system

Abstract

The system according to this embodiment aims to automate and streamline employees' PC work. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a verification unit, and a comparison unit. The collection unit checks the activity of employees' PC work. The analysis unit analyzes the activity of work collected by the collection unit and identifies tasks that can be automatically generated. The generation unit automatically generates and provides tools such as GAS and macros based on the tasks identified by the analysis unit. The verification unit automatically verifies the effectiveness of the tools generated by the generation unit. The comparison unit compares the usage time when the tools are implemented based on the effectiveness verified by the verification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently automate the PC operations of employees, and there is room for improvement.

[0005] The system according to the embodiment aims to automate and improve the efficiency of the PC operations of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a verification unit, and a comparison unit. The collection unit checks the activity of employees' PC work. The analysis unit analyzes the activity of work collected by the collection unit and identifies tasks that can be automatically generated. The generation unit automatically generates and provides tools such as GAS and macros based on the tasks identified by the analysis unit. The verification unit automatically verifies the effectiveness of the tools generated by the generation unit. The comparison unit compares the usage time when the tools are implemented based on the effectiveness verified by the verification unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and streamline employees' PC work. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable 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).

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

[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 business automation system according to an embodiment of the present invention is a system that checks the activity of employees' PC work, identifies tasks that can be automatically generated, and creates and provides the necessary tools. This business automation system checks the activity of employees' PC work and automatically identifies tasks using a generation AI. Next, based on the identified tasks, it automatically generates and provides tools such as GAS and macros. Furthermore, it can automatically verify the screen operation content and the effect of automation, and compare the usage time if implemented. For example, the business automation system checks the activity of employees' PC work. At this time, it records in detail what kind of work the employee is doing. For example, it records tasks such as checking and replying to emails, creating Excel documents, and creating PowerPoint presentations. This allows the system to understand the activity of employees. Next, the business automation system uses a generation AI to analyze the recorded activity of tasks and identify tasks that can be automatically generated. The generation AI analyzes the content of the tasks and determines which tasks can be automated. For example, tasks such as creating email formats, creating Excel macros, and automatically creating PowerPoint presentations are identified as tasks that can be automated. Based on the identified tasks, the business automation system automatically generates and provides tools such as GAS and macros. The generation AI automatically generates the most suitable tools for identified tasks and provides them to employees. For example, it uses Google Apps Script (GAS) for creating email formats, macros for creating Excel macros, and the generation AI for automatically creating PowerPoint presentations. Furthermore, the business automation system automatically verifies screen operations and the effects of automation. The generation AI records the operations performed by employees and verifies the effects of automation. For example, it compares the time taken for manually performed tasks with that taken for automated tasks to verify how much time can be saved. It is possible to compare usage time after implementation. The generation AI compares the time taken for manually performed tasks with that taken for automated tasks and shows how much time can be saved. This allows employees to confirm the effects of automation. This mechanism improves employee work efficiency and promotes business automation. For example, it is effective in various departments such as accounting and clerical work, call centers, sales progress management, review-related departments, and work content management for remote employees.In terms of user experience, users simply activate Business Optimize AI at the start of their workday and continue with their usual tasks. At the end of the workday, the system provides formulas, automation, and generative AI, demonstrating the time savings achieved through increased efficiency. It also generates a demo video showing the process, and the content can be made more committed by extending the learning period. This allows the business automation system to streamline employee tasks and verify the effectiveness of the automation.

[0029] The business automation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a verification unit, and a comparison unit. The collection unit checks the activity of employees' PC work. The collection unit records in detail the content of the work performed by the employee, for example. For example, the collection unit records the content of work such as checking and replying to emails, creating Excel documents, and creating PowerPoint presentations. The collection unit can understand the activity of employees' work. The analysis unit analyzes the activity of work collected by the collection unit and identifies tasks that can be automatically generated. For example, the analysis unit analyzes the recorded activity of work and identifies which tasks can be automated. For example, the analysis unit identifies tasks such as creating email formats, creating Excel macros, and automatically creating PowerPoint presentations as tasks that can be automated. The generation unit automatically generates and provides tools such as GAS or macros based on the tasks identified by the analysis unit. For example, the generation unit automatically generates the most suitable tool for the identified task and provides it to the employee. For example, the generation unit uses GAS for creating email formats, macros for creating Excel macros, and generation AI for automatically creating PowerPoint presentations. The verification unit automatically verifies the effectiveness of the tools generated by the generation unit. The verification unit, for example, records the operations performed by employees and verifies the effectiveness of automation. For example, the verification unit compares the time taken for manually performed tasks with the time taken for automated tasks to verify how much time can be saved. The comparison unit compares the usage time if the system is implemented based on the effectiveness verified by the verification unit. For example, the comparison unit compares the time taken for manually performed tasks with the time taken for automated tasks to show how much time can be saved. As a result, the business automation system according to the embodiment can streamline employees' work and confirm the effectiveness of automation.

[0030] The data collection unit monitors employees' PC work activities. Specifically, the unit uses keyloggers and screen capture software to record in detail the tasks performed by employees. This allows for accurate tracking of which applications employees are using, which files they are working with, and what operations they are performing. For example, the unit records the actions employees take when checking and replying to emails, and understands the content of emails being sent and received. It also records in detail what data is entered and what calculations are performed when creating Excel documents. For PowerPoint presentations, it records the slide structure, design, and content editing. This allows the data collection unit to accurately understand employee work patterns and provide foundational data for determining which tasks can be automated. Furthermore, the data collection unit centrally manages the collected data, making it accessible to the analysis and generation units. By adjusting the frequency and accuracy of data collection, the data collection unit can flexibly adapt to specific tasks and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the workflow data collected by the data collection unit to identify tasks that can be automated. Specifically, the analysis unit uses AI to analyze the collected data and determine which tasks can be automated. For example, the analysis unit analyzes email confirmation and reply operations and determines that the creation of standardized email formats can be automated. In the case of Excel document creation, it analyzes data input and calculation patterns and determines that automation using macros is possible. In the case of PowerPoint creation, it analyzes slide structure and design patterns and determines that automatic creation using generation AI is possible. The analysis unit can process data in real time using AI and analyze workflows quickly and accurately. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term workflow trends and evaluate the possibility of future automation. For example, based on historical workflow data, it can analyze the frequency and time of specific tasks to determine automation priorities. In addition, the analysis unit can use anomaly detection algorithms to detect unusual workflow patterns and abnormal data, and issue warnings early. This allows the analysis unit to handle not only real-time business analysis but also long-term business management and anomaly detection, thereby improving the reliability and efficiency of the entire system.

[0032] The generation unit automatically generates and provides tools such as GAS and macros based on the tasks identified by the analysis unit. Specifically, the generation unit uses AI to generate code to automatically create the most suitable tools for the identified tasks. For example, for email formatting, it uses scripts to automatically generate standard email templates. For Excel macro creation, it uses VBA (Visual Basic for Applications) to generate macros that automate data entry and calculations. For automatic PowerPoint creation, it uses generation AI to automatically generate slide structure and design. The generation unit provides these tools to employees to improve work efficiency. Furthermore, the generation unit can customize and update the generated tools. For example, it can add or improve tool functions based on employee feedback. The generation unit also monitors the usage of the generated tools and performs maintenance as needed. In this way, the generation unit can improve the efficiency of employee work and enhance the overall system performance.

[0033] The verification unit automatically verifies the effectiveness of the tools generated by the generation unit. Specifically, the verification unit records the operations performed by employees and uses AI to analyze the data in order to verify the effectiveness of automation. For example, the verification unit compares the time taken for manually performed tasks with that taken for automated tasks to verify how much time can be saved. It also verifies the accuracy and quality of the tasks and evaluates how much improvement can be seen through automation. Based on this data, the verification unit can quantitatively evaluate the effectiveness of automation and identify areas for improvement in the system. Furthermore, the verification unit monitors the usage of the generated tools and collects employee feedback. This allows the verification unit to continuously evaluate the effectiveness of the tools and provide information to improve the overall performance of the system.

[0034] The comparison unit compares the usage time after implementation based on the effects verified by the verification unit. Specifically, the comparison unit compares the time spent on manually performed tasks with that of automated tasks and visualizes the data to show how much time can be saved. For example, it uses graphs and charts to clearly show the time-saving effect on tasks. The comparison unit also evaluates the improvement in accuracy and quality of tasks and shows how much improvement is seen. In this way, the comparison unit can quantitatively evaluate the effects of automation and clearly demonstrate the benefits of implementation. Furthermore, the comparison unit can perform comparisons according to different tasks and conditions and propose the optimal automation method. For example, it can compare which tool is most effective for a specific task or condition and propose the optimal automation method. In this way, the comparison unit can provide information to improve the overall performance of the system and streamline employees' work.

[0035] The data collection unit can record in detail the tasks performed by employees. The data collection unit records tasks by methods such as acquiring operation logs and saving screenshots. For example, the data collection unit can record operations performed by employees in real time so that they can be reviewed later. The data collection unit can also periodically back up the tasks to prevent data loss. This allows the data collection unit to accurately understand the flow of work by recording the tasks performed by employees in detail. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee operation logs into AI and have the AI ​​perform the task recording.

[0036] The analysis unit can analyze recorded work content and identify tasks that can be automated. For example, the analysis unit analyzes recorded work content and determines which tasks can be automated. For example, the analysis unit identifies tasks based on criteria such as the presence or absence of repetitive tasks and rule-based tasks. For example, the analysis unit identifies tasks such as creating email formats, creating Excel macros, and automatically creating PowerPoint presentations as tasks that can be automated. In this way, the analysis unit can advance the automation of tasks by analyzing recorded work content and identifying tasks that can be automated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input recorded work content into AI and have the AI ​​perform the task of identifying tasks that can be automated.

[0037] The generation unit can automatically generate and provide the most suitable tools for the identified tasks. For example, the generation unit can automatically generate the most suitable tools for the identified tasks and provide them to employees. For example, the generation unit uses GAS for creating email formats, macros for creating Excel macros, and generation AI for automatically creating PowerPoint presentations. In this way, the generation unit can improve work efficiency by automatically generating and providing the most suitable tools for the identified tasks. Some or all of the above-described processes in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can input the identified tasks into the generation AI and have the generation AI generate the most suitable tools.

[0038] The verification unit can compare the time taken for manually performed tasks with that taken for automated tasks to verify their effectiveness. For example, the verification unit can compare the time taken for manually performed tasks with that taken for automated tasks to verify how much time can be saved. For example, the verification unit can record the time taken for manually performed tasks and compare it with the time taken for automated tasks. The verification unit can also verify other effects, such as comparing error rates. In this way, the verification unit can confirm the effectiveness of automation by comparing the time taken for manually performed tasks with that for automated tasks and verifying their effectiveness. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the time taken for manually performed tasks and automated tasks into the AI ​​and have the AI ​​perform the effectiveness verification.

[0039] The comparison unit can compare the time taken for manually performed tasks with the time taken for automated tasks and show how much time can be saved. For example, the comparison unit can compare manual work time and automated work time to clarify specific measurement methods and criteria for time savings. This allows the comparison unit to visually confirm the effectiveness of automation by comparing the time taken for manually performed tasks with automated tasks and showing how much time can be saved. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the time taken for manually performed tasks and automated tasks into AI and have the AI ​​perform the time savings comparison.

[0040] The data collection unit can analyze an employee's past work history and select the optimal recording method. For example, the data collection unit prioritizes recording tasks that the employee has frequently performed in the past. For example, the data collection unit can suggest efficient recording methods based on the employee's past work history. For example, the data collection unit analyzes an employee's past work history and optimizes the timing of recording. In this way, the data collection unit streamlines the recording of work content by analyzing an employee's past work history and selecting the optimal recording method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input an employee's past work history into AI and have the AI ​​select the optimal recording method.

[0041] The data collection unit can filter the recorded work content based on the employee's current projects and areas of interest. For example, the data collection unit prioritizes recording work content related to ongoing projects. For example, the data collection unit filters and records work content related to the employee's areas of interest. For example, the data collection unit adjusts the recorded work content according to the progress of projects. This allows the data collection unit to prioritize recording important work content by filtering it 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 using AI. For example, the data collection unit can input employee project information into AI and have the AI ​​perform the filtering of work content.

[0042] The data collection unit can prioritize recording highly relevant tasks by considering the employee's geographical location when recording work content. For example, if an employee is in a specific location, the data collection unit will prioritize recording tasks related to that location. For example, the data collection unit will record highly relevant tasks based on the employee's travel history. For example, the data collection unit will record the most relevant tasks based on the employee's current location. This makes the recording of work content more efficient by allowing the data collection unit to prioritize recording highly relevant tasks by considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into AI and have the AI ​​perform the recording of work content.

[0043] The data collection unit can analyze employees' social media activity and record relevant tasks when recording work content. For example, the data collection unit can analyze the content of employees' social media posts and record relevant work content. For example, the data collection unit can filter and record work content based on social media activity history. For example, the data collection unit can record relevant work content considering social media trends. This makes the recording of work content more efficient by allowing the data collection unit to analyze employees' social media activity and record relevant tasks. Some or all of the above processes 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 activity data into AI and have the AI ​​perform the recording of work content.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the tasks during the analysis. For example, the analysis unit performs a detailed analysis for tasks with high importance. For example, the analysis unit performs a simplified analysis for tasks with low importance. The analysis unit adjusts the depth of the analysis according to the importance of the tasks. In this way, the analysis unit can perform a detailed analysis of important tasks by adjusting the level of detail of the analysis based on the importance of the tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of business during analysis. For example, the analysis unit can apply a specific accounting analysis algorithm to accounting operations. For example, the analysis unit can apply a specific sales analysis algorithm to sales operations. For example, the analysis unit can apply a specific technical analysis algorithm to technical operations. In this way, the analysis unit can perform optimal analysis according to the content of the business by applying different analysis algorithms depending on the category of business. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input business category data into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of task implementation. For example, the analysis unit prioritizes analyzing tasks that are urgent. For example, the analysis unit prioritizes analyzing tasks that are due soon. For example, the analysis unit postpones analyzing tasks that have ample time before implementation. In this way, the analysis unit can prioritize the analysis of urgent tasks by determining the priority of analysis based on the timing of task implementation. 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 task implementation timing data into AI and have the AI ​​perform the determination of analysis priorities.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the tasks during the analysis. For example, the analysis unit will prioritize the analysis of tasks that are highly relevant. For example, the analysis unit will postpone the analysis of tasks that are less relevant. The analysis unit adjusts the order of analysis according to the relevance of the tasks. In this way, the analysis unit can prioritize the analysis of tasks that are highly relevant by adjusting the order of analysis based on the relevance of the tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input task relevance data into AI and have AI perform the adjustment of the order of analysis.

[0048] The generation unit can adjust the level of detail of the tools it generates based on the importance of the task during generation. For example, the generation unit generates detailed tools for high-importance tasks. For example, it generates simplified tools for low-importance tasks. The generation unit adjusts the level of detail of the tools according to the importance of the task. In this way, the generation unit can provide tools suitable for important tasks by adjusting the level of detail of the tools it generates based on the importance of the task. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input task importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the tools.

[0049] The generation unit can apply different generation algorithms depending on the category of business during generation. For example, the generation unit can apply a specific accounting generation algorithm to accounting tasks. For example, the generation unit can apply a specific sales generation algorithm to sales tasks. For example, the generation unit can apply a specific technology generation algorithm to technical tasks. In this way, the generation unit can provide tools suitable for the business by applying the optimal generation algorithm according to the category of business. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input business category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0050] The generation unit can determine the priority of the tools to be generated based on the timing of the tasks at the time of generation. For example, the generation unit will prioritize generating tools for tasks that are urgent. For example, the generation unit will prioritize generating tools for tasks that are due soon. For example, the generation unit will postpone generating tools for tasks that have ample time before implementation. In this way, the generation unit can provide tools suitable for urgent tasks by determining the priority of the tools to be generated based on the timing of the tasks. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input task implementation timing data into the generation AI and have the generation AI perform the task of determining the priority of the tools.

[0051] The generation unit can adjust the order in which it generates tools based on the relevance of the tasks during generation. For example, the generation unit can prioritize generating tools for highly relevant tasks. For example, it can postpone generating tools for less relevant tasks. The generation unit can adjust the order in which it generates tools according to the relevance of the tasks. In this way, the generation unit can provide tools suitable for highly relevant tasks by adjusting the order in which it generates tools based on the relevance of the tasks. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input task relevance data into a generation AI and have the generation AI perform the adjustment of the tool order.

[0052] The verification unit can adjust the level of detail of the verification based on the importance of the task during the verification process. For example, the verification unit performs detailed verification for tasks of high importance. For example, the verification unit performs simplified verification for tasks of low importance. The verification unit adjusts the depth of verification according to the importance of the task. In this way, the verification unit can perform detailed verification of important tasks by adjusting the level of detail of the verification based on the importance of the task. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input task importance data into AI and have AI perform the adjustment of the level of detail of the verification.

[0053] The verification unit can apply different verification algorithms depending on the category of work during verification. For example, the verification unit can apply a specific accounting verification algorithm to accounting work. For example, the verification unit can apply a specific sales verification algorithm to sales work. For example, the verification unit can apply a specific technical verification algorithm to technical work. In this way, the verification unit can perform verification appropriate to the work by applying the most suitable verification algorithm according to the category of work. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input work category data into AI and have AI execute the application of verification algorithms.

[0054] The verification unit can determine the priority of verification based on the timing of the tasks to be performed during the verification process. For example, the verification unit may prioritize verification of tasks that are urgent. For example, the verification unit may prioritize verification of tasks that are due soon. For example, the verification unit may postpone verification of tasks that have ample time to be performed. In this way, by determining the priority of verification based on the timing of the tasks to be performed, the verification unit can prioritize verification of urgent tasks. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input task timing data into AI and have AI perform the determination of verification priorities.

[0055] The verification unit can adjust the order of verification based on the relevance of the tasks during verification. For example, the verification unit will prioritize verification of highly relevant tasks. For example, the verification unit will postpone verification of less relevant tasks. The verification unit adjusts the order of verification according to the relevance of the tasks. In this way, the verification unit can prioritize the verification of highly relevant tasks by adjusting the order of verification based on the relevance of the tasks. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input task relevance data into AI and have AI perform the adjustment of the order of verification.

[0056] The comparison unit can adjust the level of detail of the comparison based on the importance of the tasks during the comparison process. For example, the comparison unit performs a detailed comparison for tasks of high importance. For example, the comparison unit performs a simplified comparison for tasks of low importance. The comparison unit adjusts the depth of the comparison according to the importance of the tasks. In this way, the comparison unit can perform a detailed comparison of important tasks by adjusting the level of detail of the comparison based on the importance of the tasks. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail of the comparison.

[0057] The comparison unit can apply different comparison algorithms depending on the category of business during the comparison process. For example, the comparison unit can apply a specific accounting comparison algorithm to accounting operations. For example, the comparison unit can apply a specific sales comparison algorithm to sales operations. For example, the comparison unit can apply a specific technology comparison algorithm to technical operations. This allows the comparison unit to perform comparisons appropriate to the business by applying the most suitable comparison algorithm according to the category of business. Some or all of the above-described processes in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input business category data into AI and have AI perform the application of comparison algorithms.

[0058] The comparison unit can determine the priority of comparisons based on the timing of task implementation. For example, the comparison unit prioritizes comparisons for urgent tasks. For example, it prioritizes comparisons for tasks with an upcoming implementation date. For example, it postpones comparisons for tasks with ample time for implementation. In this way, the comparison unit can prioritize comparisons for urgent tasks by determining the priority of comparisons based on the timing of task implementation. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input task implementation date data into AI and have the AI ​​perform the determination of comparison priorities.

[0059] The comparison unit can adjust the order of comparisons based on the relevance of the tasks during the comparison process. For example, the comparison unit prioritizes comparing tasks that are highly relevant. For example, it postpones comparing tasks that are less relevant. The comparison unit adjusts the order of comparisons according to the relevance of the tasks. In this way, the comparison unit can prioritize the comparison of tasks that are highly relevant by adjusting the order of comparisons based on the relevance of the tasks. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input task relevance data into AI and have AI perform the adjustment of the comparison order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The collection unit can collect audio data of an employee's work environment when monitoring their PC work activities and provide it to the analysis unit. The analysis unit analyzes the collected audio data to understand the type and status of the tasks the employee is performing. For example, it can determine that an employee is performing telephone answering tasks from audio data of a phone call. The generation unit can generate and provide speech recognition tools and automated response tools based on the tasks identified by the analysis unit. The verification unit can use the audio data to evaluate the accuracy and response speed of the generated tools when verifying their effectiveness. The comparison unit can compare audio data of manually performed tasks and automated tasks to show how much time can be saved.

[0062] The data collection unit can collect environmental data, such as temperature and humidity, of employees' work environments when monitoring their PC work activities and provide it to the analysis unit. The analysis unit analyzes the collected environmental data to understand the comfort level of the employees' work environment. For example, it can determine whether the environment is comfortable for employees to work in based on changes in temperature and humidity. The generation unit can generate and provide environmental adjustment tools and comfort improvement tools based on the tasks identified by the analysis unit. The verification unit can evaluate the effectiveness of the generated tools using environmental data when verifying their effectiveness. The comparison unit can compare the environmental data of manually performed tasks with automated tasks to show how much the comfort level has improved.

[0063] The data collection unit can collect posture data of employees while they are working on their PCs and provide it to the analysis unit. The analysis unit analyzes the collected posture data to determine whether the employees have good or bad posture. For example, it can identify deterioration of posture due to prolonged sitting. The generation unit can generate and provide posture improvement tools and ergonomic tools based on the tasks identified by the analysis unit. The verification unit can use posture data to evaluate the effectiveness of the generated tools. The comparison unit can compare posture data from manually performed tasks and automated tasks to show how much posture improvement has been achieved.

[0064] The collection unit can collect eye-tracking data from employees while they are working on their PCs and provide it to the analysis unit. The analysis unit analyzes the collected eye-tracking data to understand which parts of the screen or document the employee is focusing on. For example, it can determine which screen or document the employee is concentrating on based on their eye movements. The generation unit can generate and provide eye-tracking tools and concentration-enhancing tools based on the tasks identified by the analysis unit. The verification unit can use the eye-tracking data to evaluate the effectiveness of the generated tools. The comparison unit can compare eye-tracking data from manually performed tasks with automated tasks to show how much the level of concentration has improved.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection department monitors and meticulously records employees' PC work activities. For example, the data collection department records tasks such as checking and replying to emails, creating Excel documents, and creating PowerPoint presentations to understand employees' work processes. Step 2: The analysis unit analyzes the workflow data collected by the data collection unit and identifies tasks that can be automatically generated. For example, the analysis unit analyzes the recorded workflow data and identifies tasks such as creating email templates, creating Excel macros, and automatically generating PowerPoint presentations as tasks that can be automated. Step 3: The generation unit automatically generates and provides tools such as GAS and macros based on the tasks identified by the analysis unit. For example, the generation unit uses GAS for creating email formats, macros for creating Excel macros, and generation AI for automatically creating PowerPoint presentations. Step 4: The verification unit automatically verifies the effectiveness of the tools generated by the generation unit. For example, the verification unit records the operations performed by employees, compares the time taken for manually performed tasks with that taken for automated tasks, and verifies how much time can be saved. Step 5: The comparison unit compares the usage time after implementation based on the effects verified by the verification unit. For example, it compares the time spent on manually performed tasks with the time spent on automated tasks to show how much time can be saved.

[0067] (Example of form 2) The business automation system according to an embodiment of the present invention is a system that checks the activity of employees' PC work, identifies tasks that can be automatically generated, and creates and provides the necessary tools. This business automation system checks the activity of employees' PC work and automatically identifies tasks using a generation AI. Next, based on the identified tasks, it automatically generates and provides tools such as GAS and macros. Furthermore, it can automatically verify the screen operation content and the effect of automation, and compare the usage time if implemented. For example, the business automation system checks the activity of employees' PC work. At this time, it records in detail what kind of work the employee is doing. For example, it records tasks such as checking and replying to emails, creating Excel documents, and creating PowerPoint presentations. This allows the system to understand the activity of employees. Next, the business automation system uses a generation AI to analyze the recorded activity of tasks and identify tasks that can be automatically generated. The generation AI analyzes the content of the tasks and determines which tasks can be automated. For example, tasks such as creating email formats, creating Excel macros, and automatically creating PowerPoint presentations are identified as tasks that can be automated. Based on the identified tasks, the business automation system automatically generates and provides tools such as GAS and macros. The generation AI automatically generates the most suitable tools for identified tasks and provides them to employees. For example, it uses Google Apps Script (GAS) for creating email formats, macros for creating Excel macros, and the generation AI for automatically creating PowerPoint presentations. Furthermore, the business automation system automatically verifies screen operations and the effects of automation. The generation AI records the operations performed by employees and verifies the effects of automation. For example, it compares the time taken for manually performed tasks with that taken for automated tasks to verify how much time can be saved. It is possible to compare usage time after implementation. The generation AI compares the time taken for manually performed tasks with that taken for automated tasks and shows how much time can be saved. This allows employees to confirm the effects of automation. This mechanism improves employee work efficiency and promotes business automation. For example, it is effective in various departments such as accounting and clerical work, call centers, sales progress management, review-related departments, and work content management for remote employees.In terms of user experience, users simply activate Business Optimize AI at the start of their workday and continue with their usual tasks. At the end of the workday, the system provides formulas, automation, and generative AI, demonstrating the time savings achieved through increased efficiency. It also generates a demo video showing the process, and the content can be made more committed by extending the learning period. This allows the business automation system to streamline employee tasks and verify the effectiveness of the automation.

[0068] The business automation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a verification unit, and a comparison unit. The collection unit checks the activity of employees' PC work. The collection unit records in detail the content of the work performed by the employee, for example. For example, the collection unit records the content of work such as checking and replying to emails, creating Excel documents, and creating PowerPoint presentations. The collection unit can understand the activity of employees' work. The analysis unit analyzes the activity of work collected by the collection unit and identifies tasks that can be automatically generated. For example, the analysis unit analyzes the recorded activity of work and identifies which tasks can be automated. For example, the analysis unit identifies tasks such as creating email formats, creating Excel macros, and automatically creating PowerPoint presentations as tasks that can be automated. The generation unit automatically generates and provides tools such as GAS or macros based on the tasks identified by the analysis unit. For example, the generation unit automatically generates the most suitable tool for the identified task and provides it to the employee. For example, the generation unit uses GAS for creating email formats, macros for creating Excel macros, and generation AI for automatically creating PowerPoint presentations. The verification unit automatically verifies the effectiveness of the tools generated by the generation unit. The verification unit, for example, records the operations performed by employees and verifies the effectiveness of automation. For example, the verification unit compares the time taken for manually performed tasks with the time taken for automated tasks to verify how much time can be saved. The comparison unit compares the usage time if the system is implemented based on the effectiveness verified by the verification unit. For example, the comparison unit compares the time taken for manually performed tasks with the time taken for automated tasks to show how much time can be saved. As a result, the business automation system according to the embodiment can streamline employees' work and confirm the effectiveness of automation.

[0069] The data collection unit monitors employees' PC work activities. Specifically, the unit uses keyloggers and screen capture software to record in detail the tasks performed by employees. This allows for accurate tracking of which applications employees are using, which files they are working with, and what operations they are performing. For example, the unit records the actions employees take when checking and replying to emails, and understands the content of emails being sent and received. It also records in detail what data is entered and what calculations are performed when creating Excel documents. For PowerPoint presentations, it records the slide structure, design, and content editing. This allows the data collection unit to accurately understand employee work patterns and provide foundational data for determining which tasks can be automated. Furthermore, the data collection unit centrally manages the collected data, making it accessible to the analysis and generation units. By adjusting the frequency and accuracy of data collection, the data collection unit can flexibly adapt to specific tasks and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0070] The analysis unit analyzes the workflow data collected by the data collection unit to identify tasks that can be automated. Specifically, the analysis unit uses AI to analyze the collected data and determine which tasks can be automated. For example, the analysis unit analyzes email confirmation and reply operations and determines that the creation of standardized email formats can be automated. In the case of Excel document creation, it analyzes data input and calculation patterns and determines that automation using macros is possible. In the case of PowerPoint creation, it analyzes slide structure and design patterns and determines that automatic creation using generation AI is possible. The analysis unit can process data in real time using AI and analyze workflows quickly and accurately. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term workflow trends and evaluate the possibility of future automation. For example, based on historical workflow data, it can analyze the frequency and time of specific tasks to determine automation priorities. In addition, the analysis unit can use anomaly detection algorithms to detect unusual workflow patterns and abnormal data, and issue warnings early. This allows the analysis unit to handle not only real-time business analysis but also long-term business management and anomaly detection, thereby improving the reliability and efficiency of the entire system.

[0071] The generation unit automatically generates and provides tools such as GAS and macros based on the tasks identified by the analysis unit. Specifically, the generation unit uses AI to generate code to automatically create the most suitable tools for the identified tasks. For example, for email formatting, it uses scripts to automatically generate standard email templates. For Excel macro creation, it uses VBA (Visual Basic for Applications) to generate macros that automate data entry and calculations. For automatic PowerPoint creation, it uses generation AI to automatically generate slide structure and design. The generation unit provides these tools to employees to improve work efficiency. Furthermore, the generation unit can customize and update the generated tools. For example, it can add or improve tool functions based on employee feedback. The generation unit also monitors the usage of the generated tools and performs maintenance as needed. In this way, the generation unit can improve the efficiency of employee work and enhance the overall system performance.

[0072] The verification unit automatically verifies the effectiveness of the tools generated by the generation unit. Specifically, the verification unit records the operations performed by employees and uses AI to analyze the data in order to verify the effectiveness of automation. For example, the verification unit compares the time taken for manually performed tasks with that taken for automated tasks to verify how much time can be saved. It also verifies the accuracy and quality of the tasks and evaluates how much improvement can be seen through automation. Based on this data, the verification unit can quantitatively evaluate the effectiveness of automation and identify areas for improvement in the system. Furthermore, the verification unit monitors the usage of the generated tools and collects employee feedback. This allows the verification unit to continuously evaluate the effectiveness of the tools and provide information to improve the overall performance of the system.

[0073] The comparison unit compares the usage time after implementation based on the effects verified by the verification unit. Specifically, the comparison unit compares the time spent on manually performed tasks with that of automated tasks and visualizes the data to show how much time can be saved. For example, it uses graphs and charts to clearly show the time-saving effect on tasks. The comparison unit also evaluates the improvement in accuracy and quality of tasks and shows how much improvement is seen. In this way, the comparison unit can quantitatively evaluate the effects of automation and clearly demonstrate the benefits of implementation. Furthermore, the comparison unit can perform comparisons according to different tasks and conditions and propose the optimal automation method. For example, it can compare which tool is most effective for a specific task or condition and propose the optimal automation method. In this way, the comparison unit can provide information to improve the overall performance of the system and streamline employees' work.

[0074] The data collection unit can record in detail the tasks performed by employees. The data collection unit records tasks by methods such as acquiring operation logs and saving screenshots. For example, the data collection unit can record operations performed by employees in real time so that they can be reviewed later. The data collection unit can also periodically back up the tasks to prevent data loss. This allows the data collection unit to accurately understand the flow of work by recording the tasks performed by employees in detail. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee operation logs into AI and have the AI ​​perform the task recording.

[0075] The analysis unit can analyze recorded work content and identify tasks that can be automated. For example, the analysis unit analyzes recorded work content and determines which tasks can be automated. For example, the analysis unit identifies tasks based on criteria such as the presence or absence of repetitive tasks and rule-based tasks. For example, the analysis unit identifies tasks such as creating email formats, creating Excel macros, and automatically creating PowerPoint presentations as tasks that can be automated. In this way, the analysis unit can advance the automation of tasks by analyzing recorded work content and identifying tasks that can be automated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input recorded work content into AI and have the AI ​​perform the task of identifying tasks that can be automated.

[0076] The generation unit can automatically generate and provide the most suitable tools for the identified tasks. For example, the generation unit can automatically generate the most suitable tools for the identified tasks and provide them to employees. For example, the generation unit uses GAS for creating email formats, macros for creating Excel macros, and generation AI for automatically creating PowerPoint presentations. In this way, the generation unit can improve work efficiency by automatically generating and providing the most suitable tools for the identified tasks. Some or all of the above-described processes in the generation unit may be performed using, for example, generation AI, or without generation AI. For example, the generation unit can input the identified tasks into the generation AI and have the generation AI generate the most suitable tools.

[0077] The verification unit can compare the time taken for manually performed tasks with that taken for automated tasks to verify their effectiveness. For example, the verification unit can compare the time taken for manually performed tasks with that taken for automated tasks to verify how much time can be saved. For example, the verification unit can record the time taken for manually performed tasks and compare it with the time taken for automated tasks. The verification unit can also verify other effects, such as comparing error rates. In this way, the verification unit can confirm the effectiveness of automation by comparing the time taken for manually performed tasks with that for automated tasks and verifying their effectiveness. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the time taken for manually performed tasks and automated tasks into the AI ​​and have the AI ​​perform the effectiveness verification.

[0078] The comparison unit can compare the time taken for manually performed tasks with the time taken for automated tasks and show how much time can be saved. For example, the comparison unit can compare manual work time and automated work time to clarify specific measurement methods and criteria for time savings. This allows the comparison unit to visually confirm the effectiveness of automation by comparing the time taken for manually performed tasks with automated tasks and showing how much time can be saved. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the time taken for manually performed tasks and automated tasks into AI and have the AI ​​perform the time savings comparison.

[0079] The data collection unit can estimate an employee's emotions and adjust the frequency of recording work content based on the estimated emotions. For example, if an employee is stressed, the data collection unit can reduce the recording frequency to allow them to concentrate on their work. For example, if an employee is relaxed, the data collection unit can record detailed work content frequently. For example, if an employee is busy, the data collection unit can record only important work content. In this way, the data collection unit can reduce the workload by adjusting the frequency of recording work content according to the employee's emotions. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input employee emotion data into AI and have the AI ​​adjust the recording frequency.

[0080] The data collection unit can analyze an employee's past work history and select the optimal recording method. For example, the data collection unit prioritizes recording tasks that the employee has frequently performed in the past. For example, the data collection unit can suggest efficient recording methods based on the employee's past work history. For example, the data collection unit analyzes an employee's past work history and optimizes the timing of recording. In this way, the data collection unit streamlines the recording of work content by analyzing an employee's past work history and selecting the optimal recording method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input an employee's past work history into AI and have the AI ​​select the optimal recording method.

[0081] The data collection unit can filter the recorded work content based on the employee's current projects and areas of interest. For example, the data collection unit prioritizes recording work content related to ongoing projects. For example, the data collection unit filters and records work content related to the employee's areas of interest. For example, the data collection unit adjusts the recorded work content according to the progress of projects. This allows the data collection unit to prioritize recording important work content by filtering it 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 using AI. For example, the data collection unit can input employee project information into AI and have the AI ​​perform the filtering of work content.

[0082] The data collection unit can estimate an employee's emotions and determine the priority of tasks to record based on the estimated emotions. For example, if an employee is stressed, the data collection unit will postpone less important tasks. For example, if an employee is relaxed, the data collection unit will prioritize recording detailed tasks. For example, if an employee is busy, the data collection unit will prioritize recording only important tasks. This allows the data collection unit to improve work efficiency by prioritizing tasks 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 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 data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into an AI and have the AI ​​determine the priority of tasks.

[0083] The data collection unit can prioritize recording highly relevant tasks by considering the employee's geographical location when recording work content. For example, if an employee is in a specific location, the data collection unit will prioritize recording tasks related to that location. For example, the data collection unit will record highly relevant tasks based on the employee's travel history. For example, the data collection unit will record the most relevant tasks based on the employee's current location. This makes the recording of work content more efficient by allowing the data collection unit to prioritize recording highly relevant tasks by considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into AI and have the AI ​​perform the recording of work content.

[0084] The data collection unit can analyze employees' social media activity and record relevant tasks when recording work content. For example, the data collection unit can analyze the content of employees' social media posts and record relevant work content. For example, the data collection unit can filter and record work content based on social media activity history. For example, the data collection unit can record relevant work content considering social media trends. This makes the recording of work content more efficient by allowing the data collection unit to analyze employees' social media activity and record relevant tasks. Some or all of the above processes 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 activity data into AI and have the AI ​​perform the recording of work content.

[0085] The analysis unit can estimate the emotions of employees and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if an employee is relaxed, the analysis unit provides a detailed analysis result. For example, if an employee is busy, the analysis unit provides a concise analysis result. In this way, the analysis unit deepens the understanding of the analysis results by adjusting the presentation of the analysis according to the emotions of the employees. 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 AI and have the AI ​​adjust the presentation of the analysis.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the tasks during the analysis. For example, the analysis unit performs a detailed analysis for tasks with high importance. For example, the analysis unit performs a simplified analysis for tasks with low importance. The analysis unit adjusts the depth of the analysis according to the importance of the tasks. In this way, the analysis unit can perform a detailed analysis of important tasks by adjusting the level of detail of the analysis based on the importance of the tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of business during analysis. For example, the analysis unit can apply a specific accounting analysis algorithm to accounting operations. For example, the analysis unit can apply a specific sales analysis algorithm to sales operations. For example, the analysis unit can apply a specific technical analysis algorithm to technical operations. In this way, the analysis unit can perform optimal analysis according to the content of the business by applying different analysis algorithms depending on the category of business. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input business category data into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0088] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the employee is stressed, the analysis unit will perform a short, concise analysis. If the employee is relaxed, the analysis unit will perform a detailed analysis. If the employee is busy, the analysis unit will perform a brief analysis. This allows the analysis unit to deepen its understanding of the analysis results by adjusting the length of the analysis 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 analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the timing of task implementation. For example, the analysis unit prioritizes analyzing tasks that are urgent. For example, the analysis unit prioritizes analyzing tasks that are due soon. For example, the analysis unit postpones analyzing tasks that have ample time before implementation. In this way, the analysis unit can prioritize the analysis of urgent tasks by determining the priority of analysis based on the timing of task implementation. 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 task implementation timing data into AI and have the AI ​​perform the determination of analysis priorities.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the tasks during the analysis. For example, the analysis unit will prioritize the analysis of tasks that are highly relevant. For example, the analysis unit will postpone the analysis of tasks that are less relevant. The analysis unit adjusts the order of analysis according to the relevance of the tasks. In this way, the analysis unit can prioritize the analysis of tasks that are highly relevant by adjusting the order of analysis based on the relevance of the tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input task relevance data into AI and have AI perform the adjustment of the order of analysis.

[0091] The generation unit can estimate an employee's emotions and adjust the presentation of the generated tools based on the estimated emotions. For example, if an employee is stressed, the generation unit generates a simple and easy-to-use tool. If an employee is relaxed, the generation unit generates a tool with detailed features. If an employee is busy, the generation unit generates a tool that can be used quickly. In this way, the generation unit improves the usability of the tools by adjusting the presentation of the generated tools according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input employee emotion data into a generation AI and have the generation AI adjust the presentation of the tools.

[0092] The generation unit can adjust the level of detail of the tools it generates based on the importance of the task during generation. For example, the generation unit generates detailed tools for high-importance tasks. For example, it generates simplified tools for low-importance tasks. The generation unit adjusts the level of detail of the tools according to the importance of the task. In this way, the generation unit can provide tools suitable for important tasks by adjusting the level of detail of the tools it generates based on the importance of the task. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input task importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the tools.

[0093] The generation unit can apply different generation algorithms depending on the category of business during generation. For example, the generation unit can apply a specific accounting generation algorithm to accounting tasks. For example, the generation unit can apply a specific sales generation algorithm to sales tasks. For example, the generation unit can apply a specific technology generation algorithm to technical tasks. In this way, the generation unit can provide tools suitable for the business by applying the optimal generation algorithm according to the category of business. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input business category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0094] The generation unit can estimate an employee's emotions and adjust the length of the tools it generates based on the estimated emotions. For example, if an employee is stressed, the generation unit generates a short, concise tool. If an employee is relaxed, the generation unit generates a tool with detailed explanations. If an employee is busy, the generation unit generates a tool that can be used quickly. This improves the usability of the tools by adjusting the length of the tools generated according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input employee emotion data into a generation AI and have the generation AI adjust the length of the tools.

[0095] The generation unit can determine the priority of the tools to be generated based on the timing of the tasks at the time of generation. For example, the generation unit will prioritize generating tools for tasks that are urgent. For example, the generation unit will prioritize generating tools for tasks that are due soon. For example, the generation unit will postpone generating tools for tasks that have ample time before implementation. In this way, the generation unit can provide tools suitable for urgent tasks by determining the priority of the tools to be generated based on the timing of the tasks. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input task implementation timing data into the generation AI and have the generation AI perform the task of determining the priority of the tools.

[0096] The generation unit can adjust the order in which it generates tools based on the relevance of the tasks during generation. For example, the generation unit can prioritize generating tools for highly relevant tasks. For example, it can postpone generating tools for less relevant tasks. The generation unit can adjust the order in which it generates tools according to the relevance of the tasks. In this way, the generation unit can provide tools suitable for highly relevant tasks by adjusting the order in which it generates tools based on the relevance of the tasks. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input task relevance data into a generation AI and have the generation AI perform the adjustment of the tool order.

[0097] The verification unit can estimate the employee's emotions and adjust the presentation of the verification based on the estimated emotions. For example, if the employee is stressed, the verification unit provides simple and easy-to-understand verification results. For example, if the employee is relaxed, the verification unit provides detailed verification results. For example, if the employee is busy, the verification unit provides concise verification results. This allows the verification unit to deepen the understanding of the verification results by adjusting the presentation of the verification according to the employee's emotions. 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 verification unit may be performed using AI or not using AI. For example, the verification unit can input employee emotion data into AI and have the AI ​​adjust the presentation of the verification.

[0098] The verification unit can adjust the level of detail of the verification based on the importance of the task during the verification process. For example, the verification unit performs detailed verification for tasks of high importance. For example, the verification unit performs simplified verification for tasks of low importance. The verification unit adjusts the depth of verification according to the importance of the task. In this way, the verification unit can perform detailed verification of important tasks by adjusting the level of detail of the verification based on the importance of the task. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input task importance data into AI and have AI perform the adjustment of the level of detail of the verification.

[0099] The verification unit can apply different verification algorithms depending on the category of work during verification. For example, the verification unit can apply a specific accounting verification algorithm to accounting work. For example, the verification unit can apply a specific sales verification algorithm to sales work. For example, the verification unit can apply a specific technical verification algorithm to technical work. In this way, the verification unit can perform verification appropriate to the work by applying the most suitable verification algorithm according to the category of work. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input work category data into AI and have AI execute the application of verification algorithms.

[0100] The verification unit can estimate the employee's emotions and adjust the length of the verification based on the estimated emotions. For example, if the employee is stressed, the verification unit will perform a short, concise verification. For example, if the employee is relaxed, the verification unit will perform a detailed verification. For example, if the employee is busy, the verification unit will perform a brief verification. This allows the verification unit to deepen its understanding of the verification results by adjusting the length of the verification 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 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 verification unit may be performed using AI or not using AI. For example, the verification unit can input employee emotion data into AI and have the AI ​​adjust the length of the verification.

[0101] The verification unit can determine the priority of verification based on the timing of the tasks to be performed during the verification process. For example, the verification unit may prioritize verification of tasks that are urgent. For example, the verification unit may prioritize verification of tasks that are due soon. For example, the verification unit may postpone verification of tasks that have ample time to be performed. In this way, by determining the priority of verification based on the timing of the tasks to be performed, the verification unit can prioritize verification of urgent tasks. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input task timing data into AI and have AI perform the determination of verification priorities.

[0102] The verification unit can adjust the order of verification based on the relevance of the tasks during verification. For example, the verification unit will prioritize verification of highly relevant tasks. For example, the verification unit will postpone verification of less relevant tasks. The verification unit adjusts the order of verification according to the relevance of the tasks. In this way, the verification unit can prioritize the verification of highly relevant tasks by adjusting the order of verification based on the relevance of the tasks. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input task relevance data into AI and have AI perform the adjustment of the order of verification.

[0103] The comparison unit can estimate the employee's emotions and adjust the way the comparison is presented based on the estimated employee's emotions. For example, if the employee is stressed, the comparison unit provides a simple and easy-to-understand comparison result. For example, if the employee is relaxed, the comparison unit provides a detailed comparison result. For example, if the employee is busy, the comparison unit provides a concise comparison result. In this way, the comparison unit deepens the understanding of the comparison result by adjusting the way the comparison is presented according to the employee's emotions. 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 comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit can input employee emotion data into AI and have the AI ​​adjust the way the comparison is presented.

[0104] The comparison unit can adjust the level of detail of the comparison based on the importance of the tasks during the comparison process. For example, the comparison unit performs a detailed comparison for tasks of high importance. For example, the comparison unit performs a simplified comparison for tasks of low importance. The comparison unit adjusts the depth of the comparison according to the importance of the tasks. In this way, the comparison unit can perform a detailed comparison of important tasks by adjusting the level of detail of the comparison based on the importance of the tasks. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail of the comparison.

[0105] The comparison unit can apply different comparison algorithms depending on the category of business during the comparison process. For example, the comparison unit can apply a specific accounting comparison algorithm to accounting operations. For example, the comparison unit can apply a specific sales comparison algorithm to sales operations. For example, the comparison unit can apply a specific technology comparison algorithm to technical operations. This allows the comparison unit to perform comparisons appropriate to the business by applying the most suitable comparison algorithm according to the category of business. Some or all of the above-described processes in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input business category data into AI and have AI perform the application of comparison algorithms.

[0106] The comparison unit can estimate the employee's emotions and adjust the length of the comparison based on the estimated emotions. For example, if the employee is stressed, the comparison unit will perform a short, concise comparison. If the employee is relaxed, the comparison unit will perform a detailed comparison. If the employee is busy, the comparison unit will perform a brief comparison. This allows the comparison unit to deepen its understanding of the comparison results by adjusting the length of the comparison 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 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 comparison unit may be performed using AI or not. For example, the comparison unit can input employee emotion data into an AI and have the AI ​​adjust the length of the comparison.

[0107] The comparison unit can determine the priority of comparisons based on the timing of task implementation. For example, the comparison unit prioritizes comparisons for urgent tasks. For example, it prioritizes comparisons for tasks with an upcoming implementation date. For example, it postpones comparisons for tasks with ample time for implementation. In this way, the comparison unit can prioritize comparisons for urgent tasks by determining the priority of comparisons based on the timing of task implementation. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input task implementation date data into AI and have the AI ​​perform the determination of comparison priorities.

[0108] The comparison unit can adjust the order of comparisons based on the relevance of the tasks during the comparison process. For example, the comparison unit prioritizes comparing tasks that are highly relevant. For example, it postpones comparing tasks that are less relevant. The comparison unit adjusts the order of comparisons according to the relevance of the tasks. In this way, the comparison unit can prioritize the comparison of tasks that are highly relevant by adjusting the order of comparisons based on the relevance of the tasks. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input task relevance data into AI and have AI perform the adjustment of the comparison order.

[0109] The comparison unit can estimate the employee's emotions and adjust the length of the comparison based on the estimated emotions. For example, if the employee is stressed, the comparison unit will perform a short, concise comparison. If the employee is relaxed, the comparison unit will perform a detailed comparison. If the employee is busy, the comparison unit will perform a brief comparison. This allows the comparison unit to deepen its understanding of the comparison results by adjusting the length of the comparison 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 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 comparison unit may be performed using AI or not. For example, the comparison unit can input employee emotion data into an AI and have the AI ​​adjust the length of the comparison.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The collection unit can collect audio data of an employee's work environment when monitoring their PC work activities and provide it to the analysis unit. The analysis unit analyzes the collected audio data to understand the type and status of the tasks the employee is performing. For example, it can determine that an employee is performing telephone answering tasks from audio data of a phone call. The generation unit can generate and provide speech recognition tools and automated response tools based on the tasks identified by the analysis unit. The verification unit can use the audio data to evaluate the accuracy and response speed of the generated tools when verifying their effectiveness. The comparison unit can compare audio data of manually performed tasks and automated tasks to show how much time can be saved.

[0112] The data collection unit can collect employee biometric data (heart rate, skin electrical activity, etc.) when monitoring employees' PC work activities and provide it to the analysis unit. The analysis unit analyzes the collected biometric data to understand the employee's stress level and concentration level. For example, it can determine if an employee is experiencing stress from an increase in heart rate or a change in skin electrical activity. The generation unit can generate and provide stress reduction tools and concentration improvement tools based on the tasks identified by the analysis unit. The verification unit can evaluate the effectiveness of the generated tools using biometric data when verifying their effectiveness. The comparison unit can compare biometric data from manually performed tasks with automated tasks to show how much stress has been reduced.

[0113] The data collection unit can collect environmental data, such as temperature and humidity, of employees' work environments when monitoring their PC work activities and provide it to the analysis unit. The analysis unit analyzes the collected environmental data to understand the comfort level of the employees' work environment. For example, it can determine whether the environment is comfortable for employees to work in based on changes in temperature and humidity. The generation unit can generate and provide environmental adjustment tools and comfort improvement tools based on the tasks identified by the analysis unit. The verification unit can evaluate the effectiveness of the generated tools using environmental data when verifying their effectiveness. The comparison unit can compare the environmental data of manually performed tasks with automated tasks to show how much the comfort level has improved.

[0114] The data collection unit can collect posture data of employees while they are working on their PCs and provide it to the analysis unit. The analysis unit analyzes the collected posture data to determine whether the employees have good or bad posture. For example, it can identify deterioration of posture due to prolonged sitting. The generation unit can generate and provide posture improvement tools and ergonomic tools based on the tasks identified by the analysis unit. The verification unit can use posture data to evaluate the effectiveness of the generated tools. The comparison unit can compare posture data from manually performed tasks and automated tasks to show how much posture improvement has been achieved.

[0115] The collection unit can collect eye-tracking data from employees while they are working on their PCs and provide it to the analysis unit. The analysis unit analyzes the collected eye-tracking data to understand which parts of the screen or document the employee is focusing on. For example, it can determine which screen or document the employee is concentrating on based on their eye movements. The generation unit can generate and provide eye-tracking tools and concentration-enhancing tools based on the tasks identified by the analysis unit. The verification unit can use the eye-tracking data to evaluate the effectiveness of the generated tools. The comparison unit can compare eye-tracking data from manually performed tasks with automated tasks to show how much the level of concentration has improved.

[0116] The data collection unit can estimate an employee's emotions and adjust the frequency of recording work content based on the estimated emotions. For example, if an employee is stressed, the data collection unit can reduce the recording frequency to allow them to concentrate on their work. For example, if an employee is relaxed, the data collection unit can record detailed work content frequently. For example, if an employee is busy, the data collection unit can record only important work content. In this way, the data collection unit can reduce the workload by adjusting the frequency of recording work content according to the employee's emotions. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input employee emotion data into AI and have the AI ​​adjust the recording frequency.

[0117] The analysis unit can estimate the emotions of employees and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if an employee is relaxed, the analysis unit provides a detailed analysis result. For example, if an employee is busy, the analysis unit provides a concise analysis result. In this way, the analysis unit deepens the understanding of the analysis results by adjusting the presentation of the analysis according to the emotions of the employees. 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 AI and have the AI ​​adjust the presentation of the analysis.

[0118] The generation unit can estimate an employee's emotions and adjust the presentation of the generated tools based on the estimated emotions. For example, if an employee is stressed, the generation unit generates a simple and easy-to-use tool. If an employee is relaxed, the generation unit generates a tool with detailed features. If an employee is busy, the generation unit generates a tool that can be used quickly. In this way, the generation unit improves the usability of the tools by adjusting the presentation of the generated tools according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input employee emotion data into a generation AI and have the generation AI adjust the presentation of the tools.

[0119] The verification unit can estimate the employee's emotions and adjust the presentation of the verification based on the estimated emotions. For example, if the employee is stressed, the verification unit provides simple and easy-to-understand verification results. For example, if the employee is relaxed, the verification unit provides detailed verification results. For example, if the employee is busy, the verification unit provides concise verification results. This allows the verification unit to deepen the understanding of the verification results by adjusting the presentation of the verification according to the employee's emotions. 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 verification unit may be performed using AI or not using AI. For example, the verification unit can input employee emotion data into AI and have the AI ​​adjust the presentation of the verification.

[0120] The comparison unit can estimate the employee's emotions and adjust the way the comparison is presented based on the estimated employee's emotions. For example, if the employee is stressed, the comparison unit provides a simple and easy-to-understand comparison result. For example, if the employee is relaxed, the comparison unit provides a detailed comparison result. For example, if the employee is busy, the comparison unit provides a concise comparison result. In this way, the comparison unit deepens the understanding of the comparison result by adjusting the way the comparison is presented according to the employee's emotions. 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 comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit can input employee emotion data into AI and have the AI ​​adjust the way the comparison is presented.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection department monitors and meticulously records employees' PC work activities. For example, the data collection department records tasks such as checking and replying to emails, creating Excel documents, and creating PowerPoint presentations to understand employees' work processes. Step 2: The analysis unit analyzes the workflow data collected by the data collection unit and identifies tasks that can be automatically generated. For example, the analysis unit analyzes the recorded workflow data and identifies tasks such as creating email templates, creating Excel macros, and automatically generating PowerPoint presentations as tasks that can be automated. Step 3: The generation unit automatically generates and provides tools such as GAS and macros based on the tasks identified by the analysis unit. For example, the generation unit uses GAS for creating email formats, macros for creating Excel macros, and generation AI for automatically creating PowerPoint presentations. Step 4: The verification unit automatically verifies the effectiveness of the tools generated by the generation unit. For example, the verification unit records the operations performed by employees, compares the time taken for manually performed tasks with that taken for automated tasks, and verifies how much time can be saved. Step 5: The comparison unit compares the usage time after implementation based on the effects verified by the verification unit. For example, it compares the time spent on manually performed tasks with the time spent on automated tasks to show how much time can be saved.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, verification unit, and comparison unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and checks the activity of employees' PC work. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the activity of the collected work and determines which work can be automatically generated. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates and provides a tool based on the determined work. The verification unit is implemented by the control unit 46A of the smart device 14 and automatically verifies the effectiveness of the generated tool. The comparison unit is implemented by the specific processing unit 290 of the data processing device 12 and compares the usage time when implemented. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, verification unit, and comparison unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and checks the activity of employees working on their PCs. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the activity of the collected tasks to determine which tasks can be automatically generated. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and automatically generates and provides a tool based on the determined tasks. The verification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automatically verifies the effectiveness of the generated tool. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and compares the usage time when implemented. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, verification unit, and comparison unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and confirms the activity of employees working on their PCs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the activity of the collected tasks to determine which tasks can be automatically generated. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates and provides tools based on the determined tasks. The verification unit is implemented by the control unit 46A of the headset terminal 314 and automatically verifies the effectiveness of the generated tools. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares the usage time when implemented. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, verification unit, and comparison unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and confirms the movements of employees' PC work. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the movements of the collected work to determine which work can be automatically generated. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates and provides a tool based on the determined work. The verification unit is implemented by the control unit 46A of the robot 414 and automatically verifies the effectiveness of the generated tool. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares the usage time when implemented. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The data collection department monitors the activity of employees working on their PCs, An analysis unit analyzes the workflow collected by the aforementioned collection unit and identifies tasks that can be automatically generated, Based on the business processes identified by the aforementioned analysis unit, a generation unit automatically generates and provides tools such as GAS and macros. A verification unit that automatically verifies the effectiveness of the tool generated by the generation unit, The system includes a comparison unit that performs a comparison of usage time in the case of implementation based on the effects verified by the verification unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Record in detail the tasks performed by each employee. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the recorded work content to identify tasks that can be automated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It automatically generates and provides the most suitable tools for the identified tasks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The verification unit, We will compare the time spent on manually performed tasks with the time spent on automated tasks to verify the effectiveness of the automation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The comparison unit is, This compares the time spent on manually performed tasks with the time spent on automated tasks, showing how much time can be saved. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates employee emotions and adjusts the frequency of recording work activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze employees' past work history and select the most suitable record-keeping method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When recording work details, filter them based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Estimate employees' emotions and prioritize tasks to record based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When recording work details, prioritize recording highly relevant tasks by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When recording work activities, analyze employees' social media activity and record related work activities. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the business. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the timing of the work. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate employee emotions and adjust the way tools are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is We estimate employee emotions and adjust the way tools are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, adjust the level of detail of the generated tools based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied depending on the business category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Estimate employee sentiment and adjust the length of the tool generated based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the priority of the tools to be generated is determined based on the timing of the work. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, adjust the order of the tools generated based on their relevance to the business. The system described in Appendix 1, characterized by the features described herein. (Note 26) The verification unit, We estimate employee sentiment and adjust the presentation of the verification based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The verification unit, During verification, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 28) The verification unit, During verification, different verification algorithms are applied depending on the category of the task. The system described in Appendix 1, characterized by the features described herein. (Note 29) The verification unit, The system estimates employee sentiment and adjusts the length of the verification based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The verification unit, During verification, the priority of verification is determined based on the timing of the work. The system described in Appendix 1, characterized by the features described herein. (Note 31) The verification unit, During verification, adjust the order of verification based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 32) The comparison unit is, We estimate employee sentiment and adjust the way comparisons are expressed based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The comparison unit is, When making comparisons, adjust the level of detail based on the importance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 34) The comparison unit is, When making comparisons, different comparison algorithms are applied depending on the category of the business. The system described in Appendix 1, characterized by the features described herein. (Note 35) The comparison unit is, Estimate employee sentiment and adjust the length of comparisons based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The comparison unit is, When making comparisons, prioritize the comparisons based on when the tasks were performed. The system described in Appendix 1, characterized by the features described herein. (Note 37) The comparison unit is, When making comparisons, adjust the order of comparisons based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 data collection department monitors the activity of employees working on their PCs, An analysis unit analyzes the workflow collected by the aforementioned collection unit and identifies tasks that can be automatically generated, Based on the business processes identified by the aforementioned analysis unit, a generation unit automatically generates and provides tools such as GAS and macros. A verification unit that automatically verifies the effectiveness of the tool generated by the generation unit, The system includes a comparison unit that performs a comparison of usage time in the case of implementation based on the effects verified by the verification unit. A system characterized by the following features.

2. The aforementioned collection unit is Record in detail the tasks performed by each employee. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the recorded work content to identify tasks that can be automated. The system according to feature 1.

4. The generating unit is It automatically generates and provides the most suitable tools for the identified tasks. The system according to feature 1.

5. The verification unit, We will compare the time spent on manually performed tasks with the time spent on automated tasks to verify the effectiveness of the automation. The system according to feature 1.

6. The comparison unit is, This compares the time spent on manually performed tasks with the time spent on automated tasks, showing how much time can be saved. The system according to feature 1.

7. The aforementioned collection unit is The system estimates employee emotions and adjusts the frequency of recording work activities based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze employees' past work history and select the most suitable record-keeping method. The system according to feature 1.

9. The aforementioned collection unit is When recording work details, filter them based on the employee's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is Estimate employees' emotions and prioritize tasks to record based on those estimated emotions. The system according to feature 1.

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