system

Abstract

The system according to this embodiment aims to identify and streamline potential routine tasks. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, a creation unit, and a proposal unit. The collection unit collects employee PC and browser operation logs. The analysis unit analyzes the operation logs collected by the collection unit and identifies routine tasks. The determination unit presents the routine tasks identified by the analysis unit to the employee, who then makes a determination. The creation unit creates a work manual for the routine tasks determined by the determination unit. If the content of the work manual created by the creation unit is correct, the proposal unit proposes automating and streamlining the routine tasks.

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 the chatbot's 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 prior art, there was a problem that it was impossible to notice a method of discovering and streamlining potential routine tasks.

[0005] The system according to the embodiment aims to discover and streamline potential routine tasks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, a creation unit, and a proposal unit. The collection unit collects employee PC and browser operation logs. The analysis unit analyzes the operation logs collected by the collection unit and identifies routine tasks. The determination unit presents the routine tasks identified by the analysis unit to the employee, who then makes a determination. The creation unit creates a work manual for the routine tasks determined by the determination unit. If the content of the work manual created by the creation unit is correct, the proposal unit proposes automating and streamlining the routine tasks. [Effects of the Invention]

[0007] The system according to this embodiment can identify and streamline potential routine tasks. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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).

[0015] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The AI ​​assistant system according to an embodiment of the present invention is a system that analyzes employees' PC and browser operation logs to discover and streamline potential routine tasks. This AI assistant system visualizes employees' daily work hours from logs and clarifies how much time is spent on what tasks on a weekly and monthly basis. Next, an AI agent analyzes the logs and discovers routine tasks. The discovered routine tasks are presented to the employee, who then determines that they are routine tasks. For the determined routine tasks, the AI ​​agent creates a work manual from the operation logs. If the content of the work manual is correct, the AI ​​agent proposes automating and streamlining the routine tasks using programming such as macros, GAS, and RPA. Through this mechanism, potential routine tasks are visualized, routine tasks are prevented from becoming dependent on individuals through manual creation, and work errors are reduced and work efficiency is improved through the programmatic automation of routine tasks. For example, employees' daily work hours are visualized from logs. For example, detailed data such as how long employees use which applications and how long they browse which websites is collected. This clarifies how much time employees spend on each task. Next, the AI ​​agent analyzes the collected logs to identify routine tasks. For example, it can identify patterns in routine tasks, such as using the same application at the same time every day or regularly browsing the same website. The identified routine tasks are presented to employees, who then determine if they are routine tasks. For the determined routine tasks, the AI ​​agent creates a work manual from the operation logs. For example, a manual is generated with detailed instructions, such as how to operate a specific application or how to use a specific website. Employees review this manual to verify its accuracy. If the work manual is correct, the AI ​​agent suggests automating and streamlining the routine tasks using programming such as macros, GAS, and RPA. For example, it may suggest a macro to automate the operation of a specific application or a GAS script to automate the use of a specific website.This allows employees to automate routine tasks, reduce errors, and improve work efficiency. This system makes potential routine tasks visible, prevents reliance on individual employees through manual creation, and reduces errors and improves efficiency through automated routine tasks. For example, automating daily data entry tasks can significantly reduce work time and prevent errors. Similarly, automating regularly performed report creation tasks can improve efficiency. The AI ​​assistant system analyzes employee PC and browser operation logs to identify and streamline potential routine tasks.

[0029] The AI ​​assistant system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, a creation unit, and a suggestion unit. The collection unit collects the employee's PC and browser operation logs. The collection unit can collect operation logs such as clicks, keystrokes, and URL history. The collection unit may also include AI processing. The analysis unit analyzes the operation logs collected by the collection unit and discovers routine tasks. The analysis unit can identify patterns in routine tasks, such as using the same application at the same time every day or regularly browsing the same website. The analysis unit includes AI processing. The determination unit presents the routine tasks discovered by the analysis unit to the employee, who then determines that the task is a routine task. The determination unit can, for example, set the method of notifying the employee of the discovered routine task and the format of the presented content. The determination unit may also include AI processing. The creation unit creates a work manual for the routine tasks determined by the determination unit. The creation unit can generate a manual with detailed procedures, such as the operation procedure for a specific application or the usage method for a specific website. The creation unit includes AI processing. The proposal unit proposes the automation and efficiency improvements of routine tasks if the content of the business manual created by the creation unit is correct. The proposal unit can propose the automation and efficiency improvements of routine tasks using programming such as macros, GAS, and RPA. The proposal unit may also include AI processing. As a result, the AI ​​assistant system according to the embodiment can analyze the employee's PC and browser operation logs to discover and optimize potential routine tasks.

[0030] The data collection unit collects employee PC and browser operation logs. Specifically, the data collection unit can collect operation logs such as clicks, keystrokes, and URL history. This allows for a detailed understanding of what operations employees are performing. For example, click logs record which buttons and links employees clicked, keystroke logs record the characters and commands employees entered, and URL history records the history of websites employees accessed. This data is collected in real time and sent to a central database. The data collection unit may also include AI processing. The AI ​​can analyze the collected data in real time and detect abnormal operations or suspicious activity. For example, it can detect abnormal operations such as the use of a specific application at an unusual time of day or keystrokes being entered in an unusual pattern. This allows the data collection unit to efficiently collect employee operation logs and detect abnormal operations early. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and judgment units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes operation logs collected by the collection unit to identify routine tasks. Specifically, the analysis unit can identify patterns in routine tasks, such as using the same application at the same time every day or regularly browsing the same website. The analysis unit includes AI processing. The AI ​​uses machine learning algorithms to extract patterns from operation logs and identify routine tasks. For example, the AI ​​detects that an employee opens a specific application at the same time every day and determines that this operation is a routine task. The AI ​​also detects that an employee regularly browses the same website and determines that this operation is a routine task. This allows the analysis unit to efficiently analyze collected operation logs and identify routine tasks. Furthermore, the analysis unit can also analyze long-term work patterns using historical data and statistical information. For example, based on past operation logs, it can analyze how frequently a particular task is performed and evaluate its importance and priority. The analysis unit can also use anomaly detection algorithms to detect unusual operation patterns or abnormal operations and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term business analysis and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The judgment unit presents routine tasks identified by the analysis unit to employees, who then determine if those tasks are routine tasks. Specifically, the judgment unit can configure the method of notifying employees of discovered routine tasks and the format of the presentation content. The judgment unit may also include AI processing. When presenting discovered routine tasks to employees, the AI ​​can select the optimal notification method and presentation content. For example, the AI ​​can provide notifications at the optimal timing based on the employee's work status and past operation history. The AI ​​can also provide detailed explanations of the discovered routine tasks, their importance, and potential for efficiency improvements. This allows the judgment unit to efficiently present routine tasks to employees and support work efficiency improvements. Furthermore, the judgment unit can collect feedback from employees and continuously improve the accuracy and effectiveness of the presentation content. For example, employees can review the presented routine tasks and determine whether they are actually routine tasks. Based on employee feedback, the judgment unit can also improve the presentation content and review the notification method. This allows the judgment unit to respond flexibly according to the employee's work status and improve the overall system performance.

[0033] The creation unit creates work manuals for routine tasks determined by the judgment unit. Specifically, the creation unit can generate detailed manuals for operating specific applications or using specific websites. The creation unit includes AI processing. The AI ​​can analyze the operation logs of the determined routine tasks and automatically generate optimal operating procedures. For example, the AI ​​can analyze the operation procedures of a specific application and generate a detailed manual for those procedures. The AI ​​can also analyze how to use a specific website and generate a manual for its usage. This allows the creation unit to efficiently create work manuals and improve employee work efficiency. Furthermore, the creation unit can continuously update the content of the created work manuals to respond to the latest business conditions. For example, if the work content or operating procedures change, the AI ​​analyzes the latest operation logs and automatically updates the manual content. The creation unit can also collect feedback from employees and improve the content and format of the manuals. This allows the creation unit to always provide high-quality work manuals based on the latest information and improve employee work efficiency.

[0034] The proposal department will propose the automation and efficiency improvements of routine tasks, provided that the business manuals created by the creation department are accurate. Specifically, the proposal department can propose the automation and efficiency improvements of routine tasks using programming such as macros, GAS, and RPA. The proposal department may also include AI processing. The AI ​​can analyze the content of the created business manuals and propose the most suitable automation method. For example, the AI ​​can analyze the operation procedures of a specific application and generate macros or scripts to automate those procedures. Alternatively, the AI ​​can analyze the usage procedures of a specific website and propose GAS or RPA to automate those procedures. This allows the proposal department to efficiently propose the automation of routine tasks and improve employee work efficiency. Furthermore, the proposal department can evaluate the effectiveness of the proposed automation methods and continuously improve them. For example, it can monitor the execution results of proposed macros and scripts, and if the effect is insufficient, the AI ​​can automatically propose improvement plans. The proposal department can also collect feedback from employees and continuously improve the accuracy and effectiveness of the automation methods. This allows the proposal department to always provide the most suitable automation methods and maximize employee work efficiency.

[0035] The data collection unit can visualize employees' daily work hours. For example, the unit collects detailed data such as how long employees use each application and how long they browse each website. Based on the collected data, the unit can visualize employees' daily work hours through methods such as graphs and time-based summaries. This visualization of employees' daily work hours can improve work efficiency. Some or all of the above-described processes in the data collection unit may be performed using AI or without AI. For example, the data collection unit can input employee operation logs into a generating AI and have the generating AI perform the visualization of work hours.

[0036] The analysis unit can analyze the collected operation logs and identify patterns in routine tasks. For example, the analysis unit can identify patterns in routine tasks such as using the same application at the same time every day or regularly browsing the same website. The analysis unit can identify patterns in routine tasks based on criteria such as the frequency, time of day, and order of operations in the collected operation logs. By identifying patterns in routine tasks, it is possible to improve the efficiency of operations. 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 the collected operation logs into a generating AI and have the generating AI perform the identification of patterns in routine tasks.

[0037] The judgment unit presents the discovered routine tasks to the employee, allowing the employee to determine that the task is a routine task. The judgment unit can, for example, set the method of notifying the employee of the discovered routine task and the format of the presented content. The judgment unit can set the notification method and the format of the presented content when presenting routine tasks to the employee. This allows employees to improve work efficiency by determining whether a task is a routine task. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the discovered routine task into a generating AI and have the generating AI execute the content to be presented to the employee.

[0038] The creation unit can generate business manuals from operation logs. For example, it can generate detailed manuals outlining the operation procedures for a specific application or the usage of a specific website. The creation unit can set the content and format of the business manual based on the operation logs. This prevents the reliance on individual expertise for specific tasks. Some or all of the above-described processes in the creation unit may be performed using AI or not. For example, the creation unit can input operation logs into a generation AI and have the generation AI create the business manual.

[0039] The proposal department can propose the automation and efficiency improvements of routine tasks using programming such as macros, GAS, and RPA. For example, the proposal department might propose macros to automate the operation of a specific application or GAS scripts to automate the use of a specific website. If the content of the business manual is correct, the proposal department will propose the automation and efficiency improvements of routine tasks. This will improve the efficiency of operations by proposing the automation and efficiency improvements of routine tasks. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the content of the business manual into a generating AI and have the generating AI execute proposals for the automation and efficiency improvements of routine tasks.

[0040] The data collection unit can analyze an employee's past operation history and select the optimal collection method. For example, the data collection unit can discover from past operation history that operations are concentrated during specific time periods and focus on collecting logs during those times. Based on past operation history, if a particular application is frequently used, the data collection unit can prioritize collecting operation logs for that application. The data collection unit can analyze past operation history, identify operation patterns, and efficiently collect logs based on those patterns. This allows the optimal collection method to be selected by analyzing an employee's past operation history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past operation history data into a generating AI and have the generating AI select the optimal collection method.

[0041] The collection unit can filter operation logs based on the employee's current projects and areas of interest when collecting them. For example, the collection unit can prioritize collecting operation logs related to projects currently underway. The collection unit can filter operation logs related to the employee's areas of interest and collect only important data. The collection unit can dynamically select and collect necessary operation logs according to the progress of the project. This allows for the collection of only important data by filtering based on the employee's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input employee project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering of operation logs.

[0042] The data collection unit can prioritize the collection of highly relevant logs by considering the employee's geographical location when collecting operation logs. For example, if an employee is in the office, the data collection unit will prioritize collecting operation logs from within the office. If an employee is on a business trip, the data collection unit can prioritize collecting operation logs from their business trip destination. If an employee is working remotely, the data collection unit can prioritize collecting operation logs from their home. This allows for the priority collection of highly relevant logs by considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant logs.

[0043] The collection unit can analyze employees' social media activity and collect relevant logs when collecting operation logs. For example, the collection unit can collect relevant operation logs based on information shared by employees on social media. The collection unit can collect operation logs related to topics of interest from employees' social media activity. The collection unit can adjust the timing of operation log collection based on the time of day of social media activity. This allows for the collection of relevant logs by analyzing employees' social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input employee social media activity data into a generating AI and have the generating AI perform the collection of relevant logs.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the operation logs during the analysis. For example, the analysis unit can perform a detailed analysis of high-importance operation logs to provide deeper insights. For low-importance operation logs, the analysis unit can perform a simplified analysis to efficiently provide results. The analysis unit can dynamically allocate analysis resources according to the importance of the operation logs. This allows for efficient analysis by adjusting the level of detail based on the importance of the operation logs. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input operation log importance data into a generating AI and have the generating 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 the operation log during analysis. For example, the analysis unit can apply an analysis algorithm optimized for a specific application to application usage logs. For website browsing logs, it can apply an analysis algorithm appropriate to the category of the website. For file operation logs, it can apply an analysis algorithm appropriate to the type of file. This allows for efficient analysis by applying different analysis algorithms depending on the category of the operation log. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input operation log category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the timing of operation log collection during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent operation logs to provide real-time insights. The analysis unit can also analyze past operation logs to grasp long-term trends. The analysis unit can dynamically allocate analysis resources based on the timing of operation log collection. This allows for efficient analysis by determining the priority of analysis based on the timing of operation log collection. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input operation log collection timing data into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the operation logs during analysis. For example, the analysis unit can prioritize the analysis of highly relevant operation logs to quickly provide important insights. The analysis unit can postpone the analysis of less relevant operation logs, enabling efficient analysis. The analysis unit can dynamically allocate analysis resources based on the relevance of the operation logs. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the operation logs. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance data of the operation logs into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of operation logs during the judgment process. For example, the judgment unit analyzes the interrelationships of operation logs and makes a judgment based on the relevant logs. The judgment unit can improve the accuracy of its judgment by considering the interrelationships of operation logs. The judgment unit can dynamically analyze the interrelationships of operation logs and make judgments efficiently. As a result, the accuracy of the judgment can be improved by considering the interrelationships of operation logs. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the interrelationship data of operation logs into a generating AI and have the generating AI perform the improvement of the judgment accuracy.

[0049] The judgment unit can make a judgment by considering the attribute information of the person who submitted the operation log. For example, the judgment unit can make a judgment by considering the job title and job duties of the person who submitted the operation log. The judgment unit can make a judgment by considering the skill level of the person who submitted the operation log. The judgment unit can make a judgment by considering the past work history of the person who submitted the operation log. In this way, the accuracy of the judgment can be improved by considering the attribute information of the person who submitted the operation log. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the attribute information data of the person who submitted the operation log into a generating AI and have the generating AI perform the task of improving the accuracy of the judgment.

[0050] The determination unit can perform a determination while considering the geographical distribution of operation logs. For example, the determination unit can analyze the geographical distribution of operation logs and perform a determination while considering the characteristics of each region. Based on the geographical distribution, the determination unit can prioritize the determination of relevant operation logs. The determination unit can improve the accuracy of the determination by considering the geographical distribution. In this way, the accuracy of the determination can be improved by considering the geographical distribution of operation logs. Some or all of the above processing in the determination unit may be performed using AI or not. For example, the determination unit can input geographical distribution data of operation logs into a generating AI and have the generating AI perform the task of improving the accuracy of the determination.

[0051] The judgment unit can improve the accuracy of its judgment by referring to relevant literature in the operation log during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to relevant literature in the operation log. The judgment unit can adjust the criteria for judgment based on the relevant literature. The judgment unit can dynamically refer to relevant literature and perform judgments efficiently. This allows the accuracy of judgment to be improved by referring to relevant literature in the operation log. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the relevant literature data from the operation log into a generating AI and have the generating AI perform the improvement of judgment accuracy.

[0052] The creation unit can adjust the level of detail in the manual based on the importance of the operation log when creating the manual. For example, the creation unit can create a manual with detailed procedures for operation logs with high importance, and a manual with simplified procedures for operation logs with low importance. The creation unit can dynamically adjust the content of the manual according to the importance of the operation log. This allows for efficient manual creation by adjusting the level of detail based on the importance of the operation log. Some or all of the above processing in the creation unit may be performed using AI, or not. For example, the creation unit can input operation log importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the manual.

[0053] The creation unit can apply different manual creation algorithms depending on the category of the operation log when creating a manual. For example, the creation unit can apply a manual creation algorithm optimized for a specific application to application usage logs. For website browsing logs, it can apply a manual creation algorithm that corresponds to the category of the website. For file operation logs, it can apply a manual creation algorithm that corresponds to the type of file. This allows for efficient manual creation by applying different manual creation algorithms depending on the category of the operation log. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input operation log category data into a generation AI and have the generation AI execute the application of different manual creation algorithms.

[0054] The creation unit can determine the priority of manuals based on the timing of operation log collection when creating manuals. For example, the creation unit can prioritize manual creation based on the latest operation log. The creation unit can also create manuals that reflect long-term trends based on past operation logs. The creation unit can dynamically adjust the content of manuals based on the timing of operation log collection. This allows for efficient manual creation by determining the priority of manuals based on the timing of operation log collection. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input operation log collection timing data into a generation AI and have the generation AI perform the manual priority determination.

[0055] The creation unit can adjust the order of manuals based on the relevance of operation logs when creating manuals. For example, the creation unit can prioritize creating manuals based on highly relevant operation logs. The creation unit can efficiently create manuals by postponing less relevant operation logs. The creation unit can dynamically adjust the content of manuals based on the relevance of operation logs. This allows for efficient manual creation by adjusting the order of manuals based on the relevance of operation logs. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input the relevance data of operation logs into a generation AI and have the generation AI perform the adjustment of the manual order.

[0056] The proposal unit can adjust the level of detail in its proposals based on the importance of the routine tasks. For example, for high-importance routine tasks, the proposal unit can provide detailed proposals and specifically show how to improve efficiency. For low-importance routine tasks, the proposal unit can provide simplified proposals, enabling efficient proposal creation. The proposal unit can dynamically adjust the content of its proposals according to the importance of the routine tasks. This allows for efficient proposal creation by adjusting the level of detail based on the importance of the routine tasks. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input routine task importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0057] The suggestion unit can apply different suggestion algorithms depending on the category of the routine task when making suggestions. For example, for routine tasks related to using an application, the suggestion unit can apply a suggestion algorithm optimized for a specific application. For routine tasks related to browsing websites, the suggestion unit can apply a suggestion algorithm that corresponds to the category of the website. For routine tasks related to file operations, the suggestion unit can apply a suggestion algorithm that corresponds to the type of file. This allows for efficient suggestion generation by applying different suggestion algorithms depending on the category of the routine task. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input routine task category data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0058] The proposal unit can determine the priority of proposals based on the timing of routine tasks. For example, the proposal unit can prioritize proposals based on the most recent routine tasks. The proposal unit can also make proposals that reflect long-term trends based on past routine tasks. The proposal unit can dynamically adjust the content of proposals based on the timing of routine tasks. This allows for efficient proposal generation by determining the priority of proposals based on the timing of routine tasks. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input routine task execution timing data into a generating AI and have the generating AI determine the priority of proposals.

[0059] The proposal unit can adjust the order of proposals based on the relevance of routine tasks when making proposals. For example, the proposal unit can prioritize proposals based on highly relevant routine tasks. The proposal unit can efficiently make proposals by postponing less relevant routine tasks. The proposal unit can dynamically adjust the content of proposals based on the relevance of routine tasks. This allows for efficient proposals by adjusting the order of proposals based on the relevance of routine tasks. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input routine task relevance data into a generating AI and have the generating AI adjust the order of proposals.

[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 analysis unit can analyze collected operation logs and estimate employees' skill levels. For example, it can evaluate employees' skill levels based on factors such as the frequency of use of specific applications, the accuracy of their operations, and their speed. Based on these skill levels, the analysis unit can propose appropriate training programs. This helps improve employee skills and promotes increased work efficiency.

[0062] The data collection unit can consider the context of an operation when collecting employee operation logs. For example, it can prioritize the collection of operation logs related to a specific project to understand the project's progress. Based on the context of the operation, the data collection unit can filter relevant operation logs and collect data efficiently. This allows for understanding the project's progress and improving work efficiency.

[0063] The analysis unit can analyze collected operation logs and evaluate employee work performance. For example, it can evaluate employee performance based on factors such as accuracy, speed, and error frequency. Based on the performance evaluation, the analysis unit can suggest areas for improvement and the need for training. This helps to improve employee performance and promote operational efficiency.

[0064] The data collection unit can take into account the employee's health condition when collecting operation logs. For example, if an employee is feeling fatigued, the frequency of operation log collection can be reduced to alleviate their burden. If the employee is healthy, the frequency of operation log collection can be increased to collect more detailed data. This allows for the collection of operation logs in accordance with the employee's health condition, thereby improving work efficiency and promoting employee health management.

[0065] The analysis department can analyze collected operation logs to identify areas for improvement in employees' work environments. For example, it can suggest improvements to the work environment based on the frequency of use and efficiency of operation of specific applications. Based on these improvements, the analysis department can propose the introduction of appropriate tools and resources. This can improve employees' work environments and promote increased work efficiency.

[0066] The data collection unit can adjust the collection method according to the employee's work content when collecting operation logs. For example, it can focus on collecting PC operation logs for employees who do a lot of desk work, and focus on collecting mobile device operation logs for employees who do a lot of fieldwork. By collecting operation logs according to the content of work, data can be collected efficiently, and work efficiency can be improved.

[0067] The analysis unit can analyze collected operation logs and evaluate employee workload. For example, it can evaluate workload based on the time and frequency spent on specific tasks. Based on the workload evaluation, the analysis unit can propose task reallocation or additional resources. This allows for proper management of employee workload, leading to increased work efficiency and reduced employee stress.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The collection unit collects employee PC and browser operation logs. For example, it can collect operation logs such as clicks, keystrokes, and URL history. The collection unit may also include AI processing. Step 2: The analysis unit analyzes the operation logs collected by the collection unit to identify routine tasks. For example, it can identify patterns in routine tasks, such as using the same application at the same time every day or regularly browsing the same website. The analysis unit includes AI processing. Step 3: The judgment unit presents the routine tasks discovered by the analysis unit to the employee, who then determines that the task is a routine task. For example, the method of notifying the employee of the discovered routine tasks and the format of the presentation can be set. The judgment unit may also include AI processing. Step 4: The creation unit creates a work manual for the routine tasks determined by the judgment unit. For example, it can generate a manual with detailed instructions, such as how to operate a specific application or how to use a specific website. The creation unit includes AI processing. Step 5: The proposal team, if the content of the business manual created by the creation team is correct, proposes ways to automate and streamline routine tasks. For example, they can propose ways to automate and streamline routine tasks using programming such as macros, GAS, or RPA. The proposal team may also include AI processing.

[0070] (Example of form 2) The AI ​​assistant system according to an embodiment of the present invention is a system that analyzes employees' PC and browser operation logs to discover and streamline potential routine tasks. This AI assistant system visualizes employees' daily work hours from logs and clarifies how much time is spent on what tasks on a weekly and monthly basis. Next, an AI agent analyzes the logs and discovers routine tasks. The discovered routine tasks are presented to the employee, who then determines that they are routine tasks. For the determined routine tasks, the AI ​​agent creates a work manual from the operation logs. If the content of the work manual is correct, the AI ​​agent proposes automating and streamlining the routine tasks using programming such as macros, GAS, and RPA. Through this mechanism, potential routine tasks are visualized, routine tasks are prevented from becoming dependent on individuals through manual creation, and work errors are reduced and work efficiency is improved through the programmatic automation of routine tasks. For example, employees' daily work hours are visualized from logs. For example, detailed data such as how long employees use which applications and how long they browse which websites is collected. This clarifies how much time employees spend on each task. Next, the AI ​​agent analyzes the collected logs to identify routine tasks. For example, it can identify patterns in routine tasks, such as using the same application at the same time every day or regularly browsing the same website. The identified routine tasks are presented to employees, who then determine if they are routine tasks. For the determined routine tasks, the AI ​​agent creates a work manual from the operation logs. For example, a manual is generated with detailed instructions, such as how to operate a specific application or how to use a specific website. Employees review this manual to verify its accuracy. If the work manual is correct, the AI ​​agent suggests automating and streamlining the routine tasks using programming such as macros, GAS, and RPA. For example, it may suggest a macro to automate the operation of a specific application or a GAS script to automate the use of a specific website.This allows employees to automate routine tasks, reduce errors, and improve work efficiency. This system makes potential routine tasks visible, prevents reliance on individual employees through manual creation, and reduces errors and improves efficiency through automated routine tasks. For example, automating daily data entry tasks can significantly reduce work time and prevent errors. Similarly, automating regularly performed report creation tasks can improve efficiency. The AI ​​assistant system analyzes employee PC and browser operation logs to identify and streamline potential routine tasks.

[0071] The AI ​​assistant system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, a creation unit, and a suggestion unit. The collection unit collects the employee's PC and browser operation logs. The collection unit can collect operation logs such as clicks, keystrokes, and URL history. The collection unit may also include AI processing. The analysis unit analyzes the operation logs collected by the collection unit and discovers routine tasks. The analysis unit can identify patterns in routine tasks, such as using the same application at the same time every day or regularly browsing the same website. The analysis unit includes AI processing. The determination unit presents the routine tasks discovered by the analysis unit to the employee, who then determines that the task is a routine task. The determination unit can, for example, set the method of notifying the employee of the discovered routine task and the format of the presented content. The determination unit may also include AI processing. The creation unit creates a work manual for the routine tasks determined by the determination unit. The creation unit can generate a manual with detailed procedures, such as the operation procedure for a specific application or the usage method for a specific website. The creation unit includes AI processing. The proposal unit proposes the automation and efficiency improvements of routine tasks if the content of the business manual created by the creation unit is correct. The proposal unit can propose the automation and efficiency improvements of routine tasks using programming such as macros, GAS, and RPA. The proposal unit may also include AI processing. As a result, the AI ​​assistant system according to the embodiment can analyze the employee's PC and browser operation logs to discover and optimize potential routine tasks.

[0072] The data collection unit collects employee PC and browser operation logs. Specifically, the data collection unit can collect operation logs such as clicks, keystrokes, and URL history. This allows for a detailed understanding of what operations employees are performing. For example, click logs record which buttons and links employees clicked, keystroke logs record the characters and commands employees entered, and URL history records the history of websites employees accessed. This data is collected in real time and sent to a central database. The data collection unit may also include AI processing. The AI ​​can analyze the collected data in real time and detect abnormal operations or suspicious activity. For example, it can detect abnormal operations such as the use of a specific application at an unusual time of day or keystrokes being entered in an unusual pattern. This allows the data collection unit to efficiently collect employee operation logs and detect abnormal operations early. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and judgment units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0073] The analysis unit analyzes operation logs collected by the collection unit to identify routine tasks. Specifically, the analysis unit can identify patterns in routine tasks, such as using the same application at the same time every day or regularly browsing the same website. The analysis unit includes AI processing. The AI ​​uses machine learning algorithms to extract patterns from operation logs and identify routine tasks. For example, the AI ​​detects that an employee opens a specific application at the same time every day and determines that this operation is a routine task. The AI ​​also detects that an employee regularly browses the same website and determines that this operation is a routine task. This allows the analysis unit to efficiently analyze collected operation logs and identify routine tasks. Furthermore, the analysis unit can also analyze long-term work patterns using historical data and statistical information. For example, based on past operation logs, it can analyze how frequently a particular task is performed and evaluate its importance and priority. The analysis unit can also use anomaly detection algorithms to detect unusual operation patterns or abnormal operations and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term business analysis and anomaly detection, thereby improving the reliability and security of the entire system.

[0074] The judgment unit presents routine tasks identified by the analysis unit to employees, who then determine if those tasks are routine tasks. Specifically, the judgment unit can configure the method of notifying employees of discovered routine tasks and the format of the presentation content. The judgment unit may also include AI processing. When presenting discovered routine tasks to employees, the AI ​​can select the optimal notification method and presentation content. For example, the AI ​​can provide notifications at the optimal timing based on the employee's work status and past operation history. The AI ​​can also provide detailed explanations of the discovered routine tasks, their importance, and potential for efficiency improvements. This allows the judgment unit to efficiently present routine tasks to employees and support work efficiency improvements. Furthermore, the judgment unit can collect feedback from employees and continuously improve the accuracy and effectiveness of the presentation content. For example, employees can review the presented routine tasks and determine whether they are actually routine tasks. Based on employee feedback, the judgment unit can also improve the presentation content and review the notification method. This allows the judgment unit to respond flexibly according to the employee's work status and improve the overall system performance.

[0075] The creation unit creates work manuals for routine tasks determined by the judgment unit. Specifically, the creation unit can generate detailed manuals for operating specific applications or using specific websites. The creation unit includes AI processing. The AI ​​can analyze the operation logs of the determined routine tasks and automatically generate optimal operating procedures. For example, the AI ​​can analyze the operation procedures of a specific application and generate a detailed manual for those procedures. The AI ​​can also analyze how to use a specific website and generate a manual for its usage. This allows the creation unit to efficiently create work manuals and improve employee work efficiency. Furthermore, the creation unit can continuously update the content of the created work manuals to respond to the latest business conditions. For example, if the work content or operating procedures change, the AI ​​analyzes the latest operation logs and automatically updates the manual content. The creation unit can also collect feedback from employees and improve the content and format of the manuals. This allows the creation unit to always provide high-quality work manuals based on the latest information and improve employee work efficiency.

[0076] The proposal department will propose the automation and efficiency improvements of routine tasks, provided that the business manuals created by the creation department are accurate. Specifically, the proposal department can propose the automation and efficiency improvements of routine tasks using programming such as macros, GAS, and RPA. The proposal department may also include AI processing. The AI ​​can analyze the content of the created business manuals and propose the most suitable automation method. For example, the AI ​​can analyze the operation procedures of a specific application and generate macros or scripts to automate those procedures. Alternatively, the AI ​​can analyze the usage procedures of a specific website and propose GAS or RPA to automate those procedures. This allows the proposal department to efficiently propose the automation of routine tasks and improve employee work efficiency. Furthermore, the proposal department can evaluate the effectiveness of the proposed automation methods and continuously improve them. For example, it can monitor the execution results of proposed macros and scripts, and if the effect is insufficient, the AI ​​can automatically propose improvement plans. The proposal department can also collect feedback from employees and continuously improve the accuracy and effectiveness of the automation methods. This allows the proposal department to always provide the most suitable automation methods and maximize employee work efficiency.

[0077] The data collection unit can visualize employees' daily work hours. For example, the unit collects detailed data such as how long employees use each application and how long they browse each website. Based on the collected data, the unit can visualize employees' daily work hours through methods such as graphs and time-based summaries. This visualization of employees' daily work hours can improve work efficiency. Some or all of the above-described processes in the data collection unit may be performed using AI or without AI. For example, the data collection unit can input employee operation logs into a generating AI and have the generating AI perform the visualization of work hours.

[0078] The analysis unit can analyze the collected operation logs and identify patterns in routine tasks. For example, the analysis unit can identify patterns in routine tasks such as using the same application at the same time every day or regularly browsing the same website. The analysis unit can identify patterns in routine tasks based on criteria such as the frequency, time of day, and order of operations in the collected operation logs. By identifying patterns in routine tasks, it is possible to improve the efficiency of operations. 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 the collected operation logs into a generating AI and have the generating AI perform the identification of patterns in routine tasks.

[0079] The judgment unit presents the discovered routine tasks to the employee, allowing the employee to determine that the task is a routine task. The judgment unit can, for example, set the method of notifying the employee of the discovered routine task and the format of the presented content. The judgment unit can set the notification method and the format of the presented content when presenting routine tasks to the employee. This allows employees to improve work efficiency by determining whether a task is a routine task. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the discovered routine task into a generating AI and have the generating AI execute the content to be presented to the employee.

[0080] The creation unit can generate business manuals from operation logs. For example, it can generate detailed manuals outlining the operation procedures for a specific application or the usage of a specific website. The creation unit can set the content and format of the business manual based on the operation logs. This prevents the reliance on individual expertise for specific tasks. Some or all of the above-described processes in the creation unit may be performed using AI or not. For example, the creation unit can input operation logs into a generation AI and have the generation AI create the business manual.

[0081] The proposal department can propose the automation and efficiency improvements of routine tasks using programming such as macros, GAS, and RPA. For example, the proposal department might propose macros to automate the operation of a specific application or GAS scripts to automate the use of a specific website. If the content of the business manual is correct, the proposal department will propose the automation and efficiency improvements of routine tasks. This will improve the efficiency of operations by proposing the automation and efficiency improvements of routine tasks. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the content of the business manual into a generating AI and have the generating AI execute proposals for the automation and efficiency improvements of routine tasks.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of operation log collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of operation log collection to alleviate the burden. If the user is relaxed, the data collection unit can increase the frequency of operation log collection to collect more detailed data. If the user is concentrating, the data collection unit can temporarily stop collecting operation logs to avoid interfering with their work. This reduces the user's burden by adjusting the timing of operation log collection based on their 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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of operation log collection.

[0083] The data collection unit can analyze an employee's past operation history and select the optimal collection method. For example, the data collection unit can discover from past operation history that operations are concentrated during specific time periods and focus on collecting logs during those times. Based on past operation history, if a particular application is frequently used, the data collection unit can prioritize collecting operation logs for that application. The data collection unit can analyze past operation history, identify operation patterns, and efficiently collect logs based on those patterns. This allows the optimal collection method to be selected by analyzing an employee's past operation history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past operation history data into a generating AI and have the generating AI select the optimal collection method.

[0084] The collection unit can filter operation logs based on the employee's current projects and areas of interest when collecting them. For example, the collection unit can prioritize collecting operation logs related to projects currently underway. The collection unit can filter operation logs related to the employee's areas of interest and collect only important data. The collection unit can dynamically select and collect necessary operation logs according to the progress of the project. This allows for the collection of only important data by filtering based on the employee's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input employee project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering of operation logs.

[0085] The data collection unit can estimate the user's emotions and determine the priority of operation logs to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority operation logs. If the user is relaxed, the data collection unit can collect detailed operation logs to improve the accuracy of the analysis. If the user is focused, the data collection unit can temporarily stop collecting operation logs to avoid interrupting their work. This allows for the priority collection of important operation logs by determining the priority of operation logs based on the user'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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of operation logs.

[0086] The data collection unit can prioritize the collection of highly relevant logs by considering the employee's geographical location when collecting operation logs. For example, if an employee is in the office, the data collection unit will prioritize collecting operation logs from within the office. If an employee is on a business trip, the data collection unit can prioritize collecting operation logs from their business trip destination. If an employee is working remotely, the data collection unit can prioritize collecting operation logs from their home. This allows for the priority collection of highly relevant logs by considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant logs.

[0087] The collection unit can analyze employees' social media activity and collect relevant logs when collecting operation logs. For example, the collection unit can collect relevant operation logs based on information shared by employees on social media. The collection unit can collect operation logs related to topics of interest from employees' social media activity. The collection unit can adjust the timing of operation log collection based on the time of day of social media activity. This allows for the collection of relevant logs by analyzing employees' social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input employee social media activity data into a generating AI and have the generating AI perform the collection of relevant logs.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results to gain deeper insights. If the user is focused, the analysis unit can provide concise analysis results for quick understanding. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be made easy for the user to understand. 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 or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the operation logs during the analysis. For example, the analysis unit can perform a detailed analysis of high-importance operation logs to provide deeper insights. For low-importance operation logs, the analysis unit can perform a simplified analysis to efficiently provide results. The analysis unit can dynamically allocate analysis resources according to the importance of the operation logs. This allows for efficient analysis by adjusting the level of detail based on the importance of the operation logs. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input operation log importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of the operation log during analysis. For example, the analysis unit can apply an analysis algorithm optimized for a specific application to application usage logs. For website browsing logs, it can apply an analysis algorithm appropriate to the category of the website. For file operation logs, it can apply an analysis algorithm appropriate to the type of file. This allows for efficient analysis by applying different analysis algorithms depending on the category of the operation log. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input operation log category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis to gain deeper insights. If the user is focused, the analysis unit can provide a concise analysis to allow for quick understanding. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. 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 or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0092] The analysis unit can determine the priority of analysis based on the timing of operation log collection during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent operation logs to provide real-time insights. The analysis unit can also analyze past operation logs to grasp long-term trends. The analysis unit can dynamically allocate analysis resources based on the timing of operation log collection. This allows for efficient analysis by determining the priority of analysis based on the timing of operation log collection. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input operation log collection timing data into a generating AI and have the generating AI determine the priority of analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the operation logs during analysis. For example, the analysis unit can prioritize the analysis of highly relevant operation logs to quickly provide important insights. The analysis unit can postpone the analysis of less relevant operation logs, enabling efficient analysis. The analysis unit can dynamically allocate analysis resources based on the relevance of the operation logs. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the operation logs. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance data of the operation logs into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0094] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated user emotions. For example, if the user is stressed, the judgment unit can relax the judgment criteria to reduce the burden. If the user is relaxed, the judgment unit can apply strict judgment criteria to make an accurate judgment. If the user is focused, the judgment unit can dynamically adjust the judgment criteria to make an efficient judgment. In this way, the burden on the user can be reduced by adjusting the judgment criteria based on the user'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 judgment unit may be performed using AI or not using AI. For example, the judgment unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the judgment criteria.

[0095] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of operation logs during the judgment process. For example, the judgment unit analyzes the interrelationships of operation logs and makes a judgment based on the relevant logs. The judgment unit can improve the accuracy of its judgment by considering the interrelationships of operation logs. The judgment unit can dynamically analyze the interrelationships of operation logs and make judgments efficiently. As a result, the accuracy of the judgment can be improved by considering the interrelationships of operation logs. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the interrelationship data of operation logs into a generating AI and have the generating AI perform the improvement of the judgment accuracy.

[0096] The judgment unit can make a judgment by considering the attribute information of the person who submitted the operation log. For example, the judgment unit can make a judgment by considering the job title and job duties of the person who submitted the operation log. The judgment unit can make a judgment by considering the skill level of the person who submitted the operation log. The judgment unit can make a judgment by considering the past work history of the person who submitted the operation log. In this way, the accuracy of the judgment can be improved by considering the attribute information of the person who submitted the operation log. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the attribute information data of the person who submitted the operation log into a generating AI and have the generating AI perform the task of improving the accuracy of the judgment.

[0097] The judgment unit can estimate the user's emotions and adjust the order in which the judgment results are displayed based on the estimated emotions. For example, if the user is stressed, the judgment unit can prioritize displaying important results. If the user is relaxed, the judgment unit can display detailed results in a sequential manner. If the user is focused, the judgment unit can quickly display concise results. By adjusting the order in which the judgment results are displayed based on the user's emotions, the system can provide results that are easy for the user to understand. 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 judgment unit may be performed using AI or not. For example, the judgment unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the judgment results.

[0098] The determination unit can perform a determination while considering the geographical distribution of operation logs. For example, the determination unit can analyze the geographical distribution of operation logs and perform a determination while considering the characteristics of each region. Based on the geographical distribution, the determination unit can prioritize the determination of relevant operation logs. The determination unit can improve the accuracy of the determination by considering the geographical distribution. In this way, the accuracy of the determination can be improved by considering the geographical distribution of operation logs. Some or all of the above processing in the determination unit may be performed using AI or not. For example, the determination unit can input geographical distribution data of operation logs into a generating AI and have the generating AI perform the task of improving the accuracy of the determination.

[0099] The judgment unit can improve the accuracy of its judgment by referring to relevant literature in the operation log during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to relevant literature in the operation log. The judgment unit can adjust the criteria for judgment based on the relevant literature. The judgment unit can dynamically refer to relevant literature and perform judgments efficiently. This allows the accuracy of judgment to be improved by referring to relevant literature in the operation log. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the relevant literature data from the operation log into a generating AI and have the generating AI perform the improvement of judgment accuracy.

[0100] The creation unit can estimate the user's emotions and adjust the way the manual is written based on those emotions. For example, if the user is stressed, the creation unit can provide a simple and visually easy-to-understand manual. If the user is relaxed, the creation unit can provide a manual with detailed explanations. If the user is focused, the creation unit can provide a manual that gets straight to the point. In this way, by adjusting the way the manual is written based on the user's emotions, a manual that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into a generative AI and have the generative AI adjust the way the manual is written.

[0101] The creation unit can adjust the level of detail in the manual based on the importance of the operation log when creating the manual. For example, the creation unit can create a manual with detailed procedures for operation logs with high importance, and a manual with simplified procedures for operation logs with low importance. The creation unit can dynamically adjust the content of the manual according to the importance of the operation log. This allows for efficient manual creation by adjusting the level of detail based on the importance of the operation log. Some or all of the above processing in the creation unit may be performed using AI, or not. For example, the creation unit can input operation log importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the manual.

[0102] The creation unit can apply different manual creation algorithms depending on the category of the operation log when creating a manual. For example, the creation unit can apply a manual creation algorithm optimized for a specific application to application usage logs. For website browsing logs, it can apply a manual creation algorithm that corresponds to the category of the website. For file operation logs, it can apply a manual creation algorithm that corresponds to the type of file. This allows for efficient manual creation by applying different manual creation algorithms depending on the category of the operation log. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input operation log category data into a generation AI and have the generation AI execute the application of different manual creation algorithms.

[0103] The creation unit can estimate the user's emotions and adjust the length of the manual based on the estimated emotions. For example, if the user is stressed, the creation unit can provide a short, concise manual. If the user is relaxed, the creation unit can provide a longer manual with detailed explanations. If the user is focused, the creation unit can provide a concise manual. By adjusting the length of the manual based on the user's emotions, the creation unit can provide a manual of an appropriate length for the user. 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 creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the manual.

[0104] The creation unit can determine the priority of manuals based on the timing of operation log collection when creating manuals. For example, the creation unit can prioritize manual creation based on the latest operation log. The creation unit can also create manuals that reflect long-term trends based on past operation logs. The creation unit can dynamically adjust the content of manuals based on the timing of operation log collection. This allows for efficient manual creation by determining the priority of manuals based on the timing of operation log collection. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input operation log collection timing data into a generation AI and have the generation AI perform the manual priority determination.

[0105] The creation unit can adjust the order of manuals based on the relevance of operation logs when creating manuals. For example, the creation unit can prioritize creating manuals based on highly relevant operation logs. The creation unit can efficiently create manuals by postponing less relevant operation logs. The creation unit can dynamically adjust the content of manuals based on the relevance of operation logs. This allows for efficient manual creation by adjusting the order of manuals based on the relevance of operation logs. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input the relevance data of operation logs into a generation AI and have the generation AI perform the adjustment of the manual order.

[0106] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and visually easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed explanations. If the user is focused, the suggestion unit can provide suggestions that get straight to the point. In this way, by adjusting the way suggestions are presented based on the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0107] The proposal unit can adjust the level of detail in its proposals based on the importance of the routine tasks. For example, for high-importance routine tasks, the proposal unit can provide detailed proposals and specifically show how to improve efficiency. For low-importance routine tasks, the proposal unit can provide simplified proposals, enabling efficient proposal creation. The proposal unit can dynamically adjust the content of its proposals according to the importance of the routine tasks. This allows for efficient proposal creation by adjusting the level of detail based on the importance of the routine tasks. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input routine task importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0108] The suggestion unit can apply different suggestion algorithms depending on the category of the routine task when making suggestions. For example, for routine tasks related to using an application, the suggestion unit can apply a suggestion algorithm optimized for a specific application. For routine tasks related to browsing websites, the suggestion unit can apply a suggestion algorithm that corresponds to the category of the website. For routine tasks related to file operations, the suggestion unit can apply a suggestion algorithm that corresponds to the type of file. This allows for efficient suggestion generation by applying different suggestion algorithms depending on the category of the routine task. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input routine task category data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0109] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. If the user is focused, the suggestion unit can provide concise suggestions. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0110] The proposal unit can determine the priority of proposals based on the timing of routine tasks. For example, the proposal unit can prioritize proposals based on the most recent routine tasks. The proposal unit can also make proposals that reflect long-term trends based on past routine tasks. The proposal unit can dynamically adjust the content of proposals based on the timing of routine tasks. This allows for efficient proposal generation by determining the priority of proposals based on the timing of routine tasks. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input routine task execution timing data into a generating AI and have the generating AI determine the priority of proposals.

[0111] The proposal unit can adjust the order of proposals based on the relevance of routine tasks when making proposals. For example, the proposal unit can prioritize proposals based on highly relevant routine tasks. The proposal unit can efficiently make proposals by postponing less relevant routine tasks. The proposal unit can dynamically adjust the content of proposals based on the relevance of routine tasks. This allows for efficient proposals by adjusting the order of proposals based on the relevance of routine tasks. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input routine task relevance data into a generating AI and have the generating AI adjust the order of proposals.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can analyze collected operation logs and estimate employees' skill levels. For example, it can evaluate employees' skill levels based on factors such as the frequency of use of specific applications, the accuracy of their operations, and their speed. Based on these skill levels, the analysis unit can propose appropriate training programs. This helps improve employee skills and promotes increased work efficiency.

[0114] The judgment unit can estimate an employee's emotions and adjust the priority of tasks based on those estimates. For example, if an employee is stressed, less important tasks can be postponed to reduce their burden. If an employee is relaxed, more important tasks can be prioritized. By adjusting task priorities according to an employee's emotions, this system can improve work efficiency and reduce employee stress.

[0115] The data collection unit can consider the context of an operation when collecting employee operation logs. For example, it can prioritize the collection of operation logs related to a specific project to understand the project's progress. Based on the context of the operation, the data collection unit can filter relevant operation logs and collect data efficiently. This allows for understanding the project's progress and improving work efficiency.

[0116] The analysis unit can analyze collected operation logs and evaluate employee work performance. For example, it can evaluate employee performance based on factors such as accuracy, speed, and error frequency. Based on the performance evaluation, the analysis unit can suggest areas for improvement and the need for training. This helps to improve employee performance and promote operational efficiency.

[0117] The assessment unit can estimate an employee's emotions and adjust work feedback based on those estimates. For example, if an employee is stressed, it can prioritize providing positive feedback to improve their motivation. If an employee is relaxed, it can provide constructive feedback to promote work improvement. In this way, by providing feedback tailored to the employee's emotions, it is possible to improve work efficiency and boost employee motivation.

[0118] The data collection unit can take into account the employee's health condition when collecting operation logs. For example, if an employee is feeling fatigued, the frequency of operation log collection can be reduced to alleviate their burden. If the employee is healthy, the frequency of operation log collection can be increased to collect more detailed data. This allows for the collection of operation logs in accordance with the employee's health condition, thereby improving work efficiency and promoting employee health management.

[0119] The analysis department can analyze collected operation logs to identify areas for improvement in employees' work environments. For example, it can suggest improvements to the work environment based on the frequency of use and efficiency of operation of specific applications. Based on these improvements, the analysis department can propose the introduction of appropriate tools and resources. This can improve employees' work environments and promote increased work efficiency.

[0120] The judgment unit can estimate an employee's emotions and adjust the progress of their work based on those emotions. For example, if an employee is stressed, the progress of their work can be slowed down to reduce their burden. If an employee is relaxed, the progress of their work can be accelerated to allow them to work more efficiently. In this way, by adjusting the progress of work according to the employee's emotions, it is possible to improve work efficiency and reduce employee stress.

[0121] The data collection unit can adjust the collection method according to the employee's work content when collecting operation logs. For example, it can focus on collecting PC operation logs for employees who do a lot of desk work, and focus on collecting mobile device operation logs for employees who do a lot of fieldwork. By collecting operation logs according to the content of work, data can be collected efficiently, and work efficiency can be improved.

[0122] The analysis unit can analyze collected operation logs and evaluate employee workload. For example, it can evaluate workload based on the time and frequency spent on specific tasks. Based on the workload evaluation, the analysis unit can propose task reallocation or additional resources. This allows for proper management of employee workload, leading to increased work efficiency and reduced employee stress.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects employee PC and browser operation logs. For example, it can collect operation logs such as clicks, keystrokes, and URL history. The collection unit may also include AI processing. Step 2: The analysis unit analyzes the operation logs collected by the collection unit to identify routine tasks. For example, it can identify patterns in routine tasks, such as using the same application at the same time every day or regularly browsing the same website. The analysis unit includes AI processing. Step 3: The judgment unit presents the routine tasks discovered by the analysis unit to the employee, who then determines that the task is a routine task. For example, the method of notifying the employee of the discovered routine tasks and the format of the presentation can be set. The judgment unit may also include AI processing. Step 4: The creation unit creates a work manual for the routine tasks determined by the judgment unit. For example, it can generate a manual with detailed instructions, such as how to operate a specific application or how to use a specific website. The creation unit includes AI processing. Step 5: The proposal team, if the content of the business manual created by the creation team is correct, proposes ways to automate and streamline routine tasks. For example, they can propose ways to automate and streamline routine tasks using programming such as macros, GAS, or RPA. The proposal team may also include AI processing.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, creation unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects employee PC and browser operation logs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected operation logs to identify routine tasks. The determination unit is implemented by the control unit 46A of the smart device 14 and presents the discovered routine tasks to the employee, who then determines that the tasks are routine tasks. The creation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates a work manual for the determined routine tasks. The proposal unit is implemented by the control unit 46A of the smart device 14 and, if the content of the work manual is correct, proposes automated efficiency improvements for routine tasks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, creation unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects employee PC and browser operation logs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected operation logs to identify routine tasks. The determination unit is implemented by the control unit 46A of the smart glasses 214 and presents the discovered routine tasks to the employee, who then determines that the tasks are routine tasks. The creation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates a work manual for the determined routine tasks. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and, if the content of the work manual is correct, proposes automated efficiency improvements for routine tasks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, creation unit, and proposal 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 collects employee PC and browser operation logs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected operation logs to identify routine tasks. The determination unit is implemented by the control unit 46A of the headset terminal 314 and presents the discovered routine tasks to the employee, who then determines that the tasks are routine tasks. The creation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates a work manual for the determined routine tasks. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and, if the content of the work manual is correct, proposes automated efficiency improvements for routine tasks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, creation unit, and proposal unit, is implemented in at least one of 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 collects employee PC and browser operation logs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected operation logs to identify routine tasks. The determination unit is implemented by the control unit 46A of the robot 414 and presents the discovered routine tasks to the employee, who then determines that the tasks are routine tasks. The creation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates a work manual for the determined routine tasks. The proposal unit is implemented by the control unit 46A of the robot 414 and, if the content of the work manual is correct, proposes automated efficiency improvements for routine tasks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The data collection department collects employee PC and browser operation logs, An analysis unit analyzes the operation logs collected by the aforementioned collection unit to identify routine tasks, The routine tasks discovered by the aforementioned analysis unit are presented to the employee, and the employee makes a judgment in the judgment unit, A creation unit creates a work manual for routine tasks determined by the aforementioned determination unit, If the contents of the business manual created by the creation unit are correct, the system includes a proposal unit that proposes ways to automate and streamline routine tasks. A system characterized by the following features. (Note 2) The aforementioned collection unit is Visualize employees' daily work hours. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected operation logs are analyzed to identify patterns in routine tasks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, The identified routine tasks are presented to employees, who then determine whether or not they are routine tasks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Create a business manual from operation logs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose automating and streamlining routine tasks using programming tools such as macros, GAS, and RPA. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of operation log collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze employees' past operation history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting operation logs, 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 It estimates the user's emotions and determines the priority of the operation logs to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting operation logs, the system prioritizes collecting logs that are highly relevant, taking into account 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 collecting operation logs, analyze employees' social media activity and collect relevant logs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the operation log. 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 operation log. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the operation logs were collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the operation logs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, When making a judgment, the accuracy of the judgment is improved by considering the interrelationships of the operation logs. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, When making a determination, the attribute information of the person who submitted the operation log is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, It estimates the user's emotions and adjusts the order in which the judgment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, When making a determination, the geographical distribution of the operation logs is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, During the decision-making process, we improve the accuracy of the decision by referring to relevant literature in the operation log. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned creation unit, The system estimates the user's emotions and adjusts the wording of the manual based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned creation unit, When creating manuals, adjust the level of detail in the manual based on the importance of the operation logs. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned creation unit, When creating manuals, different manual creation algorithms are applied depending on the category of the operation log. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned creation unit, The system estimates the user's emotions and adjusts the length of the manual based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned creation unit, When creating manuals, prioritize the manuals based on when the operation logs were collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned creation unit, When creating manuals, adjust the order of the manuals based on the relevance of the operation logs. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the routine tasks. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of routine work. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, prioritize it based on when routine tasks are performed. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of routine tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 collects employee PC and browser operation logs, An analysis unit analyzes the operation logs collected by the aforementioned collection unit to identify routine tasks, The routine tasks discovered by the aforementioned analysis unit are presented to the employee, and the employee makes a judgment in the judgment unit, A creation unit creates a work manual for routine tasks determined by the aforementioned determination unit, If the contents of the business manual created by the creation unit are correct, the system includes a proposal unit that proposes ways to automate and streamline routine tasks. A system characterized by the following features.

2. The aforementioned collection unit is Visualize employees' daily work hours. The system according to feature 1.

3. The aforementioned analysis unit, The collected operation logs are analyzed to identify patterns in routine tasks. The system according to feature 1.

4. The determination unit, The identified routine tasks are presented to employees, who then determine whether or not they are routine tasks. The system according to feature 1.

5. The aforementioned creation unit, Create a business manual from operation logs. The system according to feature 1.

6. The aforementioned proposal section is, We propose automating and streamlining routine tasks using programming tools such as macros, GAS, and RPA. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of operation log collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze employees' past operation history and select the optimal data collection method. The system according to feature 1.

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