system

Abstract

We provide the system. [Solution] A means for analyzing the work content entered by the user and generating an optimized plan based on the date and time, A means of providing efficient work execution methods by analyzing past work history, A means of analyzing the content of an electronic message and suggesting an appropriate reply, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern individuals and enterprises are forced to efficiently manage and execute a large number of tasks, which consumes a great deal of time and labor. In addition, the use of various digital tools has complicated the business process, and there is a demand for efficiency, especially in scheduling and e-mail processing. Furthermore, there is a lack of feedback for improving future work efficiency by utilizing the knowledge obtained from business records. The purpose of this invention is to solve these problems and achieve an improvement in business efficiency and a reduction in time.

Means for Solving the Problems

[0005] The present invention provides a system that allows users to input work details via voice or text, and generates an optimized plan based on the date and time of the analyzed work. This system includes means for analyzing past work history to provide efficient work execution methods, and has a function to allow users to receive feedback based on past data. Furthermore, it can improve the efficiency of message processing by analyzing the content of electronic messages and suggesting appropriate replies. This series of functions makes it possible to simplify complex work management and improve the efficiency of work execution.

[0006] "Job description" refers to the content of the work and tasks that users are required to perform on a daily basis.

[0007] "Analysis" refers to the process of organizing input information and data and analyzing them in order to understand their structure and meaning.

[0008] "Optimization" refers to the act of adjusting and reorganizing resources and procedures to achieve the best possible results under certain conditions.

[0009] A "plan" refers to a set of predetermined steps or procedures for actions or activities carried out to achieve a specific objective.

[0010] "Work history" refers to data that records the details and results of work performed in the past.

[0011] "Efficiency" is a concept that refers to the way in which effort and resources are used to achieve a goal while eliminating waste.

[0012] "Electronic messages" refer to emails and messages sent and received via communication methods such as the internet.

[0013] A "reply message" refers to a written response created in response to a received message.

[0014] "Feedback" refers to the act of returning information about the results and impact of an action or task that has been performed. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system for efficiently managing and executing users' work tasks. Users can input their work tasks via voice or text through a terminal. This data is transmitted to a server via the terminal. The server uses information analysis means to analyze the work tasks in detail and sets priorities and categories according to their content.

[0037] Next, the server uses optimization techniques to generate a date and time-based plan based on the analysis results. This plan works in conjunction with the user's calendar application to place tasks on appropriate dates, supporting efficient work execution even in busy schedules.

[0038] Furthermore, the server can systematically analyze past business history using analytical tools, and based on this analysis, it provides users with feedback to improve business efficiency. This feedback is displayed on the terminal in a visually easy-to-understand format.

[0039] Furthermore, electronic messages received by the terminal are sent to a server, where their content is automatically analyzed using analytical tools. The server generates an appropriate reply and suggests it to the user. This function significantly reduces the time users spend processing messages.

[0040] For example, if a user enters a task such as "Prepare for next week's project meeting," the server will classify this task as "high priority" and automatically update the calendar with a preparation schedule tailored to the meeting date. It will also analyze past meeting history and provide feedback on more efficient and time-efficient preparation methods.

[0041] Thus, the present invention is implemented in a way that automates and streamlines the user's business processes. Throughout all stages of the system, the server, terminals, and users cooperate to achieve seamless business management.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users record their work details using voice or text input via a terminal. This information is captured in real time, and the format of the input is checked for accuracy.

[0045] Step 2:

[0046] The terminal formats the business data received from the user and sends it to the server according to security protocols. The data reaches the server via the network.

[0047] Step 3:

[0048] The server analyzes the received business data and extracts important keywords using natural language processing. This analysis aims to categorize business processes and automatically set their priorities.

[0049] Step 4:

[0050] The server generates an optimal schedule based on the analysis results. It references past work history and current calendar information, and incorporates this into the user's work plan.

[0051] Step 5:

[0052] The server generates schedule information and synchronizes it with the calendar application. The schedule is added to the user's calendar, and the user is notified via their device.

[0053] Step 6:

[0054] The device sends received electronic messages to the server. The data of received emails is encrypted using the appropriate protocol and securely transferred to the server.

[0055] Step 7:

[0056] The server analyzes the content of the electronic message and extracts the necessary reply information. Based on the analysis results, it automatically generates appropriate templates and wording for the reply.

[0057] Step 8:

[0058] The terminal presents the user with a suggested reply received from the server. The user can review the suggested content, edit it if necessary, and then send it.

[0059] These steps function as a series of steps, providing a system that streamlines the management and execution of tasks by users.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In today's work environment, users are required to efficiently manage and perform complex and diverse tasks. However, manual scheduling and task organization require considerable time and effort, leading to decreased work efficiency. Furthermore, electronic communication often demands quick and appropriate responses, which can be burdensome. A system is needed to solve these problems.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for interpreting work information entered by the user and creating an optimized plan based on the date and time; means for analyzing past work history and providing efficient methods for completing work; and means for interpreting the content of electronic communications and generating appropriate response statements. This enables the user to efficiently manage and perform tasks through automated processes and reduces the processing burden of electronic communications.

[0065] A "user" is someone who uses the system to input work details and is subject to management.

[0066] "Work information" refers to information about tasks and activities that users input in order to perform their work.

[0067] "Interpretation" is the process of analyzing input data and understanding its meaning and intent.

[0068] A "date and time-optimized plan" is a plan that efficiently schedules tasks using date and time information entered into the work.

[0069] "Past work history" refers to information that records previous work or tasks.

[0070] An "efficient method of completing work" refers to the techniques and procedures for completing tasks quickly and effectively.

[0071] "Electronic communication" refers to communication conducted using digital methods such as email and messaging.

[0072] A "response" is a document or message sent back in response to an received electronic communication.

[0073] A "visually easy-to-understand format" is one in which information is presented visually using graphs and charts, making it easy to understand.

[0074] This system is a platform for efficiently managing and executing user tasks. Users can input task information via a terminal in voice or text format. The terminal securely transmits the received task information to the server over the internet. The HTTPS protocol is used for this transmission.

[0075] The server uses commonly available natural language processing software to analyze the received work information. This analysis allows the server to automatically set the priority and category of the work. Based on the analysis results, the server uses a scheduling algorithm to generate an optimal schedule based on the date and time, and integrates this schedule with the user's appointment management application. A standard calendar API is used for this integration.

[0076] Electronic communications received by the user are sent from the terminal to the server, where their content is analyzed by a generative AI model. The server then uses the AI ​​model to generate prompt sentences to suggest appropriate responses. This allows users to quickly and easily create responses to electronic communications. An example of a prompt sentence might be, "Please tell me what preparations are needed for the next meeting."

[0077] Furthermore, the server stores past work history in a database and uses data analysis tools to generate efficient work completion methods. This feedback is provided to the user through the terminal in a visually easy-to-understand format. This allows the user to further improve their work efficiency.

[0078] In this way, servers, terminals, and users work together as a unified team, enabling efficient management and execution of tasks through automated processes.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user inputs work information through a terminal. Input can be in voice or text format. For example, the user might input "Prepare for Monday's meeting." The input data is converted into speech recognition or text data by the terminal and sent to the server via the internet. The output is the text data received by the server.

[0082] Step 2:

[0083] The server uses natural language processing software to analyze the received text data. This involves extracting keywords from the data and performing data calculations to determine the priority and category of the tasks. As a result of the analysis, the data is categorized, for example, "high-priority meeting preparation," and a priority is set. The output is the analyzed business data.

[0084] Step 3:

[0085] The server uses a scheduling algorithm based on analyzed business data to generate an optimal work plan. Specifically, it generates a schedule that takes into account task priorities and date / time information. The generated schedule is reflected in the user's scheduling application via an API. For example, a meeting preparation task might be assigned the day before the meeting. The output is schedule data.

[0086] Step 4:

[0087] The terminal receives electronic communication and sends the data to the server. The server inputs the received communication into a generating AI model for interpretation and creates a prompt to generate an appropriate response. The server then proposes the generated response to the user. This process allows the user to quickly generate an appropriate response. The output is the proposed response.

[0088] Step 5:

[0089] The server stores past work history in a database and analyzes it using analytical tools. It extracts efficient work completion methods and areas for improvement from the user's past performance data. The analysis results are sent to the terminal in a visually easy-to-understand format and displayed to the user as feedback. The output is visualized feedback.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In modern factories, efficient management and optimization of production operations are crucial. However, traditional systems rely on manual processes for adjusting production schedules and prioritizing tasks, resulting in significant time and effort required to improve work efficiency. Furthermore, the inability to effectively utilize past production data meant that overall operational efficiency was not fully realized.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing past work history and providing efficient work execution methods; and means for analyzing the work content in the factory in real time, setting priorities, and issuing instructions to the automation system. This enables improved work efficiency in factory production operations and further optimization of operations by utilizing past data.

[0095] A "user" is an entity that uses the system to input business details and optimize management.

[0096] "Job description" refers to information used for production management and work instructions, and is entered in voice or text format.

[0097] "Analysis" is the process of meticulously organizing and classifying the business data entered by users, and then creating an optimized plan based on that information.

[0098] A "date and time-optimized plan" is a plan that takes into account the priorities of tasks and the resources needed to create an efficient schedule.

[0099] "Past work history" refers to a collection of data on work performance recorded to date, and is the set of information that will be analyzed.

[0100] "Efficient work execution methods" refer to the means and processes for carrying out tasks in the most effective and rapid way possible.

[0101] "Factory work content" refers to the specific tasks and work instructions that are performed by the factory's automated equipment.

[0102] "Real-time analysis" means immediately evaluating the input information and performing the necessary processing.

[0103] Setting priorities is the act of assigning an urgency or importance level to a task or work, thereby facilitating more efficient processing.

[0104] "Issuing instructions to an automated system" refers to giving instructions to automated equipment or devices to perform specific actions based on information obtained through analysis.

[0105] The system for implementing this invention involves a server, a terminal, and a user working together to automate and streamline tasks. First, the user inputs the task details into the terminal via voice or text. The terminal sends this task details to the server, where analysis is performed. The analysis uses speech recognition software such as Google® Cloud Speech-to-Text API and a custom analysis algorithm written in Python.

[0106] Based on the analysis results, the server generates an optimized plan using a scheduling system such as Apache® Airflow. This plan reflects the priority and deadline of each task, and instructions are sent to the automated equipment in the factory. These instructions allow factory robots to perform tasks in real time, improving operational efficiency.

[0107] Furthermore, the server systematically analyzes past work history and provides users with feedback on how to perform tasks efficiently. For example, by using prompts such as, "Please propose the optimal production schedule to produce 100 units of part X by next Monday. Please also let me know if there are any efficiency improvement methods based on past data," further optimization of operations can be achieved.

[0108] This system enables factory production lines to operate in a data-driven manner, allowing work to proceed under a real-time, optimized production schedule. Implementing this invention significantly improves operational efficiency and productivity.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users input work details using their devices via voice or text. The input data is converted to text using a speech recognition API. This converted text data is then sent to the server.

[0112] Step 2:

[0113] The server analyzes the received text data. Using an analysis engine, it extracts important keywords and information from the work content and sets the priority and category of the tasks. This process outputs each work task as a dataset containing the information necessary for scheduling.

[0114] Step 3:

[0115] The server utilizes a scheduling system to generate an optimal work schedule based on the analysis results. It processes the input task priorities and deadlines, generating a date and time-based plan. This plan data is output as a list including specific dates and assigned personnel.

[0116] Step 4:

[0117] The server sends the generated work schedule as instructions to the factory automation equipment. The factory robots then automatically perform the corresponding tasks in real time based on the received instructions. The robots report the progress of their work according to the schedule and send the completion status to the server.

[0118] Step 5:

[0119] The server continuously analyzes past work history and generates feedback to improve work efficiency. This visualized feedback is sent to the user's terminal, suggesting improvements and more efficient methods for performing tasks. This feedback provides valuable information for optimizing future schedules.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention provides a system that utilizes an emotion engine to improve the efficiency of user task management. Users can input task details via voice or text through a terminal. This input data is transmitted to a server in real time, where the server analyzes the task details and generates an optimized plan based on the date and time.

[0122] The emotion engine recognizes the user's emotional state by analyzing the tone of voice during voice input and facial expression data acquired using the device's built-in camera. This information is taken into consideration when automatically determining task priorities and reflecting them in the schedule. For example, if the user is feeling stressed, the system will suggest rearranging tasks to reduce the workload.

[0123] Furthermore, the server analyzes past work history and provides users with efficient work execution methods. This analysis is visualized on the user's terminal as feedback, which can be used for future improvements.

[0124] On the other hand, in processing electronic messages, the server analyzes the message content and generates an appropriate reply based on the user's emotions recognized by the emotion engine. This ensures that the tone of communication matches the user's emotions, enabling more personalized responses.

[0125] For example, if a user inputs via voice, "I feel pressured about the meeting to review the project's progress," the emotion engine will detect feelings such as anxiety and tension, and the server will suggest scheduling tasks for meeting preparation as early as possible. Furthermore, the server will generate and suggest reassuring replies to emails the user has received.

[0126] Thus, the present invention provides a business management system incorporating an emotion engine, thereby enabling flexible work execution that takes into account the user's emotional state, and aiming to improve efficiency and reduce stress.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user inputs work details into the terminal via voice or text. The terminal captures the input and converts it into a predefined format.

[0130] Step 2:

[0131] The terminal sends the input content to the server. The data is encrypted via a security protocol and transmitted securely to the server.

[0132] Step 3:

[0133] The server analyzes the received work data. Natural language processing is used to extract keywords and automatically set the work category and priority.

[0134] Step 4:

[0135] The emotion engine sends voice tone and facial expression data to the server. Simultaneously, the server analyzes this emotion data to identify the user's emotional state.

[0136] Step 5:

[0137] The server optimizes existing work schedules by considering emotional data. If a user is experiencing stress, tasks are rearranged or rescheduled based on their emotions.

[0138] Step 6:

[0139] The server sends schedule data to the device so that the optimized schedule is reflected in the user's calendar. The device then synchronizes this information with its calendar application for display.

[0140] Step 7:

[0141] The terminal sends the received electronic message to the server. The server analyzes the message's content and determines its importance and the urgency of a reply.

[0142] Step 8:

[0143] The emotion engine generates an appropriate reply that reflects the user's current emotional state. The server sends this to the terminal for the user to review and edit.

[0144] Step 9:

[0145] The user edits their reply as needed and sends the finalized message. The device sends the updated message and completes the process.

[0146] In this way, using an emotion engine enables flexible and efficient business management that is attentive to the user's emotions.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] In today's work environment, efficiently managing individual tasks while taking into account users' emotional states is a challenging task. To reduce user stress and emotional burden and improve work efficiency, it is necessary to analyze emotional changes in real time and optimize tasks based on that analysis. Conventional systems have difficulty integrating emotional analysis into task management, and have not been able to achieve flexible and efficient task execution that is tailored to individual emotions.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing voice data and facial expression data to recognize the user's emotional state; and means for determining the priority of tasks based on the recognized emotional state and optimizing and rearranging the work content. This enables flexible work management and improved efficiency in response to the user's emotions.

[0152] "Job description" refers to the specific tasks and projects that users are expected to perform in their daily activities and work.

[0153] "Analysis" refers to a method of processing data based on given audio data, text data, and other information in order to understand its meaning and emotions.

[0154] A "date and time-optimized plan" refers to a plan that determines the optimal schedule for carrying out tasks based on date and time information.

[0155] "Emotional state" refers to the emotional situation or state of mind derived from the user's voice, facial expressions, and other behavioral indicators.

[0156] "Past work history" refers to records of tasks performed by the user, their results, procedures, and so on.

[0157] "Efficient work execution methods" refer to the optimal methods and processes for carrying out tasks quickly and effectively.

[0158] "The content of an electronic message" refers to the internal information of communications, emails, and other messages exchanged via electronic means in a digital format.

[0159] A "reply message" is a text generated as a response to a received message, and is used to return information to the sender.

[0160] This invention is a system that streamlines work management and enables flexible work execution that takes into account the user's emotional state. The user inputs work details into a terminal in voice or text format. The terminal collects voice and facial expression data using a microphone and camera and transmits it to a server. The server processes the collected data using an emotion analysis engine to recognize the user's emotional state. This emotion analysis utilizes voice analysis software and image analysis algorithms.

[0161] The server analyzes work content using a natural language processing engine and generates an appropriate work schedule. It also automatically determines task priorities based on the analyzed sentiment information and rearranges tasks as needed. Based on past work history, it also provides users with efficient work execution methods.

[0162] For example, if a user voice-inputs "I feel anxious about preparing for my presentation" into their device, the server will sense this anxiety and prioritize scheduling tasks for preparation. Additionally, an AI model will suggest reassuring replies to emails the user receives.

[0163] A concrete example of a prompt for a generating AI model is, "Please provide an appropriate response when the user feels anxious." Such prompts enable the system to achieve optimal task management tailored to the user's emotional state.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] Users input their work details into the terminal in either voice or text format. Voice input is captured via a microphone, while text data is collected via keyboard or touch input. These inputs are the data received, and in the case of voice data, waveform information is generated. The terminal uses a camera to capture facial expressions and acquire still images that represent the user's emotions.

[0167] Step 2:

[0168] The terminal encrypts the acquired voice data, text data, and facial expression data and sends it to the server. This ensures the security of user data. The server receives the encrypted packets and decodes them into an analyzable format. Through this process, the server prepares the input dataset for analysis.

[0169] Step 3:

[0170] The server inputs voice data into a voice analysis program, which analyzes the tone of voice to identify emotions. The server then processes facial expression data into an image analysis algorithm to recognize the user's emotional state. The output obtained at this stage is the emotion recognition result. Specifically, emotion labels such as anxiety and stress are assigned.

[0171] Step 4:

[0172] The server analyzes the business content entered as text through a natural language processing engine to determine the priority and type of task. The output generated by this analysis is the data necessary for task optimization and prioritization. For example, task A may be determined to be high priority, while task B may be determined to be low priority.

[0173] Step 5:

[0174] The server generates a work schedule based on emotional states and work analysis results. The inputs are emotional labels and task priority data, and the output is a schedule optimized for dates and times to be presented to the user. In actual operation, decisions are made to postpone certain tasks to reduce the workload of users experiencing stress.

[0175] Step 6:

[0176] The server analyzes the text of electronic messages and inputs prompts into an AI model that generates emotionally appropriate replies. These prompts may include phrases like, "Please provide an appropriate reply when the user feels anxious." The AI ​​model creates a reassuring and appropriate response and returns its output to the server. The server then sends this suggestion to the user's device, allowing the user to review and adjust it as needed.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] In today's business environment, efficient task management and task management that considers user emotions are crucial. However, conventional systems do not adequately address flexible task management that takes user emotional states into account, leading to a demand for stress reduction and improved work efficiency. Furthermore, providing user-friendly interfaces from mobile and wearable devices is also a critical challenge.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing past work history and providing efficient work execution methods; means for analyzing the content of electronic messages and suggesting appropriate replies; means for determining the user's emotional state using sentiment analysis technology and suggesting adjustments to the workload; and means for providing real-time task management support to the user through a mobile terminal or wearable terminal. This enables flexible work execution in accordance with the user's emotional state, thereby improving work efficiency and reducing stress.

[0182] A "user" is an individual user who utilizes the system, and is the entity that inputs work details and provides emotional states.

[0183] "Job description" refers to information about the tasks and projects that users are supposed to perform, and is the subject of analysis by the system.

[0184] "Analysis" is the process of extracting specific patterns and meanings from entered work content and user sentiment data.

[0185] "Means for generating plans" refers to functions that optimize the schedule and progress of tasks based on analysis results.

[0186] "Past work history" refers to data about the tasks a user has previously performed and the results thereof.

[0187] "Electronic messages" refer to the content of communications exchanged between users via email or messaging apps.

[0188] "Emotion analysis technology" refers to algorithms and methods for inferring a user's emotional state from their voice and facial expression data.

[0189] "Mobile devices" refer to portable computers such as smartphones and handheld devices.

[0190] "Wearable devices" refer to computer devices that are worn on the body, such as smart glasses and smartwatches.

[0191] "Real-time" refers to the temporal characteristic of responding immediately to user requests.

[0192] "Task management" refers to the process of organizing the tasks and activities that a user needs to perform and executing them efficiently.

[0193] The system that realizes this invention is one in which the user inputs work details via a mobile terminal or wearable device, and that data is analyzed on a server. Hardware includes portable devices such as smartphones and smart glasses, and a cloud server. Software includes a speech recognition system using the Google Speech-to-Text API, emotion analysis technology using Microsoft® Azure® Emotion Analysis, a task management system via the Asana API, and a reply generation AI model using OpenAI® GPT-3®.

[0194] First, the user inputs their work details via voice or text through their device. This input is converted into text data using a speech recognition system and sent to the server in real time. The server analyzes the received work details, generates an optimized plan, and uses Microsoft Azure sentiment analysis to determine the user's emotional state. If the user is experiencing stress, suggestions are made to adjust the workload.

[0195] Furthermore, the server analyzes past work history and proposes efficient ways to perform tasks. This proposal is displayed on the terminal as visualized feedback. Also, when a user receives an electronic message, the server analyzes the message content and generates a response that matches the user's emotions based on the OpenAI GPT-3 model.

[0196] For example, if a user says, "I feel anxious about the public briefing on the new garbage collection project," sentiment analysis technology will detect the user's anxiety and reassign related tasks to a higher priority. Examples of prompts include, "I'm feeling a little anxious about today's tasks; how should I prioritize them?" or "Please suggest ways to efficiently prepare for the upcoming meeting." This allows for real-time, personalized work support for the user.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] Users input work details via voice or text using mobile devices or wearable devices. This input data is converted from speech to text using the Google Speech-to-Text API. The output is the work details in text format.

[0200] Step 2:

[0201] The terminal sends the converted text data to the server in real time. The server analyzes the received text data to extract key points and priorities of the work content. The output is structured data of the analyzed work content.

[0202] Step 3:

[0203] The server uses Microsoft Azure's Emotion Analysis to analyze the user's emotional state from the audio provided during input and the video feed from the device's camera. This process extracts emotional states such as "stress." The output is data related to the user's emotional state.

[0204] Step 4:

[0205] The server uses the analyzed work content and sentiment state to reconstruct the optimal task schedule via the Asana API. It takes into account task priorities and deadlines, and adjusts the load as needed. The output from this process is an optimized task list.

[0206] Step 5:

[0207] The server references the user's work history database and compiles information on efficient work execution methods. It analyzes past successful techniques and progress, and provides feedback to the user. This output is a visualized improvement report.

[0208] Step 6:

[0209] When a user receives an electronic message, the server analyzes its content. Using OpenAI GPT-3, it automatically generates an appropriate reply tailored to the user's emotional state. The output is the suggested reply.

[0210] Step 7:

[0211] The device receives feedback and suggestions from the server and notifies the user in real time. This allows the user to manage tasks efficiently and emotionally. The output consists of notifications and suggestions for improvement to the user.

[0212] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0219] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] This invention provides a system for efficiently managing and executing users' work tasks. Users can input their work tasks via voice or text through a terminal. This data is transmitted to a server via the terminal. The server uses information analysis means to analyze the work tasks in detail and sets priorities and categories according to their content.

[0229] Next, the server uses optimization techniques to generate a date and time-based plan based on the analysis results. This plan works in conjunction with the user's calendar application to place tasks on appropriate dates, supporting efficient work execution even in busy schedules.

[0230] Furthermore, the server can systematically analyze past business history using analytical tools, and based on this analysis, it provides users with feedback to improve business efficiency. This feedback is displayed on the terminal in a visually easy-to-understand format.

[0231] Furthermore, electronic messages received by the terminal are sent to a server, where their content is automatically analyzed using analytical tools. The server generates an appropriate reply and suggests it to the user. This function significantly reduces the time users spend processing messages.

[0232] For example, if a user enters a task such as "Prepare for next week's project meeting," the server will classify this task as "high priority" and automatically update the calendar with a preparation schedule tailored to the meeting date. It will also analyze past meeting history and provide feedback on more efficient and time-efficient preparation methods.

[0233] Thus, the present invention is implemented in a way that automates and streamlines the user's business processes. Throughout all stages of the system, the server, terminals, and users cooperate to achieve seamless business management.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users record their work details using voice or text input via a terminal. This information is captured in real time, and the format of the input is checked for accuracy.

[0237] Step 2:

[0238] The terminal formats the business data received from the user and sends it to the server according to security protocols. The data reaches the server via the network.

[0239] Step 3:

[0240] The server analyzes the received business data and extracts important keywords using natural language processing. This analysis aims to categorize business processes and automatically set their priorities.

[0241] Step 4:

[0242] The server generates an optimal schedule based on the analysis results. It references past work history and current calendar information, and incorporates this into the user's work plan.

[0243] Step 5:

[0244] The server generates schedule information and synchronizes it with the calendar application. The schedule is added to the user's calendar, and the user is notified via their device.

[0245] Step 6:

[0246] The device sends received electronic messages to the server. The data of received emails is encrypted using the appropriate protocol and securely transferred to the server.

[0247] Step 7:

[0248] The server analyzes the content of the electronic message and extracts the necessary reply information. Based on the analysis results, it automatically generates appropriate templates and wording for the reply.

[0249] Step 8:

[0250] The terminal presents the user with a suggested reply received from the server. The user can review the suggested content, edit it if necessary, and then send it.

[0251] These steps function as a series of steps, providing a system that streamlines the management and execution of tasks by users.

[0252] (Example 1)

[0253] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0254] In today's work environment, users are required to efficiently manage and perform complex and diverse tasks. However, manual scheduling and task organization require considerable time and effort, leading to decreased work efficiency. Furthermore, electronic communication often demands quick and appropriate responses, which can be burdensome. A system is needed to solve these problems.

[0255] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0256] In this invention, the server includes means for interpreting work information entered by the user and creating an optimized plan based on the date and time; means for analyzing past work history and providing efficient methods for completing work; and means for interpreting the content of electronic communications and generating appropriate response statements. This enables the user to efficiently manage and perform tasks through automated processes and reduces the processing burden of electronic communications.

[0257] A "user" is someone who uses the system to input work details and is subject to management.

[0258] "Work information" refers to information about tasks and activities that users input in order to perform their work.

[0259] "Interpretation" is the process of analyzing input data and understanding its meaning and intent.

[0260] A "date and time-optimized plan" is a plan that efficiently schedules tasks using date and time information entered into the work.

[0261] "Past work history" refers to information that records previous work or tasks.

[0262] An "efficient method of completing work" refers to the techniques and procedures for completing tasks quickly and effectively.

[0263] "Electronic communication" refers to communication conducted using digital methods such as email and messaging.

[0264] A "response" is a document or message sent back in response to an received electronic communication.

[0265] A "visually easy-to-understand format" is one in which information is presented visually using graphs and charts, making it easy to understand.

[0266] This system is a platform for efficiently managing and executing user tasks. Users can input task information via a terminal in voice or text format. The terminal securely transmits the received task information to the server over the internet. The HTTPS protocol is used for this transmission.

[0267] The server uses commonly available natural language processing software to analyze the received work information. This analysis allows the server to automatically set the priority and category of the work. Based on the analysis results, the server uses a scheduling algorithm to generate an optimal schedule based on the date and time, and integrates this schedule with the user's appointment management application. A standard calendar API is used for this integration.

[0268] Electronic communications received by the user are sent from the terminal to the server, where their content is analyzed by a generative AI model. The server then uses the AI ​​model to generate prompt sentences to suggest appropriate responses. This allows users to quickly and easily create responses to electronic communications. An example of a prompt sentence might be, "Please tell me what preparations are needed for the next meeting."

[0269] Furthermore, the server stores past work history in a database and uses data analysis tools to generate efficient work completion methods. This feedback is provided to the user through the terminal in a visually easy-to-understand format. This allows the user to further improve their work efficiency.

[0270] In this way, servers, terminals, and users work together as a unified team, enabling efficient management and execution of tasks through automated processes.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user inputs work information through a terminal. Input can be in voice or text format. For example, the user might input "Prepare for Monday's meeting." The input data is converted into speech recognition or text data by the terminal and sent to the server via the internet. The output is the text data received by the server.

[0274] Step 2:

[0275] The server uses natural language processing software to analyze the received text data. This involves extracting keywords from the data and performing data calculations to determine the priority and category of the tasks. As a result of the analysis, the data is categorized, for example, "high-priority meeting preparation," and a priority is set. The output is the analyzed business data.

[0276] Step 3:

[0277] The server uses a scheduling algorithm based on analyzed business data to generate an optimal work plan. Specifically, it generates a schedule that takes into account task priorities and date / time information. The generated schedule is reflected in the user's scheduling application via an API. For example, a meeting preparation task might be assigned the day before the meeting. The output is schedule data.

[0278] Step 4:

[0279] The terminal receives electronic communication and sends the data to the server. The server inputs the received communication into a generating AI model for interpretation and creates a prompt to generate an appropriate response. The server then proposes the generated response to the user. This process allows the user to quickly generate an appropriate response. The output is the proposed response.

[0280] Step 5:

[0281] The server stores past work history in a database and analyzes it using analytical tools. It extracts efficient work completion methods and areas for improvement from the user's past performance data. The analysis results are sent to the terminal in a visually easy-to-understand format and displayed to the user as feedback. The output is visualized feedback.

[0282] (Application Example 1)

[0283] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0284] In modern factories, efficient management and optimization of production operations are important. However, in conventional systems, since the adjustment of production schedules and the prioritization of work contents rely on manual labor, it has required time and effort to improve work efficiency. In addition, there has been a problem that the overall efficiency of operations has not been sufficiently achieved because past production data has not been effectively utilized.

[0285] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0286] In this invention, the server includes means for analyzing the work contents input by the user and generating an optimized plan based on the date and time, means for analyzing past work histories and providing an efficient method for performing work, and means for analyzing the work contents in the factory in real time, setting priorities, and issuing instructions to an automation system. As a result, it becomes possible to improve the work efficiency in the production operations of the factory and to further optimize the operations by utilizing past data.

[0287] The "user" is the entity that inputs work contents using the system and aims to optimize management.

[0288] The "work contents" are information for performing production management and work instructions, and are input in voice or text format.

[0289] "Analysis" is an act of arranging and classifying in detail the work contents input by the user and creating an optimized plan based on the information.

[0290] The "plan optimized based on the date and time" is a plan aimed at constructing an efficient schedule considering the priorities of work and the necessary resources.

[0291] The "past work histories" are a group of data on work achievements recorded so far, and are a set of information to be analyzed.

[0292] "Efficient work execution methods" refer to the means and processes for carrying out tasks in the most effective and rapid way possible.

[0293] "Factory work content" refers to the specific tasks and work instructions that are performed by the factory's automated equipment.

[0294] "Real-time analysis" means immediately evaluating the input information and performing the necessary processing.

[0295] Setting priorities is the act of assigning an urgency or importance level to a task or work, thereby facilitating more efficient processing.

[0296] "Issuing instructions to an automated system" refers to giving instructions to automated equipment or devices to perform specific actions based on information obtained through analysis.

[0297] The system for implementing this invention involves a server, a terminal, and a user working together to automate and streamline tasks. First, the user inputs the task details into the terminal via voice or text. The terminal sends this task details to the server, where analysis is performed. The analysis uses speech recognition software such as the Google Cloud Speech-to-Text API and a custom analysis algorithm written in Python.

[0298] Based on the analysis results, the server generates an optimized plan using a scheduling system such as Apache Airflow. This plan reflects the priority and deadline of each task, and instructions are sent to the automated equipment in the factory. These instructions allow factory robots to perform tasks in real time, improving operational efficiency.

[0299] In addition, the server systematically analyzes past business histories and provides feedback to users on efficient ways to conduct business. For example, by using prompts such as "Please propose the optimal production schedule to produce 100 pieces of part X by next Monday. Also, please tell me if there are any efficiency improvement methods based on past data.", further business optimization can be achieved.

[0300] With this system, the production lines in the factory are operated in a data-driven manner and can proceed with work based on an optimized production schedule in real time. By implementing the invention in this way, the efficiency and productivity of business can be significantly improved.

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] The user inputs business content in voice or text using a terminal. The input data is converted into text data if it is voice data using a speech recognition API. This converted text data is sent to the server.

[0304] Step 2:

[0305] The server analyzes the received text data. Using an analysis engine, important keywords and information are extracted from the business content, and the priority and category of the business are set. Through this process, each business task is output as a dataset with information necessary for scheduling.

[0306] Step 3:

[0307] The server utilizes a scheduling system to generate an optimal work schedule based on the analysis results. It processes the priority, due date, etc. of the input business and generates a plan based on the date and time. This plan data is output as a list including specific schedules and responsible parties.

[0308] Step 4:

[0309] The server sends the generated work schedule as instructions to the factory automation equipment. The factory robots then automatically perform the corresponding tasks in real time based on the received instructions. The robots report the progress of their work according to the schedule and send the completion status to the server.

[0310] Step 5:

[0311] The server continuously analyzes past work history and generates feedback to improve work efficiency. This visualized feedback is sent to the user's terminal, suggesting improvements and more efficient methods for performing tasks. This feedback provides valuable information for optimizing future schedules.

[0312] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0313] This invention provides a system that utilizes an emotion engine to improve the efficiency of user task management. Users can input task details via voice or text through a terminal. This input data is transmitted to a server in real time, where the server analyzes the task details and generates an optimized plan based on the date and time.

[0314] The emotion engine recognizes the user's emotional state by analyzing the tone of voice during voice input and facial expression data acquired using the device's built-in camera. This information is taken into consideration when automatically determining task priorities and reflecting them in the schedule. For example, if the user is feeling stressed, the system will suggest rearranging tasks to reduce the workload.

[0315] Furthermore, the server analyzes past work history and provides users with efficient work execution methods. This analysis is visualized on the user's terminal as feedback, which can be used for future improvements.

[0316] On the other hand, in processing electronic messages, the server analyzes the message content and generates an appropriate reply based on the user's emotions recognized by the emotion engine. This ensures that the tone of communication matches the user's emotions, enabling more personalized responses.

[0317] For example, if a user inputs via voice, "I feel pressured about the meeting to review the project's progress," the emotion engine will detect feelings such as anxiety and tension, and the server will suggest scheduling tasks for meeting preparation as early as possible. Furthermore, the server will generate and suggest reassuring replies to emails the user has received.

[0318] Thus, the present invention provides a business management system incorporating an emotion engine, thereby enabling flexible work execution that takes into account the user's emotional state, and aiming to improve efficiency and reduce stress.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user inputs work details into the terminal via voice or text. The terminal captures the input and converts it into a predefined format.

[0322] Step 2:

[0323] The terminal sends the input content to the server. The data is encrypted via a security protocol and transmitted securely to the server.

[0324] Step 3:

[0325] The server analyzes the received work data. Natural language processing is used to extract keywords and automatically set the work category and priority.

[0326] Step 4:

[0327] The emotion engine sends voice tone and facial expression data to the server. Simultaneously, the server analyzes this emotion data to identify the user's emotional state.

[0328] Step 5:

[0329] The server optimizes existing work schedules by considering emotional data. If a user is experiencing stress, tasks are rearranged or rescheduled based on their emotions.

[0330] Step 6:

[0331] The server sends schedule data to the device so that the optimized schedule is reflected in the user's calendar. The device then synchronizes this information with its calendar application for display.

[0332] Step 7:

[0333] The terminal sends the received electronic message to the server. The server analyzes the message's content and determines its importance and the urgency of a reply.

[0334] Step 8:

[0335] The emotion engine generates an appropriate reply that reflects the user's current emotional state. The server sends this to the terminal for the user to review and edit.

[0336] Step 9:

[0337] The user edits their reply as needed and sends the finalized message. The device sends the updated message and completes the process.

[0338] In this way, using an emotion engine enables flexible and efficient business management that is attentive to the user's emotions.

[0339] (Example 2)

[0340] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0341] In today's work environment, efficiently managing individual tasks while taking into account users' emotional states is a challenging task. To reduce user stress and emotional burden and improve work efficiency, it is necessary to analyze emotional changes in real time and optimize tasks based on that analysis. Conventional systems have difficulty integrating emotional analysis into task management, and have not been able to achieve flexible and efficient task execution that is tailored to individual emotions.

[0342] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0343] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing voice data and facial expression data to recognize the user's emotional state; and means for determining the priority of tasks based on the recognized emotional state and optimizing and rearranging the work content. This enables flexible work management and improved efficiency in response to the user's emotions.

[0344] "Job description" refers to the specific tasks and projects that users are expected to perform in their daily activities and work.

[0345] "Analysis" refers to a method of processing data based on given audio data, text data, and other information in order to understand its meaning and emotions.

[0346] A "date and time-optimized plan" refers to a plan that determines the optimal schedule for carrying out tasks based on date and time information.

[0347] "Emotional state" refers to the emotional situation or state of mind derived from the user's voice, facial expressions, and other behavioral indicators.

[0348] "Past work history" refers to records of tasks performed by the user, their results, procedures, and so on.

[0349] "Efficient work execution methods" refer to the optimal methods and processes for carrying out tasks quickly and effectively.

[0350] "The content of an electronic message" refers to the internal information of communications, emails, and other messages exchanged via electronic means in a digital format.

[0351] A "reply message" is a text generated as a response to a received message, and is used to return information to the sender.

[0352] This invention is a system that streamlines work management and enables flexible work execution that takes into account the user's emotional state. The user inputs work details into a terminal in voice or text format. The terminal collects voice and facial expression data using a microphone and camera and transmits it to a server. The server processes the collected data using an emotion analysis engine to recognize the user's emotional state. This emotion analysis utilizes voice analysis software and image analysis algorithms.

[0353] The server analyzes work content using a natural language processing engine and generates an appropriate work schedule. It also automatically determines task priorities based on the analyzed sentiment information and rearranges tasks as needed. Based on past work history, it also provides users with efficient work execution methods.

[0354] For example, if a user voice-inputs "I feel anxious about preparing for my presentation" into their device, the server will sense this anxiety and prioritize scheduling tasks for preparation. Additionally, an AI model will suggest reassuring replies to emails the user receives.

[0355] A concrete example of a prompt for a generating AI model is, "Please provide an appropriate response when the user feels anxious." Such prompts enable the system to achieve optimal task management tailored to the user's emotional state.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] Users input their work details into the terminal in either voice or text format. Voice input is captured via a microphone, while text data is collected via keyboard or touch input. These inputs are the data received, and in the case of voice data, waveform information is generated. The terminal uses a camera to capture facial expressions and acquire still images that represent the user's emotions.

[0359] Step 2:

[0360] The terminal encrypts the acquired voice data, text data, and facial expression data and sends it to the server. This ensures the security of user data. The server receives the encrypted packets and decodes them into an analyzable format. Through this process, the server prepares the input dataset for analysis.

[0361] Step 3:

[0362] The server inputs voice data into a voice analysis program, which analyzes the tone of voice to identify emotions. The server then processes facial expression data into an image analysis algorithm to recognize the user's emotional state. The output obtained at this stage is the emotion recognition result. Specifically, emotion labels such as anxiety and stress are assigned.

[0363] Step 4:

[0364] The server analyzes the business content entered as text through a natural language processing engine to determine the priority and type of task. The output generated by this analysis is the data necessary for task optimization and prioritization. For example, task A may be determined to be high priority, while task B may be determined to be low priority.

[0365] Step 5:

[0366] The server generates a work schedule based on emotional states and work analysis results. The inputs are emotional labels and task priority data, and the output is a schedule optimized for dates and times to be presented to the user. In actual operation, decisions are made to postpone certain tasks to reduce the workload of users experiencing stress.

[0367] Step 6:

[0368] The server analyzes the text of electronic messages and inputs prompts into an AI model that generates emotionally appropriate replies. These prompts may include phrases like, "Please provide an appropriate reply when the user feels anxious." The AI ​​model creates a reassuring and appropriate response and returns its output to the server. The server then sends this suggestion to the user's device, allowing the user to review and adjust it as needed.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] In today's business environment, efficient task management and task management that considers user emotions are crucial. However, conventional systems do not adequately address flexible task management that takes user emotional states into account, leading to a demand for stress reduction and improved work efficiency. Furthermore, providing user-friendly interfaces from mobile and wearable devices is also a critical challenge.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing past work history and providing efficient work execution methods; means for analyzing the content of electronic messages and suggesting appropriate replies; means for determining the user's emotional state using sentiment analysis technology and suggesting adjustments to the workload; and means for providing real-time task management support to the user through a mobile terminal or wearable terminal. This enables flexible work execution in accordance with the user's emotional state, thereby improving work efficiency and reducing stress.

[0374] A "user" is an individual user who utilizes the system, and is the entity that inputs work details and provides emotional states.

[0375] "Job description" refers to information about the tasks and projects that users are supposed to perform, and is the subject of analysis by the system.

[0376] "Analysis" is the process of extracting specific patterns and meanings from entered work content and user sentiment data.

[0377] "Means for generating plans" refers to functions that optimize the schedule and progress of tasks based on analysis results.

[0378] "Past work history" refers to data about the tasks a user has previously performed and the results thereof.

[0379] "Electronic messages" refer to the content of communications exchanged between users via email or messaging apps.

[0380] "Emotion analysis technology" refers to algorithms and methods for inferring a user's emotional state from their voice and facial expression data.

[0381] "Mobile devices" refer to portable computers such as smartphones and handheld devices.

[0382] "Wearable devices" refer to computer devices that are worn on the body, such as smart glasses and smartwatches.

[0383] "Real-time" refers to the temporal characteristic of responding immediately to user requests.

[0384] "Task management" refers to the process of organizing the tasks and activities that a user needs to perform and executing them efficiently.

[0385] The system that realizes this invention is one in which the user inputs work details via a mobile terminal or wearable device, and that data is analyzed on a server. Hardware includes portable devices such as smartphones and smart glasses, and a cloud server. Software includes a speech recognition system using the Google Speech-to-Text API, emotion analysis technology using Microsoft Azure's Emotion Analysis, a task management system via the Asana API, and a reply generation AI model using OpenAI GPT-3.

[0386] First, the user inputs their work details via voice or text through their device. This input is converted into text data using a speech recognition system and sent to the server in real time. The server analyzes the received work details, generates an optimized plan, and uses Microsoft Azure sentiment analysis to determine the user's emotional state. If the user is experiencing stress, suggestions are made to adjust the workload.

[0387] Furthermore, the server analyzes past work history and proposes efficient ways to perform tasks. This proposal is displayed on the terminal as visualized feedback. Also, when a user receives an electronic message, the server analyzes the message content and generates a response that matches the user's emotions based on the OpenAI GPT-3 model.

[0388] For example, if a user says, "I feel anxious about the public briefing on the new garbage collection project," sentiment analysis technology will detect the user's anxiety and reassign related tasks to a higher priority. Examples of prompts include, "I'm feeling a little anxious about today's tasks; how should I prioritize them?" or "Please suggest ways to efficiently prepare for the upcoming meeting." This allows for real-time, personalized work support for the user.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] Users input work details via voice or text using mobile devices or wearable devices. This input data is converted from speech to text using the Google Speech-to-Text API. The output is the work details in text format.

[0392] Step 2:

[0393] The terminal sends the converted text data to the server in real time. The server analyzes the received text data to extract key points and priorities of the work content. The output is structured data of the analyzed work content.

[0394] Step 3:

[0395] The server uses Microsoft Azure's Emotion Analysis to analyze the user's emotional state from the audio provided during input and the video feed from the device's camera. This process extracts emotional states such as "stress." The output is data related to the user's emotional state.

[0396] Step 4:

[0397] The server uses the analyzed work content and sentiment state to reconstruct the optimal task schedule via the Asana API. It takes into account task priorities and deadlines, and adjusts the load as needed. The output from this process is an optimized task list.

[0398] Step 5:

[0399] The server references the user's work history database and compiles information on efficient work execution methods. It analyzes past successful techniques and progress, and provides feedback to the user. This output is a visualized improvement report.

[0400] Step 6:

[0401] When a user receives an electronic message, the server analyzes its content. Using OpenAI GPT-3, it automatically generates an appropriate reply tailored to the user's emotional state. The output is the suggested reply.

[0402] Step 7:

[0403] The device receives feedback and suggestions from the server and notifies the user in real time. This allows the user to manage tasks efficiently and emotionally. The output consists of notifications and suggestions for improvement to the user.

[0404] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0405] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0406] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0410] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0411] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0412] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0413] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0414] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0415] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0416] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0417] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0418] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0419] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0420] This invention provides a system for efficiently managing and executing users' work tasks. Users can input their work tasks via voice or text through a terminal. This data is transmitted to a server via the terminal. The server uses information analysis means to analyze the work tasks in detail and sets priorities and categories according to their content.

[0421] Next, the server uses optimization techniques to generate a date and time-based plan based on the analysis results. This plan works in conjunction with the user's calendar application to place tasks on appropriate dates, supporting efficient work execution even in busy schedules.

[0422] Furthermore, the server can systematically analyze past business history using analytical tools, and based on this analysis, it provides users with feedback to improve business efficiency. This feedback is displayed on the terminal in a visually easy-to-understand format.

[0423] Furthermore, electronic messages received by the terminal are sent to a server, where their content is automatically analyzed using analytical tools. The server generates an appropriate reply and suggests it to the user. This function significantly reduces the time users spend processing messages.

[0424] For example, if a user enters a task such as "Prepare for next week's project meeting," the server will classify this task as "high priority" and automatically update the calendar with a preparation schedule tailored to the meeting date. It will also analyze past meeting history and provide feedback on more efficient and time-efficient preparation methods.

[0425] Thus, the present invention is implemented in a way that automates and streamlines the user's business processes. Throughout all stages of the system, the server, terminals, and users cooperate to achieve seamless business management.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] Users record their work details using voice or text input via a terminal. This information is captured in real time, and the format of the input is checked for accuracy.

[0429] Step 2:

[0430] The terminal formats the business data received from the user and sends it to the server according to security protocols. The data reaches the server via the network.

[0431] Step 3:

[0432] The server analyzes the received business data and extracts important keywords using natural language processing. This analysis aims to categorize business processes and automatically set their priorities.

[0433] Step 4:

[0434] The server generates an optimal schedule based on the analysis results. It references past work history and current calendar information, and incorporates this into the user's work plan.

[0435] Step 5:

[0436] The server generates schedule information and synchronizes it with the calendar application. The schedule is added to the user's calendar, and the user is notified via their device.

[0437] Step 6:

[0438] The device sends received electronic messages to the server. The data of received emails is encrypted using the appropriate protocol and securely transferred to the server.

[0439] Step 7:

[0440] The server analyzes the content of the electronic message and extracts the necessary reply information. Based on the analysis results, it automatically generates appropriate templates and wording for the reply.

[0441] Step 8:

[0442] The terminal presents the user with a suggested reply received from the server. The user can review the suggested content, edit it if necessary, and then send it.

[0443] These steps function as a series of steps, providing a system that streamlines the management and execution of tasks by users.

[0444] (Example 1)

[0445] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0446] In today's work environment, users are required to efficiently manage and perform complex and diverse tasks. However, manual scheduling and task organization require considerable time and effort, leading to decreased work efficiency. Furthermore, electronic communication often demands quick and appropriate responses, which can be burdensome. A system is needed to solve these problems.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes means for interpreting work information entered by the user and creating an optimized plan based on the date and time; means for analyzing past work history and providing efficient methods for completing work; and means for interpreting the content of electronic communications and generating appropriate response statements. This enables the user to efficiently manage and perform tasks through automated processes and reduces the processing burden of electronic communications.

[0449] A "user" is someone who uses the system to input work details and is subject to management.

[0450] "Work information" refers to information about tasks and activities that users input in order to perform their work.

[0451] "Interpretation" is the process of analyzing input data and understanding its meaning and intent.

[0452] A "date and time-optimized plan" is a plan that efficiently schedules tasks using date and time information entered into the work.

[0453] "Past work history" refers to information that records previous work or tasks.

[0454] An "efficient method of completing work" refers to the techniques and procedures for completing tasks quickly and effectively.

[0455] "Electronic communication" refers to communication conducted using digital methods such as email and messaging.

[0456] A "response" is a document or message sent back in response to an received electronic communication.

[0457] A "visually easy-to-understand format" is one in which information is presented visually using graphs and charts, making it easy to understand.

[0458] This system is a platform for efficiently managing and executing user tasks. Users can input task information via a terminal in voice or text format. The terminal securely transmits the received task information to the server over the internet. The HTTPS protocol is used for this transmission.

[0459] The server uses commonly available natural language processing software to analyze the received work information. This analysis allows the server to automatically set the priority and category of the work. Based on the analysis results, the server uses a scheduling algorithm to generate an optimal schedule based on the date and time, and integrates this schedule with the user's appointment management application. A standard calendar API is used for this integration.

[0460] Electronic communications received by the user are sent from the terminal to the server, where their content is analyzed by a generative AI model. The server then uses the AI ​​model to generate prompt sentences to suggest appropriate responses. This allows users to quickly and easily create responses to electronic communications. An example of a prompt sentence might be, "Please tell me what preparations are needed for the next meeting."

[0461] Furthermore, the server stores past work history in a database and uses data analysis tools to generate efficient work completion methods. This feedback is provided to the user through the terminal in a visually easy-to-understand format. This allows the user to further improve their work efficiency.

[0462] In this way, servers, terminals, and users work together as a unified team, enabling efficient management and execution of tasks through automated processes.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The user inputs work information through a terminal. Input can be in voice or text format. For example, the user might input "Prepare for Monday's meeting." The input data is converted into speech recognition or text data by the terminal and sent to the server via the internet. The output is the text data received by the server.

[0466] Step 2:

[0467] The server uses natural language processing software to analyze the received text data. This involves extracting keywords from the data and performing data calculations to determine the priority and category of the tasks. As a result of the analysis, the data is categorized, for example, "high-priority meeting preparation," and a priority is set. The output is the analyzed business data.

[0468] Step 3:

[0469] The server uses a scheduling algorithm based on analyzed business data to generate an optimal work plan. Specifically, it generates a schedule that takes into account task priorities and date / time information. The generated schedule is reflected in the user's scheduling application via an API. For example, a meeting preparation task might be assigned the day before the meeting. The output is schedule data.

[0470] Step 4:

[0471] The terminal receives electronic communication and sends the data to the server. The server inputs the received communication into a generating AI model for interpretation and creates a prompt to generate an appropriate response. The server then proposes the generated response to the user. This process allows the user to quickly generate an appropriate response. The output is the proposed response.

[0472] Step 5:

[0473] The server stores past work history in a database and analyzes it using analytical tools. It extracts efficient work completion methods and areas for improvement from the user's past performance data. The analysis results are sent to the terminal in a visually easy-to-understand format and displayed to the user as feedback. The output is visualized feedback.

[0474] (Application Example 1)

[0475] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0476] In modern factories, efficient management and optimization of production operations are crucial. However, traditional systems rely on manual processes for adjusting production schedules and prioritizing tasks, resulting in significant time and effort required to improve work efficiency. Furthermore, the inability to effectively utilize past production data meant that overall operational efficiency was not fully realized.

[0477] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0478] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing past work history and providing efficient work execution methods; and means for analyzing the work content in the factory in real time, setting priorities, and issuing instructions to the automation system. This enables improved work efficiency in factory production operations and further optimization of operations by utilizing past data.

[0479] A "user" is an entity that uses the system to input business details and optimize management.

[0480] "Job description" refers to information used for production management and work instructions, and is entered in voice or text format.

[0481] "Analysis" is the process of meticulously organizing and classifying the business data entered by users, and then creating an optimized plan based on that information.

[0482] A "date and time-optimized plan" is a plan that takes into account the priorities of tasks and the resources needed to create an efficient schedule.

[0483] "Past work history" refers to a collection of data on work performance recorded to date, and is the set of information that will be analyzed.

[0484] "Efficient work execution methods" refer to the means and processes for carrying out tasks in the most effective and rapid way possible.

[0485] "Factory work content" refers to the specific tasks and work instructions that are performed by the factory's automated equipment.

[0486] "Real-time analysis" means immediately evaluating the input information and performing the necessary processing.

[0487] Setting priorities is the act of assigning an urgency or importance level to a task or work, thereby facilitating more efficient processing.

[0488] "Issuing instructions to an automated system" refers to giving instructions to automated equipment or devices to perform specific actions based on information obtained through analysis.

[0489] The system for implementing this invention involves a server, a terminal, and a user working together to automate and streamline tasks. First, the user inputs the task details into the terminal via voice or text. The terminal sends this task details to the server, where analysis is performed. The analysis uses speech recognition software such as the Google Cloud Speech-to-Text API and a custom analysis algorithm written in Python.

[0490] Based on the analysis results, the server generates an optimized plan using a scheduling system such as Apache Airflow. This plan reflects the priority and deadline of each task, and instructions are sent to the automated equipment in the factory. These instructions allow factory robots to perform tasks in real time, improving operational efficiency.

[0491] Furthermore, the server systematically analyzes past work history and provides users with feedback on how to perform tasks efficiently. For example, by using prompts such as, "Please propose the optimal production schedule to produce 100 units of part X by next Monday. Please also let me know if there are any efficiency improvement methods based on past data," further optimization of operations can be achieved.

[0492] This system enables factory production lines to operate in a data-driven manner, allowing work to proceed under a real-time, optimized production schedule. Implementing this invention significantly improves operational efficiency and productivity.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] Users input work details using their devices via voice or text. The input data is converted to text using a speech recognition API. This converted text data is then sent to the server.

[0496] Step 2:

[0497] The server analyzes the received text data. Using an analysis engine, it extracts important keywords and information from the work content and sets the priority and category of the tasks. This process outputs each work task as a dataset containing the information necessary for scheduling.

[0498] Step 3:

[0499] The server utilizes a scheduling system to generate an optimal work schedule based on the analysis results. It processes the input task priorities and deadlines, generating a date and time-based plan. This plan data is output as a list including specific dates and assigned personnel.

[0500] Step 4:

[0501] The server sends the generated work schedule as instructions to the factory automation equipment. The factory robots then automatically perform the corresponding tasks in real time based on the received instructions. The robots report the progress of their work according to the schedule and send the completion status to the server.

[0502] Step 5:

[0503] The server continuously analyzes past work history and generates feedback to improve work efficiency. This visualized feedback is sent to the user's terminal, suggesting improvements and more efficient methods for performing tasks. This feedback provides valuable information for optimizing future schedules.

[0504] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0505] This invention provides a system that utilizes an emotion engine to improve the efficiency of user task management. Users can input task details via voice or text through a terminal. This input data is transmitted to a server in real time, where the server analyzes the task details and generates an optimized plan based on the date and time.

[0506] The emotion engine recognizes the user's emotional state by analyzing the tone of voice during voice input and facial expression data acquired using the device's built-in camera. This information is taken into consideration when automatically determining task priorities and reflecting them in the schedule. For example, if the user is feeling stressed, the system will suggest rearranging tasks to reduce the workload.

[0507] Furthermore, the server analyzes past work history and provides users with efficient work execution methods. This analysis is visualized on the user's terminal as feedback, which can be used for future improvements.

[0508] On the other hand, in processing electronic messages, the server analyzes the message content and generates an appropriate reply based on the user's emotions recognized by the emotion engine. This ensures that the tone of communication matches the user's emotions, enabling more personalized responses.

[0509] For example, if a user inputs via voice, "I feel pressured about the meeting to review the project's progress," the emotion engine will detect feelings such as anxiety and tension, and the server will suggest scheduling tasks for meeting preparation as early as possible. Furthermore, the server will generate and suggest reassuring replies to emails the user has received.

[0510] Thus, the present invention provides a business management system incorporating an emotion engine, thereby enabling flexible work execution that takes into account the user's emotional state, and aiming to improve efficiency and reduce stress.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] The user inputs work details into the terminal via voice or text. The terminal captures the input and converts it into a predefined format.

[0514] Step 2:

[0515] The terminal sends the input content to the server. The data is encrypted via a security protocol and transmitted securely to the server.

[0516] Step 3:

[0517] The server analyzes the received work data. Natural language processing is used to extract keywords and automatically set the work category and priority.

[0518] Step 4:

[0519] The emotion engine sends voice tone and facial expression data to the server. Simultaneously, the server analyzes this emotion data to identify the user's emotional state.

[0520] Step 5:

[0521] The server optimizes existing work schedules by considering emotional data. If a user is experiencing stress, tasks are rearranged or rescheduled based on their emotions.

[0522] Step 6:

[0523] The server sends schedule data to the device so that the optimized schedule is reflected in the user's calendar. The device then synchronizes this information with its calendar application for display.

[0524] Step 7:

[0525] The terminal sends the received electronic message to the server. The server analyzes the message's content and determines its importance and the urgency of a reply.

[0526] Step 8:

[0527] The emotion engine generates an appropriate reply that reflects the user's current emotional state. The server sends this to the terminal for the user to review and edit.

[0528] Step 9:

[0529] The user edits their reply as needed and sends the finalized message. The device sends the updated message and completes the process.

[0530] In this way, using an emotion engine enables flexible and efficient business management that is attentive to the user's emotions.

[0531] (Example 2)

[0532] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0533] In today's work environment, efficiently managing individual tasks while taking into account users' emotional states is a challenging task. To reduce user stress and emotional burden and improve work efficiency, it is necessary to analyze emotional changes in real time and optimize tasks based on that analysis. Conventional systems have difficulty integrating emotional analysis into task management, and have not been able to achieve flexible and efficient task execution that is tailored to individual emotions.

[0534] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0535] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing voice data and facial expression data to recognize the user's emotional state; and means for determining the priority of tasks based on the recognized emotional state and optimizing and rearranging the work content. This enables flexible work management and improved efficiency in response to the user's emotions.

[0536] "Job description" refers to the specific tasks and projects that users are expected to perform in their daily activities and work.

[0537] "Analysis" refers to a method of processing data based on given audio data, text data, and other information in order to understand its meaning and emotions.

[0538] A "date and time-optimized plan" refers to a plan that determines the optimal schedule for carrying out tasks based on date and time information.

[0539] "Emotional state" refers to the emotional situation or state of mind derived from the user's voice, facial expressions, and other behavioral indicators.

[0540] "Past work history" refers to records of tasks performed by the user, their results, procedures, and so on.

[0541] "Efficient work execution methods" refer to the optimal methods and processes for carrying out tasks quickly and effectively.

[0542] "The content of an electronic message" refers to the internal information of communications, emails, and other messages exchanged via electronic means in a digital format.

[0543] A "reply message" is a text generated as a response to a received message, and is used to return information to the sender.

[0544] This invention is a system that streamlines work management and enables flexible work execution that takes into account the user's emotional state. The user inputs work details into a terminal in voice or text format. The terminal collects voice and facial expression data using a microphone and camera and transmits it to a server. The server processes the collected data using an emotion analysis engine to recognize the user's emotional state. This emotion analysis utilizes voice analysis software and image analysis algorithms.

[0545] The server analyzes work content using a natural language processing engine and generates an appropriate work schedule. It also automatically determines task priorities based on the analyzed sentiment information and rearranges tasks as needed. Based on past work history, it also provides users with efficient work execution methods.

[0546] For example, if a user voice-inputs "I feel anxious about preparing for my presentation" into their device, the server will sense this anxiety and prioritize scheduling tasks for preparation. Additionally, an AI model will suggest reassuring replies to emails the user receives.

[0547] A concrete example of a prompt for a generating AI model is, "Please provide an appropriate response when the user feels anxious." Such prompts enable the system to achieve optimal task management tailored to the user's emotional state.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] Users input their work details into the terminal in either voice or text format. Voice input is captured via a microphone, while text data is collected via keyboard or touch input. These inputs are the data received, and in the case of voice data, waveform information is generated. The terminal uses a camera to capture facial expressions and acquire still images that represent the user's emotions.

[0551] Step 2:

[0552] The terminal encrypts the acquired voice data, text data, and facial expression data and sends it to the server. This ensures the security of user data. The server receives the encrypted packets and decodes them into an analyzable format. Through this process, the server prepares the input dataset for analysis.

[0553] Step 3:

[0554] The server inputs voice data into a voice analysis program, which analyzes the tone of voice to identify emotions. The server then processes facial expression data into an image analysis algorithm to recognize the user's emotional state. The output obtained at this stage is the emotion recognition result. Specifically, emotion labels such as anxiety and stress are assigned.

[0555] Step 4:

[0556] The server analyzes the business content entered as text through a natural language processing engine to determine the priority and type of task. The output generated by this analysis is the data necessary for task optimization and prioritization. For example, task A may be determined to be high priority, while task B may be determined to be low priority.

[0557] Step 5:

[0558] The server generates a work schedule based on emotional states and work analysis results. The inputs are emotional labels and task priority data, and the output is a schedule optimized for dates and times to be presented to the user. In actual operation, decisions are made to postpone certain tasks to reduce the workload of users experiencing stress.

[0559] Step 6:

[0560] The server analyzes the text of electronic messages and inputs prompts into an AI model that generates emotionally appropriate replies. These prompts may include phrases like, "Please provide an appropriate reply when the user feels anxious." The AI ​​model creates a reassuring and appropriate response and returns its output to the server. The server then sends this suggestion to the user's device, allowing the user to review and adjust it as needed.

[0561] (Application Example 2)

[0562] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0563] In today's business environment, efficient task management and task management that considers user emotions are crucial. However, conventional systems do not adequately address flexible task management that takes user emotional states into account, leading to a demand for stress reduction and improved work efficiency. Furthermore, providing user-friendly interfaces from mobile and wearable devices is also a critical challenge.

[0564] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0565] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing past work history and providing efficient work execution methods; means for analyzing the content of electronic messages and suggesting appropriate replies; means for determining the user's emotional state using sentiment analysis technology and suggesting adjustments to the workload; and means for providing real-time task management support to the user through a mobile terminal or wearable terminal. This enables flexible work execution in accordance with the user's emotional state, thereby improving work efficiency and reducing stress.

[0566] A "user" is an individual user who utilizes the system, and is the entity that inputs work details and provides emotional states.

[0567] "Job description" refers to information about the tasks and projects that users are supposed to perform, and is the subject of analysis by the system.

[0568] "Analysis" is the process of extracting specific patterns and meanings from entered work content and user sentiment data.

[0569] "Means for generating plans" refers to functions that optimize the schedule and progress of tasks based on analysis results.

[0570] "Past work history" refers to data about the tasks a user has previously performed and the results thereof.

[0571] "Electronic messages" refer to the content of communications exchanged between users via email or messaging apps.

[0572] "Emotion analysis technology" refers to algorithms and methods for inferring a user's emotional state from their voice and facial expression data.

[0573] "Mobile devices" refer to portable computers such as smartphones and handheld devices.

[0574] "Wearable devices" refer to computer devices that are worn on the body, such as smart glasses and smartwatches.

[0575] "Real-time" refers to the temporal characteristic of responding immediately to user requests.

[0576] "Task management" refers to the process of organizing the tasks and activities that a user needs to perform and executing them efficiently.

[0577] The system that realizes this invention is one in which the user inputs work details via a mobile terminal or wearable device, and that data is analyzed on a server. Hardware includes portable devices such as smartphones and smart glasses, and a cloud server. Software includes a speech recognition system using the Google Speech-to-Text API, emotion analysis technology using Microsoft Azure's Emotion Analysis, a task management system via the Asana API, and a reply generation AI model using OpenAI GPT-3.

[0578] First, the user inputs their work details via voice or text through their device. This input is converted into text data using a speech recognition system and sent to the server in real time. The server analyzes the received work details, generates an optimized plan, and uses Microsoft Azure sentiment analysis to determine the user's emotional state. If the user is experiencing stress, suggestions are made to adjust the workload.

[0579] Furthermore, the server analyzes past work history and proposes efficient ways to perform tasks. This proposal is displayed on the terminal as visualized feedback. Also, when a user receives an electronic message, the server analyzes the message content and generates a response that matches the user's emotions based on the OpenAI GPT-3 model.

[0580] For example, if a user says, "I feel anxious about the public briefing on the new garbage collection project," sentiment analysis technology will detect the user's anxiety and reassign related tasks to a higher priority. Examples of prompts include, "I'm feeling a little anxious about today's tasks; how should I prioritize them?" or "Please suggest ways to efficiently prepare for the upcoming meeting." This allows for real-time, personalized work support for the user.

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] Users input work details via voice or text using mobile devices or wearable devices. This input data is converted from speech to text using the Google Speech-to-Text API. The output is the work details in text format.

[0584] Step 2:

[0585] The terminal sends the converted text data to the server in real time. The server analyzes the received text data to extract key points and priorities of the work content. The output is structured data of the analyzed work content.

[0586] Step 3:

[0587] The server uses Microsoft Azure's Emotion Analysis to analyze the user's emotional state from the audio provided during input and the video feed from the device's camera. This process extracts emotional states such as "stress." The output is data related to the user's emotional state.

[0588] Step 4:

[0589] The server uses the analyzed work content and sentiment state to reconstruct the optimal task schedule via the Asana API. It takes into account task priorities and deadlines, and adjusts the load as needed. The output from this process is an optimized task list.

[0590] Step 5:

[0591] The server references the user's work history database and compiles information on efficient work execution methods. It analyzes past successful techniques and progress, and provides feedback to the user. This output is a visualized improvement report.

[0592] Step 6:

[0593] When a user receives an electronic message, the server analyzes its content. Using OpenAI GPT-3, it automatically generates an appropriate reply tailored to the user's emotional state. The output is the suggested reply.

[0594] Step 7:

[0595] The device receives feedback and suggestions from the server and notifies the user in real time. This allows the user to manage tasks efficiently and emotionally. The output consists of notifications and suggestions for improvement to the user.

[0596] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0597] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0598] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0602] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0603] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0604] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0605] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0606] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0607] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0608] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0609] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0610] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0611] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0612] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] This invention provides a system for efficiently managing and executing users' work tasks. Users can input their work tasks via voice or text through a terminal. This data is transmitted to a server via the terminal. The server uses information analysis means to analyze the work tasks in detail and sets priorities and categories according to their content.

[0614] Next, the server uses optimization techniques to generate a date and time-based plan based on the analysis results. This plan works in conjunction with the user's calendar application to place tasks on appropriate dates, supporting efficient work execution even in busy schedules.

[0615] Furthermore, the server can systematically analyze past business history using analytical tools, and based on this analysis, it provides users with feedback to improve business efficiency. This feedback is displayed on the terminal in a visually easy-to-understand format.

[0616] Furthermore, electronic messages received by the terminal are sent to a server, where their content is automatically analyzed using analytical tools. The server generates an appropriate reply and suggests it to the user. This function significantly reduces the time users spend processing messages.

[0617] For example, if a user enters a task such as "Prepare for next week's project meeting," the server will classify this task as "high priority" and automatically update the calendar with a preparation schedule tailored to the meeting date. It will also analyze past meeting history and provide feedback on more efficient and time-efficient preparation methods.

[0618] Thus, the present invention is implemented in a way that automates and streamlines the user's business processes. Throughout all stages of the system, the server, terminals, and users cooperate to achieve seamless business management.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] Users record their work details using voice or text input via a terminal. This information is captured in real time, and the format of the input is checked for accuracy.

[0622] Step 2:

[0623] The terminal formats the business data received from the user and sends it to the server according to security protocols. The data reaches the server via the network.

[0624] Step 3:

[0625] The server analyzes the received business data and extracts important keywords using natural language processing. This analysis aims to categorize business processes and automatically set their priorities.

[0626] Step 4:

[0627] The server generates an optimal schedule based on the analysis results. It references past work history and current calendar information, and incorporates this into the user's work plan.

[0628] Step 5:

[0629] The server generates schedule information and synchronizes it with the calendar application. The schedule is added to the user's calendar, and the user is notified via their device.

[0630] Step 6:

[0631] The device sends received electronic messages to the server. The data of received emails is encrypted using the appropriate protocol and securely transferred to the server.

[0632] Step 7:

[0633] The server analyzes the content of the electronic message and extracts the necessary reply information. Based on the analysis results, it automatically generates appropriate templates and wording for the reply.

[0634] Step 8:

[0635] The terminal presents the user with a suggested reply received from the server. The user can review the suggested content, edit it if necessary, and then send it.

[0636] These steps function as a series of steps, providing a system that streamlines the management and execution of tasks by users.

[0637] (Example 1)

[0638] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] In today's work environment, users are required to efficiently manage and perform complex and diverse tasks. However, manual scheduling and task organization require considerable time and effort, leading to decreased work efficiency. Furthermore, electronic communication often demands quick and appropriate responses, which can be burdensome. A system is needed to solve these problems.

[0640] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0641] In this invention, the server includes means for interpreting work information entered by the user and creating an optimized plan based on the date and time; means for analyzing past work history and providing efficient methods for completing work; and means for interpreting the content of electronic communications and generating appropriate response statements. This enables the user to efficiently manage and perform tasks through automated processes and reduces the processing burden of electronic communications.

[0642] A "user" is someone who uses the system to input work details and is subject to management.

[0643] "Work information" refers to information about tasks and activities that users input in order to perform their work.

[0644] "Interpretation" is the process of analyzing input data and understanding its meaning and intent.

[0645] A "date and time-optimized plan" is a plan that efficiently schedules tasks using date and time information entered into the work.

[0646] "Past work history" refers to information that records previous work or tasks.

[0647] An "efficient method of completing work" refers to the techniques and procedures for completing tasks quickly and effectively.

[0648] "Electronic communication" refers to communication conducted using digital methods such as email and messaging.

[0649] A "response" is a document or message sent back in response to an received electronic communication.

[0650] A "visually easy-to-understand format" is one in which information is presented visually using graphs and charts, making it easy to understand.

[0651] This system is a platform for efficiently managing and executing user tasks. Users can input task information via a terminal in voice or text format. The terminal securely transmits the received task information to the server over the internet. The HTTPS protocol is used for this transmission.

[0652] The server uses commonly available natural language processing software to analyze the received work information. This analysis allows the server to automatically set the priority and category of the work. Based on the analysis results, the server uses a scheduling algorithm to generate an optimal schedule based on the date and time, and integrates this schedule with the user's appointment management application. A standard calendar API is used for this integration.

[0653] Electronic communications received by the user are sent from the terminal to the server, where their content is analyzed by a generative AI model. The server then uses the AI ​​model to generate prompt sentences to suggest appropriate responses. This allows users to quickly and easily create responses to electronic communications. An example of a prompt sentence might be, "Please tell me what preparations are needed for the next meeting."

[0654] Furthermore, the server stores past work history in a database and uses data analysis tools to generate efficient work completion methods. This feedback is provided to the user through the terminal in a visually easy-to-understand format. This allows the user to further improve their work efficiency.

[0655] In this way, servers, terminals, and users work together as a unified team, enabling efficient management and execution of tasks through automated processes.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The user inputs work information through a terminal. Input can be in voice or text format. For example, the user might input "Prepare for Monday's meeting." The input data is converted into speech recognition or text data by the terminal and sent to the server via the internet. The output is the text data received by the server.

[0659] Step 2:

[0660] The server uses natural language processing software to analyze the received text data. This involves extracting keywords from the data and performing data calculations to determine the priority and category of the tasks. As a result of the analysis, the data is categorized, for example, "high-priority meeting preparation," and a priority is set. The output is the analyzed business data.

[0661] Step 3:

[0662] The server uses a scheduling algorithm based on analyzed business data to generate an optimal work plan. Specifically, it generates a schedule that takes into account task priorities and date / time information. The generated schedule is reflected in the user's scheduling application via an API. For example, a meeting preparation task might be assigned the day before the meeting. The output is schedule data.

[0663] Step 4:

[0664] The terminal receives electronic communication and sends the data to the server. The server inputs the received communication into a generating AI model for interpretation and creates a prompt to generate an appropriate response. The server then proposes the generated response to the user. This process allows the user to quickly generate an appropriate response. The output is the proposed response.

[0665] Step 5:

[0666] The server stores past work history in a database and analyzes it using analytical tools. It extracts efficient work completion methods and areas for improvement from the user's past performance data. The analysis results are sent to the terminal in a visually easy-to-understand format and displayed to the user as feedback. The output is visualized feedback.

[0667] (Application Example 1)

[0668] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] In modern factories, efficient management and optimization of production operations are crucial. However, traditional systems rely on manual processes for adjusting production schedules and prioritizing tasks, resulting in significant time and effort required to improve work efficiency. Furthermore, the inability to effectively utilize past production data meant that overall operational efficiency was not fully realized.

[0670] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0671] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing past work history and providing efficient work execution methods; and means for analyzing the work content in the factory in real time, setting priorities, and issuing instructions to the automation system. This enables improved work efficiency in factory production operations and further optimization of operations by utilizing past data.

[0672] A "user" is an entity that uses the system to input business details and optimize management.

[0673] "Job description" refers to information used for production management and work instructions, and is entered in voice or text format.

[0674] "Analysis" is the process of meticulously organizing and classifying the business data entered by users, and then creating an optimized plan based on that information.

[0675] A "date and time-optimized plan" is a plan that takes into account the priorities of tasks and the resources needed to create an efficient schedule.

[0676] "Past work history" refers to a collection of data on work performance recorded to date, and is the set of information that will be analyzed.

[0677] "Efficient work execution methods" refer to the means and processes for carrying out tasks in the most effective and rapid way possible.

[0678] "Factory work content" refers to the specific tasks and work instructions that are performed by the factory's automated equipment.

[0679] "Real-time analysis" means immediately evaluating the input information and performing the necessary processing.

[0680] Setting priorities is the act of assigning an urgency or importance level to a task or work, thereby facilitating more efficient processing.

[0681] "Issuing instructions to an automated system" refers to giving instructions to automated equipment or devices to perform specific actions based on information obtained through analysis.

[0682] The system for implementing this invention involves a server, a terminal, and a user working together to automate and streamline tasks. First, the user inputs the task details into the terminal via voice or text. The terminal sends this task details to the server, where analysis is performed. The analysis uses speech recognition software such as the Google Cloud Speech-to-Text API and a custom analysis algorithm written in Python.

[0683] Based on the analysis results, the server generates an optimized plan using a scheduling system such as Apache Airflow. This plan reflects the priority and deadline of each task, and instructions are sent to the automated equipment in the factory. These instructions allow factory robots to perform tasks in real time, improving operational efficiency.

[0684] Furthermore, the server systematically analyzes past work history and provides users with feedback on how to perform tasks efficiently. For example, by using prompts such as, "Please propose the optimal production schedule to produce 100 units of part X by next Monday. Please also let me know if there are any efficiency improvement methods based on past data," further optimization of operations can be achieved.

[0685] This system enables factory production lines to operate in a data-driven manner, allowing work to proceed under a real-time, optimized production schedule. Implementing this invention significantly improves operational efficiency and productivity.

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] Users input work details using their devices via voice or text. The input data is converted to text using a speech recognition API. This converted text data is then sent to the server.

[0689] Step 2:

[0690] The server analyzes the received text data. Using an analysis engine, it extracts important keywords and information from the work content and sets the priority and category of the tasks. This process outputs each work task as a dataset containing the information necessary for scheduling.

[0691] Step 3:

[0692] The server utilizes a scheduling system to generate an optimal work schedule based on the analysis results. It processes the input task priorities and deadlines, generating a date and time-based plan. This plan data is output as a list including specific dates and assigned personnel.

[0693] Step 4:

[0694] The server sends the generated work schedule as instructions to the factory automation equipment. The factory robots then automatically perform the corresponding tasks in real time based on the received instructions. The robots report the progress of their work according to the schedule and send the completion status to the server.

[0695] Step 5:

[0696] The server continuously analyzes past work history and generates feedback to improve work efficiency. This visualized feedback is sent to the user's terminal, suggesting improvements and more efficient methods for performing tasks. This feedback provides valuable information for optimizing future schedules.

[0697] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0698] This invention provides a system that utilizes an emotion engine to improve the efficiency of user task management. Users can input task details via voice or text through a terminal. This input data is transmitted to a server in real time, where the server analyzes the task details and generates an optimized plan based on the date and time.

[0699] The emotion engine recognizes the user's emotional state by analyzing the tone of voice during voice input and facial expression data acquired using the device's built-in camera. This information is taken into consideration when automatically determining task priorities and reflecting them in the schedule. For example, if the user is feeling stressed, the system will suggest rearranging tasks to reduce the workload.

[0700] Furthermore, the server analyzes past work history and provides users with efficient work execution methods. This analysis is visualized on the user's terminal as feedback, which can be used for future improvements.

[0701] On the other hand, in processing electronic messages, the server analyzes the message content and generates an appropriate reply based on the user's emotions recognized by the emotion engine. This ensures that the tone of communication matches the user's emotions, enabling more personalized responses.

[0702] For example, if a user inputs via voice, "I feel pressured about the meeting to review the project's progress," the emotion engine will detect feelings such as anxiety and tension, and the server will suggest scheduling tasks for meeting preparation as early as possible. Furthermore, the server will generate and suggest reassuring replies to emails the user has received.

[0703] Thus, the present invention provides a business management system incorporating an emotion engine, thereby enabling flexible work execution that takes into account the user's emotional state, and aiming to improve efficiency and reduce stress.

[0704] The following describes the processing flow.

[0705] Step 1:

[0706] The user inputs work details into the terminal via voice or text. The terminal captures the input and converts it into a predefined format.

[0707] Step 2:

[0708] The terminal sends the input content to the server. The data is encrypted via a security protocol and transmitted securely to the server.

[0709] Step 3:

[0710] The server analyzes the received work data. Natural language processing is used to extract keywords and automatically set the work category and priority.

[0711] Step 4:

[0712] The emotion engine sends voice tone and facial expression data to the server. Simultaneously, the server analyzes this emotion data to identify the user's emotional state.

[0713] Step 5:

[0714] The server optimizes existing work schedules by considering emotional data. If a user is experiencing stress, tasks are rearranged or rescheduled based on their emotions.

[0715] Step 6:

[0716] The server sends schedule data to the device so that the optimized schedule is reflected in the user's calendar. The device then synchronizes this information with its calendar application for display.

[0717] Step 7:

[0718] The terminal sends the received electronic message to the server. The server analyzes the message's content and determines its importance and the urgency of a reply.

[0719] Step 8:

[0720] The emotion engine generates an appropriate reply that reflects the user's current emotional state. The server sends this to the terminal for the user to review and edit.

[0721] Step 9:

[0722] The user edits their reply as needed and sends the finalized message. The device sends the updated message and completes the process.

[0723] In this way, using an emotion engine enables flexible and efficient business management that is attentive to the user's emotions.

[0724] (Example 2)

[0725] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0726] In today's work environment, efficiently managing individual tasks while taking into account users' emotional states is a challenging task. To reduce user stress and emotional burden and improve work efficiency, it is necessary to analyze emotional changes in real time and optimize tasks based on that analysis. Conventional systems have difficulty integrating emotional analysis into task management, and have not been able to achieve flexible and efficient task execution that is tailored to individual emotions.

[0727] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0728] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing voice data and facial expression data to recognize the user's emotional state; and means for determining the priority of tasks based on the recognized emotional state and optimizing and rearranging the work content. This enables flexible work management and improved efficiency in response to the user's emotions.

[0729] "Job description" refers to the specific tasks and projects that users are expected to perform in their daily activities and work.

[0730] "Analysis" refers to a method of processing data based on given audio data, text data, and other information in order to understand its meaning and emotions.

[0731] A "date and time-optimized plan" refers to a plan that determines the optimal schedule for carrying out tasks based on date and time information.

[0732] "Emotional state" refers to the emotional situation or state of mind derived from the user's voice, facial expressions, and other behavioral indicators.

[0733] "Past work history" refers to records of tasks performed by the user, their results, procedures, and so on.

[0734] "Efficient work execution methods" refer to the optimal methods and processes for carrying out tasks quickly and effectively.

[0735] "The content of an electronic message" refers to the internal information of communications, emails, and other messages exchanged via electronic means in a digital format.

[0736] A "reply message" is a text generated as a response to a received message, and is used to return information to the sender.

[0737] This invention is a system that streamlines work management and enables flexible work execution that takes into account the user's emotional state. The user inputs work details into a terminal in voice or text format. The terminal collects voice and facial expression data using a microphone and camera and transmits it to a server. The server processes the collected data using an emotion analysis engine to recognize the user's emotional state. This emotion analysis utilizes voice analysis software and image analysis algorithms.

[0738] The server analyzes work content using a natural language processing engine and generates an appropriate work schedule. It also automatically determines task priorities based on the analyzed sentiment information and rearranges tasks as needed. Based on past work history, it also provides users with efficient work execution methods.

[0739] For example, if a user voice-inputs "I feel anxious about preparing for my presentation" into their device, the server will sense this anxiety and prioritize scheduling tasks for preparation. Additionally, an AI model will suggest reassuring replies to emails the user receives.

[0740] A concrete example of a prompt for a generating AI model is, "Please provide an appropriate response when the user feels anxious." Such prompts enable the system to achieve optimal task management tailored to the user's emotional state.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] Users input their work details into the terminal in either voice or text format. Voice input is captured via a microphone, while text data is collected via keyboard or touch input. These inputs are the data received, and in the case of voice data, waveform information is generated. The terminal uses a camera to capture facial expressions and acquire still images that represent the user's emotions.

[0744] Step 2:

[0745] The terminal encrypts the acquired voice data, text data, and facial expression data and sends it to the server. This ensures the security of user data. The server receives the encrypted packets and decodes them into an analyzable format. Through this process, the server prepares the input dataset for analysis.

[0746] Step 3:

[0747] The server inputs voice data into a voice analysis program, which analyzes the tone of voice to identify emotions. The server then processes facial expression data into an image analysis algorithm to recognize the user's emotional state. The output obtained at this stage is the emotion recognition result. Specifically, emotion labels such as anxiety and stress are assigned.

[0748] Step 4:

[0749] The server analyzes the business content entered as text through a natural language processing engine to determine the priority and type of task. The output generated by this analysis is the data necessary for task optimization and prioritization. For example, task A may be determined to be high priority, while task B may be determined to be low priority.

[0750] Step 5:

[0751] The server generates a work schedule based on emotional states and work analysis results. The inputs are emotional labels and task priority data, and the output is a schedule optimized for dates and times to be presented to the user. In actual operation, decisions are made to postpone certain tasks to reduce the workload of users experiencing stress.

[0752] Step 6:

[0753] The server analyzes the text of electronic messages and inputs prompts into an AI model that generates emotionally appropriate replies. These prompts may include phrases like, "Please provide an appropriate reply when the user feels anxious." The AI ​​model creates a reassuring and appropriate response and returns its output to the server. The server then sends this suggestion to the user's device, allowing the user to review and adjust it as needed.

[0754] (Application Example 2)

[0755] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] In today's business environment, efficient task management and task management that considers user emotions are crucial. However, conventional systems do not adequately address flexible task management that takes user emotional states into account, leading to a demand for stress reduction and improved work efficiency. Furthermore, providing user-friendly interfaces from mobile and wearable devices is also a critical challenge.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0758] In this invention, the server includes means for analyzing the work content entered by the user and generating an optimized plan based on the date and time; means for analyzing past work history and providing efficient work execution methods; means for analyzing the content of electronic messages and suggesting appropriate replies; means for determining the user's emotional state using sentiment analysis technology and suggesting adjustments to the workload; and means for providing real-time task management support to the user through a mobile terminal or wearable terminal. This enables flexible work execution in accordance with the user's emotional state, thereby improving work efficiency and reducing stress.

[0759] A "user" is an individual user who utilizes the system, and is the entity that inputs work details and provides emotional states.

[0760] "Job description" refers to information about the tasks and projects that users are supposed to perform, and is the subject of analysis by the system.

[0761] "Analysis" is the process of extracting specific patterns and meanings from entered work content and user sentiment data.

[0762] "Means for generating plans" refers to functions that optimize the schedule and progress of tasks based on analysis results.

[0763] "Past work history" refers to data about the tasks a user has previously performed and the results thereof.

[0764] "Electronic messages" refer to the content of communications exchanged between users via email or messaging apps.

[0765] "Emotion analysis technology" refers to algorithms and methods for inferring a user's emotional state from their voice and facial expression data.

[0766] "Mobile devices" refer to portable computers such as smartphones and handheld devices.

[0767] "Wearable devices" refer to computer devices that are worn on the body, such as smart glasses and smartwatches.

[0768] "Real-time" refers to the temporal characteristic of responding immediately to user requests.

[0769] "Task management" refers to the process of organizing the tasks and activities that a user needs to perform and executing them efficiently.

[0770] The system that realizes this invention is one in which the user inputs work details via a mobile terminal or wearable device, and that data is analyzed on a server. Hardware includes portable devices such as smartphones and smart glasses, and a cloud server. Software includes a speech recognition system using the Google Speech-to-Text API, emotion analysis technology using Microsoft Azure's Emotion Analysis, a task management system via the Asana API, and a reply generation AI model using OpenAI GPT-3.

[0771] First, the user inputs their work details via voice or text through their device. This input is converted into text data using a speech recognition system and sent to the server in real time. The server analyzes the received work details, generates an optimized plan, and uses Microsoft Azure sentiment analysis to determine the user's emotional state. If the user is experiencing stress, suggestions are made to adjust the workload.

[0772] Furthermore, the server analyzes past work history and proposes efficient ways to perform tasks. This proposal is displayed on the terminal as visualized feedback. Also, when a user receives an electronic message, the server analyzes the message content and generates a response that matches the user's emotions based on the OpenAI GPT-3 model.

[0773] For example, if a user says, "I feel anxious about the public briefing on the new garbage collection project," sentiment analysis technology will detect the user's anxiety and reassign related tasks to a higher priority. Examples of prompts include, "I'm feeling a little anxious about today's tasks; how should I prioritize them?" or "Please suggest ways to efficiently prepare for the upcoming meeting." This allows for real-time, personalized work support for the user.

[0774] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0775] Step 1:

[0776] Users input work details via voice or text using mobile devices or wearable devices. This input data is converted from speech to text using the Google Speech-to-Text API. The output is the work details in text format.

[0777] Step 2:

[0778] The terminal sends the converted text data to the server in real time. The server analyzes the received text data to extract key points and priorities of the work content. The output is structured data of the analyzed work content.

[0779] Step 3:

[0780] The server uses Microsoft Azure's Emotion Analysis to analyze the user's emotional state from the audio provided during input and the video feed from the device's camera. This process extracts emotional states such as "stress." The output is data related to the user's emotional state.

[0781] Step 4:

[0782] The server uses the analyzed work content and sentiment state to reconstruct the optimal task schedule via the Asana API. It takes into account task priorities and deadlines, and adjusts the load as needed. The output from this process is an optimized task list.

[0783] Step 5:

[0784] The server references the user's work history database and compiles information on efficient work execution methods. It analyzes past successful techniques and progress, and provides feedback to the user. This output is a visualized improvement report.

[0785] Step 6:

[0786] When a user receives an electronic message, the server analyzes its content. Using OpenAI GPT-3, it automatically generates an appropriate reply tailored to the user's emotional state. The output is the suggested reply.

[0787] Step 7:

[0788] The device receives feedback and suggestions from the server and notifies the user in real time. This allows the user to manage tasks efficiently and emotionally. The output consists of notifications and suggestions for improvement to the user.

[0789] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0790] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0791] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0792] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0793] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0794] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0795] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0796] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0797] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0798] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0799] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0800] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0801] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0802] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0803] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0804] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0805] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0806] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0807] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0808] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0809] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0810] The following is further disclosed regarding the embodiments described above.

[0811] (Claim 1)

[0812] A means for analyzing the work content entered by the user and generating an optimized plan based on the date and time,

[0813] A means of providing efficient work execution methods by analyzing past work history,

[0814] A means of analyzing the content of an electronic message and suggesting an appropriate reply,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, which receives the aforementioned business content as voice or text input.

[0818] (Claim 3)

[0819] The system according to claim 1, which automatically determines the priority and deadline of tasks and reflects the plan in date information.

[0820] "Example 1"

[0821] (Claim 1)

[0822] A means for interpreting work information entered by the user and creating an optimized plan based on the date and time,

[0823] A means of providing efficient work completion methods by analyzing past work history,

[0824] A means for interpreting the content of electronic communications and generating an appropriate response,

[0825] A means of linking the generated plan to the schedule management application of an information device,

[0826] A means of providing feedback in a visually easy-to-understand format,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, which receives the aforementioned work information as voice or text input.

[0830] (Claim 3)

[0831] The system according to claim 1, which automatically evaluates the importance and deadline of a task and reflects the plan in date and time information.

[0832] "Application Example 1"

[0833] (Claim 1)

[0834] A means for analyzing the work content entered by the user and generating an optimized plan based on the date and time,

[0835] A means of providing efficient work execution methods by analyzing past work history,

[0836] A means of analyzing factory work in real time, setting priorities, and issuing instructions to the automation system,

[0837] A means of analyzing the content of an electronic message and suggesting an appropriate reply,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, which receives the aforementioned business content as voice or text input.

[0841] (Claim 3)

[0842] The system according to claim 1, which automatically determines the priority and deadline of tasks, reflects the plan in date information, and issues instructions to automated equipment in a factory.

[0843] "Example 2 of combining an emotion engine"

[0844] (Claim 1)

[0845] A means for analyzing the work content entered by the user and generating an optimized plan based on the date and time,

[0846] A means of recognizing the user's emotional state by analyzing voice data and facial expression data,

[0847] A means of determining the priority of tasks based on recognized emotional states, and optimizing and reallocating the content of those tasks,

[0848] A means of providing efficient work execution methods by analyzing past work history,

[0849] A means of analyzing the content of electronic messages and suggesting appropriate replies based on the user's emotions,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, which receives the aforementioned work content as voice or text input and optimizes the work based on the emotional state.

[0853] (Claim 3)

[0854] The system according to claim 1, which uses an emotion engine to automatically determine the priority and deadline of tasks, reflects the plan in date information, and provides flexible responses that match the user's emotions.

[0855] "Application example 2 when combining with an emotional engine"

[0856] (Claim 1)

[0857] A means for analyzing the work content entered by the user and generating an optimized plan based on the date and time,

[0858] A means of providing efficient work execution methods by analyzing past work history,

[0859] A means of analyzing the content of an electronic message and suggesting an appropriate reply,

[0860] A method for determining a user's emotional state using emotion analysis technology and proposing adjustments to their workload,

[0861] A means of providing users with real-time task management support through a mobile device or wearable device,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, which receives the aforementioned business content as voice or text input and applies sentiment analysis technology.

[0865] (Claim 3)

[0866] The system according to claim 1, which automatically determines the priority and deadline of tasks and reflects emotional states in the plan. [Explanation of Symbols]

[0867] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for analyzing the work content entered by the user and generating an optimized plan based on the date and time, A means of providing efficient work execution methods by analyzing past work history, A means of analyzing the content of an electronic message and suggesting an appropriate reply, A system that includes this.

2. The system according to claim 1, which receives the aforementioned business content as voice or text input.

3. The system according to claim 1, which automatically determines the priority and deadline of tasks and reflects the plan in date information.

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