System

Abstract

An object of the system according to the embodiment is to automate and efficiently perform management of a regular task and distribution to a task consignor.SOLUTION: A system includes a regular task registration part, a task determination part, a task distribution part, an acceptance selection part, a work start report part, and a work end report part. The routine work registration part registers the details of routine work. The task determination part automatically determines a task corresponding to each task consignor on the basis of the information of the periodic task registered by the periodic task registration part. A business distribution part distributes the details of the business discriminated by the business discrimination part to the tablet of the business consignor in real time. The acceptance selection unit selects whether or not the task consignor accepts the task distributed to the tablet. The work start report unit presses a work start button at a timing when the task consignor starts a regular task. The work completion reporting part presses a work completion button as soon as the work consignor completes the work.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the management of routine tasks and distribution to contractors was often done manually, which led to a lack of efficiency.

[0005] The system according to the embodiment aims to automate and efficiently manage regular tasks and deliver them to contractors. [Means for solving the problem]

[0006] The system according to the embodiment includes a periodic task registration unit, a task determination unit, a task distribution unit, a contract selection unit, a work start reporting unit, and a work completion reporting unit. The periodic task registration unit registers details of periodic tasks. The task determination unit automatically determines the tasks corresponding to each contractor based on the information on periodic tasks registered by the periodic task registration unit. The task distribution unit distributes details of the tasks determined by the task determination unit to the contractor's tablet in real time. The contract selection unit selects whether the contractor will accept the tasks distributed to the tablet. The work start reporting unit presses a work start button when the contractor starts the periodic task. The work completion reporting unit presses a work completion button once the contractor has completed the task. [Effects of the Invention]

[0007] The system according to the embodiment can automate and efficiently manage regular tasks and deliver them to contractors. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The recurring task management system according to an embodiment of the present invention is a system that uses a generation AI to automate the management and distribution of recurring tasks. In this recurring task management system, an administrator registers details of recurring tasks in an internal system such as a CRM, and the generation AI automatically determines appropriate tasks for each contractor based on that information and distributes them in real time. This is expected to improve the efficiency of tasks and the accuracy of management.

[0029] A periodic task management system according to an embodiment includes a periodic task registration unit, a task determination unit, a task distribution unit, a task selection unit, a work start reporting unit, and a work completion reporting unit. The periodic task registration unit registers details of periodic tasks. For example, an administrator registers details of the periodic tasks, such as the procedure, frequency, time designation, and urgency, in an internal system such as CRM. The task determination unit automatically determines the tasks to be handled by each contractor based on the information on the periodic tasks registered by the periodic task registration unit. For example, a generation AI analyzes the contractor's schedule and past work history to select the optimal tasks. The task distribution unit distributes details of the tasks determined by the task determination unit to the contractor's tablet in real time. For example, the generation AI notifies the contractor's tablet of details of the tasks selected. The task selection unit allows the contractor to select whether to accept the tasks distributed to the tablet. For example, if the contractor selects "I will accept this task," the task is confirmed. The work start reporting unit presses a work start button when the contractor starts the regular work. For example, the contractor presses the "work start button" when starting the work. The work completion reporting unit presses a work completion button as soon as the contractor finishes the work. For example, the contractor presses the "work completion button" when finishing the work. As a result, the regular work management system according to the embodiment is expected to improve the efficiency of work and the accuracy of management.

[0030] The periodic task registration unit can analyze past work data using generation AI and automatically suggest optimal procedures and frequencies. For example, when an administrator registers a periodic task, the periodic task registration unit uses generation AI to analyze past work data and automatically suggest optimal procedures and frequencies. For example, it suggests optimal cleaning procedures and frequencies based on past cleaning work data. The generation AI analyzes past work data using machine learning models and natural language processing technology to suggest optimal procedures and frequencies. Past work data includes work history data and work log data. Procedures are suggested based on work procedure manuals and standard operating procedures. Frequencies are suggested based on criteria such as daily, weekly, and monthly. This makes it possible to improve work efficiency by suggesting optimal procedures and frequencies based on past work data.

[0031] The periodic task registration unit can automatically evaluate the importance and scope of impact of tasks using generation AI and prioritize them. For example, when an administrator registers a periodic task, the periodic task registration unit uses generation AI to automatically evaluate the importance and scope of impact of the task and prioritize it. For example, it will register tasks with a high degree of urgency first. The generation AI evaluates the importance of tasks based on criteria such as the impact and urgency of the task and prioritizes them. Importance is evaluated based on the impact and urgency of the task. The scope of impact is evaluated based on the departments and number of people affected, etc. Priority is determined based on importance and scope of impact. This enables efficient task management by evaluating the importance and scope of impact of tasks and prioritizing them.

[0032] The periodic task registration unit uses voice input so that the generation AI can analyze the voice data and automatically convert it into text. For example, when an administrator registers a periodic task, the periodic task registration unit uses voice input so that the generation AI can analyze the voice data and automatically convert it into text. For example, an administrator might input "cleaning work every Monday at 9 a.m." The generation AI analyzes the voice data using voice recognition technology and converts it into text data. Voice input is performed using a microphone. The voice data is saved in an audio file format. The text is converted based on criteria such as voice recognition accuracy and text format. This makes it possible to efficiently register tasks by using voice input.

[0033] The periodic task registration unit can support efficient task registration by having the generation AI automatically suggest similar tasks registered by other managers. For example, when a manager registers a periodic task, the periodic task registration unit can support efficient task registration by having the generation AI automatically suggest similar tasks registered by other managers. For example, it can suggest similar tasks based on past cleaning work data. The generation AI analyzes task data registered by other managers and suggests similar tasks. Similar tasks are suggested based on criteria such as the similarity of task content and commonality of work procedures. This enables efficient task registration by suggesting similar tasks registered by other managers.

[0034] The task determination unit can analyze the real-time location information of the contractor, and determine and distribute the most suitable task. For example, the task determination unit uses a generation AI to analyze the real-time location information of the contractor, and determine and distribute the most suitable task. For example, it may distribute cleaning work to a nearby contractor. The generation AI obtains and analyzes the location information of the contractor using GPS data, Wi-Fi location information, etc. The location information is evaluated based on criteria such as GPS data and Wi-Fi location information. This enables efficient task distribution by distributing the most suitable task based on the contractor's real-time location information.

[0035] The task determination unit can analyze the contractor's past performance data and deliver appropriate tasks according to the difficulty of the tasks. In the task determination unit, for example, the generation AI analyzes the contractor's past performance data and delivers appropriate tasks according to the difficulty of the tasks. For example, it delivers tasks with high difficulty based on past cleaning work data. The generation AI analyzes the contractor's performance data, such as work time and work results, and evaluates the difficulty of the tasks. The performance data is evaluated based on criteria such as work time and work results. The difficulty is evaluated based on criteria such as the complexity of the task and the required skill level. This makes it possible to deliver appropriate tasks by delivering tasks according to the difficulty of the tasks based on the contractor's past performance data.

[0036] The task determination unit can analyze the skill set of the contractor and prioritize delivery of tasks that will lead to skill improvement. For example, the task determination unit uses a generation AI to analyze the skill set of the contractor and prioritize delivery of tasks that will lead to skill improvement. For example, delivery of tasks that will improve cleaning skills. The generation AI analyzes the skill set of the contractor, such as technical skills and soft skills, and evaluates tasks that will lead to skill improvement. Skill sets are evaluated based on criteria such as technical skills and soft skills. Skill improvement is evaluated based on criteria such as training programs and skill assessment tests. This allows the contractor's skills to be improved by delivering tasks that will lead to skill improvement based on the contractor's skill set.

[0037] The consignment selection unit can support the decision to accept the work by having the generation AI automatically present the advantages and disadvantages of the work. For example, when a contractor accepts a work, the consignment selection unit supports the decision to accept the work by having the generation AI automatically present the advantages and disadvantages of the work. For example, it presents the remuneration and time required for the work. The generation AI analyzes the advantages and disadvantages of the work and presents them to the contractor. The advantages are evaluated based on criteria such as remuneration and opportunities for skill improvement. The disadvantages are evaluated based on criteria such as the difficulty of the work and the time burden. In this way, the advantages and disadvantages of the work are presented to support the contractor's decision to accept the work.

[0038] The contractor selection unit can display the evaluations and feedback of other contractors to support the contractor's decision to accept the work. For example, when a contractor accepts a work, the contractor selection unit displays the evaluations and feedback of other contractors to support the contractor's decision to accept the work. For example, it displays the evaluations of past contractors. The generation AI analyzes the evaluations and feedback of other contractors and presents them to the contractor. The evaluation is based on criteria such as the evaluation scores and feedback content of other contractors. The feedback is provided in the form of text feedback, voice feedback, etc. In this way, the contractor's decision to accept the work is supported by displaying the evaluations and feedback of other contractors.

[0039] The outsourcing selection unit allows the generation AI to recommend the most suitable work based on past outsourcing history. For example, when a business contractor accepts a task, the generation AI recommends the most suitable work based on past outsourcing history. For example, it recommends work that has been successful in the past. The generation AI analyzes the business contractor's past outsourcing history and recommends the most suitable work. The outsourcing history is evaluated based on criteria such as past outsourcing work and number of times it has been accepted. The most suitable work is evaluated based on criteria such as suitability for the work and skill matching. This supports the business contractor's decision to accept the task by recommending the most suitable work based on past outsourcing history.

[0040] The outsourcing selection unit displays the remuneration and incentives for the work in real time, thereby increasing the willingness to accept the work. For example, when a contractor accepts a work, the outsourcing selection unit displays the remuneration and incentives for the work in real time, thereby increasing the willingness to accept the work. For example, it displays the remuneration amount and bonuses. The generation AI analyzes the remuneration and incentives for the work and presents them to the contractor. Remuneration is evaluated based on criteria such as monetary remuneration and non-monetary remuneration. Incentives are evaluated based on criteria such as bonuses and perks. In this way, by displaying the remuneration and incentives for the work, the willingness of the contractor to accept the work is increased.

[0041] The work start reporting unit allows the generation AI to automatically optimize the work environment (for example, adjusting lighting and temperature) when the contractor presses the work start button. The work start reporting unit allows the generation AI to automatically optimize the work environment (for example, adjusting lighting and temperature) when the contractor presses the work start button. For example, it adjusts the brightness of the lighting. The generation AI analyzes elements of the work environment such as lighting and temperature and sets the optimal settings. The work environment is evaluated based on criteria such as lighting, temperature, and noise level. Optimization is performed based on criteria such as lighting adjustment method and temperature setting criteria. Lighting is adjusted based on criteria such as LED lighting and dimming function. Temperature adjustment is performed based on criteria such as air conditioner setting temperature and use of temperature sensor. This automatically optimizes the work environment, improving the work efficiency of the contractor.

[0042] The work start reporting unit can have the generation AI automatically play music or environmental sounds to increase the contractor's concentration when work begins. The work start reporting unit, for example, has the generation AI automatically play music or environmental sounds to increase the contractor's concentration when work begins. For example, classical music to increase concentration is played. The generation AI analyzes music or environmental sounds to increase the contractor's concentration and plays the most appropriate music or environmental sounds. Concentration is evaluated based on criteria such as a concentration test and work efficiency. Music is played based on criteria such as classical music or environmental music. Environmental sounds are played based on criteria such as natural sounds or white noise. In this way, the work efficiency of the contractor is improved by playing music or environmental sounds that increase concentration.

[0043] The work start reporting unit allows the generation AI to automatically prepare the tools and materials necessary for the work when the work start button is pressed. For example, the work start reporting unit allows the generation AI to automatically prepare the tools and materials necessary for the work when the work start button is pressed. For example, it prepares the tools necessary for cleaning work. The generation AI analyzes the tools and materials necessary for the work and prepares the most appropriate ones. Tools are evaluated based on criteria such as work equipment and software tools. Materials are evaluated based on criteria such as manuals and reference materials. This improves the work efficiency of the contractor by automatically preparing the tools and materials necessary for the work.

[0044] The work start reporting unit can support efficient work by having the generation AI automatically adjust collaboration with other contractors when work starts. The work start reporting unit can support efficient work by, for example, having the generation AI automatically adjust collaboration with other contractors when work starts. For example, adjusting the allocation of cleaning work. The generation AI analyzes collaboration with other contractors and makes optimal adjustments. Collaboration is evaluated based on criteria such as the use of communication tools and the method of work allocation. Adjustments are made based on criteria such as schedule adjustments and adjustments to work content. This automatically adjusts collaboration with other contractors, supporting efficient work.

[0045] The work content confirmation unit allows the generation AI to visually explain the work procedures using videos and animations when the contractor confirms the work content. For example, when the contractor confirms the work content, the work content confirmation unit allows the generation AI to visually explain the work procedures using videos and animations. For example, cleaning work procedures are explained using videos. The generation AI analyzes the work procedures and creates the most appropriate videos and animations. The work procedures are evaluated based on criteria such as step-by-step procedure manuals and video manuals. The videos are created based on criteria such as MP4 format and animated videos. The animations are created based on criteria such as 2D animation and 3D animation. In this way, by visually explaining the work procedures using videos and animations, the contractor's understanding is deepened and work efficiency is improved.

[0046] The work content confirmation unit allows the generation AI to monitor the progress of work in real time while the work is being carried out and provide advice as needed. The work content confirmation unit, for example, allows the generation AI to monitor the progress of work in real time while the work is being carried out and provide advice as needed. For example, the work content confirmation unit monitors the progress of cleaning work and provides advice. The generation AI analyzes the progress of the work and provides optimal advice. The progress is evaluated based on criteria such as the completion rate of the work and the progress status of the work. Monitoring is carried out based on criteria such as real-time monitoring and regular progress confirmation. Advice is provided in the form of text advice, audio advice, etc. This allows the progress of the work to be monitored in real time and advice to be provided as needed, thereby improving the work efficiency of the contractor.

[0047] When the work content confirmation unit confirms work content, the generation AI can automatically present past success stories and best practices. For example, when the work content confirmation unit confirms work content, the generation AI automatically presents past success stories and best practices. For example, it presents success stories for cleaning work. The generation AI analyzes past success stories and best practices and presents them to the contractor. Success stories are evaluated based on criteria such as success stories and best practices from past projects. Best practices are evaluated based on criteria such as industry standard methods and the sharing of success stories. In this way, by presenting past success stories and best practices, the work efficiency of the contractor is improved.

[0048] The work content confirmation unit supports the generation AI in real-time communication with other contractors while working, thereby promoting cooperative work. The work content confirmation unit, for example, supports the generation AI in real-time communication with other contractors while working, thereby promoting cooperative work. For example, it supports coordination in cleaning work. The generation AI analyzes communication with other contractors and provides optimal support. Real-time communication is evaluated based on criteria such as chat tools and video conferencing tools. Collaborative work is evaluated based on criteria such as how tasks are divided and how collaborative work is carried out. This supports real-time communication with other contractors, promoting cooperative work and improving work efficiency.

[0049] The work completion reporting unit allows the generation AI to automatically evaluate the results of work and provide feedback when the contractor presses the work completion button. For example, the work completion reporting unit allows the generation AI to automatically evaluate the results of work and provide feedback when the contractor presses the work completion button. For example, it evaluates cleaning work. The generation AI analyzes the results of work and provides optimal feedback. The results of work are evaluated based on criteria such as the degree of completion of the work and quality evaluation. The evaluation is based on criteria such as the evaluation scores of other contractors and the content of feedback. Feedback is provided in the form of text feedback, voice feedback, etc. In this way, the work results can be automatically evaluated and feedback can be provided, thereby improving the work efficiency and quality of the contractor.

[0050] The work completion reporting section allows the generation AI to automatically prepare for the next task when a task is completed, supporting a smooth transition of tasks. For example, the work completion reporting section allows the generation AI to automatically prepare for the next task when a task is completed, supporting a smooth transition of tasks. For example, it may prepare for the next cleaning task. The generation AI analyzes the details of the next task and the necessary tools and materials, and makes optimal preparations. The next task is evaluated based on criteria such as the details of the next task and the necessary tools and materials. A smooth transition of tasks is evaluated based on criteria such as the method of handing over the task and the preparation of the work environment. This supports a smooth transition of tasks by automatically preparing for the next task.

[0051] The work completion reporting unit allows the generation AI to automatically save work records when the work completion button is pressed, making them available for later reference. The work completion reporting unit allows the generation AI to automatically save work records when the work completion button is pressed, making them available for later reference. For example, saving cleaning work records. The generation AI analyzes work records and selects the optimal storage method. Work records are evaluated based on criteria such as work logs and work reports. Storage is based on criteria such as cloud storage or local storage. Reference is based on criteria such as search functions and filtering functions. This improves the traceability of work by automatically saving work records and making them available for later reference.

[0052] In the work completion reporting section, the generation AI automatically notifies other contractors of the work progress when the work is completed, allowing the entire team to share their status. In the work completion reporting section, for example, the generation AI automatically notifies other contractors of the work progress when the work is completed, allowing the entire team to share their status. For example, notifying of the progress of cleaning work. The generation AI analyzes the work progress and selects the optimal notification method. Work progress is evaluated based on criteria such as the completion rate of the work and the progress of the work. Notifications are made based on criteria such as real-time notifications and periodic notifications. The overall team status is evaluated based on criteria such as progress reports and status updates. This improves team collaboration by automatically notifying work progress and sharing the overall team status.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The periodic task management system can also be equipped with a health management unit that monitors the health status of contractors. For example, it can measure the heart rate and blood pressure of contractors while they are working, and issue an alert if an abnormality is detected. The health management unit uses wearable devices to collect and analyze health data in real time. This allows the system to constantly monitor the health status of contractors and prevent health risks.

[0055] The Regular Task Registration Department can also evaluate the ecological footprint of tasks and suggest environmentally friendly procedures and frequency. For example, it can make suggestions to optimize the amount of detergent and water used in cleaning work. The Generative AI analyzes the ecological footprint based on past data and suggests procedures and frequency that minimize environmental impact. This not only improves work efficiency but also contributes to environmental protection.

[0056] The regular work registration unit can also perform a risk assessment of the work and provide special warnings for high-risk work. For example, it can provide additional safety procedures and precautions for work involving heights or handling hazardous materials. The generation AI evaluates the risk of work based on past accident data and risk assessment criteria and provides necessary warnings. This can improve work safety.

[0057] The regular task registration unit can also automatically calculate the cost of tasks and make suggestions for cost reduction. For example, it can make suggestions for optimizing the cost of materials used in cleaning work. The generation AI analyzes task costs based on past data and suggests procedures and material selections for cost reduction. This not only improves the efficiency of tasks but also contributes to cost reduction.

[0058] The regular task registration unit can also evaluate the social impact of tasks and prioritize suggestions for tasks that are socially beneficial. For example, it can prioritize suggestions for tasks that contribute significantly to the local community. The generation AI analyzes the social impact of tasks and evaluates tasks that are socially beneficial. This not only improves the efficiency of tasks, but also the degree of social contribution.

[0059] The task determination unit can also analyze the learning history of the task contractor and prioritize tasks that will lead to skill improvement. For example, tasks that will improve cleaning skills can be delivered. The generation AI analyzes the learning history and training data of the task contractor and evaluates tasks that will lead to skill improvement. This allows the task contractor to improve their skills.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The regular task registration unit registers the details of the regular task. For example, an administrator registers details of the regular task, such as the procedure, frequency, time specification, and urgency, in an internal system such as CRM. Step 2: The task determination unit automatically determines the tasks to be performed for each contractor based on the information on regular tasks registered by the regular task registration unit. For example, the generation AI analyzes the contractor's schedule and past work history to select the most suitable tasks. Step 3: The task distribution unit distributes the details of the tasks identified by the task determination unit to the client's tablet in real time. For example, it notifies the client's tablet of the details of the tasks selected by the generation AI. Step 4: The outsourcing selection unit selects whether the outsourcer will accept the work delivered to the tablet. For example, if the outsourcer selects "I will accept this work," the work is confirmed. Step 5: The work start reporting unit presses the work start button when the contractor starts the regular work. For example, the contractor presses the "work start button" when starting work. Step 6: The work completion reporting unit presses the work completion button as soon as the contractor has completed the work. For example, the work completion reporting unit presses the "work completion button" when the contractor has completed the work.

[0062] (Example 2) The recurring task management system according to an embodiment of the present invention is a system that uses a generation AI to automate the management and distribution of recurring tasks. In this recurring task management system, an administrator registers details of recurring tasks in an internal system such as a CRM, and the generation AI automatically determines appropriate tasks for each contractor based on that information and distributes them in real time. This is expected to improve the efficiency of tasks and the accuracy of management.

[0063] A periodic task management system according to an embodiment includes a periodic task registration unit, a task determination unit, a task distribution unit, a task selection unit, a work start reporting unit, and a work completion reporting unit. The periodic task registration unit registers details of periodic tasks. For example, an administrator registers details of the periodic tasks, such as the procedure, frequency, time designation, and urgency, in an internal system such as CRM. The task determination unit automatically determines the tasks to be handled by each contractor based on the information on the periodic tasks registered by the periodic task registration unit. For example, a generation AI analyzes the contractor's schedule and past work history to select the optimal tasks. The task distribution unit distributes details of the tasks determined by the task determination unit to the contractor's tablet in real time. For example, the generation AI notifies the contractor's tablet of details of the tasks selected. The task selection unit allows the contractor to select whether to accept the tasks distributed to the tablet. For example, if the contractor selects "I will accept this task," the task is confirmed. The work start reporting unit presses a work start button when the contractor starts the regular work. For example, the contractor presses the "work start button" when starting the work. The work completion reporting unit presses a work completion button as soon as the contractor finishes the work. For example, the contractor presses the "work completion button" when finishing the work. As a result, the regular work management system according to the embodiment is expected to improve the efficiency of work and the accuracy of management.

[0064] The periodic task registration unit can analyze past work data using generation AI and automatically suggest optimal procedures and frequencies. For example, when an administrator registers a periodic task, the periodic task registration unit uses generation AI to analyze past work data and automatically suggest optimal procedures and frequencies. For example, it suggests optimal cleaning procedures and frequencies based on past cleaning work data. The generation AI analyzes past work data using machine learning models and natural language processing technology to suggest optimal procedures and frequencies. Past work data includes work history data and work log data. Procedures are suggested based on work procedure manuals and standard operating procedures. Frequencies are suggested based on criteria such as daily, weekly, and monthly. This makes it possible to improve work efficiency by suggesting optimal procedures and frequencies based on past work data.

[0065] The periodic task registration unit can automatically evaluate the importance and scope of impact of tasks using generation AI and prioritize them. For example, when an administrator registers a periodic task, the periodic task registration unit uses generation AI to automatically evaluate the importance and scope of impact of the task and prioritize it. For example, it will register tasks with a high degree of urgency first. The generation AI evaluates the importance of tasks based on criteria such as the impact and urgency of the task and prioritizes them. Importance is evaluated based on the impact and urgency of the task. The scope of impact is evaluated based on the departments and number of people affected, etc. Priority is determined based on importance and scope of impact. This enables efficient task management by evaluating the importance and scope of impact of tasks and prioritizing them.

[0066] The regular task registration unit can measure the stress level of a manager using the emotion estimation function, and make suggestions to reduce the workload if the stress level is high. For example, when a manager registers regular tasks, the regular task registration unit can measure the stress level using the emotion estimation function, and make suggestions to reduce the workload if the stress level is high. For example, if the stress level is high, the frequency of the task can be reduced. The emotion estimation function measures the stress level of a manager using facial expression recognition technology and voice analysis technology. The stress level is evaluated based on a stress check sheet, biometric data, etc. The workload is evaluated based on the workload, working hours, mental workload, etc. In this way, the manager's health can be maintained by measuring the stress level of the manager and making suggestions to reduce the workload.

[0067] The periodic task registration unit uses voice input so that the generation AI can analyze the voice data and automatically convert it into text. For example, when an administrator registers a periodic task, the periodic task registration unit uses voice input so that the generation AI can analyze the voice data and automatically convert it into text. For example, an administrator might input "cleaning work every Monday at 9 a.m." The generation AI analyzes the voice data using voice recognition technology and converts it into text data. Voice input is performed using a microphone. The voice data is saved in an audio file format. The text is converted based on criteria such as voice recognition accuracy and text format. This makes it possible to efficiently register tasks by using voice input.

[0068] The periodic task registration unit can support efficient task registration by having the generation AI automatically suggest similar tasks registered by other managers. For example, when a manager registers a periodic task, the periodic task registration unit can support efficient task registration by having the generation AI automatically suggest similar tasks registered by other managers. For example, it can suggest similar tasks based on past cleaning work data. The generation AI analyzes task data registered by other managers and suggests similar tasks. Similar tasks are suggested based on criteria such as the similarity of task content and commonality of work procedures. This enables efficient task registration by suggesting similar tasks registered by other managers.

[0069] The periodic task registration unit can use the emotion estimation function to analyze the emotion of the manager regarding the task to be registered and provide positive feedback. For example, when the manager registers a periodic task, the periodic task registration unit uses the emotion estimation function to analyze the emotion regarding the task and provide positive feedback. For example, it displays "Good job" when the task is registered. The emotion estimation function analyzes the emotion of the manager using facial expression recognition technology and voice analysis technology. The emotion is evaluated based on criteria such as positive emotion and negative emotion. The feedback is provided in the form of text feedback, voice feedback, or the like. In this way, the manager's emotion is analyzed and positive feedback is provided, thereby improving motivation for registering the task.

[0070] The task determination unit can analyze the real-time location information of the contractor, and determine and distribute the most suitable task. For example, the task determination unit uses a generation AI to analyze the real-time location information of the contractor, and determine and distribute the most suitable task. For example, it may distribute cleaning work to a nearby contractor. The generation AI obtains and analyzes the location information of the contractor using GPS data, Wi-Fi location information, etc. The location information is evaluated based on criteria such as GPS data and Wi-Fi location information. This enables efficient task distribution by distributing the most suitable task based on the contractor's real-time location information.

[0071] The task determination unit can analyze the contractor's past performance data and deliver appropriate tasks according to the difficulty of the tasks. In the task determination unit, for example, the generation AI analyzes the contractor's past performance data and delivers appropriate tasks according to the difficulty of the tasks. For example, it delivers tasks with high difficulty based on past cleaning work data. The generation AI analyzes the contractor's performance data, such as work time and work results, and evaluates the difficulty of the tasks. The performance data is evaluated based on criteria such as work time and work results. The difficulty is evaluated based on criteria such as the complexity of the task and the required skill level. This makes it possible to deliver appropriate tasks by delivering tasks according to the difficulty of the tasks based on the contractor's past performance data.

[0072] The task determination unit can use the emotion estimation function to analyze the current emotional state of the contractor and prioritize delivery of tasks that are less stressful. For example, the task determination unit uses the generation AI to analyze the current emotional state of the contractor using the emotion estimation function and prioritize delivery of tasks that are less stressful. For example, it delivers tasks that are less stressful. The emotion estimation function analyzes the emotional state of the contractor using facial expression recognition technology and voice analysis technology. The emotional state is evaluated based on criteria such as emotion score and stress level. Stress is evaluated based on criteria such as a stress check sheet and biometric data. In this way, the emotional state of the contractor is analyzed and tasks that are less stressful are delivered, thereby reducing the contractor's stress.

[0073] The task determination unit can analyze the skill set of the contractor and prioritize delivery of tasks that will lead to skill improvement. For example, the task determination unit uses a generation AI to analyze the skill set of the contractor and prioritize delivery of tasks that will lead to skill improvement. For example, delivery of tasks that will improve cleaning skills. The generation AI analyzes the skill set of the contractor, such as technical skills and soft skills, and evaluates tasks that will lead to skill improvement. Skill sets are evaluated based on criteria such as technical skills and soft skills. Skill improvement is evaluated based on criteria such as training programs and skill assessment tests. This allows the contractor's skills to be improved by delivering tasks that will lead to skill improvement based on the contractor's skill set.

[0074] The task determination unit can use the emotion estimation function to identify tasks that motivate the contractor most and deliver those tasks preferentially. For example, the task determination unit uses the generation AI to identify tasks that motivate the contractor most using the emotion estimation function and delivers those tasks preferentially. For example, it delivers tasks that are highly motivating. The emotion estimation function analyzes the emotional state of the contractor using facial expression recognition technology and voice analysis technology. The emotional state is evaluated based on criteria such as emotion score and motivation level. Motivation is evaluated based on criteria such as questionnaire surveys and behavioral data. This improves the efficiency and quality of work by delivering tasks that increase the contractor's motivation.

[0075] The consignment selection unit can support the decision to accept the work by having the generation AI automatically present the advantages and disadvantages of the work. For example, when a contractor accepts a work, the consignment selection unit supports the decision to accept the work by having the generation AI automatically present the advantages and disadvantages of the work. For example, it presents the remuneration and time required for the work. The generation AI analyzes the advantages and disadvantages of the work and presents them to the contractor. The advantages are evaluated based on criteria such as remuneration and opportunities for skill improvement. The disadvantages are evaluated based on criteria such as the difficulty of the work and the time burden. In this way, the advantages and disadvantages of the work are presented to support the contractor's decision to accept the work.

[0076] The contractor selection unit can display the evaluations and feedback of other contractors to support the contractor's decision to accept the work. For example, when a contractor accepts a work, the contractor selection unit displays the evaluations and feedback of other contractors to support the contractor's decision to accept the work. For example, it displays the evaluations of past contractors. The generation AI analyzes the evaluations and feedback of other contractors and presents them to the contractor. The evaluation is based on criteria such as the evaluation scores and feedback content of other contractors. The feedback is provided in the form of text feedback, voice feedback, etc. In this way, the contractor's decision to accept the work is supported by displaying the evaluations and feedback of other contractors.

[0077] The contract selection unit can use the emotion estimation function to analyze the emotional state of the contracted person and suggest tasks that will elicit positive emotions. For example, when the contracted person accepts a task, the contract selection unit uses the emotion estimation function to analyze the emotional state and suggest tasks that will elicit positive emotions. For example, the contract selection unit suggests tasks that the contracted person will enjoy. The emotion estimation function analyzes the emotional state of the contracted person using facial expression recognition technology and voice analysis technology. The emotional state is evaluated based on criteria such as an emotion score and positive emotions. Positive emotions are evaluated based on criteria such as satisfaction and a sense of accomplishment. In this way, the efficiency and quality of work are improved by analyzing the emotional state of the contracted person and suggesting tasks that will elicit positive emotions.

[0078] The outsourcing selection unit allows the generation AI to recommend the most suitable work based on past outsourcing history. For example, when a business contractor accepts a task, the generation AI recommends the most suitable work based on past outsourcing history. For example, it recommends work that has been successful in the past. The generation AI analyzes the business contractor's past outsourcing history and recommends the most suitable work. The outsourcing history is evaluated based on criteria such as past outsourcing work and number of times it has been accepted. The most suitable work is evaluated based on criteria such as suitability for the work and skill matching. This supports the business contractor's decision to accept the task by recommending the most suitable work based on past outsourcing history.

[0079] The outsourcing selection unit displays the remuneration and incentives for the work in real time, thereby increasing the willingness to accept the work. For example, when a contractor accepts a work, the outsourcing selection unit displays the remuneration and incentives for the work in real time, thereby increasing the willingness to accept the work. For example, it displays the remuneration amount and bonuses. The generation AI analyzes the remuneration and incentives for the work and presents them to the contractor. Remuneration is evaluated based on criteria such as monetary remuneration and non-monetary remuneration. Incentives are evaluated based on criteria such as bonuses and perks. In this way, by displaying the remuneration and incentives for the work, the willingness of the contractor to accept the work is increased.

[0080] The outsourcing selection unit can use the emotion estimation function to identify the tasks that the outsourcee feels most satisfied with and prioritize proposing those tasks. For example, when the outsourcee accepts a task, the outsourcing selection unit uses the emotion estimation function to identify the tasks that the outsourcee feels most satisfied with and prioritize proposing those tasks. For example, it can propose tasks that have previously generated high satisfaction. The emotion estimation function analyzes the outsourcee's emotional state using facial expression recognition technology and voice analysis technology. The emotional state is evaluated based on criteria such as emotion score and satisfaction level. Satisfaction level is evaluated based on criteria such as questionnaire surveys and behavioral data. This allows the outsourcee to propose the tasks that they feel most satisfied with, thereby improving the efficiency and quality of work.

[0081] The work start reporting unit allows the generation AI to automatically optimize the work environment (for example, adjusting lighting and temperature) when the contractor presses the work start button. The work start reporting unit allows the generation AI to automatically optimize the work environment (for example, adjusting lighting and temperature) when the contractor presses the work start button. For example, it adjusts the brightness of the lighting. The generation AI analyzes elements of the work environment such as lighting and temperature and sets the optimal settings. The work environment is evaluated based on criteria such as lighting, temperature, and noise level. Optimization is performed based on criteria such as lighting adjustment method and temperature setting criteria. Lighting is adjusted based on criteria such as LED lighting and dimming function. Temperature adjustment is performed based on criteria such as air conditioner setting temperature and use of temperature sensor. This automatically optimizes the work environment, improving the work efficiency of the contractor.

[0082] The work start reporting unit can have the generation AI automatically play music or environmental sounds to increase the contractor's concentration when work begins. The work start reporting unit, for example, has the generation AI automatically play music or environmental sounds to increase the contractor's concentration when work begins. For example, classical music to increase concentration is played. The generation AI analyzes music or environmental sounds to increase the contractor's concentration and plays the most appropriate music or environmental sounds. Concentration is evaluated based on criteria such as a concentration test and work efficiency. Music is played based on criteria such as classical music or environmental music. Environmental sounds are played based on criteria such as natural sounds or white noise. In this way, the work efficiency of the contractor is improved by playing music or environmental sounds that increase concentration.

[0083] The work start reporting unit allows the generation AI to automatically prepare the tools and materials necessary for the work when the work start button is pressed. For example, the work start reporting unit allows the generation AI to automatically prepare the tools and materials necessary for the work when the work start button is pressed. For example, it prepares the tools necessary for cleaning work. The generation AI analyzes the tools and materials necessary for the work and prepares the most appropriate ones. Tools are evaluated based on criteria such as work equipment and software tools. Materials are evaluated based on criteria such as manuals and reference materials. This improves the work efficiency of the contractor by automatically preparing the tools and materials necessary for the work.

[0084] The work start reporting unit can support efficient work by having the generation AI automatically adjust collaboration with other contractors when work starts. The work start reporting unit can support efficient work by, for example, having the generation AI automatically adjust collaboration with other contractors when work starts. For example, adjusting the allocation of cleaning work. The generation AI analyzes collaboration with other contractors and makes optimal adjustments. Collaboration is evaluated based on criteria such as the use of communication tools and the method of work allocation. Adjustments are made based on criteria such as schedule adjustments and adjustments to work content. This automatically adjusts collaboration with other contractors, supporting efficient work.

[0085] The work start reporting unit can use the emotion estimation function to identify an environment in which the contractor can be most relaxed and automatically provide that environment. The work start reporting unit can, for example, use the emotion estimation function at the start of work to identify an environment in which the contractor can be most relaxed and automatically provide that environment. For example, relaxing music can be played. The emotion estimation function analyzes the contractor's emotional state using facial expression recognition technology and voice analysis technology. The emotional state is evaluated based on criteria such as an emotion score and a degree of relaxation. A relaxing environment is evaluated based on criteria such as lighting, music, and temperature. This improves work efficiency by providing the contractor with an environment in which they can be most relaxed.

[0086] The work content confirmation unit allows the generation AI to visually explain the work procedures using videos and animations when the contractor confirms the work content. For example, when the contractor confirms the work content, the work content confirmation unit allows the generation AI to visually explain the work procedures using videos and animations. For example, cleaning work procedures are explained using videos. The generation AI analyzes the work procedures and creates the most appropriate videos and animations. The work procedures are evaluated based on criteria such as step-by-step procedure manuals and video manuals. The videos are created based on criteria such as MP4 format and animated videos. The animations are created based on criteria such as 2D animation and 3D animation. In this way, by visually explaining the work procedures using videos and animations, the contractor's understanding is deepened and work efficiency is improved.

[0087] The work content confirmation unit allows the generation AI to monitor the progress of work in real time while the work is being carried out and provide advice as needed. The work content confirmation unit, for example, allows the generation AI to monitor the progress of work in real time while the work is being carried out and provide advice as needed. For example, the work content confirmation unit monitors the progress of cleaning work and provides advice. The generation AI analyzes the progress of the work and provides optimal advice. The progress is evaluated based on criteria such as the completion rate of the work and the progress status of the work. Monitoring is carried out based on criteria such as real-time monitoring and regular progress confirmation. Advice is provided in the form of text advice, audio advice, etc. This allows the progress of the work to be monitored in real time and advice to be provided as needed, thereby improving the work efficiency of the contractor.

[0088] The work content confirmation unit can analyze the emotional state of the contractor using the emotion estimation function and make suggestions to reduce stress. The work content confirmation unit, for example, uses the emotion estimation function to analyze the emotional state of the contractor while they are working and make suggestions to reduce stress. For example, it can play relaxing music. The emotion estimation function analyzes the emotional state of the contractor using facial expression recognition technology and voice analysis technology. The emotional state is evaluated based on criteria such as an emotion score and a stress level. Stress is evaluated based on criteria such as a stress check sheet and biometric data. In this way, the contractor's health can be maintained by analyzing the emotional state of the contractor and making suggestions to reduce stress.

[0089] When the work content confirmation unit confirms work content, the generation AI can automatically present past success stories and best practices. For example, when the work content confirmation unit confirms work content, the generation AI automatically presents past success stories and best practices. For example, it presents success stories for cleaning work. The generation AI analyzes past success stories and best practices and presents them to the contractor. Success stories are evaluated based on criteria such as success stories and best practices from past projects. Best practices are evaluated based on criteria such as industry standard methods and the sharing of success stories. In this way, by presenting past success stories and best practices, the work efficiency of the contractor is improved.

[0090] The work content confirmation unit supports the generation AI in real-time communication with other contractors while working, thereby promoting cooperative work. The work content confirmation unit, for example, supports the generation AI in real-time communication with other contractors while working, thereby promoting cooperative work. For example, it supports coordination in cleaning work. The generation AI analyzes communication with other contractors and provides optimal support. Real-time communication is evaluated based on criteria such as chat tools and video conferencing tools. Collaborative work is evaluated based on criteria such as how tasks are divided and how collaborative work is carried out. This supports real-time communication with other contractors, promoting cooperative work and improving work efficiency.

[0091] The work content confirmation unit can use the emotion estimation function to identify a method by which the contractor can most efficiently proceed with the work and propose that method. For example, the work content confirmation unit uses the emotion estimation function during work to identify a method by which the contractor can most efficiently proceed with the work and proposes that method. For example, it optimizes work procedures. The emotion estimation function analyzes the emotional state of the contractor using facial expression recognition technology and voice analysis technology. The emotional state is evaluated based on criteria such as emotion score and efficiency. A method by which the work can be efficiently proceeded with is evaluated based on criteria such as optimization of work procedures and how to use tools. In this way, work efficiency is improved by proposing a method by which the contractor can most efficiently proceed with the work.

[0092] The work completion reporting unit allows the generation AI to automatically evaluate the results of work and provide feedback when the contractor presses the work completion button. For example, the work completion reporting unit allows the generation AI to automatically evaluate the results of work and provide feedback when the contractor presses the work completion button. For example, it evaluates cleaning work. The generation AI analyzes the results of work and provides optimal feedback. The results of work are evaluated based on criteria such as the degree of completion of the work and quality evaluation. The evaluation is based on criteria such as the evaluation scores of other contractors and the content of feedback. Feedback is provided in the form of text feedback, voice feedback, etc. In this way, the work results can be automatically evaluated and feedback can be provided, thereby improving the work efficiency and quality of the contractor.

[0093] The work completion reporting section allows the generation AI to automatically prepare for the next task when a task is completed, supporting a smooth transition of tasks. For example, the work completion reporting section allows the generation AI to automatically prepare for the next task when a task is completed, supporting a smooth transition of tasks. For example, it may prepare for the next cleaning task. The generation AI analyzes the details of the next task and the necessary tools and materials, and makes optimal preparations. The next task is evaluated based on criteria such as the details of the next task and the necessary tools and materials. A smooth transition of tasks is evaluated based on criteria such as the method of handing over the task and the preparation of the work environment. This supports a smooth transition of tasks by automatically preparing for the next task.

[0094] The work completion reporting unit allows the generation AI to automatically save work records when the work completion button is pressed, making them available for later reference. The work completion reporting unit allows the generation AI to automatically save work records when the work completion button is pressed, making them available for later reference. For example, saving cleaning work records. The generation AI analyzes work records and selects the optimal storage method. Work records are evaluated based on criteria such as work logs and work reports. Storage is based on criteria such as cloud storage or local storage. Reference is based on criteria such as search functions and filtering functions. This improves the traceability of work by automatically saving work records and making them available for later reference.

[0095] In the work completion reporting section, the generation AI automatically notifies other contractors of the work progress when the work is completed, allowing the entire team to share their status. In the work completion reporting section, for example, the generation AI automatically notifies other contractors of the work progress when the work is completed, allowing the entire team to share their status. For example, notifying of the progress of cleaning work. The generation AI analyzes the work progress and selects the optimal notification method. Work progress is evaluated based on criteria such as the completion rate of the work and the progress of the work. Notifications are made based on criteria such as real-time notifications and periodic notifications. The overall team status is evaluated based on criteria such as progress reports and status updates. This improves team collaboration by automatically notifying work progress and sharing the overall team status.

[0096] The work completion reporting unit can use the emotion estimation function to enable the contractor to report the completion of work in a way that gives the contractor the greatest sense of accomplishment. The work completion reporting unit, for example, uses the emotion estimation function to enable the contractor to report the completion of work in a way that gives the contractor the greatest sense of accomplishment when the work is completed. For example, a message that gives the contractor a sense of accomplishment is displayed. The emotion estimation function analyzes the contractor's emotional state using facial expression recognition technology and voice analysis technology. The emotional state is evaluated based on criteria such as an emotion score and a sense of accomplishment. The sense of accomplishment is evaluated based on criteria such as a questionnaire survey and behavioral data. This allows the contractor to report the completion of work in a way that gives the contractor the greatest sense of accomplishment, thereby improving the contractor's motivation.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The periodic task management system can also be equipped with a health management unit that monitors the health status of contractors. For example, it can measure the heart rate and blood pressure of contractors while they are working, and issue an alert if an abnormality is detected. The health management unit uses wearable devices to collect and analyze health data in real time. This allows the system to constantly monitor the health status of contractors and prevent health risks.

[0099] The Regular Task Registration Department can also evaluate the ecological footprint of tasks and suggest environmentally friendly procedures and frequency. For example, it can make suggestions to optimize the amount of detergent and water used in cleaning work. The Generative AI analyzes the ecological footprint based on past data and suggests procedures and frequency that minimize environmental impact. This not only improves work efficiency but also contributes to environmental protection.

[0100] The regular work registration unit can also perform a risk assessment of the work and provide special warnings for high-risk work. For example, it can provide additional safety procedures and precautions for work involving heights or handling hazardous materials. The generation AI evaluates the risk of work based on past accident data and risk assessment criteria and provides necessary warnings. This can improve work safety.

[0101] The regular task registration unit can also use the emotion estimation function to measure the motivation level of managers and make suggestions to reduce the workload if motivation is low. For example, if motivation is low, the frequency of tasks can be reduced. The emotion estimation function measures the motivation level of managers using facial expression recognition technology and voice analysis technology. This allows managers to maintain their health and work efficiency by measuring their motivation level and making suggestions to reduce the workload.

[0102] The regular task registration unit can also automatically calculate the cost of tasks and make suggestions for cost reduction. For example, it can make suggestions for optimizing the cost of materials used in cleaning work. The generation AI analyzes task costs based on past data and suggests procedures and material selections for cost reduction. This not only improves the efficiency of tasks but also contributes to cost reduction.

[0103] The regular task registration unit can use the emotion estimation function to measure the manager's fatigue level and suggest a break if fatigue is high. For example, if fatigue is high, it can display the message "We recommend taking a break." The emotion estimation function measures the manager's fatigue level using facial expression recognition technology and voice analysis technology. This allows the manager's health to be maintained by measuring the manager's fatigue level and suggesting breaks at appropriate times.

[0104] The regular task registration unit can also evaluate the social impact of tasks and prioritize suggestions for tasks that are socially beneficial. For example, it can prioritize suggestions for tasks that contribute significantly to the local community. The generation AI analyzes the social impact of tasks and evaluates tasks that are socially beneficial. This not only improves the efficiency of tasks, but also the degree of social contribution.

[0105] The task determination unit can also use the emotion estimation function to analyze the current emotional state of the task contractor and prioritize delivery of tasks that elicit positive emotions. For example, tasks that the task contractor can enjoy are delivered. The emotion estimation function analyzes the emotional state of the task contractor using facial expression recognition technology and voice analysis technology. This makes it possible to analyze the emotional state of the task contractor and deliver tasks that elicit positive emotions, thereby improving the motivation of the task contractor.

[0106] The task determination unit can also analyze the learning history of the task contractor and prioritize tasks that will lead to skill improvement. For example, tasks that will improve cleaning skills can be delivered. The generation AI analyzes the learning history and training data of the task contractor and evaluates tasks that will lead to skill improvement. This allows the task contractor to improve their skills.

[0107] The task determination unit can also use the emotion estimation function to analyze the current emotional state of the task contractor and prioritize delivery of tasks that pose less stress. For example, tasks that pose less stress are delivered. The emotion estimation function analyzes the emotional state of the task contractor using facial expression recognition technology and voice analysis technology. This makes it possible to reduce the stress of the task contractor by analyzing the emotional state of the task contractor and delivering tasks that pose less stress.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The regular task registration unit registers the details of the regular task. For example, an administrator registers details of the regular task, such as the procedure, frequency, time specification, and urgency, in an internal system such as CRM. Step 2: The task determination unit automatically determines the tasks to be performed for each contractor based on the information on regular tasks registered by the regular task registration unit. For example, the generation AI analyzes the contractor's schedule and past work history to select the most suitable tasks. Step 3: The task distribution unit distributes the details of the tasks identified by the task determination unit to the client's tablet in real time. For example, it notifies the client's tablet of the details of the tasks selected by the generation AI. Step 4: The outsourcing selection unit selects whether the outsourcer will accept the work delivered to the tablet. For example, if the outsourcer selects "I will accept this work," the work is confirmed. Step 5: The work start reporting unit presses the work start button when the contractor starts the regular work. For example, the contractor presses the "work start button" when starting work. Step 6: The work completion reporting unit presses the work completion button as soon as the contractor has completed the work. For example, the work completion reporting unit presses the "work completion button" when the contractor has completed the work.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0154] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a periodic business registration unit for registering details of periodic business; a task determination unit that automatically determines tasks corresponding to each contractor based on the information on the periodic tasks registered by the periodic task registration unit; a business distribution unit that distributes details of the business determined by the business determination unit to the tablet of the business contractor in real time; an acceptance selection unit for selecting whether or not the outsourcer accepts the work delivered to the tablet; a work start reporting unit that causes the contractor to press a work start button when the contractor starts a regular work; a work completion reporting unit for pressing a work completion button when the contractor completes the work. A system characterized by:

2. The periodic business registration unit Using voice input, the generative AI analyzes the voice data and automatically converts it into text 2. The system of claim 1.

3. The business determination unit Analyzing the real-time location information of the contractor, determining the most suitable business and delivering it.

2. The system of claim 1.

4. The entrustment selection unit Generative AI automatically presents the advantages and disadvantages of the above-mentioned work and assists in the decision to accept the contract.

2. The system of claim 1.

5. The work start reporting unit When the contractor presses the work start button, the generation AI automatically optimizes the work environment (for example, adjusting lighting and temperature).

2. The system of claim 1.

6. The work content confirmation department: Analyzing the emotional state of the contractor using an emotion estimation function and making suggestions to reduce stress 2. The system of claim 1.

7. The work completion reporting unit Analyzing the emotional state of the contractor using an emotion estimation function and providing positive feedback 2. The system of claim 1.

8. The periodic business registration unit Measure the stress level of managers using emotion estimation function, and if stress level is high, make suggestions to reduce the burden of the work.

2. The system of claim 1.

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