system

Abstract

The system according to this embodiment aims to analyze employees' daily work reports and task contents to identify tasks that can be automated or made more efficient. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, a proposal unit, and a provision unit. The collection unit collects employees' daily work reports and task details. The analysis unit analyzes the information collected by the collection unit. The identification unit identifies tasks that can be automated or made more efficient based on the information analyzed by the analysis unit. The proposal unit proposes specific automation methods for the tasks identified by the identification unit. The provision unit provides the automation methods proposed by the proposal unit to the administrator.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently analyze the work reports and task contents of employees and identify tasks that can be automated or made more efficient.

[0005] The system according to the embodiment aims to analyze the work reports and task contents of employees and identify tasks that can be automated or made more efficient.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, a proposal unit, and a provision unit. The collection unit collects employees' daily work reports and task details. The analysis unit analyzes the information collected by the collection unit. The identification unit identifies tasks that can be automated or made more efficient based on the information analyzed by the analysis unit. The proposal unit proposes specific automation methods for the tasks identified by the identification unit. The provision unit provides the automation methods proposed by the proposal unit to the administrator. [Effects of the Invention]

[0007] The system according to this embodiment can analyze employees' daily work reports and task details to identify tasks that can be automated or made more efficient. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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), etc.

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The HelpMe AI system according to an embodiment of the present invention is a system that uses a generating AI to analyze employees' work reports and task contents and identify tasks that can be automated or made more efficient. This HelpMe AI system works by having employees input work reports and task contents, and the generating AI analyzes them to identify tasks that can be automated or made more efficient. For example, tasks that involve a lot of routine work, such as standardized data entry and report creation, can often be automated. Next, the generating AI proposes specific automation methods for the identified tasks that can be automated or made more efficient. For example, it might propose a tool to automate data entry or a template to streamline report creation. This allows employees to use automation tools to improve the efficiency of their work. Furthermore, the generating AI provides managers with suggestions for effective task automation. Based on the suggestions provided by the generating AI, managers can optimize their operations. For example, automating specific tasks can reduce the workload on employees and improve overall productivity. In this way, the HelpMe AI system analyzes employees' work and identifies tasks that can be automated or made more efficient, providing an environment where employees can focus on more important tasks. It also provides managers with suggestions for effective task automation, thereby optimizing operations and improving productivity across the entire company. This allows companies to strengthen their competitiveness and support business success. The HelpMe AI system can improve productivity by automating and streamlining employee tasks.

[0029] The HelpMe AI system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, a proposal unit, and a provision unit. The collection unit collects employees' daily work reports and task details. The collection unit can, for example, collect daily work reports and task details entered by employees. The collection unit can collect daily work reports and task details using manual input or automatic collection methods. The collection unit can, for example, collect daily work reports manually entered by employees. The collection unit can also automatically collect task details from employees' task management tools. Furthermore, the collection unit can periodically collect employees' daily work reports and task details. The analysis unit analyzes the information collected by the collection unit using a generation AI. The analysis unit analyzes daily work reports and task details using, for example, natural language processing technology. The generation AI analyzes daily work reports and task details in detail and identifies tasks that can be automated or made more efficient. For example, the generation AI analyzes the contents of daily work reports and identifies routine work such as standardized data entry and report creation. The Identification Unit identifies tasks that can be automated or streamlined based on the information analyzed by the Analysis Unit. For example, the Identification Unit identifies tasks that can be automated based on the information analyzed by the Generating AI. The Identification Unit identifies routine tasks such as standardized data entry and report creation as tasks that can be automated. The Proposal Unit proposes specific automation methods for the tasks identified by the Identification Unit. For example, the Proposal Unit proposes tools for automating data entry tasks or templates for streamlining report creation. The Proposal Unit can use the Generating AI to propose the optimal automation method for the identified tasks. The Provision Unit provides the automation methods proposed by the Proposal Unit to the administrator. For example, the Provision Unit provides the administrator with proposals for effective task automation. The Provision Unit can use the Generating AI to provide the administrator with the optimal automation method. As a result, the HelpMe AI system according to the embodiment analyzes employees' daily work reports and task contents and identifies tasks that can be automated or streamlined, thereby providing an environment in which employees can focus on more important tasks. Some or all of the above-described processing in the Provision Unit may be performed using AI, for example, or without AI.For example, the provisioning department can input the automation methods proposed by the proposal department into the generation AI, and have the generation AI execute the generation of proposals to be provided to the administrator.

[0030] The data collection unit collects employee work reports and task details. For example, it can collect work reports and task details entered by employees. Specifically, it retrieves data from systems and tools that employees use to record their daily work. In the case of manual entry, employees enter their work reports through dedicated forms or applications, and this data is sent to the data collection unit. In the case of automated collection, it integrates with task management tools and project management software to automatically collect the progress of tasks and projects performed by employees. For example, task management tools can retrieve detailed information such as the start time, end time, progress status, and assigned person for each task. Furthermore, the data collection unit can collect this data regularly. For example, it can collect data at specific times daily, weekly, or monthly to ensure that the information is always up-to-date. This allows the data collection unit to understand employee work status in real time and secure foundational data to provide to the analysis unit. The collected data is stored in a secure database and managed so that the analysis unit and other departments can access it as needed.

[0031] The analysis unit uses generative AI to analyze information collected by the data collection unit. For example, the analysis unit uses natural language processing technology to analyze daily work reports and task contents. The generative AI analyzes daily work reports and task contents in detail to identify tasks that can be automated or made more efficient. Specifically, the generative AI analyzes the text data of daily work reports and extracts frequently repeated tasks and routine work. For example, it identifies routine work such as data entry, report writing, and email sending. The generative AI learns the patterns of these tasks and determines which tasks are suitable for automation. The generative AI also evaluates the priority and urgency of tasks and proposes the best methods for efficiency. Based on past data and work history, the analysis unit can also analyze work trends and performance fluctuations to predict future work and identify areas for improvement. For example, if there is a tendency for the workload to increase at a particular time, preparations for automation can be made for that time. This allows the analysis unit to quickly and accurately analyze the collected data and provide concrete insights for business automation and efficiency improvements.

[0032] The Specialization Department identifies tasks that can be automated or streamlined based on information analyzed by the Analysis Department. For example, the Specialization Department identifies tasks that can be automated based on information analyzed by the Generative AI. Specifically, the Specialization Department lists routine and standardized tasks extracted by the Generative AI and evaluates how suitable these tasks are for automation. For example, data entry tasks are performed according to a standardized format and are therefore judged to be easy to automate. On the other hand, report creation tasks can have a wide range of content, so partial automation may be deemed more appropriate. The Specialization Department evaluates the resources and tools necessary for automating tasks and selects the optimal automation method. For example, an automated input tool using OCR (Optical Character Recognition) technology may be suitable for data entry tasks. Also, an automated generation tool using templates may be suitable for report creation tasks. The Specialization Department evaluates whether the automation of these tasks is feasible and develops a concrete automation plan. This allows the Specialization Department to clarify the specific steps toward automating and streamlining tasks and provide a basis for proposing specific automation methods to the Proposal Department.

[0033] The proposal department will propose specific automation methods for tasks identified by the designated department. For example, the proposal department will propose tools to automate data entry tasks or templates to streamline report creation. Specifically, the proposal department can use generation AI to propose the optimal automation method for the identified tasks. For example, for data entry tasks, it will propose an automated input tool using OCR technology, and for report creation tasks, it will propose an automated generation tool using templates. The proposal department will also provide specific guidelines on how to implement and use these tools and templates. Furthermore, the proposal department will evaluate the effects and benefits of automating tasks and make specific proposals to managers. For example, it will show specific benefits such as time and cost savings through automation and increased productivity through improved work efficiency. The proposal department will also propose follow-up and support systems after implementation to help managers proceed with automation with confidence. In this way, the proposal department can specifically propose the optimal automation method for identified tasks and provide support to managers to effectively promote automation.

[0034] The Service Provider department provides administrators with automation methods proposed by the Proposal department. For example, the Service Provider department provides administrators with proposals for effective business process automation. Specifically, the Service Provider department can use generative AI to provide administrators with the optimal automation methods. For example, it can provide administrators with automation tools and templates proposed by the Proposal department and provide detailed explanations on how to implement and use them. The Service Provider department supports administrators in effectively implementing and operating automation tools. For example, it provides pre-implementation training and post-implementation support systems to ensure that administrators can proceed with automation with confidence. The Service Provider department also regularly evaluates the effectiveness of the proposed automation methods and makes improvement suggestions as needed. For example, it monitors the usage and effectiveness of automation tools and makes suggestions for improvements and additional suggestions. In this way, the Service Provider department can provide administrators with the optimal automation methods and support the improvement of business efficiency and productivity. Furthermore, the Service Provider department collects feedback from administrators and works with the Proposal department and specific departments to improve the automation methods. In this way, the Service Provider department can continuously provide valuable proposals to administrators and effectively advance business process automation.

[0035] The data collection unit can analyze employees' past work reports and task details to select the optimal collection method. For example, the collection unit can analyze the submission times of past work reports and collect them during the time slot with the highest submission rate. It can also analyze past task details and collect them when a specific task is completed. Furthermore, the collection unit can analyze the content of past work reports and select a simpler collection method if many reports are concise. This allows for efficient information gathering by selecting the optimal collection method through the analysis of past work reports and task details. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data from past work reports and task details into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter the collected work reports and task details based on the employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting only work reports related to ongoing projects. It can also prioritize collecting task details related to the employee's areas of interest. Furthermore, the data collection unit can filter and collect only the necessary information according to the project's progress. This allows for the efficient collection of highly relevant information by filtering based on the employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant information by considering the employee's geographical location when collecting daily work reports and task details. For example, if an employee is on a business trip, the data collection unit will prioritize collecting daily work reports related to their business trip location. It can also prioritize collecting daily work reports from home if an employee is working remotely. Furthermore, if an employee is in a specific office, the data collection unit can prioritize collecting task details related to that office. This enables efficient information collection by prioritizing the collection of highly relevant information while considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0038] The data collection unit can analyze employees' social media activity and collect relevant information when collecting work reports and task details. For example, the data collection unit can collect work-related posts that employees have shared on social media. It can also collect industry news that employees follow on social media. Furthermore, the data collection unit can collect information on work-related groups that employees participate in on social media. This allows for the efficient collection of relevant information by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media data into a generating AI and have the generating AI collect relevant information.

[0039] The analysis unit can adjust the level of detail in its analysis based on the importance of the daily work reports and task content. For example, the analysis unit can perform a detailed analysis on daily work reports of high importance. It can also perform a concise analysis on task content of low importance. Furthermore, the analysis unit can adjust how the analysis results are displayed according to their importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the daily work reports and task content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of daily work reports and task content into a generating AI and have the generating AI perform the adjustment of the level of detail in the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the daily work report or task content during analysis. For example, the analysis unit can apply a pattern recognition algorithm to data entry tasks. It can also apply a natural language processing algorithm to report creation tasks. Furthermore, it can apply a project management-specific analysis algorithm to project management tasks. By applying different analysis algorithms depending on the category of the daily work report or task content, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input category data of daily work reports and task content into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the submission dates of daily work reports and task details during the analysis process. For example, the analysis unit will prioritize the analysis of daily work reports with approaching submission deadlines. It can also postpone the analysis of task details that have passed their submission deadlines. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission dates. This enables efficient analysis by determining the priority of analysis based on the submission dates of daily work reports and task details. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the submission dates of daily work reports and task details into a generating AI and have the generating AI perform the priority determination.

[0042] The analysis unit can adjust the order of analysis based on the relevance of daily work reports and task contents during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant daily work reports. It can also postpone the analysis of less relevant task contents. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of daily work reports and task contents. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data of daily work reports and task contents into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The identification unit can improve the accuracy of identification by considering the interrelationships between daily work reports and task contents during the identification process. For example, the identification unit analyzes the relationships between daily work reports and task contents to identify related tasks. The identification unit can also evaluate and identify the relationships between task contents based on the content of daily work reports. Furthermore, the identification unit can improve the accuracy of identification by considering the interrelationships between task contents. As a result, the accuracy of identification is improved by considering the interrelationships between daily work reports and task contents. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input data on the interrelationships between daily work reports and task contents into a generating AI and have the generating AI perform the identification accuracy improvement.

[0044] The identification unit can perform identification by considering the attribute information of the submitter of the work report or task details. For example, the identification unit can improve the accuracy of identification by considering the submitter's position and job duties. The identification unit can also improve the accuracy of identification by referring to the submitter's past work history. Furthermore, the identification unit can improve the accuracy of identification by considering the submitter's skill set. As a result, the accuracy of identification is improved by considering the attribute information of the submitter of the work report or task details. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the identification accuracy improvement.

[0045] The identification unit can perform identification while considering the geographical distribution of daily work reports and task content. For example, the identification unit can prioritize the identification of tasks that are geographically close. It can also postpone the identification of tasks that are geographically far away. Furthermore, the identification unit can improve the accuracy of identification based on geographical distribution. Thus, the accuracy of identification is improved by considering the geographical distribution of daily work reports and task content. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input geographical distribution data of daily work reports and task content into a generating AI and have the generating AI perform the identification accuracy improvement.

[0046] The identification unit can improve the accuracy of identification by referring to work reports and related literature on task content at the time of identification. For example, the identification unit can improve the accuracy of identification by referring to literature related to the content of work reports. The identification unit can also improve the accuracy of identification by referring to literature related to task content. Furthermore, the identification unit can improve the accuracy of identification based on related literature. In this way, the accuracy of identification is improved by referring to work reports and related literature on task content. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input work report and related literature data on task content into a generating AI and have the generating AI perform the accuracy improvement of identification.

[0047] The proposal department can adjust the level of detail in its proposals based on the importance of tasks that can be automated or streamlined. For example, the proposal department can provide detailed proposals for high-priority tasks, and concise proposals for low-priority tasks. Furthermore, the proposal department can adjust how the proposal content is displayed according to its importance. This allows for more efficient proposals by adjusting the level of detail based on the importance of tasks that can be automated or streamlined. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input importance data for tasks that can be automated or streamlined into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0048] The proposal department can apply different proposal algorithms depending on the category of tasks that can be automated or streamlined when making a proposal. For example, for data entry tasks, the proposal department can propose an automation tool specifically for data entry. It can also propose a template specifically for report creation for report creation tasks. Furthermore, it can propose an automation tool specifically for project management tasks for project management tasks. This allows for highly accurate proposals by applying different proposal algorithms depending on the category of tasks that can be automated or streamlined. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input category data of tasks that can be automated or streamlined into a generating AI and have the generating AI apply different proposal algorithms.

[0049] The proposal department can prioritize proposals based on the submission timing of tasks that can be automated or streamlined. For example, the proposal department can prioritize proposals for tasks with approaching deadlines. It can also postpone proposals for tasks whose submission deadlines have passed. Furthermore, the proposal department can dynamically adjust the priority of proposals according to the submission timing. This enables efficient proposals by prioritizing proposals based on the submission timing of tasks that can be automated or streamlined. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input submission timing data for tasks that can be automated or streamlined into a generating AI and have the generating AI perform the priority determination.

[0050] The proposal department can adjust the order of proposals based on the relevance of tasks that can be automated or made more efficient. For example, the proposal department can prioritize proposing tasks that are highly relevant. It can also postpone proposing tasks that are less relevant. Furthermore, the proposal department can dynamically adjust the order of proposals according to their relevance. This allows for efficient proposals by adjusting the order of proposals based on the relevance of tasks that can be automated or made more efficient. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input relevance data of tasks that can be automated or made more efficient into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0051] The service provider can provide optimal suggestions by referring to the administrator's past business automation history at the time of delivery. For example, the service provider can provide optimal suggestions based on the automation tools the administrator has previously adopted. The service provider can also make suggestions by referring to the administrator's past success stories in business automation. Furthermore, the service provider can make suggestions to avoid the administrator's past failures in business automation. In this way, optimal suggestions are possible by referring to the administrator's past business automation history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the administrator's past business automation history data into a generating AI and have the generating AI execute the generation of optimal suggestions.

[0052] The service provider can provide optimal suggestions by considering the administrator's geographical location information at the time of delivery. For example, if the administrator is on a business trip, the service provider can provide suggestions related to the destination. Furthermore, if the administrator is working remotely, the service provider can provide suggestions related to work from home. Additionally, if the administrator is in a specific office, the service provider can provide suggestions related to that office. This allows for optimal suggestions by considering the administrator's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the administrator's geographical location data into a generating AI and have the generating AI generate optimal suggestions.

[0053] The service provider can provide optimal suggestions while considering the administrator's work schedule. For example, the service provider can provide suggestions at the optimal time to match the administrator's schedule. The service provider can also prioritize providing suggestions of high importance while considering the administrator's work schedule. Furthermore, the service provider can adjust the content of the suggestions based on the administrator's schedule. This makes it possible to provide optimal suggestions by considering the administrator's work schedule. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the administrator's work schedule data into a generating AI and have the generating AI generate optimal suggestions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can collect employee health data and link it to daily work reports and task details. For example, it can collect employee heart rate and sleep data and link it to work performance. The data collection unit can also adjust workloads based on employee health status. Furthermore, based on employee health data, the data collection unit can suggest ways to improve work efficiency and automate processes. This enables the optimization of work processes while considering employee health.

[0056] A specific department can identify tasks that can be automated or streamlined by considering employees' skill sets. For example, the department can analyze employees' skill sets and automate tasks that match those skills. It can also identify tasks that can be streamlined based on employees' skill sets. Furthermore, the department can optimize task assignments by considering employees' skill sets. This enables the optimization of tasks while taking employees' skill sets into account.

[0057] The data collection unit can adjust its collection methods when collecting employee work reports and task details, taking into account the employee's past work performance. For example, a simpler collection method can be applied to employees with high past work performance, while a more detailed collection method can be applied to employees with low past work performance. Furthermore, the collection frequency can be adjusted based on past work performance. This enables efficient information collection that takes into account the employee's past work performance.

[0058] The analysis unit can improve the accuracy of its analysis of daily work reports and task contents by considering the employee's work history. For example, it can analyze an employee's past work history and select an analysis algorithm to apply to similar tasks. It can also evaluate the reliability of the analysis results based on the employee's work history. Furthermore, it can determine the priority of the analysis by considering the employee's work history. This enables highly accurate analysis that takes into account the employee's work history.

[0059] The task identification department can take employees' career goals into consideration when identifying daily work reports and task content. For example, it can prioritize identifying tasks that align with employees' career goals. It can also identify tasks that allow for skill development based on employees' career goals. Furthermore, it can improve the accuracy of task identification by considering employees' career goals. This makes it possible to identify tasks that take employees' career goals into account.

[0060] The proposal department can adjust the content of proposals to take into account the work style of the employees. For example, if an employee works remotely, they can provide proposals suitable for remote work. Similarly, if an employee prefers working in a team, they can provide proposals that facilitate teamwork. Furthermore, they can prioritize proposals based on the employee's work style. This enables effective proposals that consider the individual work style of each employee.

[0061] The service provider can offer optimal proposals at the time of delivery, taking into account the manager's work objectives. For example, they can prioritize proposals that align with the manager's work objectives. They can also offer proposals from a long-term perspective based on the manager's work objectives. Furthermore, they can adjust the content of the proposals to take the manager's work objectives into consideration. This makes it possible to offer optimal proposals that take the manager's work objectives into account.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects employee work reports and task details. The collection unit can collect work reports manually entered by employees, as well as task details automatically collected from task management tools. The collection unit can also collect this information on a regular basis. Step 2: The analysis unit uses generative AI to analyze the information collected by the collection unit. The analysis unit uses natural language processing technology to analyze daily work reports and task contents to identify tasks that can be automated or made more efficient. For example, it identifies routine tasks such as standardized data entry and report writing. Step 3: The Identification Department identifies tasks that can be automated or made more efficient based on the information analyzed by the Analysis Department. Based on the information analyzed by the Generating AI, the Identification Department identifies routine tasks such as standardized data entry and report creation as tasks that can be automated. Step 4: The proposal department proposes specific automation methods for the tasks identified by the designated department. The proposal department proposes tools for automating data entry tasks and templates for streamlining report creation. Using generation AI, the optimal automation method for the identified tasks can be proposed. Step 5: The provisioning department provides the administrator with the automation methods proposed by the proposaling department. The provisioning department can provide the administrator with effective business automation proposals and offer the optimal automation method using generative AI.

[0064] (Example of form 2) The HelpMe AI system according to an embodiment of the present invention is a system that uses a generating AI to analyze employees' work reports and task contents and identify tasks that can be automated or made more efficient. This HelpMe AI system works by having employees input work reports and task contents, and the generating AI analyzes them to identify tasks that can be automated or made more efficient. For example, tasks that involve a lot of routine work, such as standardized data entry and report creation, can often be automated. Next, the generating AI proposes specific automation methods for the identified tasks that can be automated or made more efficient. For example, it might propose a tool to automate data entry or a template to streamline report creation. This allows employees to use automation tools to improve the efficiency of their work. Furthermore, the generating AI provides managers with suggestions for effective task automation. Based on the suggestions provided by the generating AI, managers can optimize their operations. For example, automating specific tasks can reduce the workload on employees and improve overall productivity. In this way, the HelpMe AI system analyzes employees' work and identifies tasks that can be automated or made more efficient, providing an environment where employees can focus on more important tasks. It also provides managers with suggestions for effective task automation, thereby optimizing operations and improving productivity across the entire company. This allows companies to strengthen their competitiveness and support business success. The HelpMe AI system can improve productivity by automating and streamlining employee tasks.

[0065] The HelpMe AI system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, a proposal unit, and a provision unit. The collection unit collects employees' daily work reports and task details. The collection unit can, for example, collect daily work reports and task details entered by employees. The collection unit can collect daily work reports and task details using manual input or automatic collection methods. The collection unit can, for example, collect daily work reports manually entered by employees. The collection unit can also automatically collect task details from employees' task management tools. Furthermore, the collection unit can periodically collect employees' daily work reports and task details. The analysis unit analyzes the information collected by the collection unit using a generation AI. The analysis unit analyzes daily work reports and task details using, for example, natural language processing technology. The generation AI analyzes daily work reports and task details in detail and identifies tasks that can be automated or made more efficient. For example, the generation AI analyzes the contents of daily work reports and identifies routine work such as standardized data entry and report creation. The Identification Unit identifies tasks that can be automated or streamlined based on the information analyzed by the Analysis Unit. For example, the Identification Unit identifies tasks that can be automated based on the information analyzed by the Generating AI. The Identification Unit identifies routine tasks such as standardized data entry and report creation as tasks that can be automated. The Proposal Unit proposes specific automation methods for the tasks identified by the Identification Unit. For example, the Proposal Unit proposes tools for automating data entry tasks or templates for streamlining report creation. The Proposal Unit can use the Generating AI to propose the optimal automation method for the identified tasks. The Provision Unit provides the automation methods proposed by the Proposal Unit to the administrator. For example, the Provision Unit provides the administrator with proposals for effective task automation. The Provision Unit can use the Generating AI to provide the administrator with the optimal automation method. As a result, the HelpMe AI system according to the embodiment analyzes employees' daily work reports and task contents and identifies tasks that can be automated or streamlined, thereby providing an environment in which employees can focus on more important tasks. Some or all of the above-described processing in the Provision Unit may be performed using AI, for example, or without AI.For example, the provisioning department can input the automation methods proposed by the proposal department into the generation AI, and have the generation AI execute the generation of proposals to be provided to the administrator.

[0066] The data collection unit collects employee work reports and task details. For example, it can collect work reports and task details entered by employees. Specifically, it retrieves data from systems and tools that employees use to record their daily work. In the case of manual entry, employees enter their work reports through dedicated forms or applications, and this data is sent to the data collection unit. In the case of automated collection, it integrates with task management tools and project management software to automatically collect the progress of tasks and projects performed by employees. For example, task management tools can retrieve detailed information such as the start time, end time, progress status, and assigned person for each task. Furthermore, the data collection unit can collect this data regularly. For example, it can collect data at specific times daily, weekly, or monthly to ensure that the information is always up-to-date. This allows the data collection unit to understand employee work status in real time and secure foundational data to provide to the analysis unit. The collected data is stored in a secure database and managed so that the analysis unit and other departments can access it as needed.

[0067] The analysis unit uses generative AI to analyze information collected by the data collection unit. For example, the analysis unit uses natural language processing technology to analyze daily work reports and task contents. The generative AI analyzes daily work reports and task contents in detail to identify tasks that can be automated or made more efficient. Specifically, the generative AI analyzes the text data of daily work reports and extracts frequently repeated tasks and routine work. For example, it identifies routine work such as data entry, report writing, and email sending. The generative AI learns the patterns of these tasks and determines which tasks are suitable for automation. The generative AI also evaluates the priority and urgency of tasks and proposes the best methods for efficiency. Based on past data and work history, the analysis unit can also analyze work trends and performance fluctuations to predict future work and identify areas for improvement. For example, if there is a tendency for the workload to increase at a particular time, preparations for automation can be made for that time. This allows the analysis unit to quickly and accurately analyze the collected data and provide concrete insights for business automation and efficiency improvements.

[0068] The Specialization Department identifies tasks that can be automated or streamlined based on information analyzed by the Analysis Department. For example, the Specialization Department identifies tasks that can be automated based on information analyzed by the Generative AI. Specifically, the Specialization Department lists routine and standardized tasks extracted by the Generative AI and evaluates how suitable these tasks are for automation. For example, data entry tasks are performed according to a standardized format and are therefore judged to be easy to automate. On the other hand, report creation tasks can have a wide range of content, so partial automation may be deemed more appropriate. The Specialization Department evaluates the resources and tools necessary for automating tasks and selects the optimal automation method. For example, an automated input tool using OCR (Optical Character Recognition) technology may be suitable for data entry tasks. Also, an automated generation tool using templates may be suitable for report creation tasks. The Specialization Department evaluates whether the automation of these tasks is feasible and develops a concrete automation plan. This allows the Specialization Department to clarify the specific steps toward automating and streamlining tasks and provide a basis for proposing specific automation methods to the Proposal Department.

[0069] The proposal department will propose specific automation methods for tasks identified by the designated department. For example, the proposal department will propose tools to automate data entry tasks or templates to streamline report creation. Specifically, the proposal department can use generation AI to propose the optimal automation method for the identified tasks. For example, for data entry tasks, it will propose an automated input tool using OCR technology, and for report creation tasks, it will propose an automated generation tool using templates. The proposal department will also provide specific guidelines on how to implement and use these tools and templates. Furthermore, the proposal department will evaluate the effects and benefits of automating tasks and make specific proposals to managers. For example, it will show specific benefits such as time and cost savings through automation and increased productivity through improved work efficiency. The proposal department will also propose follow-up and support systems after implementation to help managers proceed with automation with confidence. In this way, the proposal department can specifically propose the optimal automation method for identified tasks and provide support to managers to effectively promote automation.

[0070] The Service Provider department provides administrators with automation methods proposed by the Proposal department. For example, the Service Provider department provides administrators with proposals for effective business process automation. Specifically, the Service Provider department can use generative AI to provide administrators with the optimal automation methods. For example, it can provide administrators with automation tools and templates proposed by the Proposal department and provide detailed explanations on how to implement and use them. The Service Provider department supports administrators in effectively implementing and operating automation tools. For example, it provides pre-implementation training and post-implementation support systems to ensure that administrators can proceed with automation with confidence. The Service Provider department also regularly evaluates the effectiveness of the proposed automation methods and makes improvement suggestions as needed. For example, it monitors the usage and effectiveness of automation tools and makes suggestions for improvements and additional suggestions. In this way, the Service Provider department can provide administrators with the optimal automation methods and support the improvement of business efficiency and productivity. Furthermore, the Service Provider department collects feedback from administrators and works with the Proposal department and specific departments to improve the automation methods. In this way, the Service Provider department can continuously provide valuable proposals to administrators and effectively advance business process automation.

[0071] The data collection unit can estimate employees' emotions and adjust the timing of collecting work reports and task details based on the estimated emotions. For example, if an employee is feeling stressed, the data collection unit can postpone the collection of work reports until the employee is relaxed. It can also avoid collecting work reports when employees are concentrating. Furthermore, if an employee is tired, the data collection unit can postpone the collection of work reports until the next day. By adjusting the collection timing according to employees' emotions, the burden on employees is reduced, and efficient information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI adjust the collection timing.

[0072] The data collection unit can analyze employees' past work reports and task details to select the optimal collection method. For example, the collection unit can analyze the submission times of past work reports and collect them during the time slot with the highest submission rate. It can also analyze past task details and collect them when a specific task is completed. Furthermore, the collection unit can analyze the content of past work reports and select a simpler collection method if many reports are concise. This allows for efficient information gathering by selecting the optimal collection method through the analysis of past work reports and task details. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data from past work reports and task details into a generating AI and have the generating AI select the optimal collection method.

[0073] The data collection unit can filter the collected work reports and task details based on the employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting only work reports related to ongoing projects. It can also prioritize collecting task details related to the employee's areas of interest. Furthermore, the data collection unit can filter and collect only the necessary information according to the project's progress. This allows for the efficient collection of highly relevant information by filtering based on the employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0074] The data collection unit can estimate employees' emotions and prioritize the work reports and tasks to be collected based on those estimated emotions. For example, if an employee is stressed, the data collection unit will postpone the collection of less important work reports. Conversely, if an employee is relaxed, the data collection unit can prioritize the collection of more important work reports. Furthermore, if an employee is in a hurry, the data collection unit can prioritize the collection of concise task contents. This enables efficient information collection by prioritizing the work reports and tasks to be collected according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI perform the priority determination.

[0075] The data collection unit can prioritize the collection of highly relevant information by considering the employee's geographical location when collecting daily work reports and task details. For example, if an employee is on a business trip, the data collection unit will prioritize collecting daily work reports related to their business trip location. It can also prioritize collecting daily work reports from home if an employee is working remotely. Furthermore, if an employee is in a specific office, the data collection unit can prioritize collecting task details related to that office. This enables efficient information collection by prioritizing the collection of highly relevant information while considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0076] The data collection unit can analyze employees' social media activity and collect relevant information when collecting work reports and task details. For example, the data collection unit can collect work-related posts that employees have shared on social media. It can also collect industry news that employees follow on social media. Furthermore, the data collection unit can collect information on work-related groups that employees participate in on social media. This allows for the efficient collection of relevant information by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media data into a generating AI and have the generating AI collect relevant information.

[0077] The analysis unit can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide concise and to-the-point analysis results. If an employee is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if an employee is in a hurry, the analysis unit can provide analysis results using graphs and charts for quick understanding. In this way, by adjusting the presentation of the analysis according to the employee's emotions, easy-to-understand analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0078] The analysis unit can adjust the level of detail in its analysis based on the importance of the daily work reports and task content. For example, the analysis unit can perform a detailed analysis on daily work reports of high importance. It can also perform a concise analysis on task content of low importance. Furthermore, the analysis unit can adjust how the analysis results are displayed according to their importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the daily work reports and task content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of daily work reports and task content into a generating AI and have the generating AI perform the adjustment of the level of detail in the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the category of the daily work report or task content during analysis. For example, the analysis unit can apply a pattern recognition algorithm to data entry tasks. It can also apply a natural language processing algorithm to report creation tasks. Furthermore, it can apply a project management-specific analysis algorithm to project management tasks. By applying different analysis algorithms depending on the category of the daily work report or task content, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input category data of daily work reports and task content into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0080] The analysis unit can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a short, concise analysis. If an employee is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if an employee is in a hurry, the analysis unit can provide a short analysis for quick understanding. By adjusting the length of the analysis according to the employee's emotions, the analysis can provide easily understandable results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0081] The analysis unit can determine the priority of analysis based on the submission dates of daily work reports and task details during the analysis process. For example, the analysis unit will prioritize the analysis of daily work reports with approaching submission deadlines. It can also postpone the analysis of task details that have passed their submission deadlines. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission dates. This enables efficient analysis by determining the priority of analysis based on the submission dates of daily work reports and task details. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the submission dates of daily work reports and task details into a generating AI and have the generating AI perform the priority determination.

[0082] The analysis unit can adjust the order of analysis based on the relevance of daily work reports and task contents during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant daily work reports. It can also postpone the analysis of less relevant task contents. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of daily work reports and task contents. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data of daily work reports and task contents into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0083] The identification unit can estimate an employee's emotions and adjust the criteria for identifying tasks that can be automated or streamlined based on the estimated emotions. For example, if an employee is stressed, the identification unit will prioritize automating tasks that reduce stress. Similarly, if an employee is relaxed, the unit can prioritize identifying tasks that can be streamlined. Furthermore, if an employee is in a hurry, the unit can quickly identify tasks that can be automated. This allows for efficient task identification by adjusting the criteria according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input employee emotion data into a generative AI and have the generative AI adjust the criteria.

[0084] The identification unit can improve the accuracy of identification by considering the interrelationships between daily work reports and task contents during the identification process. For example, the identification unit analyzes the relationships between daily work reports and task contents to identify related tasks. The identification unit can also evaluate and identify the relationships between task contents based on the content of daily work reports. Furthermore, the identification unit can improve the accuracy of identification by considering the interrelationships between task contents. As a result, the accuracy of identification is improved by considering the interrelationships between daily work reports and task contents. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input data on the interrelationships between daily work reports and task contents into a generating AI and have the generating AI perform the identification accuracy improvement.

[0085] The identification unit can perform identification by considering the attribute information of the submitter of the work report or task details. For example, the identification unit can improve the accuracy of identification by considering the submitter's position and job duties. The identification unit can also improve the accuracy of identification by referring to the submitter's past work history. Furthermore, the identification unit can improve the accuracy of identification by considering the submitter's skill set. As a result, the accuracy of identification is improved by considering the attribute information of the submitter of the work report or task details. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the identification accuracy improvement.

[0086] The identification unit can estimate an employee's emotions and adjust the display method of the identified task based on the estimated employee's emotions. For example, if an employee is stressed, the identification unit can provide a simple and highly visible display method. It can also provide a display method with detailed information if the employee is relaxed. Furthermore, if an employee is in a hurry, it can provide a concise display method. This allows for easy-to-understand displays by adjusting the display method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input employee emotion data into a generative AI and have the generative AI adjust the display method.

[0087] The identification unit can perform identification while considering the geographical distribution of daily work reports and task content. For example, the identification unit can prioritize the identification of tasks that are geographically close. It can also postpone the identification of tasks that are geographically far away. Furthermore, the identification unit can improve the accuracy of identification based on geographical distribution. Thus, the accuracy of identification is improved by considering the geographical distribution of daily work reports and task content. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input geographical distribution data of daily work reports and task content into a generating AI and have the generating AI perform the identification accuracy improvement.

[0088] The identification unit can improve the accuracy of identification by referring to work reports and related literature on task content at the time of identification. For example, the identification unit can improve the accuracy of identification by referring to literature related to the content of work reports. The identification unit can also improve the accuracy of identification by referring to literature related to task content. Furthermore, the identification unit can improve the accuracy of identification based on related literature. In this way, the accuracy of identification is improved by referring to work reports and related literature on task content. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input work report and related literature data on task content into a generating AI and have the generating AI perform the accuracy improvement of identification.

[0089] The suggestion department can estimate an employee's emotions and adjust the way suggestions are presented based on those emotions. For example, if an employee is stressed, the suggestion department can provide concise and to-the-point suggestions. If an employee is relaxed, the suggestion department can provide more detailed suggestions. Furthermore, if an employee is in a hurry, the suggestion department can provide suggestions using graphs and charts for quick understanding. By adjusting the presentation of suggestions according to the employee's emotions, it becomes possible to create suggestions that are easy to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0090] The proposal department can adjust the level of detail in its proposals based on the importance of tasks that can be automated or streamlined. For example, the proposal department can provide detailed proposals for high-priority tasks, and concise proposals for low-priority tasks. Furthermore, the proposal department can adjust how the proposal content is displayed according to its importance. This allows for more efficient proposals by adjusting the level of detail based on the importance of tasks that can be automated or streamlined. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input importance data for tasks that can be automated or streamlined into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0091] The proposal department can apply different proposal algorithms depending on the category of tasks that can be automated or streamlined when making a proposal. For example, for data entry tasks, the proposal department can propose an automation tool specifically for data entry. It can also propose a template specifically for report creation for report creation tasks. Furthermore, it can propose an automation tool specifically for project management tasks for project management tasks. This allows for highly accurate proposals by applying different proposal algorithms depending on the category of tasks that can be automated or streamlined. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input category data of tasks that can be automated or streamlined into a generating AI and have the generating AI apply different proposal algorithms.

[0092] The suggestion unit can estimate an employee's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if an employee is stressed, the suggestion unit can provide a short, concise suggestion. If an employee is relaxed, the suggestion unit can provide a more detailed suggestion. Furthermore, if an employee is in a hurry, the suggestion unit can provide a short suggestion that can be quickly understood. By adjusting the length of the suggestion according to the employee's emotions, it becomes possible to provide suggestions that are easy to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input employee emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0093] The proposal department can prioritize proposals based on the submission timing of tasks that can be automated or streamlined. For example, the proposal department can prioritize proposals for tasks with approaching deadlines. It can also postpone proposals for tasks whose submission deadlines have passed. Furthermore, the proposal department can dynamically adjust the priority of proposals according to the submission timing. This enables efficient proposals by prioritizing proposals based on the submission timing of tasks that can be automated or streamlined. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input submission timing data for tasks that can be automated or streamlined into a generating AI and have the generating AI perform the priority determination.

[0094] The proposal department can adjust the order of proposals based on the relevance of tasks that can be automated or made more efficient. For example, the proposal department can prioritize proposing tasks that are highly relevant. It can also postpone proposing tasks that are less relevant. Furthermore, the proposal department can dynamically adjust the order of proposals according to their relevance. This allows for efficient proposals by adjusting the order of proposals based on the relevance of tasks that can be automated or made more efficient. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input relevance data of tasks that can be automated or made more efficient into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0095] The service provider can estimate an employee's emotions and adjust the way suggestions are presented based on those emotions. For example, if an employee is stressed, the service provider can provide concise and to-the-point suggestions. If an employee is relaxed, the service provider can also provide detailed suggestions. Furthermore, if an employee is in a hurry, the service provider can provide suggestions using graphs and charts for quick understanding. By adjusting the presentation of suggestions according to the employee's emotions, it becomes possible to provide suggestions that are easy to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0096] The service provider can provide optimal suggestions by referring to the administrator's past business automation history at the time of delivery. For example, the service provider can provide optimal suggestions based on the automation tools the administrator has previously adopted. The service provider can also make suggestions by referring to the administrator's past success stories in business automation. Furthermore, the service provider can make suggestions to avoid the administrator's past failures in business automation. In this way, optimal suggestions are possible by referring to the administrator's past business automation history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the administrator's past business automation history data into a generating AI and have the generating AI execute the generation of optimal suggestions.

[0097] The service provider can estimate an employee's emotions and prioritize the suggestions they offer based on those emotions. For example, if an employee is stressed, the service provider might postpone less important suggestions. Conversely, if an employee is relaxed, the service provider might prioritize more important suggestions. Furthermore, if an employee is in a hurry, the service provider might prioritize suggestions that require immediate attention. This allows for more efficient suggestions by prioritizing them according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0098] The service provider can provide optimal suggestions by considering the administrator's geographical location information at the time of delivery. For example, if the administrator is on a business trip, the service provider can provide suggestions related to the destination. Furthermore, if the administrator is working remotely, the service provider can provide suggestions related to work from home. Additionally, if the administrator is in a specific office, the service provider can provide suggestions related to that office. This allows for optimal suggestions by considering the administrator's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the administrator's geographical location data into a generating AI and have the generating AI generate optimal suggestions.

[0099] The service provider can provide optimal suggestions while considering the administrator's work schedule. For example, the service provider can provide suggestions at the optimal time to match the administrator's schedule. The service provider can also prioritize providing suggestions of high importance while considering the administrator's work schedule. Furthermore, the service provider can adjust the content of the suggestions based on the administrator's schedule. This makes it possible to provide optimal suggestions by considering the administrator's work schedule. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the administrator's work schedule data into a generating AI and have the generating AI generate optimal suggestions.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can collect employee health data and link it to daily work reports and task details. For example, it can collect employee heart rate and sleep data and link it to work performance. The data collection unit can also adjust workloads based on employee health status. Furthermore, based on employee health data, the data collection unit can suggest ways to improve work efficiency and automate processes. This enables the optimization of work processes while considering employee health.

[0102] The analysis unit can estimate employees' emotions and determine analysis priorities based on those estimated emotions. For example, if an employee is stressed, it can prioritize analyzing tasks that can reduce stress. If an employee is relaxed, it can prioritize analyzing tasks that can be made more efficient. Furthermore, if an employee is in a hurry, it can prioritize analyzing tasks that require quick analysis. This allows for more efficient analysis by prioritizing analysis according to employees' emotions.

[0103] A specific department can identify tasks that can be automated or streamlined by considering employees' skill sets. For example, the department can analyze employees' skill sets and automate tasks that match those skills. It can also identify tasks that can be streamlined based on employees' skill sets. Furthermore, the department can optimize task assignments by considering employees' skill sets. This enables the optimization of tasks while taking employees' skill sets into account.

[0104] The suggestion department can estimate employees' emotions and adjust the timing of suggestions based on those estimates. For example, if an employee is stressed, the suggestion can be postponed. Conversely, if an employee is relaxed, the suggestion can be brought forward. Furthermore, if an employee is in a hurry, the suggestion can be made quickly. By adjusting the timing of suggestions according to employees' emotions, more effective suggestions can be made.

[0105] The service provider can estimate an employee's emotions and adjust the content of the suggestions based on those estimates. For example, if an employee is stressed, it can offer suggestions to reduce stress. If an employee is relaxed, it can offer suggestions that can improve efficiency. Furthermore, if an employee is in a hurry, it can offer suggestions that require immediate attention. By adjusting the content of suggestions according to the employee's emotions, it becomes possible to provide more effective suggestions.

[0106] The data collection unit can adjust its collection methods when collecting employee work reports and task details, taking into account the employee's past work performance. For example, a simpler collection method can be applied to employees with high past work performance, while a more detailed collection method can be applied to employees with low past work performance. Furthermore, the collection frequency can be adjusted based on past work performance. This enables efficient information collection that takes into account the employee's past work performance.

[0107] The analysis unit can improve the accuracy of its analysis of daily work reports and task contents by considering the employee's work history. For example, it can analyze an employee's past work history and select an analysis algorithm to apply to similar tasks. It can also evaluate the reliability of the analysis results based on the employee's work history. Furthermore, it can determine the priority of the analysis by considering the employee's work history. This enables highly accurate analysis that takes into account the employee's work history.

[0108] The task identification department can take employees' career goals into consideration when identifying daily work reports and task content. For example, it can prioritize identifying tasks that align with employees' career goals. It can also identify tasks that allow for skill development based on employees' career goals. Furthermore, it can improve the accuracy of task identification by considering employees' career goals. This makes it possible to identify tasks that take employees' career goals into account.

[0109] The proposal department can adjust the content of proposals to take into account the work style of the employees. For example, if an employee works remotely, they can provide proposals suitable for remote work. Similarly, if an employee prefers working in a team, they can provide proposals that facilitate teamwork. Furthermore, they can prioritize proposals based on the employee's work style. This enables effective proposals that consider the individual work style of each employee.

[0110] The service provider can offer optimal proposals at the time of delivery, taking into account the manager's work objectives. For example, they can prioritize proposals that align with the manager's work objectives. They can also offer proposals from a long-term perspective based on the manager's work objectives. Furthermore, they can adjust the content of the proposals to take the manager's work objectives into consideration. This makes it possible to offer optimal proposals that take the manager's work objectives into account.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects employee work reports and task details. The collection unit can collect work reports manually entered by employees, as well as task details automatically collected from task management tools. The collection unit can also collect this information on a regular basis. Step 2: The analysis unit uses generative AI to analyze the information collected by the collection unit. The analysis unit uses natural language processing technology to analyze daily work reports and task contents to identify tasks that can be automated or made more efficient. For example, it identifies routine tasks such as standardized data entry and report writing. Step 3: The Identification Department identifies tasks that can be automated or made more efficient based on the information analyzed by the Analysis Department. Based on the information analyzed by the Generating AI, the Identification Department identifies routine tasks such as standardized data entry and report creation as tasks that can be automated. Step 4: The proposal department proposes specific automation methods for the tasks identified by the designated department. The proposal department proposes tools for automating data entry tasks and templates for streamlining report creation. Using generation AI, the optimal automation method for the identified tasks can be proposed. Step 5: The provisioning department provides the administrator with the automation methods proposed by the proposaling department. The provisioning department can provide the administrator with effective business automation proposals and offer the optimal automation method using generated AI.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee work reports and task details using the receiving device 38 of the smart device 14. The analysis unit analyzes the collected information using AI generated by the identification processing unit 290 of the data processing unit 12. The identification unit identifies tasks that can be automated or made more efficient based on the information analyzed by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes specific automation methods for the tasks identified by the identification processing unit 290 of the data processing unit 12. The provision unit provides proposals to administrators using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects employee work reports and task details using the microphone 238 of the smart glasses 214. The analysis unit analyzes the collected information using AI generated by the identification processing unit 290 of the data processing unit 12. The identification unit identifies tasks that can be automated or made more efficient based on the information analyzed by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes specific automation methods for the tasks identified by the identification processing unit 290 of the data processing unit 12. The provision unit provides proposals to administrators using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects employee work reports and task details using the microphone 238 of the headset terminal 314. The analysis unit analyzes the collected information using AI generated by the identification processing unit 290 of the data processing unit 12. The identification unit identifies tasks that can be automated or made more efficient based on the information analyzed by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes specific automation methods for the tasks identified by the identification processing unit 290 of the data processing unit 12. The provision unit provides proposals to administrators using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, proposal unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects employee work reports and task details using the microphone 238 of the robot 414. The analysis unit analyzes the collected information using AI generated by the identification processing unit 290 of the data processing unit 12. The identification unit identifies tasks that can be automated or made more efficient based on the information analyzed by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes specific automation methods for the tasks identified by the identification processing unit 290 of the data processing unit 12. The provision unit provides proposals to administrators using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The collection department collects employees' daily work reports and task details, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, an identification unit identifies tasks that can be automated or made more efficient, A proposal unit proposes specific automation methods for the tasks identified by the aforementioned design unit, The system comprises a provisioning unit that provides the administrator with the automation method proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates employees' emotions and adjusts the timing of collecting daily work reports and task details based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze employees' past work reports and task details to select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting daily work reports and task details, filter them based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates employee emotions and prioritizes the work reports and tasks to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting daily work reports and task details, prioritize the collection of highly relevant information by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting daily work reports and task details, we analyze employees' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the emotions of employees and adjust the representation of the analysis based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the work reports and task content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the work report or task content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates employee sentiment and adjusts the length of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the submission timing of daily work reports and task details. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of daily work reports and task content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, Estimate employee sentiment and adjust the criteria for identifying tasks that can be automated or streamlined based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, At specific times, improve the accuracy of the identification process by considering the interrelationships between daily work reports and task details. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, When identifying a person, the identification process takes into account the attribute information of the person who submitted the work report or task details. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, We estimate employee sentiment and adjust how identified tasks are displayed based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, When identifying a task, the geographical distribution of daily work reports and task contents should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When a task is identified, the accuracy of that identification is improved by referring to relevant literature such as daily work reports and task details. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, We estimate the employees' emotions and adjust the way we present proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the tasks that can be automated or made more efficient. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of tasks that can be automated or made more efficient. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Estimate the employee's feelings and adjust the length of the suggestion based on those feelings. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of submissions for tasks that can be automated or streamlined. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of tasks that can be automated or made more efficient. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We estimate employees' emotions and adjust the way we present suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the solution, we refer to the administrator's past history of business process automation to offer the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, Estimate employee sentiment and prioritize suggestions based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we will consider the administrator's geographical location to offer the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we will offer the most suitable proposal while taking into account the administrator's work schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects employees' daily work reports and task details, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, an identification unit identifies tasks that can be automated or made more efficient, A proposal unit proposes specific automation methods for the tasks identified by the aforementioned design unit, The system comprises a provisioning unit that provides the administrator with the automation method proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates employees' emotions and adjusts the timing of collecting daily work reports and task details based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze employees' past work reports and task details to select the most suitable data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting daily work reports and task details, filter them based on employees' current projects and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is The system estimates employee emotions and prioritizes the work reports and tasks to be collected based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting daily work reports and task details, prioritize the collection of highly relevant information by considering the geographical location of employees. The system according to feature 1.

7. The aforementioned collection unit is When collecting daily work reports and task details, we analyze employees' social media activity and collect relevant information. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the emotions of employees and adjust the representation of the analysis based on the estimated emotions of the employees. The system according to feature 1.

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