: User

[1422] Operation: The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[1423] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[1424] Processing steps for analyzing, classifying, and organizing task information

[1425] Step 1:

[1426] Subject: Server

[1427] Operation: The server receives task information sent by the user.

[1428] Example: The server receives task information "Prepare fiscal year-end financial report."

[1429] Step 2:

[1430] Subject: Server

[1431] Operation: The server launches a natural language processing module to perform text analysis on the received task information, extracting the task name, deadline, person in charge, and required materials.

[1432] Example: The server extracts the task name "Preparing the year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[1433] Step 3:

[1434] Subject: Server

[1435] Operation: The server generates a task list based on the extracted task information and classifies and organizes each task into an appropriate category.

[1436] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[1437] Process steps for collecting communication logs

[1438] Step 1:

[1439] Subject: Server

[1440] How it works: The server starts a process to automatically collect a user's communication history, including emails, chat logs, and meeting notes.

[1441] Example: The server retrieves email logs from Google Mail for the past three months.

[1442] Processing steps for analyzing communication logs and identifying important contacts and schedules

[1443] Step 1:

[1444] Subject: Server

[1445] Operation: The server analyzes the collected communication logs using natural language processing technology.

[1446] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[1447] Step 2:

[1448] Subject: Server

[1449] How it works: The server uses the analysis results to identify important business contacts and upcoming schedules.

[1450] Example: A server identifies "year-end closing meeting on December 15, 2023" and the associated contact (e.g., accountant).

[1451] Process steps for linking with business calendars

[1452] Step 1:

[1453] Subject: Server

[1454] Behavior: The server calls an API to automatically enter the specified schedule and contacts into the business calendar.

[1455] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar.

[1456] Step 2:

[1457] Subject: Server

[1458] Operation: The server sets a reminder notification for the entered schedule and notifies the user.

[1459] Example: Set the server to send a reminder notification on "December 13, 2023".

[1460] Processing steps for forecasting annual schedules

[1461] Step 1:

[1462] Subject: Server

[1463] Operation: The server launches a process to collect and analyze historical business data.

[1464] Example: A server retrieves the "financial report filing schedule for the past five years" from a database.

[1465] Step 2:

[1466] Subject: Server

[1467] How it works: The server predicts the annual schedule based on the collected data and generates a new schedule.

[1468] Example: The server predicts "December 31, 2023" as the next "year-end financial statement submission date" and registers it in the schedule.

[1469] Business Priority and Resource Optimization Process Steps

[1470] Step 1:

[1471] Subject: Server

[1472] How it works: The server prioritizes tasks based on the predicted schedule and current task information.

[1473] Example: The server sets "Prepare Year-End Financial Report" as a high priority.

[1474] Step 2:

[1475] Subject: Server

[1476] Operation: The server executes a process to optimize resource allocation based on the configured priorities.

[1477] Example: The server assigns "Additional staff in the accounting department" to the task "Prepare year-end financial report."

[1478] In this way, the present invention enables smooth business continuity through automated handover processing using AI, even in the event of a sudden resignation or transfer, thereby reducing the burden on successors and improving business efficiency across the entire company.

[1479] Example 1

[1480] 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."

[1481] Sudden resignations or transfers can cause confusion and prevent smooth work handovers. In such situations, it can be difficult for successors to quickly grasp the necessary information and take over work efficiently. Important tasks, deadlines, and contact information may be overlooked, leading to work stagnation and mistakes. Therefore, there is a need for a system that streamlines work handovers and collects and organizes all necessary information.

[1482] 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.

[1483] In this invention, the server includes means for using natural language processing when analyzing task information entered by a user, means for using a generative AI model when analyzing collected communication logs, means for predicting an annual schedule using past data with the generative AI model, and means for setting priorities for tasks based on the predicted schedule and optimizing resources through an information system. This enables a successor to smoothly and efficiently take over tasks through automated handover processing, even in the event of a sudden resignation or transfer.

[1484] "Business task information" refers to information including details of tasks related to business entered by a user.

[1485] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[1486] A "generative AI model" is an algorithm that uses artificial intelligence to generate new information from data.

[1487] A "communication log" is a record of emails, chats, meeting notes, and the like that a user makes in the course of their work.

[1488] A "schedule management system" is software or a system for managing work schedules and tasks.

[1489] An "annual schedule" is a calendar that lists important dates, times, and tasks for work throughout the year.

[1490] "Priority" refers to the ranking determined based on the importance and urgency of a business task.

[1491] "Resources" refer to the personnel, equipment, time, and other resources required to carry out a task.

[1492] An "information system" is a computer system used to manage an organization's information.

[1493] "Notifications" are alerts or messages that the system sends to inform the user.

[1494] This invention is a system that uses generative AI technology to ensure smooth work handovers in the event of sudden resignation or transfer. This system provides functions such as input, analysis, classification, and organization of work task information, collection and analysis of communication logs, work calendar integration, annual schedule forecasting, and work priority and resource optimization. The following hardware and software are used to implement this system.

[1495] Hardware and software used

[1496] Server: A computer system for analyzing task information, collecting and analyzing communication logs, forecasting schedules, and optimizing resources. For example, a cloud-based server (AWS, Google Cloud) is used.

[1497] Natural language processing libraries: Used to analyze user-entered text. These include Python's "NLTK" and "spaCy."

[1498] Generative AI models: Used for data analysis and prediction, using machine learning libraries such as TensorFlow and PyTorch.

[1499] API: Used for communication log collection and calendar integration. For example, "Microsoft Graph API" and "Google Calendar API."

[1500] Web browser: Used as an interface for users to enter work task information.

[1501] System processing flow and operation

[1502] Entering task information for work

[1503] Users enter task information using a dedicated input form, which functions as an interface displayed in a web browser, and users enter detailed information such as the task name, deadline, person in charge, and required materials.

[1504] Examples:

[1505] Task Name: Prepare year-end financial statements

[1506] Deadline: December 31, 2023

[1507] Person in charge: Sato from the Accounting Department

[1508] Required documents: Past financial data, accounting software documents

[1509] Sending task information to the server

[1510] When the user presses the "Send" button, the user's device sends the entered task information to the server. The transmission is securely performed using the HTTPS protocol.

[1511] Analysis, classification, and organization of task information

[1512] The server analyzes the received task information and extracts detailed information using natural language processing techniques. Based on the extracted information, the server organizes the tasks in a list format.

[1513] Specific behavior:

[1514] The server uses the Python NLTK library to parse the input text and classify it based on the characteristics of the task.

[1515] Collection of communication logs

[1516] The server collects communication information such as users' emails, chat logs, and meeting notes via a specified API.

[1517] Specific behavior:

[1518] The server uses the Microsoft Graph API to fetch relevant emails and chat logs from the user's mailbox.

[1519] Analyze communication logs and identify important contacts and schedules

[1520] The server analyzes the collected communication logs to identify important contacts and schedules, using a generative AI model for the analysis.

[1521] Specific behavior:

[1522] The server uses the Google Cloud Natural Language API to analyze text from emails and chat logs to extract important events and contact information.

[1523] Adding schedules to business calendars and notifications

[1524] The server adds the identified schedule and contacts to a business calendar (e.g., Google Calendar) and notifies the user of the information.

[1525] Specific behavior:

[1526] The server creates the event through the Google Calendar API and sends an email notification to the user.

[1527] Annual schedule forecast

[1528] The server analyzes past business data and uses a generative AI model to predict future annual schedules.

[1529] Specific behavior:

[1530] The server reads past schedule data and uses the TensorFlow library to train a model to predict future schedules.

[1531] Business prioritization and resource optimization

[1532] The server prioritizes work based on predicted schedules and uses project management tools to optimize resources.

[1533] Specific behavior:

[1534] The server uses Jira's API to prioritize tasks and assign the necessary resources (e.g., specific team members).

[1535] This system allows users to have their successors take over quickly in the event of a sudden resignation or transfer, thanks to an automated handover process that utilizes generative AI models. This prevents important tasks and deadlines from being overlooked, improving work efficiency and continuity.

[1536] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1537] Program processing flow and specific explanation

[1538] Step 1:

[1539] User input of work task information

[1540] Subject: User

[1541] Input: Task information such as task name, deadline, assignee, and required materials

[1542] Operation and output: The user enters task information related to their work using a dedicated input form. They fill in the input form with detailed information such as the task name, deadline, person in charge, and required materials, and then press the "Submit" button. This collects the task information.

[1543] Example: A user enters the task name "Preparing fiscal year-end financial statements," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[1544] Step 2:

[1545] Sending task information to the server

[1546] Subject: Device

[1547] Input: Task information entered by the user

[1548] Operation and output: When the user presses the "Submit" button, the device securely transmits the entered task information to the server using the HTTPS protocol. The information is serialized in JSON format and transmitted.

[1549] Specific operation: The device sends a POST request to the endpoint (e.g. https: / / api.company.com / tasks).

[1550] Step 3:

[1551] Analysis, classification, and organization of task information by the server

[1552] Subject: Server

[1553] Input: Received task information

[1554] Operation and output: The server analyzes the received task information and extracts detailed data using natural language processing techniques. This data extraction is performed using Python's "NLTK" and "spaCy." Based on the analyzed data, it organizes the tasks in a list format and links related documents.

[1555] Specific operation: The server analyzes the task "Preparing year-end financial statements," links related documents (such as past financial data), and organizes them into a list.

[1556] Step 4:

[1557] Collection of communication logs

[1558] Subject: Server

[1559] Input: User emails, chat logs, MTG notes, etc.

[1560] Operation and output: The server collects communication information such as user emails, chat logs, and meeting notes via API. This information is collected using the Microsoft Graph API and Slack API. The collected communication logs are stored in a database.

[1561] What it does: The server uses the Microsoft Graph API to fetch relevant emails and chat logs from the user's mailbox and save them in a database.

[1562] Step 5:

[1563] Analyze communication logs and identify important contacts and schedules

[1564] Subject: Server

[1565] Input: Collected communication logs

[1566] Operation and output: The server analyzes the collected communication logs to identify important contacts and schedules related to the work. The analysis is performed using generative AI models and natural language processing algorithms. The results are stored in a database and linked to the schedule management system.

[1567] How it works: The server uses the Google Cloud Natural Language API to analyze the text of emails and chat logs, identifying important schedules such as "End of fiscal year closing meeting (December 15, 2023)" and contact information such as accountants.

[1568] Step 6:

[1569] Adding schedules to business calendars and notifications

[1570] Subject: Server

[1571] Input: Identified schedule and contacts

[1572] Actions and Output: The server adds the identified schedules and contacts to the business calendar and sends notifications to the user using the Google Calendar API or Microsoft Outlook Calendar API. The notifications are delivered to the user via email or in-app notifications.

[1573] Specific operation: The server uses the Google Calendar API to create an event on the business calendar and notify the user of the "Year-end closing meeting on December 15, 2023."

[1574] Step 7:

[1575] Annual schedule forecast

[1576] Subject: Server

[1577] Input: Past business data

[1578] Operation and output: The server analyzes past business data and uses a generative AI model to predict the upcoming annual schedule. This prediction is made using TensorFlow and PyTorch. The prediction results are stored in a database and displayed to the user.

[1579] Specific operation: The server inputs past schedule data into the model and predicts the submission deadline for 2023 as "December 31, 2023."

[1580] Step 8:

[1581] Business prioritization and resource optimization

[1582] Subject: Server

[1583] Input: Forecasted Schedule

[1584] Operation and output: The server prioritizes tasks and optimizes resources based on the predicted schedule. This is done using the Jira API and Asana API. The optimized resource information is notified to the user.

[1585] Specific actions: The server sets "Preparing Year-End Financial Statements" to high priority, secures the necessary resources (e.g., additional accounting staff), and notifies the user.

[1586] Through these steps, the system enables quick and efficient handover of work even in the event of sudden resignation or transfer.

[1587] (Application example 1)

[1588] 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."

[1589] In logistics centers, when an employee suddenly resigns or is transferred, there is a problem of work not being smoothly handed over. This can lead to delays and mistakes, which can lead to reduced efficiency. Furthermore, task priorities and resource optimization are not achieved, negatively impacting the productivity of the entire logistics center. To solve these issues, it is necessary to organize and classify work task information, identify important contacts and schedules, forecast annual schedules, and even propose the shortest routes for logistics work and identify urgent orders.

[1590] 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.

[1591] In this invention, the server includes means for inputting business task information, means for analyzing and classifying and organizing task information, means for collecting communication logs related to business tasks, means for analyzing the communication logs and identifying important contacts and schedules, means for linking the identified schedules and contacts with a business calendar, means for predicting an annual schedule from past business data, means for optimizing business priorities and resources based on the predicted schedule, means for analyzing and organizing picking data and delivery lists for logistics work based on tasks and proposing the shortest routes, and means for identifying urgent or additional orders for logistics work based on the collected communication logs. This enables smooth business handover and improved business efficiency even in the event of a sudden resignation or transfer at a logistics center.

[1592] The "work task information input means" is an interface that allows staff at the logistics center to input their own task information into the system.

[1593] The "task information analysis means" is a system that analyzes input task information and classifies and organizes each piece of information into a specific format.

[1594] The "communication log collection means" is a system that automatically collects business-related communication data such as emails and chat logs.

[1595] The "communication log analysis means" is a system for analyzing collected communication logs and identifying important contacts and schedules.

[1596] The "business calendar linking means" is a system for synchronizing the identified schedule and contacts with the business calendar and sending necessary notifications.

[1597] The "annual schedule forecasting tool" is a system that analyzes past business data and predicts important upcoming deadlines and tasks.

[1598] The "task priority and resource optimization means" is a system that sets the priority of tasks based on a predicted schedule and optimally allocates the necessary resources.

[1599] The "Picking Data Analysis and Organization Method" is a system that analyzes picking data for logistics work based on tasks, and classifies and organizes the data for efficient product collection.

[1600] The "shortest route suggestion means" is a system that suggests efficient picking routes based on categorized and organized picking data.

[1601] The "urgent order identification means" is a system for identifying urgent orders or additional orders for logistics work based on the collected communication logs.

[1602] This invention is a system for smoothly transferring work duties at a logistics center when an employee suddenly resigns or is transferred. This system is realized by combining the following means. The main technologies used are Python programs and AWS (Amazon Web Services).

[1603] Hardware and Software Configuration

[1604] AWS EC2: Hosts application servers.

[1605] AWS Comprehend: A natural language processing (NLP) service for text analysis.

[1606] Google Calendar API: An API for integrating with business calendars.

[1607] Python: A programming language that implements the entire program.

[1608] Specific processing of the system

[1609] A means of inputting task information for work

[1610] Subject: Device

[1611] Operation: Staff at the distribution center use smartphones or tablets to enter their task information into the system, including the task name, deadline, shelf number, and required quantity.

[1612] Example: For "picking work," enter the deadline "December 15, 2023," shelf number "A23," and required quantity "10."

[1613] Task information analysis method

[1614] Subject: Server

[1615] Operation: Receives input task information and parses it using AWS Comprehend, which categorizes and organizes each piece of information into a specific format.

[1616] Example: Analyzing "picking work" and classifying and organizing products for efficient product collection.

[1617] Communication log collection method

[1618] Subject: Server

[1619] Operation: Automatically collects communication data related to the logistics center, such as emails and chat logs.

[1620] Example: Collect information about "urgent additional orders" from the logistics center chat log.

[1621] Communication log analysis method

[1622] Subject: Server

[1623] Operation: Analyze the collected communication logs and identify important contacts and schedules. Again, use AWS Comprehend.

[1624] Example: Identifying the date and time of an "urgent reorder" and the associated contact.

[1625] Business calendar integration method

[1626] Subject: Server

[1627] Interaction: Synchronize identified schedules and contacts with your business calendar using the Google Calendar API and send necessary notifications.

[1628] Example: Add "Picking work on December 15, 2023" to the business calendar and send a reminder notification.

[1629] Annual schedule forecasting tool

[1630] Subject: Server

[1631] Operation: Analyze past business data and predict the upcoming annual schedule.

[1632] Example: Forecasting tasks for December and setting a schedule for preparations for the busy end-of-year season each year.

[1633] Business priorities and resource optimization measures

[1634] Subject: Server

[1635] Operation: Prioritize work and optimally allocate necessary resources based on the forecast schedule.

[1636] Example: Optimizing resources by increasing the number of picking staff during peak periods.

[1637] Picking data analysis and organization tool

[1638] Subject: Server

[1639] Operation: Based on task information, analyzes picking data for logistics work, and classifies and organizes it for efficient product collection.

[1640] Example: A picking sheet is displayed with an optimized route based on the shelf number of each product.

[1641] Shortest route suggestion method

[1642] Subject: Server

[1643] Operation: Based on the classified and organized picking data, an efficient picking route is proposed.

[1644] Example: The route display guides picking work in the order of "shelf number A23 → B15 → C8."

[1645] Urgent Order Identification Method

[1646] Subject: Server

[1647] Operation: Identify urgent or additional logistics orders based on collected communication logs.

[1648] Example: From the collected logs, identify the "urgent additional order on December 14, 2023" and reflect the data in the system.

[1649] Prompt Sentence Examples

[1650] 1. A scene where a staff member inputs "picking work":

[1651] Prompt: "Please enter the task name, due date, shelf number, and required quantity."

[1652] 2. A scene where a log saying "A new order has been received" is collected from the communication log:

[1653] Prompt: "Please enter the log data you want to collect next."

[1654] This invention improves operational efficiency and reduces work errors and delays even when sudden resignations or transfers occur at a logistics center. It also prevents important tasks and deadlines from being missed, improving productivity throughout the organization.

[1655] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1656] Step 1:

[1657] A means of inputting task information for work

[1658] Subject: User

[1659] Operation: Logistics center staff enter their task information into the system using a smartphone or tablet.

[1660] Input: Task name, due date, shelf number, required quantity

[1661] Output: The entered task information is sent to the server.

[1662] Specific operation: For example, for "picking work," the deadline "December 15, 2023," shelf number "A23," and required quantity "10" are entered.

[1663] Step 2:

[1664] Task information analysis method

[1665] Subject: Server

[1666] Operation: Receives input task information and parses it using AWS Comprehend.

[1667] Input: Task information entered in Step 1

[1668] Output: Classified task information as the analysis result

[1669] Specific operation: Analyzes "picking work" and classifies and organizes products based on product name, required quantity, and shelf number for efficient product collection.

[1670] Step 3:

[1671] Communication log collection method

[1672] Subject: Server

[1673] Operation: Automatically collects communication data related to the logistics center, such as emails, chat logs, and work records.

[1674] Input: Communication data (email, chat log)

[1675] Output: Collected communication log data

[1676] Specific actions: For example, accessing a mail server or chat system and collecting logs such as "urgent additional order."

[1677] Step 4:

[1678] Communication log analysis method

[1679] Subject: Server

[1680] Operation: Analyze the collected communication logs using AWS Comprehend to identify important contacts and schedules.

[1681] Input: Communication log data collected in Step 3

[1682] Output: A list of important contacts and schedules

[1683] Specific operation: For example, identify the "urgent additional order date and time" and the contact information of the relevant person.

[1684] Step 5:

[1685] Business calendar integration method

[1686] Subject: Server

[1687] Interaction: Synchronize identified schedules and contacts with your business calendar using the Google Calendar API and send necessary notifications.

[1688] Input: Schedule and contacts identified in Step 4

[1689] Output: Schedules and notifications reflected in the business calendar

[1690] Specific actions: For example, add "Picking work on December 15, 2023" to the work calendar and send a reminder notification to staff.

[1691] Step 6:

[1692] Annual schedule forecasting tool

[1693] Subject: Server

[1694] Operation: Analyze past business data and predict the upcoming annual schedule.

[1695] Input: Past business data

[1696] Output: Projected annual schedule

[1697] Specific actions: Analyze tasks for the busy end-of-year period each year and set a preparation schedule for December.

[1698] Step 7:

[1699] Business priorities and resource optimization measures

[1700] Subject: Server

[1701] Operation: Prioritize work and optimally allocate necessary resources based on the forecast schedule.

[1702] Input: Annual schedule forecasted in Step 6

[1703] Output: Optimized resource allocation plan

[1704] Specific actions: For example, increasing the number of picking staff during peak periods to optimize resources.

[1705] Step 8:

[1706] Picking data analysis and organization tool

[1707] Subject: Server

[1708] Operation: Based on task information, analyzes picking data for logistics work, and classifies and organizes it for efficient product collection.

[1709] Input: Task information entered in Step 1

[1710] Output: Categorized and organized picking data

[1711] Specific operation: Based on the shelf number of each product, the picking sheet is optimized and displayed along the shortest route.

[1712] Step 9:

[1713] Shortest route suggestion method

[1714] Subject: Server

[1715] Operation: Based on the classified and organized picking data, an efficient picking route is proposed.

[1716] Input: Picking data categorized and organized in step 8

[1717] Output: Proposed picking route

[1718] Specific operation: For example, guide picking work along a route such as "shelf number A23 → B15 → C8."

[1719] Step 10:

[1720] Urgent Order Identification Method

[1721] Subject: Server

[1722] Operation: Identify urgent or additional logistics orders based on collected communication logs.

[1723] Input: Communication log data collected in Step 3

[1724] Output: Identified urgent order information

[1725] Specific operations: Identify the "urgent additional order on December 14, 2023" from the collected logs and reflect the corresponding data in the system.

[1726] 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.

[1727] This invention is a system that combines generative AI technology with an emotion engine to solve the problem of insufficient task handovers. This system achieves more effective and efficient task handovers by integrating task information input, analysis, classification and organization, communication log collection and analysis, business calendar integration, annual schedule forecasting, task priority and resource optimization, and an emotion engine that recognizes user emotions.

[1728] What the program does

[1729] 1. Enter task information:

[1730] Subject: User

[1731] The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[1732] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[1733] 2. Analysis and classification of task information:

[1734] Subject: Server

[1735] The server receives task information sent by the user, performs text analysis, and extracts the task name, deadline, person in charge, and necessary materials. It then generates a task list based on the extracted task information and classifies and organizes each task into the appropriate category.

[1736] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[1737] 3. Collecting communication logs:

[1738] Subject: Server

[1739] The server automatically collects communication history such as users' emails, chat logs, and meeting notes.

[1740] Example: The server retrieves email logs from Google Mail for the past three months.

[1741] 4. Analyzing communication logs and identifying important contacts and schedules:

[1742] Subject: Server

[1743] The server analyzes the collected communication logs using natural language processing technology to identify important contacts and schedules related to business operations.

[1744] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[1745] 5. Integration with business calendars:

[1746] Subject: Server

[1747] The server automatically enters the identified schedule and contact information into the business calendar and notifies the user. It also sets reminder notifications for the entered schedule.

[1748] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar and send a reminder to the user.

[1749] 6. Annual Schedule Forecast:

[1750] Subject: Server

[1751] The server analyzes past business data, predicts the upcoming annual schedule, and generates a new schedule.

[1752] Example: The server retrieves the "financial report submission schedule for the past five years" from the database and predicts "December 31, 2023" as the next "year-end financial report submission date."

[1753] 7. Business Prioritization and Resource Optimization:

[1754] Subject: Server

[1755] The server prioritizes tasks and optimizes resources based on the predicted schedule and current task information.

[1756] Example: A server sets "Preparing year-end financial reports" as a high priority and allocates the necessary resources (e.g., additional accounting staff) for it.

[1757] Emotion engine integration

[1758] 1. Emotion recognition using emotion engine:

[1759] Subject: Server

[1760] The server uses the user's input and behavior log to drive the emotion engine and recognize the user's emotions.

[1761] Example: A server analyzes a user's keystrokes and mouse movements to detect stress levels.

[1762] 2. Adjusting work priorities and resource optimization based on sentiment data:

[1763] Subject: Server

[1764] The server takes into account the emotional data recognized by the emotion engine and adjusts the priority of tasks and resource allocation.

[1765] Example: If a user is under high stress, the server will adjust the priority of the task to be assigned to a less demanding task.

[1766] 3. Adjust reminder notifications based on your emotions:

[1767] Subject: Server

[1768] The server adjusts the content and timing of the reminder notification based on the emotional state recognized by the emotion engine.

[1769] Example: If the user is feeling stressed, the server can flexibly adjust the arrival time of the reminder notification (for example, send it a little later).

[1770] This system allows users to smoothly continue their work even in the event of a sudden resignation or transfer, thanks to automated handover processing and support from an emotion engine. This reduces the burden on successors and improves work efficiency across the company. It also provides more human-like support by optimizing tasks and adjusting notifications based on the user's emotional state.

[1771] The processing flow will be explained below.

[1772] Processing steps for entering task information for work

[1773] Step 1:

[1774] Subject: User

[1775] Operation: The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[1776] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[1777] Processing steps for analyzing, classifying, and organizing task information

[1778] Step 1:

[1779] Subject: Server

[1780] Operation: The server receives task information sent by the user.

[1781] Example: The server receives task information "Prepare fiscal year-end financial report."

[1782] Step 2:

[1783] Subject: Server

[1784] Operation: The server launches a natural language processing module to perform text analysis on the received task information, extracting the task name, deadline, person in charge, and required materials.

[1785] Example: The server extracts the task name "Preparing the year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[1786] Step 3:

[1787] Subject: Server

[1788] Operation: The server generates a task list based on the extracted task information and classifies and organizes each task into an appropriate category.

[1789] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[1790] Process steps for collecting communication logs

[1791] Step 1:

[1792] Subject: Server

[1793] How it works: The server starts a process to automatically collect a user's communication history, including emails, chat logs, and meeting notes.

[1794] Example: The server retrieves email logs from Google Mail for the past three months.

[1795] Processing steps for analyzing communication logs and identifying important contacts and schedules

[1796] Step 1:

[1797] Subject: Server

[1798] Operation: The server analyzes the collected communication logs using natural language processing technology.

[1799] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[1800] Step 2:

[1801] Subject: Server

[1802] How it works: The server uses the analysis results to identify important business contacts and upcoming schedules.

[1803] Example: A server identifies "year-end closing meeting on December 15, 2023" and the associated contact (e.g., accountant).

[1804] Process steps for linking with business calendars

[1805] Step 1:

[1806] Subject: Server

[1807] Behavior: The server calls an API to automatically enter the specified schedule and contacts into the business calendar.

[1808] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar.

[1809] Step 2:

[1810] Subject: Server

[1811] Operation: The server sets a reminder notification for the entered schedule and notifies the user.

[1812] Example: Set the server to send a reminder notification on "December 13, 2023".

[1813] Processing steps for forecasting annual schedules

[1814] Step 1:

[1815] Subject: Server

[1816] Operation: The server launches a process to collect and analyze historical business data.

[1817] Example: A server retrieves the "financial report filing schedule for the past five years" from a database.

[1818] Step 2:

[1819] Subject: Server

[1820] How it works: The server predicts the annual schedule based on the collected data and generates a new schedule.

[1821] Example: The server predicts "December 31, 2023" as the next "year-end financial statement submission date" and registers it in the schedule.

[1822] Business Priority and Resource Optimization Process Steps

[1823] Step 1:

[1824] Subject: Server

[1825] How it works: The server prioritizes tasks based on the predicted schedule and current task information.

[1826] Example: The server sets "Prepare Year-End Financial Report" as a high priority.

[1827] Step 2:

[1828] Subject: Server

[1829] Operation: The server executes a process to optimize resource allocation based on the configured priorities.

[1830] Example: The server assigns "Additional staff in the accounting department" to the task "Prepare year-end financial report."

[1831] Processing steps for emotion engine integration

[1832] Step 1:

[1833] Subject: Server

[1834] Operation: The server uses the user's input and behavior log to drive the emotion engine and recognize the user's emotions.

[1835] Example: A server analyzes a user's keystrokes and mouse movements to detect stress levels.

[1836] Step 2:

[1837] Subject: Server

[1838] Operation: The server takes into account the emotional data recognized by the emotion engine and adjusts task priorities and resource allocation.

[1839] Example: If a user is under high stress, the server will adjust the priority of the task to be assigned to a less demanding task.

[1840] Step 3:

[1841] Subject: Server

[1842] How it works: The server adjusts the content and timing of reminder notifications based on the emotional state recognized by the emotion engine.

[1843] Example: If the user is feeling stressed, the server can flexibly adjust the arrival time of the reminder notification (for example, send it a little later).

[1844] In this way, this invention enables smooth business continuity even in the event of sudden resignation or transfer by integrating an AI-based automated handover process with an emotion engine. This reduces the burden on successors and improves business efficiency across the company. It also provides more human-like support by optimizing tasks and adjusting notifications based on the user's emotional state.

[1845] Example 2

[1846] 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."

[1847] In conventional work handovers, there have been problems with the handover not proceeding smoothly due to insufficient or inaccurate task information, insufficient understanding of communication logs, etc. Furthermore, the emotional state of the person in charge of the handover can affect work efficiency, and insufficient management of this makes it difficult to optimize work priorities and resource allocation. To solve these problems, accurate analysis of task information, appropriate analysis of communication logs, and work adjustments based on emotional data are required.

[1848] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1849] In this invention, the server includes means for inputting work task information, means for analyzing and classifying and organizing the task information, means for collecting communication logs related to the work tasks, means for analyzing the communication logs and identifying important contacts and dates, means for linking the identified dates and contacts with a work calendar, means for predicting an annual schedule from past work data, means for optimizing work priorities and resources based on the predicted schedule, means for recognizing user emotions using an emotion engine, and means for adjusting work priorities and resource allocation based on the recognized emotion data. This enables effective and efficient work handover and realizes appropriate work distribution and resource management that takes into account the user's emotional state.

[1850] "Task information for a business" is information such as the task name, deadline, person in charge, and necessary materials related to the business that is input by the user into the system.

[1851] "Means for inputting task information" refers to an interface or device that allows a user to input work task information into the system.

[1852] "Means for analyzing, classifying, and organizing task information" refers to the process or algorithm by which the server analyzes the input task information and classifies and organizes each task into an appropriate category.

[1853] "Communication log" refers to the communication history such as emails, chats, and meeting notes that are generated when a user performs their work.

[1854] "Means for collecting communication logs" refers to a function or device that allows the server to automatically collect users' emails, chat logs, meeting notes, etc.

[1855] "Means for analyzing communication logs and identifying important contacts and schedules" refers to the process or algorithm for analyzing communication logs collected by the server and identifying important business-related contacts and schedules.

[1856] "Business calendar" refers to a calendar application or system used to manage business-related schedules.

[1857] "Means for linking with business calendars" refers to the processes or algorithms by which the server automatically enters identified schedules and contacts into a business calendar and notifies the user.

[1858] "Means for predicting annual schedules" refers to the processes and algorithms that the server uses to analyze past business data and predict future annual schedules.

[1859] "Means for optimizing task priorities and resources" refers to the processes and algorithms that allow the server to prioritize tasks and optimally allocate required resources based on predicted schedules and current task information.

[1860] An "emotion engine" refers to the technology and algorithms that analyze user input and behavioral logs to recognize the user's emotions and psychological state.

[1861] "Means for recognizing emotions" refers to the process or algorithm for recognizing a user's emotions using an emotion engine.

[1862] "Means for adjusting task priorities and resource allocations based on emotional data" refers to processes or algorithms for dynamically adjusting task priorities and resource allocations based on recognized emotional data.

[1863] This invention is a system that combines generative AI technology with an emotion engine to solve the problem of insufficient task handovers. This system realizes more effective and efficient task handovers by integrating task information input, analysis, classification and organization, communication log collection and analysis, business calendar integration, annual schedule forecasting, task priority and resource optimization, and an emotion engine that recognizes user emotions.

[1864] Entering task information for work

[1865] Hardware and software: devices (computers, tablets, smartphones, etc.), business management software

[1866] What happens:

[1867] The user uses a terminal to input task information for work. The input information includes the task name, deadline, person in charge, and required materials. As a specific example, a user inputs the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[1868] Analysis, classification, and organization of task information

[1869] Hardware and software: Server, natural language processing engine

[1870] What happens:

[1871] The server receives task information sent by the user and performs text analysis using a natural language processing engine to extract the task name, deadline, person in charge, and required documents. It then generates a task list based on the extracted task information and classifies and organizes each task into appropriate categories. For example, the server might classify "Preparing the fiscal year-end financial report" into the "Financial Report" category and organize related documents and reference links.

[1872] Collection of communication logs

[1873] Hardware and software: Servers, communication log collection software (email API, chat API, etc.)

[1874] What happens:

[1875] The server automatically collects communication history such as users' emails, chat logs, meeting notes, etc. For example, the server retrieves email logs from the "email API" for the past three months.

[1876] Analyze communication logs and identify important contacts and schedules

[1877] Hardware and software: Server, natural language processing engine

[1878] What happens:

[1879] The server analyzes the collected communication logs using natural language processing technology to identify important business-related contacts and schedules. For example, the server can identify "minutes of meetings regarding fiscal year-end settlement" or "emails responding to important customers" and extract the relevant information.

[1880] Integration with business calendars

[1881] Hardware and software: Server, business calendar API (e.g. Google Calendar API)

[1882] What happens:

[1883] The server automatically enters the identified schedule and contacts into the business calendar and notifies the user. It also sets reminder notifications. As a specific example, the server uses the "Business Calendar API" to add "Year-end Closing Meeting on December 15, 2023" to the calendar and sends a reminder notification to the user.

[1884] Annual schedule forecast

[1885] Hardware and software: Servers, data analysis engines

[1886] What happens:

[1887] The server analyzes past business data and predicts the upcoming annual schedule. For example, the server retrieves the "financial report submission schedule for the past five years" from the database and predicts "December 31, 2023" as the next "year-end financial report submission date."

[1888] Business prioritization and resource optimization

[1889] Hardware and software: Servers, resource management engines

[1890] What happens:

[1891] The server prioritizes tasks based on the predicted schedule and current task information, and optimally allocates the necessary resources. For example, the server may assign "preparing the fiscal year-end financial report" as a high priority and secure the necessary resources (e.g., additional accounting staff).

[1892] Emotion engine integration

[1893] Emotion recognition by emotion engine

[1894] Hardware and software: Server, sentiment analysis engine

[1895] What happens:

[1896] The server uses the user's input and behavior logs to drive the emotion engine and recognize the user's emotions. For example, the server analyzes the user's keystrokes and mouse movements to detect their stress level.

[1897] Adjusting work priorities and resource optimization based on sentiment data

[1898] Hardware and software: Server, sentiment analysis engine

[1899] What happens:

[1900] The server adjusts task priorities and resource allocation based on the emotional data recognized by the emotion engine. For example, if a user is in a high-stress state, the server will assign less demanding tasks a high priority.

[1901] Adjusting reminder notifications based on emotions

[1902] Hardware and software: Server, sentiment analysis engine

[1903] What happens:

[1904] The server adjusts the content and timing of reminder notifications based on the emotional state recognized by the emotion engine. For example, if the user is feeling stressed, the server will adjust the time at which the reminder notification arrives to a slightly later date.

[1905] Prompt Sentence Examples

[1906] "Add a task to the work calendar to prepare the year-end financial report, and enter the person responsible and due date."

[1907] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1908] Step 1:

[1909] The user uses the terminal to input task information for the work.

[1910] Input includes the task name, deadline, person in charge, and required materials. This information is sent to the system.

[1911] Input: Task name: "Prepare fiscal year-end financial report", deadline: "December 31, 2023", person in charge: "Sato from the Accounting Department"

[1912] Output: Submitted task information

[1913] Specific behavior:

[1914] When the user fills in the task information in the input form on the terminal and presses the "send" button, the task information is sent to the server.

[1915] Step 2:

[1916] The server receives the task information sent by the user and performs text analysis.

[1917] This analysis extracts task names, deadlines, people in charge, and required materials, and stores the extracted information in a database.

[1918] Input: Submitted task information

[1919] Output: Extracted task name, due date, assignee, required materials

[1920] Specific behavior:

[1921] The server uses a "natural language processing engine" to analyze the text of the submitted task information, extract important elements, and store them in a database.

[1922] Step 3:

[1923] The server generates a list of tasks based on the analyzed task information and classifies and organizes each task into an appropriate category.

[1924] This information is visualized as a list of tasks.

[1925] Input: Extracted task name, deadline, assignee, required materials

[1926] Output: A categorized list of tasks

[1927] Specific behavior:

[1928] The server processes the task information it receives using a classification algorithm, assigns it to a category such as "financial reporting," and displays it as a list of tasks.

[1929] Step 4:

[1930] The server automatically collects communication history such as users' emails, chat logs, and meeting notes.

[1931] The collected communication logs are stored in a database.

[1932] Input: User communication history data

[1933] Output: Collected communication logs

[1934] Specific behavior:

[1935] The server uses "communication log collection software" to collect communication logs from interfaces such as "email API" for the past three months and stores them in a database.

[1936] Step 5:

[1937] The server analyzes the collected communication logs and identifies important business-related contacts and schedules.

[1938] The results of this analysis are reflected in the business calendar.

[1939] Input: Collected communication logs

[1940] Output: Important contacts and schedules

[1941] Specific behavior:

[1942] The server uses a "natural language processing engine" to analyze the collected communication logs and extract important information such as "meeting minutes" and "responses to important customers."

[1943] Step 6:

[1944] The server automatically enters the identified schedule and contacts into the business calendar and notifies the user.

[1945] Also, set reminder notifications.

[1946] Input: Important contacts and schedules

[1947] Output: Schedule entries and notifications in the business calendar

[1948] Specific behavior:

[1949] The server uses the Calendar API to add schedules such as "Meeting on December 15, 2023" to the business calendar, set reminders, and notify the user.

[1950] Step 7:

[1951] The server analyzes past business data and predicts the upcoming annual schedule.

[1952] The predicted schedule is notified to the user.

[1953] Input: Past business data

[1954] Output: Projected annual schedule

[1955] Specific behavior:

[1956] The server uses a "data analysis engine" to analyze past schedule data, generate predictions such as the "next fiscal year-end financial statement submission date," and notify the user.

[1957] Step 8:

[1958] The server prioritizes tasks based on the predicted schedule and current task information, and optimally allocates the necessary resources.

[1959] Input: Projected schedule and current task information

[1960] Output: Set work priorities and resource allocations

[1961] Specific behavior:

[1962] The server uses a "resource management engine" to set task priorities and optimally allocate resources (e.g., personnel and time) based on those priorities.

[1963] Step 9:

[1964] The server runs an emotion engine to recognize the user's emotion data.

[1965] It recognizes emotions from user input and behavior logs and saves the results.

[1966] Input: User input and action logs

[1967] Output: Recognized emotion data

[1968] Specific behavior:

[1969] The server uses an "emotion analysis engine" to analyze the user's keystrokes and mouse movements to recognize and record their stress level and emotional state.

[1970] Step 10:

[1971] The server adjusts the priority of tasks and resource allocation based on the emotion data.

[1972] This allows less taxing tasks to be prioritized when the user's stress level is high.

[1973] Input: Recognized emotion data

[1974] Output: Aligned work priorities and resource allocation

[1975] Specific behavior:

[1976] Based on the recognized emotion data, the server adjusts the priority setting algorithm, assigning low-burden tasks a higher priority, and reallocating resources.

[1977] Step 11:

[1978] The server adjusts the content and timing of the reminder notification based on the emotional state recognized by the emotion engine.

[1979] This delays the timing of the reminder notification when the user's stress level is high.

[1980] Input: Recognized emotion data

[1981] Output: Adjusted reminder notification

[1982] Specific behavior:

[1983] The server analyzes the emotional data and runs an algorithm that delays the timing of sending reminder notifications if the stress level is high.

[1984] (Application example 2)

[1985] 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."

[1986] In today's society, where there is a demand for improved work efficiency that takes into account the lack of task handovers and human emotions, the lack of smooth employee task handovers, resulting in oversights and confusion during the handover process, is a major issue. Another problem is the lack of support that takes into account the stress and emotions of employees performing the handover. Appropriate information sharing and emotional support are particularly essential for on-site work, such as in factories. Therefore, a system that integrates the efficiency of task handovers with emotion recognition is needed.

[1987] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1988] In this invention, the server includes means for inputting business task information, means for analyzing and classifying and organizing the task information, means for collecting communication history related to the business tasks, means for analyzing the communication history and identifying important contacts and dates, means for linking the identified dates and contacts with a business calendar, means for predicting an annual schedule from past business data, means for optimizing business priorities and resources based on the predicted schedule, and means for adjusting business priorities and resources based on an emotion engine that recognizes user emotion data. This makes business handover more efficient and enables optimal business support that takes human emotions into consideration.

[1989] "Business task information" refers to details of specific tasks and work to be performed in a business.

[1990] "Analysis" is the process of analyzing input data or information and extracting specific patterns or meanings.

[1991] "Classification and organization" refers to the process of dividing and organizing information according to specific categories and orders based on analyzed information.

[1992] "Communication history" is a record of business communications that a user has had, such as emails, chats, and meeting notes.

[1993] "Important Contacts" refers to the contact information of parties and personnel essential to the performance of business.

[1994] A "date" is the date and time when a particular task or event is scheduled to occur.

[1995] A "business calendar" is a calendar system for managing business schedules and plans.

[1996] An "annual plan" refers to a work schedule for one year, predicted based on past data.

[1997] "Business priority" is a criterion for determining which business tasks should be performed first, based on their importance and urgency.

[1998] "Resource optimization" refers to the optimal allocation of resources such as personnel and equipment in order to carry out business operations efficiently.

[1999] An "emotion engine" is a system that recognizes the user's emotional state and provides appropriate feedback and support.

[2000] "Emotion data" is data that expresses the user's emotional state using numerical values ​​or categories.

[2001] As an example of how to implement this invention, we will show an example of introducing a system that combines generative AI technology and an emotion engine for factory workers. Below, we will explain the specific process of this system and the technology and data processing methods used.

[2002] System Overview

[2003] The system is comprised mainly of a server and user devices (e.g., smart glasses), and supports employee work handover and efficiency through the management of work task information, emotion recognition, analysis of communication history, and integration with work calendars.

[2004] 1. Entering work task information

[2005] The user inputs task information (e.g., task name, deadline, and person in charge) through the smart glasses interface, which is then sent to the server and stored in a database.

[2006] 2. Analysis and classification of task information

[2007] The server analyzes the input task information and uses natural language processing techniques (e.g., TfidfVectorizer) to extract task names, deadlines, and assignees, then classifies the tasks into categories using KMeans clustering.

[2008] 3. Collection and analysis of communication history

[2009] The server collects communication logs such as users' emails and chat history, analyzes them using natural language processing technology, and identifies important contacts and schedules, linking them to task information.

[2010] 4. Integration with business calendars

[2011] The server automatically enters the identified important contacts and dates into the business calendar using calendar integration technologies such as the Google Calendar API, making it easier to manage task schedules.

[2012] 5. Annual schedule forecast

[2013] The server analyzes past business data and runs an algorithm to predict annual schedules, which then predicts important upcoming dates and automatically generates a new schedule.

[2014] 6. Business Prioritization and Resource Optimization

[2015] The server prioritizes tasks based on the predicted schedule and executes algorithms to optimize resources accordingly, for example, by optimally allocating resources to the necessary personnel.

[2016] 7. Emotion engine integration

[2017] The emotion engine recognizes user emotions based on user input and behavioral logs. This process uses machine learning frameworks such as Keras and TensorFlow. Based on the recognized emotion data, work priorities and resources are adjusted.

[2018] Specific examples

[2019] The user uses the smart glasses to input task information such as "Preparing the year-end financial report, 2023-12-31, Accounting Department staff."

[2020] The server collects emails and chat logs and identifies "important meeting minutes" and "customer response emails."

[2021] The server automatically adds important dates such as "year-end closing meeting" to Google Calendar and sets reminder notifications.

[2022] The generative AI model analyzes the prompt, "I'm overwhelmed with the amount of tasks I have to do right now..." and determines that the user is in a state of high stress.

[2023] This embodiment allows factory workers to efficiently take over tasks through smart glasses and receive optimal support based on emotion recognition.

[2024] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2025] Step 1:

[2026] The user uses the smart glasses interface to input task information, including the task name, deadline, and assignee, which is then sent to the server and stored in a database.

[2027] Step 2:

[2028] The server analyzes the received business task information. It uses the natural language processing technology TfidfVectorizer to perform text analysis to extract task names, deadlines, and responsible persons. Based on the analysis results, it organizes the task information and compiles it into a list.

[2029] Step 3:

[2030] The server applies the KMeans clustering algorithm to categorize the business task information. This categorizes tasks into related categories and groups similar tasks together in the task list. The input data is the analysis results, and the output data is the category information for each task.

[2031] Step 4:

[2032] The server collects communication logs such as user emails and chat history. To do this, it connects to the email server or chat server via API and obtains the log data. The collected communication logs are stored in a database.

[2033] Step 5:

[2034] The server analyzes the collected communication logs and uses natural language processing technology to identify important contacts and schedules. The important information obtained from this analysis is used in the next step. The input data is the communication logs, and the output data is the identified important contacts and schedule information.

[2035] Step 6:

[2036] The server automatically enters important contacts and dates into the business calendar using calendar integration technology such as the Google Calendar API, and the entered information is reflected in the user's calendar application.

[2037] Step 7:

[2038] The server analyzes past business data and predicts the annual schedule. This identifies important dates for the next year and generates a new schedule. The input data is past business data, and the output data is the predicted annual schedule.

[2039] Step 8:

[2040] The server prioritizes tasks based on the predicted annual schedule and runs a resource optimization algorithm, which prioritizes each task and allocates the necessary resources.

[2041] Step 9:

[2042] The server runs an emotion engine based on user input and behavior logs to recognize the user's emotions. The emotion engine uses machine learning frameworks such as Keras and TensorFlow. The input data is the user's behavior log and input data, and the output data is the recognized emotion information.

[2043] Step 10:

[2044] The server adjusts task priorities and resources based on the recognized emotion data. For example, if a user is in a high-stress state, it adjusts resource and task allocation to prioritize less demanding tasks.

[2045] The above processing steps make the handover of work more efficient and provide optimal support based on emotion recognition.

[2046] 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.

[2047] 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.

[2048] 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.

[2049] [Fourth embodiment]

[2050] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2051] 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.

[2052] 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).

[2053] 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.

[2054] 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.

[2055] 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).

[2056] 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. 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.

[2057] 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.

[2058] 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.

[2059] 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.

[2060] 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.

[2061] 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.

[2062] 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."

[2063] This invention is a system that uses generative AI technology to ensure smooth work handovers in the event of sudden resignation or transfer. This system provides functions such as inputting, analyzing, classifying and organizing task information, collecting and analyzing communication logs, linking with business calendars, forecasting annual schedules, and optimizing work priorities and resources.

[2064] What the program does

[2065] 1. Enter task information:

[2066] Subject: User

[2067] Users input task information into the system, including the task name, deadline, person in charge, and required materials.

[2068] Example: A user enters "Preparing year-end financial statements" as the task name, "December 31, 2023" as the deadline, and "Sato from the accounting department" as the person in charge.

[2069] 2. Analysis and classification of task information:

[2070] Subject: Server

[2071] The server receives task information entered by the user, performs text analysis, extracts detailed task information, and organizes it in a list format.

[2072] Example: The server categorizes the "preparing year-end financial statements" task and links related documents and data (e.g., historical financial data).

[2073] 3. Collecting communication logs:

[2074] Subject: Server

[2075] The server collects communication information such as users' emails, chat logs, and meeting notes.

[2076] Example: The server retrieves the minutes of the "accounting meeting" and "customer correspondence emails" from the user's mailbox.

[2077] 4. Analyzing communication logs and identifying important contacts and schedules:

[2078] Subject: Server

[2079] The server analyzes the collected communication logs and identifies important contacts and schedules related to business.

[2080] Example: A server identifies an event called "Year-End Closing Meeting (December 15, 2023)" and identifies the associated contact (e.g., accountant).

[2081] 5. Integration with business calendars:

[2082] Subject: Server

[2083] The server adds the identified schedule and contacts to the business calendar and notifies the user.

[2084] Example: The server adds "Year-end closing meeting on December 15, 2023" to the business calendar and sends a reminder to the user.

[2085] 6. Annual Schedule Forecast:

[2086] Subject: Server

[2087] The server analyzes past business data and predicts the upcoming year's schedule, setting important deadlines and tasks in advance.

[2088] Example: A server models the deadlines for financial statements at the end of each year and predicts the deadline for 2023 as December 31, 2023.

[2089] 7. Business Prioritization and Resource Optimization:

[2090] Subject: Server

[2091] The server sets the priority of operations and optimizes resources based on the predicted schedule, thereby improving business efficiency.

[2092] Example: The server sets "Preparing year-end financial statements" as a "high" priority and allocates the necessary resources (e.g., additional accounting staff) for it.

[2093] This system allows users to smoothly and effectively transfer their work even in the event of a sudden resignation or transfer, thanks to automated handover processing using AI. Successors can clearly understand tasks and schedules, allowing them to take over work quickly.

[2094] This system not only improves business continuity and reduces the burden on successors, but also contributes to improving the efficiency of the entire organization. It also prevents important tasks and deadlines from being overlooked, helping to ensure that work is carried out reliably.

[2095] The processing flow will be explained below.

[2096] Processing steps for entering task information for work

[2097] Step 1:

[2098] Subject: User

[2099] Operation: The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[2100] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[2101] Processing steps for analyzing, classifying, and organizing task information

[2102] Step 1:

[2103] Subject: Server

[2104] Operation: The server receives task information sent by the user.

[2105] Example: The server receives task information "Prepare fiscal year-end financial report."

[2106] Step 2:

[2107] Subject: Server

[2108] Operation: The server launches a natural language processing module to perform text analysis on the received task information, extracting the task name, deadline, person in charge, and required materials.

[2109] Example: The server extracts the task name "Preparing the year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[2110] Step 3:

[2111] Subject: Server

[2112] Operation: The server generates a task list based on the extracted task information and classifies and organizes each task into an appropriate category.

[2113] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[2114] Process steps for collecting communication logs

[2115] Step 1:

[2116] Subject: Server

[2117] How it works: The server starts a process to automatically collect a user's communication history, including emails, chat logs, and meeting notes.

[2118] Example: The server retrieves email logs from Google Mail for the past three months.

[2119] Processing steps for analyzing communication logs and identifying important contacts and schedules

[2120] Step 1:

[2121] Subject: Server

[2122] Operation: The server analyzes the collected communication logs using natural language processing technology.

[2123] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[2124] Step 2:

[2125] Subject: Server

[2126] How it works: The server uses the analysis results to identify important business contacts and upcoming schedules.

[2127] Example: A server identifies "year-end closing meeting on December 15, 2023" and the associated contact (e.g., accountant).

[2128] Process steps for linking with business calendars

[2129] Step 1:

[2130] Subject: Server

[2131] Behavior: The server calls an API to automatically enter the specified schedule and contacts into the business calendar.

[2132] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar.

[2133] Step 2:

[2134] Subject: Server

[2135] Operation: The server sets a reminder notification for the entered schedule and notifies the user.

[2136] Example: Set the server to send a reminder notification on "December 13, 2023".

[2137] Processing steps for forecasting annual schedules

[2138] Step 1:

[2139] Subject: Server

[2140] Operation: The server launches a process to collect and analyze historical business data.

[2141] Example: A server retrieves the "financial report filing schedule for the past five years" from a database.

[2142] Step 2:

[2143] Subject: Server

[2144] How it works: The server predicts the annual schedule based on the collected data and generates a new schedule.

[2145] Example: The server predicts "December 31, 2023" as the next "year-end financial statement submission date" and registers it in the schedule.

[2146] Business Priority and Resource Optimization Process Steps

[2147] Step 1:

[2148] Subject: Server

[2149] How it works: The server prioritizes tasks based on the predicted schedule and current task information.

[2150] Example: The server sets "Prepare Year-End Financial Report" as a high priority.

[2151] Step 2:

[2152] Subject: Server

[2153] Operation: The server executes a process to optimize resource allocation based on the configured priorities.

[2154] Example: The server assigns "Additional staff in the accounting department" to the task "Prepare year-end financial report."

[2155] In this way, the present invention enables smooth business continuity through automated handover processing using AI, even in the event of a sudden resignation or transfer, thereby reducing the burden on successors and improving business efficiency across the entire company.

[2156] Example 1

[2157] 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."

[2158] Sudden resignations or transfers can cause confusion and prevent smooth work handovers. In such situations, it can be difficult for successors to quickly grasp the necessary information and take over work efficiently. Important tasks, deadlines, and contact information may be overlooked, leading to work stagnation and mistakes. Therefore, there is a need for a system that streamlines work handovers and collects and organizes all necessary information.

[2159] 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.

[2160] In this invention, the server includes means for using natural language processing when analyzing task information entered by a user, means for using a generative AI model when analyzing collected communication logs, means for predicting an annual schedule using past data with the generative AI model, and means for setting priorities for tasks based on the predicted schedule and optimizing resources through an information system. This enables a successor to smoothly and efficiently take over tasks through automated handover processing, even in the event of a sudden resignation or transfer.

[2161] "Business task information" refers to information including details of tasks related to business entered by a user.

[2162] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[2163] A "generative AI model" is an algorithm that uses artificial intelligence to generate new information from data.

[2164] A "communication log" is a record of emails, chats, meeting notes, and the like that a user makes in the course of their work.

[2165] A "schedule management system" is software or a system for managing work schedules and tasks.

[2166] An "annual schedule" is a calendar that lists important dates, times, and tasks for work throughout the year.

[2167] "Priority" refers to the ranking determined based on the importance and urgency of a business task.

[2168] "Resources" refer to the personnel, equipment, time, and other resources required to carry out a task.

[2169] An "information system" is a computer system used to manage an organization's information.

[2170] "Notifications" are alerts or messages that the system sends to inform the user.

[2171] This invention is a system that uses generative AI technology to ensure smooth work handovers in the event of sudden resignation or transfer. This system provides functions such as input, analysis, classification, and organization of work task information, collection and analysis of communication logs, work calendar integration, annual schedule forecasting, and work priority and resource optimization. The following hardware and software are used to implement this system.

[2172] Hardware and software used

[2173] Server: A computer system for analyzing task information, collecting and analyzing communication logs, forecasting schedules, and optimizing resources. For example, a cloud-based server (AWS, Google Cloud) is used.

[2174] Natural language processing libraries: Used to analyze user-entered text. These include Python's "NLTK" and "spaCy."

[2175] Generative AI models: Used for data analysis and prediction, using machine learning libraries such as TensorFlow and PyTorch.

[2176] API: Used for communication log collection and calendar integration. For example, "Microsoft Graph API" and "Google Calendar API."

[2177] Web browser: Used as an interface for users to enter work task information.

[2178] System processing flow and operation

[2179] Entering task information for work

[2180] Users enter task information using a dedicated input form, which functions as an interface displayed in a web browser, and users enter detailed information such as the task name, deadline, person in charge, and required materials.

[2181] Examples:

[2182] Task Name: Prepare year-end financial statements

[2183] Deadline: December 31, 2023

[2184] Person in charge: Sato from the Accounting Department

[2185] Required documents: Past financial data, accounting software documents

[2186] Sending task information to the server

[2187] When the user presses the "Send" button, the user's device sends the entered task information to the server. The transmission is securely performed using the HTTPS protocol.

[2188] Analysis, classification, and organization of task information

[2189] The server analyzes the received task information and extracts detailed information using natural language processing techniques. Based on the extracted information, the server organizes the tasks in a list format.

[2190] Specific behavior:

[2191] The server uses the Python NLTK library to parse the input text and classify it based on the characteristics of the task.

[2192] Collection of communication logs

[2193] The server collects communication information such as users' emails, chat logs, and meeting notes via a specified API.

[2194] Specific behavior:

[2195] The server uses the Microsoft Graph API to fetch relevant emails and chat logs from the user's mailbox.

[2196] Analyze communication logs and identify important contacts and schedules

[2197] The server analyzes the collected communication logs to identify important contacts and schedules, using a generative AI model for the analysis.

[2198] Specific behavior:

[2199] The server uses the Google Cloud Natural Language API to analyze text from emails and chat logs to extract important events and contact information.

[2200] Adding schedules to business calendars and notifications

[2201] The server adds the identified schedule and contacts to a business calendar (e.g., Google Calendar) and notifies the user of the information.

[2202] Specific behavior:

[2203] The server creates the event through the Google Calendar API and sends an email notification to the user.

[2204] Annual schedule forecast

[2205] The server analyzes past business data and uses a generative AI model to predict future annual schedules.

[2206] Specific behavior:

[2207] The server reads past schedule data and uses the TensorFlow library to train a model to predict future schedules.

[2208] Business prioritization and resource optimization

[2209] The server prioritizes work based on predicted schedules and uses project management tools to optimize resources.

[2210] Specific behavior:

[2211] The server uses Jira's API to prioritize tasks and assign the necessary resources (e.g., specific team members).

[2212] This system allows users to have their successors take over quickly in the event of a sudden resignation or transfer, thanks to an automated handover process that utilizes generative AI models. This prevents important tasks and deadlines from being overlooked, improving work efficiency and continuity.

[2213] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2214] Program processing flow and specific explanation

[2215] Step 1:

[2216] User input of work task information

[2217] Subject: User

[2218] Input: Task information such as task name, deadline, assignee, and required materials

[2219] Operation and output: The user enters task information related to their work using a dedicated input form. They fill in the input form with detailed information such as the task name, deadline, person in charge, and required materials, and then press the "Submit" button. This collects the task information.

[2220] Example: A user enters the task name "Preparing fiscal year-end financial statements," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[2221] Step 2:

[2222] Sending task information to the server

[2223] Subject: Device

[2224] Input: Task information entered by the user

[2225] Operation and output: When the user presses the "Submit" button, the device securely transmits the entered task information to the server using the HTTPS protocol. The information is serialized in JSON format and transmitted.

[2226] Specific operation: The device sends a POST request to the endpoint (e.g. https: / / api.company.com / tasks).

[2227] Step 3:

[2228] Analysis, classification, and organization of task information by the server

[2229] Subject: Server

[2230] Input: Received task information

[2231] Operation and output: The server analyzes the received task information and extracts detailed data using natural language processing techniques. This data extraction is performed using Python's "NLTK" and "spaCy." Based on the analyzed data, it organizes the tasks in a list format and links related documents.

[2232] Specific operation: The server analyzes the task "Preparing year-end financial statements," links related documents (such as past financial data), and organizes them into a list.

[2233] Step 4:

[2234] Collection of communication logs

[2235] Subject: Server

[2236] Input: User emails, chat logs, MTG notes, etc.

[2237] Operation and output: The server collects communication information such as user emails, chat logs, and meeting notes via API. This information is collected using the Microsoft Graph API and Slack API. The collected communication logs are stored in a database.

[2238] What it does: The server uses the Microsoft Graph API to fetch relevant emails and chat logs from the user's mailbox and save them in a database.

[2239] Step 5:

[2240] Analyze communication logs and identify important contacts and schedules

[2241] Subject: Server

[2242] Input: Collected communication logs

[2243] Operation and output: The server analyzes the collected communication logs to identify important contacts and schedules related to the work. The analysis is performed using generative AI models and natural language processing algorithms. The results are stored in a database and linked to the schedule management system.

[2244] How it works: The server uses the Google Cloud Natural Language API to analyze the text of emails and chat logs, identifying important schedules such as "End of fiscal year closing meeting (December 15, 2023)" and contact information such as accountants.

[2245] Step 6:

[2246] Adding schedules to business calendars and notifications

[2247] Subject: Server

[2248] Input: Identified schedule and contacts

[2249] Actions and Output: The server adds the identified schedules and contacts to the business calendar and sends notifications to the user using the Google Calendar API or Microsoft Outlook Calendar API. The notifications are delivered to the user via email or in-app notifications.

[2250] Specific operation: The server uses the Google Calendar API to create an event on the business calendar and notify the user of the "Year-end closing meeting on December 15, 2023."

[2251] Step 7:

[2252] Annual schedule forecast

[2253] Subject: Server

[2254] Input: Past business data

[2255] Operation and output: The server analyzes past business data and uses a generative AI model to predict the upcoming annual schedule. This prediction is made using TensorFlow and PyTorch. The prediction results are stored in a database and displayed to the user.

[2256] Specific operation: The server inputs past schedule data into the model and predicts the submission deadline for 2023 as "December 31, 2023."

[2257] Step 8:

[2258] Business prioritization and resource optimization

[2259] Subject: Server

[2260] Input: Forecasted Schedule

[2261] Operation and output: The server prioritizes tasks and optimizes resources based on the predicted schedule. This is done using the Jira API and Asana API. The optimized resource information is notified to the user.

[2262] Specific actions: The server sets "Preparing Year-End Financial Statements" to high priority, secures the necessary resources (e.g., additional accounting staff), and notifies the user.

[2263] Through these steps, the system enables quick and efficient handover of work even in the event of sudden resignation or transfer.

[2264] (Application example 1)

[2265] 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."

[2266] In logistics centers, when an employee suddenly resigns or is transferred, there is a problem of work not being smoothly handed over. This can lead to delays and mistakes, which can lead to reduced efficiency. Furthermore, task priorities and resource optimization are not achieved, negatively impacting the productivity of the entire logistics center. To solve these issues, it is necessary to organize and classify work task information, identify important contacts and schedules, forecast annual schedules, and even propose the shortest routes for logistics work and identify urgent orders.

[2267] 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.

[2268] In this invention, the server includes means for inputting business task information, means for analyzing and classifying and organizing task information, means for collecting communication logs related to business tasks, means for analyzing the communication logs and identifying important contacts and schedules, means for linking the identified schedules and contacts with a business calendar, means for predicting an annual schedule from past business data, means for optimizing business priorities and resources based on the predicted schedule, means for analyzing and organizing picking data and delivery lists for logistics work based on tasks and proposing the shortest routes, and means for identifying urgent or additional orders for logistics work based on the collected communication logs. This enables smooth business handover and improved business efficiency even in the event of a sudden resignation or transfer at a logistics center.

[2269] The "work task information input means" is an interface that allows staff at the logistics center to input their own task information into the system.

[2270] The "task information analysis means" is a system that analyzes input task information and classifies and organizes each piece of information into a specific format.

[2271] The "communication log collection means" is a system that automatically collects business-related communication data such as emails and chat logs.

[2272] The "communication log analysis means" is a system for analyzing collected communication logs and identifying important contacts and schedules.

[2273] The "business calendar linking means" is a system for synchronizing the identified schedule and contacts with the business calendar and sending necessary notifications.

[2274] The "annual schedule forecasting tool" is a system that analyzes past business data and predicts important upcoming deadlines and tasks.

[2275] The "task priority and resource optimization means" is a system that sets the priority of tasks based on a predicted schedule and optimally allocates the necessary resources.

[2276] The "Picking Data Analysis and Organization Method" is a system that analyzes picking data for logistics work based on tasks, and classifies and organizes the data for efficient product collection.

[2277] The "shortest route suggestion means" is a system that suggests efficient picking routes based on categorized and organized picking data.

[2278] The "urgent order identification means" is a system for identifying urgent orders or additional orders for logistics work based on the collected communication logs.

[2279] This invention is a system for smoothly transferring work duties at a logistics center when an employee suddenly resigns or is transferred. This system is realized by combining the following means. The main technologies used are Python programs and AWS (Amazon Web Services).

[2280] Hardware and Software Configuration

[2281] AWS EC2: Hosts application servers.

[2282] AWS Comprehend: A natural language processing (NLP) service for text analysis.

[2283] Google Calendar API: An API for integrating with business calendars.

[2284] Python: A programming language that implements the entire program.

[2285] Specific processing of the system

[2286] A means of inputting task information for work

[2287] Subject: Device

[2288] Operation: Staff at the distribution center use smartphones or tablets to enter their task information into the system, including the task name, deadline, shelf number, and required quantity.

[2289] Example: For "picking work," enter the deadline "December 15, 2023," shelf number "A23," and required quantity "10."

[2290] Task information analysis method

[2291] Subject: Server

[2292] Operation: Receives input task information and parses it using AWS Comprehend, which categorizes and organizes each piece of information into a specific format.

[2293] Example: Analyzing "picking work" and classifying and organizing products for efficient product collection.

[2294] Communication log collection method

[2295] Subject: Server

[2296] Operation: Automatically collects communication data related to the logistics center, such as emails and chat logs.

[2297] Example: Collect information about "urgent additional orders" from the logistics center chat log.

[2298] Communication log analysis method

[2299] Subject: Server

[2300] Operation: Analyze the collected communication logs and identify important contacts and schedules. Again, use AWS Comprehend.

[2301] Example: Identifying the date and time of an "urgent reorder" and the associated contact.

[2302] Business calendar integration method

[2303] Subject: Server

[2304] Interaction: Synchronize identified schedules and contacts with your business calendar using the Google Calendar API and send necessary notifications.

[2305] Example: Add "Picking work on December 15, 2023" to the business calendar and send a reminder notification.

[2306] Annual schedule forecasting tool

[2307] Subject: Server

[2308] Operation: Analyze past business data and predict the upcoming annual schedule.

[2309] Example: Forecasting tasks for December and setting a schedule for preparations for the busy end-of-year season each year.

[2310] Business priorities and resource optimization measures

[2311] Subject: Server

[2312] Operation: Prioritize work and optimally allocate necessary resources based on the forecast schedule.

[2313] Example: Optimizing resources by increasing the number of picking staff during peak periods.

[2314] Picking data analysis and organization tool

[2315] Subject: Server

[2316] Operation: Based on task information, analyzes picking data for logistics work, and classifies and organizes it for efficient product collection.

[2317] Example: A picking sheet is displayed with an optimized route based on the shelf number of each product.

[2318] Shortest route suggestion method

[2319] Subject: Server

[2320] Operation: Based on the classified and organized picking data, an efficient picking route is proposed.

[2321] Example: The route display guides picking work in the order of "shelf number A23 → B15 → C8."

[2322] Urgent Order Identification Method

[2323] Subject: Server

[2324] Operation: Identify urgent or additional logistics orders based on collected communication logs.

[2325] Example: From the collected logs, identify the "urgent additional order on December 14, 2023" and reflect the data in the system.

[2326] Prompt Sentence Examples

[2327] 1. A scene where a staff member inputs "picking work":

[2328] Prompt: "Please enter the task name, due date, shelf number, and required quantity."

[2329] 2. A scene where a log saying "A new order has been received" is collected from the communication log:

[2330] Prompt: "Please enter the log data you want to collect next."

[2331] This invention improves operational efficiency and reduces work errors and delays even when sudden resignations or transfers occur at a logistics center. It also prevents important tasks and deadlines from being missed, improving productivity throughout the organization.

[2332] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2333] Step 1:

[2334] A means of inputting task information for work

[2335] Subject: User

[2336] Operation: Logistics center staff enter their task information into the system using a smartphone or tablet.

[2337] Input: Task name, due date, shelf number, required quantity

[2338] Output: The entered task information is sent to the server.

[2339] Specific operation: For example, for "picking work," the deadline "December 15, 2023," shelf number "A23," and required quantity "10" are entered.

[2340] Step 2:

[2341] Task information analysis method

[2342] Subject: Server

[2343] Operation: Receives input task information and parses it using AWS Comprehend.

[2344] Input: Task information entered in Step 1

[2345] Output: Classified task information as the analysis result

[2346] Specific operation: Analyzes "picking work" and classifies and organizes products based on product name, required quantity, and shelf number for efficient product collection.

[2347] Step 3:

[2348] Communication log collection method

[2349] Subject: Server

[2350] Operation: Automatically collects communication data related to the logistics center, such as emails, chat logs, and work records.

[2351] Input: Communication data (email, chat log)

[2352] Output: Collected communication log data

[2353] Specific actions: For example, accessing a mail server or chat system and collecting logs such as "urgent additional order."

[2354] Step 4:

[2355] Communication log analysis method

[2356] Subject: Server

[2357] Operation: Analyze the collected communication logs using AWS Comprehend to identify important contacts and schedules.

[2358] Input: Communication log data collected in Step 3

[2359] Output: A list of important contacts and schedules

[2360] Specific operation: For example, identify the "urgent additional order date and time" and the contact information of the relevant person.

[2361] Step 5:

[2362] Business calendar integration method

[2363] Subject: Server

[2364] Interaction: Synchronize identified schedules and contacts with your business calendar using the Google Calendar API and send necessary notifications.

[2365] Input: Schedule and contacts identified in Step 4

[2366] Output: Schedules and notifications reflected in the business calendar

[2367] Specific actions: For example, add "Picking work on December 15, 2023" to the work calendar and send a reminder notification to staff.

[2368] Step 6:

[2369] Annual schedule forecasting tool

[2370] Subject: Server

[2371] Operation: Analyze past business data and predict the upcoming annual schedule.

[2372] Input: Past business data

[2373] Output: Projected annual schedule

[2374] Specific actions: Analyze tasks for the busy end-of-year period each year and set a preparation schedule for December.

[2375] Step 7:

[2376] Business priorities and resource optimization measures

[2377] Subject: Server

[2378] Operation: Prioritize work and optimally allocate necessary resources based on the forecast schedule.

[2379] Input: Annual schedule forecasted in Step 6

[2380] Output: Optimized resource allocation plan

[2381] Specific actions: For example, increasing the number of picking staff during peak periods to optimize resources.

[2382] Step 8:

[2383] Picking data analysis and organization tool

[2384] Subject: Server

[2385] Operation: Based on task information, analyzes picking data for logistics work, and classifies and organizes it for efficient product collection.

[2386] Input: Task information entered in Step 1

[2387] Output: Categorized and organized picking data

[2388] Specific operation: Based on the shelf number of each product, the picking sheet is optimized and displayed along the shortest route.

[2389] Step 9:

[2390] Shortest route suggestion method

[2391] Subject: Server

[2392] Operation: Based on the classified and organized picking data, an efficient picking route is proposed.

[2393] Input: Picking data categorized and organized in step 8

[2394] Output: Proposed picking route

[2395] Specific operation: For example, guide picking work along a route such as "shelf number A23 → B15 → C8."

[2396] Step 10:

[2397] Urgent Order Identification Method

[2398] Subject: Server

[2399] Operation: Identify urgent or additional logistics orders based on collected communication logs.

[2400] Input: Communication log data collected in Step 3

[2401] Output: Identified urgent order information

[2402] Specific operations: Identify the "urgent additional order on December 14, 2023" from the collected logs and reflect the corresponding data in the system.

[2403] 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.

[2404] This invention is a system that combines generative AI technology with an emotion engine to solve the problem of insufficient task handovers. This system achieves more effective and efficient task handovers by integrating task information input, analysis, classification and organization, communication log collection and analysis, business calendar integration, annual schedule forecasting, task priority and resource optimization, and an emotion engine that recognizes user emotions.

[2405] What the program does

[2406] 1. Enter task information:

[2407] Subject: User

[2408] The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[2409] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[2410] 2. Analysis and classification of task information:

[2411] Subject: Server

[2412] The server receives task information sent by the user, performs text analysis, and extracts the task name, deadline, person in charge, and necessary materials. It then generates a task list based on the extracted task information and classifies and organizes each task into the appropriate category.

[2413] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[2414] 3. Collecting communication logs:

[2415] Subject: Server

[2416] The server automatically collects communication history such as users' emails, chat logs, and meeting notes.

[2417] Example: The server retrieves email logs from Google Mail for the past three months.

[2418] 4. Analyzing communication logs and identifying important contacts and schedules:

[2419] Subject: Server

[2420] The server analyzes the collected communication logs using natural language processing technology to identify important contacts and schedules related to business operations.

[2421] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[2422] 5. Integration with business calendars:

[2423] Subject: Server

[2424] The server automatically enters the identified schedule and contact information into the business calendar and notifies the user. It also sets reminder notifications for the entered schedule.

[2425] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar and send a reminder to the user.

[2426] 6. Annual Schedule Forecast:

[2427] Subject: Server

[2428] The server analyzes past business data, predicts the upcoming annual schedule, and generates a new schedule.

[2429] Example: The server retrieves the "financial report submission schedule for the past five years" from the database and predicts "December 31, 2023" as the next "year-end financial report submission date."

[2430] 7. Business Prioritization and Resource Optimization:

[2431] Subject: Server

[2432] The server prioritizes tasks and optimizes resources based on the predicted schedule and current task information.

[2433] Example: A server sets "Preparing year-end financial reports" as a high priority and allocates the necessary resources (e.g., additional accounting staff) for it.

[2434] Emotion engine integration

[2435] 1. Emotion recognition using emotion engine:

[2436] Subject: Server

[2437] The server uses the user's input and behavior log to drive the emotion engine and recognize the user's emotions.

[2438] Example: A server analyzes a user's keystrokes and mouse movements to detect stress levels.

[2439] 2. Adjusting work priorities and resource optimization based on sentiment data:

[2440] Subject: Server

[2441] The server takes into account the emotional data recognized by the emotion engine and adjusts the priority of tasks and resource allocation.

[2442] Example: If a user is under high stress, the server will adjust the priority of the task to be assigned to a less demanding task.

[2443] 3. Adjust reminder notifications based on your emotions:

[2444] Subject: Server

[2445] The server adjusts the content and timing of the reminder notification based on the emotional state recognized by the emotion engine.

[2446] Example: If the user is feeling stressed, the server can flexibly adjust the arrival time of the reminder notification (for example, send it a little later).

[2447] This system allows users to smoothly continue their work even in the event of a sudden resignation or transfer, thanks to automated handover processing and support from an emotion engine. This reduces the burden on successors and improves work efficiency across the company. It also provides more human-like support by optimizing tasks and adjusting notifications based on the user's emotional state.

[2448] The processing flow will be explained below.

[2449] Processing steps for entering task information for work

[2450] Step 1:

[2451] Subject: User

[2452] Operation: The user inputs task information from the terminal, including the task name, deadline, person in charge, and required materials.

[2453] Example: A user enters the task name "Prepare fiscal year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department" into the system interface.

[2454] Processing steps for analyzing, classifying, and organizing task information

[2455] Step 1:

[2456] Subject: Server

[2457] Operation: The server receives task information sent by the user.

[2458] Example: The server receives task information "Prepare fiscal year-end financial report."

[2459] Step 2:

[2460] Subject: Server

[2461] Operation: The server launches a natural language processing module to perform text analysis on the received task information, extracting the task name, deadline, person in charge, and required materials.

[2462] Example: The server extracts the task name "Preparing the year-end financial report," the deadline "December 31, 2023," and the person in charge "Sato from the accounting department."

[2463] Step 3:

[2464] Subject: Server

[2465] Operation: The server generates a task list based on the extracted task information and classifies and organizes each task into an appropriate category.

[2466] Example: The server categorizes "Preparing Year-End Financial Reports" under the category of "Financial Reporting" and organizes the related required documents and reference links.

[2467] Process steps for collecting communication logs

[2468] Step 1:

[2469] Subject: Server

[2470] How it works: The server starts a process to automatically collect a user's communication history, including emails, chat logs, and meeting notes.

[2471] Example: The server retrieves email logs from Google Mail for the past three months.

[2472] Processing steps for analyzing communication logs and identifying important contacts and schedules

[2473] Step 1:

[2474] Subject: Server

[2475] Operation: The server analyzes the collected communication logs using natural language processing technology.

[2476] Example: The server identifies and classifies "minutes of meetings regarding fiscal year-end settlement" and "important customer correspondence emails."

[2477] Step 2:

[2478] Subject: Server

[2479] How it works: The server uses the analysis results to identify important business contacts and upcoming schedules.

[2480] Example: A server identifies "year-end closing meeting on December 15, 2023" and the associated contact (e.g., accountant).

[2481] Process steps for linking with business calendars

[2482] Step 1:

[2483] Subject: Server

[2484] Behavior: The server calls an API to automatically enter the specified schedule and contacts into the business calendar.

[2485] Example: The server uses the Google Calendar API to add "Year-end closing meeting on December 15, 2023" to the business calendar.

[2486] Step 2:

[2487] Subject: Server

[2488] Operation: The server sets a reminder notification for the entered schedule and notifies the user.

[2489] Example: Set the server to send a reminder notification on "December 13, 2023".

[2490] Processing steps for forecasting annual schedules

[2491] Step 1:

[2492] Subject: Server

[2493] Operation: The server launches a process to collect and analyze historical business data.

[2494] Example: A server retrieves the "financial report filing schedule for the past five years" from a database.

[2495] Step 2:

[2496] Subject: Server

[2497] How it works: The server predicts the annual schedule based on the collected data and generates a new schedule.

[2498] Example: The server predicts "December 31, 2023" as the next "year-end financial statement submission date" and registers it in the schedule.

[2499] Business Priority and Resource Optimization Process Steps

[2500] Step 1:

[2501] Subject: Server

[2502] How it works: The server prioritizes tasks based on the predicted schedule and current task information.

[2503] Example: The server sets "Prepare Year-End Financial Report" as a high priority.

[2504] Step 2:

[2505] Subject: Server

[2506] Operation: The server executes a process to optimize resource allocation based on the configured priorities.

[2507] Example: The server assigns "Additional staff in the accounting department" to the task "Prepare year-end financial report."

[2508] Processing steps for emotion engine integration

[2509] Step 1:

[2510] Subject: Server

[2511] Operation: The server uses the user's input and behavior log ...

Claims

1. A means for inputting task information for a business; means for analyzing, classifying, and organizing the task information; means for collecting a communication log related to the business task; A means for analyzing the communication log and identifying important contacts and schedules; A means for linking the identified schedules and contacts with a business calendar; A method for predicting annual schedules from past business data, A means of optimizing work priorities and resources based on predicted schedules; A system including:

2. 10. The system of claim 1, further comprising means for providing reminders of important schedules identified in the business calendar.

3. The system according to claim 1 , further comprising means for visualizing the input and analysis results of the business task information as a task list.

Images